Appetite, Volume 50, Issue 01 (January 2008)

ARTICLE IN PRESS Appetite 50 (2008) 1–11 www.elsevier.com/locate/appet Research Review How emotions affect eating: A ...

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ARTICLE IN PRESS

Appetite 50 (2008) 1–11 www.elsevier.com/locate/appet

Research Review

How emotions affect eating: A five-way model Michael Macht Department of Psychology, University of Wu¨rzburg, Marcusstr. 9-11, 97070 Wu¨rzburg, Germany Received 18 April 2006; received in revised form 13 July 2007; accepted 16 July 2007

Abstract Despite the importance of affective processes in eating behaviour, it remains difficult to predict how emotions affect eating. Emphasizing individual differences, previous research did not pay full attention to the twofold variability of emotion-induced changes of eating (variability across both individuals and emotions). By contrast, the present paper takes into account both individual characteristics and emotion features, and specifies five classes of emotion-induced changes of eating: (1) emotional control of food choice, (2) emotional suppression of food intake, (3) impairment of cognitive eating controls, (4) eating to regulate emotions, and (5) emotioncongruent modulation of eating. These classes are distinguished by antecedent conditions, eating responses and mediating mechanisms. They point to basic functional principles underlying the relations between emotions and biologically based motives: interference, concomitance and regulation. Thus, emotion-induced changes of eating can be a result of interference of eating by emotions, a byproduct of emotions, and a consequence of regulatory processes (i.e., emotions may regulate eating, and eating may regulate emotions). r 2007 Elsevier Ltd. All rights reserved. Keywords: Emotion; Eating; Mood; Emotional eating; Restrained eating; Motivation; Food choice

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Variability of emotion-induced changes of eating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Variability across individuals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Variability across emotions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Previous theories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A five-way model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Food-induced emotions control food choice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Intense emotions suppress food intake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Negative and positive emotions impair cognitive eating controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Negative emotions elicit eating to regulate emotions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Emotions modulate eating in congruence with emotion features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Emotion-induced eating in restrained and emotional eaters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Emotion-induced eating in normal eaters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 2 2 3 4 4 5 5 6 6 7 7 7 8 8

Introduction

E-mail address: [email protected] 0195-6663/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.appet.2007.07.002

Emotions serve distinct motivational functions and contribute essentially to control of basic behavioural

ARTICLE IN PRESS M. Macht / Appetite 50 (2008) 1–11

2

systems in animals and men (e.g. Frederickson, 1998; Frijda, 1986; Izard & Ackerman, 2000; Plutchik, 1984; Tomkins, 1963). Specific emotions such as anger, fear, sadness, and joy, as well as moods that are thought to last longer and be more diffuse (e.g. Ekman, 1992; Frijda, 1993) have been found to affect eating responses along the entire process of ingestion: motivation to eat (Macht & Simons, 2000), affective responses to foods (Ferber & Cabanac, 1987; Willner & Healy, 1994), food choice (e.g. Gibson, 2006; Oliver & Wardle, 1999), chewing (Macht, 1998), eating speed (Krebs, Macht, Weyers, Weijers, & Janke, 1996), amount ingested (Greeno & Wing, 1994 for a review), as well as metabolism and digestion (Blair, Wing, & Wald, 1991; Wing, Blair, Epstein, & McDermott, 1990). Surveys demonstrate that most people experience changes of eating in response to emotional stress (on average 30% an increase and 48% a decrease of appetite or intake, Table 1). Epidemiological data indicate that stress-related eating is associated with increased body weight (Laitinen, Ek, & Sovio, 2002). Not surprisingly, effects of emotions on eating have been studied extensively, but due to their variability it remains difficult to predict how an emotion affects eating in a given group of persons. Emotions can increase food intake in one group of persons, e.g. restrained eaters, but decrease food intake in another group, e.g. non-restrained eaters. Furthermore, different emotions may increase or decrease eating in the same group of individuals. For example, boredom may be associated with increased appetite, but sadness with decreased appetite (e.g. Pudel & Richter, 1980). This twofold variability (across individuals and emotions) has been neglected by previous research. Based on an individual difference model, it was mostly examined whether eating habits predispose people to increase food intake in response to negative emotions (Greeno & Wing, 1994). The present paper introduces an integrative model that takes into account both individual characteristics and emotion features. First, the empirical evidence on varia-

Table 1 Changes of eating in response to emotional stress reported in surveys Change of eating 11% more appetite, 70% less appetite 16% more appetite, 38% less appetite 25% more appetite, 32% less appetite 44% eat more, 48% eat less 4% eat more, 55% eat less 49% eat more, 51 do not eat more 55% eat more, 45% do not eat more 38% eat more, 42% eat less

N

Reference

364 1950

Krumbacher and Meyer (1963) Pudel and Richter (1980)

1024

Pudel (1984)

80

Willenbring et al. (1986)

475 500

Popper et al. (1989) Spillman (1990)

101

Weinstein et al. (1997)

212

Oliver and Wardle (1999)

bility of emotion-induced changes of eating is summarized briefly. Variability of emotion-induced changes of eating Variability across individuals A series of experiments showed that restrained eaters (as identified by questionnaire; e.g. Herman & Mack, 1975; Stunkard & Messick, 1985) consume more food than nonrestrained eaters in response to fear and negative mood states (for a review, see Greeno & Wing, 1994, also Heatherton, Striepe, & Wittenberg, 1998; Rotenberg & Flood, 1999; Rutledge & Linden, 1998; Tuschen, Florin, & Baucke, 1993). Restrained eating refers to a persistent pattern of eating-related cognitions and behaviours in order to reduce or to maintain body weight (Herman & Mack, 1975). Another experiment demonstrated that emotional eaters (also identified by questionnaire; van Strien, Frijters, Bergers, & Defares, 1986) consume more sweet, high-fat foods in response to emotional stress than non-emotional eaters (Oliver, Wardle & Gibson, 2000). Field studies showed that negative emotional states in everyday life can be associated with a tendency to eat as an emotion regulation strategy (Macht, Haupt, & Ellgring, 2005; Macht & Simons, 2000). Emotional eating is thought to be instigated in order to cope with negative emotions (Bruch, 1973; Kaplan & Kaplan, 1957). Probably, the influences of both eating habits are reflected in effects of emotions on binge eating. Negative emotions have been found to increase the tendency to binge in bulimia nervosa and the binge eating disorder (experiments: Agras & Telch, 1998; Cattanach, Malley, & Rodin, 1988; Chua, Touyz, & Hill, 2004; Gluck, Geliebter, Hung, & Yahav, 2004; Telch & Agras, 1996; field studies: Alpers & Tuschen-Caffier, 2001; Cooper & Bowskill, 1986; Davis, Freeman, & Garner, 1988; Johnson & Larson, 1982; Kenardy, Arnow, & Agras, 1996). Taken together, studies indicate that restrained eaters increase food intake, emotional eaters consume more sweet, high-fat foods, and binge eaters tend to binge in response to negative emotions. By contrast, normal eaters (i.e. normal-weight persons whose emotional and restrained eating scores fall within the normal range) are usually assumed to reduce food intake in response to negative emotions (Herman & Polivy, 1984; Schachter, Goldman, & Gordon, 1968). The evidence does not support this assumption. Table 2 summarizes experimental studies in normal-eating populations: 43% of the results indicate an increase, 39% a decrease and 26% no change of eating in response to emotions (as shown by Bellisle et al. (1990), in the latter studies possibly one part of the sample reduced and the other part increased food intake). Similarly, surveys and field studies showed that stress and negative emotions can be associated with both increased and decreased motivation to eat and food intake


3

Table 2 Effects of emotions on eating: experimental studies in normal subjects Effect

Eating measure

Emotion

Reference

Increase

Food intake

Negative mood Negative mood Fear Fear Fear Arousal Arousal Arousal Boredom Anger Arousal/stress Joy Joy Anger Negative mood Negative mood Fear Fear Fear Fear Fear Arousal Depressive mood Sadness Sadness Arousal Arousal Emotion Negative mood Negative mood Negative mood Negative mood Fear Fear Fear Fear

Lowe and Maycock (1988) Willner et al. (1998) Pine (1985) Pines and Gal (1977) Bellisle et al. (1990) Glass (1967) Cantor et al. (1982) Grunberg and Straub (1992) Abramson and Stinson (1977) Macht (1996) Ferber and Cabanac (1987) Macht et al. (2002) Macht et al. (2002) Macht (1996) Willner et al. (1998) Baucom and Aiken (1981) Schachter et al. (1968) Herman and Polivy (1975) Heatherton et al. (1991) Herman et al. (1987) Bellisle et al. (1990) Grunberg and Straub (1992) Willner and Healy (1994) Macht et al. (2002) Macht et al. (2002) Macht (1998) Macht (1998) Reference Frost et al. (1982) Tuschen et al. (1993) Telch and Agras (1996) Ruderman (1985) Reznick and Balch (1977) Heatherton et al. (1991) Schotte et al. (1990) Cools et al. (1992)

Hedonic responses Motivation to eat

Decrease

Chocolate craving Food intake

Hedonic responses Motivation to eat

Effect No effect

Chewing Eating measure Food intake

(Table 1; de Castro, 1986; Hill, Weaver, & Blundell, 1991; Macdiarmid & Hetherington, 1995; McCann, Warnick, & Knopp, 1990; Michaud et al., 1990; Pollard, Steptoe, Canaan, Davies, & Wardle, 1995; Rosenfield & Stevenson, 1988; Slochower, 1983; Steptoe, Lipsey, & Wardle, 1998; Stone & Brownell, 1994; Striegel-Moore et al., 1999; Wardle, Steptoe, Oliver, & Lipsey, 2000; Weidner, Kohlmann, Dotzauer, & Burns, 1996). Based on this evidence, it is difficult to predict how normal eaters change eating in response to emotions. Variability across emotions Dimensions used to classify emotions, namely valence (pleasure–displeasure), arousal (e.g. Greenwald, Cook, & Lang, 1989; Russell & Feldman Barrett, 1999) and intensity (Frijda, Ortony, Sonnemans, & Clore, 1992), have been found to make an essential contribution to variability of emotion-induced changes of eating. There is clear evidence that emotions differing in valence and arousal or intensity exert different influences on eating.

Animal studies point to a role of intensity for the direction of effects of emotional stress on food intake. Rats decrease food intake during intense noise (Alario, Gamallo, Beato, & Trancho, 1987; Pare´, 1964), in response to intense electric shock (Strongman, 1965; Weiss, 1968), and during chronic stress (Sampson, Muscat, Philips, & Willner, 1992; Willner, Muscat, & Papp, 1992), but increase food intake or eating speed in response to low or moderate noise and electric shock (Krebs et al., 1996; Kupferman, 1964; Rasbury & Shemberg, 1971; Sterritt, 1962, 1965; Sterritt & Shemberg, 1963; Strongman, Coles, Remington, & Wookey, 1970; Ullman, 1951, 1952). Results of a human study correspond to these data (Mehrabian, 1980). Participants imagined themselves in various emotions and reported how much they would eat during each of these emotions. Compared with low-arousal states (e.g. boredom, depressive mood) high-arousal states (e.g. tension, fear) were reported to inhibit food intake. These results support the view that high-arousal or intense emotions decrease food intake whereas low- to moderate emotions may increase food intake (Robbins & Fray, 1980).


4 Table 3 Previous models of emotion-induced changes of eating Type of model

Explained phenomenon

Mediating mechanism

Scope

Reference

Psychological models

Increased food intake in response to emotions dto. Increased food intake in response to negative emotions

Disinhibition of restrained eating

Restrained eaters

Impairment of cognitive capacity Reduction of negative emotions by eating

Restrained eaters Emotional eaters

Binge eating

Escape from aversive self-awareness

Decreased food intake in response to stress and fear Decreased hedonic reactions during chronic mild stress Eating induced by tail-pinch Increased eating speed in a stressful environment

Antagonistic physiological stress responses Reduced brain dopamine

Restrained eaters, bulimics Animals, humans

Herman and Polivy (1984) Boon et al. (2002) Bruch (1973), Slochower (1983), Booth (1994) Heatherton and Baumeister (1991) Schachter et al. (1968) Willner et al. (1992)

Physiological models

Biological models

Eating bouts in response to arousal and frustration

Animals, humans

Activation of endogenous opiates Disposition to ensure energy supply

Rats Animals

Reduction of arousal through eating

Animal, humans

Human studies demonstrated that positive and negative emotions could differ markedly in their effects on eating. Negative emotions such as anger, fear, and sadness may increase impulsive eating (i.e. fast and irregular eating directed at any food available), eating to regulate the emotional state and consumption of junk food, but decrease food pleasantness. By contrast, joy and other positive emotions increase food pleasantness and consumption of healthy foods (Lyman, 1982; Macht, 1999; Macht, Roth, & Ellgring, 2002). To summarize, high-arousal or intense emotions suppress eating, and negative emotions can increase or decrease food intake. Little is known about effects of positive emotions on eating and about differences between negative emotions such as anger, sadness and fear in their effects on eating. Clinical studies indicate that anger is an important antecedent of binge eating (Arnow, Kenardy, & Agras, 1992). It has also been suggested that disgust contributes to the maintenance of pathological eating patterns and may complicate attempts to normalize eating (Troop, Treasure, & Serpell, 2002). Previous theories During the past decades, a number of theories have attempted to explain the manifold effects of emotions on eating. Whereas emotion-induced increase of food intake was explained by psychological mechanisms (Bruch, 1969, 1973; Heatherton & Baumeister, 1991; Herman & Polivy, 1984; Slochower, 1983), physiological mechanisms were proposed to explain emotion-induced decrease of intake (Herman & Polivy, 1984; Schachter et al., 1968; Willner, 1997). Biologically oriented theories emphasized the adaptive function of changes of feeding behaviour in stressful situations (Cantor, 1981; Lima, 1987; Whishaw, Dringenberg, & Comery, 1992). As can be seen from

Morley et al. (1983) Lima (1987), Whishaw et al. (1992) Cantor (1981)

Table 3, previous theories aim at different phenomena, propose different mediating mechanisms and differ in scope. It has been a major problem for research that theories were mostly treated separately. Therefore, a general framework is provided in the following. It is suggested that emotions affect eating in five basic ways, which encompass phenomena addressed by previous theories. A five-way model As shown above, a number of factors were identified that make an essential contribution to emotion-induced changes of eating: arousal/intensity, valence and food relatedness of emotions as well as restrained and emotional eating. Taking into account these factors as antecedents, effects of emotions on eating can be classified into five basic classes: (1) Emotions aroused by food stimuli affect food choice. (2) Emotions high in arousal or intensity suppress eating due to incompatible emotional responses. (3) Emotions moderate in arousal or intensity affect eating depending on motivations to eat: (a) In restrained eating, negative and positive emotions enhance food intake due to impairment of cognitive control. (b) In emotional eating, negative emotions elicit the tendency to be regulated by eating and, as a consequence, enhance intake of sweet and high-fat foods. (c) In normal eating, emotions affect eating in congruence with their cognitive and motivational features. The flow diagram in Fig. 1 gives a visual presentation.


5

Emotion

Food-induced emotion?

yes

Control of food choice

Preference Craving Avoidance

Suppression

Decreased food intake

Restrained eating

Impairment of cognitive eating controls

Increased food intake

Emotional eating

Eating to regulate emotions

Increased intake of sweet, high-fat foods

no

yes Intense emotion?

no

Eating habits related to emotions?

yes

no

Emotion features relevant to eating?

yes

Congruent modulation

Change of eating congruent with emotion features

no

No change of eating

Fig. 1. A flow diagram to predict basic classes of emotion-induced changes of eating.

Emotions of low arousal or intensity are not expected to affect eating. In the following, each class is described in detail. Food-induced emotions control food choice Food-induced emotions are powerful determinants of food choice (Galef, 1996; Martins & Pliner, 2005; Rozin & Schulkin, 1990). Tasting energy-dense food such as sugar and fat evokes positive affective responses that promote ingestion, whereas tasting bitter compounds, which are correlated with toxins, evokes negative affective responses that promote rejection (Rosenstein & Oster, 1988; Steiner, 1979). The biological basis of these affective reactions is demonstrated in homology across primate species (Steiner, Hawilo, Glaser, & Berridge, 2001). Notably, food-induced emotional responses encompass full-blown emotions, in particular, neophobia and disgust (Raudenbush & Frank, 1999; Rozin & Fallon, 1987). Appetitive food-related stimuli can elicit a strong desire to eat or craving that can be conceived as an additional variant of food-induced emotions. Such craving co-occurs with a number of autonomic responses (Nederkoorn,

Smulders, Havermans, & Jansen, 2004; Nederkoorn, Smulders, & Jansen, 2000) which vary across individuals and can contribute to binge eating (Jansen, 1998). A number of studies demonstrated heightened cue reactivity in restrained eaters, binge eaters and bulimics as compared to controls (Carter, Bulik, McIntosh, & Joyce, 2001; Federoff, Polivy, & Herman, 2003; Karhunen, Lappalainen, Tammela, Turpeinen, & Uusitupa, 1997; Staiger, Dawe, & McCarthy, 2000), although lack of differences between groups has also been reported (e.g. Klaijner, Herman, Polivy, & Chhabra, 1980; Overduin, Jansen, & Eilkes, 1997). It is important to recognize that in some persons by contingent pairing of emotions and eating, emotions may become food-related cues and thus elicit eating (Booth, 1994). Intense emotions suppress food intake Animal studies demonstrated that intense or chronic stress sharply decreases food intake (for reviews see Greeno & Wing, 1994; Robbins & Fray, 1980, Section ‘‘Variability across emotions’’). Consequently, it was proposed that a decreased food intake is the ‘‘natural’’ response to stress

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M. Macht / Appetite 50 (2008) 1–11

(Herman & Polivy, 1984; Schachter et al., 1968). In fact, intense emotions are linked with both behaviours and physiological responses that interfere with eating. For example, intense sadness is associated with behavioural deactivation and withdrawal from the environment. Intense fear motivates flight and avoidance, and is also associated with autonomic responses inhibiting motivation to eat. Stress-induced physiological changes may interfere with digestion by delaying glucose absorption and gastrointestinal transit (Blair et al., 1991; Wing et al., 1990). Negative and positive emotions impair cognitive eating controls Restraint theory suggested that emotional stress disinhibits dietary restraint and therefore increases food intake (Herman & Polivy, 1984). In this view, negative emotions were thought to undermine restrained eaters’ ability to continue dieting, because they impose a ‘‘more urgent concern (i.e., how to cope with the stressor) than even dieting’’ and lead to eating ‘‘as if the diet boundary had been knocked down’’ (p. 152). The diet boundary was conceived as ‘‘the dieter’s self-imposed quota for consumption on a given occasion’’ (p. 149). Although this appeared to be a plausible explanation, the common experimental design comparing individuals with high or low scores on restraint scales was not appropriate to test putative mediating mechanisms (Greeno & Wing, 1994). An alternative explanation was suggested by Boon, Stroebe, Schut, and Jansen (1998). According to their ‘‘limited capacity hypothesis’’, restrained eaters’ food intake is increased, if their cognitive capacity to maintain restricted food intake is limited by distraction (be it emotional or not). Processing of emotional stimuli requires attention, and since cognitive capacity is limited, cognitive control over eating may be impaired. The overeating response was attributed to ironic processes, i.e., the more people exert cognitive control, the higher their vulnerability to overeating will be. A number of experimental studies supported this assumption (Boon, Stroebe, Schut, & IJntema, 2002; Boon et al., 1998; Lattimore & Caswell, 2004; Lattimore & Maxwell, 2004; Vreugdenberg, Bryan, & Kemps, 2003; Wallis & Hetherington, 2004; Ward & Mann, 2000). Negative emotions elicit eating to regulate emotions Emotional eating theory suggested that certain individuals eat in order to cope with stress and are therefore at risk of becoming obese (Bruch, 1969, 1973; Kaplan, 1957; Slochower, 1983). As the German colloquialism ‘‘Kummerspeck’’ (literally: fat of sorrow) shows, it is a popular view that emotional eating causes obesity. Although this view proved to be over-simplistic (Greeno & Wing, 1994), evidence did emerge for a role of emotional eating in a subgroup of obese persons. Obese binge eaters experience an increased tendency to binge in negative mood (Agras &

Telch, 1998; Gluck et al., 2004) and can be treated successfully by improving their skills to regulate emotions (Telch, Agras, & Linehan, 2001; Wiser & Telch, 1999). Moreover, studies indicate that healthy, normal-weight persons regulate negative emotions by eating (Macht, 1999; Macht et al., 2005; Macht & Simons, 2000). Emotional eating theory originated from psychodynamic thinking, but can be related to a number of more recent approaches. The core assumption of the theory (i.e., that negative emotions induce eating and are, as a result, reduced) has been viewed from a learning perspective, with negative emotions as eliciting stimuli, eating as operant behaviour, and its consequence, the eating-induced reduction of negative emotions, as negative reinforcement (Booth, 1994). Regulation was also taken into account by biological approaches which conceptualized arousalinduced eating as ‘‘adjunctive behaviour’’ or ‘‘displacement activity’’ that reduces arousal (Cantor, 1981). Finally, eating has been viewed as a strategy to improve negative mood (Thayer, 1989, 2001), to mask stress (masking hypothesis; Polivy & Herman, 1999), or to escape from aversive self-awareness (escape theory; Heatherton & Baumeister, 1991). In spite of its popularity and plausibility, the concept of emotional eating does however face some unresolved problems. Above all, the underlying mechanisms are far from clear. Usually, nutrient-dependent physiological changes are proposed to mediate mood-elevating effects of eating. Carbohydrate-rich meals may lead to a postprandial improvement of mood (for reviews see Benton & Donohoe, 1999; Christensen, 1993, 1997; Gibson & Green, 2002) and also to reduced stress responses in emotionally labile subjects (Markus, Panhuysen, & Tuiten, 1998). These effects were explained by meal-induced increase of brain serotonin availability (Wurtman, 1982). However, relatively small proportions of protein in a meal may deteriorate the increase of tryptophan levels at the blood–brain barrier and, thus, an increase of brain serotonin. Since most carbohydrate-rich meals in everyday life contain some protein, the ecological validity of the serotonin hypothesis can be questioned (Benton, 2002). Another physiological mechanism is suggested by animal data demonstrating that ingestion of high fat and carbohydrate ‘‘comfort foods’’ (Wansink, Cheney, & Chan, 2003) reduces activity of the hypothalamo-pituitary– adrenal axis and dampens stress responses (Dallman et al., 2003). The main problem with both the serotonin and the endocrine hypothesis is that emotional effects of eating can occur only with delay. Such delayed effects might be useful for coping with chronic stress, but not for responding to negative emotions, as they arise from unpredictable stimuli such as daily hassles. In order to cope with these emotions, it would be most efficient to reduce their intensity immediately. Indeed, sweet solutions have been shown to rapidly calm stress responses in human newborns (Smith, Fillion, & Blass, 1990). Similarly, in adults, experimentally


induced negative mood is improved immediately and selectively after eating palatable food, and this effect is most pronounced in persons scoring high on emotional eating (Macht & Mu¨ller, 2007). This suggests that immediate positive affective reactions elicited by palatable foods diminish the impact of stress. Additionally, it may be important here that eating distracts from the experience of negative emotions (Spitzer & Rodin, 1983). One might speculate that immediate effects of eating based on hedonistic mechanisms and distraction, play a major role in the regulation of everyday emotions and constitute a common phenomenon that applies to the general population. By contrast, in binge eating, immediate hedonic effects may be backed up or substituted by delayed and long-lasting physiological effects of chronic high fat and carbohydrate consumption. Emotions modulate eating in congruence with emotion features During negative mood, negative verbal information is retrieved more readily than positive information, and conversely, positive information is retrieved more readily during positive mood (for a review, see Berkowitz, 2000, pp. 78–83). This congruency effect was also shown for recognition of palatable and unpalatable food flavours (Pliner & Steverango, 1994) as well as for emotion-induced changes of eating. Sadness has been found to decrease and joy to increase food pleasantness and motivation to eat (Macht et al., 2002; Willner & Healy, 1994). These changes can be explained by emotion features. Sadness is associated with a slowing of cognitive processes and motor activity (Izard & Ackerman, 2000), with an absence of interest (Frijda, 1986, p. 22), and with a lowering of attention to the outside (Barr-Zisowitz, 2000). By contrast, joy is associated with an increased capability to perceive and process stimuli and an increased readiness to engage in activities (Frederickson, 1998; Frijda, 1986; Izard & Ackerman, 2000). Such emotion-congruent changes of eating can be conceived as a by-product of emotional activation, and should mostly occur, if emotions are elicited during eating. Discussion The present paper proposes five basic classes of emotioninduced changes of eating that can be predicted by antecedent conditions and point to basic functional principles relating emotions and eating. Depending on emotion features and eating habits emotion-induced changes of eating can be a result of interference (suppression of food intake; impairment of cognitive eating controls), a by-product of emotions (emotion-congruent modulation of eating), and a consequence of regulatory processes (emotional control of food choice; eating to regulate emotions). Future research may examine whether these principles can be applied to relations between emotions and other biologically motivated behaviours

7

such as thirst, sex and aggression. To improve understanding of the manifold effects of emotions on eating research has to disentangle mediating mechanisms and to identify relations between classes of emotion-induced changes of eating. In particular, two problems remain to be resolved. Emotion-induced eating in restrained and emotional eaters Negative emotions increase food intake in restrained eaters as well as intake of sweet, high-fat food in emotional eaters (Section ‘‘Variability across individual’’). To explain these findings, it was suggested that emotional eaters eat to reduce negative emotions. By contrast, restrained eaters were thought to increase food intake, because emotions impair cognitive control over their restricted eating pattern (Sections ‘‘Negative and positive emotions impair cognitive eating controls’’ and ‘‘Negative emotions elicit eating to regulate emotions’’). Although restrained and emotional eating appear to reflect different neurobiological processes (Volkow et al., 2003), it remains unclear whether increased consumption in response to emotions is in fact mediated by different mechanisms in restrained and emotional eaters. The present model suggests two ways to test empirically the assumption of different mechanisms. First, emotional and restrained eaters should differ in their eating responses to various emotions and stressors. Provided that cognitive control of eating can be impaired by a variety of influences, restrained eaters should increase food intake in response to both negative and positive emotions and also to cognitive demand. By contrast, emotional eaters should increase eating only in response to negative emotions. Second, emotioninduced eating should exert different effects on emotional state in emotional and restrained eaters. Consistent with the assumption that emotional eaters increase food intake to reduce negative emotions, they can be expected to report reduced negative emotions due to eating. By contrast, restrained eaters should experience no change of mood due to emotion-induced eating or may even report deterioration of mood (because of fear to gain weight). There is some evidence supporting these predictions. Restrained eaters have indeed been shown to increase food intake not only in response to negative emotions, but also to positive emotions (Cools, Schotte, & McNnally, 1992; Tuschen et al., 1993) and cognitive demand (e.g. Wallis & Hetherington, 2004); it remains, however, to be examined whether emotional eaters’ intake can also be increased by positive emotions and non-emotional stressors. Also, improvement of experimentally induced negative mood by eating has been found to be correlated with emotional, but not with restrained eating (Macht & Mu¨ller, 2007). In spite of this evidence, however, more studies comparing emotion-induced changes of eating in restrained and emotional eaters are needed.

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Emotion-induced eating in normal eaters

References

In normal eaters, negative emotions decrease or increase food intake. The five-way model explains decreased intake, depending on emotion intensity, by emotion-congruent modulation (Section ‘‘Intense emotions suppress food intake) or emotional suppression of eating (Section ‘‘Emotions modulate eating in congruence with emotion features’’), but it remains unclear how to explain increased intake in normal eaters. To understand this phenomenon, it should be recognized that emotional and restrained eating are regarded as continuous variables and that persons are classified as emotional or restrained eaters, if they score high on these continua. For example, emotional eaters may be defined as lying above the 80th percentile of scores in the most appropriate norm group (van Strien, 2002; personal communication). These persons can be expected to eat in order to regulate negative emotions frequently and consistently across situations. Presumably, a high proportion of their meals and snacks is based on the motivation to eat in order to cope with negative emotions. Persons with low scores of the emotional eating scale (e.g. below the 50th percentile) can be expected to experience this motivation, if at all, less frequently and less consistently. However, normal eaters with moderate emotional eating scores (e.g. between the 60th and 80th percentile) may occasionally also eat to cope with negative emotions. In these persons, a transient tendency to regulate emotions by eating may enhance consumption during emotions, although they are not classified as emotional eaters. It has indeed been demonstrated that normal eaters increase motivation to regulate emotions by eating in response to negative emotions induced in the laboratory (Bekker, van de Meerendonk, & Mollerus, 2004) and experienced in everyday life (Macht et al., 2005; Macht & Simons, 2000). Similarly, normal eaters may occasionally restrict eating, although they do not score very high on restraint scales, and thus enhance their susceptibility to emotion-induced eating. It can therefore be assumed that normal eaters increase intake due to transient shifts in motivations to eat. In other words, impairment of cognitive eating controls and emotion regulation eating (Sections ‘‘Negative and positive emotions impair cognitive eating controls’’ and Negative emotions elicit eating to regulate emotions) may occasionally also occur in normal eaters. Future research is needed to test the hypothesis that transient shifts in motivations to eat enhance susceptibility in normal eaters to increase intake in response to emotions. A suitable starting point for an experimental approach could be a study by Willner et al. (1998), who reinforced responses with chocolate under a progressively increasing work requirement and showed that craving for and intake of chocolate were increased by depressive mood. It remains to be examined whether the linking of operant responding for food and depressive mood can transiently increase motivation to eat in order to regulate mood in persons not classified as typical emotional eaters.

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Research report

Television use and food choices of children: Qualitative approach Giovanna Medeiros Rataichesck Fiatesa,, Renata D.M.C. Ambonib, Evanilda Teixeirab Departamento de Nutric- aõ, Centro de Cieˆncias da Sau´de, Universidade Federal de Santa Catarina, Floriano´polis 88010-970, SC, Brasil Departamento de Cieˆncia dos Alimentos, Centro de Cieˆncias Agra´rias, Universidade Federal de Santa Catarina, Floriano´polis 88034-001, SC, Brasil a

b

Received 1 September 2006; received in revised form 29 March 2007; accepted 9 May 2007

Abstract This study reports the results of 12 focus group interviews with primary school students (7–10 years old, n ¼ 57) in Floriano´polis, Brazil, regarding their food choices, television (TV) viewing, and physical activity habits. In 2005, an average Brazilian child aged 4–11 years watched TV almost 5 h per day. Intensive TV use in childhood and adolescence may contribute to sedentarism and unhealthy eating habits, and excessive viewing might have long-lasting adverse effects on health. Results indicated that frequent ingestion of snack foods was not a habit for most students, possibly because of an acknowledged parental interference, but these were the food items they bought with their own money. Daily TV viewing was reported by almost all students, especially during meals and before bedtime, but students still found the time to be physically active. Most of them mentioned going to sports classes and engaging in active play regularly. No attempts by the parents to regulate TV viewing in the household were mentioned. The habit of eating while watching TV, together with the students’ behavior as primary consumers of food products, pointed to the need for strategies that will reduce TV viewing and educate the children as consumers. r 2007 Elsevier Ltd. All rights reserved. Keywords: Children; Food choices; Television viewing; Consumer behavior

Introduction This article examines the behavior of a group of Brazilian children regarding their food choices, television (TV) viewing, and physical activity habits. Nowadays, children’s food preferences seem to influence food selection in ways that are inconsistent with dietary guidelines (Birch, 1999). Dietary patterns that result in high intakes of fat, saturated fat, and refined carbohydrate as well as low intakes of fruit, vegetables, and complex carbohydrate are becoming increasingly common, escalating the risks of coronary heart disease, certain cancers, diabetes, hypertension, and obesity (Coon & Tucker, 2002). Excessive TV viewing during childhood and adolescence plays an important role in this matter, since it has been associated with higher intakes of energy, fat, sweet and salty snacks, and carbonated beverages as well as with lower intakes of fruit and vegetables (Coon, Goldberg, Rogers, & Tucker, 2001). One of the reasons why TV has such effect is Corresponding author. Tel.: +554837219784; fax: +554837219542.

E-mail address: gifi[email protected] (G.M.R. Fiates). 0195-6663/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.appet.2007.05.002

because food is the most frequently advertised product category on children’s TV programming, and it has been established that exposure to food advertisements effectively promotes consumption of the advertised products (Halford, Gillespie, Brown, Pontin, & Dovey, 2004). TV also contributes to sedentarism, displacing more energetic activities that could help in burning the excessive calories contained in nutritionally unbalanced diets (Gortmaker et al., 1996; Hancox, Milne, & Poulton, 2004). The average American child spends 5.5 h per day with a variety of media, mostly TV (Strasburger, 2004). In Brazil, the situation is not different. In 2005, Brazilian kids aged 4–11 years watched TV around 4 h and 52 min per day (Instituto Midiativa, 2006). In a study about TV programming in Brazil, Almeida, Nascimento, and Quaioti (2002) analyzed 432 h of advertising in Brazilian commercial TV during weekdays and 216 h during Saturdays; they found that foods were the most advertised category of products, especially those rich in fat and sugar. The fact that children can be very sensitive to the effects of advertising has led to a large body of research. In Chile, the high percentage of children watching TV and the

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marked influence of commercials over their food preferences convinced researchers that an urgent educational strategy was necessary to promote healthy eating habits (Olivares, Albala, Garcia, & Jofre´, 1999). In a study with Spanish students, Ruano and Pujol (1997) concluded that unhealthy dietary habits clustered in high consumers of TV. In Turkey, Arnas (2006) reported that TV advertisements affected young children’s unhealthy food consumption. In the United Kingdom, Halford et al. (2004) concluded that the ability to recognize food advertisements significantly correlated with the amount of food eaten after exposure to them. In the United States, Coon et al. (2001) verified that watching TV during meals negatively affected the dietary patterns of children and their families. As one can see, TV viewing can certainly influence the behavior of children to an extent that can cause concern. Throughout the world, children do not only influence family purchase decisions in a variety of product categories, but also have considerable amount of money to spend with their own needs, which qualifies them to be an important primary market (McNeal, 1998). It is estimated that children between the ages of 4 and 14 living in urban environments spend approximately US$ 300 a year in their personal wants and needs. In the US alone, children spend more than US$ 7 billion a year in food and beverage (McNeal, 2000). Although it is likely that some aspects of children’s consumer behavior are universal across cultures, local studies provide valuable opportunities to generate information about what is happening in different settings. There have been no studies about the consumer behavior of Brazilian children, or how their TV viewing habits affect their food choices. The present pilot study, which is exploratory in nature, aimed to obtain preliminary data about this relevant and yet unknown situation in Brazil. We expected that the students from the sample, who only attend school for half periods (like virtually every student in Brazil), dedicated most of their leisure time to watching TV, and that this would negatively influence their eating habits and physical activity levels. We expected to find high consumption of snack foods, together with low intakes of fruits and vegetables, and low levels of exercise. Methods

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Also, starting at the age of eight, kids already have money of their own to spend, and sometimes spend it independently (John, 1999). Since this is a relatively unexplored area, we chose to apply a qualitative approach to promote idea generation via group interaction. Bringing children together to discuss an idea or concept minimizes direct inquiry and produces richer responses because the children are not only responding to the researcher, but to other members of the group as well (McNeal, 2000; Heary & Hennessy, 2002; Green & Thorogood, 2004). Participants A total of 57 students (28 male, 29 female), aged 7–10 years (mean age: 8.9 years), returned the consent forms signed by their parents. The participants’ response rate (40.4%) fairly represents the overall student population. The participants were composed of 43% of the 1st graders, 28% of 2nd graders, 38% of 3rd graders, and 49% of 4th graders. Additional data about the sample are presented in Table 1. No children had special needs. The majority of the students were white and from middle-class families living near the school. Nonparticipation in the study was due to failure in returning the signed consent forms or absenteeism on the day of the focus group interview. Focus group interviews The students participated in 12 focus group sessions. Participants were divided into groups on the basis of grade and gender with an average of about five students in each focus group (range of 4–6 students per group). Two focus groups were formed from 1st grade students (one with boys and one with girls, n ¼ 9), two from 2nd grade students (both mixed, n ¼ 9); three from 3rd grade students (two with boys and one with girls, n ¼ 15) and five from 4th grade students (two with boys and three with girls, n ¼ 24). The mixed groups with 2nd-graders were formed because there were not enough participants to create female or male only groups. Interviews were conducted within the school setting, during school hours, in a special room with little furniture and distraction.

Table 1 Characteristics of interview participants

Study design The study was conducted in a primary private school in Floriano´polis, Brazil. The protocol was approved by the Federal University of Santa Catarina Human Research Ethics Committee (Protocol # 293/05). All 141 students from the seven existing classrooms of 1st to 4th grade were invited to participate in the study, and received informed consent forms to be signed by the parents. This age group was chosen because some of the most important consumer knowledge and skills are developed during this period.

Boys ðn ¼ 28Þ

Freq. (%)

Girls ðn ¼ 29Þ

Freq. (%)

Total ðnÞ

Freq. (%)

Age/grade 7 years (1st grade) 8 years (2nd grade) 9 years (3rd grade) 10 years (4th grade)

05 04 10 09

9 7 18 15

04 05 05 15

7 9 9 26

09 09 15 24

16 16 26 42

School period Morning Afternoon

13 15

23 26

06 23

10 40

19 38

33 67

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G.M.R. Fiates et al. / Appetite 50 (2008) 12–18

Can a food be healthy and tasty at the same time? Give examples. Do you like eating fruits? How often? What about vegetables? Do you like eating snack foods? How often? What else do you like to eat? Do your parents try to interfere with your eating habits? When you have your own money to spend, what food items do you buy? Do you like watching TV? When? What are your favorite shows? Do you usually eat something while watching TV? Do your parents try to interfere with your TV viewing habits? Between watching TV and playing outside, what do you prefer? Do you practice any sports?

Fig. 1. Focus group interview structure: sample of main questions.

Each focus group interview was conducted in Portuguese by the same moderator (first author), using a discussion guide with open-ended questions to ensure consistency in interviewing style (Fig. 1). Moreover, a semi-structured questioning route was used to ensure consistency in questions asked across groups, yet allow for some flexibility in accordance with the topics raised and level of participation within the groups. Focus group interviews were audio-recorded with participants’ permission and lasted approximately 30 min. Interviews were transcribed verbatim, in Portuguese, and then translated to English. A total of 6.6 h of tape-recorded interviews were obtained to produce a manuscript. Data were analyzed through content analysis (O’Dea, 2003). With this method, ideas or trends were coded in the transcript margin; then, they were selectively retrieved and reassembled together, and grouped according to common themes, using the cut-and-paste technique (Bardin, 2004). Words and phrases used by participants were analyzed to determine the degree of similarity among responses. We considered the context in which responses were given, with particular emphasis on the stimulus that elicited each response. We also considered the frequency of comments to measure the significance of specific topics. Frequencies were used only in the broadest of terms (for example, many, some, a few). Quotes were pulled from the transcripts to illustrate categories. The specific quotes were selected on the basis that they illustrated a variety of response types, including responses which were typical or common; unusual responses; responses which represented a concise summary of a discussion topic; or responses showing a range of viewpoints on a topic. After this systematic process was completed, the data were categorized as follows:

Consumption of fruits, vegetables and snack foods Parental interference over food choices Food consumer behavior

TV viewing habits Physical activity

Results Consumption of fruits, vegetables, and snack foods Most of the interviewed students referred to the ingestion of fruits and vegetables with many positive remarks denoting a regular intake of salads and fresh fruit. Very few students reported intense rejection of fruits and vegetables; negative comments were mostly on restrictions about the way of eating, use of seasoning, or addition of toppings. ‘‘Everyday I eat three fruits: one in the morning, other at noon and another one at night.’’ (2nd-grade boy) ‘‘The salad I like best is beets! No seasoning, of coursey’’ (4th-grade boy) ‘‘I only [eat fruit] when I feel like ityonly when my mother buys strawberries, but it has to be with some kind of sweet added to it, like condensed milkyor sugary’’ (1st-grade girl) ‘‘yI don’t really eat them; I just suck the juice and throw away the rest.’’ (4th-grade girl) Snack food intake was a topic that generated several controversies. Many students admitted liking and eating more than they should. More students, however, made negative remarks and considered snacks not a regular item in their diets. ‘‘I kind of think, actually [about eating healthy foods], but I go towards not so healthy items, because I think they taste bettery’’ (4th-grade girl) ‘‘I eat a lot of chocolateyalmost everything I eat everyday is cookies and chocolateyand milky’’ (2ndgrade girl) ‘‘y I hate chocolatey if someone offers me, I eat, but rarelyy I really don’t like.’’ (3rd-grade boy) ‘‘yice cream only once a week or even only once a month, but I don’t really like ice cream.’’ (4th-grade girl) ‘‘I had esofagitisyI couldn’t eat chocolate, soda, chipsynow it’s been treated, but I still don’t eat much because my mom forbids, I eat whatyonce a monthy’’ (4th-grade boy) Parental interference over food choices Students provided many comments about how their parents interfered with their food choices. This topic was brought to discussion because we thought that the students’ habits or product requests could generate conflict between them and their parents. Parental control over the children’s food choices seemed to be exercised in several ways: (1) some simply did not buy what their children wanted; (2) some bought and restricted access to them at


home; (3) others tried educating, negotiating or forcing; and (4) yet others appealed for health reasons. ‘‘My motherykeeps them in a kitchen cabinetywhen she is sleeping I go there and eat everythingythen she put a locker in the cabinetymade only two copies of the keyy’’ (3rd- grade boy) ‘‘ythey say I must eat at least two or three vegetables per day, fruitsy’’ (4th-grade boy) ‘‘My mom and dad, we have a dealyif I eat lettuce, I don’t have to eat apples, which I don’t like.’’ (4th-grade girl) ‘‘ymy mother warned me that if I did not eat the vegetables in my plate I would stay all day long eating what was left from my lunchythen I started to eat vegetables.’’ (4th-grade boy) ‘‘When I get something to eat while watching TV he says I can only have two pieces of it and then put it awayybecause if not I keep eatingythen I get fat and out of shapey(3rd-grade boy) Most of the students seemed to accept what their parents were doing, acknowledging that they knew better and were acting in their best interest. A few of them reported getting annoyed, indicating that some degree of conflict was taking place. ‘‘They are rightythey want our healthy’’ (3rd-grade girl) ‘‘We feel hurt, because we cannot have what we wantythey [the parents] go like this: you’ve had enough, now you can’t have it anymorey’’ (2nd-grade girl) Food consumer behavior Many interviewed students reported having money to spend, either as allowance or as pocket money. When prompted to talk about buying food items, every statement obtained referred to buying snack foods, such as sweet and salted snacks, candies, gum, lollypops, and ice pops. Places where they usually shopped were supermarkets, newsstands, shopping malls, and the school cafeteria. These little purchases were often done independently. ‘‘I like candy very muchyonce I went to the mall and we bought a lot of candyythen we went to the play area, won those tokens, you know, traded them all for gumy’’ (4th-grade boy) ‘‘Sometimes my mom gives me money, but it’s only for buying stuff at the school canteenyI buy popsiclesy’’ (1st grade girl) ‘‘yI sneak to the supermarket and buy a lot of stuffyTic Tacsy’’ (3rd-grade boy) TV viewing habits The subject of TV viewing generated much interest. Almost all students reported watching TV daily, at all

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hours, but especially during meals and at night. Some were really passionate about it, while others regarded it as a last resort, when there was nothing more interesting left to do or no friends to play with. Their favorite shows were cartoons and kid’s programs, followed by soap operas and movies. By analyzing the shows they usually watched and the times they were aired, we found that the students watched a lot of adult programming and cable TV. Many students reported having TV sets in their bedrooms, which they left on until they fell asleep. Very few students mentioned watching more TV programs on weekends than on weekdays. ‘‘I will watch anything that is on TVy (1st-grade boy) ‘‘I don’t watch much TV becauseyI’ve got books to read; sometimes I play with my sister or with my dog.’’ (3rd grade girl) ‘‘If I have to choose between using the computer, watching TV or playing soccer, I’d rather play soccer.’’ (4th-grade girl) ‘‘yI try to do other things, but when I have nothing left to do, then I go watch TVy’’ (4th-grade boy) When asked to talk about their parents’ attitudes towards their TV viewing habits, all the students could remember were warnings about the mess caused by bringing food to the living room or the bedroom, or the parents turned the TV off as punishment for some wrongdoing. Absolutely no comments were made about any attempts to control the hours spent by the children in front of the TV, indicating a situation where there seems to be no conflict at all. ‘‘ywhen I am watching cartoons I eat in the living roomymy mom says: you will have to clean everything, try not to make a messy’’ (1st grade boy) ‘‘My father doesn’t want me to eat in his bedroom, but in my bedroom it is oky’’ (1st grade girl) The ingestion of food during TV viewing periods appeared as a very strong habit in the studied population. Virtually, every student admitted eating while watching TV, from simple candies to whole meals, in the kitchen, living room, or bedroom. ‘‘yI always stay in front of the television eating something; I eaty whatever is in front of mey’’ (2nd-grade boy) ‘‘When I get back from school, I go straight to bedytell my grandma to make me a plate of foodythere is a little table, then I eatyand watch cartoonsy’’ (3rdgrade boy) ‘‘ythere was a TV set in my bedroom, then she [my mom] put it in the kitchen to watch the newsythen I watch from the kitchen tabley(4th-grade girl) ‘‘Afternoon snackyand lunchyand dinnery [in front of the TV]’’ (2nd-grade girl) Many students mentioned that eating in front of the TV set clearly interfered in the amount of food they ate. Some


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reported eating more; others said they almost forgot to eat. Either way, watching TV distracted them to a point where they paid no attention to the amount of food being eaten. ‘‘My mother hands me a full plate, and leavesyI stay watching TVyI don’t touch the foodywhen I see that she is coming back, then I remember to eaty’’ (4th grade girl) ‘‘Every day, I am watching Xuxa and my mom tells me to go eat in the kitchenyif she lets me eat in the living room I eat everything, when I eat in the kitchen I eat almost nothingy’’ (1st-grade girl) ‘‘y I turn the TV on, start eating, then sometimes I look more at the television than I look at my foodyI end up eating less!’’ (4th grade girl) ‘‘Sometimes we don’t realize what we are eating, and then we get stuffedy’’ (4th grade girl) Physical activity TV viewing apparently did not keep the students from being physically active. Many attended sports classes before or after school (swimming, soccer, dance), and even more reported active leisure activities at home, outside their apartments, with neighbors and friends (bike, roller skating, soccer). Boys were clearly more active than girls. This was the only difference we could observe between sexes. Some of the boys were so restless that they even reported trying to play soccer inside their apartments. ‘‘ycan’t play ball in the kitchen, can’t play in the living room, can’t play in my room, and can’t play in my sister’s roomy’’ (3rd-grade boy) ‘‘When it’s a hot day and I am allowed to play, my friend goes to my placeyor I go to hisyride our bikesy (3rd grade boy) ‘‘Sometimes I go skating, I do karate, once in a while I go to the University campus with my father, and we play basketball, handball, when I go to the beach I swim a loty’’ (4th grade boy) ‘‘I live in an apartment, my bike is too small, I don’t do sports; I stay in front of the computer, eat and sleep.’’ (4th-grade girl) Discussion In this study, a qualitative methodology was used to investigate TV viewing habits and food choices in a group of primary school students. Results showed that TV viewing was indeed an important part of the children’s leisure hours, and that eating was a habit strongly associated with it. The findings also provided key insights into the parents’ concerns, from the children’s viewpoint. The interviewed students could clearly describe their parents’ interference over their food choices, but not over the time they spent watching TV. Contrary to what we expected, the majority of students reported eating fruits and vegetables regularly, and snack

foods only occasionally but mainly when purchased with their own money. TV has a negative influence in the domestic environment, stimulating the consumption of unhealthy foods and reducing the period during which the parents are the main socializing power in their children’s lives (Hart, Bishop, & Truby, 2002; Harrison, 2005). This seems not to be true for the studied population, since children could easily mention parental attitudes that affected their food choices and eating habits. Some strategies adopted by them, however, could backfire. Restricting children’s access to snack foods makes the restricted foods more attractive, and the pressure to eat may discourage fruit and vegetable intake (Fisher & Birch, 1999; Fisher, Mitchell, Smiciklas–Wright, & Birch, 2002). Children’s food choices are shaped by many factors; some are endogenous to the individual child and others are environmental. These include the foods made available to children and the modeling of food behaviors by caregivers, especially parents, who are responsible for choosing most of the food that makes its way into the family kitchen (Harrison, 2005). Apparently, the food choices made by the children when spending their own money were more difficult to control. Food items purchased by the children as primary consumers were various types of sweet and salted snacks; no fruits or natural fruit juices were mentioned even once. Besides supermarkets, newsstands, and malls, snack food purchases occurred in the school cafeteria. In Floriano´polis, southern Brazil, where this study was conducted, school canteens are prohibited by law to sell candy, gum, lollypops, fried snacks and soda, which certainly limits the unhealthy options (Santa Catarina, 2001). Nevertheless, students can still spend money (and apparently were doing so) with unhealthy snacks such as ice cream, chocolate, mini pizza, hot dogs, and hamburgers. The findings were similar to those reported by Olivares, Yań˜es, and Dı´ az (2003) in Chile that whenever Chilean students had money to buy foods, they bought French fries, chocolates, cookies, and other sweet and salted products rich in fat, soft drinks, yogurt and fast food. Coincidentally, these were the same foods advertised in the ads they liked on TV. We also proved that the children who participated in our study were allowed to watch TV at all hours, apparently without any interference from the parents. They might be heavily exposed to food advertisements for unhealthy products, since these were the most advertised items both in daytime and nighttime TV programming (Almeida, Nascimento, & Quaioti, 2002; Arnas, 2006; Coon & Tucker, 2002; Kuribayashi, Roberts, & Johnson, 2001). It is true that children have a natural fondness for sweets and snacks, but it is also possible that the TV ads are accomplishing their goal and influencing the purchases of the children. Eating in front of the TV was a common situation for the children in this study. Research has shown that children indeed snack more while watching TV (Borzekowski &


Robinson, 2001; Francis, Lee, & Birch, 2003); that snacking while watching TV was associated with increased overall caloric intake and calories from fat (Gore, Foster, Di Lillo, Kirk, & West, 2003); and that TV ads influence the types and amounts of foods they choose to eat (Arnas, 2006; Harrison, 2005). The students in the present study not only snacked, but they were used to eat their meals with the TV on, denoting a family habit that could have nutritional implications. According to Coon et al. (2001), the dietary patterns of children from families in which TV viewing is a normal part of meal routines may include fewer fruits and vegetables, and more pizzas, snack food, and sodas than the dietary patterns of children from families in which TV viewing and eating are separate activities. Brazilian children do not go to school full-time; they either spend mornings or afternoons in school, giving them plenty of spare time to dedicate to leisure activities. What we learned from the students’ opinions was that their TV viewing habits did not stop them from being physically active. Although we could not determine how much of their spare time was dedicated to activities that required energy expenditure, our results differed considerably from previous research that concluded that TV viewing contributes to sedentarism because it displaces more energetic activities (Gortmaker et al., 1996; Hancox et al., 2004). This was unexpected, since previous studies conducted with schoolchildren in Floriano´polis revealed that the prevalence of overweight and obesity was 22.1% (De Assis et al., 2005). When compared to a similar group of French children, the ones from Floriano´polis were heavier, had lower levels of physical activity, and watched more TV (De Assis et al., 2006). A possible explanation for our findings could be that the spare time available either before or after school was enough for the students in the present study to both exercise and watch TV. Comments about the physical environment (living in small apartments, lack of space to play) suggested that the children might have no other option than filling their daily schedules with TV watching. Several studies have come up recently, aimed at reducing the time children spend in front of the TV through educational strategies to be carried out by parents and teachers (Dennison, Russo, Burdick, & Jenkins, 2004; Ford, McDonald, Owens, & Robinson, 2002; Jason & Fries, 2004). Other approaches proposed by the academic community deal with the issue of advertising regulations. In some European countries, like Belgium, it is forbidden to broadcast commercials before, during, and after children’s programs. Sweden has introduced a total ban on TV advertising directed at children under 12 years of age (Valkenburg, 2000). Research is also being conducted about how advertising could be used to promote nutritional messages and influence the adoption of good eating behaviors (Bannon & Schwartz, 2006; McNeal, 2000). Thus, even though they could be considered regular TV watchers, the students in this study benefited from parental guidance that led them to eat fruits and vegetables

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regularly, and found time to exercise. When left on their own, however, the students spent their money with a variety of potentially unhealthy snack foods. Since consumption has become part of the way in which children are brought up and socialized, they should probably receive some kind of formal consumer education, in order to become educated, accomplished consumers (Benn, 2002). In our opinion, the more disturbing findings were the habit of eating while watching TV and the absence of parental control over the children’s TV viewing time. Both situations point to the need of developing educational strategies directed towards the children and their families, addressing the issue of excessive TV viewing and its implications. Aside from the media, schools are influential environments in children’s lives, and could serve as effective mediums in relaying information to promote modification of ‘‘bad’’ behaviors and adoption of new, ‘‘healthier’’ ones. Limitations Several methodological issues need to be considered when interpreting the findings presented in this study. This study’s sample comprised students who volunteered to participate, and therefore may have been more likely to have an interest in matters related to nutrition than others. Also, students may have misreported their eating behaviors to be more socially desirable than they actually are. It also should be noted that the study comprised only one school, therefore, results are necessarily of a local nature and findings may differ from those of other regions or countries. There were, however, a number of strengths to the present study. Conclusions were based on responses from 57 students, which is relatively large for a qualitative study. There was a diverse range of students in the sample, not only from different genders, but also of different ages, and, probably, different classes and parental education levels. This study was of exploratory nature; results suggested that the qualitative approach generated valuable and interesting data that can be tested later on by other methods, such as a quantitative survey to be completed by a larger number of students. Additional work is necessary so that more solid conclusions can be obtained. References Almeida, S. S., Nascimento, P. C. B. D., & Quaioti, T. C. B. (2002). Quantidade e qualidade de produtos alimentı´ cios anunciados na televisaõ brasileira. Revista de Sau´de Pu´blica, 36, 353–355. Arnas, Y. A. (2006). The effects of television food advertisement on children’s food purchasing requests. Pediatrics International, 48, 138–145. Bannon, K., & Schwartz, M. B. (2006). Impact of nutrition messages on children’s food choice: Pilot study. Appetite, 46, 124–129. Bardin, L. (2004). Ana´lise de Conteu´do. Lisboa: Edic- o˜es 70. Benn, J. (2002). Consumer education: educational considerations and perspectives. International Journal of Consumer Studies, 26, 169–177.

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Birch, L. L. (1999). Development of food preferences. Annual Review of Nutrition, 19, 41–62. Borzekowski, D. L., & Robinson, T. N. (2001). The 30-second effect: an experiment revealing the impact of television commercials on food preferences of preschoolers. Journal of the American Dietetic Association, 101, 42–46. Coon, K. A., Goldberg, J. G., Rogers, B. L., & Tucker, K. L. (2001). Relationships between use of television during meals and children’s food consumption patterns. Pediatrics, 107, 1–9. Coon, K. A., & Tucker, K. L. (2002). Television and children’s consumption patterns. A review of the literature. Minerva Pediatrica, 54, 423–436. De Assis, M. A. A., Rolland Cachera, M.-F., Grosseman, S., de Vasconcelos, F. A., Luna, M. E., Calvo, M. C., et al. (2005). Obesity, overweight and thinness in schoolchildren of the city of Florianopolis, Southern Brazil. European Journal of Clinical Nutrition, 59, 1015–1021. De Assis, M. A. A., Rolland-Cachera, M.-F., Vasconcelos, F. A. G., Bellisle, F., Calvo, M. C. M., Luna, M. E. P., et al. (2006). Overweight and thinness in 7–9 year old children from Floriano´polis, Southern Brazil: a comparison with a French study using similar protocol. Revista de Nutric- aõ, 19, 299–308. Dennison, B. A., Russo, T. J., Burdick, P., & Jenkins, P. (2004). An intervention to reduce television viewing by preschool children. Archives of Pediatric and Adolescent Medicine, 158, 170–176. Fisher, J. O., & Birch, L. L. (1999). Restricting access to a palatable food affects children’s behavioral response, food selection and intake. American Journal of Clinical Nutrition, 69, 1264–1272. Fisher, J. O., Mitchell, D. C., Smiciklas-Wright, H., & Birch, L. L. (2002). Parental influences on young girls fruit and vegetable, micronutrient, and fat intakes. Journal of the American Dietetic Association, 102, 58–64. Ford, B. S., McDonald, T. E., Owens, A. S., & Robinson, T. N. (2002). Primary care interventions to reduce television viewing in AfricanAmerican children. American Journal of Preventive Medicine, 22, 106–109. Francis, L. A., Lee, Y., & Birch, L. L. (2003). Parental weight status and girls’ television viewing, snacking and body mass indexes. Obesity Research, 11, 143–151. Gore, S. A., Foster, J. A., Di Lillo, V. G., Kirk, K., & West, D. S. (2003). Television viewing and snacking. Eating Behaviors, 4, 399–405. Gortmaker, S. L., Must, A., Sobol, A. M., Peterson, K., Colditz, G. A., Graham, A., et al. (1996). Television viewing as a cause of increasing obesity among children in the United States, 1986–1990. Archives of Pediatrics and Adolescent Medicine, 150, 356–362. Green, J., & Thorogood, N. (2004). Qualitative methods for health research. London: Sage. Halford, J. C. G., Gillespie, J., Brown, V., Pontin, E. E., & Dovey, T. M. (2004). Effect of television advertisements for foods on food consumption in children. Appetite, 42, 221–225.

Hancox, R. J., Milne, B. J., & Poulton, R. (2004). Association between child and adolescent television viewing and adult health: A longitudinal birth cohort study. Lancet, 364, 257–262. Harrison, K. (2005). Is ‘‘fat free’’ good for me? A panel study of television viewing and children’s nutritional knowledge and reasoning. Health Communication, 17, 117–132. Hart, K. H., Bishop, J. A., & Truby, H. (2002). An investigation into school children’s knowledge and awareness of food and nutrition. Journal of Human Nutrition and Dietetics, 15, 129–140. Heary, C. M., & Hennessy, E. (2002). The use of focus group interviews in pediatric health care research. Journal of Pediatric Psychology, 27, 47–57. Instituto Midiativa. Brasileiro veˆ TV mais de 5 horas por dia. [WWW page]. URL /www.midiativa.org.br/index.php/midiativa/content/ view/full/2717S. Jason, L. A., & Fries, M. (2004). Helping parents reduce children’s television viewing. Research on Social Work Practice, 14, 121–131. John, D. R. (1999). Consumer socialization of children: A retrospective look at twenty-five years of research. Journal of Consumer Research, 26, 1–42. Kuribayashi, A., Roberts, M. C., & Johnson, R. J. (2001). Actual nutritional information of products advertised to children and adults on Saturday. Children’s Health Care, 30, 309–322. McNeal, J. U. (1998). Tapping the three kids’ markets. American Demographics, 20, 37–41. McNeal, J. U. (2000). Children as consumers of commercial and social products. Washington, DC: Pan American Health Organization. O’Dea, J. A. (2003). Why do kids eat healthful food? Perceived benefits of and barriers to healthful eating and physical activity among children and adolescents. Journal of the American Dietetic Association, 103, 497–501. Olivares, S. C., Albala, C. A., Garcia, F. B., & Jofre´, I. C. (1999). Publicidad televisiva y preferencias alimentarias en escolares de la Regioń Metropolitana. Revista Me´dica de Chile, 127, 791–799. Olivares, S. C., Yań˜es, R., & Dı´ az, N. (2003). Publicidad de alimentos y conductas alimentarias en escolares de 51 a 81 ba´sico. Revista Chilena de Nutricioń, 30, 36–42. Ruano, I. R., & Pujol, M. E. S. (1997). Ha´bitos de vida en una poblacioń escolar de Mataro´ (Barcelona) asociados al nu´mero de veces diarias que se ve televisioń y al consumo de azućares. Revista Espanõla de Salud Pu´blica, 71, 487–498. Santa Catarina. Lei n1 12.061, de 18 de dezembro de 2001. Dispo˜e sobre crite´rios de concessaõ de servic- os de lanches e bebidas nas unidades educacionais, localizadas no Estado de Santa Catarina. Lex: Dia´rio Oficial do Estado de Santa Catarina, p. 1, 20 dez, 2001. Strasburger, V. C. (2004). Children, adolescents and the media. Current Problems in Pediatric and Adolescent Health Care, 34, 54–113. Valkenburg, P. M. (2000). Media and youth consumerism. Journal of Adolescent Health, 27(Suppl.), 52–56.

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Research report

Repeated cue exposure effects on subjective and physiological indices of chocolate craving Dinska Van Gucht, Debora Vansteenwegen, Tom Beckers, Dirk Hermans, Frank Baeyens, Omer Van den Bergh Department of Psychology, University of Leuven, Tiensestraat 102, 3000 Leuven, Belgium Received 2 February 2007; received in revised form 19 April 2007; accepted 9 May 2007

Abstract The aim of this study is to investigate the effects of repeated unreinforced exposure to chocolate cues in persons reporting chocolate craving. Participants in the experimental group (n ¼ 40) received 10 consecutive brief exposures to chocolate cues in each of two sessions, separated by 1–3 days. Control participants (n ¼ 18) received two exposures at the start and end of each session. Chocolate craving was measured (alternately) through subjective report and the amount of saliva secretion to chocolate cues. Results showed a between-sessions decrease in both craving measures in the experimental group, whereas no differences in craving between sessions were observed in the control group. These results provide evidence for the effects of cue exposure treatment in chocolate craving. r 2007 Elsevier Ltd. All rights reserved. Keywords: Chocolate craving; Cue exposure; Salivation

Introduction Food craving, which is defined as an intense desire or longing (Kozlowski & Wilkinson, 1987; Weingarten & Elston, 1990) to eat a particular food (Marlatt, 1987; Rozin, 1976), is a common phenomenon in the general population (Weingarten & Elston, 1991). In Western cultures, the food most frequently craved is chocolate, especially among women (Pelchat, 1997; Rogers & Smit, 2000; Rozin, Levine, & Stoess, 1991). Although for the majority of people chocolate craving is not harmful (Lafay et al., 2001), it may be subjectively experienced as unwanted, being an element of an unhealthy life style and possibly contributing to weight problems. In some cases, it may become a real problem when it rises to the level of binge eating (Kales, 1990). Therefore, it is important to gain insight into the craving process and to develop ways that can help to reduce chocolate craving (Kemps, Tiggemann, & Hart, 2005). Moreover, given that chocolate craving is highly prevalent Corresponding author.

E-mail address: [email protected] (O. Van den Bergh). 0195-6663/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.appet.2007.05.003

and, unlike for example alcohol dependency and binge eating (Jansen, 1998), much less confounded by psychiatric comorbidity, it allows craving and craving reduction techniques to be investigated in a relatively pure manner in easily accessible populations (Weingarten & Elston, 1991). Only a few studies have hitherto been devoted to the reduction of chocolate craving, none of which has yielded clear results. One study looked at suppression of cravingrelated thoughts, but this did not seem to have a substantial effect (Johnston, Bulik, & Anstiss, 1999). Kemps et al. (2005) recently found that visuo-spatial working memory-based techniques reduced imagery vividness and self-reported chocolate craving in both female chocolate cravers and non-cravers. However, contrary to what was predicted, irrelevant speech also reduced both vividness and craving, be it to a lesser extent. For any attempt to reduce chocolate craving, it is relevant to understand the critical cues and processes underlying the craving response to chocolate. Several accounts have been proposed (e.g., Rozin et al., 1991), which we will briefly discuss. Chocolate is known for its ‘‘melt in the mouth’’ sensation that is attributed to one of its main fat

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ingredients, cocoa butter. Chocolate is also high in sugar. The combination of sugar and fat has a particular appeal (Drewnowski & Greenwood, 1983). Chocolate also has a uniquely attractive aroma. As a result, the taste and flavor of chocolate have a high incentive value (Rozin et al., 1991). Comparing the role of sensory and pharmacological properties of chocolate revealed that the former but not the latter satisfied the craving response (Michener & Rozin, 1994). In line with this, chocolate cravers reported that non-chocolate substitutes were inadequate to abate their craving (Polivy, Coleman, & Herman, 2005; Weingarten & Elston, 1991). In sum, results suggest that pharmacological factors play little—if any—role in the satisfaction of craving (Rogers & Smit, 2000). Despite findings that depressed mood may increase chocolate craving (Willner et al., 1998) and that people report to self-medicate with chocolate (Schuman, Gitlin, & Fairbanks, 1987), a recent review revealed that the moodenhancing effect of chocolate consumption is short-lived and that chocolate prolongs a dysphoric mood when consumed for comforting (Parker, Parker, & Brotchie, 2006). Women often report a stronger craving for chocolate in their perimenstruum (e.g., Hill & Heaton-Brown, 1994), suggesting a role for sex hormones (Bruinsma & Taren, 1999). However, Spanish women reported much less premenstrual chocolate craving than American women, suggesting a cultural origin (Osman & Sobal, 2006; Zellner, Garriga-Trillo, Centeno, & Wadsworth, 2004). One way to frame this is by assuming that chocolate craving is triggered by classically conditioned food cues. Zellner and Edwards (2001) state that conditioning is strongly involved in the production of food cravings; neutral stimuli or cues that have been associated with food intake can, over time, elicit reactivity that can be experienced as craving for the food. So, with repeated consumption of chocolate during the perimenstruum, moods and typical feelings during this period may become conditioned cues for the high incentive value of the sensory properties of chocolate. Chocolate craving is then seen as a mediator of cue-induced eating. Craving or urge to use a given drug is considered an important source of maintenance and relapse in addiction literature (Drummond, Tiffany, Glautier, & Remington, 1995; Jansen, 1998). Exposure-based therapies, inspired by Pavlovian conditioning models of addiction, aim to extinguish this craving. These therapies consist of repeated exposures to drug cues while preventing drug use (response prevention) and have proven successful to extinguish craving. Given its success in the domain of alcohol abuse (Drummond et al., 1995), bulimia nervosa (Carter, McIntosh, Joyce, Frampton, & Bulik, 2006) and binge eating (Jansen, 1998; Jansen, Broekmate, & Heymans, 1992), the conditioning model of food craving (Jansen, 1998; Zellner & Edwards, 2001) seems to provide the best heuristic tool when trying to devise techniques to reduce chocolate craving. Specifically, Jansen et al. (1992) have

demonstrated cue exposure to be efficacious in binge eaters, reducing the binge frequency. Nevertheless, techniques for chocolate craving reduction based on the conditioning model have not been put to empirical test yet. In the present study, we repeatedly presented chocolate cues to chocolate cravers in a clinical analogue study in two consecutive sessions. Chocolate craving was measured in two ways and this alternately: By subjective report and by the amount of saliva secretion to chocolate cues, using the cotton roll method. This method appears to yield a sensitive, valid and reliable index of craving (Nederkoorn, de Wit, Smulders, & Jansen, 2001; Tuomisto et al., 1999; White, 1977). Research on addiction has shown that effects of exposure are often limited to the context in which exposure is conducted (e.g. Thewissen, Snijders, Havermans, van den Hout, & Jansen, 2006). Therefore, the experiment also contained a mood induction manipulation aimed at exploring to what extent reduction of craving would generalize to different mood contexts. However, our mood manipulation failed to induce the intended mood changes (see infra); therefore, the contextual control of chocolate craving could not be addressed. Method Participants Participants (n ¼ 58) were recruited on the basis of a questionnaire, administered to 335 students, that labeled them as chocolate cravers. They met the criterion for chocolate craver when they reported to (a) be ‘‘very bad’’/ ‘‘bad’’/‘‘rather bad’’ at postponing a chocolate craving, (b) ‘‘rather like’’/‘‘very much like’’ to gain more control over their chocolate craving and (c) find it ‘‘neutral’’/ ‘‘rather difficult’’/‘‘very difficult’’ to gain more control over their chocolate craving. All were female psychology students at the University of Leuven, aged between 20 and 24 years (M ¼ 20.71, SD ¼ 0.80). All participated on a voluntary basis. Measures Chocolate craving was measured, alternately, through subjective report and the amount of saliva secretion to chocolate cues. Subjective report Self-reported craving was measured using an online scale. The scale, presented on a computer screen in front of the participant, ran from bottom to top and ranged from 0 (no craving for chocolate at all) to 100 (extreme craving for chocolate). It was divided in 10 boxes representing 10 units each. Above the scale the question ‘‘How strong is your craving for chocolate at this moment?’’ appeared. During the time of an exposure trial (2 min), participants were instructed to rate their craving at any time, but at

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least every time they heard a tone (every 30 s; at 0.50 , 10 , 1.50 and 20 into the trial). They were told that their craving could either change or not and that it was important to reevaluate their craving at every specific moment. Since participants hardly ever reported their craving in absence of the tones, only the four scores reported at the times of the tones were taken into account for the analyses. Per trial a craving score was calculated by taking the mean of those four scores. Saliva secretion The amount of salivation was measured by weighing the amount of saliva absorbed by rolls of cotton before and after application to the mouth. This amount can be interpreted as an indicator of craving (Nederkoorn et al., 2001). Participants were shown where and how to place the cotton rolls at fixed locations in the mouth, consistently in the same order (left, right, middle), and how to take them out. It was emphasized that swallowing needed to be restricted as much as possible. After each cotton roll measurement participants were instructed to take a little sip of water. Procedure The experiment consisted of two 1-h sessions with 1–3 days between sessions. Participants were instructed not to eat nor drink coffee 3 h prior to the sessions and to abstain from chocolate and other candies 24 h prior to the sessions. Each participant was asked beforehand what kind and brand of chocolate she preferred; this was then used for cue exposure. The experiment also implied mood induction manipulations in order to explore to what extent reduction of craving would generalize to different mood contexts. In the first session half of the participants were shown a film fragment (4 min, 40 s) from Dead poets society (Witt, Thomas, & Weir, 1989) intended to induce a negative mood. The other half watched two film fragments: one (3 min, 26 s) from A fish called Wanda (Shamberg & Crichton, 1988) and the other (3 min, 32 s) from There’s something about Mary (Farrelly & Farrelly, 1998) intended to induce a positive mood (clips were selected on the basis of film excerpts used in previous research by Schaefer, Nils, Sanchez, & Philippot, 2007). In the second session participants were instructed to look at both the negative movie clip and the two positive fragments, to see whether reduced craving would generalize to the same and a different mood context. The valence of the mood induction (first session) and the order (positive or negative mood induction; second session) were counterbalanced across participants. To measure emotional changes the Positive and Negative Affect Schedule (PANAS) Scales (Watson, Clark, & Tellegen, 1988) were used (Dutch validated version: Engelen, De Peuter, Victoir, Van Diest, & Van den Bergh, 2006). The mood manipulations did not yield the

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intended effects1 and mood-related aspects will not be discussed further. First the two sessions for the experimental group (n ¼ 40) will be described. Then the procedural differences for the control group (n ¼ 18) will be mentioned. Session 1 Participants were first asked to fill out an informed consent form, a biographical questionnaire and the PANAS Scales. An exercise trial with the cotton rolls followed, which served as a baseline measurement of saliva secretion (BS ¼ baseline salivation). Following BS measurement, participants were exposed to chocolate for the first time. The chocolate (an unwrapped bar) was presented on a plate, which was placed behind the monitor, out of sight of the participant. When an exposure trial started, the experimenter placed the plate in front of the participant. At the end of each trial, the plate was again placed behind the monitor. All cue exposure trials lasted 27 min, during which time participants either had to report their craving online or salivation was measured. Participants were instructed to first look at the chocolate for 30 s, and to then hold the chocolate for another 30 s, followed by 1 min of constantly smelling the chocolate. At the end of the first trial only, participants were allowed—after smelling the chocolate—to taste a very small piece. Craving during the first exposure trial was measured by means of an online scale (C1 ¼ first online craving). A second exposure trial followed (S0 ¼ salivation measure) after approximately 1 min. After this trial, film fragments were shown, followed by four cue exposure trials. Craving was measured alternately by means of the cotton rolls and the online scale (S1, C2, S2, C3). Next, the same film fragments were presented and followed by another four cue exposure trials (S3, C4, S4, C5). The last trial (C5) lasted for 10 min (9 min of constant smelling, with a tone probing for online craving every 30 s) or until a participant rated her craving to be lower than 10 on the online scale. In sum, the first session consisted of 10 cue exposure trials with five salivation measurements and five online craving measurements, in the following order: (BS), C1, S0, (film), S1, C2, S2, C3, (film), S3, C4, S4, C5. At the end of the session participants were again asked to fill out the PANAS Scales. Session 2 This session was similar to the first session, with the following exceptions. Participants were now presented with both the negative and the positive mood movie clips. The first clip was presented between S0 and S1, the second between C3 and S3. An additional set of PANAS Scales 1 Apart from a significant induction effect in the NA-scores for session 1, F(1, 56) ¼ 4.93, po.05; our mood manipulations failed to result in reliable mood change effects. Consequently, mood induction was not included as a factor in statistical analyses.


Session 1 Session 2

80 60 40 20

Experimental group

Results Online craving scores As can be seen in Fig. 1, there was a significant decrease from session 1 to session 2 for the experimental group, F(1, 56) ¼ 35.14, po.001, whereas there was no difference between sessions for the control group, Fo1. The group session interaction was significant, F(1, 56) ¼ 11.91, po.01, but the group session trial interaction was not, F(2, 112) ¼ 1.54, n.s. In the experimental group, we observed no decrease in craving from start to end (C1–C5) in session 1, Fo1, and even an increase in session 2, F(1, 56) ¼ 7.59, po.01. From the beginning of the experiment (C1 in session 1) to the end (C5 in session 2), a significant decrease in reported craving was observed, F(1, 56) ¼ 6.08, po.05. In the control group, we saw an increase in reported craving from C1 to C5 both in session 1, F(1, 56) ¼ 6.42, 2

Right before the S0 measure, participants were instructed to taste a small piece of chocolate. Due to the chocolate tasting right before, the S0 trial can be seen as a maximal amount of saliva secreted and is therefore not included in the analysis.

C5

M(C2,C3,C4) Control group

Session 1 Session 2

1.60 1.50 1.40 1.30 1.20 1.10

Experimental group

Trial

S4

M(S1,S2,S3)

BS

S4

M(S1,S2,S3)

1.00 BS

A 2 2 3 (group session trial) ANOVA was performed on the amount of saliva secreted. The three trials that were included were (1) the BS, (2) the mean of S1, S2 and S3 since these were the three trials where the experimental group was exposed to chocolate whereas the control group was not [M(S1,S2,S3)], (3) and the last exposure trial S4.2 Similarly, a 2 2 3 (group session trial) ANOVA was run on the online craving scores, with the following three trials: (1) C1, (2) the mean of C2, C3 and C4 since these were the three trials where the experimental group was exposed to chocolate whereas the control group was not [M(C2,C3,C4)], (3) and the last exposure trial C5. We followed these ANOVAs up with planned comparisons to check for effects of exposure within and between sessions in both groups.

Trial

Fig. 1. Mean chocolate craving scores on the online scale ranging from 0 (no craving for chocolate at all) to 100 (extreme craving for chocolate) (+SE) for the experimental and control group, by session and trial, with C1 ¼ first online craving measure; M(C2,C3,C4) ¼ the mean of the trials C2, C3 and C4; C5 ¼ last online craving measurement.

Saliva secreted in grams

Data analysis

C1

C5

0 M(C2,C3,C4)

Control group Participants in the control group were treated in the same way as participants in the experimental group, except that they only received exposure to chocolate at the beginning of each session and at the end. More precisely, they were only exposed to chocolate at C1, S0, S4 and C5. Their craving was measured in the same way as for participants in the experimental group at the other trials (S1, C2, S2, C3, S3 and C4) but without chocolate exposure.

100

C1

was administered right before the second clip. The last exposure trial (C5) lasted 2 min.

Mean reported online craving

22

Control group

Fig. 2. Mean amount of saliva secreted in grams (+SE) for the experimental and control group, by session and trial, with BS ¼ baseline saliva; M(S1,S2,S3) ¼ the mean of the trials S1, S2 and S3; S4 ¼ last salivation measurement.

po.05, and in session 2, F(1, 56) ¼ 9.67, po.05. From the beginning of the experiment (C1 in session 1) to the end (C5 in session 2), a significant increase in reported craving was observed, F(1, 56) ¼ 4.18, po.01 [interaction between groups: F(1, 56) ¼ 9.43, po.01]. The mean reported craving at C2, C3 and C4 (when participants were not exposed) was significantly lower than at C1 and C5 (when exposure did take place) in both sessions, F(1, 56) ¼ 78.72, po.001. Note that subjective craving at C1 did not differ between groups in session 1, F(1, 56) ¼ 2.35, p4.05. Amount of salivation As can be seen in Fig. 2, there was a marginally significant decrease from session 1 to session 2 for the experimental group, F(1, 56) ¼ 3.52, p ¼ .07, whereas there was no difference between sessions at all for the control


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group, Fo1. Similar to the online craving data, the mean amount of saliva secreted in session 2 was lower than in session 1 in the experimental group but not in the control group. However, neither the three-way interaction, nor the group by session interaction turned out significant, Fo1. Note that BS did not differ between groups in session 1, Fo1.

physiological, behavioral) for tracking extinction learning’’ (p. 165). Their plea is consistent with Bouton’s (2000) and Havermans and Jansen’s argument (2003) that learning theory can provide a strong model to conceptualize problems of maintenance and change of health-related behavior. The current experiment constitutes one of the first steps in this direction.

Discussion

Acknowledgments

In this clinical analogue study, chocolate cravers were repeatedly exposed to their favorite chocolate in two consecutive sessions in order to extinguish their cueinduced craving. Both subjective craving and the amount of saliva secretion were measured. In the experimental group, reported craving for chocolate was not reduced within a single session of repeated exposure to the cues, but did diminish between sessions and from the beginning of the experiment to the end. In contrast, in the control group, no differences between sessions were found and reported craving increased within both sessions and from the beginning to the end of the experiment. These results show that a cue-exposure manipulation was effective in reducing subjective craving in the experimental group and not in the control group. The data pattern that emerges for salivation was in line with these findings, rendering it unlikely that the subjective craving results would be due to mere demand. In general, our data are in accordance with a conditioning model in showing that exposure is effective to reduce chocolate craving. This mirrors the positive effects of exposure-based therapies for bulimia nervosa, alcoholism and phobias (e.g. Carter et al., 2006; Drummond et al., 1995; O¨st, 1997). However, also differences emerged. For example, a typical within-session pattern during exposure to fear cues in anxiety disorders implies an initial increase in fear reactions followed by a continuous decrease until the end of the exposure session, while fear is reduced across trials (Foa & Kozak, 1986). The present results are in line with the between-session, but not with the within-session part. Possibly, different expectations may play a role here: Exposure to fear cues is typically framed in a fear reduction context, whereas in the present study no clear information was given as to the course of the sessions. It is likely that at the end of a session participants were still expecting to receive chocolate, possibly leading to a within-session increase of craving. Another possible explanation is the short duration of our experiment (28 min in session 1; 20 min in session 2); Jansen et al. (1992) found that craving in binge eaters only extinguished after half an hour of cue exposure. Although cue exposure has been proven efficient for the treatment of alcoholism (Drummond et al., 1995), a review by Conklin and Tiffany (2002) concluded that no consistent evidence was available for the efficacy of cue exposure for the treatment of other addictions and that there is a need for ‘‘cue-reactivity work investigating the value of different response measures (e.g. self-report,

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Research report

Validation of a scale for the assessment of food cravings among smokers Benjamin A. Tolla,, Nicole A. Katulakb, Pamela Williams-Piehotab,1, Stephanie O’Malleya a

Department of Psychiatry, Yale University School of Medicine, 1 Long Wharf Drive—Box 18, New Haven, CT 06511, USA b Department of Psychology, Yale University, 1 Long Wharf Drive—Box 18, New Haven, CT 06511, USA Received 8 May 2007; accepted 11 May 2007

Abstract Weight gain associated with smoking cessation impedes attempts to quit smoking and may lead to obesity. One factor that might contribute to weight gain is cravings for sweet or rich foods. To date, no reliable measure exists for evaluating these cravings. The purpose of the current study was to validate an assessment of craving for sweet or rich foods for use among smokers. With a sample of 385 smokers enrolled in a clinical trial for smoking cessation, the study examined the factor structure, internal consistency, and convergent and predictive validity of the Questionnaire on Craving for Sweet or Rich Foods (QCSRF). A two-factor model best represented the data. Factor 1 contained six items assessing perceptions about the ability of sweet or rich foods to relieve negative affect and about self-control over eating. Factor 2 contained three items assessing the intensity of cravings. Both factors demonstrated high internal consistency and good convergent and predictive validity. These results suggest the QCSRF is a reliable and valid measure for examining cravings for sweet or rich foods among smokers. r 2007 Elsevier Ltd. All rights reserved. Keywords: Food craving; Sweet; Rich; Smoking; Weight gain

Introduction People who quit smoking gain an average of 10 to 13 pounds within the first 1 to 2 years of quitting (Hudmon, Gritz, Clayton, & Nisenbaum, 1999; Klesges et al., 1997). This weight gain often prevents successful attempts to quit smoking (Borrelli & Mermelstein, 1998; Meyers, Klesges, Winders, Ward, Peterson, & Eck, 1997; Ockene et al., 2000) and may lead to obesity (Koh-Banerjee et al., 2003). Postcessation weight gain has been attributed to several processes, including changes in metabolism related to a reduction in nicotine intake (Perkins, 1992) and an increase in the body weight set point among smokers who quit (Cabanac & Frankham, 2002; Perkins, 1993). Another primary contributing factor to weight gain after quitting smoking is eating more (Perkins, 1993). After smoking cessation, individuals may eat more calorie-dense foods Corresponding author. Tel.: +203 974 5767; fax: +203 974 5790.

E-mail address: [email protected] (B.A. Toll). Pamela Williams-Piehota, Ph.D. is now at Research Triangle Institute, Research Triangle Park, North Carolina. 1

0195-6663/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.appet.2007.05.001

(Grunberg, 1982; Hall, McGee, Tunstall, Duffy, & Benowitz, 1989) and more total calories per day (e.g, Hatsukami, LaBounty, Hughes, & Laine, 1993; Perkins, Epstein, & Pastor, 1990; Spring, Wurtman, Gleason, Wurtman, & Kessler, 1991). Increases in caloric intake during smoking abstinence tend to occur in the presence of simultaneous cigarette and food cravings (Kos, Hasenfratz, & Battig, 1996), and in general, increased caloric consumption has been associated with food craving, particularly craving for sweet or rich foods (Mercer & Holder, 1997). Research on food craving has been somewhat scarce (see Weingarten & Elston, 1990 for a review) and specific to studies involving bulimics (Waters, Hill, & Waller, 2001), menstruating (Cohen, Sherwin, & Fleming, 1987; Kurzer, 1997; Michener, Rozin, Freeman, & Gale, 1999) or pregnant women (Worthington-Roberts, Little, Lambert, & Wu, 1989), and ‘‘chocolate addicts’’ (Bruinsma & Taren, 1999; Macdiarmid & Hetherington, 1995). However, this limited literature on food craving among these specific populations bears many similarities to larger bodies of research on craving in other domains (Weingarten & Elston, 1990). Research on craving for cigarettes

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B.A. Toll et al. / Appetite 50 (2008) 25–32

(Heishman, Singleton, & Moolchan, 2003; Shadel, Niaura, & Abrams, 2004; Willner, Hardman, & Eaton, 1995), alcohol (Sinha & O’Malley, 1999), and other drugs of abuse (Weinstein, Feldtkeller, Malizia, Wilson, Bailey, & Nutt, 1998) is far more plentiful and provides a window into the areas of food craving that have yet to be explored. One such topic that has been investigated only minimally in food craving research is its conceptualization. That is, does craving represent a unified construct, or does it have a multidimensional nature? A review of drug craving measures supports the idea that craving is best described and assessed in terms of multiple constructs (Sayette, Shiffman, Tiffany, Niaura, Martin, & Shadel, 2000; Tiffany & Drobes, 1991). Accordingly, studies on cigarette and marijuana use have conceptualized craving with separate constructs relating to the intensity of desires or cravings, the anticipation of the immediate effects of fulfilling cravings or urges, and the intentions to satisfy these cravings (Heishman, Singleton, & Moolchan, 2003; Singleton, Trotman, Zavahir, Taylor, & Heishman, 2002; Tiffany, 1990). The Food Craving Questionnaire (FCQ; Cepeda-Benito, Gleaves, Williams, & Erath, 2000), used primarily in eating disorders research, also characterizes craving as multidimensional, with factors representing constructs similar to those used to conceptualize drug craving. Another well-researched aspect of cravings for tobacco and other drugs, which has shed some light on preliminary food craving research, involves the biochemical mechanisms hypothesized to underlie craving. Several neurological pathways have been implicated in drug use, craving, and addiction including those related to neurotransmitters such as dopamine, opioid peptides, serotonin, GABA, and glutamate (Koob, 2000). Like drug craving, craving for foods, especially those high in sugar and fat, may also involve the opioid reward system (e.g., Drewnowski, 1992, Mercer & Holder, 1997) and the serotoninergic system (Wurtman & Wurtman, 1995). In particular, changes in endogenous opioid activity may elicit food craving and result from food intake (Mercer & Holder, 1997), and certain levels of brain serotonin may trigger cravings for foods rich in carbohydrates and fats and result from satisfying these cravings (Wurtman & Wurtman, 1995). The notion that the same biological processes and chemicals associated with cravings for substances of abuse are implicated in cravings for sweet or rich foods is of particular interest to researchers of smoking cessation and weight gain. Because both eating sweet, rich foods and smoking may affect levels of endogenous opioids and serotonin, cravings for sweet or rich foods and cigarette cravings may co-occur during attempts to quit smoking. Furthermore, satisfaction of either type of craving may alleviate the need to satisfy the other. Accordingly, several controlled studies have shown that food deprivation increases self-administration of nicotine (de la Garza & Johanson, 1987; Donny, Caggiula, Mielke, Jacobs, Rose, & Sved, 1998) and nicotine craving (Saules, Pomerleau,

Snedecor, Brouwer, & Rosenberg, 2004) and that smoking deprivation increases food consumption (Hatsukami, LaBounty, Hughes, & Laine, 1993), particularly that of sweet foods (Rodin, 1987). Hence, some smokers attempting cessation may experience cravings for sugary foods, which tend to be high in fat and calories and to lead to weight gain. Although research on the relationship between smoking cessation and sweet cravings is equivocal (e.g., Pomerleau, Garcia, Drewnowski, & Pomerleau, 1991), studies indicating that glucose may reduce cigarette cravings and withdrawal discomfort after quitting smoking (McRobbie & Hajek, 2004; West, 2001) support this idea. Despite studies that point to a relationship between smoking and craving for sweet or rich foods, a standard scale for testing this relationship or these types of cravings has not been developed. Various measures of food craving have been created for specific use among eating-disordered and other populations (e.g., Gendall, Joyce, Sullivan, & Bulik, 1998), and scales have been designed to measure cravings very narrowly with lists of specific foods (Harvey, Wing, & Mullen, 1993; White, Whisenhunt, Williamson, Greenway, & Netemeyer, 2002) or very broadly with questions about food craving in general (Cepeda-Benito, Gleaves, Williams, & Erath, 2000). Still, no published scale exists to assess cravings for sweet or rich foods, despite evidence suggesting that foods from this category may be craved often, particularly among smokers, and may contribute largely to excess caloric intake and weight gain associated with quitting smoking. Thus, the purpose of the current study was to refine and validate a self-report questionnaire for the assessment of craving for sweet or rich foods among smokers. To this end, this study examines the factor structure, internal consistency, and convergent and predictive validity of the Questionnaire on Cravings for Sweet or Rich Foods (QCSRF).

Method Participants The study sample included 385 daily smokers (48.1% female; 87.3% white) enrolled in a clinical trial comparing the effectiveness of the nicotine patch in combination with placebo or naltrexone for smoking cessation (O’Malley et al., 2006). Participants were eligible for the trial if they were at least 18 years of age, smoked an average of at least 20 cigarettes per day for at least one year, and had a baseline expired carbon monoxide (CO) level of at least 10 ppm. Participants were excluded for unstable medical or psychiatric problems, current alcohol dependence, or opiate use. The mean age of eligible participants was 45.95 (SD ¼ 11.17), and the mean number of cigarettes smoked per day was 27.70 (SD ¼ 10.30). On average, participants reported having smoked for 28.67 years (SD ¼ 11.09).

ARTICLE IN PRESS B.A. Toll et al. / Appetite 50 (2008) 25–32

Materials The Questionnaire on Craving for Sweet or Rich Foods (QCSRF) is a 14-item, self-report measure adapted from the Alcohol Urge Questionnaire (Bohn, Krahn, & Staehler, 1995) by M.J. Bohn and D. Krahn (personal communications), who originally developed these items to assess food cravings among alcohol dependent individuals whose reported cravings for sweet and rich foods increase after abstaining from alcohol. The QCSRF contains one question on craving sweet or rich foods ‘‘at this moment’’ and five questions on craving these foods ‘‘during the past week,’’ which are rated on a seven-point scale from ‘‘none at all’’ to ‘‘more than ever.’’ Eight additional questions assess current cravings and are formatted according to the Alcohol Urge Questionnaire (Bohn, Krahn, & Staehler, 1995), with cravings rated on a seven-point Likert-type scale from ‘‘strongly disagree’’ to ‘‘strongly agree’’. The Weight Control Subscale of the Smoking Consequences Questionnaire-Adult (SCQ; Copeland, Brandon, & Quinn, 1995) is a 5-item subscale of the 55-item selfreport SCQ that assesses nicotine expectancy factors (negative consequences, positive reinforcement, sensory satisfaction, negative reinforcement, negative affect reduction, and weight control). Subjects rate the likelihood of each item on a 10-point Likert scale from 0 to 9. The SCQ has been validated among current smokers, ex-smokers, and individuals entering smoking cessation treatment and has demonstrated good reliability (subscale coefficient alphas ranged from 0.83 to 0.96.) Scores on the Weight Control Subscale of the SCQ are consistently higher for women than men (Cepeda-Benito & Ferrer, 2000; Copeland, Brandon, & Quinn, 1995). The Dieting and Bingeing Severity Scale (DBSS; Kurth, Krahn, Nairn, & Drewnowski, 1995) is a 25-item survey that classifies individuals according to the frequency and severity of dieting and bingeing behaviors. The DBSS has been validated with the Structured Clinical Interview for DSM-IIIR (SCID) in a sample of college women and has demonstrated adequate reliability (Cronbach’s alpha ¼ 0.77). The DBSS is useful for identifying risk factors for developing eating disorders including binge eating, excessive weight control behaviors, and extreme concerns about eating and body weight/shape. Individuals with excessive concerns about weight and shape are identified according to their responses to the following three items: ‘‘I feel out of control when I eat’’; ‘‘I am terrified of gaining weight’’; and ‘‘I feel satisfied with the shape of my body’’. These items have the following descriptors: ‘‘never’’, ‘‘rarely’’, ‘‘sometimes’’, ‘‘often’’, ‘‘always’’. To meet criteria for excessive concerns, respondents need to endorse at least two of these questions as either ‘‘often’’ or ‘‘always’’ on the first two questions or ‘‘never’’ or ‘‘rarely’’ on the third question. Basic demographic information (e.g., age, gender, and race) and smoking background were assessed at baseline. Smoking background included current smoking status as well as smoking history, such as number of cigarettes

27

smoked per day, number of years smoked, number and length of quit attempts, and amount of weight gained in previous attempts to quit smoking. The Timeline Followback interview method (Sobell & Sobell, 1992, 2003) was used to obtain reports of daily cigarette use at baseline and during treatment. Carbon monoxide levels (CO) were also obtained to verify smoking abstinence. Procedure The QCSRF, DBSS (Kurth et al., 1995), and the Weight Control Subscale of the SCQ (Copeland, Brandon, & Quinn, 1995) were administered, as parts of a larger battery of questionnaires, to all participants as baseline assessments before they attempted to quit smoking. At intake and at each of the six weekly appointments during treatment, weight, carbon monoxide levels (CO) and reports of daily cigarette use were obtained. The QCSRF was readministered at each of these appointments during treatment. During treatment, all subjects received 21 mg transdermal nicotine patches for six weeks, beginning on the day they were scheduled to quit smoking. In addition, subjects were randomized to receive either placebo naltrexone or one of three doses of active naltrexone beginning on the day after their quit date. Smoking abstinence during treatment was defined as no smoking, not even a puff, and was verified by an exhaled CO level of 10 ppm or less. Additional details about the study can be found in O’Malley et al. (2006). Baseline data for the entire sample was used to investigate the factor structure and the convergent validity of the QCSRF. Predictive validity was tested only for those in the placebo naltrexone group (n ¼ 90). Results Factor analysis A principal components analysis with varimax rotation was performed on the 14 items comprising the QCSRF using SPSS 11.0 for Windows (SPSS, Inc., 2000). Rotation was used to enhance the scientific utility and interpretability of the component solution (Tabachnick & Fidell, 1996). This analysis revealed three factors with eigenvalues greater than 1, accounting for 66.1% of the total variance. However, the scree plot demonstrated a break in slope between components 2 and 3, suggesting a two-factor solution (Zwick & Velicer, 1986). In addition, the third component accounted for only 7.5% of the variance and included only two items with meaningful loadings. Therefore, a second principal components analysis was conducted with varimax rotation that forced a two-factor solution. To demonstrate good simple structure, only items that loaded over 0.40 on one factor and less than 0.40 on all other factors were assigned to factors (Hatcher, 1994). Factor 1 contained six items, and Factor 2 contained

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three items. These items and their loadings are displayed in Table 1. The content of the items from each factor was examined and used to develop factor interpretations and subscale/ factor names. Factor 1 appeared to assess perceptions about the ability of sweet or rich foods to relieve negative affect and about levels of self-control over eating. Therefore, this factor was named ‘‘Relief/Control.’’ Higher scores on this factor represented higher expectations that food would relieve negative affect and lower expectations that one would have control over eating. Factor 2 measured the intensity of desire for sweet or rich food in the past week; therefore, this factor was named ‘‘Intensity.’’ Higher scores on these items related to more intense urges. Reliability In order to evaluate the degree of homogeneity or consistency of the items within the scale for each factor, alpha reliability coefficients were calculated. Internalconsistency reliability of the QCSRF was good for both the Relief/Control Factor (coefficient alpha ¼ 0.87) and the Intensity Factor (coefficient alpha ¼ 0.90; DeVellis, 1991). Convergent validity Research suggests that individuals who engage in weight control efforts or who are concerned about controlling their weight experience food cravings more often than individuals who are not concerned about controlling their weight (Gendall, Sullivan, Joyce, Fear, & Bulik, 1997; Lafay et al., 2001; Pelchat, 1997). Therefore, we compared QCSRF scores to scores on the Weight Control Subscale of the SCQ (Copeland, Brandon, & Quinn, 1995) and on the

Dieting and Bingeing Severity Scale (Kurth et al., 1995) in order to examine convergent validity (the evidence of similarity between two related constructs; DeVellis, 1991). For the SCQ, we first classified participants into two groups based on their SCQ Weight Control subscale scores: those who smoked for weight control (i.e., weightcontrol smokers; the top third of the sample) and those who did not (i.e., non-weight-control smokers; the bottom third of the sample). Then, QCSRF scores were analyzed using independent samples t-tests for each factor. For the Relief/Control Factor, there was a significant difference between the groups, t (256) ¼ 3.56, po0.05, with weightcontrol smokers having higher scores (M ¼ 8.71, SD ¼ 4.37) as compared to non-weight-control smokers (M ¼ 6.85, SD ¼ 4.01). For the Intensity Factor, there was also a significant difference between groups, t (257) ¼ 2.83, po0.05, with weight-control smokers again having higher scores (M ¼ 12.89, SD ¼ 7.16) as compared to the non-weight-control group (M ¼ 10.52, SD ¼ 6.21). These results support the idea that food cravings are experienced more in individuals who smoke for weight control and provide evidence of good convergent validity for both factors of the QCSRF. We then classified individuals according to whether or not they had excessive concerns about weight and body shape based on DBSS scores. Individuals who met DBSS criteria for excessive weight concern had higher Relief/ Control Factor scores (M ¼ 14.51, SD ¼ 8.72) and higher Intensity Factor scores (M ¼ 9.00, SD ¼ 5.03) than those who did not meet these criteria (M ¼ 11.07, SD ¼ 6.14), t (372) ¼ 3.32, po0.05, and (M ¼ 7.51, SD ¼ 4.07), t (370) ¼ 2.24, po0.05, for each of these factors respectively. Similarly, those who indicated on the DBSS that they had ever exercised for weight control reported more intense food cravings (M ¼ 8.13, SD ¼ 4.15), as indicated by higher Intensity Factor scores, than those who

Table 1 Rotated two-factor matrix for the questionnaire on craving for sweet or rich foods (N ¼ 375) Item

1. Rate the intensity of desire for sweet or rich food you feel at this moment. 2. Rate your strongest desire for sweet or rich food during the past week. 3. Rate how intense your desire for sweet or rich food was, overall, during the past week. 4. To what extent have you thought about sweet or rich food? 5. When you have smelled sweet or rich food or seen someone eating or advertisements for food, how much have you been able to taste it? 6. When you have passed by a grocery or other food store, how much do you feel a ‘‘need’’ to eat sweet or rich food? 7. Having something sweet and rich to eat would make me feel less depressed. 8. I could easily control how much sweet and rich food I ate right now. 9. I crave something sweet and rich to eat right now. 10. I would not enjoy having something sweet and rich right now. 11. Having something sweet and rich to eat would make me happier now. 12. I have an urge for something sweet and rich to eat. 13. I would be less irritable now if I could have something sweet and rich to eat. 14. I would not be able to control how much rich and sweet food I ate if I had something here to eat.

Relief/control factor

Intensity factor

0.54 0.09 0.23 0.29 0.45

0.56 0.88 0.87 0.84 0.57

0.51

0.63

0.62 0.32 0.77 0.15 0.81 0.78 0.83 0.54

0.32 0.38 0.33 0.34 0.21 0.27 0.15 0.28

Note. Varimax rotation method was used. Bold items were assigned to factors. Italicized items loaded on both factors and were not assigned.


had never exercised for weight control (M ¼ 6.89, SD ¼ 4.23), t (370) ¼ 2.75, po0.05. Those whose DBSS responses indicated that they had ever had an eating binge reported higher Relief/Control Factor scores (M ¼ 12.86, SD ¼ 7.79) than those who had never had one (M ¼ 10.92, SD ¼ 5.90), t (372) ¼ 2.64, po0.05. Accordingly, DBSS reports of more frequent dieting were also associated with higher Relief/Control Factor scores r (374) ¼ 0.11, po0.05 and higher Intensity Factor scores r (372) ¼ 0.11, po0.05.

29

quit smoking during the first week had significantly lower scores (M ¼ 11.05; SD ¼ 5.87) than those who reported smoking (M ¼ 14.23; SD ¼ 8.49), t (84) ¼ 2.01, po0.05. Similarly, there was a trend for participants who reported quitting to have lower Intensity Factor scores (M ¼ 7.42; SD ¼ 4.13) than those who smoked (M ¼ 9.08; SD ¼ 4.69), t (84) ¼ 1.64, p ¼ 0.10. These findings indicate that scores on both factors of the QCSRF are associated with quitting smoking, showing evidence of good predictive validity for the measure.

Predictive validity Gender differences In order to examine predictive validity, we analyzed how QCSRF scores related to weight gain and to success at quitting smoking using data from the participants in the placebo group. First, because smoking cessation is associated with weight gain (Hudmon et al., 1999; Klesges et al., 1997), which could be related in part to eating in response to cravings, we investigated the convergence of the QCSRF with weight gain. When we examined weight gain reported during prior attempts at smoking cessation, we found that higher Intensity Factor scores at baseline (i.e., week 0) were associated with the reported amount of weight gained in previous quit attempts r (289) ¼ 0.16, po0.05. We then examined the relationship between weight gain from baseline to week 6 for successful quitters in the placebo group only (n ¼ 35) and the QCSRF, using area under the curve scores. When attempting to detect relationships between repeated measures and another variable, the ‘‘area under the curve’’ (AUC) can be calculated to account for multiple time points. One advantage of using this method is that multiple comparisons, which represent problems for statistical analyses, are not needed. Using a standard method, we computed the AUC for all QCSRF time points (Pruessner, Kirschbaum, Meinlschmid, & Hellhammer, 2003). We then used a median split to classify subjects with regard to weight gain (i.e., participants that gained 5 pounds or less and those that gained over 5 pounds) and compared these two groups. These analyses did not show a relationship between AUC for the Relief/Control Factor. However, participants who gained less weight over the course of the study treatment had somewhat higher AUC scores on the Intensity Factor (M ¼ 322.00, SD ¼ 145.87) than those who gained more weight (M ¼ 245.75, SD ¼ 146.58), t (29) ¼ 1.45, p ¼ 0.16, although this effect was nonsignificant. The lack of significant relationships between the QCSRF factors and weight gain may have been due to the restricted range of weight gain during the initial weeks of quitting (week 6 weight change: M ¼ 4.40, SD ¼ 3.89) and the small sample size. We then examined whether or not QCSRF scores would differ for participants who successfully abstained from smoking over the first week of treatment (n ¼ 60) compared to participants who were unsuccessful in maintaining smoking abstinence during this period (n ¼ 26). For the Relief/Control Factor, participants who

Research has shown that women, more often than men, experience food cravings in general (Lafay et al., 2001) and cravings for sweets more than savory foods (Pelchat, 1997; Weingarten & Elston, 1991; Zellner, Garriga-Trillo, Rohm, Centeno, & Parker, 1999). Although these studies included samples from the general population, which tends to consist of only a minority of smokers, we hypothesized that females in our smoking sample would also exhibit stronger cravings than male smokers. To test this prediction, we compared scores between women and men on the two factors of the QCSRF. As expected, women exhibited higher Intensity Factor scores (M ¼ 8.14, SD ¼ 4.67) than men (M ¼ 7.25, SD ¼ 3.69), t (374) ¼ 2.05, po0.05. However, women and men did not differ significantly in Relief/Control Factor scores. Discussion The goal of the current study was to refine and validate a food craving measure, the Questionnaire on Craving for Sweet or Rich Foods (QCSRF), for use among smokers. Psychometric analyses revealed two factors, one measuring perceptions about the ability of sweet or rich food to relieve negative affect and about self-control over eating and the other assessing the intensity of urges. The emergence of these two factors is consistent with research on craving for various drugs (Heishman, Singleton, & Moolchan, 2003; Singleton et al., 2002; Tiffany, 1990) and with findings from a measure of food craving used in research on bulimia (Cepeda-Benito, Gleaves, Williams, & Erath, 2000). In addition, the QCSRF demonstrated high internal consistency, and evidence was found for good convergent validity with measures of weight control and measures of dieting and bingeing behavior. Gender differences consistent with previous research (Lafay et al., 2001; Pelchat, 1997; Weingarten & Elston, 1991; Zellner et al., 1999) also emerged for the Intensity Factor of the QCSRF. Good predictive validity was also demonstrated by showing an association between QCSRF scores and quitting smoking and past weight gain, but not with weight gained during the current quit attempt. Despite the significant association of QCSRF scores and weight gain during previous quit attempts, reports of craving during treatment did not significantly predict weight gain during

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treatment. Although this may be due to the restricted range of weight gain and the small sample size of the placebo group, this finding is in line with other research that shows a variable relationship between smoking cessation and weight gain (e.g., Filozof, Fernandez-Pinilla, & FernandezCruz, 2004). The failure to find a closer correspondence between food cravings and weight gain is perhaps not surprising because individuals may engage in a range of behaviors that may ultimately influence how much weight they gain, for example, eating in response to craving, restricting food intake, or increasing exercise. Moreover, individuals with high food craving may resume smoking either to directly suppress weight gain or because the combined effort to resist eating and smoking diminishes their ability to succeed at smoking cessation by depleting their self-control resources (Baumeister, Bratslavsky, Muraven, & Tice, 1998; Perkins, 1994; Perkins et al., 2001). Consistent with the hypothesis that food craving will predict smoking cessation failure, our study found that those who were unable to successfully maintain abstinence from smoking in the first week had higher food craving scores than those who were successful. These findings suggest that the QCSRF may provide a tool for investigating how food cravings and strategies to limit weight gain influence weight gain and the ability to quit smoking. This research could help identify and intervene with those smokers for whom cravings are a significant barrier to smoking cessation. The present study had several limitations. First, many of the effect sizes of the indices of reliability and validity were small. However, smoking cessation research has clearly shown that small effects are often clinically relevant. For instance, a recent review of over 100 clinical trials of Nicotine Replacement Therapy revealed a small (Odds Ratio ¼ 1.77; CI ¼ 1.66–1.88) but clinically relevant effect (Silagy, Lancaster, Stead, Mant, & Fowler, 2004). Second, the sample for the study was limited to participants enrolled in a smoking cessation clinical trial and all received nicotine patches; additional validation of the scale using a sample of smokers not actively trying to quit or smokers who quit without the use of a medication may be beneficial. Third, because this was a preliminary investigation of the QCSRF and naltrexone treatment may have affected food cravings (Zimmerman, Rechlin, Plaskacewicz, Plaskacewicz, Barocka, Wildt, & Kaschka, 1997), we limited some of our analyses to the placebo group. Future studies should test the reliability of the QCSRF at multiple points in time prior to quitting smoking as well as compare QCSRF scores of smokers before and after cessation attempts and at various follow-ups. A fourth limitation involves the questionnaire wording, specifically the terms ‘‘sweet or rich foods.’’ This wording was initially chosen because food craving research shows that sugary and fatty foods (e.g., chocolate) are those most frequently craved (Hill & Heaton-Brown, 1994), especially in the United States (Zellner et al., 1999). However, use of the word ‘‘or’’ makes it impossible to know the types of

foods and their macronutrient composition. This is particularly important given that the serotonin effect of carbohydrate intake may only occur in the absence of protein (Wurtman & Wurtman, 1995). Also, the terms ‘‘cravings,’’ ‘‘sweet,’’ and ‘‘rich’’ were not defined for the subjects in the study, despite multiple possible interpretations of their meanings. However, these terms were used in all QCSRF questions, so any errors of interpretation were likely constant across conditions. Fifth, the QCSRF only measures cravings for foods classified as sweet or rich, whereas urges for other foods may also contribute to weight gain in smokers attempting to quit. For example, while women in studies primarily consisting of nonsmokers tend to crave sweet more than savory foods, the opposite may be true for men (Pelchat, 1997; Weingarten & Elston, 1991; Zellner et al., 1999). Although one study in smokers found increases in snacking without a shift in preference for sweet foods after one day of abstinence from smoking (Gilbert & Pope, 1982), commonalities in research on the biochemical pathways involved in smoking and food intake and cravings, as well as some controlled studies (e.g., McRobbie & Hajek, 2004; Perkins, Epstein, Sexton, & Pastor, 1990; Rodin, 1987; West, 2001), support the link between smoking and sweet or rich foods, in particular, as opposed to other types of foods. Moreover, many studies examine associations between drug dependency and cravings for sweets and not other types of foods (Mercer & Holder, 1997). Future work using this scale may benefit from: 1) including a more comprehensive measure of cravings (e.g., a specific food listing) or additional categories such as salty foods, and 2) providing definitions of ‘‘cravings,’’ ‘‘sweet’’ and ‘‘rich’’ to participants before they complete the assessment. The current study investigated the psychometric properties of the QCSRF using a sample of smokers and provides evidence for its potential use as a valuable tool in studies of smokers and smoking cessation. Specifically, it could be used to expand upon past research on increases in caloric intake or weight gain after quitting smoking and to examine how medications may modify this relationship. Additionally, it would be interesting to examine correlations between QCSRF scores and scores on measures of craving for cigarettes and of withdrawal symptoms postquit (e.g., QSU; Tiffany & Drobes, 1991; Minnesota Nicotine Withdrawal Scale; Hughes, 1992). Future research could also investigate the relationship between QCSRF scores and success with smoking cessation and how this relationship may be mediated by weight gain, weight concern, and the satisfaction of cravings for sweet or rich food. The focus of the current study was to examine sweet or rich food cravings among smokers and hence was limited to a smoking sample. However, research in multiple domains suggests that food craving, particularly for sugar and fat, may play a significant role in a variety of psychological and physiological disorders including eating and mood disorders (Yanovski, 2003) as well as obesity


(Delahanty, Meigs, Hayden, Williamson, & Nathan, 2002) and diabetes (Strachan, Ewing, Frier, Harper, & Deary, 2004). Accordingly, the QCSRF could be used to investigate other non-smoking populations, particularly those with eating, metabolic, and other weight-related disorders.

Acknowledgements We would like to thank Drs. Tanja Wranik and Amy Latimer for comments on earlier drafts of this paper. This research was supported in part by NIH grants K12DA00167, K05-AA014715, P50-AA15632, and P50DA13334.

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Research Report

Food, eating, and weight concerns of men in recovery from substance addiction Jennifer Cowan, Carol Devine 351A MVR Hall, Division of Nutritional Sciences, Cornell University, Ithaca, NY 14853-4401, USA Received 11 August 2006; received in revised form 12 May 2007; accepted 18 May 2007

Abstract Dysfunctional eating patterns and excessive weight gains have been observed during recovery from drug and alcohol addictions. Yet, little is known about food choice behaviors among substance abusers and the role that food plays in their lives. The purpose of this study was to understand how men at different stages of recovery from substance addictions experienced food and eating, weight changes, and used food in recovery. A purposive, multi-ethnic sample of 25 urban men at different stages of recovery from drug and alcohol addictions participated in qualitative interviews. Data analysis based on the constant comparative method revealed three main themes: excess weight gain, meaningful use of food, and disordered eating and a struggle to eat healthy that differed by recovery stage (early, mid, and later recovery). Men in early recovery described dysfunctional eating practices such as mood and binge eating, the use of food as a substitute for drug use, and the use of food to satisfy cravings. Men in mid to later recovery expressed weight concerns and distress about efforts to lose weight. The findings also suggest that food deprivation in active addiction and interactions between stage of recovery and living environment may contribute to changes in food choice behaviors and to excess weight gains in recovery. These findings highlight behavioral interactions between food and substance abuse and opportunities for nutrition and weight interventions in recovery. r 2007 Elsevier Ltd. All rights reserved. Keywords: Substance abuse; Eating behavior; Recovery stage; Food meaning; Weight

Introduction Overweight and dysfunctional eating patterns have been observed among people in recovery from drug and alcohol addictions (Hatcher, 2004; Hodgkins, Jacobs, & Gold, 2003; Jackson & Grilo, 2002). Overeating, binge eating, and the use of foods, especially those high in fats and sugars, as substitutes for alcohol and drug use have been reported. High rates of eating disorders have also been reported in this population (Cepik, Arikan, Boratav, & Isik, 1995; Hudson, Weiss, Pope, McElroy, & Mirin, 1992; Krahn, 1991; Mitchell, Pyle, & Specker, 1992; Wiseman et al., 1995), but little is known about how substance abusers themselves experience food and eating in recovery and the roles that food plays during the recovery process. Recovery from drug and alcohol addiction has been viewed as a behavior-change process in which total Corresponding author.

E-mail address: [email protected] (J. Cowan). 0195-6663/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.appet.2007.05.006

abstinence from alcohol and illicit drugs is achieved while promoting overall health and well-being. It is also the period in which the addicted person begins to learn new skills while slowly moving away from old habits and patterns of thinking (Gorski, 1989a, 1990; McDuff, Solounias, RachBeisel, & Johnson, 1994; Moyer, 1989; Rioux & Van Meter, 1990). A developmental model rooted in a cognitive-behavioral framework (Rawson, Obert, McCann, & Marinelli-Casey, 1993) has been suggested as a way of helping recovering addicts and treatment professionals identify appropriate treatment goals and evaluate progress during recovery (Gorski, 1990). This model describes six progressive recovery stages: (1) transition or pretreatment phase, in which the addicted individual recognizes the addiction problem; (2) stabilization, marked by detoxification and recuperation from acute withdrawal; (3) early recovery, in which the compulsion to use chemicals is reduced and the sober individual becomes more comfortable with being abstinent; (4) middle recovery, in which the individual begins to experience a balance and

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J. Cowan, C. Devine / Appetite 50 (2008) 33–42

productive daily life; (5) late recovery, in which the addicted individual begins to move towards healthy living; and (6) maintenance, marked by an awareness for continued growth and development (Gorski, 1990; Gorski & Kelly, 1996; McDuff et al., 1994). This developmental framework for understanding recovery guided participant recruiting, and it informed the interpretation of the emergent findings of this qualitative study. We chose this framework because it is widely used in relapse prevention programs (e.g., McDuff et al., 1994; Moyer, 1989; Rawson et al., 1993), and because one of the aims of this study was to inform nutrition interventions for men in recovery from substance abuse. In the population at large, food is used not only to satisfy hunger but also serves many roles in providing social interaction, comfort, identity, and pleasure in people’s lives (Angulo, 1987; Sobal & Nelson, 2003; Wanksink, Cheney, & Chan, 2003; Westenhoefer & Pudel, 1993). Some dietary practices have been linked to increased risk for chronic food-related diseases such as diabetes and cardiovascular diseases; and these chronic conditions are more common in the substance abuse population (Anders & Anders, 1999; Howard, Arnsten, & Gourrevitch, 2004; Parillo & Riccardi, 2004; Poilkolainen, 1998; Rehm, Gmel, Sempos, & Trevisan, 2002; Selwyn, 1996; Sone, Yamada, Mizuno, Aida, & Ohashi, 2004; Sutter & Vetter, 1999; van de Weil, 2004). Obesity plays a key role in these chronic conditions (Howard et al., 2004; Sutter & Vetter, 1999) because the excess weight gained in recovery may contribute to these health problems. There is a need to understand the perceptions and behaviors related to food choices among men in recovery from drug and alcohol addictions to promote improved health and reduced disease in this population. Our aim in this study was to understand how men at different stages of recovery from substance addictions experienced weight changes and how they described the functions and meaning of food and eating behaviors in recovery. We conducted the study to inform nutrition interventions to address weight, nutrition, and health concerns during recovery. Methods We used a grounded theory approach in this analysis (Charmaz, 2000; Glaser & Strauss, 1967; Strauss & Corbin, 1998) because we were interested in understanding the multiple experiences, meanings, and functions of weight change and food and eating from the perspective of men in recovery from substance abuse. We also hoped to develop a new conceptual understanding of the issues of interest to inform the design of interventions with men in recovery settings. Twenty-five men in recovery from drug and alcohol addictions were purposively recruited to vary in race/ ethnicity, age, living environment (halfway houses, rehabilitation program, supportive, and independent living), and

recovery status, from a city of approximately 700,000 people in Upstate New York. Adult men were recruited for this study because men are more widely affected by alcohol and drug addictions (Drug and Alcohol Service Information System Report (DASIS), 2005; National Survey on Drug Use and Health Report (NSDUH), 2004). All of the study participants were recruited through personal contacts at drug treatment agencies and at Alcoholics Anonymous meetings in the research community. Study participants were recruited from four types of recovery environments: (1) a halfway house, a drug-free residential group community that provided 24-h recovery services; (2) a highly structured rehabilitation program that provided group housing and rehabilitative services; (3) a supportive living environment that provided apartments and weekly recovery counseling services; and (4) independent living where men lived independently at home and participated in community recovery support groups. We recruited participants from different recovery environments because we wanted to include men at different stages of recovery. Halfway houses and rehabilitation programs typically house men in early recovery; supportive and independent livings are used in later stages of recovery. The treatment of nicotine dependence was not a primary objective of these treatment facilities. No formal smoking cessation program was offered, and the residents were allowed to smoke while in treatment. We used social cognitive theory (Bandura, 1977) and a life course perspective (Devine, Conners, Bisogni, & Sobal, 1998) in the study design and data collection. The lead researcher, trained in qualitative research methods, conducted 1-h face-to-face interviews in English with study participants using a semi-structured interview guide (Denzin & Lincoln, 2003). We tested the open-ended interview guide with three men in recovery. We also conducted 15–20 min follow-up telephone interviews with participants 5–7 months after the initial face-to-face interviews to update information on weight and eating behaviors in recovery. We audiotape-recorded all face-to-face interviews, transcribed them verbatim and reviewed them for accuracy. Study participants provided demographic information (age, drug of choice, ethnic identity, recovery status, living situation, treatment history, and self-reported weight and height). The interviewer wrote detailed field notes after each interview and follow-up telephone call to provide the context for the interview, comments on data quality, and preliminary analytic observations. Following the face-toface interview, each participant completed the Eating Attitude Test (EAT-26), a standardized instrument used to assess eating disorders (Garner, Olmstead, Bohr, & Garfinkel, 1982). All the study participants provided informed consent and received a gift certificate to a local supermarket. The University Institutional Review Board (IRB) reviewed the study procedures. We conducted data analysis guided by the constant comparative method (Glaser & Strauss, 1967) continuously

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throughout the study. Researchers read interviews and field notes, and discussed each case until consensus was achieved. We used open and axial coding (Strauss & Corbin, 1998) to identify emergent thematic categories from interview transcripts. In this process, it emerged that participants’ descriptions of the meaning and function of food, weight, and eating differed by recovery status. We then developed a recovery timeline for each participant from active addiction to recovery and examined emergent patterns in participants’ descriptions of their eating behaviors over time. We next used participants’ narratives to develop a data matrix to explicitly examine differences in body weight, weight concerns, food function and meaning, and eating behaviors (Miles & Huberman, 1994). Participants commonly referred to ‘‘early’’ and ‘‘later’’ stages of recovery. Men described common experiences with food, eating, and weight and similar ways of interpreting them in each of these stages. The ‘‘mid’’ recovery group emerged as a transitional stage in which participants described transitions in self-awareness and meaning about weight, food, and eating. The three distinctive stages of recovery that emerged from this matrix were used in subsequent analysis. We then connected these findings to the published literature. We used peer debriefing through discussions about emergent themes and member checks with study participants, and community members to verify interpretations (Lincoln & Guba, 1985). We calculated body mass index (BMI, weight in kg/height in m2) from each participant’s self-reported weight and height at the time of the interview. We also calculated the mean EAT score for each participant (Garner et al., 1982).

Table 1 Characteristics of study participants Numbers Gender Male

25

Age (years) 24–30 31–40 41–50 Over 50

3 7 11 4

Race/ethnicity Black White Latino Other

11 10 3 1

Primary drug of choice Alcohol Cocaine (including crack) Heroin Marijuana

5 16 2 2

Mean body mass index (BMI) at the time of interviewa Early recovery 26 Mid recovery 27 Later recovery 29 a

Self-reported weight and height.

Table 2 Study participants by stage of recovery (months of sobriety) living in the four recovery environments Living situations

Early recovery (1–6 months)

Mid recovery (7–13 months)

Later recovery (14–36 months)

Halfway house Rehabilitation program Supportive living Independent living

7 2

2 1

0 1

0 1

1 2

5 3

Findings Twenty-five men in recovery from substance abuse participated in this study (Table 1). The participants differed in type of drug use, age, and living environment. Men from three major race/ethnic groups participated: black, Latino and white of European background. Participants fell into one of three emergent recovery stages: (1) early recovery (1–6 months), (2) mid recovery (7–13 months), and (3) later recovery (14–36 months) (Table 2). The mean ages of the participants in early, mid, and later recovery were 39, 43, and 42 years, respectively. Three dominant themes emerged from participants’ descriptions of their experiences in recovery: excess weight gain, meaningful use of food (e.g., as a drug substitute or mood regulator), and disordered eating and the struggle to eat healthy. All of these themes differed by recovery status. Weight changes Most of the participants recalled that they rarely ate while they were in active addiction and consequently experienced severe weight loss during that time. Some men reported eating as little as twice a week. In contrast, during recovery, all but two study participants gained weight;

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many described gaining what they saw as too much weight over short periods of time. One participant who had been in recovery for 3 months said this about his weight since recovery, ‘‘I weigh more now than I ever have in my life! When I went intoywhen I first started my recovery I weighed like 160 pounds, and now I weigh over 200 [pounds]. The highest I’ve ever weighed in my life like 190yactually I think I’m a little overweight right now.’’ More than half of the men in the study, particularly those interviewed during later recovery, fell above the normal BMI range based on self-reported weight. More than half of the participants interviewed in early recovery reported that they had stopped smoking at the start of their treatment while only three of the nine men in later recovery described themselves as non-smokers. All of the six men interviewed when they were in mid recovery

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described themselves as smokers. Some of the smokers reported that they had not quit smoking because they were afraid they would gain weight. Weight changes in early recovery (1–6 months) All 10 participants interviewed when they were in early recovery reported gaining weight since entering recovery. In early recovery, men presented weight gain as inevitable and desirable to replace pounds lost during active addiction. These men estimated that they had gained between 14 and 45 lb within the first 6 months of recovery. One man who had been in recovery 1 month reported gaining 14 lb in 28 days. A participant who had been in recovery for almost 5 months contrasted his weight changes during active addiction and during recovery: When I first came here [in recovery] I was weighing like 130 [pounds]. I weigh like 170 now, you know, big difference! ‘Cause when I was out there I was not eating. And I wouldn’t eat properly. And I was constantly running so the little bit that I was feeding into my body was going, ‘cause I was in and out, in and out. Weight changes in mid recovery (7–13 months) All six participants interviewed during mid recovery expressed consciousness of weight gain and the desire to lose the extra weight they had gained in early recovery. The men in mid recovery reported gaining between 33 and 60 lb within the first stages of their recovery. Three of these men were able to lose some weight (1–15 lb) during mid recovery using a variety of strategies. One man who had gained 40 lb since entering recovery said he was ‘‘slowly pulling’’ away from fried foods in an attempt to lose some weight. Another man who had been in recovery for 9 months described his strategy to lose weight: ‘‘I can lose weight if I stop eating for two days. I can be down to my other weight I want to be.’’ A man who had been in recovery for 7 months described his weight loss success: I’ve gained—well in the halfway house [early recovery] I figured I gained about fifty pounds. I was there for six months. [Now] I’ve just, you know, tried to wean myself off of so much, [I] try to cut down a little bit and it’s been working. Since I’ve come here [supportive living] I’ve noticed that I have lost weight. I’ve lost about fifteen pounds. Weight changes in later recovery (14–36 months) Most of the nine participants interviewed during the later stage of recovery recalled gaining a lot of weight in early recovery; they were currently losing weight and/or wishing to lose the excess weight gained previously. The participants in this phase of recovery reported an estimated total weight gain in recovery between 15 and 80 lb, most of which had occurred in early recovery. Five of these men reported an estimated weight loss between 8 and 65 lb

during later recovery and four still hoped to lose some weight. One man who had been in recovery for 15 months was depressed by his current weight, and his doctor had told him he needed to lose some weight. Another man who had lost 65 lb in 6 months through an ‘‘extreme’’ weight loss regimen described his weight concerns since recovery: I came in [recovery] weighing about 180, which is probably about my normal weight. I just had unlimited access to foody with my addictive personality and my addictive eating styles—I just went wild, you know, and that’s where I went from 180 pounds to 240. I came into supportive living at probably about 230–235 but after six months of being here [supportive living] it didn’t go down. It sure went upylast time I weighed myself, I was about 242, uh, and I gotten bigger after that but I didn’t weigh myself. So I was over 240 pounds and that was at January 1st of this year, yeahyI lost a lot of weight and I was on my feet as far as eating healthyymy weight’s been stable between 180 and 185 [pounds]. And I’m still sugar and flour freey Meaningful use of food Meaning of food in early recovery (1–6 months) The men interviewed in early recovery described many functions and meanings of food in their lives. Men described using food, usually sweets and ‘‘junk’’ foods, as a drug substitute, to regulate moods, to alleviate boredom, to satisfy cravings, and to provide structure to the days. One participant who had been in recovery for only 1 month told how he had replaced his drug use with food: I just eat just to, just to eat. Just to eat! ‘Cause once you come off narcotics, you have to replace that narcotic with something else. And a lot of people chain smoke. If they smoke, they go to chain smoking. Um, if not, they eat. And they eat constantly! You know, ‘cause, you’re replacing it [narcotics]. You’re replacing that drug with something else, you know! So, I replace my drug with food, with food and working out. Most of the participants in this early phase of recovery also described having mood ‘‘swings.’’ They often used food to regulate their moods or ate more frequently when they were sad and depressed, nervous/anxious, or upset. A man in recovery for 5 months expressed that when he was anxious and depressed, eating was a soothing activity. He said: Moods and eating yeah, when I’m depressed, anxiousyeating’s often some kind of a soothing activity and I might have a harder time you know controlling the portions. I might snack more frequently, I might choose more instantly gratifying foods like sugary foods, that’s sweet maybe not so healthy.


These men also described how they used food to alleviate boredom. One man interviewed in early recovery when asked about his eating simply said, ‘‘I’ll eat and I won’t even be hungry, I just eat it anyway.’’ The men in early recovery also used food to satisfy (food) cravings. One diabetic participant said that he had ‘‘real bad cravings’’ for chocolate and on one occasion he ate so many chocolate bars his face became ‘‘numbed’’. The participants interviewed in early recovery also commonly expressed that food provided some structure while they were in recovery from drug and alcohol addictions. One man who had been in recovery for 3 months when asked about his eating pattern said: So basically what I’m trying to do since I’ve been here [halfway house recovery program] is I’m trying to be more structured. I’m trying to eat at least three times per day, breakfast, lunch and dinner. Meaning of food in mid recovery (7–13 months) Most of the men interviewed in mid recovery also expressed the theme that food provided some structure in their lives. A man who had been in recovery in the halfway house for 7 months stated that he had a food routine, which was to prepare him for independent living: [Eating] More of a routine, like I said breakfast, lunch and dinner! Where, as before in addiction it might be every two days or basically whatever. It’s reallyyit’s basically for when we get back out there and uh, go to independent living. You know, live like normal people do or did [a little laugh]ywhen you get your own apartment. It’s good to have standards like that to uhymoreybe uh, responsible. But, uh, basically breakfast, lunch and dinner, maybe a late night snack. It’s a good routine, basically. I tried, like I said, to get into a routine with everything I doy Few participants interviewed in mid recovery described using food as a drug substitute, to alleviate boredom and/ or to satisfy food cravings. One man who had been in recovery for 7 months, stated that he had to have his coffee first thing in the morning instead of drugs to start his day. Only one participant reported using food to alleviate boredom: ‘‘Usually when I’m bored, you knowyboredom leads me to food. You knowysomething to munch on, you knowysomething to use my hands with umyI think that’s the only time that I’m really attracted to food.’’ Those study participants who reported using food to satisfy food cravings were usually reflecting back to food cravings experienced in early recovery. One man who had been in recovery for 7 months reflected, ‘‘I couldn’t get enough sugar when I first got into the halfway house [early recovery].’’ Meaning of food in later recovery (14–36 months) Unlike participants interviewed in early recovery, the men in later recovery did not describe current use of food

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as a substitute for drugs but mainly reflected back to early recovery when food had served this function in their lives. One participant who had been in recovery for 27 months said this about food as drug substitute when he first came into recovery: It [eating] was like a drug addiction. I would go to the store and buy candy and a lot of junk food, and eat it compulsive. I had to have some. I felt deprived if I didn’t have cakes and ice cream just before I went to bed at night. Only one participant interviewed in later recovery stated that he had used food to alleviate boredom and to satisfy food cravings. He had been in recovery for 15 months and said he ate until he was full when he was bored. Like the men in mid recovery, most of the participants interviewed in the later recovery phase reported that food provided some structure in recovery and that they were currently trying to maintain their food structure. One participant who had been in recovery for 14 months said: I’ve managed to maintain a normal eating regimen currently. Sometimes it varies. Some days I may not have time to eat or maybe in a hurry and I just neglect to eat. But usually in the past nine or ten months I’ve been pretty disciplined as far as eatingyI’m eating in the morning, afternoon, evening. Another man who had been interviewed while in late recovery emphasized that it was important for him to have a food structure because he was an ‘‘impulsive type of person’’. He also said ‘‘I have to have a plan, I have to have the right food in the house,’’ when he was asked about his eating patterns in recovery. Disordered eating and the struggle to eat healthy Eating behaviors in early recovery (1–6 months) The eating behaviors described by the study participants differed by their stage of recovery. Most of the participants interviewed in early recovery expressed that they regularly ate large amounts of food in a short period of time (binging) and that they had often hoarded food during their recovery. Two participants diagnosed with diabetes and hypertension reported that they were struggling to control their food intake because of their health. A study participant who had been in the program for 5 months described his binging behavior: Over the past months [I] just would eat and eat and eat ‘til I’m stuffed. I’ll eat and eat and I’ll feel so stuffed and then I’ll get really good disgusted with myself, and just say, why did I take it to that extremeyI really didn’t like it but I still didn’t feel like I could control it. The participants interviewed in early recovery described food hoarding as a common behavior in the halfway house. One man complained that food was stolen and eaten on a regular basis in the halfway house even when someone

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else’s name was written on it. Another participant described hoarding pork chops and eating them in his room even though eating in the bedroom was against the program policy: I eat two pork chops, I’d come back later on that night and I eat like two more. I take them up to my room and eat in the room, right. I go in and grab it, wrap it up and hide it right here [in his pocket] then have it in the room. Eating behaviors in mid recovery (7–13 months) Most of the participants in mid recovery described overeating, especially their favorite foods, on special occasions at restaurants and/or at family events. One participant who had been in recovery for 7 months described it this way: I can eat likey a whole pizza. And uh, totally know I’m full and still eat, and still eat, and still eat. And basically that’s what I thinkythat’s the only food I do it withy But, like I said, it all goes back toyfrom years ago. Pizza has always been my favorite and I could sit and eat it until I get sick. For some reason, that’s the only food that does it to me. None of the participants interviewed in mid recovery reported hoarding food but reflected back to food hoarding in early recovery. One participant who had been in recovery for 7 months and had transitioned from a halfway house program into supportive living described his memory of food-hoarding behaviors earlier in his recovery: The halfway house more or lessyall the food was comfort food, you know. It’s been so long, every one gets in there and the first thing they do is go to the kitchen, you know, becauseywe were never worried about before, you know. And now there’s some people there that were worrying about they’d never get it again, so hiding it, and putting it in different spots so they know it was theirs or something like that. Most of the study participants in this stage of recovery reported some struggle for control over food and their eating, particularly the struggle to eat healthier. One man who had been in the halfway house for 7 months and was trying to eat less red meat expressed his struggles for control over food: ‘‘There’s a lot of red meat here [halfway house]. I’m finding it hard because of that.’’ That’s one thing I got to watch out for and stay away from because of the red meats and all that. It’s hard for me in here and then when I go out, I love eating that stuff but I’ve been doing I think OK but I can’t totally get to where I want to be in hereyCause I do like eating that food and I know it’s not healthy for me. Another participant who had been in recovery for 10 months felt that he lacked nutrition knowledge:

I guess I would like to develop some type of eating pattern, you know, um, and, I think it’s lack of, you know, knowledge in what is good, and stuff like that. And, you know, what could be done quick! I would like to find, I guess, better ways of eating. Eating behaviors in later recovery (14–36 months) Most of the men interviewed in later recovery described overeating less frequent than those interviewed in early or mid recovery. The men in this stage of recovery described eating large portions or overeating only on special occasions such as eating out at restaurants and family events. One participant in recovery for 23 months said, ‘‘I overeat at times. I tend to gorge myself too, at dinnertime or at night. One of my goals is to start eating less portions.’’ One man (in recovery for 33 months) described his overeating episodes at family events: ‘‘Not often, I can go back to the family gatherings it’s like a spread! You want everything, so you eat ‘til you like you’re about to burst. I eat ‘til I become miserable.’’ All of the study participants interviewed in later recovery expressed some struggle to control their eating in recovery: struggle to eat healthier or to eat smaller portion sizes. One man in later recovery when asked about food and eating simply said, ‘‘I try and eat pretty healthy it’s just I think I overeat at timesyjust need to have more control, more disciplineylike smaller portions.’’ Another man (in recovery for 29 months) shared his struggles with eating the wrong foods: Mostly, I’d say my food is grease. It’s high carbs, you know, in factyI need to experience with more of the correct way of eatingyjust like last week, in my, inserviceywe was talking about nutrition. And, I eat a lot of sugar uhyit’s wrong. I did eat wrong. I know I eat wrong. And I can’t stop and change that. I can’t! I need help in ity I just know I eat wrong. The mean Eating Attitude Test (EAT-26) scores also differed by the three recovery stage (early: 15, mid: 3.33, later: 7.89). Three out of the four participants who scored at or above the cut-off score for eating disorders (20) were in early recovery. Discussion This investigation provided new insights into the meaning of food and eating to men at different stages of recovery from substance abuse. Participants in this analysis provided descriptions of weight changes, meaningful use of food, and disordered eating and struggles to eat healthy that differed by their stage of recovery. Our findings suggest that regaining weight lost during active addiction was dominant and perceived as positive by the participants in early recovery. This is consistent with the evidence that most drug addicts have poor diets during active addiction and are usually malnourished and underweight when they enter recovery (Cepik et al., 1995; Hauser


& Iber, 1989). The new finding that emerged from our data, which suggests that weight gain in early recovery may exceed the weight lost during active addiction and become a concern to men in recovery needs to be confirmed with longitudinal measured weights. If true, this suggests that treatment should address healthy weight goals early in the recovery process. Both animal and human studies in a variety of populations provide evidence that weight gain follows food deprivation or restriction. Studies in eating disordered populations have reported positive associations between food deprivation and/or restrained eating and bingeing (e.g., Lowe, Foster, Kerzhnerman, Swain, & Wadden, 2001; Ouwens, Van Strien, & Van der Staak, 2003; Ricciardelli, Williams, & Finemore, 2001; Van Strien, Cleven, & Schippers, 2000). Similarly, animal experiments of eating behavior after food deprivation found an increase in food hoarding, food seeking behaviors (e.g., Day & Bartness, 2003; Day, Mintz, & Bartness, 1999; Pierre, Skjoldager, Bennett, & Renner, 2001), and food consumption in proportion to the degree of body mass loss (Wood & Bartness, 1996). Previous studies conducted in recovery populations also support the idea that changes in eating behaviors and weight may occur as the drug addicts recover (Hatcher, 2004; Hauser & Iber, 1989; Hodgkins et al., 2003). The classic Minnesota starvation experiment may also provide some insight into the eating behaviors of people previously in a semi-starvation state (Keys, Brozek, Henschel, Mickelsem, & Taylor, 1950). This experiment provided a unique and complete account of the body’s response to sustained food deprivation followed by periods of refeeding. Like the men in the Minnesota experiment who were in a semi-starvation state, many of our participants in recovery reported that they rarely ate while they were in active addiction. Once abstinence was achieved and food became more available to the participants as they entered early recovery, recovering addicts gained excessive weight and exhibited disordered eating behaviors such as binge eating and hoarding food, similar to those documented in the Minnesota Starvation Study (Keys et al., 1950). Increases in food intake after periods of food restriction may be due to the need to maintain body weight to its biologically suitable levels. Consequently, weight regain may be an important factor in food consumption after periods of food restrictions, which would support the participants’ descriptions of severe weight loss in active addiction and bingeing behaviors in early recovery. Abnormal neurological mechanisms in the brain may play a role in the eating pathologies observed in the study participants in early recovery. Drugs of abuse (e.g., alcohol and cocaine) are believed to release dopamine (DA) in the brain, the neurotransmitter involved with the ‘‘reinforcing properties of food’’ (Wang, Volkow, Thanos, & Fowlers, 2004). Dopamine acts as a strong inhibitor of food intake by preventing the expression of hypothalamic neuropeptides responsible for stimulating food intake (see also

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Gillard, Dang, & Stanley, 1993; Meguid et al., 2000). Low dopamine brain activities, reduction in dopamine release and/or decreased dopamine receptors (DA D2) stimulation, are associated with dysfunctional eating patterns (Wang et al., 2004). Positron Emission Tomography (PET) studies that have measured DA D2 receptors in obese and drug-addicted individuals reported significantly lower levels in these groups (Volkow et al., 1993, 1996, 2001; Wang et al., 1997, 2004). Our study participants in early recovery may have compromised neurobiological systems, which cause them to use food as a temporary compensation for the decrease in dopamine activities in the brain (Wang et al., 2004). Our study findings also suggest that food had different meanings and uses at different stages of recovery. While food as a substitute for drugs, to regulate moods, alleviate boredom, or satisfy cravings dominated early recovery; food and meals that provided structure and preparation for future living situations dominated later recovery. A developmental framework is consistent with the changes in food meaning and use described by the men in the different stages of recovery in this study. In a developmental model of recovery, as the recovering addicts move from one stage to another in the recovery process they develop new skills that are critical to the next stage of recovery (Gorski, 1989b). Therefore, some behaviors that the recovering addicts may display early in their sobriety may not necessarily be exhibited later on in the recovery process because the individuals are changing as they learn new skills. For example, excessive caffeine and nicotine use following detoxification (pre-early stage of recovery) usually decline over time (Gorski, 1989b). The developmental stages in recovery may be associated with the change in dopamine activities in the brain over time. The developmental model of recovery (Gorski, 1990) is also a good fit with behavioral differences in eating behavior during recovery. In our sample, which covered three stages of recovery, there is some evidence supporting a development process with eating behavior as well as weight changes and concerns in recovery. The experiences of these men suggest that dysfunctional eating practices such as mood and binge eating, the use of food as a substitute for drug use and the use of food to satisfy cravings were experienced more commonly in early recovery than in mid or later recovery. The participants interviewed during early recovery also scored higher on the eating disorders assessment (EAT-26), than those in mid to later recovery. Also, three out of the four men who scored at or above the cut-off score on the eating disorders assessment were in early recovery. Our finding that men indicate dysfunctional eating patterns in early recovery is consistent with previous studies. One study conducted by Farkas and Dwyer (1984) suggested that cravings for sweet and the use of sweets to satisfy the cravings for alcohol and drugs occur frequently in the early stages of recovery. Similarly, foodhoarding behaviors were reported only by the participants

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in early recovery, which is consistent with previous studies in which food-hoarding behaviors were observed when normal feeding was administered following periods of food deprivation (Day & Bartness, 2003; Day et al., 1999; Keys et al., 1950; Wood & Bartness, 1996). This suggests that any intervention conducted in this population should take recovery stages into consideration, particularly during early recovery when a normal diet is reintroduced. A further question is raised by the living situations of men in different stages of recovery. Because the men at different stages of recovery lived in different environments, it is difficult to disentangle the relationship of the eating environment and the stage of recovery to food choice behaviors and weight changes. Within the same recovery environment, were experiences related to the environment or to the stage of recovery? However, some participants at different stages of recovery differed in their eating and weight experiences. This observation provides supports for a stage of recovery interpretation. On the other hand, some participants who were interviewed in mid and later recovery reflected back on earlier recovery experiences and described a past environment, the halfway house, as having contributed to their poor food choices and excess weight gain. These experiences are consistent with an environmental interpretation. The food and eating environment has been identified as an influence on food choice behaviors leading to excess weight gain and obesity (e.g., Hill & Peters, 1998; Jeffery & Utter, 2003). It is likely that interactions between recovery stage and the food environment may provide an alternative explanation for the differences described by our study participants. An interaction interpretation is consistent with social cognitive theory, the theoretical framework that helped to guide the study design and data analysis. This theory suggests that a person’s actions are determined through reciprocal interactions between personal and cognitive factors, behaviors, and environmental influences (Bandura, 1977). Therefore, the study participants’ weight changes, eating behaviors, and the way in which food was interpreted in recovery may be a function of the recovery stage as well as the food environment. A longitudinal study with a larger sample of men at different stages of recovery living in different environments is needed to sort out these issues. Tobacco use may be another explanation for the differences observed in the eating patterns of the participants at different stages of recovery. Five of the eight participants in early recovery were non-smokers; they reported that they had stopped smoking following detoxification. It is well documented that smoking tobacco reduces appetite, and that smoking cessation can lead to hyperphagia and weight gain (e.g., Carney & Goldberg, 1984; Chen et al., 2005; Grunberg, 1982; John, Meyer, Rumpf, Hapke, & Schumann, 2006; Shiffman, West, & Gilbert, 2004). There is also some evidence suggesting that appetite tends to be higher in recent quitters than in daily smokers (Etter, 2005). Therefore, smoking cessation at the

beginning of the recovery process could have exacerbated the effects of drug and alcohol abstinence on appetite and weight gain of the six non-smokers interviewed in early recovery. Further research should study the role of interactions among these factors on appetite and weight gain in this population. This study provides support for the importance of nutrition education and dietary counseling that is tailored to stage of recovery. Most of the participants in this study lacked from the start and did not gain during recovery, the necessary nutrition skills to move forward into healthy eating. These findings highlight the need for more attention towards food and nutrition in the recovery population. Recovering addicts who become more aware about nutrition and who are more involved in improving their personal health and nutritional status are more likely to embrace better eating patterns (Hauser & Iber, 1989). Because drug addicts may be at increased risk for chronic diseases such as diabetes, cardiovascular diseases (coronary heart disease, hypertension, stroke), liver problems, and digestive problems such as peptic ulcers and chronic pancreatitis (e.g., Howard et al., 2004; Johnson, Barzargan, & Cherpitel, 2001; Rehm et al., 2002; Sone et al., 2004; van de Weil, 2004) it is especially important to provide nutritional support appropriate to each stage of recovery. Moreover, it is vital for people in recovery programs to learn new and healthy ways of eating early in order to give them the skills for healthful eating when they are living on their own. The qualitative research methods and the emphasis on weight changes and eating patterns at different stages in recovery provided a better understanding of food choice behaviors and the ways in which food is used in recovery from the perspective of men in recovery from substance addiction. These approaches also provided some insight into the weight concerns observed in the recovery population. Our study sample was limited to an urban male population studied in a cross-sectional design. These findings will be most useful in understanding food behaviors, weight changes and concerns, and the perceptions of food functions of urban men with similar characteristics. Longitudinal investigation to address these food and weight concerns at different stages of recovery from substance addiction is warranted. Implications for research and practice The goal of this study was to understand how men in recovery from substance addiction experienced food and eating, and used food for the purpose of improving their nutritional health and well-being at different stages in the recovery process. Our intention was also to highlight body weight changes and concerns experienced by adult men in various stages of recovery for the improvement of programs or policies concerning nutrition education in recovery facilities.


This study’s findings emphasized the need for more attention towards food and nutrition at the different stages of recovery and the potential for restructuring treatment programs so that eating behaviors and nutrition education address specific needs at each stage of recovery. The results also revealed the need for a better programmatic approach in which weight gain issues are targeted more systematically, perhaps through nutrition skill building at different stages of recovery. For example, in early recovery when excessive weight gain and disordered eating such as binging and hoarding food occurred frequently, more support is needed to encourage healthy eating patterns in order to facilitate healthy weight gain. These findings also highlighted the need for more attention towards physical activity as an accompanying systematic approach to combat the excessive weight gain in recovery. Moreover, individuals in mid recovery who are usually faced with more responsibilities to purchase and prepare their own meals need to learn good shopping and budgeting skills, and healthy food choices, which will promote a healthy lifestyle. For people in later recovery, maintaining a healthy lifestyle, which includes healthy eating will be essential to a successful recovery thus warrants continued nutritional support perhaps through follow-up services tailored to the needs of recovering alumni living on their own. This study emphasized the differences in weight changes, meaningful use of food, and eating behaviors at different stages of recovery and the opportunities for nutrition interventions at each stage. The excessive weight gain that usually occurs in early recovery and the struggles to lose weight later on in the recovery process further highlight the importance of early nutrition and weight interventions in drug and alcohol treatment facilities. Acknowledgments This research project was supported by a grant from Division of Nutritional Sciences at Cornell University and a fellowship from the State University of New York. The authors would like to acknowledge the helpful comments of Drs. Barbara Strupp and John Cawley and the contributions of the men who shared their perspectives with us. References Anders, R., & Anders, L. (1999). Association between alcohol consumption and mortality, myocardial infarction, and stroke in 25 year follow up of 49, 618 young Swedish men. British Medical Journal, 319(25), 821–822. Angulo, J. (1987). Three meanings of food: The psychological, ecological and symbolic perspecitves. Transformation, 2(2-3), 33–37. Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice-Hall. Carney, R. M., & Goldberg, A. P. (1984). Weight gain after cessation of cigarette smoking: A possible role for adipose-tissue lipoprotein lipase. The New England Journal of Medicine, 310, 614–616.

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ARTICLE IN PRESS


Research Report

Sensitivity to reward and body mass index (BMI): Evidence for a non-linear relationship Caroline Davisa,b,, John Foxc a

Kinesiology & Health Sciences, York University, 343 Bethune College, 4700 Keele Street, Toronto Ont., Canada, M3J 1P3 Centre for Addiction and Mental Health, 343 Bethune College, 4700 Keele Street, Toronto, Canada, M3J 1P3Toronto, Ont., Canada c McMaster University, Hamilton, Ont., Canada

b

Received 8 November 2006; received in revised form 21 May 2007; accepted 22 May 2007

Abstract The role of brain reward mechanisms, and associated personality traits, are of growing interest to researchers in the field of eating behaviours and obesity. However, the direction of causal influence has produced some debate and inconsistency in the literature. Some have argued that a reward deficiency syndrome increases the risk for obesity while others have claimed that a heightened sensitivity to reward is linked to higher body mass index (BMI). To reconcile these two perspectives, a non-linear relationship between reward sensitivity and BMI was predicted. Previous research has also not considered whether sex moderates the relationship between these two variables. Results indicated that although men had significantly higher reward sensitivity than women, the interaction between sex and BMI was not significant. As predicted, the quadratic term of BMI was statistically significant indicating a moderately positive relationship between BMI and reward sensitivity in the normal and overweight range of BMI (18–30) after which the relationship changed direction and was inversely related in those with moderate and extreme obesity. This curvilinear relationship meshes with, and helps to integrate, the two perspectives described above and found in previous studies. It would behoove future researchers to identify behaviours that might explain the association between a psychobiological trait like reward sensitivity and a physical characteristic like body size, and to explicate the possibly reciprocal causal mechanisms that underlie this association. r 2007 Elsevier Ltd. All rights reserved. Keywords: Sensitivity to reward; BMI; Overeating; Dopamine; Non-linear

Dopamine (DA) availability in the brain’s mesocorticolimbic pathway has been widely implicated in the regulation of our emotional capacity to feel pleasure and our desire to seek out natural rewards in our environment (Berridge, 2003; Wise, 2004). As with all biological systems, however, there is considerable response variability across individuals. Gray’s (1987) reinforcement sensitivity theory describes a personality dimension, based on individual differences in the activation threshold of brain reward regions—what Gray has called the behavioural activation system (BAS). Those with a more sensitive BAS (i.e. high

reward sensitivity) are more prone to detect signals of reward in their environment, to approach with greater alacrity potentially rewarding stimuli, and to experience more positive affect (pleasure/reinforcement) when they are in situations with cues of reward. Accordingly, those with a more sensitive or reactive DA pathway1 are likely to have a greater capacity for reward and a stronger drive to engage in behaviours with potentially pleasurable outcomes (Cohen, Young, Baek, Kessler, & Ranganath, 2005). Alternatively, a sluggish DA system is thought to foster an anhedonic demeanor and a relative insensitivity to natural rewards (Depue & Collins, 1999; Evans et al., 2006).

Corresponding author. Kinesiology & Health Sciences, York University, 343 Bethune College, 4700 Keele Street, Toronto Ont., Canada, M3J 1P3. E-mail address: [email protected] (C. Davis).

1 The strength of the DA signal and the level of DA availability are influenced by many factors such as the DA transporter, the density of DA receptors, the ability of the neuron to synthesize DA, and its ability to secrete DA into the synapse.

Introduction


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C. Davis, J. Fox / Appetite 50 (2008) 43–49

There is now substantial evidence, from animal studies and human research, that brain reward regions play an important role in the development of obesity through their regulation of appetite (Di Chiara, 2005; Kelley, Baldo, Pratt, & Will, 2005; Need, Ahmadi, Spector, & Goldstein, 2005). However, the manner in which reward sensitivity impacts eating behaviour—in other words, the direction of causal influence—has produced some debate and inconsistency in the scientific literature. Reward Deficiency Syndrome One argument favours the view that hypo-dopaminergic functioning—what has been called a reward deficiency syndrome (RDS)–is a key factor in the development of obesity (e.g. Pagoto, Spring, Cook, McChargue, & Schneider, 2006; Wang et al., 2002; Wang et al., 2001; Wang, Volkow, Thanos, & Fowler, 2004). When there is sub-optimal functioning of the brain reward cascade, which could be caused by certain genetic variants in the DA system, that person is likely to need a DA ‘fix’ to feel good and to improve the DA deficit (Blum & Braverman, 2001). In other words, a sluggish or under-reactive DA reward pathway will foster a diminished motivation to engage in rewarding behaviours.2 In this context, highly palatable foods can serve a ‘self-medicating’ function similar to drugs of abuse because of their ability to enhance DA activation and improve mood (Bowirrat & Oscar-Berman, 2005). Supporting evidence for the RDS theory is based largely on the finding of lower DA D2 receptor densities in obese adults compared to their non-obese counterparts as assessed by neuroimaging scans (Wang et al., 2001) and some genetic research (e.g. Blum et al., 1996). Indirect evidence also comes from the findings that certain DA antagonist drugs (such as clozapine) used in the treatment of psychotic disorders cause increased appetite and significant weight gain in some patients (e.g. de Leon, Diaz, Josiassen, Cooper, & Simpson, 2007; Guo et al., 2007; Masi, Mucci, & Pari, 2006). Currently unknown genetic factors are likely to contribute to the variability of appetite response among patients, resulting in substantial interest in the pharmacogenetics of neuroleptic drug use (Reynolds, Templeman, & Godlewska, 2006). Heightened sensitivity to reward Supporting the hedonic route to obesity, studies have found that sensitivity to reward (SR) —as assessed by selfreport questionnaires—correlates with eating habits that contribute to weight gain. For instance, this trait has been positively associated with emotional overeating, preference for high fat food, binge eating, and food 2 It is important to emphasize that hypo-functioning of the DA pathways may be a genetically inherited condition or it may be caused by various environmental factors such as chronic stress or substance abuse.

cravings (Davis et al., 2007; Davis, Strachan, & Berkson, 2004; Davis & Woodside, 2002; Franken & Muris, 2005; Loxton & Dawe, 2001). To date, only a few studies have investigated direct links between SR and body weight in humans. Franken and Muris (2005) reported a positive relationship between these two variables, but, their sample comprised only young females (mean age of 20 years), and more than 90% of the sample was either normal weight or below. In an earlier study, Davis et al. (2004) found some evidence for a non-linear relationship between BMI and physical anhedonia—a personality trait that has some conceptual overlap with SR. Although their sample reflected a broader range of BMI values, and was therefore more representative of the general adult population, it too was limited in its generalizability because it included only female participants. Argument for a curvilinear relationship As a means of reconciling the alternative viewpoints described above, an inverted-U function is proposed to describe the relationship between SR and BMI whereby both ends of the BMI dimension could reflect low reward sensitivity, albeit from differing motivational processes. Such a proposition is not only theoretically intuitive, but it meshes well with the existing data. First, it was expected that a positive association would be found between SR and BMI in the non-obese range of the body-weight continuum. There is good evidence that within normal limits increases in mesolimbic DA are associated with more appetitive behaviours as seen, for example, in a recent study by Volkow et al. (2002). Amplification of the DA signal via a small dose of oral methylphenidate3 increased the desire of their participants to eat in response to a palatable food cue. Also, according to reinforcement sensitivity theory (Gray, 1987), those with a greater SR have a stronger appetitive drive, and therefore should be more likely to engage in hedonic eating than their less reward-sensitive counterparts. Over time, this pattern of behaviour may foster an energy imbalance associated with incremental weight gain.4 Secondly, at the higher (obese) end of the body-weight continuum, an inverse relationship between reward sensitivity and BMI was expected. Such a finding would map onto the RDS evidence—in particular, the often-cited study by Wang et al. (2001) who found a negative relationship between DA D2 receptor densities and BMI. In other words, the most morbidly obese participants in 3 Methylphenidate is a psychomotor stimulant which binds to the DA transporter, thereby inhibiting the reuptake of DA into the cell, and increasing its availability in the synapse. 4 It is important to keep in mind, however, that DA activation is likely to have a biphasic effect on appetite response as seen by the appetitesuppressing effects of powerful DA agonists like amphetamine (e.g. Kuo, 2003). In other words, an increase of the DA signal within the moderate/ normal range is likely to foster appetitive motivation, while augmentation to very high levels is likely to have an anorexic effect.

ARTICLE IN PRESS C. Davis, J. Fox / Appetite 50 (2008) 43–49

their study had the fewest DA D2 receptors and were assumed to be the most deficient in hedonic capacity. Wang and colleagues have argued that highly palatable foods, eaten in large quantities, can compensate for a reward deficit in the same manner as addictive drugs. With this pattern of excessive and high-calorie consumption, weight gain could be rapid and prodigious. It is important, however, to consider that Wang et al’s data could reflect the sequelae of overeating rather than a factor antecedent to its onset, especially since all cases in their study were in the morbidly obese range of BMI. Excessive food consumption is likely to cause a down-regulation of post-synaptic D2 receptors as is typically seen in the case of chronic drug addicts (see Leshner & Koob, 1999 for a review) and animals subjected to repeated intake of sweet and fatty food (Kelley, Will, Steininger, Zhang, & Haber, 2003). In summary, we are predicting a non-linear relationship between reward sensitivity and BMI because a reward deficit could relate either to low or to high BMI. For some individuals, a low SR may contribute to reduced food intake (and consequently low body weight) because of diminished motivation for, and pleasure from, eating. In other individuals, a reward deficiency might foster overeating of highly palatable food (and a relatively high body weight) as a means of ‘self-medication’ to compensate for a sluggish brain reward system. A third process is also relevant to the dynamics of our predicted curvilinear relationship. Individuals who are inherently highly sensitive to reward—those who we argue are prone to hedonic overeating—could over time experience a down-regulation of DA pathways and reward sensitivity as a result of the chronic over-consumption of calorically dense foods. Such a neuroadaption would mimic that found in addicts who abuse addictive substances (see Davis et al., 2004; Wang et al., 2004). The current study represents the first attempt to examine, in a comprehensive way, the relationship between reward sensitivity and BMI in a large sample of men and women who represent a broad range of BMI values, including a substantial proportion of moderate and morbidly obese participants. It is also the first to examine sex differences and their possible moderating effect on the relationship between reward sensitivity and BMI. In light of our prediction that sensitivity has an inverted-U relationship with BMI, we fit a quadratic regression model to the data, allowing as well for the possible interaction of BMI with sex. In addition, to enable us to discover a nonlinear relationship different from the one that we predicted, we fit a non-parametric regression model to the data.

45

years, SD ¼ 7.13) and BMI (men: x¯ ¼ 28:24, SD ¼ 5.56; women: x¯ ¼ 30:67, SD ¼ 8.01). In both groups, the participants were predominantly Caucasian with a smaller proportion of Afro-Canadians. Individuals of South Asian (e.g. Korean, Japanese) and Chinese descent were excluded from the study because of evidence that a uniform risk-classification system based on BMI is inappropriate across all ethnicities. In particular, it is recommended that in South Asian and Chinese populations, BMI cut-offs for optimal health should be far below the WHO values currently in common use (Razak et al., 2005). Participants were recruited from newspaper advertisements and from posters placed at several large urban institutions (e.g. hospitals and universities) asking for ‘healthy adults’ to take part in a psychology study. In order to attract a sufficiently large sample of obese individuals, some of our advertisements and posters asked for ‘Plus-Sized Adults’ to participate in the research. Volunteers were screened during an initial telephone interview and excluded if they were: (i) pregnant, (ii) not fluent in English, (iii) had not resided in Canada for at least 5 years, and (iv) were currently being treated for a serious medical condition such as angina or diabetes, or had any psychiatric conditions such as depression, substance abuse, or an eating disorder.5 If participants passed the telephone screening interview, they were assessed individually, after giving signed informed consent, in one of two research facilities. Trained research assistants conducted a more comprehensive screening using a standard medical and psychiatric interview protocol. Following this, the questionnaire package was completed, height and weight were measured, and each individual was paid a small stipend for participation. The data used in this study are part of a larger, ongoing project whose results will be published at a later date. Measures SR was assessed by one scale of the Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ) (Torrubia, Avila, Molto, & Caseras, 2001), and comprises 24 forced-choice items reflecting the respondent’s approach responses under various conditions of reward. The scale items reflect both the anticipation of reward (e.g. Does the good prospect of obtaining money motivate you strongly to do some things?) and pleasure experienced from rewarding activities (e.g. Does you attention easily stray from your work in the presence of an attractive stranger?). This scale was developed to

Methods Participants and procedure Study participants included 369 healthy adult men (n ¼ 138) and women (n ¼ 231). They were similar in age (men: x¯ ¼ 28:24 years, SD ¼ 7.66; women: x¯ ¼ 30:67

5 By eliminating participants with depression, the representativeness of the sample is somewhat compromised, especially because of the high prevalence of obesity in those with depression (Ohayon, 2007). However, since most people in treatment for depression take anti-depression medications, many of which affect appetite and body weight, it was our feeling that the medication factor could present a potential confound in the interpretation of our results.

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assess Gray’s psychobiological model of personality (see Gray, 1987, 1990), a well-validated theory based on two independent neurobiological motivational systems: the BIS and the BAS. High reward sensitivity (reflected by high scores on the SR scale) is seen as a reflection of a strong BAS—i.e. a heightened tendency to approach pleasurable stimuli in one’s environment, and to find the associated behaviours rewarding. Low SR scores would reflect a deficient SR. This scale has shown good internal consistency, temporal stability, and concurrent validity (e.g. Caseras, Avila, & Torrubia, 2003). Nevertheless, psychometric evaluation of the factor structure of the SPSRQ in two recent studies has identified a number of poor items (Cogswell, Alloy, van Dulmen, & Fresco, 2006; O’Connor, Colder, & Hawk, 2004). Indeed, in both studies the same 7 items were identified as problematic in the SR scale, and their authors noted some improvement in the factor structure of the questionnaire— without sacrificing the validity of the scales—when these items were trimmed from the item pool. Cogswell et al. (2006) strongly recommend that researchers will benefit from continued evaluation of the full versus the trimmed version of these scales in future studies. For this reason, both the 24-item original SR scale and the trimmed 17-item scale were employed in the data analysis. The alpha coefficients in the present study were 0.76 for the full scale and 0.74 for the trimmed scale, and their inter-correlation was 0.96. BMI (weight[kg]/height[metres2]) was calculated from height and weight measured with the participant wearing indoor clothing and standing in stocking feet.

Results Descriptive statistics Table 1 presents the means, standard deviations, extremes, and quartiles for BMI and SR, shown separately for male (n ¼ 136) and female (n ¼ 230) subjects. Only subjects with complete data on all variables in the analysis are included: 3 subjects were omitted because they did not answer all of the questions comprising the SR scale.6 Male subjects have a statistically significantly lower average BMI than female subjects (t ¼ 3.39, df ¼ 354, po.001, twosided) and higher average SR (t ¼ 5.29, df ¼ 259, po.001, two-sided).7 Variation in BMI is smaller for males than for females; variation in SR is slightly larger for males. 6 The results reported below use the 17-item trimmed version of the scale, but we obtained essentially identical results with the full 24 items, as is unsurprising given the very high correlation between the two versions of the scale. 7 The degrees of freedom for each t-test were computed by the commonly employed Welch–Satterthwaite approximation, appropriate when the variances are not assumed to be equal, and are reported here to the nearest whole number.

Table 1 Distribution of BMI and sensitivity to reward by sex

BMI Males Females

Mean

SD

Minimum

Q1

Median

Q3

Maximum

28.23 30.65

5.60 8.01

19.03 17.16

23.67 24.41

27.38 29.79

31.21 35.84

46.09 51.60

0 0

6.75 4

8 7

11 9

17 16

Sensitivity to reward Males 8.62 3.59 Females 6.64 3.22

Table 2 Analysis-of-variance table for the regression model fit to sensitivity to reward Source

df

Sum of squares

Mean square

F

p

Sex BMI Linear Quadratic Sex BMI Linear Quadratic Error

1 2 1 1 2 1 1 360

256.7 94.9 24.4 70.5 0.32 0.11 0.21 4021.9

256.7 47.5 24.4 70.5 0.16 0.11 0.21 11.2

23.0 4.2 2.2 6.3 0.01 0.01 0.02

o0.001 0.015 0.14 0.012 0.99 0.92 0.89

Total

365

4451.7

Regression analysis As explained, we undertook a quadratic regression of SR on BMI, expecting to find a positive relationship between the two variables at low levels of BMI and a negative relationship at high levels. We employed an orthogonal polynomial basis for the quadratic in BMI.8 The regression model includes a dummy variable for sex, coded 1 for males and 0 for females, and interaction regressors for BMI and sex. As well, so that we could detect a relationship between BMI and SR different from the one anticipated, we also fit a non-parametric-regression model to the data, employing a smoothing spline in BMI, and selecting the equivalent degrees of freedom for the spline by generalized cross-validation (Wood, 2000). An analysis-of-variance table for the quadratic regression appears in Table 2. The interaction between sex and BMI is essentially absent (p ¼ 0.99): The regression curves for male and female subjects are almost perfectly parallel. There is, however, strong evidence of a sex effect on SR (po0.001), and moderately strong evidence of an effect of BMI (p ¼ 0.015); moreover, the quadratic component of the BMI term is statistically significant (p ¼ 0.013).

8 The linear regressor is therefore centred at 0, and the quadratic regressor is uncorrelated with the linear regressor. The fitted quadratic curve itself, however, is identical to that produced by employing the raw quadratic regressors BMI and BMI2.

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Table 3 displays the coefficients for the additive quadratic regression of SR on BMI and sex. At fixed levels of BMI, male subjects on average are nearly 2 points higher than female subjects in SR, a difference that is only slightly smaller than the marginal difference in means for the sexes. Sex and BMI account for about 10% of the variation in SR. The quadratic partial relationship between SR and BMI is displayed in the ‘effect plot’ (Fox, 1987, 2003) in Fig. 1, panel (a): This graph was constructed by setting the dummy regressor for sex to 0.5, representing a hypothetical group composed equally of males and females, allowing BMI to range over the values observed in the data, and computing fitted values from the regression model in Table 3. The broken lines in the graph represent a point-wise 95% confidence envelope around the fitted quadratic regression. Panel (b) of Fig. 1 shows a similar effect plot for the additive non-parametric regression that we fit to the data. The equivalent degrees of freedom for the BMI spline term in the model is 2.61—slightly more than the 2 degrees of freedom used by the quadratic regression in panel (a). Not only are the two fitted curves very similar, but the two models produce nearly identical fits to the data: Fitted values for the 366 subjects under the two models correlate in excess of 0.99.

Discussion The role of brain reward mechanisms, and associated personality traits, are of growing interest to researchers in the field of eating behaviours and obesity. As we predicted, results from the present study identified a non-linear relationship between SR and BMI in a sample of men and women who represent a broad spread of values on both variables. It was also clearly evident that this relationship was essentially identical in men and women, although the former reported significantly greater reward sensitivity. There is increasing recognition that many biopsychosocial relationships are not linear in the full range of their expression (Waldman, Atwater, & Davidson, 2004; Wong & Lachenbruch, 1996), including factors that influence BMI (Oh & Seo, 2001; Payne & Morrison, 2002; Vandewater, Shim, & Caplovitz, 2004). In these cases, it is generally more appropriate to use a quadratic model, or other non-linear models to represent the relationship between the two variables. SR and BMI Among normal-weight and overweight participants, the relationship between reward sensitivity and BMI was in the positive direction, but changed to a negative association in the cohort who were moderately and morbidly obese. In considering this inverted-U relationship, it is probable that the causal direction of influence between the two variables is dynamic and even reciprocal, and is dependent on how each interfaces with other relevant psycho-social factors. Undoubtedly there are many routes to obesity. Those with a relatively high SR-by definition, individuals with a strong appetitive drive—are likely to have more difficulty resisting the temptation of readily available, rich and tasty foods in our current ‘supermarket’ environment. Gradually, and over time, weight gain will ensue, which can bring its own, sometimes stressful, consequences. Concerning those at the opposite end of the reward sensitivity continuum, our findings suggest that a doublepronged mechanism could link low SR with body weight

Table 3 Coefficients and standard errors for the additive quadratic regression of sensitivity to reward on sex and BMI Coefficient Intercept Sex (male ¼ 1) BMI Linear Quadratic

Standard error

6.72 1.77

0.22 0.37

5.19 8.47

3.38 3.36

R2 ¼ 0.096

Quadratic Regression 8

b Sensitivity to Reward

Sensitivity to Reward

a

7 6 5 4

47

Additive Nonparametric Regression 8 7 6 5 4

3 20 25 30 35 40 45 50 BMI

20 25 30 35 40 45 50 BMI

Fig. 1. Fitted sensitivity to reward by BMI, for a hypothetical group composed equally of males and females. The ‘‘rug plot’’ at the bottom of each panel shows the distribution of BMI. The broken lines give a 95-percent point-wise confidence envelope around the fit.

ARTICLE IN PRESS 48


via two different motivational responses to food intake. On the one hand, a relatively depressed appetite (and therefore low body weight) could occur—as is typically seen in the melancholic form of depression (Gold & Chrousos, 2002)—because of the subjectively diminished reward potential of food. On the other hand, a low SR might promote the use of highly palatable food to boost an anhedonic affect in order to compensate for what is believed to be a DA deficit. If ‘self-medication’ with food occurs frequently, and to a great degree, the outcome would almost certainly be a high BMI. One important limitation of this study is its sole reliance on a self-report assessment of the reward sensitivity construct. To date, there has been very little human research bridging the gap between neurobiological studies of brain reward functioning and psychological research using personality measures of SR. In other words, evidence that psycho-behavioural assessments of this trait correlate with biological markers of DA activation in the mesocorticolimbic regions is minimal. Exceptions are two recent studies. One (White, Lott, & de Wit, 2006) found a positive association between response to amphetamine (greater liking, more wanting of the drug) and a questionnaire measure of reward sensitivity (using the Multidimensional Personality Questionnaire-Brief Form: Patrick, Curtin, & Tellegen, 2002), while the second demonstrated that selfreport reward sensitivity (as assessed by the Drive subscale of the BIS/BAS: Carver & White, 1994) was positively correlated with degree of activation of brain reward regions in response to visual images of appetizing food (Beaver et al., 2006). Before we can claim definitively that variation in brain reward mechanisms can be conceptualized and assessed in any meaningful way as a personality trait— and thereby related to risk for obesity—we need to have a converging base of human research that links personality and behaviour to relevant biological markers.

References Beaver, J. D., Lawrence, A. D., Van Ditzhuijzen, J., Davis, M. H., Woods, A., & Calder, A. J. (2006). Individual differences in reward drive predict neural responses to images of food. Journal of Neuroscience, 26, 5160–5166. Berridge, K. C. (2003). Pleasures of the brain. Brain and Cognition, 52, 106–128. Blum, K., & Braverman, E. R. (2001). ‘‘Reward deficiency syndrome’’: An emerging concept. Molecular Psychiatry, 6(Suppl 1), S3. Blum, K., Sheridan, P. J., Wood, R. C., Braverman, E. R., Chen, T. J. H., Cull, J. G., et al. (1996). The D-2 dopamine receptor gene as a determinant of reward deficiency syndrome. Journal of the Royal Society of Medicine, 89, 396–400. Bowirrat, A., & Oscar-Berman, M. (2005). Relationship between dopaminergic neurotransmission, alcoholism, and reward deficiency syndrome. American Journal of Medical Genetics Part B-Neuropsychiatric Genetics, 132B, 29–37. Carver, C. S., & White, T. L. (1994). Behavioral inhibition, behavioral activation, and affective responese to impending reward and punishment: The BIS/BAS scales. Journal of Personality and Social Psychology, 67, 319–333.

Caseras, X., Avila, C., & Torrubia, R. (2003). The measurement of individual differences in Behavioural Inhibition and Behavioural Activation Systems: A comparison of personality scales. Personality and Individual Differences, 34, 999–1013. Cogswell, A., Alloy, L. B., van Dulmen, M. H. M., & Fresco, D. M. (2006). A psychometric evaluation of behavioral inhibition and approach self-report measure. Personality and Individual Differences, 40, 1649–1658. Cohen, M. X., Young, J., Baek, J-M., Kessler, C., & Ranganath, C. (2005). Individual differences in extraversion and dopamine genetics predict neural reward responses. Cognitive Brain Research, 25, 851–861. Davis, C., Patte, K., Levitan, R. D., Reid, C., Tweed, S., & Curtis, C. (2007). From motivation to behaviour: A model of reward sensitivity, overeating, and food preferences in the risk profile for obesity. Appetite, 48, 12–19. Davis, C., Strachan, S., & Berkson, M. (2004). Sensitivity to reward: Implications for overeating and overweight. Appetite, 42, 131–138. Davis, C., & Woodside, D. B. (2002). Sensitivity to the rewarding effects of food and exercise in the eating disorders. Comprehensive Psychiatry, 43, 189–194. de Leon, J., Diaz, F. J., Josiassen, R. C., Cooper, T. B., & Simpson, G. M. (2007). Weight gain during a double-blind multidosage clozapine study. Journal of Clinical Psychopharmacology, 27, 22–27. Depue, R. A., & Collins, P. F. (1999). Neurobiology of the structure of personality: Dopamine, facilitation of incentive motivation, and extraversion. The Behavioral and Brain Sciences, 22, 491–517 (discussion 518–569). Di Chiara, G. (2005). Dopamine in disturbances of food and drug motivated behavior: A case of homology? Physiology & Behavior, 86, 9–10. Evans, A. H., Lawrence, A. D., Potts, J., MacGregor, L., Katzenschlager, R., Shaw, K., et al. (2006). Relationship between impulsive sensation seeking traits, smoking, alcohol and caffeine intake, and Parkinson’s disease. Journal of Neurology, Neurosurgery, and Psychiatry, 77, 317–321. Fox, J. (1987). Effect displays for generalized linear models. Sociological Methodology, 17, 347–361. Fox, J. (2003). Effect displays in R for generalized linear models. Journal of Statistical Software, 8(15), 1–27. Franken, I. H. A., & Muris, P. (2005). Individual differences in reward sensitivity are related to food craving and relative body weight in healthy women. Appetite, 45, 198–201. Gold, P. W., & Chrousos, G. P. (2002). Organization of the stress system and its dysregulation in melancholic and atypical depression: High vs low CRH/NE states. Molecular Psychiatry, 7, 254–275. Gray, J. A. (1987). The neuropsychology of emotion and personality. In S. M. Stahl, S. D. Iverson, & E. C. Goodman (Eds.), Cognitive Neurochemistry (pp. 171–190). Oxford, UK: Oxford University Press. Gray, J. A. (1990). Brain systems that mediate both emotion and cognition. Cognition and Emotion, 4, 269–288. Guo, J. J., Keck, P. E., Jr., Corey-Lisle, P. K., Li, H., Jiang, D., Jang, R., et al. (2007). Risk of diabetes mellitus associated with atypical antipsychotic use among medicaid patients with bipolar disorder: A nested case-control study. Pharmacotherapy, 27, 27–35. Kelley, A. E., Baldo, B. A., Pratt, W. E., & Will, M. J. (2005). Corticostriatal-hypothalamic circuitry and food motivation: Integration of energy, action and reward. Physiology & Behavior, 86, 773–795. Kelley, A. E., Will, M. J., Steininger, T. L., Zhang, M., & Haber, S. N. (2003). Restricted daily consumption of a highly palatable food (chocolate Ensure) alters striatal enkephalin gene expression. European Journal of Neuroscience, 18, 2592–2598. Kuo, D. Y. (2003). Further evidence of the mediation of both subtypes of dopamine D1/D2 receptors and cerebral neuropeptide Y (NPY) in amphetamine-induced appetite suppression. Behavioural Brain Research, 147, 149–155. Leshner, A. I., & Koob, G. F. (1999). Drugs of abuse and the brain. Proceedings of the Association of American Physicians, 111, 99–108.

ARTICLE IN PRESS C. Davis, J. Fox / Appetite 50 (2008) 43–49 Loxton, N. J., & Dawe, S. (2001). Alcohol abuse and dysfunctional eating in adolescent girls: The influence of individual differences in sensitivity to reward and punishment. International Journal of Eating Disorders, 29, 455–462. Masi, G., Mucci, M., & Pari, C. (2006). Children with schizophreniaClinical picture and pharmacological treatment. CNS Drugs, 20, 841–866. Need, A. C., Ahmadi, K. R., Spector, T. D., & Goldstein, D. B. (2005). Obesity is associated with genetic variants that alter dopamine availability. Annals of Human Genetics, 70, 293–303. O’Connor, R. M., Colder, C. R., & Hawk, L. W., Jr. (2004). Confirmatory factor analysis of the sensitivity to punishment and sensitivity to reward questionnaire. Personality and Individual Differences, 37, 985–1002. Oh, H. S., & Seo, W. S. (2001). The compound relationship of smoking and alcohol consumption with obesity. Yonsei Medical Journal, 42, 480–487. Ohayon, M. M. (2007). Epidemiology of depression and its treatment in the general population. Journal of Psychiatric Research, 41, 207–213. Pagoto, S. L., Spring, B., Cook, J. W., McChargue, D., & Schneider, K. (2006). High BMI and reduced engagement and enjoyment of pleasant events. Personality and Individual Differences, 40, 1421–1431. Patrick, C. J., Curtin, J. J., & Tellegen, A. (2002). Development and validation of a brief form of the Multidimensional Personality Questionnaire. Psychological Assessment, 14, 150–163. Payne, R. L., & Morrison, D. (2002). The differential effects of negative affectivity on measures of well-being versus job satisfaction and organizational commitment. Anxiety Stress and Coping, 15, 231–244. Razak, F., Anand, S., Vuksan, V., Davis, B., Jacobs, R., Teo, K. K., et al. (2005). Ethnic differences in the relationships between obesity and glucose-metabolism abnormalities: A cross-sectional population-based study. International Journal of Obesity, 29, 656–667. Reynolds, G. P., Templeman, L. A., & Godlewska, B. R. (2006). Pharmacogenetics of schizophrenia. Expert Opinion on Pharmacotherapy, 7, 1429–1440.

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Torrubia, R., Avila, C., Molto, J., & Caseras, X. (2001). The sensitivity to punishment and sensitivity to reward questionnaire (SPSRQ) as a measure of Gray’s anxiety and impulsivity dimensions. Personality and Individual Differences, 31, 837–862. Vandewater, E. A., Shim, M. S., & Caplovitz, A. G. (2004). Linking obesity and activity level with children’s television and video game use. Journal of Adolescence, 27, 71–85. Volkow, N. D., Wang, G.-J., Fowler, J. S., Logan, J., Jayne, M., Francesschi, D., et al. (2002). ‘‘Nonhedonic’’ food motivation in humans involves dopamine in the dorsal striatum and methyphenidate amplifies this effect. Synapse, 44, 175–180. Waldman, D. A., Atwater, L. E., & Davidson, R. A. (2004). The role of individualism and the Five-Factor Model in the prediction of performance in a leaderless group discussion. Journal of Personality, 72, 1–28. Wang, G. J., Volkow, N. D., Felder, C., Fowler, J. S., Levy, A. V., Pappas, N. R., et al. (2002). Enhanced resting activity of the oral somatosensory cortex in obese subjects. Neuroreport, 13, 1151–1155. Wang, G.-J., Volkow, N. D., Logan, J., Pappas, N. R., Wong, C. T., Zhu, et al. (2001). Brain dopamine and obesity. The Lancet, 357, 354–357. Wang, G. J., Volkow, N. D., Thanos, P. K., & Fowler, J. S. (2004). Similarity between obesity and drug addiction as assessed by neurofunctional imaging: A concept review. Journal of Affective Disorders, 23, 39–53. White, T. L., Lott, D. C., & de Wit, H. (2006). Personality and subjective effects of acute amphetamine in healthy volunteers. Neuropsychopharmacology, 31, 1064–1074. Wise, R. A. (2004). Dopamine, learning and motivation. Nature Reviews Neuroscience, 5, 483–494. Wong, W. K., & Lachenbruch, P. A. (1996). Tutorial in biostatistics designing studies for dose response. Statistics in Medicine, 15, 343–359. Wood, S. N. (2000). Modelling and smoothing parameter estimation with multiple quadratic penalties. Journal of the Royal Statistical Society, Series B, 62, 413–428.

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Research Report

Effects of short-term moderate alcohol administration on oxidative stress and nutritional status in healthy males Giovanni Addoloratoa,, Lorenzo Leggioa,c, Veronica Ojettia, Esmeralda Capristoa, Giovanni Gasbarrinia, Antonio Gasbarrinib a

Institutes of Internal Medicine, Catholic University of Rome, L.go A. Gemelli 8, I-00168, Rome, Italy b Medical Pathology, Catholic University of Rome c Center for Alcohol and Addiction Studies, Department of Psychiatry and Human Behaviour, Brown University Medical School, Providence (RI), USA Received 28 March 2007; received in revised form 21 May 2007; accepted 22 May 2007

Abstract The effects of moderate amounts of different alcoholic beverages on oxidative stress and nutritional parameters were investigated in 40 healthy subjects. Ethanol 40 g/day was administered at the two main meals for 30 days by beer (group A), wine (group B) or spirit (group C); controls (group D) maintaned abstinence. Malondyaldeide (MDA), adenosine-triphosphate (ATP), reduced-glutathione (GSH), E-vitamin and nutritional status were evaluated at the start (T0) and the end (T1) of the study. At T1 controls did not present significant changes in the assessed parameters, while a significant increase of malondyaldeide (MDA) and a significant decrease of reducedglutathione and E-vitamin in group A, B and C and of ATP in group C were observed. Fat mass (FM) increased slightly in group A and B and decreased in group C. Ethanol decreased antioxidant parameters and increased lipoperoxidation parameters. However some of these changes appeared attenuated when ethanol was consumed in beer or wine. Finally, short-term moderate ethanol intake appeared to influence the FM, although it was not able to significantly affect nutritional or body composition. r 2007 Elsevier Ltd. All rights reserved. Keywords: Ethanol; Beer; Wine; Oxidative stress; Lipid peroxidation; Antioxidants

Introduction The impact of alcohol consumption on individual’s health is related to drinking behaviour patterns as well as to genetic factors (Lieber, 1995). Beneficial and adverse effects due to alcohol intake have been reported in several epidemiologic studies, showing a J-shaped relationship between the amount of alcohol consumed and mortality (Criqui and Ringel, 1994; Stefanini, Caputo, & Addolorato, 1998). In particular, although acute and chronic alcohol abuse are related to an increase in mortality, particularly due to liver diseases (Gramenzi et al., 2006; Lieber, 1988), hemorragic stroke and cardiovascular diseases (Lucas, Brown, Wassef, & Giles, 2005), consumption of moderate doses of ethanol seems to be associated Corresponding author. Institute of Internal Medicine Catholic University of Rome. E-mail address: [email protected] (G. Addolorato).


with a lower death rate from coronary heart disease (CAD) and thrombotic stroke (Klatsky, 2002; Renaud and De Lorgeril, 1992). Apart from the mechanisms involved in the pathogenesis of tissue injury, mortality and morbidity related to alcohol abuse, great attention has been recently devoted both to alcohol induced nutritional disorders (Addolorato, Capristo, Greco, Stefanini, & Gasbarrini, 1997, 1998; Addolorato et al., 2006) and alcohol related oxidative stress (Addolorato et al., 2001; Lieber, 2005). Gender-related difference seems to play an important role in these mechanisms (Addolorato et al., 1999; Gasbarrini et al., 2001). On the other hand an increase of the antioxidant activity in blood of subjects with a moderate alcohol intake has been recently indicated as a possible protective mechanism against CAD mortality (Gorinstein et al., 1997), in addition to an alcohol-related action on the high-densitity-lipoprotein cholesterol (HDL-C) (Rapaport, 1989) and on platelet aggregation (Renaud & De Lorgeril, 1992).

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Wine and beer are the most common ethanol-containing beverages in developed countries; both beverages have many non alcohol components with antioxidant properties (Maxwell, Cruickshank, & Thorpe, 1994; Rimm, Klatsky, Grobbee, & Stampfer, 1996). Wine is the beverage mainly investigated, and has been found to contain antioxidants, vasorelaxants and stimulants of anticoagulation mechanisms (Maxwell et al., 1994; Rimm et al., 1996). Beer also contains many different substances with nutritional value, such as vitamins, minerals, organic and inorganic salts and phenolic compounds. Among these compounds, phenols— essential in determining the taste and in maintaining the foam—are well-documented antioxidants (Bors, Michel, & Saran, 1984; Chimi, Cillard, Cillard, & Rahmani, 1991; De Whalley, Rankin, Hoult, Jessup, & Leake, 1990; Nardini et al., 1995) contributing to physical and chemical stability of the packaged beer. Animal studies suggested that the daily consumption of beer may have beneficial effects (Kondo, 2004). Moreover, a previous study by our lab showed that the prolonged administration of moderate amount of beer in a well-balanced diet does not cause oxidative stress and it is able to attenuate the oxidative action of ethanol by itself in rats (Gasbarrini et al., 1998). At present there are few experimental studies in humans investigating the effect of moderate alcohol administration by wine, beer and spirit on oxidative stress and nutritional status parameters. Therefore, the aim of the present study 30

*

20

10

Subjects and Methods Subjects A total of 40 non-smoking Caucasian male, healthy, social drinker, subjects with a mean alcohol consumption of 30710 g per day of ethanol for at least 5 y were recruited from the medical staff. In accordance with previous reports (Bisaga & Evans, 2004; Seppala, Stromberg, & Bergman, 1984), after 2 weeks of total abstinence from alcohol (T0), the subjects were randomly divided into four groups: Group A:10 subjects (mean age 2876 years; body mass index –(BMI)- 25.872.4) consumed 40 g of ethanol

-5

A

-10

*

D B

*

C

*

-15

*

*

-20 -25

5 0

was to evaluate the influence of 30 days of moderate amount of beer, wine and spirit administration in healthy subjects on some parameters of both oxidant and antioxidant status and of body composition Fig 1. A dose of 40 grams/day was chosen in order to reproduce in an experimental setting the possible effects of the mean amount of alcohol typically consumed in our country by healthy subjects (Caputo et al., 1998) and usually these subjects were defined as moderate drinkers (Rigamonti et al., 2003). Finally, a daily alcohol consumption of 40 g/day or less in male subjects is defined as a moderate alcohol intake according to the World Health Organization (WHO) guidelines (see Guerrini, Gentili, & Guazzelli, 2006).

0

25

15

51

-30 A

B

D

C

GSH

MDA 0

D

0

-5

-5

-10

-10

-15

A

-20

*

A

D

B

C

-15 C

*

-25

*

-20 -25 -30

-30 B

*

E-VITAMIN

ATP

Fig 1. Variation expressed in percentage between the start (T0) and the end (T1) of the study of plasma concentrations of malondyaldeide (MDA), reduced glutathione (GSH), alpha-tocopherol (E vitamin) and adenosine triphosphate (ATP) of the groups examined. MDA significantly increased in all subjects exposed to ethanol (group A: +9.5%; group B: +19.0%; group C: +7.3%) (po0.05). A significant decrease of GSH (group A: 4.2%; group B: 5.1%; group C 9.0%) and of E vitamin (group A: 14.5%; group B: 32.4%; group C: 17.6%) were found in all subjects exposed to ethanol (po0.05). Plasmatic levels of ATP significantly decreased only in group C ( 12.0%; po0.05). *po0.05 in T1 with respect to T0.

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G. Addolorato et al. / Appetite 50 (2008) 50–56

per day in a lager type beer (1000 ml; 4% ethanol) for 30 days. Group B:10 subjects (mean age 3175 years; BMI 24.371.9) consumed 40 g of ethanol per day in red wine (400 ml; 11% ethanol) for 30 days. Group C: 10 subjects (mean age 2773 years; BMI 24.072.1) consumed 40 g of ethanol per day in spirit (120 ml of distillate 40% volume) for 30 days. Group D: 10 subjects (mean age 2973 years; BMI 24.972.3) maintained abstinence from alcohol during the study period and served as control group. In these subjects alcohol abstinence was confirmed by subjects and collateral interview, in particular, collateral interviews involving family members living with the subject during the study (relative or partner) (Sobell, Agarwal, & Sobell, 1997). Beer, wine and spirit used in this study were produced in Italy. The amount of alcoholic beverages was fractioned into two administration on any day of the study and was consumed by subjects with the main daily meals consisting of a normal mediterranean diet. During the study period the subjects had to pursue their usual life style. No drugs influencing plasma levels of antioxidants and lipids were allowed throughout the experimental period. The protocol was carefully explained to the volunteers and their written informed consent was obtained. The protocol complied fully with the guidelines of the Ethics Committee of the Universita` Cattolica del Sacro Cuore in Rome, Italy. Blood sample processing Blood samples were collected from a peripheral vein. All samples were taken using ethylenddiaminetetracetic acid (EDTA) as an anticoagulant; they were immediately refrigerated in ice, protected from the light and centrifuged for 5 min at 2000 rpm within 20 min. Plasma was stored at 80 1C. All biochemical analyses were performed within one week. Plasma redox status and biochemical parameters All the blood parameters were evaluated at the start (T0) and at the end (T1) of the study. Plasma malondyaldeide (MDA, a lipoperoxidation marker) levels were evaluated as follows: 1 ml of 50% trichloroacetic acid was added to 0.5 ml of sample, and the protein-free supernatant was used for MDA measurement by the thiobarbituric acid reaction (Buege and Aust, 1978). Consumption of two of the main natural antioxidant compounds, reduced glutathione (GSH) and alpha-tocopherol (E-vitamin), was recorded in order to better characterize oxidative stress. GSH plasma levels were measured by chromatography method according to Neuschwander-Tetri and Roll (1989). E-vitamin plasma levels were determined according to Bieri, Talliver, and Catignani (1979). Plasma sample, protected from the light, were added to a double volume of ethanol containing tocopherol acetate as the internal

standard, and to three volume N-heptane containing butylated hydroxytoluene. They were then extracted, evaporated under nitrogen, and resuspended in the mobile phase with the addiction of tetrahydrofurane (10%). The mobile phase consisted in Acetonitrile–metyl alcohol mixture; the stainless steel column was sturry-packed with Spheri-S RP-18, the flow rate being set at 2 ml/min and the temperature at 37 1C. Adenosine triphosphate (ATP) plasma levels were determined by bioluminescence according to Douillet et al. (2005). All the main biochemical blood parameters including total cholesterol, triglicerydes, HDL-C, and liver parameters were assessed by standardized automated techniques. Electrocardiograph examination, systolic and diastolic blood pressure and heart rate were evaluated in all subjects at T0 and T1. Nutritional measurements Body weight was measured to the nearest 0.1 kg with a beam scale, and height was measured to the nearest 0.5 cm with a wall-mounted stadiometer while the subjects were wearing light clothes and no shoes. BMI was computed as the ratio between body weight (kg) and height (m2). Fat mass (FM), fat free mass (FFM) and total body water (TBW) were assessed by bioimpedance (BIA). BIA was performed using a radiofrequency current of 800 mA at 50 KHz between a set of electrodes attached to the dorsum of the hand and foot (Body Composition Analyzer, Medileader, Parma, Italy), as previously described (Addolorato et al., 1997). All subjects compiled a food diary during the study period. Each food item provided and each item remaining uneaten was weighed to the nearest gram on precision scales (KS-01, Rowenta, Berlin, Germany). The nutrient content of all food items was carefully calculated by a dietician using computerized food tables (Food Processor 11, ESHA Research, Salem, OR) modified according to the food tables of the National Institute of Nutrition, Rome, Italy. Total diet was controlled, and provided by our lab. With the exception of tap water, subjects were not allowed to eat or drink anything other than the food supplied. The mean energy intake was 9.7 MJ (2.3 Mcal), range 9.4–10 MJ, and did not differ between the four treatments. Diet was controlled (equal for all treatments) for antioxidant vitamin intake. Statistical analysis Statistical analysis was performed using one-factor analysis of variance and Scheffe F-test method for multiple comparison. A po0.05 was considered to be significant. Results Plasma redox status Table 1 reports the plasma concentrations of ATP, GSH, MDA and E vitamin of the groups examined at the start of


the study and after 30 days. No significant differences in ATP, GSH, MDA and E vitamin were found in control before and after the 30-day study period. Conversely, plasmatic values of MDA increased significantly in all subjects exposed to ethanol (group A: +9.5%; group B: +19.0%; group C: +7.3%) (po0.05); moreover a significant decrease in plasmatic levels of GSH (group A: 4.2%; group B: 5.1%; group C 9.0%) and of E vitamin (group A: 14.5%; group B: 32.4%; group C: 17.6%) were found in all subjects consuming ethanol (po0.05). Plasma levels of ATP decreased significantly in

Table 1 Adenosine triphosphate (ATP), reduced glutathione (GSH), malondyaldeide (MDA) and alpha-tocopherol (E-vitamin) plasma levels in the 4 Groups examined at the start (T0) and at the end of the study (T1). Group

Redox Parameters

T0 (7 SD)

T1 (7 SD)

A

ATP GSH MDA E-Vitamin ATP GSH MDA E-Vitamin

0.8370.11 1.1970.04 0.9470.04 10.3371.08 0.8270.03 1.1770.05 0.8970.07 9.8270.64

0.8170.02 1.1470.03* 1.0370.05* 8.8371.34* 0.8070.02 1.1170.03* 1.0670.05* 6.6370.84*

2.4 4.2 9.5 14.5 2.3 5.1 19.0 32.4

ATP GSH MDA E-Vitamin ATP GSH MDA E-Vitamin

0.9170.04 1.2070.04 0.9570.04 10.4770.83 0.9170.04 1.1770.04 0.9870.05 9.7471.15

0.8070.03* 1.1070.05* 1.0270.03* 8.6271.07* 0.9270.05 1.1670.04 1.0270.04 9.7271.53

12.0 9.0 7.3 17.6 1 0.8 4 0.2

B

C

D

(%)

*po0.05 (respect to T0). %: variation between T1 and T0 expressed in percentage.

53

group C ( 12.0%; po0.05) while this parameter was stable in groups A ( 2.4%) and B ( 2.3%) (table 1). Biochemical parameters Table 2 shows the main blood parameters in the examined subjects at T0 and T1. All the assessed parameters remained in each group in the normal range, including liver parameters. However, in the control group (D) all the investigated blood parameters remained unmodified while in the three alcohol exposed groups some modifications were observed, in particular in HDLCholesterol (po0.05) (Table 2). Haemodynamic parameters None of the subjects had an abnormal electrocardiograph. No significant changes in electrocardiograph examination, heart rate, systolic and diastolic blood pressure were found in experimental subjects and controls before and after the study period. Nutritional status No change in dietary consumption was found in any of the subjects evaluated. No significant changes in BMI were found in all examined subjects before and after the study period. No significant modifications were observed in all examined groups as regard to FM, FFM and TBW. However, while FFM and FM were unmodified in the control group (group D), FM was increased in subjects drinking beer (group A. T0:17.0176.62 kg; T1:17.487 6.29) and wine (group B. T0:12.975.3 kg; T1:14.375.9 kg) and decreased in subjects exposed to spirit (group C. T0:16.274.35 kg; T1:14.6274.91 kg). FFM was stable in

Table 2 Blood Parameters evaluated in all four Groups at the start (T0) and at the end (T1) of the study. Normal values

AST ALT GGT AP Amy Glu Chol HDL-C TG K+ Cl Prot Creat UN

7–45 UI/L 7–45 UI/L 8–61 UI/L 98–279 UI/L o160 U/L 60–110 mg/dl) 130–200 mg/dl 445 mg/dl 20–170 mg/dl 3.5–5 mEq/l 98–108 mEq/l 5.5–8.5 g/dl 0.7–1.2 mg/dL 10–23 mg/dL

GROUP A

GROUP B

GROUP C

GROUP D

T0 (7SD)

T1 (7SD)

T0 (7SD)

T1 (7SD)

T0 (7SD)

T1 (7SD)

T0 (7SD)

T1 (7SD)

1574 975 17715 116745 104739 69719 133745 3974 74733 3.4170.48 90710 5.7771.03 0.8470.14 1372

1773 1175* 1679 133748* 126732* 82712* 151739* 4775* 83743 3.8270.5* 10273* 6.6570.8* 0.8970.12 1572*

1374 1078 21717 97727 109741 6079 136743 4177 937101 4.0170.5 8678 5.5170.7 0.7870.12 1373

2377* 1376 2676 128725* 143743* 7979* 170754* 4979* 108772 4.1770.4 10273* 7.0470.4* 0.9170.24 1474

1877 1279 33726 137726 160766 79713 158733 38712 114758 3.6770.4 9779 6.6770.77 0.9470.1 1372

1876 1274 3276 170760 144758 9078* 162747 47711* 97742 4.5170.4* 10573* 6.5370.4 1.0170.1* 1472

2079 973 2272 116723 162752 68710 159738 4776 97753 3.8570.56 10476 7.0170.64 0.9670.07 1472

2177 1073 2074 111733 150753 72712 167743 5178 94754 4.5170.56 10277 7.3370.81 0.9570.09 1474

*po0.05; AST: a spartate aminotransferase; ALT: alanine aminotransferase; GGT : gamma-glutamyl transpeptidase; AP: alcaline phosphatase; Amy: Amylase; Glu: glucose; Chol: cholesterol; HDL-C: high density lipoprotein cholesterol; TG: tryglycerides; K+: Potassium; Cl : Chloro; Prot: proteins; Creat: creatinine; UN: urea nitrogen.

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subjects exposed to beer (T0:62.8474.44 kg; T1:62.777 4.31 kg) and wine (T0: 60.174.7 kg; T1:59.674.3 kg) and increased in subjects exposed to spirit (T0:60.976.15 kg; T1:63.176.33 kg). Discussion The present study shows a significant increase of plasma MDA—a marker of lipoperoxidation—and a significant decrease of plasma GSH and E vitamin—the two main antioxidant compounds-in all subjects exposed to ethanol for 30 days. Conversely, no modification of these parameters were observed in control, alcohol-free subjects. However ATP was reduced only in subjects drinking alcohol by spirit while no modification of ATP was observed both in subjects drinking alcohol by beer or wine and in control. Present data are in line with the alcohol related oxidative stress reported both in animals (Addolorato et al., 2001) and humans (Lieber, 2005). The possible protective effects of alcoholic beverage have been extensively reported. In particular, alcohol appears to be a protective factor if consumed in moderate amount and regularly during meals (Vogel, 2002) and this protective effect seems to be independent of the type of beverage consumed (Graziano et al., 1999) although microcompounds contained in the alcohol beverages could be involved in this mechanism (Renaud, Gue´guen, Siest, & Salamon, 1999). Other than the antioxidant action of the microcompounds (Miyagi, Miwa, & Inoue, 1997), several factors have been suggested to maintain the protective effects of a moderate intake of alcohol beverages, including an increased of HDL-cholesterol (Graziano et al., 1993), of the fibrinolysis processes (Aikens et al., 1997; Dimmitt et al., 1998; Grenett et al., 1998), of vasodilation (Venkov, Myers, Tanner, & Vaughan, 1999), decrease in blood pressure levels (Hatton, Bukoski, Edgar, & McCarron, 1992), protection against ischemia-reperfusion iniury (Addolorato et al., 2001; Chen, Gray, & Mochly-Rosen, 1999), and inhibition of proliferation and migration of vascular smooth cells (Hendrickson, Cahill, McKillop, Sitzmann, & Redmond, 1998; Hendrickson et al., 1999). With regard to the possible antioxidant action of microcompounds present in alcoholic beverages, the present study failed in finding it since MDA was increased and GSH and E vitamin were reduced in alcohol-exposed subjects. In particular, the present study showed that ethanol, although in low doses, was able to determine a decrease of plasma antioxidant status. However, while a significant ATP reduction was observed in subjects exposed to spirit, ATP levels were unmodified when ethanol was consumed by beer or wine in the same way as subjects not exposed to alcohol (group D). Since ATP represents a parameter of energy level and antioxidant status (Gasbarrini et al., 1996), this result could indicate that the decrease in plasma parameters of antioxidant status appears to be attenuated when ethanol is consumed in beer or wine. The present data are in line with a previous preclinical

observation by our lab showing that a moderate consumption of beer does not appear to cause oxidative stress in rats, and beer seems to attenuate the oxidative action by ethanol itself (Gasbarrini et al., 1998). A previous clinical study performed in healthy men on the effects of low alcohol doses on antioxidant compounds (van der Gaad, van den Berg, van der Berg, Schaafsma, & Hendriks, 2000) showed different results from our data with respect to glutathione activity and E vitamin. The differences between the two studies could be partially related to the possible different genetic and/or environmental factors between the two population evaluated (Netherlands and Italy). Another possible difference could be related to the different antioxidant capacity of alcoholic beverages originating from different countries, as have been suggested recently (Meynell et al., 2006). Furthermore, it should be underlined that our study was conducted after a 2-week wash-out period, according to previous reports (Bisaga & Evans, 2004; Seppala, Stromberg, & Bergman, 1984). However, a considerable variation on the wash-out time is present in the literature (Laitinen et al., 1991; Patat et al., 1995) and future studies are needed to better standardize the correct one. In our study an increase of HDL-cholesterol was observed in all subjects exposed to ethanol but not in the control group; this result is in line with previous similar data by Graziano et al. (1993; 1999). The increase of the HDL-cholesterol could be related to the possible protective effects of moderate amounts of alcohol intake on cardiovascular diseases (Lucas et al., 2005). In our subjects, ethanol seemed to increase some liver parameters; however, it should be underlined that all these parameters remained in the normal range and consequently these modifications were not relevant. In fact, although also moderate consumption of alcohol could be related to systemic or single-organ effects (Meyerhoff et al., 2005), in our sample no symptoms were reported and/or registered. This observation is in line with a previous experimental study in which no side-effects were observed in healthy male subjects after a 6-week administration of 30 g/day ethanol (Bleich et al., 2001). However, it should be underlined that the present study represents an experimental setting with a short-term alcohol administration. Consequently present data could not be representative of the effects on health of long-term moderate alcohol intake. Further studies are needed to clarify the contrasting data on moderate alcohol consumption and health benefits. For example, Beulens et al. (2007) have suggested that in the general population men with hypertension drinking moderately and safely may not need to change their drinking habits; on the other hand Zilkens et al. (2005) have reported that an intake of 40 g/day of alcohol for 4 weeks by red wine or beer could elevate blood pressure in normotensive men. As regard to the possible effects of moderate alcohol intake on nutritional status, FM was decreased in subjects drinking spirit and increased in subjects drinking wine and


beer, although these modifications were not significant. Ethanol represents a high energy substrate providing 7.2 Kcal (29.7 KJ) per gram; however these calories are defined as ‘‘empty’’, since they are inefficiently utilizable (Lieber, 1993). In fact, ‘‘empty calories’’ act by displacing other nutrients in the diet, and causing primary malnutrition through decreased intake of essential nutrients (Lieber, 1993) and a decrease in FM in chronic alcoholic patients (Addolorato et al., 1997). These effects were due mainly to both an increase of energetic expenditure related to the microsomial ethanol oxidation system (MEOS) induction, and to an increase of fat oxidation related to the mitochondria system induction due to a free radical action (Addolorato et al., 1998). These data seem to be similar to those of the present study, thus indicating that these mechanisms could also occur with moderate doses of ethanol consumed by itself. Conversely, when the same quantities of ethanol are contained in alcohol beverage such as beer or wine it is conceivable that the free radical action on the MEOS and mitochondria systems could be counterbalanced by the non alcoholic compounds with antioxidant action. In this case FM could be increased since drinking during meals determines a prompt alcohol oxidation in competition with the metabolism of other nutrients (Addolorato et al., 1997; Sonko et al., 1994). In conclusion, ethanol per se seems to be able to increase oxidative parameters in humans, also in a moderate amount; however these changes appear to be attenuated when ethanol is consumed as beer or wine. The short-term administration of moderate amounts of ethanol in healthy males seems to influence FM, although it is not able to significantly affect nutritional status and body composition. FM seems to be influenced differently by spirit intake with respect to beer and wine intake. Future studies on larger samples are needed to clarify the role of the different alcoholic beverages on body composition. Acknowledgement The present study was partially supported by ‘‘Associazione Ricerca in Medicina’’ Foundation, Bologna-Rome (Italy). References Addolorato, G., Capristo, E., Greco, A. V., Stefanini, G. F., & Gasbarrini, G. (1997). Energy expenditure, substrate oxidation and body composition in chronic alcoholism: new findings from metabolic assessment. Alcoholism: Clinical and Experimental Research, 21, 962–967. Addolorato, G., Capristo, E., Greco, A. V., Stefanini, G. F., & Gasbarrini, G. (1998). Influence of chronic alcohol abuse on body weight and energy metabolism: is excess ethanol consumption a risk factor for obesity or malnutrition? Journal of Internal Medicine, 244, 387–395. Addolorato, G., Capristo, E., Caputo, F., Greco, A. V., Ceccanti, M., Stefanini, G. F., & Gasbarrini, G. (1999). Nutritional status and body fluid distribution in chronic alcoholics compared with controls. Alcoholism: Clinical and Experimental Research, 23, 1232–1237.

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Research Report

Front-of-pack nutrition labelling: Testing effectiveness of different nutrition labelling formats front-of-pack in four European countries Gerda I.J. Feunekesa,, Ilse A. Gortemakera, Astrid A. Willemsa, Rene´ Liona, Marcelle van den Kommerb a

Unilever Food and Health Research Institute, Olivier van Noortlaan 120, 3133 AT Vlaardingen, The Netherlands b Unilever N.V., Vitality Programme Foods, Weena 455, 3013 AL Rotterdam, The Netherlands Received 19 October 2006; received in revised form 23 May 2007; accepted 28 May 2007

Abstract In two studies, the impact of eight front-of-pack nutrition labelling formats that differed in complexity was investigated across four European countries. In total 1630 men and women (18–55 yrs) were recruited from Internet panels in the United Kingdom, Germany, Italy and the Netherlands for study 1 and 776 in Italy and the United Kingdom for study 2. Participants evaluated several products (healthier and less healthy variants of the same product category) with a front-of-pack nutrition labelling format. The first study evaluated different labelling formats on consumer friendliness (comprehension, liking and credibility) and the second study measured the effect of the different labelling formats on decision-making (usage intention and process time). The results indicated minor differences in consumer friendliness and usage intention between simpler (such as Healthier Choice Tick, Smileys and Stars) and more complex frontof-pack nutrition labelling formats (such as Multiple Traffic Light, Wheel of Health and GDA scores). Endorsement by national and international health organisations strongly increased the labelling formats’ credibility. Participants needed significantly less time to evaluate simpler front-of-pack labelling compared to the more complex labelling format. Thus simpler front-of-pack labelling formats seem more appropriate in a shopping environment where quick decisions are made. r 2007 Elsevier Ltd. All rights reserved. Keywords: Labelling; Food choice; Public health; Behavioural change; Comprehension; Intention; Nutrition; Logo

Introduction There is robust evidence that dietary factors are related to the development of chronic diseases such as heart disease, stroke, obesity and diabetes (Astrup, 2001; Joint FAO/WHO Expert Consultation, 2003; Kromhout, Menotti, Kesteloot, & Sans, 2002). The World Health Organization has recommended that food manufactures reduce levels of saturated fatty acids, trans fatty acids, sodium and sugar in their products in order to reduce the burden of chronic diseases on society (WHO, 2004). One way to help consumers reduce the intake of these nutrients is to improve the product composition; another is to motivate consumers to make healthier choices. Both should be done simultaneously. Therefore, in addition to enhanCorresponding author.

E-mail address: [email protected] (G.I.J. Feunekes). 0195-6663/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.appet.2007.05.009

cing the nutritional profile of products using the Unilever Nutrition Enhancement Programme score (Nijman et al., 2007), we investigated the effectiveness of front-of-pack nutrition labelling formats that would help consumers make healthier choices and that could be used across different countries. In order to make healthier choices, consumers must be able to distinguish healthier products from less healthy ones. This can be done by making the nutritional composition of foods transparent in the form of nutrition labels, either in the form of front-of-pack or back-of-pack nutritional information. A front-of-pack logo in addition to the traditional numerical nutrition fact box on the back of the pack may be more effective in helping consumers make a healthy choice than back-of-pack nutritional information alone (Geiger, Wyse, Parent, & Hansen, 1991; Scott & Worsley, 1994). However, research on nutrition labelling formats is relatively scarce and the

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majority has investigated back-of-pack nutrition labelling formats and is qualitative in nature (see for example Cowburn & Stockley, 2005; Higginson, Rayner, Draper, & Kirk, 2002). Furthermore, only a small part of that research has investigated the effect of different formats on behavioural change (see Scott & Worsley, 1994, for an exception). Therefore, the objective of the research reported in this paper was to investigate how well a number of front-of-pack nutrition labelling formats were understood by consumers and how effective they could be in helping consumers make healthier choices. Although back-of-pack nutrition labels were designed to help consumers make healthier choices (Jordan Lin, Lee, & Yen, 2004; Kurtzweil, 1993), research conducted in Europe, the US and Australia/New Zealand suggests that the majority of consumers find back-of pack nutrition labels confusing, especially the numerical information and the terminology used (Byrd-Bredbenner, Wong, & Cotte, 2000; Cowburn & Stockley, 2005; EUFIC, 2005; Sadler, 1999; Scott & Worsley, 1997; Shannon, 1994; Shine, O’Reilly, & O’Sullivan, 1997; Wandel, 1999). Cowburn and Stockley (2005) reviewed the literature on nutrition labelling formats and concluded that in particular vulnerable groups such as older consumers and consumers with lower levels of education and income are likely to have difficulties in understanding nutrition labels. Their review also showed that consumers had difficulty converting information from ‘g per 100 g’ to ‘g per serving’ and interpreting serving size information. Results from a study by Vijwanathan and Hastak (2002) suggested that adding some kind of benchmark (e.g., as a percentage of the recommended daily intake) can help consumers put nutritional information into context. In order to make healthier choices consumers have to take into account several nutrients simultaneously. A study by Black and Rayner (1992) showed that consumers find it difficult to make these comparisons. To simplify their task, consumers tended to use a single nutrient (like fat) as a measure to compare products on overall health. This may lead consumers to make the wrong choice—products low in fat could well be high in other nutrients, such as sugar or salt. A simple front-of-pack label that summarises the whole nutritional profile and provides an overall interpretation of the healthiness of the product should therefore facilitate and improve consumers’ decision-making with regard to healthy foods. Furthermore, such a label would not require detailed nutritional knowledge. Another advantage of simple labels is that these reduce the cognitive effort and the time needed to process the information compared to more detailed labels (Geiger et al., 1991; Scott & Worsley, 1994). In a supermarket environment, consumers generally have limited opportunity to process information and their motivation to do this is likely to be low when shopping for groceries, resulting in relatively superficial processing of information (cf. Eagly & Chaiken, 1993; Fiske & Neuberg, 1990; Petty, Cacioppo, & Schumann, 1983). Research by Hoyer (1984) showed that

consumers take buying decisions in a supermarket in seconds rather than minutes. Other findings support the idea that consumers only glance at nutrition information and do not further process the information at the point of purchase (Higginson et al., 2002; Scott & Worsley, 1997). In conclusion, the available research suggests that a frontof-pack label would facilitate making healthier choices by incorporating benchmark information that enables consumers to interpret the information and/or by providing an advice that includes an interpretation of the nutritional information. Ever since the introduction of the traditional numerical nutrition fact box, different parties (retailers, manufactures, governmental and non-governmental organisations) have tried to design front-of-pack nutrition information labels that complement it and are easier to understand and use. These labels vary from complex detailed nutrition labels to simple symbols. Detailed nutrition labelling formats enable consumers to make an informed choice by providing information on key nutrients in a friendlier way compared to the traditional nutrition fact box. Simple symbols provide an interpretation of the healthiness of the overall product, thus reducing the processing load (Scott & Worsley, 1994). Examples of more detailed labels are ‘Guideline Daily Amounts (GDA)’ and ‘Wheel of Health’. GDA shows the amount in grams and percentages for calories, sugar, fat, saturates and salt per serving (Tesco, 2006). The ‘Wheel of Health’ is similar to the ‘Multiple Traffic Light’ label, which is recommended by the UK Food Standards Agency (FSA, 2005). It shows the amount of the five key nutrients energy, total fat, saturated fatty acids, sugar and salt in each serving. The nutrients can score green, amber or red, respectively, indicating ‘‘Go’’, ‘‘Ok’’ and ‘‘Think before you eat too much of this y although a little bit will never hurt’’(Sainsbury, 2006). Note that neither the ‘GDA’ nor the ‘Wheel of Health’ provides an overall interpretation of the information. Examples of simple symbols are the ‘Green Keyhole’ (Sweden; Kinnunen, 2000; Larsson, Lissner, & Wilhelmsen, 1999; Weinehall, Hellsten, Boman, & Hallmans, 2001), ‘Shop Smart With Heart’ (Canada; Kinnunen, 2000), ‘Pick The Tick’ (Australia and New Zealand; Kinnunen, 2000; Scott & Worsley, 1994; Young & Swinburn, 2002) and ‘Smart Spot’ (PepsiCo, 2006). There is thus a multitude of front-of-pack labels that aim to help consumers make a healthier choice. The verdict is still out as to which of these labelling formats is best understood by consumers and which makes it easiest for consumers to make a healthier choice. The aim of the first study was to evaluate the different front-of-pack nutrition labelling formats on their consumer friendliness and their ability to help consumers differentiate between healthier and less healthy variants of the same product category, to see whether the different labelling formats met the basic requirements. The aim of the second study was to investigate the effect of the labelling formats

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on decision-making when taking into account the shopping environment. In addition to our main research question, we investigated whether there were any significant differences in comprehension and intention to change behaviour across different countries. Furthermore, as research in the USA and New Zealand had shown that consumers with lower levels of overall education are less likely to read nutrition and ingredient information (Bender & Derby, 1992; Cowburn & Stockley, 2005; Scott & Worsley, 1994), we investigated differences in comprehension of the labels for consumers with different levels of education. The impact of endorsement on the credibility of a labelling format was also explored. Finally, we investigated whether participants thought the labelling formats indicated a comparison within one product category or across food groups. Study one

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Table 1 Socio-demographic characteristics of participants by country (study 1) Demographics

% UK (n ¼ 316)

Germany (n ¼ 447)

Italy (n ¼ 430)

NL (n ¼ 437)

Gender Male Female

48.6 51.4

48.2 51.8

48.1 51.9

49.7 50.3

Education level Lowa Middleb Highc

39.9 35.6 24.5

18.9 58.9 22.1

51.8 41.0 7.3

36.3 35.8 27.9

Age 18–24 25–34 35–44 45–55

19.5 26.2 28.9 25.4

18.7 24.8 30.3 26.2

19.2 27.6 28.3 24.9

14.8 25.2 31.6 28.4

years years years years

a

Up to primary school. Up to secondary school. c Higher education/university. b

Method Participants In total, 1630 participants from four European countries participated in this study, 316 participants from the United Kingdom, 447 participants from Germany, 430 participants from Italy and 437 participants from the Netherlands. These countries were selected on the basis of geographical distribution, size of the country and spread in food cultures. Consumer samples of each country were drawn from Internet panels of a market research agency (Survey Sampling International). To create representative samples, participants were selected by means of quota sampling (based on census figures) on gender, education and age (for the age range of 18–55 yrs) (Table 1). To correct for slight differences from the census figures, data were weighted for age, education and gender for each country. Materials Type of nutrition labelling format. The different nutrition labelling formats varied in complexity from the simple ‘Healthier Choice Tick’ to the more complex ‘Wheel of Health’ (Fig. 1a–1f). The simple formats provide a judgement about the total product and the more detailed formats provide a judgement per nutrient. The Healthier Choice Tick is a single tick used only on the healthier product variants, i.e., in contrast to the other labelling formats this labelling format is absent or present on a product. Three graded nutrition-labelling formats (Stars, Smileys and the Health Protection Factor) were included because it was hypothesised that familiarity with these systems from non-food categories might increase comprehension, credibility and liking. Stars is a well-known rating system awarded to restaurants and hotels. Smileys is a similar format with ‘‘smileys’’ instead of stars. The Health Protection Factor was derived from the system that is used

Fig. 1. The nutrition labelling formats used in study 1: (a) Healthier choice tick; (b) Health protection factor; (c) Stars; (d) Smileys; (e) Multiple traffic light; (f) Wheel of health.

on sunscreen lotions. Products could receive a number from 1 to 7, with higher numbers indicating a healthier product. The Multiple Traffic Light showed the five key nutrients (energy, total fat, saturated fatty acids, sugar and salt). Each nutrient can score low (green), medium (amber) or high (red) and this was indicated by colour and in text. The Wheel of Health, used by retailer Sainsbury (UK), provides the exact amount of the five key nutrients per serving in a pie-chart format, with each slice of the pie coloured green (low), amber (medium) or red (high), depending on the nutrient score. Products. For three product categories a healthier variant and a less healthy variant of the same product category was selected by using the Unilever Nutrition Enhancement Programme score which is based on International Dietary Guidelines (Nijman et al., 2007). For dairy drinks, the healthier variant was ‘low-fat milk’ and the

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less healthy variant ‘low-fat fruit flavoured yoghurt drink’. For ice-cream, the healthier variant was ‘real fruit covered ice-cream’ and the less healthy variant ‘chocolate covered ice-cream’ and for spreads, the healthier variant was ‘vegetable oil-based margarine’ and the less healthy variant ‘butter’. All products were presented unbranded. Endorsements. Each endorsement was shown together with the Smileys format. The following endorsements were included: (a) a national nutrition organisation, (b) the World Health Organization, (c) the European Union and (d) European Food Manufacturers. Depending on the country the following national nutrition organisations were used, the ‘British Dietetic Association’, the German ‘Deutsche Gesellschaft fu¨r Erna¨hrungsforschung’, the Italian ‘Istituto Nazionale di Ricerca per gli Alimenti e la Nutrizione’ and the Dutch ‘Voedingscentrum’. Procedure The study was conducted on-line. Participants were randomly assigned to three out of the six nutrition labelling formats and each of these was presented for all three product categories. Each product category consisted of two products, a healthier and less healthy variant. The order of presentation of the nutrition labelling formats and product categories was randomised over participants, as was the presentation of the products on the left versus the right side of the screen. Participants were exposed to nine pairs of pictures of products with a front-of-pack labelling format. An enlarged version of the nutrition labelling format was shown below the product. Note that the Healthier Choice Tick was only placed on the healthier variant within each pair. All labelling formats in this test were accompanied with the text that it was endorsed by the World Health Organization. A click button was located below each product enabling participants to see the standard back-ofpack nutrition fact box in a pop-up window. They then rated the front-of-pack nutrition labelling format on liking, comprehension, credibility and perceived healthiness for both products. When participants finished rating all nine product pairs they were asked to complete questions on demographics, health behaviours and attitudes, nutritional knowledge and the endorsements. Finally, participants were asked whether they thought the label indicated a comparison within one product category or across all food groups. Measures All questions employed 5-point Likert-type rating scales, unless otherwise indicated. Consumer friendliness measures. Comprehension was measured by the question, ‘How difficult or easy is it for you to understand this health indicator’, with extremes 1 (very difficult to understand) and 5 (very easy to understand). Credibility was measured by the question, ‘How credible is

this health indicator to you’, with extremes 1 (not at all credible) and 5 (extremely credible). Liking was measured by the question, ‘How much do you like the health indicator on this product’, with extremes 1 (do not like it at all) and 5 (like it extremely). Perceived healthiness measure. Perceived healthiness of the product was measured by the question, ‘How healthy is this product to you?’ with extremes 1 (not healthy at all) and 5 (very healthy). The mean difference in perceived healthiness between the healthier and less healthy variants of the same product category for each labelling format was calculated. The size of this difference indicated to what extent the labelling formats helped consumers to differentiate between healthier and less healthy product variants. Background variables. The background variables consisted of demographic variables, knowledge, behaviour and attitudinal variables regarding health and nutrition. Demographics: Participants were asked about their age, gender, education level, income level, profession, marital status and household composition. Nutritional knowledge: Perceived nutritional knowledge was measured with the item ‘I am knowledgeable about health and nutrition issues’, with extremes 1 (strongly disagree) and 5 (strongly agree). For the analyses scores 1 and 2 were recoded as low, score 3 as middle and scores 4 and 5 as high. Perceived healthiness of diet: Perceived healthiness of diet was measured with the question, ‘How would you describe your overall diet?’, with extremes 1 (excellent) and 5 (poor). For the analyses scores 1 and 2 were recoded as excellent, score 3 as average and scores 4 and 5 as poor. Label reading: Label reading was measured with the question, ‘Do you read labels on food packages?’, with extremes 1 (always) and 5 (never). For the analyses scores 1 and 2 were recoded as always, score 3 as sometimes and scores 4 and 5 as never. Attitude towards healthy eating: Personal attitude towards healthy eating was measured with the question, ‘Which one of the following statements do you think best describes your personal attitude towards healthy eating?’. Participants answered this question by choosing one of the following statements: ‘Have to follow a special diet because of a specific health need’, ‘Eat a healthy diet because it helps keep me fit and well’, ‘Try to eat a healthy diet but find it hard to stick to’ or ‘Eat what I like and do not worry about how healthy it is’. Participants were also asked: ‘When comparing these products what were your assumptions?’ and could tick one of the following options: ‘I assumed the health indicators help me to compare ice-cream with ice-cream, spreads with spreads, and drinks with drinks’, ‘(y) help me to make choices across all foods’ or ‘I did not think about it really’.


Design and statistical analyses A 6 (Labelling Format: Healthier Choice Tick, Health Protection Factor, Smileys, Stars, Multiple Traffic Light, Wheel of Health) 3 (Product category: Dairy drink, Icecream, Spreads) 2 (Healthiness of product: Healthier product, Less healthy product) within subject factorial design was used with Countries as a between subjects variable (UK, Germany, Italy, The Netherlands). Each cell contained approximately 160 participants. These mixed model ANOVAs were performed as not all participants saw all nutrition labelling formats. The alpha level was set at 0.01. Differences between groups were analysed using Tukey–Kramer pair-wise comparisons. Significance was assessed with alpha set at 0.01. Pre-analyses revealed normal distribution of each variable. All background variables were included as independent variables in the analyses for comprehension and perceived healthiness. Results Impact of nutrition labelling format on perceived consumer friendliness (study 1) Comprehension of formats. On average, participants found all nutrition labelling formats easy to understand, with means ranging from 3.4 to 4.0 on a 5-point scale (Table 2). Main effects were found for Format and for Country. Overall, the differences, although significant, were small. The Health Protection Factor scored significantly lower than the other five formats. The Multiple Traffic Light, followed by Stars and Smileys scored highest on comprehension, po0.01. Furthermore, Dutch participants reported a slightly better understanding of the formats compared to participants from the UK, Germany and Italy, po0.01. The interaction between Country and Format was also significant. In contrast to the other countries, in the Netherlands and Italy the Health Protection Factor did not score significantly lower than the other formats (Table 2).

61

Light and Wheel of Health the most, po0.01. The Health Protection Factor, followed by Stars and Smileys were liked the least, po0.01. Participants from the UK and Italy liked the formats the most and Dutch participants liked the formats the least, po0.01. Furthermore, the interaction between Format and Country was also significant. Participants from the UK liked the Multiple Traffic Light more than the Wheel of Health, po0.01, but this difference was not found in other countries. Impact of background variables on comprehension of the labels. The interaction between Format and Perceived nutritional knowledge was significant (Table 3). Participants who perceived themselves as least knowledgeable about health and nutrition found the Wheel of Health more difficult to understand than Smileys, po0.01. The interaction between Format and Label reading was also significant. Participants who never read labels found the Wheel of Health more difficult to understand compared to Stars and Smileys, po0.01 (Table 3). No significant differences on comprehension were found for Education level (Table 3). For the other background variables, some small differences were found for Comprehension on Age, Gender, Perceived healthiness of diet and Attitude towards healthy eating, but these did not provide a consistent and interpretable pattern (data not shown, available from authors).

Credibility of formats. On average, participants found the formats reasonably credible, with means ranging from 2.9 to 3.5 on a 5-point scale (Table 2). Main effects were found for Format and for Country. The Wheel of Health and Multiple Traffic Light were perceived as most credible and the Health Protection Factor as least credible, po0.01. Participants from the UK and Italy found the formats somewhat more credible compared to Dutch and German participants, po0.01. The interaction between Format and Country was also significant. In the Netherlands and Italy, the Health Protection Factor was perceived to be as credible as the Healthier Choice Tick, Smileys and Stars, but this was not the case in the UK and Germany.

Impact nutrition labelling format on perceived healthiness (study 1) On average, participants rated the healthier products as slightly healthy (M ¼ 3.2) and the less healthy products as slightly unhealthy (M ¼ 2.3). We calculated the mean difference in perceived healthiness between the healthier and less healthy products for each labelling format and used this as the dependent measure. Mean difference scores ranged from 0.6 to 1.0 (Table 2). Main effects were found for Format and for Country. Smileys and Stars were significantly the best differentiators between healthier and less healthy product variants and the Health Protection Factor differentiated the least, po0.01. For the UK and The Netherlands, the difference in perceived healthiness between the healthier and less healthy products was greater than the difference in perceived healthiness for Italy and Germany, po0.01. Furthermore the interaction between Format and Country was also significant (Table 2). In Italy and the UK the Healthier Choice Tick scored as well as Smileys and Stars, po0.01. No substantial differences were detected for Perceived healthiness on all background variables (data not shown, available from authors).

Liking of formats. On average, participants reasonably liked the formats, with means ranging from 2.8 to 3.4 on a 5-point scale (Table 2). Main effects were found for Format and for Country. Participants liked the Multiple Traffic

Difference in perceived healthiness per product category. We also analysed the differences in perceived healthiness between healthier and less healthy products per product category. The main effect for product category was

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Table 2 Mean scores and ANOVAs for comprehension, credibility, liking and mean difference in perceived healthiness by format and country across products (study 1) Variables

Format

ANOVAs 1

Smileys (n ¼ 1630)

Stars (n ¼ 645)

Multiple traffic light (n ¼ 656)

Wheel of health (n ¼ 644)

Overall mean

Format (df ¼ 5)

Country (df ¼ 3)

Format Country (df ¼ 15)

M

M

M

M

M

M

M

F (p)

F (p)

F (p)

Comprehension UK Germany Italy NL Overall mean

35.7 (*)

19.0 (*)

5.7 (*)

3.6cd 3.7a 3.7a 4.1a 3.8c

3.1d 3.2b 3.4a 4.0a 3.4d

3.9bc 4.0a 3.7a 4.2a 3.9ab

4.0abc 4.0a 3.8a 4.1a 4.0ab

4.3a 3.9a 3.7a 4.3a 4.0a

3.7bc 4.0a 3.7a 4.0a 3.8bc

3.8 3.8 3.7 4.1 3.9

Credibility UK Germany Italy NL Overall mean

61.2 (*)

9.8 (*)

2.6 (**)

3.1cd 2.9c 3.2bc 2.9b 3.0b

2.9d 2.7d 3.1c 2.9b 2.9c

3.2bc 2.9c 3.1c 2.9b 3.0b

3.3bc 3.0bc 3.2bc 3.0b 3.1b

3.8a 3.2ab 3.4ab 3.4a 3.4a

3.5ab 3.4a 3.6a 3.4a 3.5a

3.3 3.0 3.3 3.1 3.1

Liking UK Germany Italy NL Overall mean

36.2 (*)

34.3 (*)

2.8 (**)

3.3b 3.2ab 3.5ab 2.8ab 3.2b

2.8c 2.6c 3.1c 2.5c 2.8cd

3.2b 3.1b 3.2bc 2.7bc 3.1c

3.1bc 3.0b 3.1c 2.7bc 3.0c

3.9a 3.3ab 3.6a 2.9a 3.4a

3.3b 3.5a 3.6a 3.0a 3.4ab

3.3 3.1 3.3 2.8 3.1

Mean difference perceived healthiness2 UK Germany Italy NL Overall mean

46.5 (*)

35.3 (*)

3.2 (*)

1.1ab 0.6b 0.8a 0.9bc 0.8c

0.8c 0.6b 0.4c 0.8c 0.6d

1.3a 0.8a 0.8a 1.1a 1.0a

1.2a 0.8a 0.7a 1.1ab 1.0ab

1.1ab 0.7ab 0.7ab 1.0abc 0.9bc

1.0bc 0.8ab 0.5bc 0.9bc 0.8c

1.1 0.7 0.6 1.0 0.9

Note: Means in the same row that do not share superscripts differ at po0.01 (Tukey–Kramer). *po0.0001; **po0.001. 1 Every participant received Smileys. 2 Mean differences are the difference between healthier and less healthy products for each format.

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Health protection factor (n ¼ 645)


Healthier choice tick (n ¼ 670)


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Table 3 Mean scores and ANOVAs for comprehension by format, perceived nutritional knowledge and label reading across countries and across products (study 1) Intervening variables

Format

ANOVAs



Smileys (n ¼ 1630)

Stars (n ¼ 645)



Overall mean

Intervening variable (df ¼ 2)

Intervening variable format (df ¼ 10)

M

M

M

M

M

M

M

F (p)

F (p)

Perceived nutritional knowledge Low (n ¼ 236) 3.7ab Middle (n ¼ 522) 3.7ab High (n ¼ 872) 3.9a

11.8 (*)

2.3 (*)

3.4b 3.4b 3.5b

3.9a 3.8a 4.1a

3.9ab 3.9a 4.0a

3.9ab 3.9a 4.1a

3.4b 3.7ab 4.1a

3.7 3.8 4.0

Label reading Never (n ¼ 323) Sometimes (n ¼ 373) Always (n ¼ 934)

6.9 (**)

4.7 (*)

3.5c 3.4b 3.5c

4.0ab 3.8a 4.0ab

4.2a 3.9a 4.0ab

3.8abc 3.9a 4.2a

3.6c 3.7ab 4.1ab

3.8 3.7 3.9 0.6 (ns)

2.1 (ns)

3.7bc 3.7a 3.9b

Education level

Note: Means in the same row that do not share superscripts differ at po0.01 (Tukey–Kramer).*po0.0001; **po0.01.

Table 4 Mean difference scores and ANOVAs for perceived healthiness by format and product category across countries (study 1) Variables

Mean difference Dairy drink Ice-cream Spreads

Format

ANOVAs



Smileys (n ¼ 1630)

Stars (n ¼ 645)



Overall mean

Product category (df ¼ 3)

Format product category (df ¼ 15)

M

M

M

M

M

M

M

F (p)

F (p)

205.5 (*)

17.8 (*)

0.9b 0.9a 0.7bcd

0.9b 0.3b 0.7abc

1.2a 0.9a 0.9a

1.1a 0.9a 0.8ab

1.2a 0.9a 0.5d

1.0ab 0.8a 0.6cd

1.1 0.8 0.7

Note: The mean difference is the difference between healthier and less healthy products for each format. Means in the same row that do not share superscripts differ at po0.01 (Tukey–Kramer). *po0.0001.

significant (Table 4). Overall the labelling formats differentiated most for dairy drinks and least for spreads, po0.01. The interaction between Format and Product category was also significant. In contrast to the other product categories, for spreads the Multiple Traffic Light differentiated significantly less between the healthier and less healthy product compared to Smileys and Stars, po0.01. Furthermore, we were interested in defining the most consistent format in differentiating between healthier and less healthy products across product categories. Therefore, we calculated the difference between the highest mean difference score in perceived healthiness and the lowest mean difference score in perceived healthiness for each labelling format and used this as the dependent measure. Main effects were found for Format F(5, 3239) ¼ 15.4, po0.0001 and for Country F(3, 1627) ¼ 6.8, po0.001. The Healthier Choice Tick (Mdiff ¼ 0.2), followed by Smileys (Mdiff ¼ 0.3) and Stars (Mdiff ¼ 0.3), was the most con-

sistent differentiator across product categories, po0.01. The most inconsistent differentiator was the Multiple Traffic Light (Mdiff ¼ 0.7), followed by the Health Protection Factor (Mdiff ¼ 0.5) and Wheel of Health (Mdiff ¼ 0.5), po0.01. In Germany (Mdiff ¼ 0.6), the labelling formats were slightly less consistent across product categories than in Italy (Mdiff ¼ 0.3), po0.01. The interaction between Format and Country was also significant F(15, 3239) ¼ 3.4, po0.0001. The interaction seemed to be mainly caused by the Wheel of Health, which was a significantly more consistent differentiator in Italy (Mdiff ¼ 0.1) compared to Germany (Mdiff ¼ 0.8). Credibility of endorsers Main effects were found for Endorsement type and for Country (Table 5). The nutrition labelling format (Smileys) was perceived to be far more credible when it was endorsed by an international or national organisation in the area of


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Table 5 Mean scores and ANOVAs for credibility of labelling format (Smileys) by type of endorsement (study 1) Countries

UK Germany Italy NL Overall mean

Endorsement

ANOVAs

No endorsement (n ¼ 136)

National Nutrition Organisation (n ¼ 136)

World Health Organization (n ¼ 136)

European Union (n ¼ 136)

European food manufactures (n ¼ 136)

Overall mean

Type (df ¼ 4)

Country (df ¼ 3)

Type Country (df ¼ 12)

M

M

M

M

M

M

F (p)

F (p)

F (p)

664.2 (*)

5.9 (*)

35.4 (*)

2.2c 1.9d 2.0d 2.1c 2.0e

3.3a 3.4a 2.9c 3.2a 3.2b

3.5a 3.1b 3.5a 3.0a 3.3a

2.7b 2.5c 3.2b 2.8b 2.8c

2.6b 2.3c 2.7c 2.7b 2.6d

2.9 2.6 2.9 2.7

Note: Means in the same row that do not share superscripts differ at po0.01 (Tukey–Kramer). *po0.0001.

health and nutrition, po0.01. Furthermore, endorsements by the European Union and European Food Manufactures were perceived as less credible compared to the national nutrition organisation and the World Health Organization, po0.01. The interaction between Endorsement type and Country was also significant. German participants found the format slightly more credible when endorsed by the national nutrition organisation compared to World Health Organization and Italian participants found the format more credible when endorsed by the World Health Organization compared to the national nutrition organisation, po0.01. Interpretation of labelling formats as comparison across or within product category. More than half of the participants (58%) indicated that they thought the nutrition labelling formats compared products across all food products compared to 27% of the participants who thought the format only compared products within one category and 15% who indicated that they had not thought about it. Discussion study 1 Overall, the findings suggest that all labelling formats were understood, liked and were seen as credible, except for the Health Protection Factor. Furthermore, all labelling formats were able to help consumers to differentiate between healthier and less healthy variants of the same product category. Although there were several significant differences between countries, the overall effects were quite similar. There is thus an opportunity to introduce one front-of-pack label across European countries. Furthermore, the results indicate that an official endorsement strongly increases the credibility of the labelling format. The Health Protection Factor was clearly the least consumer friendly format. It scored lowest on comprehension, credibility and liking. The low score of the Health Protection Factor may be due to the way the labelling formats were presented to participants, in that no further

explanation about the formats was given to participants. Participants did not know what the maximum score was so they may not have been able to interpret the score as high or low. Furthermore, the term ‘Health Protection Factor’ might have been perceived as strange in relation to food. Within the small differences that were found, the Multiple Traffic Light scored best with respect to the indicators used to assess consumer friendliness. However, it is also the most inconsistent differentiator between healthier and less healthy products. Given that a format should be effective for all product categories, the Multiple Traffic Light seems less ideal. The results also indicated that providing more information is not necessarily better for everyone. Participants with low perceived nutritional knowledge found the detailed Wheel of Health more difficult to understand than participants with high perceived nutritional knowledge. A similar finding appeared for people who never or hardly ever read labels versus people who always read labels. However, we found no support indicating that people with lower levels of education had more difficulty in understanding the labels. Measuring the effectiveness of different labelling formats is quite complex as there is a difference between evaluating several labels in an experimental setting where participants have time to process all the information, compared to a shopping situation where nutrition labels have to compete with many other stimuli. In a second study, we addressed this, and focussed on the impact of the labelling formats on behavioural intention. We introduced another method (shopping basket) to better mimic a shopping context. Study two Method Participants In total, 776 participants from two European countries participated in this study, 371 participants from Italy and


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Table 6 Socio-demographic characteristics of participants by country (study 2) Demographics

% Italy (n ¼ 371)

Gender Male Female

46.6 53.4

UK (n ¼ 405)

48.6 51.4

Education level Lowa Middleb Highc

30.6 67.5 19.0

33.2 28.5 38.5

Age 18–24 25–34 35–44 45–55

18.6 26.7 30.8 23.9

14.1 31.6 28.9 25.4

years years years years

a

Up to primary school. Up to secondary school. c Higher education/university. b

405 participants from the United Kingdom. As the results of the first study showed that there were minimal differences between countries, we limited it to a southern and northern European country. Consumer samples of each country were drawn from Internet panels of a market research agency (Survey Sampling International). To create representative samples, participants were selected by means of quota sampling (based on census figures) on gender, education and age (for the age range of 18–55 yrs) (Table 6). To correct for slight differences from the census figures, data were weighted for age, education and gender for each country. Participants from the first study were not contacted for the second study. Materials Type of nutrition labelling format. We included two labelling formats from the first study (i.e. the Healthier Choice Tick and Stars) (Fig. 1). Stars was included as it was the most promising simpler format together with Smileys. The Healthier Choice Tick was included because it was the simplest format. In addition to these two labelling formats, we introduced two new labelling formats, i.e. the Multiple Choice Tick and GDA scores (Fig. 2). The Multiple Choice Tick was included as a more fine-grained alternative of the Healthier Choice Tick. A product was given 0, 1, 2 or 3 ticks, with 3 ticks being the healthiest product. The Multiple Traffic Light and Wheel of Health were replaced by GDA scores. The latter came up as an interesting example of another detailed format when it was launched by Tesco’s in 2005. Products. This study employed two methods. In the first method (product pair) two product categories were included, ice-cream and spreads. For each category a healthier variant and a less healthy variant was selected by

Fig. 2. The additional nutrition labelling formats used in study 2: (a) Multiple choice tick; (b) GDA scores.

using the Unilever Nutrition Enhancement Programme score (Nijman et al., 2007). For ice-cream, the healthier variant was ‘real fruit covered ice-cream’ and the less healthy variant ‘chocolate covered ice-cream’. For spreads, the healthier variant was ‘vegetable oil-based margarine’ and the less healthy variant ‘butter’. In the second method (shopping basket) five product categories that could be consumed as a snack and two filler products (i.e. soup and an apple) were selected. For each category a healthier and less healthy variant was included: ‘real fruit covered’ and ‘chocolate covered ice-cream’; ‘cola light’ and ‘regular cola’; ‘unsalted peanuts’ and ‘salted peanuts’; ‘tea biscuit’ and ‘muffin’; ‘popcorn’ and ‘chocolate bar’. Procedure To obtain baseline measurements, participants were first exposed to the 12 products used in the study without a front-of-pack nutrition labelling format, enabling current usage and perceived healthiness of products to be assessed. Subsequently, participants were asked to complete a number of questions regarding demographics, health behaviours, health attitudes and nutritional knowledge. Participants then did the first test (product pair) using the same procedure as in the first study. Participants answered questions on liking, perceived healthiness and intended usage frequency. When participants finished this part of the test they were exposed to two collages of pictures of products that contained healthier and less healthy product variants (shopping basket). Participants answered questions on comprehension and liking of the labelling format. Subsequently participants would see the less healthy product variants that they used more than once a month, together with their healthier variant. Participants answered questions after each pair on perceived healthiness and intended usage frequency for both products. Measures Baseline measures. Current usage of the products without a front-of-pack nutrition labelling format was measured by the question ‘How often do you usually eat or drink this product?’, with answers ranging from 1 (less than once a month), 2 (once a month), 3 (two or three times a month), 4 (once a week), 5 (two or four times a week), 6 (almost daily) to 7 (several times a day). For the analyses the scale was converted to number of times per year. Current perceived healthiness of the products without a labelling format was measured by the question ‘How healthy is this product for you?’, with answers ranging from 1 (not healthy at all) to 5 (very healthy).

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Consumer friendliness and perceived healthiness of product. Comprehension and liking of the labelling format were measured as in study 1. Change in perceived healthiness was measured by calculating the difference between baseline measure of perceived healthiness and perceived healthiness of the product after exposure to the labelling format. Behavioural intention. Intended usage frequency of the product after exposure to labelling format was measured by the question ‘Having seen this product with the health indicator, how often do you intend to use this product?’, with answers ranging from 1 (less than once a month), 2 (once a month), 3 (two or three times a month), 4 (once a week), 5 (two or four times a week), 6 (almost daily) to 7 (several times a day). For the analyses the scale was converted to number of times per year. Intended change in usage frequency was measured by calculating the difference between baseline usage frequency and intended usage frequency after exposure to the labelling format. For the Healthier Choice Tick, less healthy products were followed by the question, ‘Having seen this product without the health indicator, how often do you intend to use this product?’. Time. The time (in seconds) participants took to look at and to evaluate each labelling format during the product pair test was measured. Background variables. study 1.

results, all data were analysed separately for the product pair method and shopping basket method. Furthermore, the influence of labelling formats on Perceived healthiness and Intended change in usage frequency was analysed separately for healthier and less healthy products. The alpha was set at 0.01 for ANOVAs and 0.01 for post hoc analyses. Pre-analyses revealed normal distribution of each variable. The same background variables as mentioned in study 1 were included in the analyses for Comprehension, Perceived difference in healthiness and Intended change in usage frequency. Results Impact nutrition labelling format on consumer friendliness (study 2) Comprehension of formats. Shopping basket: On average, participants found all nutrition labelling formats easy to understand, with means ranging from 3.8 to 4.4 on a 5-point scale (Table 7). A main effect was found for Format. Stars scored highest on comprehension and GDA scores lowest, po0.01. There was no main effect for Country and the interaction between Format and Country was also not significant. For the background variables, no significant differences were found for Comprehension (data not shown, available from authors).

The questions were the same as in

Design and statistical analyses Two different methods were used to test the nutrition labelling formats. The first method was similar to the one employed in the first study: front-of-pack nutrition labelling formats were presented per product pair (comparison of a healthier and less healthy product variant). A 4 (Labelling Format: Healthier Choice Tick, Multiple Choice Tick, Stars, GDA scores) 2 (Product Category: Ice-cream, Spreads) 2 (Healthiness of Product: Healthier product, Less healthy product) within subject factorial design was used with Countries as a between subject variable (UK, Italy). The second method (shopping basket) used a single factor design. The factor was type of nutrition labelling format (the 4 different formats). The nutrition labelling formats were presented on a collage of 12 products to imitate a shopping situation. Depending on the labelling format (Healthier Choice Tick, Multiple Choice Tick or Stars) the 12 products were clustered on screen into, respectively, 2, 4 or 5 categories according to their healthiness. GDA scores were shown on each product separately. Each participant was shown two of the labelling formats. Products and labelling formats were all randomised across participants. All data were analysed using the same analyses as mentioned in study 1. To facilitate interpretation of the

Liking of formats. Product pair: On average, participants reasonably liked the formats, with means ranging from 3.1 to 3.5 on a 5-point scale (Table 7). A main effect was found for Format. GDA scores was slightly more liked compared to the other formats, po0.01. There was no main effect for Country and the interaction between Format and Country was also non-significant (Table 7). Shopping basket: Also in the shopping basket, participants reasonably liked the formats, with means ranging from 3.2 to 3.6 on a 5-point scale (Table 7). A main effect was found for Format. Stars was most liked, followed by the GDA scores and Multiple Choice Tick, po0.01. There was no main effect for Country and the interaction between Format and Country was also not significant (Table 7). As the different methods showed different results we also conducted an ANOVA on liking of the formats with Format as between subjects factor and Test type (product pair vs. shopping basket) as within subjects factor. This yielded the main effect for Format, F(3, 2288) ¼ 7.9, po0.0001. We also found a main effect for Test type, F(1, 2288) ¼ 23.8, po0.0001. Participants liked the nutrition labelling formats slightly more when tested with the second method (shopping basket) compared to the first method (product pair), po0.01 (Table 7). The interaction between Format and Test type was also significant, F(3, 2288) ¼ 11.1, po0.0001. The more complex GDA scores was liked most in the first method (product pair) (po0.01).


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Table 7 Mean scores and ANOVAs for liking and comprehension by format and method across countries (study 2) Variables

Format

ANOVAs


Multiple choice tick (n ¼ 376)

Stars (n ¼ 356)

GDA scores (n ¼ 406)

Overall mean

Format (df ¼ 5)

Country (df ¼ 3)

Format country (df ¼ 15)

M

M

M

M

M

F (p)

F (p)

F (p)

Comprehension Shopping basket

4.1b

4.0b

4.4a

3.8c

4.1

19.6 (*)

5.1 (ns)

1.4 (ns)

Liking Product pair Shopping basket

3.1b 3.2b

3.2b 3.3ab

3.1b 3.6a

3.5a 3.4ab

3.2 3.4

14.3 (*) 6.9 (**)

0.4 (ns) 6.5 (ns)

0.2 (ns) 2.7 (ns)

Note: Means in the same row that do not share superscripts differ at po0.01 (Tukey–Kramer). *po0.0001; **po0.001.

Impact nutrition labelling format on behavioural intention Intended change in usage frequency. Differences in frequency between baseline and post measure were used as the dependent measure. Overall, participants intended to slightly increase their consumption of healthier products and intended to decrease their consumption of less healthy products (Fig. 4). No significant differences between the formats were found (data not shown, available from authors). For the background variables, no significant differences were found for Intended change in usage frequency (data not shown, available from authors). Time to evaluate Product pair. Twenty participants spent more than 350 s (i.e., 5.8 min) compared to an average of 80 s to evaluate a labelling format and were excluded as outliers. Analyses for the time needed by participants revealed significant main effects for Format and Product category (Table 8).

Mean difference scores

Impact nutrition labelling format on change in perceived healthiness (study 2) Differences in perceived healthiness between baseline and post measure was used as the dependent measure. Overall, the labelling formats increased the perceived healthiness of the healthier products and slightly decreased the healthiness of the less healthy products. Although significant differences were found between the different labelling formats, these did not provide a consistent and interpretable pattern across the two methods and across healthier and less healthy products (Fig. 3). For the background variables, some small differences were found for Difference in perceived healthiness but these did not provide a consistent and interpretable pattern (data not shown, available from authors).

0.8 0.6

Healthier Choice Tick

a

a

Multiple Choice Tick

Stars

a

0.2

b

0

b

-0.2 -0.4

b a

Healthier products


0.8

GDA scores

b

0.4

-0.6


In the second method (shopping basket), the simpler Stars was liked most, po0.01 (Table 7). These results indicate that the way formats are tested influences the liking of the labelling formats.

ab

0.6

b

a

Less healthy products


Stars

GDA scores

b

0.4 0.2 0 -0.2 -0.4 -0.6

ab

Healthier products

ab

b

a Less healthy products

Fig. 3. Differences in mean scores for perceived healthiness per method by format, healthier and less healthy products and across countries (study 2): (a) product pair; (b) shopping basket. Note: Means that do not share letters in the same category differ at po0.01 (Tukey–Kramer).

Participants needed significantly more time to evaluate GDA scores than the Healthier Choice Tick and Stars, po0.01. Furthermore, participants needed significantly more time to evaluate the labelling formats when tested on Ice-creams than when tested on Spreads, po0.01. No other significant effects were found (Table 8). Discussion study 2 The results indicated that simpler front-of-pack labelling formats (Healthier Choice Tick and Stars) may be more effective in helping consumers to make healthier choices in


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a supermarket environment than a more detailed complex front-of-pack labelling format (GDA scores), when considering the time participants needed to process the information and the ease of comprehension. In addition, the study showed that all formats were more likely to increase the perceived healthiness of the healthier products than to decrease the perceived healthiness of the less healthy products. The nutrition labelling formats may have confirmed participants existing knowledge about less healthy products, and may have provided new information in that healthier products were initially seen as less healthy. Contrary to what might be expected when taken into account the above results, participants’ intention to use less healthy products decreased, whereas participants’ intention



Stars

GDA scores


50 25

Healthier products


0 -25 -50 Healthier Choice Tick


Stars

GDA scores


50 25

Healthier products


0 -25 -50 -75

Fig. 4. Difference in mean scores for intended usage frequency (times per year) per method by format, healthier and less healthy products and across countries (study 2): (a) product pair; (b) shopping basket. Note: Means do not differ in the same category at po0.01 (Tukey–Kramer).

to use healthier products hardly increased. This suggests that the labelling formats do not encourage overconsumption of particular products as was suggested in a study by Scott and Worsley (1994). The results of this study suggest that the way a format is tested impacts on how participants judge products with a nutrition labelling format. For example, the labelling formats were slightly more liked when tested with the second method (shopping basket) than when tested with the first method (product pair). This shows that it is necessary to further improve the methodology to test nutrition labelling formats. General discussion A series of front-of-pack nutrition labelling formats were evaluated on their consumer friendliness (comprehension, liking and perceived credibility), on their ability to differentiate between healthier and less healthy products, and on their impact on intention to change behaviour. In the first study, the focus was on comprehension, liking and credibility of the labelling formats and additionally the impact of the labelling formats on perceived healthiness of the products. In the second study, the focus was on decision-making and we consequently included behavioural intention measures and measured the amount of time people took to process a nutrition labelling format. As far as we know, this latter measure has not been taken into account before in research on the effectiveness of labelling formats, although this is an important dependent variable if it is our aim to facilitate making a healthy choice. The results of both studies suggest that front-of-pack labelling formats are effective in helping consumers make healthier choices, e.g., participants intended to decrease the number of times per year they consumed less healthy products. The results also suggest that from the perspective of consumer friendliness there are no large differences between the different formats, with the exception of the Health Protection Factor, which scored lowest on consumer friendliness. In addition, front-of-pack nutrition labelling formats in general seem to work for everybody and across

Table 8 Mean scores and ANOVAs for time to evaluate each format by product category across countries (study 2) Variables

Time (s) Icecream Spreads Overall mean

Format

ANOVAs


Multiple choice tick (n ¼ 767)

Stars (n ¼ 757)

GDA scores (n ¼ 776)

Mean scores

Format (df ¼ 3)

Product category (df ¼ 1)

Format product category (df ¼ 3)

M

M

M

M

M

F (p)

F (p)

F (p)

9.5 (*)

123.9 (*)

3.7 (ns)

66.7ab

71.1ab

57.9a

76.1b

68.0

44.5a 55.6a

45.5a 58.3ab

48.4a 53.2a

51.9a 64.0b

47.6 59.7

Note: Means in the same row that do not share superscripts differ at po0.01 (Tukey–Kramer). *po0.001.


all countries, although there were some slight differences between subgroups and countries. Results from study 1 showed that participants who scored lowest on perceived nutritional knowledge and label reading understood the more detailed Wheel of Health less well than participants who perceived themselves as highly knowledgeable and always read labels. This suggests that too much detailed information might be less suitable for ‘vulnerable’ groups. Furthermore, some differences between countries were found, but these were not large enough to warrant different labels between countries. A caveat with respect to these latter findings is that differences between countries may have been attenuated by the different demographics in each country. However, given that we did not find any big differences between the demographic groups within each country, any confounding influences will be quite limited. We also found clear evidence that official endorsements strongly increase the credibility of the labelling format, indicating that endorsement by an international or national organisation in the area of health and nutrition is important. Furthermore, most participants indicated that they thought a nutrition labelling format compares products across food products rather than between products within one category. Consumers thus clearly expect one nutrition labelling format across food products. The results from study 2 showed that healthy choices can be made faster with the simpler front-of-pack formats Healthier Choice Tick or Stars than with the more detailed GDA scores. Participants needed almost 10 s more to evaluate products with GDA scores than products with a Healthier Choice Tick or Stars. This seems especially relevant in a shopping environment where consumers often spend little time to decide what to buy (Hoyer, 1984). In line with findings by Williams (2005), we recommend to present simple labelling formats front-of-pack and more detailed nutritional information (such as GDAs) on the back of the package. This will allow consumers to make a quick decision, whilst also providing detailed information if consumers desire this. Although consumers like the idea of a simplified front-of-pack nutrition label, there are individual differences with respect to the preferred level of detail of nutrition information (Grunert & Wills, 2007). In addition to the findings from our study, there are also other considerations that need to be taken into account when choosing a labelling format. A front-of-pack labelling format (complementing detailed nutritional back-ofpack information) is more cost effective, as only products that meet a certain standard will be labelled. In contrast, complex front-of-pack labelling formats involve a specific label reflecting the nutrient profile on each product. This will significantly increase the costs of implementing such a labelling format, making adoption of a front-of-pack label by manufacturers less likely. Furthermore, research by Young and Swinburn (2002) showed that receiving a tick is a strong incentive for food companies to make their products healthier.

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These studies also showed that measuring the effectiveness of front-of-pack labelling formats is a complex issue, as many different factors have to be taken into consideration. A caveat of this study is that processing information in an experimental setting is different from processing information in real life. In a supermarket situation consumers face many distracting factors (such as time, noise and large number of products) that will hinder detailed information processing. In our second study, we tried to create a task that better simulated the shopping environment and included a time measurement in the second study. Future research could explore this further by mimicking the shopping environment even better, e.g., by including distracters while consumers evaluate different front-of-pack nutrition labelling formats and by including actual choice. Furthermore, future studies should aim to measure actual behaviour (e.g., by creating a virtual shopping task where participants will be asked to buy products from a shopping list when they have to make a healthy meal). This may also allow us to better assess whether participants really understood the labelling formats. Participants may not really have understood a labelling format although they thought they did. Alternatively, participants may not have wanted to admit that they did not understand a nutrition labelling format. In conclusion, our results indicated that front-of-pack labelling formats help consumers make healthier choices and that there are no major differences in consumer friendliness between simpler and more detailed labelling formats. However, when taking into account the shopping environment, we suggest a simple tick logo on the front-of-pack (e.g., the Healthier Choice Tick) to complement the detailed back-of-pack nutritional information fact box. Backed up by a detailed and science-based nutritional profile, such as Unilever’s Nutrition Enhancement Programme score, such a front-of-pack labelling format could have a substantial positive impact on public health. However, a multitude of different front-of-pack labelling formats would only confuse consumers, thus decreasing the effectiveness of all front-ofpack labelling formats. The current challenge is therefore to come up with a harmonised European or even global frontof-pack labelling format across all foods.

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Research report

Enhanced salt appetite, diet and drinking in traditional Bedouin women in the Negev Micah Leshem, Amany Saadi, Nesreen Alem, Khadeja Hendi Psychology Department, University of Haifa, Haifa 31905, Israel Received 30 October 2006; received in revised form 28 May 2007; accepted 30 May 2007

Abstract The Negev Bedouin are desert dwellers in high summer heat and scarce shade and water. They are under pressure to cease their traditional way of life. To document, while still possible, how traditional Bedouin nutritional habits may have accommodated to these conditions, we evaluated sodium appetite, diet and drinking in Bedouin women (n ¼ 31) who still partially maintained their traditional way of life in isolated tribal encampments in the spring of 2005. Data were compared to urban Bedouin women (n ¼ 15), and to urban Jewish women (n ¼ 15) representing mainstream dietary habits in the same region, and to published data. About 60% (by energy) of the encampment diet is traditional, but this proportion is reduced in summer. Encampment Bedouin women rated concentrated salt solution as more preferred than other groups, added 40% more salt to an ideal test soup and had a 50% greater absolute dietary sodium intake. The sodium content of the traditional Bedouin diet is 25% higher than the Jewish women’s diet. This enhanced sodium appetite is reflected in the value of salt in their folklore. The possible causes of the enhanced salt appetite are considered. In addition, dietary intake (M ¼ 3470, SE ¼ 285 kcal) was 50% greater than in urban Jewish women. Fluid intake (2.4 L/d) was 20% greater than Jewish women, but may have been inadequate in the exposed encampments because 8 of the 31 women reported an occurrence of dehydration, 6 of them while pregnant. Encampment women BMI (30.371.1 kg/m2) was high, and health problems were typical of populations in transition. r 2007 Elsevier Ltd. All rights reserved. Keywords: Bedouin; Desert; Diet; Drinking; Negev; Salt; Salt appetite; Sodium appetite; Tradition; Women

Introduction The Bedouin way of life has undergone extensive changes in the past decades (Aburabia-Queder, 2005; Abu-Saad, Weitzman, Abu-rabiah, Abu-shareb, & Fraser, 2001; Cwikel, Lev-Wiesel, & Al-Krenawi, 2003; Degen, Benjamin, & Hoorweg, 2000; Fraser, Abu-Saad, & AbuShareb, 2001). One of the main influences is the government policy of resettlement in permanent places of habitation, which has severely restricted the nomadic existence of the Bedouin. Since the 1960s when this and subsequent policies were instigated, more than 50% of the 150,000 Bedouin have been moved to permanent places of residence in 7 main townships (Abu-Saad et al., 2001). Corresponding author.

E-mail address: [email protected] (M. Leshem). 0195-6663/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.appet.2007.05.010

Many of the rest live in encampment villages considered ‘illegal’ by the government and hence without services and amenities—water, electricity, sewage, schools, shops, etc. There they are subject to restrictions, inter alia, on grazing, and planting feed or crops, which are destroyed by government planes dusting herbicide (Aburabia-Queder, 2005; Abu-Ras, 2006; Abu-Saad, 2006; Degen et al., 2000; Golan-Agnon, 2006; Greenberg, 2006; Jabareen, 2006). These villages, are frequently demolished by the authorities, e.g. the same encampment of 25 families on 5 occasions during the winter of 2006–2007 (Gvirtz, 2007). In the encampments, the prohibitions on farming and herding in a subsistence economy, together with the increasing availability of commercial foods, have impacted the diet. In order to document the vestiges of their traditional diet, we studied Bedouin women in isolated illegal encampments. Their diet is more traditional, because

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M. Leshem et al. / Appetite 50 (2008) 71–82

unlike the men, who drive to town to work daily and eat there, the women rarely leave the encampment as they do not travel without a male family-member chaperone (Cwikel et al., 2003). Traditional life style means, inter alia, that each encampment belongs to a different tribe, with no intermarriage between tribes. It is a patriarchic society. Outsiders cannot meet the Bedouin women without prior consent of a family man. Some 30% of the women are in polygamous marriages of 2 or more wives (Aburabia-Queder, 2005). Most live in sheet metal or breeze-block one-room shacks, a few still live in tents. Using their vehicles, the men tow water containers to the encampment. The water is placed centrally and is usually carried, or sometimes piped, to the homes. Some encampments have generators that operate at night for a few hours to power refrigerators1 and TVs. Urbanized Bedouin live in townships with amenities and services. Much of traditional dietary protein was obtained from sheep, goat, and camel milk, prepared into a variety of soured and other products such as yoghurt (labane), butter (samne), g’amid, or a rock-hard, salted, and sun-dried product (afig) which is preserved, chewed, or reconstituted by boiling in water (Abu-Saad et al., 2001; Degen et al., 2000; Fraser et al., 2001). Traditional staples include ‘sahg’ bread, which is served under many dishes, rice, and vegetables—wild, grown, or salted and sun-dried. Bought rice has replaced the traditional home-grown wheat grain as the basis for many recipes. Traditional drinks are sheep, goat, and camel milk, sweet tea (5.7% w/w sugar), and unsweetened coffee made from local wild beans (Degen et al., 2000; Fraser et al., 2001). Meals are taken while seated on the ground, and served on a communal plate or large bowl and gathered to the mouth with fingers or sahg bread. All seasoning is done during food preparation. Salt, sugar or condiments are not available during the meal, so that there is no individual variation in seasoning. Salt is of interest for a number of reasons. In the absence of refrigeration, salt is a preservative, and it is possible that the high salt content of the food would engender a preference for salty foods (Beauchamp & Cowart, 1990; Mattes, 1997; Sullivan & Birch, 1990). Moreover, in a hot desert climate, where electrolyte and water loss in perspiration is high, salt is a mainstay of adequate hydration for both Bedouin and their livestock. For these reasons, in addition to the tests and questionnaires, we asked the encampment Bedouin about salt in their folklore. We also documented their recipes and weighed the ingredients (reported here only as summarized nutrient values). Determining salt appetite in humans is complicated by the absence of an ubiquitous definition. Ingestion of 1

No perishables can be stored with this form of refrigeration for more than a day or two. We found the fridges filled with sterilized milk cartons (used for drink and to prepare milk products), and suchlike.

aqueous NaCl (used in animal research) is not acceptable to humans, whose sodium intake is dependent upon food intake. Hence the evaluation of sodium appetite is compounded by the determinants of eating, and includes involuntary and discretionary sodium intake, a variety of sources, e.g., salting at table, choice of salty food items in meals, consumption of salt snacks, sodium supplementation in sports, etc., and given to diverse regulators, physiological, habitual, conditioned and cultured. Sodium intake from these various sources is not necessarily correlated, and hence a number of indices of salt preference are often combined to provide a measure of a general ‘‘salt appetite’’, as we do here (Crystal & Bernstein, 1995; Leshem, 1998; Mattes, 1997; see Kochli, Rakover, & Leshem, 2005, for a fuller discussion and references). Method The study had the approval of the University of Haifa Human Research Ethics Committee. Participants indicated their agreement to participate, and that they had the right to withdraw at any time, or refuse any test, by signing or making a mark on the letter of informed consent that was read out and explained. Participants Thirty-one Bedouin women from 7 encampments of the H’ura, Abu Tlul, Azazmah, Abda, Ksayfah, Beer Alh’mam and A’ojan tribes, participated in the final data set (Table 1). However, it was not possible to obtain data for all the tests from all the participants (some participants refused certain tests), and n varies from 27 to 31 or as stated. They were tested and interviewed in April 2005. For dietary comparisons, we used data collected in 2002–2003 from 15 Bedouin and 15 Jewish women, 20–50 y old. The urban Bedouin women lived in townships, and the Jewish women lived in the city of Beer-Sheva, in the same region in the Negev. Encampment participant selection and procedure The three researchers were female and Arabic speaking (a non-family male would not be permitted to interview the women). Villages were chosen by a Bedouin male intermediary who knew the location of the remote, traditional, encampments. In the encampment, the researchers were first interviewed by a married elder of the

Table 1 Age, BMI and height of encampment participants, n ¼ 28 Bedouin Age (y) Weight (kg) Height (cm) BMI (kg/m2)

4472 (26–64) 73.072.6 (47.5–96.2) 154.8710.2 (143–168) 30.371.1 (21.1–43.4)

ARTICLE IN PRESS M. Leshem et al. / Appetite 50 (2008) 71–82

tribe and by the men before receiving permission to talk to the women. A woman then took the researchers to a family tent where the women were gathered, and after a few minutes socializing, the researchers explained about the study and its aim of documenting the Bedouin’s traditional diet, asked about salt and its history, and answered questions. The informed consent forms were explained and signed. Some women would not, or could not (below), participate. Because the Bedouin home is not divided into rooms, it was not possible to interview and test without the other participants being present, as were their children, and although the other women could not hear the interview, they could see the tests being carried out. Each woman was tested by one researcher, with another researcher assisting by preparing the tests, weighing, recording, etc. Sometimes two women were interviewed simultaneously in different parts of the tent/hut by the 3 researchers. For each participant, the test session lasted 1.5–2 h, of which the questionnaires took 1–1.5 h. The following measures were obtained: information about age, health, dieting, tendency to vomit and diarrhoea. Height and weight were measured. Weight was obtained fully clothed except for footwear, and to adjust the weight, 0.613 kg was deducted, a mean value obtained from weighing similar clothing. White–black, and thirst visual analogue scores (VAS) A 100 mm VAS anchored by ‘‘very light’’ and ‘‘very dark’’ at the ends was used for scoring 6 monochrome 2 cm squares graded from white to black (Figure 4, Small, Zatorre, & Jones-Gotman, 2001). This test was used to explain the use of the VAS and to validate its use for the other VAS evaluations (below). VAS of thirst, anchored at the ends by ‘‘not at all thirsty’’ and ‘‘very thirsty’’ was used to evaluate thirst. Most of the women marked the scales themselves, but those that could not, pointed with their fingers, and the researchers made the mark accordingly. Taste hedonics of sweet and salty In this test oral sprays (0.8 ml) of 6 concentrations each of solutions of NaCl (0.0025, 0.01, 0.04, 0.16, 0.64, 2.56 M) and sucrose (1.1, 3.3, 10.0, 30.0, 90.0, 270.0 g/l) were prepared in bottled water (Neviot, 9 mg/l Na+). NaCl and sucrose solutions were tested separately, with the first 15 questions of the dietary questionnaire intervening between tastes (about 20 min) to reduce taste after-effects. The participants rinsed their mouth with bottled water between each concentration, which were presented in a pseudorandomized order to exclude adjacent concentrations. After each concentration, participants scored one VAS anchored at the ends with ‘‘do not taste anything’’ and ‘‘very strong taste’’, and another VAS anchored by ‘‘not tasty’’ and ‘‘tasty’’. Spray bottles were coded to maintain double-blind conditions.

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Dietary and seasoning questionnaire The questionnaire included 64 food items, including Bedouin dishes. Each required 2 answers. The researchers read the questions and wrote the participant’s response: (1) How many portions of this item have you eaten in the past week? (2) How much do you like this item (5-point scale: ‘‘dislike’’, 2, ‘‘like very much’’, +2). As explained in the Discussion below, this question emerged as inappropriate in both encampment and urban Bedouin women. Similarly, questions on table-salt use and other condiments at table were not applicable because the Bedouin do not season during the meal, although both questions were retained for the Jewish women. In addition, we asked the participants to demonstrate preparation of traditional recipes, and we documented and weighed the ingredients, and subsequently estimated their nutritional value (Meir & Reshef, 1997). Where additional recipes were found, they were added to the questionnaire. Urban Bedouin and Jewish women were tested as above on the questionnaire and salting of soup, but obtaining height and weight, and some of the other measures, was not possible. Other methodological differences were that in the urban Bedouin community, the male experimenter was only permitted with a male family member chaperone who also assisted in translating the questions and answers. Preferred levels of salt and sweet To measure the preferred level of salting, tomato soup was prepared from unsalted tomato pureé and water (1:9). The soup was divided into two, and 3% NaCl (w/v) was added to one half. The soups were maintained at 40–45 1C in thermos flasks. They were presented to the participants in partially filled 200 ml polystyrene cups and referred to as ‘‘flavoured differently’’, the word ‘‘salt’’ was not used in this connection. Participants were requested to mix the two into a third, empty cup, using 5 ml disposable spoons, until the mixture was most tasty. The cups were weighed (to 0.1 g) to calculate the sodium concentration of the mixture. A similar test evaluated preference for sweetening using unsweetened and 20% w/v sucrose tea (Kochli et al., 2005; Leshem, 1998; Shirazki, Weintraub, Reich, Gershon, & Leshem, 2006). Preference for salt and sweet snacks Whilst the dietary questionnaire was being administered, participants were offered 2 types of salty snacks (small, salty pretzels, and a salty crispy snack) and 2 sweet snacks (small chocolate spheres and cream crackers). The number and type of snack eaten was counted.

ARTICLE IN PRESS 74


Salt appetite Salt appetite was operationally defined by calculating the unweighted (divided by highest score) averaged scores, summed for all the tests, i.e. the hedonic scores for the 3 highest concentrations of oral salt spray, concentration of salt in soup, salty snacks eaten and dietary sodium. Sweet preference was similarly obtained from sweet spray, sweet snacks, and concentration of sucrose in tea (Kochli et al., 2005; Leshem, 1998).

Table 2 Component matrix of measures of tradition (% variance explained) [cumulative %] 1 (43) [43] Age Garb Fridge Housing Literate Shop

2 (20) [62]

0.918 0.847 0.616 0.457 0.918

3 (18) [81]

0.447 0.888 0.733 0.840

PROP taster status The strong genetic basis for sensitivity to PROP and similar compounds has provided a tool for anthropologists to trace family lineage and we employed it here to see whether the tribal separation might be reflected in taste sensitivity. Participants tasted filter disks impregnated with 0.025 mg PROP solution (6-n-propylthiouracil. Kindly supplied by Linda Bartoshuk). They then scaled their sensation of intensity on a labelled magnitude scale (Bartoshuk, Duffy, Lucchina, Prutkin, & Fast, 1998) by pointing with their fingers, and the researchers marked the scale. This test was administered as the last taste test because for supertasters it leaves an enduring bitter aftertaste.

for post hoc comparisons. Where variability obscured effects, the Mann–Whitney (MWU) was employed. Repeated measures ANOVA was employed for analysis of taste responses to oral sprays. Only probabilities above 0.05 are considered significant and reported. The measure of variance throughout this report is the standard error. Results Fig. 1 presents the distributions of age, height, weight and BMI of the encampment Bedouin women. Diet

Evaluation of tradition in encampment Bedouin To enable future comparisons, we documented how traditional these women were at the time of this research using the following quantifiable criteria (n ¼ 28–30): 1. 11 could sign their name, 17 could not. 2. 15 wore bought clothes, 15 wore home-made, traditional, black, embroidered clothes. 3. 19 lived in breeze-block shacks, 4 in temporary shacks, and 7 in traditional tents. 4. 12 had refrigerators, 18 did not. 5. 14 were within walking distance of a shop, 16 were not. 6. Although only relevant to the date of this investigation, 18 of the women were below 45 years of age, and 12 above. The interdependence of these criteria was analysed by allocating a value of 0 for a ‘modern’ and ‘1’ for a ‘traditional’ participant in each criterion, followed by data reduction (r40.4), which apportioned 3 factors explaining 81% of the variance (Table 2). Factor 1 comprised 5 of the 6 criteria of tradition, confirming their interdependence as measures. Factor 2 related housing standard and literacy, and factor 3 may reflect the level of available amenities such as electricity, in relating possession of a refrigerator and proximity to a shop. Statistical methods One-way analysis of variance (ANOVA) served for group comparisons, with the least squares difference test

Macronutrient and electrolyte intakes of the Bedouin were higher than for Jewish women, but were similar for encampment and urban Bedouin (Table 3). The questionnaire included 64 items of which, for the encampment Bedouin women, 31 were judged traditional and 33 ‘modern’, by Bedouin consultants. More traditional items were reportedly eaten by the participants (22.170.8 (15–30)) than modern food items (18.170.9 (11–28), (po0.001)) (Table 3). Values for nutrients obtained from traditional foods are significantly higher than from the modern foods (excepting fat, Table 3). The comparison of traditional and modern drinks is moot insofar as traditional drinks include water, milk, tea, and coffee, and modern beverages include soft drinks and sterilized flavoured milks, such as chocolate milk (recall that the drinks are not refrigerated in the desert climate). Urban Bedouin women reported a similar diet, but the diet of both Bedouin groups differed from that of urban Jewish women (Table 3). Table 3 also presents data from a national survey (Ministry of Health, 2004a). The survey’s consistently lower values could be because, inter alia, our questionnaire uses recall, recognition and cuing, whereas the survey data was collected using recall only. Fluid intake, water, and thirst Daily fluid intake in encampment Bedouin was greater by some 20% than fluid intake in urban Jewish women (Tables 3 and 4). Encampment Bedouin drank a variety of fluids, all of them more than Jewish women, except water


75

6 5 Frequency

Freqency

6

4

4 3 2

2

1 0

0 25 30 35 40 45 50 55 60 65

1.40

1.50

age (y) 6

1.70

5

5

4

4

Frequency

Frequency

1.60

height (m)

3 2

3 2 1

1 0

0 40

50

60

70

80

90

100

20

25

weight (kg)

30

35

40

45

BMI

Fig. 1. Frequency distributions of age, height, weight and BMI (kg/m2) of encampment Bedouin women.

Table 3 Macronutrient composition of the encampment diet, the contribution of modern and traditional items, and comparison to urban Bedouin and Jewish women, and national averages Daily intake

Energy (kcal) Fat (g) Protein (g) CHOb (g) Na+ (mg) K+ (mg) Ca++ (mg) Fluid (ml)

Encampment Bedouin women

Urban women (n ¼ 15+15)

Women 25–64 y

Traditional foods

Modern foods

Total

Bedouin

Jewish

(n ¼ 1701)a

18827157+ 63.376.6 110.076.8++ 294.5727.4++ 40997250++ 30437189++ 13527115++ 21047177++

15887158 54.075.7 48.775.2 189.6722.3 261877250 10827189 389742 340763

34707285** 117.379.6* 158.7710.5*** 484.1743.1*** 67177491* 41257245*** 17417124***y 24447164*

33047198* 131.5710.0** 151.2710.1* 408.2723.2* 68867618* 38517200** 13277117 24377169*

23077211 84.278.8 108.977.7 275.8726.0 45747811 26977219 1016786 20017178

1533716 5771 5871 20372 2350730 2299725 47077 –

+ po0.05, ++po0.001, traditional differs from modern. *po0.05, **po0.01, ***po0.001, Bedouin differ from Jewish women. y po0.05, different from urban Bedouin. a Ministry of Health (2004a). b Carbohydrates.

(Table 4). The amount of water drunk was similar, but it is not the same drink—the encampment women drink it from a sun-heated tank whereas Jewish women drink refrigerated bottled mineral water (Ministry of health, 2004a). Encampment women drank three times as much milk as urban women. Milk and yoghurt-like products accounted for almost 20% of their fluid intake (Table 4).

Encampment and urban Bedouin also ate more fresh vegetables and fruit than Jewish women, totalling, respectively, 546761, 528742 and 384746 g/d (p’so0.05 different from Jews), thereby adding approximately 0.45 l water to their daily fluid intake, 0.15 l more than the Jews (Meir & Reshef, 1997). This is additional to the total fluid intake shown in Tables 3 and 4.


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Table 4 Main sources of drinking fluids, ordered by intake in encampment Bedouin Daily intake (ml)

Encampment Bedouin

Urban Bedouin

Urban Jewish

Water Soft drinks Milk Tea Coffee Yoghurts Soup Total fluid

11207148 340763** 307761y 236725*** 159740 155727** 128720*** 24447164*

13307153 250752 95726 293764* 183765 110719* 176725*** 24377169*

11987161 209793 126723 72728 284784 58719 55733 20017178

Table 5 Main (45% of total) sources of dietary sodium Food itema

% of total dietary sodium Encampments Urban Bedouin Urban Jewish

*po0.05, **po0.001, ***po0.001 different from Jews. ypo0.05, Bedouin differ (MWU).

Like the dietary values, the pattern of fluid intake of urban and encampment Bedouin was generally similar (excepting milk). We did not have thirst ratings for the urban women, but encampment Bedouin thirst ratings were significantly lower than midrange (50 mm) of thirst VAS at 24.974.0 mm, t(27) ¼ 6.27, (po0.001). Dehydration Eight of the 28 respondents (26.7%) reported dehydration requiring a saline infusion (our criterion question for dehydration), and for 6 of the women it occurred during pregnancy. Each woman reported a single dehydration event. Dietary sodium Dietary sodium intake of both encampment and urban Bedouin was significantly higher, by 50%, than the intake of urban Jewish women (Table 3). Although the greater sodium intake appears to be proportional to the greater caloric intake, detailed analysis of the sodium density of the diets (Na+ mg/kcal) revealed that the traditional encampment diet was more sodium dense than both the modern encampment diet and the urban Jewish diet (respectively, 2.4170.15, 1.6470.16 (po0.001), and 1.9370.19 (po0.02, MWU)). The Bedouin obtained almost 60% of their dietary sodium from the traditional components of their diet (Table 3). Almost 40% of dietary sodium comes from soups and salting fresh vegetables, and for the Bedouin, breads bring the total to over 50% (Table 5).

Pita, sahg and breads Soup—instant powder Fresh vegetables Soup—home made Salty olives & pickles Rice Salt snacks % of total dietary Na+ a

16.0 14.5 12.4 11.3 10.5 6.1 2.6 73.4

15.9 11.6 12.7 16.8 8.1 3.0 5.0 73.1

10.9 13.9 13.8 6.4 8.4 5.6 3.4 62.4

Includes salt added in seasoning.

Table 6 Preference for salt and sweet snacks, and concentration of NaCl in soup and sucrose in tea, in encampments

Salty snacksa Sweet snacksa NaCl in soupb Sucrose in teab

Encampment Bedouin

Urban Bedouin

Urban Jewish

6.2970.79+ 3.9670.56 1.0170.07 8.5070.39

– – 0.7770.07 –

– – 0.6470.12 –

a

Morsels eaten. Percent concentration w/v. + po0.01, more than sweet snacks. po0.005 more than Jews. b

Encampment Bedouin added almost 40% more salt to the test soup than Jewish women. We did not have data for preferred level of sucrose in tea or snack preference for the urban women, but the encampment values did not differ from other groups we have tested (Kochli et al., 2005; Leshem, 1998; Shirazki et al., 2006). Salt and sweet taste hedonics and intensity The white–black curve and small variance confirms that the VAS technique was executed well by the participants (Fig. 2, top). The taste-intensity estimations show a clear dissociation between intensity and hedonic ratings, and are typical of results of other groups in this task. The flat hedonic evaluation of NaCl by concentration is unusual (see Discussion, below). The monotonically increasing hedonic evaluation of sucrose is typical. Salt appetite

Snacks and preferred level of salt in soup Salt snacks were preferred to sweet snacks (Table 6) in similar proportion to that of other groups we have tested, but the encampment women partook of some 60% fewer snacks than other groups (Kochli et al., 2005; Leshem, 1998; Shirazki et al., 2006).

We did not have sufficient measures for comparison with urban women, but the salt appetite of the encampment Bedouin was higher than that of controls, and lower than that of patients with congenital adrenal hyperplasia (CAH) of salt wasting severity, we have tested similarly, whereas values for sweet preference were comparable (Table 7).


77

100

VAS (mm)

75

50

25

0

100

100

NaCl hedonics

75

intensity VAS (mm)

VAS (mm)

75

Sucrose

50

50

25

25

0

0 2.5

10 40 160 640 concentration (mM)

2460

1.1

3.3 10 30 90 concentration (g/L)

270

Fig. 2. VAS ratings of shades of white to black (above, 0 ¼ ‘‘very light’’, 100 mm ¼ ‘‘very dark’’), and hedonics (0 ¼ ‘‘not tasty’’, 100 ¼ ‘‘tasty’’) and intensity (0 ¼ ‘‘do not taste anything’’, 100 ¼ ‘‘very strong taste’’) of NaCl and sucrose solutions. All concentration gradients were significant (po0.01) except hedonics of NaCl. Hedonics: dashed lines, intensity: continuous lines.

Table 7 Salt appetite in encampment Bedouin compared to data from healthy and salt wasting patientsa

12

Encampment Bedouin Control CAH Salt wasting CAH

Sweet preference

0.4170.02,+ 0.3470.03 0.5370.05

0.5070.02 0.4770.03 0.4570.10

po0.01 different from control CAH. + a

po0.01 different from salt-wasting CAH. From Kochli et al. (2005).

Frequency

10 Salt appetite

8 6 4 2

Dieting and chronic disease Among 30 respondents, 13 reported dieting, 7 of them because of chronic disease. Eleven restricted fat, and of these one additionally restricted salt, and one sugar and

m

ng ro st

iu ed

m

rd w ly ea k

PROP responses were similar in distribution to other reports, but did not correlate with any measure of salt preference (Fig. 3. Bartoshuk et al., 1998). We found no evidence that PROP response differed in any of the 7 tribal encampments, possibly because of the small sample size (n ¼ 3–5 in each tribe).

0

ha

PROP

very strong

strongest imaginable

Fig. 3. PROP response and taste descriptor anchors (logarithmic VAS).

salt. Of the remaining 2 dieters, 1 restricted sugar and salt, and one did not specify. None reported carbohydrate restriction. Of the 9 reporting chronic disease, 2 reported taking regular medication. Two additional women reported taking regular medication and no chronic disease.

ARTICLE IN PRESS 78


Discussion This study was designed to evaluate salt appetite in an indigenous community of Negev Bedouin that have adapted to desert conditions over generations. Because their way of life is disappearing rapidly, we needed to document their traditional diet urgently, and did so in the spring of 2005. To find the most traditional diet we went to isolated encampments, and interviewed only the women. The lifestyle is extraordinary in dietary habits, socioeconomic, educational, environmental and many other circumstances. As can be seen from the national data (Table 3, Ministry of Health, 2004a), the Bedouin differ on almost every dietary nutrient. Such differences may be methodological, as suggested above. Nevertheless, the data we present may be valuable not only as a record of Bedouin nutritional tradition, but also for comparison with particular aspects of other communities and traditions, particularly traditional desert communities. The study was carried out in unusual circumstances in comparison to populations studied in the west. Seventeen of 28 participants could not sign their name, consistent with the lack of education for encampment Bedouin women (Abu-Saad, 2006; Cwikel et al., 2003). The VAS were difficult for them to grasp. When we suggested they consider them as vertical ladders they pointed out there were no rungs for their hands. One woman refused to mark the white–black VAS explaining that since the line was black she would not use it to score a different colour, because this was lying, and punishable by God. It took us a while to comprehend this problem (this woman could not be tested). We also noted difficulties with categorization of fruits and vegetables (there are few fruits indigenous to the Negev), and colours, which some of the women could not name. The women also did not dissociate eating a food and liking it. Our questionnaire queries whether a food is eaten, and how much it is liked, but they insisted that if they ate it, they liked it; it was ‘good’ and ‘healthy’, and they would not characterize it further. Similarly, they did not dissociate hunger and appetite, and suggested that availability was their main motivation to eat. In contrast they had no difficulty in dissociating intensity and hedonics of the oral sprays (Figure 4), and the curves for the intensity estimations of black–white, and salt and sweet oral sprays, were similar to those found in other groups, attesting to the reliability of the technique. Regarding the use of recall data among Bedouin women, others have shown that it can be used with confidence (Launer et al., 1992). Diet The women reported that traditional foods are the mainstay of their nutrition, such as sheep & goat milk products, wild vegetables, rice and flour. Meat (70% chicken) accounted for less than 10% of caloric intake. However, in summer, lactation ceases in sheep and goats,

no wild vegetables remain, and vegetables (tomato, cucumber, onion, garlic), cannot be grown for lack of water. Therefore, they explained, in summer they needed dietary supplementation from ‘modern’ (shop-bought) foods because they possess too few animals (because of grazing prohibitions) to produce the preserved milk products that traditionally saw them through the summer. In the furthest encampments, the women told us that their summer diet consisted of sahg bread, wild plants, and sterilized milk from stored cartons. In addition, some Bedouin told us they still preserve tomatoes, sundried in salt. The encampment women reported a trip to town once a week or fortnight to buy ‘modern’ foods that they ate the same day, or stored, if they required no refrigeration. Even where fridges are available, they are mainly used to stock non-perishable foods. The preference for the traditional diet has been previously reported for urbanized Bedouin (Degen et al., 2000; Fraser et al., 2001) and may account for the remarkably similar nutritional values of encampment and urbanized Bedouin diets that we find (Tables 3–5). On the other hand, the food items making up the two diets, and daily eating patterns, can differ (Fraser et al., 2001). Our data are consistent with the Bedouin accounts: they show that the diet composition we found in the spring of 2005 has a slightly greater proportion of traditional food items than ‘modern’ (22 vs. 18), but deriving from them, a yet greater proportion of macronutrients (54–69%), especially sodium and other electrolytes (61–77%), and fluid (84%). ‘Modern’ beverages (e.g. soft drinks) are served on special occasions, e.g. for guests.2 It should be noted that our study was carried out in April, when traditional foods are more available. Their proportion of the diet may be reduced in summer, when little can be grown, and the limited traditional preserved foods they could prepare, exhausted, as the Bedouin explained (above). We found no consistent correlations between dietary components and our measures of tradition. It may be that dietary tradition is robustly independent of such other variables, and that for the same reasons, despite decades of separation, and differing environs, encampment and urban Bedouin diet composition remains similar, and unique (but, cf. Fraser et al., 2001, for analysis of the differences). Salt appetite Greater dietary intake entails greater absolute sodium intake, which is physiologically significant in terms of replacing loss, reserves and excretion. Dietary sodium intake of the Bedouin women is some 50% higher than that of urban Jewish women. For the encampment Bedouin, 60% of dietary sodium derives from the traditional components of the diet, which is 25% richer in sodium 2

Alcoholic drinks are unknown.


than the Jewish women’s diet. It is seems, therefore, that the traditional Bedouin diet provided high levels of sodium. The encampment Bedouin hedonic evaluation of salt concentrations by oral spray is atypical, but points to a greater preference for high concentrations of salt. Salt solution is generally aversive, and preference aversion curves typically descend from around neutrality monotonically with concentration (Kochli et al., 2005; Stone & Pangborn, 1990). Encampment Bedouin rated all concentrations of salt as hedonically similar, and close to neutral, although they clearly distinguished the concentrations of these solutions. These Bedouin find concentrated NaCl solution less aversive, or more preferred, than other groups we have tested, excepting salt wasting patients with congenital adrenal hyperplasia (CAH Kochli et al., 2005; Shirazki et al., 2006). This preference for high concentrations of salt solution in the encampment Bedouin women is similar to that of CAH patients with uncontrolled sodium loss who have an enhanced sodium appetite (Kochli et al., 2005). Moreover, encampment Bedouin mixed almost 40% more salt into the test soup than urban Jewish women, again, a concentration similar to that preferred by salt wasting CAH (Kochli et al., 2005). The preference for salt snacks over sweet snacks was similar to that of other groups, even though the Bedouin consumed some 60% fewer test snacks, explaining that it was not appropriate, and shameful for them as hosts, to eat food brought by guests (the researchers). Despite the evidence for a preference for high sodium content, and tolerance for high salt concentrations, we do not find that the Bedouin sensory evaluation of salt differs from other groups we have tested (Kochli et al., 2005; Leshem, 1998; Shirazki et al., 2006). Thus on three measures of salt intake and preference, dietary sodium, concentrated NaCl solutions, and preferred level of salt in soup, the encampment Bedouin score high. In addition, we summed these measures with the other measures we tested, that did not differ significantly from other groups, to derive a ‘salt appetite’ score (cf. Methods). This global measure enables comparisons with similarly derived sweet preference (as a control for specificity), and with other groups we have tested (Kochli et al., 2005; Leshem, 1998). On this measure of salt appetite the encampment Bedouin scored higher than other groups, but lower than CAH (cf. Table 7). A number of factors might contribute to this enhanced salt appetite. Dehydration can elevate salt appetite of the Bedouin mother because animal research has shown that dehydration, electrolyte loss, and multiple pregnancies may increase salt appetite enduringly (Frankmann, Ulrich, & Epstein, 1991; Leshem, Kavushansky, Devys, & Thornton, 2004; Sakai, Frankmann, Fine, & Epstein, 1989), although we have not found that multiple pregnancies in women increase salt appetite (Leshem, unpublished). Dehydration in humans can also condition a preference for tastes

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associated with salt (Wald & Leshem, 2003). In addition a number of factors can increase salt appetite of the offspring: dehydration during pregnancy (as occurred among some of these women), can increase salt appetite of the offspring because prenatal electrolyte loss, as by maternal vomiting, increases offspring salt appetite (Crystal & Bernstein, 1995; Leshem, 1998; Shirazki et al., 2006). Furthermore, infantile vomiting and diarrhoea increase salt appetite enduringly (Leshem, 1998), and Bedouin children suffer high rates of diarrhoea (Bilenko, Fraser, & Naggan, 1999). The consistently salty diet might also engender a preference for salty foods by ‘‘mere exposure’’, although the evidence for this is skimpy (Beauchamp & Cowart, 1990; Mattes, 1997). A relevant observation was that schoolchildren took ‘afig’ with them to chew as their food for the day—the rock-hard preserved milk product, sundried and heavily salted. It is also possible that exposure to the hot desert climate coupled with restricted water, engenders net sodium loss as occurs in CAH, and stimulates salt appetite by related mechanisms (Kochli et al., 2005). We found no evidence for a genetic predisposition for salt appetite insofar as there was no relationship with the response to PR0P (Bartoshuk et al., 1998). Thus there are many possible contributors to the high salt appetite of these Bedouin women. The salt appetite of their children and menfolk remains to be studied. Consistent with their enhanced appetite for salt, the Bedouin women told of the distinctive place of salt in their history and lore. The following accounts were similar in the different encampments. In the past, they would bring salt rocks from near the Dead Sea (approximately 60 km distant) and grind them using ‘‘alrah0 a’’—manual mill-stones. Flocks obtained their salt grazing on salty plants (e.g. anthemio op, artemidia dieleri, which they showed us). The Bedouin deduced the plants bear salt because the animals drank after eating them, although to humans they taste bitter (some of these plants are medicinal for the Bedouin). Today, because of grazing restrictions, they provide their animals with bought mineral blocks. For salt lore, they volunteered the following: ‘‘If you eat from our food that has salt, you will not harm us’’. Salt symbolizes a visit, a sitting together, hospitality, generosity and trust. Story 1: A brother asked his sister while she was cooking how much she cherished him. The sister replied that she cherished him like salt. The brother understood this reply as undervaluing him and took offence. To prove how much she loved him and how important salt is, she reduced the amount of salt in her brother’s food daily until he found the food tasteless. She then brought him salt with a gold ring in it symbolizing that her brother and salt were equally precious. Story 2: A visitor came to a Bedouin in winter when he had no salt. In order to prepare food for the guest the

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Bedouin sent his son to fetch salt. On the way back, the son was thrown from his horse and his head hit a rock and bled. When the father came looking for the boy he found him dead. The father took the salt and prepared the meal for his guest. Only after that did he give vent to his grief. An emphasis on salt in lore might reflect the enhanced physiological need, and might also contribute to salt salience and therefore intake. Other electrolytes Intake of dietary potassium and calcium was also very high. However, despite the high intake of calcium, OHD-25 (active vitamin D) is reportedly low because traditional clothes cover much of the body (Lowenthal & Shany, 1994). Water, fluid intake and thirst Water is obviously pivotal to the Bedouin. In the past encampments were located in the vicinity of rain runoff cisterns (average rainfall for the area is 50 mm/y, with none May–September) and water was also brought from afar on camels. When shepherding, the women used to take water with them in skin containers. When they ran out of water far from home they either asked for it from Bedouin they met, or sought shelter from the heat until the evening. Nowadays, because they are restricted to pasture near their encampment, they often do not bother to carry water with them when they shepherd their animals. Our data show the encampment women drank marginally greater amounts of fluid than urban Jewish women, despite their high BMI and virtually unprotected exposure to the desert climate (the study was carried out in the BeerSheva region in April (mean temperature 18.6 1C (11.5–25.6), and relative humidity 54% (28–81) (Goldreich, 1998)). Their VAS ratings of spontaneous thirst were low, but while they objected to eating the food we offered (test snacks) because we were their guests, they did ask to drink from the bottled water we brought with us. This might suggest that the attraction of available, clean, water, that did not deplete their reserves, may have overcome their etiquette and restraint. The daily fluid intake of some 2.5 L from fluids and fresh vegetables and fruits might be inadequate in desert conditions and may have contributed to the incidence of 8 women dehydrated out of 28. Of especial concern is that 6 of the 8 dehydrational occurrences were in pregnant women, given the dire infant mortality rate in this group (14.1 compared to 3.3 in the majority Jewish population (Statistical Abstract of Israel, 2006)). The potential contribution of such dehydration to salt appetite in mothers and offspring was considered above. BMI The BMI of 30.3 kg/m2 is very similar to the 30.9 previously reported for Bedouin women in the Negev

(Abou-Rbiah & Weitzman, 2002), and is much higher than reported for the more remote south Sinai Bedouin women of 0.0–0.5 SD score for women of childbearing age (Beverley & Henderson, 2003), or national averages (Ministry of Health, 2004b). We suspect that one of the reasons for the high BMI is lack of activity. The Negev encampment Bedouin are prohibited from herding flocks, or moving camp, and the women no longer carry water from distant sources. Farming is prohibited, and there is very little housework. Tradition (and distance) restricts them from taking alternative employment outside the encampment (Cwikel et al., 2003). The most persistent physical activity is kneading sahg bread throughout the day (Fraser, Weitzman, Blondheim, Shany, & AbouRbiah, 1990). The high fertility rate of 5.8570.25 (1–16) children (Cwikel et al., 2003), or 7.61 in 2005 (Statistical Abstract of Israel, 2006), may also contribute to increased BMI because multiple pregnancies can increase weight by some 7 kg within 1 year after birth (Rosser, 1992). Our data suggest that traditional food items contribute more energy in their diet, and presumably weight gain, than the modern foods. Consonant with the high BMI, the participants reported illnesses that required dietary restriction mainly of fat and salt, and mainly related to the metabolic syndrome, in keeping with the known health problems of this community (Abou-Rbiah & Weitzman, 2002; Abu-saad, et al., 2001; Cwikel et al., 2003; Fraser, Shahar, Shai, Vardi, & Bilenko, 2000). Similar clinical patterns are typical of populations in transition, such as migrants (e.g. Miller, Wilbur, Chandler, & Sorokin, 2003; Pousada et al, 2005).

Summary and conclusions In this unique group of encampment Bedouin women, the traditional diet is particularly rich in sodium, dietary sodium intake is more than double that of urban Jewish women, they add 40% more sodium to a test soup, and high concentrations of salt in aqueous test solutions are more preferred. Together, these suggest an enhanced salt appetite, which is also reflected by the importance of salt in their lore. High sodium intake may be protective in the desert environment. However, we cannot know if it be a culturally evolved strategy to counter the threat of dehydration, a physiological and learned response to early and possibly frequent dehydrational stress (Crystal & Bernstein, 1995; Kochli et al., 2005; Shirazki et al., 2006) or an evolutionary dictate (Epstein, 1991; Fessler, 2003). Clearly all of these can contribute and interact to increase salt appetite and intake. The springtime diet still retains traditional foods (54% by energy), and together with the ‘modern’ food items, it is highly caloric. Coupled with the restrictions on their traditional herding and nomadic lifestyle that have forced


a sedentary existence, it has made the Bedouin women vulnerable to the metabolic syndrome. Despite the methodological difficulties (Cwikel et al., 2003), it is incumbent upon scholars to document the Bedouin lifestyle that is undergoing forced and rapid changes (Aburabia-Queder, 2005; Abu-Ras, 2006; AbuSaad, 2006; Cwikel et al., 2003; Golan-Agnon, 2006). Such knowledge may better guide policy decisions, and serve the Bedouin community it its struggle for improvement. Acknowledgements Supported by the Israel Science Foundation Grant No. 902/00, and the Edelstein Foundation For Population Studies to ML. We are grateful to Mr. Said Al-Sana of Ben-Gurion University for invaluable help, and to Mr. Eyal Shoval, for the study of urban Bedouin and Jewish women. References Abou-Rbiah, Y., & Weitzman, S. (2002). Diabetes among Bedouins in the Negev: The transition from a rare to a highly prevalent condition. The Israel Medical Association Journal, 4, 687–689. Aburabia-Queder, S. (2005). Black feminism: Bedouin women of the Negev. MIFNEH, 46–47, 23–26 [article in Hebrew]. Abu-Ras, T. (2006). Land disputes in Israel: The case of the Bedouin of the Naqab. Adalah’s Newsletter, 24, 1–9. Abu-Saad, I. (2006). State-controlled education and identity formation among the Palestinian Arab minority in Israel. American Behavioral Scientist, 49, 1085–1100. Abu-Saad, K., Weitzman, S., Abu-rabiah, Y., Abu-shareb, H., & Fraser, D. (2001). Rapid lifestyle, diet and health changes among urban Bedouin Arabs of southern Israel. Food, Nutrition and Agriculture, 28, 45–54. Bartoshuk, L. M., Duffy, V. B., Lucchina, L. A., Prutkin, J., & Fast, K. (1998). PROP (6-n-propylthio-uracil) supertasters and the saltiness of NaCl. Annals of the New York Academy Science, 855, 793–796. Beauchamp, G. K., & Cowart, B. J. (1990). Preference for high salt concentrations among children. Developmental Psychology, 26, 539–545. Beverley, D., & Henderson, C. (2003). A cross-sectional survey of the growth and nutrition of the Bedouin of south Sinai peninsula. Annals of Tropical Paediatrics, 23, 209–214. Bilenko, N., Fraser, D., & Naggan, L. (1999). Maternal knowledge and environmental factors associated with risk of diarrhea in Israeli Bedouin children. European Journal of Epidemiology, 15, 907–912. Crystal, S. R., & Bernstein, I. L. (1995). Morning sickness: Impact on offspring salt preference. Appetite, 25, 231–240. Cwikel, J., Lev-Wiesel, R., & Al-Krenawi, A. (2003). The physical and psychosocial health of Bedouin Arab women of the Negev area of Israel. The impact of high fertility and pervasive domestic violence. Violence Against Women, 9, 240–257. Degen, A. A., Benjamin, R. W., & Hoorweg, J. C. (2000). Bedouin households and sheep production in the Negev desert, Israel. Nomadic Peoples, 4, 125–147. Epstein, A. N. (1991). Thirst and salt intake: A personal view and some suggestions. In D. J. Ramsay, & D. A. Booth (Eds.), Thirst— physiological and psychological aspects (pp. 481–501). Berlin: Springer. Fessler, D. M. T. (2003). An evolutionary explanation of the plasticity of salt preferences: Prophylaxis against sudden dehydration. Medical Hypotheses, 61, 412–415. Frankmann, S. P., Ulrich, P., & Epstein, A. N. (1991). Transient and lasting effects of reproductive episodes on NaCl intake of female rats. Appetite, 16, 193–204.

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Fraser, D., Abu-Saad, K., & Abu-Shareb, H. M. (2001). The relative importance of traditional and ‘‘modern’’ foods for Israeli Negev Bedouins. A population in transition. Nutrition, Metabolism, and Cardiovascular Diseases, 11, 66–69. Fraser, D., Shahar, D., Shai, I., Vardi, H., & Bilenko, N. (2000). Negev nutritional studies: Nutritional deficiencies in young and elderly populations. Public Health Reviews, 28, 31–46. Fraser, D., Weitzman, S., Blondheim, S., Shany, S., & Abou-Rbiah, Y. (1990). The prevalence of cardiovascular risk factors among male Bedouins: A population in transition. European Journal of Epidemiology, 6, 273–278. Golan-Agnon, D. (2006). Separate but not equal discrimination against Palestinian Arab students in Israel. American Behavioral Scientist, 49, 1075–1084. Goldreich, Y. (1998). The climate of Israel-observations, research and applications. Jerusalem: Bar-Ilan University Press, Ramat-Gan and Magnes Press, The Hebrew University (Hebrew). Greenberg, M. (2006). Authorities were aware shepherds had entered firing range—but did nothing Haaretz, 4 April. Jabareen, Y. T. (2006). Law and education critical perspectives on Arab Palestinian education in Israel. American Behavioral Scientist, 49, 1052–1074. Kochli, A., Rakover, Y., & Leshem, M. (2005). Increased salt appetite in patients with congenital adrenal hyperplasia 21-hydroxylase deficiency. American Journal of Physiology, Physiology (Regulatory, Integrative and Comparative Physiology), 288, R1673–R1681. Launer, L. J., Forman, M. R., Hundt, G. L., Sarov, B., Chang, D., Berendes, H. W., et al. (1992). Maternal recall of infant feeding events is accurate. Journal of Epidemiology and Community Health, 46, 203–206. Leshem, M. (1998). Salt preference in adolescence is predicted by common prenatal and infantile mineralofluid loss. Physiology and Behavior, 63, 699–704. Leshem, M., Kavushansky, A., Devys, J.-M., & Thornton, S. (2004). Enhancement revisited: The effects of multiple depletions on sodium intake in rats vary with strain, substrain, and gender. Physiology and Behavior, 82, 571–580. Lowenthal, M., & Shany, S. (1994). Osteomalacia in Bedouin women of the Negev. Israel Journal of Medical Sciences, 30, 520–523. Mattes, R. D. (1997). The taste for salt in humans. American Journal of Clinical Nutrition, 65, 692S–697S. Meir, C., & Reshef, A. (1997). Tables of food constituents. Jerusalem: Ministry of Health, Department of Nutrition (Hebrew). Miller, A. M., Wilbur, J., Chandler, P. J., & Sorokin, O. (2003). Cardiovascular disease risk factors and menopausal status in midlife women from the former Soviet Union. Women Health, 38, 19–36. Ministry of Health (2004a). First Israeli national health and nutrition survey 1999–2001. Part 2—what Israelis eat. Publication no. 228. Ministry of Health, Israel. Ministry of Health (2004b). State of health of the Arab population of Israel. Publication no. 226, Ministry of Health, Israel (Hebrew). Pousada, J. M., Britto, M. M., Cruz, T., Lima, M. L., Lessa, I., Lemaire, D. C., et al. (2005). The metabolic syndrome in Spanish migrants to Brazil: Unexpected results. Diabetes Research and Clinical Practice, 72, 75–80. Rosser, S. (1992). Pregnancy, weight cycling and weight gain in obesity. International Journal of Obesity Related Metabolic Disorders, 16, 145–147. Sakai, R. R., Frankmann, S. P., Fine, W. B., & Epstein, A. N. (1989). Prior episodes of sodium depletion increase the need-free sodium intake of the rat. Behavioral Neuroscience, 103, 186–192. Shirazki, A., Weintraub, Z., Reich, D., Gershon, E., & Leshem, M. (2006). Lowest neonatal serum sodium predicts sodium intake in lowbirthweight children. American Journal of Physiology (Regulatory, Integrative and Comparative Physiology). Online, December 2006. Small, D. M., Zatorre, R. J., & Jones-Gotman, M. (2001). Changes in taste intensity perception following anterior temporal lobe removal in humans. Chemical Senses, 26, 425–432.

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Statistical Abstract Of Israel (2006). No. 57. /http://www1.cbs.gov.il/ reader/shnatonenew.htmS. Stone, L. J., & Pangborn, R. M. (1990). Preferences and intake measures of salt and sugar, and their relation to personality traits. Appetite, 15, 63–79. Sullivan, S. A., & Birch, L. L. (1990). Pass the sugar, pass the salt: Experience dictates preference. Developmental Psychology, 26, 546–551.

Wald, N., & Leshem, M. (2003). Salt conditions a flavor preference or aversion after exercise depending on NaCl dose and sweat loss. Appetite, 40, 277–284. Yael Gvirtz, Sowing destruction, Editorial, Yedioth Aharonoth, 18.3.2007 (English: /http://www.israel-mfa.gov.il/MFA/Archive/Editorials/2007/ Editorials+18-Mar-2007.htmS).

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Research report

Structure of the relationship between parents’ and children’s food preferences and avoidances: An explorative study Margherita Guidetti, Nicoletta Cavazza Universita` di Modena e Reggio Emilia, Reggio Emilia, Italy Received 9 February 2007; received in revised form 11 April 2007; accepted 1 June 2007

Abstract The aims of the present research were to uncover the underlying structure of the relationship between parents’ and children’s preferences and avoidances in the food domain, and to determine whether this structure revealed any differences as a function of children’s age. Two hundred and eighty-two parent–child dyads (children aged 10–20) completed a self-administered questionnaire on eating attitudes and practices. The results led to a descriptive model of the connection between parents’ and children’s food repertories, made of four overlap and four autonomy areas. This structure was then compared with the structure that emerged from random pairs of an adult and a child/adolescent. Our findings showed that parents’ repertory had an anchoring function in the formation of that of their children, not only in terms of imitation, but also in terms of influencing the direction of differentiation and innovation. As far as the second aim of our study is concerned, only two out of eight areas (that of parent preferences limitation, and that of child autonomous avoidances) differed in relation to the children’s age. The theoretical and practical impact of these results is discussed. r 2007 Elsevier Ltd. All rights reserved. Keywords: Food preferences; Food avoidances; Parents; Children; Adolescents

Introduction Aside from innate (or genetic) predispositions, individual food preferences and avoidances begin developing in childhood both through personal experience and social interaction. The former includes learning through simple and associative conditioning (e.g., Brunstrom, 2004; Wardle et al., 2003). The latter comprises social facilitation, modeling, and deliberate influence through interpersonal and media communication (for a review, cf. Conner & Armitage, 2002). In every process listed above, the role of family is substantial, since by the age of 3–4 years eating behaviour of children is increasingly influenced by the physical and social environment (Rolls, Engell, & Birch, 2000). Humans are genetically predisposed to reject novel edible foods (food neophobia) and to prefer the more familiar ones. However, repeated exposure and social influence can Corresponding author.

E-mail address: [email protected] (M. Guidetti). 0195-6663/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.appet.2007.06.001

transform an initial neophobic response toward a new food into a preference (for a review cf. Birch, 1999). Research on parents’ influence on children’s eating habits has shown that this influence manifests itself through different processes (Birch & Fisher, 1998; Hertzler, 1983; Nicklas et al., 2001; Patrick & Nicklas, 2005): genetic transmission (e.g., Breen, Plomin, & Wardle, 2006); restriction of food experience, including the selective exposure (e.g., Cullen et al., 2001; Kratt, Reynolds, & Shewchuk, 2000) and the control, at least partial, on the physical, social and affective context of eating (e.g., Campbell, Crawford, & Ball, 2006; French, Story, Neumark-Sztainer, Fulkerson, & Hannan, 2001; NeumarkSztainer, Hannan, Story, Croll, & Perry, 2003; Videon & Manning, 2003); modeling (e.g., Addessi, Galloway, Visalberghi, & Birch, 2005; De Bourdeaudhuij & Van Oost, 2000); parental practices, that is pressures, restrictions and monitoring (e.g., Birch et al., 2001; Carper, Orlet, & Birch, 2000; Liem, Mars, & De Graaf, 2004), and parental styles related to their children’s nutrition (e.g., Kremers, Brug, de Vries, & Engels, 2003; Patrick, Nicklas,

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Hughes, & Morales, 2005; Vereecken, Keukelier, & Maes, 2004). This would thus suggest a relatively high degree of resemblance between children’s and parents’ food preferences. Many studies aimed at quantifying the extent of this similarity have found positive, though fairly weak, correlations between parents’ and children’s preferences, which might be explained by the shared cultural environment (e.g., Birch, 1980; Borah-Giddens & Falciglia, 1993; Ritchey & Olson, 1983; Rozin, 1991; Rozin, Fallon, & Mandell, 1984; Stafleu, Van Staveren, De Graaf, Burema, & Hautvast, 1995; Weidner, Archer, Healy, & Matarazzo, 1985). With reference to this, Rozin (1991) talked about ‘‘family paradox’’. Instead of using correlations, some authors crosstabulated the liked and the disliked foods of the members of each dyad. Pliner and Pelchat (1986) calculated a phistatistic for each table, whereas Skinner and colleagues (Skinner et al., 1998; Skinner, Carruth, Wendy, & Ziegler, 2002) computed a percentage of agreement (like–like matches+dislikedislike matches out of the potential correspondences) and found the highest similarity scores compared with other studies. However, these research were focused on resemblances and left out differences between parents’ and children’s food repertories (thought Skinner et al., 1998, reported also the percentage of correspondence between likes and dislikes of the members). In all these studies, preferences were collected through questionnaires (food preference questionnaire) or interviews in which participants were asked how much they liked each food listed (‘‘hedonic rating’’). Although the researchers used the term ‘‘preferences’’, in our opinion, these studies investigated the similarity between parents’ and children’s likes and dislikes. Indeed, authors generally use the term ‘‘preference’’ as a synonym of ‘‘liking’’. However, Rozin and Vollmecke (1986) suggest to distinguish between these expressions: the former presumes the availability of at least two different items, and implies the choice of one rather than the other; the latter indicates the affective reactions to a food. They stated that liking is only one of the motivations that may account for a preference. Other factors (e.g., perceived healthiness, convenience, price) can affect preferences but not liking. Rozin and Vollmecke (1986) proposed a taxonomy of preferences’ and avoidances’ underlying reasons: these are sensory-affective motivations, anticipated consequences, and ideational motivations. On the basis of different combinations of these three classes of motivations, Rozin and colleagues (Fallon & Rozin, 1983; Rozin & Fallon, 1980, 1987) have designated four types of food rejections: distaste, danger, disgust, and inappropriate. In line with this conceptualization, we will use the terms ‘‘preference’’ and ‘‘avoidance’’ as broader concepts than ‘‘like’’ and ‘‘dislike’’. Since research based on hedonic rating highlighted moderate correlations between parents’ and children’s likes and dislikes, it is possible that comparing them at a wider

level could yield different results. We believe that the connection between parents’ and children’s food repertories should be considered from a perspective that goes beyond the quantification of the similarity between likes and dislikes. Parental influence in the food domain could manifest itself not only in imitation, but also in the anchoring function that the parents’ repertory fulfills in the formation of their children’s repertory. Using their parents’ preferences and avoidances as a reference point, children could build their food repertory by reproducing that of their parents but also by contradicting it and expanding it. Thus, the structure of the relationship between parents’ and children’s repertories might include not only intradyad similarities and differences, but also autonomy zones of each participant. Although research in the past has largely focused on correlational analysis, correlation seems not completely suitable to highlight the structure of the relationship between parents’ and children’s food repertories. In our opinion, the use of cross-tabulations is a better way to stress this structure. Nevertheless, as the cross-tabs were based on hedonic rating task, they did not allow to consider the foods that are particularly preferred or avoided by only one member of the dyad but not by the other. Indeed, participants could give a judgement online, even if some items were not spontaneously comprised in their personal set of preferences and avoidances. In this way, every food would be included in one of the four possible intersections between likes and dislikes of the dyad members. Suppose that each individual hold a continuum from preferred to avoided foods, with a wider neutral zone in the middle: by asking participants to list the foods placed at the extremity of this continuum, we could obtain the overlaps between parents’ and children’s preferences and avoidances, as well as the autonomy zones, representing the intersection between one extremity of one member of the dyad and the neutral zone of the other. In other words, the autonomy zones include the foods that are salient for one member of a dyad but that are not salient for the other. Indeed, if parents’ and children’s repertories would not be linked, all the preferences and all the avoidances of one member of the dyad should fall in the neutral zone of the other. This would be the null hypothesis of complete independence between the two food repertories. In addition, the autonomy zones allow us to observe to what extent the child explores outside the parental repertory of the main preferences and avoidances. Furthermore, the research has left unexplored the question concerning to what extent the resemblance between parents’ and children’s likes develops in relation to the age of the latter. This is an important point, however, since likes and dislikes, preferences and avoidances change as children get older and are exposed longer to foods (Fischler, 1990; Logue, Logue, Uzzo, McCarty, & Smith, 1988). In addition, as children grow up, they modify their beliefs and attitudes about differing foods (Conner, 1993) and they are increasingly exposed to influences from

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outside the family (Skinner et al., 1998). It is also necessary to bear in mind the special nature of the transitional stage from childhood to adulthood, characterized by the elaboration of identity. Adolescence is typically a time of gradual shift from parental to peer influence (Erikson, 1963). Food choice is deeply linked to the construction and expression of identity both at personal and social level (Bisogni, Connors, Devine, & Sobal, 2002; Vartanian, Herman, & Polivy, 2007), and particularly in this phase of life (like much other behaviour) it fulfills a function of selfexpression. While studies focusing on the resemblance between parents’ and children’s food preferences have not dealt with the question of its variation in relation to children’s age, the research concerning the resemblance between eating habits does not present convergent results. Some researchers (Hannon, Bowen, Moinpour, & McLerran, 2003; Patterson, Rupp, Sallis, Atkins, & Nader, 1988) reported greater similarities between parents’ and children’s food consumption when children are young rather than adolescent. Instead, De Bourdeaudhuij (1996) showed higher correlations between parents and adolescent children than between parents and younger children. Still others (Lau, Quadrel, & Hartman, 1990; Rossow & Rise, 1994) indicated a certain stability in parental influence throughout their children’s lives. Lastly, Feunekes, Stafleu, De Graaf, and Van Staveren (1997) found different correlations for food items consumed at home and those consumed outside the home: for the former, associations were higher and did not differ significantly with the increasing age of children, whereas for the latter, associations were lower and more unstable. Furthermore, while correlations were never significant for father–child dyads, they were for mother–child dyads until the child reached the age of about 12. In summary, to the best of our knowledge, studies to date have not explored two questions: the first concerns the fact that association between food likes scores of children and their parents have been surprisingly weak. Nevertheless, studies carried out in this domain were limited to analyzing correlations or cross-tabulations between hedonic rating scores of parents and children. Hence, the complexity of the relationship between food repertories was reduced to a simple problem of similarity between parents’ and children’s judgement on a like–dislike continuum. The second question concerns the possible variations in the relationship linking children’s and parents’ food repertory as a function of the age of the former. The present study had two aims. First, to uncover the underlying structure of the connections between children’s and parents’ food preferences and avoidances, made of overlap and authonomy areas. Given the exploratory nature of our study, we hypothesized that the relationship between parents’ and children’s food repertories would be stronger than the relationship linking random dyads of an adult and a child/adolescent. The second aim was to

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determine whether this structure showed any differences in relation to the children’s age. Method Participants and procedure Two hundred and eighty-two Italian parent–child dyads volunteered for the study. Children were 150 females and 132 males aged 10–20 years (M ¼ 13.78, SD ¼ 2.84). They were recruited from Scout groups from Carpi (MO). Each child was coupled with the parent who usually did the food shopping and cooking: 238 mothers and 44 fathers aged 31–63 years (M ¼ 43.60, SD ¼ 4.79). The effect of parents’ and children’s sex was tested in all analyses: no significant effects were observed, and therefore will not be presented. Each member of the dyads separately completed a questionnaire on food attitudes and practices. For the purpose of this study, we analyzed the answers to two open questions. Instead of supplying participants with a list of foods to evaluate on a like–dislike continuum, we asked respondents to indicate the six foods placed at the two extremity of the preferences–avoidances continuum (‘‘please list three dishes or foods that you prefer’’; ‘‘please list three dishes or foods that you strongly dislike and would like to avoid’’). The open-ended questions are suitable to capture participants’ preferences and avoidances because they assume the availability of endless alternatives. Furthermore, this method has three other benefits. First, it allows to take into account foods that are particularly preferred or avoided by a member of the dyad but are not by the other (what we called ‘‘autonomy areas’’). Second, it might overcome the tendency shown by children to accept a narrower number of foods with respect to the adults (Pliner & Pelchat, 1986): indeed, this tendency does not influence the results if participants were asked to list their preferences and avoidances. Lastly, this strategy could strengthen our results, since giving a consistent judgement about foods listed by researcher is different from indicating spontaneously the same food category or even the same foodstuff among thousands of different food items that could be reported. Two hundred and seventy-six parent–child couples answered the questions and formed our final sample. Thus, we obtained information about preferences and avoidances areas for each member of the dyads. Through content analysis, preferred and avoided foods were grouped into 17 categories (pasta, traditional first courses, grains, soup, pizza and bread, meat, cold cuts, offal, fish, dairy products and eggs, pulses, vegetables, fruit, desserts, fast food, junk food and snacks, ethnic food). Two persons (one of the authors and another person) allocated each food to a category and disagreements between coders (76 out of 4584 item classified, that is 1.66%) were resolved throughout discussion. For example, the ‘‘vegetables’’ macro-category included tomatoes, lattuce, cabbage, and also potatoes;


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‘‘dessert’’ category comprised homemade sweet items, icecream, and chocolate; while other confectionery and chips were placed in ‘‘fast food, junk food, and snack’’ category. Measures and analysis In order to uncover the underlying structure of the relationship between children’s and parents’ food preferences and avoidances, we followed a modified version of the cross-tabulation method used by Pliner and Pelchat (1986) and Skinner et al. (1998, 2002). We gathered preferences and avoidances separately in order to compare distinct overlap areas: not only resemblances (food items preferred or avoided by both child and parent), but also differences (food items preferred by one and avoided by the other). We represented preferences and avoidances of each parent and his/her child graphically as circles intersecting each other (Fig. 1). Then we measured the size of the four overlap areas by counting foods and food categories in common among the various zones (both preferences and avoidances) and assigning 2 points for any matching foodstuff (if the parent and the child indicated the same specific food) and 1 point for any matching food category (if they both indicated a food included in the same category) between the repertories of each dyad. Two persons were responsible for allocating these points. Hence, a correspondence concerning the same specific foodstuff weighed more than a correspondence at

food category level. This was an arbitrary choice, but it is justifiable because the same preference for ‘‘pasta’’ is less reliable than the same preference for ‘‘spaghetti alla carbonara’’, for example. Suppose that a child listed as preferences ice cream, pizza and apricot, while his mother listed pizza, chocolate cake and carrots. They also listed as avoindances cabbage, gorgonzola cheese and liver, and mortadella, liver and panettone, respectively. This dyad would get a score of 3 for the preferences reproduction area (1 point for the match ice cream/chocolate cake—same ‘‘dessert’’ category—+ 2 point for the match pizza/pizza—same specific foodstuff); a score of 2 for the avoidances reproduction area (for the match liver/liver), a score of 1 for the limitation area (for the match cabbage/carrots) and a score of 1 for the opposition area (for the match ice cream/panettone). Thus, the obtained overlap areas (two positive and two negative) could vary from 0 to 6. They were called

PR=1.56

the preferences reproduction (PR) area, resulting from the intersection between parent’s and child’s preferences (positive overlap), the avoidances reproduction (AR) area, resulting from the intersection between parent’s and child’s avoidances (positive overlap), the parental repertory limitation (L) area, resulting from the intersection between parents’ preferences and child’s avoidances (negative overlap), and the opposition (O) (against parental repertory) area, resulting from the intersection between parent’s avoidances and child’s preferences (negative overlap).

As we mentioned above, the open-ended question method allowed us to take into account also the autonomy areas, which includes foods that are particularly preferred or avoided by a member of the dyad but are not by the other: PAP=3.72

CAP=4.07

L=0.72

O=0.38

CAA=4.30

PAA=4.65

AR=0.97

Fig. 1. Structure of the relationship between parents’ and children’s food repertories: intersection between preferences and avoidances of participating pair members (means of the areas size). Range: 0–6. Legend: ‘‘positive’’ overlap areas: PR ¼ preferences reproduction area; AR ¼ avoidances reproduction area; ‘‘negative’’ overlap areas: L ¼ parental repertory limitation area; O ¼ opposition (towards parental repertory) area; autonomy areas: PAP: parents’ autonomous preferences area; PAA, parents’ autonomous avoidances area; CAP, child’s autonomous preferences area; CAA, child’s autonomous avoidances area.

parent’s autonomous preferences (PAP) area, resulting from the subtraction of preferences reproduction area and limitation area from the parent’s whole preferences zone (PAP ¼ total parent’s preferences(PR+L) ¼ 6PRL), child’s autonomous preferences (CAP) area, resulting from the subtraction of preferences reproduction area and opposition area from the child’s whole preferences zone (CAP ¼ total child’s preferences(PR+O) ¼ 6PRO), parent’s autonomous avoidances (PAA) area, resulting from the subtraction of avoidances reproduction area and opposition area from the parent’s whole avoidances zone (PAA ¼ total parent’s avoidances(AR+O) ¼ 6ARO), and child’s autonomous avoidances (CAA) area, resulting from the subtraction of avoidances reproduction area and limitation area from the child’s whole avoidances zone (CAA ¼ total child’s avoidances(AR+L) ¼ 6ARL).


In order to control for the effect of participants’ shared cultural environment, we adopted a method already used by Birch (1980) and Pliner and colleagues (Pliner, 1983; Pliner & Pelchat, 1986). The extent of the four overlap areas was also computed for random pairs of an adult and a child/adolescent of the same gender and in the same age group of his/her real child. These random pairs were generated by shifting down of one record the parents’ data, within sub-samples based on gender and children’s age range. These overlap areas were then compared with those of the parent–child dyads, to test if the former were smaller than the latter. Results A 2 (parent gender) 2 (child gender) 8 (areas) mixed ANOVA design with repeated measures on the third factor, and children’s age as covariate, was performed. Table 1 shows the mean size of identified areas, for both real and random pairs. Fig. 1 graphically represents the structure of the relationship between food preferences and avoidances concerning real parent–child dyads. The analysis showed a main effect for the repeated factor, F(4.268) ¼ 22.711, po.001, Z2 ¼ .253. Pairwise comparisons (Bonferroni adjustment for multiple comparisons) confirm that every area was significantly different from the others at the .001 level (except for the AR–L and the CAP–CAA pairs, which are not significantly different). The broader overlap area was that of preferences reproduction, while the smallest was the opposition area (PAA4CAACAP4PAP4PR4ARL4O). There were no interaction effects between areas and children’s age, parent gender, and child gender. The child autonomous preferences area was significantly larger than the opposition area, and the child autonomous avoidances area was significantly larger than the limitation area, showing children’s tendency to explore outside the parental repertory. Children seemed to explore parents’ preferences area more than parents’ avoidances area: among the positive overlaps (reproductions), the one that

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reproduced parents’ preferences was broader than the one that reproduced his/her avoidances, and among the negative ones (limitation and opposition), the one that limited parents’ preferences was broader than the one that opposed his/her avoidances. Furthermore, the overlaps concerning the parents’ avoidances area were very narrow: the PAA area was the largest of all and it was considerably greater than the PAP area. The positive overlaps were greater than the negative ones (PR4L and AR4O), indicating that our participants were more inclined to imitate parental food repertories than to refuse them. An analysis of variance with repeated measures on the factor ‘‘areas’’ was performed for the random pairs as well. The results showed a main effect for the repeated factor, F(4.259) ¼ 31.134, po.001, Z2 ¼ .325: every area was significantly different from the others at list at the .01 level (except for the ARL and the CAACAP pairs, which are not significantly different). The same structure emerged from every comparison: CAP4O, po.001; CAA4L, po.001; PR4AR, po.001; L4O, po.01; PR4L, po.001; AR4O, po.001; PAA4PAP, po.001. However, as we anticipated, paired sample t-test showed that the overlapping areas between preferences and avoidances of the real parent–child dyads, with the exception of the opposition area, were significantly larger

Table 2 Correlation between areas size and children age (N ¼ 276)

Preferences reproduction area (PR) Avoidances reproduction area (AR) Limitation area (L) Opposition area (O) Child autonomous preferences area (CAP) Child autonomous avoidances area (CAA) Parent autonomous preferences area (PAP) Parent autonomous avoidances area (PAA) a

Pearson r

Sig. (2-tailed)

.005 .032 .133a .011 .001 .121a .092 .034

.938 .592 .027 .853 .986 .045 .126 .578

Correlation is significant at the .05 level (2-tailed).

Table 1 Means and standard deviation of overlap and autonomy areas both for parent–child and random dyads Parent-child dyads

Random dyads

Paired sample t-test

M

SD

M

SD

t

df

p

1.13 1.05 1.03 .60 1.42 1.29 1.21 1.41

1.11a .65b .55b .33c 4.34d 4.56e 5.01f 4.80e

.98 .82 .74 .58 1.14 1.09 1.08 1.10

5.037 4.068 2.394 1.012 5.965 5.100 3.865 4.642

260 260 260 260 260 260 260 260

.000 .000 .017 .312 .000 .000 .000 .000

‘‘Positive’’ overlap (resemblances) Preferences reproduction area (PR) 1.56a Avoidances reproduction area (AR) .97b ‘‘Negative’’ overlap (differences) Parental repertory limitation area (L) .72b Opposition (towards parental repertory) area (O) .38c Autonomy areas Parent’s autonomous Preferences area (PAP) 3.72d Child’s autonomous preferences area (CAP) 4.07e Parent’s autonomous avoidances area (PAA) 4.65f Child’s autonomous avoidances area (CAA) 4.30e

Note: In each column, means with different subscripts differ from each other by at least po.01. See paired sample t-test columns for the differences between real and random dyads.

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than that identified for the random dyads, t(260) ¼ 5.037, po.001 for the PR area, t(260) ¼ 4.068, po.001 for the AR area, and t(260) ¼ 2.394, po.05 for the limitation area. As far as the relationship between the structure illustrated and the age of the children is concerned, the cited mixed ANOVA design showed no overall effect due to the covariation of the children age. Nevertheless, we measured the single correlations between area sizes (both overlap and autonomy) and children age (see Table 2): two significant, though very weak, correlations emerged. The first involved the children’s age and the size of the limitation area, r(276) ¼ .133, po.05: as age increased, the number of foods that children refused among those particularly loved by their parents decreased. The second correlation involved the children’s age and the size of child autonomous avoidances area, r(276) ¼ .121, po.05: as age increased, the number of foods that children avoided and parents did not mention increased. Lastly, we checked the effects of some socio-demographic characteristics such as parents’ occupation, parents’ educational level, number of family members, and residence area, by including them as factors in a series of ANOVAs with children’s age as covariate. No significant effects were observed. Discussion Our findings allowed us to outline a descriptive model of the connection between parents’ and children’s food repertories, showing the overlaps between preferences and avoidances for the members of each dyad, and the autonomy areas. These results suggested that parental food repertories would have an anchoring function in the formation of those of the children, not only in terms of imitation, but also in terms of influencing the direction of differentiation and innovation. The comparison between the different areas indicated that the children were more inclined to imitate parental food repertory rather than to refuse it and that their food exploration was more directed outside the parents’ preferences area than towards the parents’ avoidances area, which remained relatively unexplored. The same structure emerged from the random pairs, showing that a shared cultural environment influences individuals’ preferences and avoidances. Nevertheless, the overlap areas regarding real dyads were greater than those of the random dyads (except for the opposition area). These findings indicate that this dynamic of imitation and exploration specifically concerned the parent–child relationship. Furthermore, our results supported the idea that similarities and differences linking parents’ and children’s food repertories are not simply due to a shared culture, but were, at least in part, specifically attributable to the shared family environment. This pattern of results is consistent with those of Pliner et al. (Pliner, 1983; Pliner & Pelchat, 1986), and go beyond

them, as the present study examined the multifaceted nature of the connection between parents’ and children’s preferences and avoidances. Both preferences and avoidances reproduction areas were significantly broader for the real dyads than for the random ones. Instead, the pattern pertinent to the negative overlap areas was differentiated: the limitation area was larger for the real parent–child dyads than for the random ones, while no significant differences were found for the opposition area. These findings suggest that the imitation of preferences and avoidances and the limitation of the parental repertory of loved foods were behaviours that children activated towards their parents specifically, while rebellion consisting in preferring what adults avoided could be observed towards adults in general. Moreover, the fact that the opposition area was the only one that did not differ significantly between real and random dyads additionally supported the idea that children did not push themselves to explore the parental avoidances area. It is interesting to note, however, that Birch (1980) obtained different results, probably due to the nature of the task used in her study (ranking of a limited number and variety of foods). Our findings differed in part from those reported by Skinner et al. (1998). In their research, for both mothers and fathers, the like–like agreement is the largest one (substantially different from the others), as it is the PR area in our study. Nevertheless, the other three overlaps were exactly in the opposite order compared with our findings (child likesparent dislikes4child dislikesparent likes4both dislikes VS AR4L4O). This difference is probably due to the research method adopted. We could expect that, using a traditional hedonic rating questionnaire, overlap areas size is dependent on the food items selected by researcher, while the open-ended question method we used may provide more stable results. Regarding the differences as a function of children’s age, our findings showed that as the children’s age increased, the negative overlap between their avoidances and parents’ preferences (limitation area) decreased, suggesting that children tended to accept a larger number of food items among those particularly loved by their parents, probably because they were acquiring ‘‘more adult-like’’ tastes. Lastly, the child’s autonomous avoidances area tended to increase, suggesting that, through the exploration outside the parental repertory, older children were encountering new foodstuffs which they found unpleasant (but not which they loved, thus preferences seemed more stable). Four limitations of our research should be noted. First, the sample was not statistically representative of the Italian adolescent population and this prevented us from being able to generalize the observed results. Second, we asked participants to list only three preferences and only three avoidances. Probably, if participants had been free to mention as many food items as they wanted, it would have been possible to produce a more complete picture. The third weakness concerns the fact that we involved only


one parent in the sample, omitting the complexity of family influence processes. Finally, in this study, we could not consider the variables that might affect the relationship between the structure observed and the children’s age. On the basis of previous research, we can identify these potentially intervening factors. First, the period of reciprocal exposure increases with children’s age and therefore it should produce rising levels of resemblance. Second, children need to achieve autonomy from their parents and to prove their loyalty to their peers while at the same time they need to maintain family ties (Chapman & Maclean, 1993; Grotevant & Cooper, 1998): the former should lead to a decrease in parental influence, while the latter should lead to its stability. Third, a double motivation underlies the changes in adolescents’ eating habits (mainly at the level of attitudes, consumption, and meal patterns): on the one hand, the need to express their own freedom from parental control and, on the other, the implementation of likes and life styles considered proper for adults. While the former motivation should imply a decrease in similarities as children grow up, the latter should result in their increase. It is also necessary to bear in mind that family influences are multidirectional; therefore, as parental influence on children’s eating habits decreases, children’s influence on their parents could increase, for example. Lastly, the social and cultural pressures that exalt thinness (Chapman & Maclean, 1993) could induce adolescents to prefer healthier foods, moving them closer to parents’ likes. Despite these limitations, our research contributes an overview of the possible connections between parents’ and children’s preferences and avoidances, going beyond the approach based just on correlation analysis. Consistently with the previous research, our findings highlighted quite small overlapping areas between preferences and avoidances of the participant dyads’ members. However, our results also underlined the fact that the relationship between food repertories is not simply a matter of resemblance between ‘‘hedonic ratings’’; this relationship is a far more complex structure and it is important to separate preferences and avoidances, and to consider not only the reproduction, but also the intersection between opposite sign areas and the autonomy zones. It remains to be seen whether our descriptive model can be generalized and whether its structure will remain valid should a wider range of preferences and avoidances, and also of likes and dislikes be considered. Further research is needed regarding the development of the observed structure as children grow up, possibly through a longitudinal study design, and the role of intervening factors. In addition, the relation between the various processes of reciprocal influence and the structure of intersections observed should be studied. Finally, it would be very interesting to consider both parents (and not just one), as well as any siblings, in order to obtain a more complete model of the entire family.

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Research Report

Experimental investigation of the effects of naturalistic dieting on bulimic symptoms: Moderating effects of depressive symptoms Katherine Presnella,, Eric Sticeb, Jennifer Tristanc a

Department of Psychology, Southern Methodist University, P.O. Box 750442, Dallas, TX 75275, USA b Oregon Research Institute, 1715 Franklin Boulevard, Eugene, OR 97403, USA c Department of Psychology, University of Texas at Austin, 1 University Station A8000, Austin, TX 78712, USA Received 1 February 2007; received in revised form 31 May 2007; accepted 1 June 2007

Abstract Prospective studies suggest that dieting increases risk for bulimic symptoms, but experimental trials indicate dieting reduces bulimic symptoms. However, these experiments may be unrepresentative of real-world weight loss dieting. In addition, the fact that most dieters do not develop eating disorders suggests moderating factors may be important. Accordingly, we randomly assigned 157 female intermittent dieters to either diet as they usually do for weight loss or eat as they normally do when not dieting for 4 weeks. Naturalistic dieting halted the weight gain shown by controls, but did not result in significant weight loss. Although there was no main effect of the dieting manipulation on bulimic symptoms, moderation analyses indicated that naturalistic dieting decreased bulimic symptoms among participants with initially low depressive symptoms. Results suggest that self-initiated weight loss dieting is not particularly effective, which appears to explain several discrepancies in the literature. Additionally, depressive symptoms may be an important determinant of bulimic symptoms that eclipses the effects of naturalistic dieting on this outcome. r 2007 Elsevier Ltd. All rights reserved. Keywords: Dieting; Weight loss; Eating disorder; Bulimia; Depression

Introduction Concerns that dieting contributes to the onset and maintenance of bulimia nervosa and obesity (Blundell, 1995; Fairburn, 1997; Polivy & Herman, 1985; Wilson, 1993) have generated controversy over the advisability of engaging in weight loss dieting (Brownell & Rodin, 1994; Lowe & Timko, 2004). Dieting has been defined as intentional and sustained restriction of caloric intake for the purposes of weight loss (Herman & Polivy, 1975; Laessle, Tuschl, Kotthaus, & Pirke, 1989; Wadden, Brownell, & Foster, 2002) and must result in a negative energy balance for weight loss to occur. Bulimia nervosa is associated with functional impairment and increases risk for future psychiatric disorders, adverse health outcomes, and obesity (Striegel-Moore, Seeley, & Lewinsohn, 2003; Wilson, Becker, & Heffernan, 2003). The potentially Corresponding author.

E-mail address: [email protected] (K. Presnell). 0195-6663/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.appet.2007.06.002

adverse effects of dieting are of added significance because low-calorie diets are currently the treatment of choice for obesity, now credited with over 110,000 deaths annually in the US alone (Flegal, Graubard, Williamson, & Gail, 2005). According to restraint theory (Polivy & Herman, 1985), dieters rely on cognitive controls rather than physiological cues to regulate food intake, which may leave them vulnerable to uncontrolled eating when these cognitive processes are disrupted or when dietary rules are violated. Polivy and Herman (1985) also suggested that dieting results in chronic hunger, which increases the likelihood of binge eating. Binge eating episodes may prompt intensified dieting efforts and the use of inappropriate compensatory behaviors that are intended to prevent weight gain, which may then develop into a self-maintaining binge-purge cycle in at-risk individuals (Fairburn, 1997). In support of these assertions, prospective studies indicate that individuals with high scores on dieting scales have a significantly increased risk of onset of bulimic symptoms (Field,

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K. Presnell et al. / Appetite 50 (2008) 91–101

Camargo, Taylor, Berkey, & Colditz, 1999; Stice & Agras, 1998; Stice, Killen, Hayward, & Taylor, 1998) and threshold or sub-threshold bulimia nervosa (Killen et al., 1994, 1996), as well as future increases in bulimic symptoms (Johnson & Wardle, 2005). Despite the seemingly compelling evidence linking dieting to bulimic symptoms, contradictory findings have emerged in experimental studies, which have the ability to rule out third-variable alternative explanations that may account for the effects from prospective studies. Randomized trials have found that assignment to a low-calorie diet results in significant reductions in binge eating among obese (Goodrick, Poston, Kimball, Reeves, & Foreyt, 1998; Reeves et al., 2001) and overweight (Klem, Wing, Simkin-Silverman, & Kuller, 1997) participants relative to assessment-only controls. Assignment to a low-calorie weight loss diet, relative to a waitlist condition, also resulted in significantly greater decreases in all of the symptoms of bulimia nervosa among non-disordered women (Groesz & Stice, 2007; Presnell & Stice, 2003) and among women with threshold and sub-threshold bulimia nervosa (Burton & Stice, 2006). Additionally, assignment to a lower-intensity weight maintenance diet resulted in significantly greater reductions in bulimic symptoms relative to assessment-only controls among non-disordered adolescent girls (Stice, Presnell, Groesz, & Shaw, 2005). The findings from these randomized experiments seem antithetical to restraint theory; however, it is possible that the behaviors that comprise monitored dieting programs are unrepresentative of dieting as it is practiced in the real world. Weight change is expected and monitored in these trials and the support and guidance offered in these programs may produce greater weight loss than occurs in dieting attempts conducted outside these programs. Monitored weight loss diets may also differ qualitatively from naturalistic weight loss attempts in terms of the types of weight loss behaviors used. Indeed, Wadden et al. (2004) recommended that researchers ascertain whether selfdirected weight loss influences binge eating to address this possibility. Although mounting evidence suggests that assigning individuals to a weight loss diet decreases bulimic symptoms, it is vital to investigate this relation in a randomized experiment that has manipulated real-world dieting to help further establish the conditions under which dieting shows an inverse relation to bulimic symptoms. Thus, the primary aim of the current study was to provide a more ecologically valid experimental test of the effects of naturalistic dieting on bulimic symptoms. Based on the experimental evidence that weight loss diets result in decreased bulimic symptoms, we hypothesized that naturalistic dieting would result in greater decreases in bulimic symptoms relative to a non-dieting control condition. We conducted an experimental psychopathology study that manipulated real-world weight loss dieting to determine whether it had an impact on bulimic symptoms because experiments offer a more powerful test of theories than

prospective studies, which are vulnerable to third-variable alternative explanations for any prospective effects (Hinshaw, 2002; Stice, 2002). Although many women engage in some form of dieting, only a small subset develop bulimia nervosa (Wilson, 2002), suggesting that individual difference factors may qualify the relation of dieting and bulimic symptoms. Therefore, investigating factors that may moderate the effects of dieting on bulimic symptoms could help elucidate the nature of this relation. Researchers have proposed that negative mood promotes binge eating because some individuals may eat in an effort to provide comfort or distraction from aversive emotions (McCarthy, 1990; Stice & Agras, 1998). Prospective studies suggest that depressed mood predicts weight gain and obesity onset (Goodman & Whitaker, 2002; Pine, Goldstein, Wolk, & Weissman, 2001). This tendency to eat in response to negative mood may particularly increase the risk for binge eating among dieters because negative mood is believed to distract them from their dietary vigilance (Ruderman, 1986). Indeed, experimentally induced negative effect has triggered eating in the laboratory among participants with elevated dietary restraint scores, but not among their lower-scoring counterparts (Cools, Schotte, & McNally, 1992; Heatherton, Herman, & Polivy, 1991). However, it is possible that lab-based mood inductions and eating behavior do not generalize to the type of experiences that characterize bulimia nervosa. In addition, none of these lab experiments manipulated dieting, which is a key limitation because the dietary restraint scales used in this literature are not valid measures of actual dietary restriction (Bathalon et al., 2000; Rolls et al., 1997; Stice, Fisher, & Lowe, 2004). Thus, a second aim of the current study was to examine whether depressed mood attenuates the effects of naturalistic weight loss dieting on bulimic symptoms. We hypothesized that the expected inverse relation between dieting and bulimic symptoms would be weaker for those with initially elevated depressive symptoms. Finally, there is evidence that dieters employ both healthy (e.g., reduced fat intake) and unhealthy (e.g., skipping meals) weight control behaviors (French, Perry, Leon, & Fulkerson, 1995). Accordingly, a third aim of the current study was to elucidate weight control behaviors employed by individuals when engaged in self-directed weight loss attempts and test whether these behaviors are associated with an actual energy deficit. Research suggests that self-identified dieters are relatively ineffective at achieving weight loss through self-directed attempts (French et al., 1994; Klesges, Isbell, & Klesges, 1992; Stice, Cameron, Killen, Hayward, & Taylor, 1999) and it has been asserted that individuals who express the need to diet may do so because they routinely overeat when not attempting to control their caloric intake (Lowe & Levine, 2005; Presnell & Stice, 2003). It is also important to document whether certain dieting behaviors are associated with increased bulimic symptoms.

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Method Participants Participants were 157 undergraduate women (M age ¼ 19.1, SD ¼ 3.7) with a history of weight loss dieting. We recruited women who self-reported periodic weight loss dieting to ensure that this population was representative of individuals who diet. We used this sampling frame rather than requiring that individuals respond above a cutoff on a self-report dieting scale because dietary restraint scales are not valid measures of dietary restriction (Bathalon et al., 2000; Rolls et al., 1997; Stice, Fisher et al., 2004). The mean body mass index (BMI) of participants was 23.6 kg/m2 (SD ¼ 3.2) at pretest, which corresponds to a healthy weight.1 The ethnic/racial composition of the sample was 69% Caucasian, 8% Asian/Pacific Islander, 4% Black, 13% Hispanic, and 6% who specified ‘‘other’’. Participants who met criteria for a current eating disorder were given a referral and excluded. Further exclusion criteria included: (a) a current physical or medical condition that may affect eating behavior (e.g., pregnancy or cancer), (b) use of a pacemaker or other internal electronic medical device, (c) current low weight, (d) use of Ephedra products as a means of dieting or losing weight, or (e) insufficient history of weight loss dieting (i.e., had not dieted at least once in the last year or dieting episode lasted less than 7 days). Four women were not eligible for the study after the initial screening due to either taking medication that affects appetite (n ¼ 1), use of products that contain Ephedra for dieting purposes (n ¼ 1), or insufficient history of dieting (n ¼ 2). Procedure The study was described as an investigation of dieting behaviors and its effects on concentration and mood. Participants were randomly assigned to a Diet as Usual condition or a Non-dieting control condition for a period of 4 weeks, and were told that we were monitoring electrolyte levels, but were not told that we were measuring their weight to minimize any reactive effects of being held accountable for weight change. Written informed consent was obtained prior to data collection. Participants were debriefed following the final assessment and written consent was obtained a second time to authorize use of participants’ data after they were informed of the true study purposes. No participants withheld their data. Participants completed assessments at pretest and at 4-week follow-up, including blinded structured interviews assessing bulimia nervosa symptoms. 1 The body mass index of participants ranged from 18 to 38; however, it should be noted that the effects of the Diet as Usual intervention were not moderated by initial body mass (B ¼ .004, ns, r ¼ .017). Age also did not moderate the effects of the dieting manipulation (B ¼ .032, ns, r ¼ .120).

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Participants were asked to describe their weight loss behaviors. Those assigned to the Diet as Usual condition were instructed to engage in the behaviors that they reported normally using when dieting to lose weight on a consistent basis during the study period (see Table 3). They were not instructed in effective weight control methods; rather, it was emphasized that they should employ the techniques they typically use when attempting to lose weight. Those in the Non-dieting control condition were asked to refrain from dieting (i.e., intentional caloric restriction) and eat as they normally do when not attempting to lose weight for a 4-week period. They received course credit for participation. The local Institutional Review Board approved this project. Measures Body mass index BMI (kg/m2) was calculated from directly measured height and weight. Height was measured using a portable direct reading stadiometer and weight and was assessed using a Tanita TBF-521 digital scale with participants’ shoes, socks, and coats removed. To provide an unobtrusive measure of weight, participants also completed a bioelectrical impedance assessment, which ostensibly measured electrolyte abnormalities for safety purposes.2 This device had a handheld readout, making it impossible for participants to determine that we were assessing their weight. BMI correlates with direct measures of total body fat such as dual energy X-ray absorptiometry (r ¼ .80–.90) and correlates with health measures including blood pressure, adverse lipoprotein profiles, atherosclerotic lesions, serum insulin levels, and diabetes mellitus (Dietz & Robinson, 1998; Pietrobelli et al., 1998). Manipulation check In order to assess the degree to which participants’ dieting behaviors resembled how they normally diet to lose weight, they were asked, ‘‘To what extent did the behaviors you engaged in over the past 4 weeks represent how you usually diet?’’ Participants responded on a 5-point scale ranging from ‘‘Not at all’’ to ‘‘Extremely’’. Bulimic symptoms The seven diagnostic items from the Eating Disorder Examination (EDE; Fairburn & Cooper, 1993), a structured psychiatric interview, assessed DSM-IV symptoms of 2 Although we collected body fat percentage using bioelectrical impedance, this was done to provide a cover story to participants to minimize any potential reactive effects of being monitored. We do not report the effects of the dieting manipulation on body fat percentage because several factors that can significantly impact estimates of bioelectrical impedance (such as hydration level, time of day, and phase in the menstrual cycle) that were not carefully controlled in the current study may have introduced error into this measurement. Thus, we did not have confidence in the accuracy of our body fat estimate and therefore did not feel it would be appropriate to report.

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bulimia nervosa (American Psychiatric Association, 2000). Female assessors attended 24 h of training, wherein they received instruction in structured interview skills, reviewed diagnostic criteria for relevant disorders, observed simulated interviews and role-played interviews. Assessors demonstrated high inter-rater agreement (kappa [k]4.80) with experts using tape-recorded interviews before collecting data. Items assessing frequency of binge eating and compensatory behaviors and overvaluation of weight and shape were averaged to form a past month bulimic symptom composite for each assessment, as done in previous studies (Presnell & Stice, 2003; Stice, Burton, & Shaw, 2004; Stice, Shaw, Burton, & Wade, 2006). Because the symptom composite was skewed, a normalizing log10 transformation was applied (untransformed means are reported in tables). In past studies this EDE symptom composite showed internal consistency (a ¼ .92–.96), temporal reliability (k ¼ .96 for diagnoses, r ¼ .90 for symptom composite), inter-rater reliability (k ¼ .86 for diagnoses), sensitivity to detecting intervention effects, and predictive validity for future onset of depression (Burton & Stice, 2006; Presnell & Stice, 2003; Stice, Burton et al., 2004; Stice et al., 2006).

regression models. An interaction term was computed for experimental condition and the moderator, with condition dummy-coded (0 ¼ Non-dieting, 1 ¼ Diet as Usual), to test the moderating effects of depressive symptoms. Predictor variables were mean centered before computing the interaction term, as recommended by Aiken and West (1991). Posttest bulimic symptoms were regressed on the main effects of condition and pretest depressive symptoms and on the condition-by-depression interaction term, controlling for pretest bulimic symptoms. Simple slope analyses probed the form of significant interactions at 1 SD above and below the mean of the moderator, following Aiken and West (1991). Finally, a linear regression model examined the effect of self-reported dieting behaviors on posttest BMI, controlling for pretest BMI. Chi-square analyses were performed on each self-reported dieting behavior to test whether a significantly greater proportion of individuals in the Diet as Usual condition reported engaging in each behavior relative to Non-dieting controls (see Table 3). Results Preliminary analyses and descriptive statistics

Depressive symptoms Depressed mood was assessed with the Beck Depression Inventory (BDI; Beck, Ward, Mendelson, Mock, & Erbaugh, 1961). The BDI is a 21-item self-report scale in which each item is rated from 0 to 3 and summed for a total score. The BDI has acceptable internal consistency (a ¼ .73–.95), reliability (test–retest r ¼ .60–.90) and convergent validity with clinician ratings of depressive symptoms (M r ¼ .75; Barrera & Garrison-Jones, 1988; Beck, Steer, & Garbin, 1988). Weight control behaviors Items were adapted from a checklist developed by French, Jeffery, and Murray (1999) to assess specific dieting behaviors as a means of gaining an understanding of what strategies are employed when individuals engage in weight loss dieting. Participants indicated whether they regularly used 16 weight control behaviors over the study period (e.g., ‘‘Skipping breakfast’’ and ‘‘Eating less fat’’). Data analyses Independent t-tests and Chi-square tests were performed on all continuous and dichotomous demographic and outcome variables (respectively) to test for differences between groups at pretest to ensure that randomization resulted in equivalent groups. Repeated measure analysis of variance (ANOVA) models tested whether participants in the Diet as Usual condition showed greater pre- to postchanges in the outcomes relative to Non-dieting control participants; paired t-tests evaluated whether there were significant pre- to post-changes in outcomes in each condition. Moderator effects were tested using multiple

Participants assigned to the Diet as Usual and Nondieting conditions did not differ significantly on ethnicity, age, parental education, BMI, or bulimic symptoms at pretest. Although self-report dieting scales do not correlate with objective measurements of caloric intake (Bathalon et al., 2000; Rolls et al., 1997; Stice, Fisher et al., 2004), we examined whether there were any group differences in baseline levels of self-reported dietary restraint to address concerns that some individuals may have been currently dieting at study initiation. There were no significant differences between groups on self-reported dietary restraint at baseline, t (155) ¼ .11, p ¼ .91. Only one participant (.6%) dropped out of the study before completion, indicating that attrition should not bias the results. Table 1 presents the means and standard deviations of the demographic variables across experimental conditions at pretest. A manipulation check verified that participants in the Diet as Usual condition reported significantly more days of dieting during the study period than those in the Nondieting group, t(154) ¼ 13.48, po.001, d ¼ 2.18. The former also indicated that the behaviors they engaged in over the study period were ‘‘Moderately’’ representative of how they usually diet (M ¼ 2.94, SD ¼ 1.12). As hypothesized, there were significantly different changes in BMI in the Diet as Usual condition from pre- to post-relative to the Non-dieting condition, as indicated by the significant time-by-condition interaction (po.05). Paired t-tests revealed that there was a significant increase in BMI from pre to post for participants in the Non-dieting condition, t(72) ¼ 2.10, po.05, and that the change in BMI from pre to post in the Diet as Usual condition was not statistically significant, t(82) ¼ .89, p ¼ .38. This

ARTICLE IN PRESS K. Presnell et al. / Appetite 50 (2008) 91–101 Table 1 Demographic characteristics for the Diet as Usual and Non-dieting control participants Pretest demographic characteristic

Diet as Usual (n ¼ 84)

Non-dieting (n ¼ 73)

Age (years) M SD

19.14 4.45

19.12 2.74

Body mass index (BMI) M SD

23.65 3.34

23.48 3.04

Beck Depression Inventory (BDI) M 8.98 SD 6.73

8.88 6.09

Ethnicity (%) African-American Asian Caucasian Hispanic Native American ‘‘Other’’ or mixed racial heritage

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symptoms (B ¼ .15, po.05, r ¼ .17), but non-significant at 1 SD above the mean of depressive symptoms (B ¼ .12, p ¼ .11, r ¼ .13).3 That is, there was a significant reduction in bulimic symptoms in the naturalistic dieting condition relative to controls for individuals with low baseline depressive symptoms, but not for those with high baseline depressive symptoms. Fig. 1 depicts these results. Weight control strategies and their effects on weight loss

6.0 2.4 72.6 13.7 0 6.8

1.4 5.5 66.7 6.0 0 4.8

corresponded to a mean weight gain of .16 kg in the Nondieting condition and a slight but non-significant increase of .09 kg in the Diet as Usual condition. The proportion of participants who gained versus lost or maintained their weight over the study period was 60.3% and 39.7% in the Non-dieting condition and 47.6% and 51.3% in the Diet as Usual condition, respectively. Main effects of experimental manipulation of naturalistic dieting on bulimic symptoms Change in the bulimic symptom composite from pre to post did not significantly differ for the two groups (p ¼ .76); rather, both groups showed a significant decrease in bulimic symptoms over the study. Separate examination of each of the three symptom domains of bulimia nervosa again revealed no significant Time Condition interactions from pre to post on binge frequency (p ¼ .76), compensatory behavior frequency (p ¼ .99), or overvaluation of shape and weight (p ¼ .52). Means and standard deviations for the outcomes in the Diet as Usual and Non-dieting groups are reported in Table 2, with results from the repeated measure ANOVA models. Moderating effect of depression on the relation of naturalistic dieting to bulimic symptoms A second aim was to examine whether depressed mood moderated the effect of the dieting manipulation on change in bulimic symptoms. There was a significant condition-bydepressive symptoms interaction (B ¼ .02, po.05, r ¼ .21) on change in bulimic symptoms. As hypothesized, the relation between condition and change in bulimic symptoms was significant at 1 SD below the mean of depressive

To further characterize naturalistic dieting, the specific weight control strategies used by participants in the Diet as Usual condition over the study period were investigated. Participants in the Diet as Usual condition reported greater frequency of skipping lunch, eating less at mealtime, reducing fat intake, snacking less, avoiding eating after a certain time of day, eating many small meals throughout the day, increasing fruit and vegetable consumption, and increasing exercise than participants in the Non-dieting condition (see Table 3). However, the only dieting behavior that emerged as a significant predictor of weight loss over the study in the Diet as Usual condition was eating less at mealtime (OR ¼ 4.13, 95% CI ¼ 1.62–10.57, p ¼ .003). None of the dieting behaviors significantly predicted future change in bulimic symptoms. Discussion The aims of the current study were to examine the effect of a naturalistic dieting manipulation on bulimic symptoms, test whether depressive symptoms moderated these effects and to explore the dieting behaviors employed by individuals engaging in self-directed weight loss attempts. A manipulation check verified that dieting was successfully manipulated, as participants showed a significantly different slope in their weight trajectory, though this took an unexpected form. Participants in the Non-dieting condition gained weight over the study period, whereas there was no significant change in BMI for participants in the Diet as Usual condition (i.e., they did not lose or gain a significant amount of weight). Although these findings were unanticipated, they do accord with evidence that self-labeled dieters, as a group, have either shown no weight loss over time (Heatherton et al., 1991; Klesges, Klem, Epkins, & Klesges, 1991) or increases in weight over time (Field et al., 2003; French et al., 1994; Klesges et al., 1992; Stice et al., 1999; Stice, Presnell, Shaw, & Rhode, 2005) relative to selflabeled non-dieters. The magnitude of the weight gain in the Non-dieting condition (.16 kg per month) is comparable to that of one study that found an average weight gain of .21 kg per month among formerly dieting college females (Lowe et al., 2006), but is more than the average weight 3 In the current sample, the mean BDI score was 8.93 (SD ¼ 6.42), which indicates minimal depressive symptoms. One standard deviation above the mean would correspond to a mild level of depressive symptoms, and one standard deviation below the mean would represent normal mood.


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Table 2 Means and standard deviations for the comparisons between the Diet as Usual and the Non-dieting control conditions, and results from the repeated measure analyses of variance Measure

Pretest

Posttest

Time Condition interaction

M

SD

M

SD

Body mass index Non-dieting Diet as Usual

23.48a 23.65

3.04 3.34

23.59b 23.63

3.05 3.37

F(1,154) ¼ 4.27, po.05, Z2 ¼ .027

Bulimic symptoms Non-dieting Diet as Usual

5.64 6.10

4.76 5.46

3.96 4.47

3.53 4.91

F(1,154) ¼ .12, p ¼ .73, Z2 ¼ .001

Binge frequency Non-dieting Diet as Usual

.27 .54

.84 2.09

.05 .10

.28 .46

F(1,154) ¼ .18, p ¼ .67, Z2 ¼ .001

Compensatory behavior frequency Non-dieting 1.66 Diet as Usual 1.68

4.01 3.00

.82 1.43

3.18 4.24

F(1,154) ¼ .07, p ¼ .79, Z2 ¼ .001

Overvaluation of shape and weight Non-dieting 3.44 Diet as Usual 3.39

1.08 1.33

3.03 2.84

1.19 1.45

F(1,154) ¼ .42, p ¼ .52, Z2 ¼ .003

Note: Means with different superscripts are significantly different at po.05.

Change in Bulimic Symptoms

0 1 SD below BDI mean

1 SD above BDI mean

-0.05 -0.1 -0.15 -0.2 -0.25 -0.3

Fig. 1. Plot of the simple slopes for the relation between dieting condition and change in bulimic symptoms (EDE interview) at 1 SD above and below the mean on depressive symptoms (BDI). m: Non-dieting, K: Diet as Usual.

gain of .08 kg per month documented in normative samples of young women (Kuczmarski et al., 2000). Nonetheless, participants in the Diet as Usual condition did not show the weight gain observed in the Non-dieting condition, providing experimental evidence that individuals who express the need to diet for weight loss purposes show a weight gain trajectory over time that is temporarily arrested through dietary efforts. However, participants did not show the weight loss typically observed in monitored weight loss treatment trials or experimental studies that manipulated weight loss dieting. One interpretation of this finding is that individuals who are attempting to lose weight may perceive their dieting to be more effective than it actually is—that is, they are eating less than they would like to eat, but do not reduce their

caloric intake sufficiently to achieve a negative energy balance (Lowe & Levine, 2005). From this perspective, real-world dieting might be better conceptualized as a modulation of a habitual overconsumption tendency, rather than a dietary intake that results in a negative energy balance necessary for weight loss. This overconsumption tendency may be rooted in greater reinforcement from eating, expectancies that eating serves a positive function (e.g., affect regulation), deficits in self-control, responsivity to food cues, or insensitivity to satiation. The present data appear to provide the first experimental evidence for the assertion that naturalistic weight loss dieting does not typically result in weight loss. These findings may help to explain several vexing findings from the literature. First, our results suggest that real-world weight loss dieters are temporarily halting an overeating tendency and are therefore temporarily consuming a similar number of kilocalories as non-dieters. This conceptualization may explain why individuals with elevated dietary restraint scale scores usually gain more weight over time than their counterparts with lower scores (French et al., 1994; Klesges et al., 1992; Stice et al., 1999), and why they do not consume fewer calories than those with lower scores according to objective and unobtrusive measures of intake (Bathalon et al., 2000; Rolls et al., 1997; Stice, Fisher et al., 2004). Second, our results may explain why experimental studies that induce true caloric-deficit diets produce different findings than prospective studies; as suggested by the validity studies that used objective measures of caloric intake, the prospective studies do not appear to have investigated the effects of true caloric-deficit weight loss diets. The current findings might be considered


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Table 3 Comparison of weight control strategies reported by participants in the Diet as Usual versus Non-dieting control condition Strategy

Skip breakfast Skip lunch Skip dinner Eat less at mealtime Decrease fat intake Snack less Avoid eating late in day Smoking Drink more water Reduce calories Eat many small meals Increase fruits/vegetables Take diet supplements Reduce sugar Increase exercise Other

Diet as Usual

Non-dieting

Difference in frequencies

n

%

n

%

w2 (1,156)

36 25 13 49 59 61 42 2 57 40 21 35 7 20 31 10

42.9 29.8 15.5 58.3 70.2 72.6 50.0 2.4 67.9 47.6 25.0 41.7 8.3 23.8 36.9 11.9

23 12 9 27 39 33 22 4 42 30 6 20 2 13 10 5

31.5 16.4 12.3 37.0 53.4 45.2 30.1 5.5 57.5 41.1 8.2 27.4 2.7 17.8 13.7 6.8

2.33 4.02 .36 7.56 5.19 12.98 6.72 .99 2.08 .79 7.92 3.71 2.32 .92 11.21 1.21

po.05.

more ecologically valid than the experimental and treatment trials because participants did not undergo a prescribed weight control intervention. Main effects of the experimental manipulation of naturalistic dieting on bulimic symptoms Contrary to hypotheses, there was no significant main effect of the dieting manipulation on change in bulimic symptoms. Indeed, both groups showed decreases in bulimic symptoms. The finding that participants in the Diet as Usual condition did not show significantly greater reductions in bulimic symptoms than those in the Nondieting condition departs from findings from randomized experiments and treatment trials which found that assignment to a weight loss diet resulted in significantly greater decreases in bulimic symptoms than assignment to a waitlist control condition (Burton & Stice, 2006; Groesz & Stice, 2007; Presnell & Stice, 2003). This may have resulted because naturalistic dieting is less effective, on average, than the dietary modifications prescribed by weight loss interventions and it therefore exerts a much weaker effect on decreases in bulimic symptoms than an effective weight loss diet. However, another study found that a weight maintenance diet produced significantly greater decreases in bulimic symptoms relative to assessment-only controls, despite no significant changes in weight among the weight maintenance group (Stice, Presnell, Shaw, & Rhode, 2005). Nonetheless, it is important to note that there was no evidence that dieting as usual resulted in increases in bulimic symptoms, despite adequate power to detect such an effect (we had a power of .83 to detect a medium effect size). In other words, the dietary behaviors used by participants in the current study did not appear to

undermine their control over eating or render them vulnerable to disinhibited eating, as suggested by restraint theory (Polivy & Herman, 1985). Collectively, there is mounting evidence that dietary control reduces, rather than promotes, binge eating (Goodrick et al., 1998; Klem et al., 1997; Reeves et al., 2001) and bulimic symptoms (Burton & Stice, 2006; Groesz & Stice, 2007; Presnell & Stice, 2003; Stice, Presnell, Groesz et al., 2005; Stice, Presnell, Shaw et al., 2005). Thus, results from experiments investigating the effects of weight loss dieting, weight maintenance dieting, and naturalistic dieting imply it may be useful to help individuals diet more effectively and sustain these behaviors over the long term. Moderating effect of depression on relation of naturalistic dieting and bulimic symptoms As predicted, depressed mood moderated the effects of dieting on bulimic symptoms. Results suggested that naturalistic dieting does produce significant reductions in bulimic symptoms, but only for individuals without elevated depressive symptoms. Thus, the beneficial effect of dieting that has emerged in other studies was only evident for participants with low depressive symptoms. This finding appears consistent with the general hypothesis that people with affective disturbances may binge eat to alleviate negative emotions (McCarthy, 1990) and that negative affect might disrupt the ability to conform to cognitive rules regarding dietary restriction (Ruderman, 1986). It also accords with findings from prospective studies that depressed mood predicts weight gain and obesity onset (Goodman & Whitaker, 2002; Pine et al., 2001). In addition, depressive symptoms such as increased appetite, hopelessness about the future and one’s ability to effectively change one’s situation or environment, or

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feelings of failure and low self-worth may also contribute to increased binge eating. Weight control strategies and their effects on weight loss To characterize naturalistic dieting, we asked participants to self-report their weight control behaviors. Participants reported using various behaviors to lose weight, including both healthy (e.g., reducing the amount of fat consumed) and ostensively unhealthy (e.g., skipping meals) behaviors. Results were consistent with some prior research on dieting behaviors, although some studies found that among adults, increased exercise is a more prevalent weight control practice, with 82–83% of adults endorsing this strategy (French et al., 1999; Levy & Heaton, 1993). Only one-third of participants in the Diet as Usual condition reported increasing their exercise as a means of losing weight in the present study. Most commonly, individuals reported reduced frequency of eating between meals or reduced portion sizes at meals. This may indicate that such individuals are consuming excess calories through snacking and oversized portions when not dieting. Of note, skipping meals also emerged as one of the most frequently endorsed behaviors in the current study, with over half of the participants in the Diet as Usual condition indicating that they regularly skipped at least one meal as a means of losing weight. This finding is consistent with other research that reported skipping meals as the most prevalent unhealthy weight reduction technique among adolescent females, with nearly 50% indicating that they use this as a method of weight loss (Grigg, Bowman, & Redman, 1996; Paxton, Wertheim, & Gibbons, 1991), although one study found that meal skipping was less prevalent among adults (French et al., 1999). Interestingly, nearly 40% of participants who were not actively dieting reported skipping meals over the study period, suggesting that these behaviors may be common among this population in both the presence and absence of weight loss attempts. The fact that none of these strategies predicted weight loss except for eating less at meals implies that dieting behaviors used by individuals who diet on their own are not particularly effective or that self-reported dieting behaviors have questionable validity. Furthermore, none of the dieting behaviors predicted increased bulimic symptoms, which suggests that even those that are typically regarded as unhealthy do not appear to confer increased risk of disordered eating. Limitations It is important to note the limitations of this study. First, it could be argued that some participants may have been currently dieting at study initiation and that the results may not reflect the effects of initiating a diet. However, random assignment to condition should ensure that any such individuals were evenly distributed across conditions (i.e., is not confounded with condition). Indeed, preliminary analyses revealed no differences between groups on baseline self-report dieting scores. Moreover, there is no

evidence that self-reported current dieting status is a valid indicator of caloric intake, or that those who endorse current dieting eat any differently than those who do not endorse current dieting.4 Second, it could be argued that our manipulation provided limited motivation for participants to diet effectively. However, the intent of this study was to experimentally manipulate dieting in an ecologically valid manner; most individuals who choose to engage in weight loss attempts on their own are not accountable for their behaviors or degree of weight loss (Levy & Heaton, 1993). Additionally, the manipulation represented an ecologically valid period of dieting, as research has suggested that a typical diet lasts between four to six weeks (Emmons, 1992; French et al., 1999; Williamson, Serdula, Anda, Levy, & Byers, 1992). Because participants indicated that their behaviors during the study were only ‘‘moderately’’ representative of their typical dieting, however, findings should be interpreted with care. Although we do not know how their behavior differed from their usual dieting attempts, it is possible that they were restricting their intake to a greater extent than they normally would because they were participating in a research study that asked them to diet. Focusing on late adolescent females is appropriate because this is the peak risk period for the onset of bulimic pathology (Stice et al., 1998); nevertheless, results should be generalized with caution to younger or older populations. Finally, the current study did not include a follow-up assessment, so the effects of discontinuing naturalistic weight control efforts cannot be determined. Despite these limitations, the study had several strengths, including utilizing an experimental design, the use of blinded diagnostic interviews to assess bulimic symptoms and a cover story to blind participants to the study hypotheses. To our knowledge, this is the first study to experimentally manipulate real-world dieting, thus providing important information regarding its effects on weight change and bulimic symptoms. Clinical implications and future directions The finding that dieting as usual is ineffective in producing weight loss converges with previous findings that indicate that naturalistic weight control efforts are not associated with reductions in weight (Field et al., 2003; French et al., 1994; Klesges et al., 1992; Stice et al., 1999; Tanofsky-Kraff et al., 2006) and imply that individuals may need to learn to control their intake more effectively. The high rates of obesity underscore the importance of 4 Nonetheless, we also tested whether baseline scores on the Dutch Restrained Eating Scale (van Strien, Frijters, van Staveren, Defares, & Deurenberg, 1986) moderated the effects of the experimental manipulation on change in bulimic symptoms to address concerns that some individuals may have been terminating a diet whereas others were initiating a diet, which may result in differential effects on bulimic symptoms. Results indicated that initial level of self-reported dieting behaviors did not moderate this relation (B ¼ .069, ns, r ¼ .081), providing little support for this possibility.


gaining a better understanding of the specific behaviors that relate to successful weight loss over the long term in order to guide weight loss interventions. One direction for future research is to identify specific dieting behaviors that predict successful weight loss or weight maintenance, versus weight gain over time, as well as those that result in increases or decreases in bulimic symptoms. Because most dieters do not employ a single method of weight control at a time, research that examines combinations of dieting behaviors would also be useful. It may also be possible to combine prevention efforts for eating disorders and obesity (Irving & Neumark-Sztainer, 2002), particularly given that similar weight management strategies that have been demonstrated to reduce bulimic symptoms have also been shown to decrease future risk for onset of obesity (Stice, Presnell, Groesz et al., 2005; Stice, Presnell, Shaw et al., 2005). Indeed, the rise in the prevalence of obesity in the United States and the accompanying health risks argue that a greater proportion of the population should be encouraged to engage in sustained weight loss efforts. However, the current findings suggest that it might be useful to help individuals with depression learn more adaptive affect regulation skills before attempting to promote weight loss dieting. Our results suggest that dieting may not reduce bulimic symptoms unless individuals have low levels of depressive symptoms, as it seems to interfere with the ability to control overeating tendencies. The pervasiveness of dieting among females in Western culture and its potential relation to bulimic symptoms and other negative outcomes underscore the importance of providing more rigorous tests of these relations. Moreover, the health compromising effects of obesity also warrant investigation into the advisability of engaging in weight control practices. If dieting does not produce deleterious effects, then a substantial portion of the population should be encouraged to engage in weight control practices as a means of reducing the health risks resulting from excess weight, given that obesity and obesity-related complications are more prevalent and account for even higher mortality than eating disorders (Fontaine, Redden, Wang, Westfall, & Allison, 2003). Acknowledgments We wish to extend our sincere appreciation to the project research assistants, the participants who made this study possible, and to Sarah Kate Bearman for her input on earlier versions of this manuscript. Data from this study were presented in part at the meetings of the 2005 Eating Disorder Research Society and the 2006 Society of Behavioral Medicine. References Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage.

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ARTICLE IN PRESS K. Presnell et al. / Appetite 50 (2008) 91–101 van Strien, T., Frijters, J. E. R., van Staveren, W. A., Defares, P. B., & Deurenberg, P. (1986). The predictive validity of the Dutch Restrained Eating Scale. International Journal of Eating Disorders, 5, 747–755. Wadden, T. A., Brownell, K. D., & Foster, G. D. (2002). Obesity: Responding to the global epidemic. Journal of Consulting and Clinical Psychology, 70, 510–525. Wadden, T. A., Foster, G. D., Sarwer, D. B., Anderson, D. A., Gladis, M., Sanderson, R. S., et al. (2004). Dieting and the development of eating disorders in obese women: Results of a randomized controlled trial. American Journal of Clinical Nutrition, 80, 560–568.

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Research Report

Emotional responses to food, body dissatisfaction and other eating disorder features in children, adolescents and young adults Caroline McNamaraa, Phillipa Haya,, Mary Katsikitisb, Anna Chur-Hansenc a

Discipline of Psychiatry, School of Medicine, James Cook University, Townsville, Qld 4811, Australia School of Social Sciences, University of the Sunshine Coast, Maroochydore DC QLD 4558, Australia c Department of Psychiatry, University of Adelaide, North Terrace, Adelaide, SA 5001, Australia

b

Received 11 January 2007; received in revised form 16 April 2007; accepted 1 June 2007

Abstract We aimed to assess and compare emotional responses to different foods in relationship to eating disorder and associated features, across gender and age groups. We hypothesized that negative emotional responses to images of foods would be higher in (i) those with higher body dissatisfaction and (ii) older females. Five hundred and thirty-six (18% Grade 5, 39% Grade 8 or 9, and 43% Grade 11 or 12) school, and 93 university students participated. Emotive responses to images of foods were assessed with a PowerPoint presentation of 16 differing food and four ‘neutral’ images shown over 30 s intervals. Responses were rated on three 10-cm visual analog scales measuring levels of happiness, fear and disgust. Body image concern was assessed with the nine-item body dissatisfaction subscale of the EDI and eating disorder symptoms with the Eating Disorder Examination Questionnaire. With increasing age all three emotional responses towards food fell and body dissatisfaction increased. Compared to females, males showed significantly higher levels of a ‘happy’ response to food, and in adult females a fear emotive response correlated positively with eating concern and body dissatisfaction. In men, positive emotive responses to food may be indicative of broader factors that reduce their vulnerability to eating disorders. r 2007 Elsevier Ltd. All rights reserved. Keywords: Anxiety; Disgust; Happiness; Food; Body dissatisfaction; Eating concern; Normal development; Gender

Introduction Central to an eating disorder are sufferer’s thoughts, behaviors and feelings around food, and the impact of the former on weight and body image. Cognitive and behavioral aspects of eating disorders have been widely recognized as important to the identification, theoretical understanding and development of treatments. For example, key diagnostic criteria for bulimia nervosa (American Psychiatric Association, 1994) comprise cognitive and behavioral features and a specific cognitive behavioral therapy is recognized as having the ‘best evidence’ of any Corresponding author. School of Medicine, University of Western Sydney, Building 3, Room 2.04, Campbelltown Campus, Australia. E-mail addresses: [email protected] (P. Hay), [email protected] (M. Katsikitis), [email protected] (A. Chur-Hansen).


treatment for bulimia nervosa (National Institute for Clinical Excellence, 2004). The contribution of emotional features of eating disorders has had relatively less attention although the DSM-IV criteria for anorexia nervosa include an ‘intense fear of gaining weight’ as a key feature, and there is a wealth of literature addressing specific emotional states for people with eating disorders. For example, many studies have reported higher rates of alexithymia, particularly in anorexia nervosa (Troop, Schmidt, & Treasure, 1995; Zonnevijlle-Bender, van Goozen, Cohen-Kettenis, van Elburg, & van Engeland, 2004) but also in obese women with binge eating disorders (Pinaquy, Chabrol, Simon, Louvet, & Barbe, 2003). (Alexithymia as applied by Troop et al. (1995) and others is a psychological construct, denoting an inability to identify and express emotions, an inability to distinguish between emotional states and physical sensations, to be concrete and utilitarian in speech

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and thought, and to have a paucity of fantasy; Sifneos, 1973.) Negative emotions such as shame, guilt and disgust around binge eating and purging behaviors (e.g. Davey, Buckland, Tantow, & Dallos, 1998; Pinaquy et al., 2003) and their reinforcement of the diet–binge–purge cycle (Fairburn, 1981) have also been extensively investigated. In addition, high rates of co-morbidity with mood and anxiety disorders are reported (Blinder, Cumella, & Sanathara, 2006; Kaye, Bulik, Thornton, Barbarich, & Masters, 2004). In contrast, there are fewer studies of direct emotional responses to foods in people with eating disorders. Davey et al. (1998) found that disgust sensitivity was related to eating disorder symptoms in 170 female students, particularly with regard to foodstuffs of animal origin. Ellison et al. (1998) have assessed the emotive responses of six anorexia nervosa patients to imagining the drinking of high calorie drinks, and found patients were significantly more anxious when doing this than controls. Neither of these studies assessed emotive responses to images of food or actual foods. Troop, Murphy, Bramon, and Treasure (2000) and Harvey, Troop, Treasure, and Murphy (2002) have investigated disgust sensitivity to foods in people with eating disorders. In their initial study, they found a positive and significant correlation between self-reported ‘drive for thinness’ and disgust sensitivity to foods on the Disgust Sensitivity Questionnaire, and a trend to higher levels of disgust sensitivity to foods in 74 eating disorder patients compared to 15 female control participants. Their later study (Harvey et al., 2002) investigated disgust using a range of 18 visual images (three of high-calorie ‘foods’: sausages and chips, alcohol and milky drinks) in 40 nonclinical females. The study found those with higher selfreported eating disorder symptoms had higher rates of both fear and disgust to the high calorie foods than those with low levels of eating disorder symptoms. Stormark and Torkildsen (2004) have also found that women with eating disorders had a delayed response to identifying the colors that a range of words (including food words) were typed in, and background colors of (i) anticipated unemotive neutral images (including pictures of a hammer, a vacuum cleaner, a house and a sofa) and (ii) anticipated emotive images (including pictures of people having a meal, cheese, a cream puff and a dinner plate). In sum, negative emotions, such as disgust and fear, have been found to mediate responses to food in people with eating disorders, and both anxiety about food and eating and fear of weight gain are important clinical features of eating disorders. Thus, we chose to examine the specific emotions of fear (evoking a sense of tension or nervousness) and disgust (evoking a sense of aversion and dislike) as these are basic emotions (Plutchik, 1990; Power & Dalgleish, 1997) that are of most direct clinical relevance, and previous study in the area of eating disorders and sufferers responses to food. In addition, we chose to study happiness as it is also a basic but positive emotion. We were interested to include a positive emotion as a contrast

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to the negative emotive responses, and in the clinical setting a goal of therapy, namely to promote eating food as a normal and pleasurable activity, and not merely to remove negative affect around foods. Effects of gender or age on emotive responsiveness in eating disorders have also been little studied. ZonnevijlleBender, van Goozen, Cohen-Kettenis, van Elburg, and de Wildt et al. (2004) found no differences in levels of alexithymia, depression or anxiety between 48 adolescents (mean age 15.1 years) and 23 adults (mean age 21.3 years) with anorexia nervosa. We therefore aimed to examine the emotional responses to images of food in relationship to body image concerns across age and gender groups in children, adolescents and young adults. We chose this age range as eating disorder problems are known to emerge most commonly in adolescence and young adulthood (e.g. Hay, 2003). In addition, as disordered eating occurs more frequently in women (Carlat & Camargo, 1991; Garfinkel et al., 1995; Woodside et al., 2001) and the overweight (Darby, Hay, Mond, Rodgers, & Owen, 2007; Haines & Neumark-Sztainer, 2006), we evaluated emotive responses by gender and body mass index (BMI) as well as age group. We hypothesized that negative emotive responses to foods would be greater in those with more body image concerns and eating disorder symptoms, that these associations would also be more likely present in older participants, that levels of negative emotions in response to images of foods would be higher in females than males and that the reverse would hold for a positive emotion. We anticipated that type of school may potentially bias the results (Dyer & Tiggemann, 1996; Tiggemann, 2001) and thus we studied students from a range of school types and conducted secondary analyses on single gender compared to coeducational schools and high or moderate fee versus low fee schools, controlling for gender and age of students.

Methods Overview Students from years 5, 8 and 11 from a range of schools in South Australia were approached by their teachers with a letter to take home to their parents explaining the study and asking them to sign a consent form to return to the school and then to the investigators. Nine schools participated, representing a range of gender mix, high and low fee schools. (In Australia low fee schools have 50–100% government support, higher fee schools have o50% government support; Department of Education Science and Training, 2004.) Two first year, first semester, undergraduate nursing and psychology university classes also participated. They attended a regional university with a high (23.4%) and higher than national average (14.5%) percent of students of lower socio-economic origin (James Cook University, 2003).


C. McNamara et al. / Appetite 50 (2008) 102–109

Procedure Trained fourth year medical students administered the study instruments and collected the data. They came to the schools and university classes on agreed days and times, collected the consent forms and administered the PowerPoint slide show and collected the responses. Participants marked in booklets their emotion ratings on visual analogs scales and completed a questionnaire on body image. Older participants (university students) also completed a questionnaire on eating disorder symptomatology. Where younger (Grade 5) children did not recognize a word or understand an item on the questionnaires a brief verbal explanation was given. Assessment instruments A set of 20 PowerPoint slides of a range of foods and four non-food images (available on request) were presented to each participant at half-minute intervals. Participants rated their level of fear, happiness and disgust, for each image on three 10-cm visual analog scales (60 VAS in total) from ‘no emotion’ to ‘most ever fear/anxiety, happiness, or disgust’. As this was a cross-sectional study of immediate emotive responses to a sequence of images of foods, we were studying fear, however included the term anxiety in the descriptor as alternately representing the emotion. A summary mean score for disgust, fear and happiness was computed for all 16 food item responses, and all analyses were conducted on these summary scores. The foods were photographed from a local supermarket and widely used trading market. They included foods it was anticipated would arouse disgust (e.g. green spotted herbal sausages), a happy or ‘excited’ response (e.g. MacDonald’s ‘Happy meal’) and anxiety or fear due to unfamiliarity (e.g. Chinese skinned ducks) or, in those with eating disorders, possible fear of binging (e.g. a chocolate block). Four nonfood images, (e.g. a chair) were included to check for aberrant responses such as rating all images as high on fear. We found no such aberrant responses and the assessment had face validity in that responses were in the predicted direction. In addition the computed mean scores were internally consistent with Cronbach alpha 40.70 for all three emotions (fear a ¼ 0.84, disgust a ¼ 0.73, happiness a ¼ 0.75) and construct validity was demonstrated in the findings of significant between group differences (see Results below). Questionnaires Body image was assessed using the nine-body dissatisfaction subscale items from Eating Disorders Inventory-I (EDI-I). This is a widely used instrument with well established reliability and validity (Garner et al., 1983) and has been found to have good internal consistency when used in a sample of 13–20 year old girls (Cronbach alpha 0.93) (Schoemaker, van Strein, & van der Staak,

1994) and when used in a sample of 11–18 year old boys and girls (Cronbach alpha 0.91 in female, 0.86 in males) with construct validity demonstrated in between gender and between age groups differences (Shore & Porter, 1990). In the university students, eating disorder symptoms over the preceding month were assessed with the Eating Disorder Examination Questionnaire (EDE-Q). The EDE-Q focuses on the past 28 days and is scored using a seven-point, forced-choice, rating scheme (Beglin & Fairburn, 1992). Eating disorder behaviors are assessed in terms of the number of episodes occurring during the past 4 weeks. Subscale scores and a global score may similarly be derived from the 22 items addressing attitudinal aspects of eating-disorder psychopathology and Australian community normative data for these are published (Mond, Hay, Rodgers, & Owen, 2006). Excepting for frequency of binge eating, a high level of agreement between EDE-Q and EDE subscale scores has been demonstrated in both clinical and general population samples (Mond, Hay, Rodgers, Owen, & Beumont, 2004). Body mass index (BMI; in kg/m2) was calculated from self-reported height and weight. Statistics Quantitative statistical analysis was completed using the computer software the Statistical Package for the Social Sciences (SPSS) Version 14.0. Inspection of data using the SPSS frequencies function reporting skewness, kurtosis and visual depiction of distribution found most data to be non-parametric. Spearman ranked correlations and the Wilcoxon or Kruskal Wallis tests for differences between groups, were used. Data were transformed to normal scores using the SPSS function of Blom for the purpose of univariate analyses of variance which were used to test between group differences when controlling for age and gender. A correction of Po0.01 was made where multiple correlations were conducted. Differences that reached a level of o0.10 but X0.01 are reported as a statistical trend. Because of known variability with age, the results of BMI analyses are presented by age group. Ethics The South Australian and Queensland arms were approved by the University of Adelaide Human Research Ethics Committee and the James Cook University Human Research Ethics Committee, respectively. All participants and children and adolescent parents, provided written informed consent. For ethical reasons, specific eating disorder features were assessed in the university students only, and not in school students to avoid possible prompting of eating disorder behaviors in a more vulnerable group.


Results Participants Five hundred and thirty-six children and adolescents were recruited from South Australian schools and 93 students from Queensland undergraduate first semester classes. Of participants, 25% attended a single gender boys school, 43% a single gender girls school and 32% mixed gender school. Forty-two percent attended a high fee, 39% a moderate fee and 19% a low fee (including 100% Statefunded) school according to the federal Australian government classification (Department of Education Science and Training, 2004). Ninety-six students (15%) were aged o12 (median age 10 years), 197 (31%) were aged

105

12–14 (median 13 years), 248 (39%) were aged 15–17 (median 16 years) and 86 (14%) were aged X18 (median 25 years). One hundred and ninety-nine (37%) of school children and 22 (24%) of university students were male (one of whom was aged less than 18). Mean scores of the EDI body image subscale are shown in Table 1. As expected, body image concerns increased with increasing age (Kruskal Wallis, K–W, w2 ¼ 51.8, d.f. ¼ 3, Po0.001). Of the university students, 26 (28%) were married or living as married, 53 (57%) were never married and single, 1 was widowed, 10 (11%) were divorced or separated and 3 declined the question. All but two listed their main occupation as student. Mean EDE-Q symptom scores (n ¼ 82) were as follows: global EDE-Q 1.7 (S.D. 1.3, median 1.5), restraint 1.4 (S.D. 1.4, median 1.0), weight

Table 1 Comparative differences between age and gender groups in emotive responses to foods and body dissatisfaction o12 years

All

Age (years)

12–14 years

15–17 years

K–W w2, d.f., P

X18 years

Mean (S.D.)

Median, n

Mean (S.D.)

Median, n

Mean (S.D.)

Median, n

Mean (S.D.)

Median, n

Mean (S.D.)

Median, n

15.7 (6.1)

15, 627a

10.0 (0.5)

10, 96

13.3 (0.6)

13, 197

16.1 (0.6)

16, 248

26.8 (9.6)

24, 86

5.54 (1.29) 2.95 (1.81) 3.69 (1.40) 4.2 (5.2)

5.5, 94

4.70 (1.47) 1.95 (1.62) 3.10 (1.24) 8.3 (6.7)

4.8, 191

4.67 (1.54) 1.67 (1.42) 2.65 (1.20) 9.3 (7.6)

4.6, 245

4.40 (1.46) 1.44 (1.26) 2.40 (1.35) 10.7 (7.7)

4.3, 77

32, 3, o.001

1.1, 76

42, 3, o.001

2.3, 74

56, 3, o.001

10.0, 84

52, 3, o.001

Emotive responses to food imagesb Happiness 4.81 4.8, (1.51) Fear 1.93 1.5, (1.60) Disgust 2.95 2.9, (1.37) Body 8.4 (7.3) 7.0, dissatisfactionc

622 621 619 640

3.0, 96 3.7, 96 2.0, 96

1.6, 190 3.0, 190 7.0, 197

1.3, 244 2.5, 244 7.0, 258

Emotive responses between genders Happiness Males Females Between gender Wilcoxon Z, P Fear Males Females Between gender Wilcoxon Z, P Disgust Males Females Between gender Wilcoxon Z, P a

5.14 (1.54) 4.61 (1.46) 4.04, o0.001

5.14, 220

2.08 (1.66) 1.83 (1.57) 1.80, 0.07

1.71, 221

2.78 (1.35) 3.00 (1.32) 1.82, 0.06

2.73, 220

4.58, 398

1.38, 396

2.92, 395

Between age groups: K–W w2, d.f., P 5.69 (1.45) 5.35 (1.100) 1.53, 0.13

5.89, 49

2.68 (1.90) 3.26 (1.69) 1.63, 0.10

2.83, 50

3.28 (1.37) 4.15 (1.32) 2.88, 0.004

3.41, 50

5.28, 44

3.31, 45

4.25, 45

4.96 (1.52) 4.70 (1.44) 1.49, 0.14

5.15, 63

2.07 (1.74) 1.88 (1.57) 0.51, 0.61

1.71, 63

2.99 (1.31) 3.16 (1.20) 0.88, 0.38

2.88, 63

4.73, 128

1.46, 127

3.05, 127

Two participants did not provide data on age. Score on Likert scale (0 minimum, 10 maximum). c Subscale of the Eating Disorder Inventory-I (Garner, Olmstead, & Polivy, 1983). b

5.10 (1.54) 4.43 (1.50) 2.95, 0.003

4.95, 87

1.93 (1.43) 1.52 (1.40) 2.54, 0.011

1.69, 87

2.60 (1.26) 2.68 (1.17) 0.78, 0.44

2.50, 87

4.39, 158

1.14, 157

2.50, 157

5.10 (1.54) 4.33 (1.44) 0.49, 0.63

4.50, 20

11, 3, 0.01

4.29, 54

18, 3, 0.001

1.93 (1.43) 1.43 (1.24) 0.69, 0.49

0.90, 20

9.9, 3, 0.02

1.10, 53

38, 3, o.001

1.71 (1.07) 2.61 (1.39) 2.48, 0.013

2.07, 20

23, 3, o0.01

2.52, 53

45, 3, o.001


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concern 2.0 (S.D. 1.6, median 1.8) shape concern 2.4 (S.D. 1.7, median 2.0) and eating concern 0.9 (S.D. 1.2, median 0.4). These are very similar to levels found in the Australian general population in young adult women (Mond et al., 2006). Mean BMI was 24.5 kg/m2 (median 22.8, S.D. 5.5, n ¼ 82). Emotive responses and age As shown in Table 1, with increasing age there was a decrease in emotive responses and all three emotive responses decreased with increasing age (viz. disgust Spearman rho, rs ¼ 0.30, Po0.001, n ¼ 619; happiness rs ¼ 0.21, Po0.001, n ¼ 622; fear rs ¼ 0.24, Po0.001, n ¼ 621). Emotive responses and gender As shown in Table 1, for all participants, males had a stronger ‘happy’ emotive response and statistical trends indicated a weaker ‘disgust’ emotive response than females and stronger anxious response to foods. As expected, they also had lower body dissatisfaction (Wilcoxon Z ¼ 7.84, Po0.001). Females aged less than 12 years were significantly more disgusted in their emotive response than males aged less than 12 years. Females aged 15–17 years were significantly less ‘happy’ in their emotive response than males aged 15–17 years, and there was a statistical trend for males in this age group to be more anxious than females. Adult females aged 18 years or more showed a statistical trend to be more disgusted than males in this age group.

correlations between body dissatisfaction and emotive responses. In young adult females (18 years or more) body dissatisfaction scores correlated positively with an anxious response but this failed to reach significance. As shown in Table 3, in young adult males and females there were no significant correlations between levels of emotive responses to foods and EDE-Q global or subscale scores. In the young adult males who completed the EDEQ there were statistical trends towards a negative correlation between a ‘happy’ response to food and EDE-Q global scores (P ¼ 0.04), weight concern (P ¼ 0.011), shape concern (P ¼ 0.06) and eating concern (P ¼ 0.012). In the young adult females who completed the EDE-Q, there were statistical trends towards a positive correlation between a ‘happy’ response to food and EDE-Q weight concern (P ¼ 0.059) and eating concern (P ¼ 0.09), and a fear response to food and EDE-Q eating concern (P ¼ 0.014). Body mass index (BMI) and emotive responses The results of BMI analyses are presented by age group. Too few (n ¼ 2) of those under 12-years old provided height and weight information for analyses. The BMI of those aged 12–14 years did not correlate with body dissatisfaction (n ¼ 197, rs ¼ 0.21, P ¼ 0.11). As expected, the BMI of those aged 15–17 years and the young adults both correlated with body dissatisfaction (n ¼ 98, rs ¼ 0.27, Po0.01; n ¼ 96, rs ¼ 0.27, Po0.01, respectively). Table 3 Correlations between eating disorder symptoms and emotive responses in adults (aged X18 years)

Eating disorder symptoms and emotive responses

Happiness

As shown in Table 2, in males there were no significant correlations found between emotive responses to food and body dissatisfaction. In younger females, there were no Table 2 Correlations between body image dissatisfaction and emotive responses n; Spearman rho (rs) Happiness

Fear

Disgust

Body image dissatisfactiona Males o12 years 49, 0.03 12–14 years 63, 0.12 15–17 years 87, 0.15 X18 years 19, 0.29

50, 0.15 63, -0.14 87, 0.07 19, -0.08

50, 0.03 63, 2.20b 87, 0.08 18, 0.05

Females o12 years 12–14 years 15–17 years X18 years

45, 127, 157, 53,

45, 127, 157, 52,

a

44, 128, 158, 54,

0.09 0.05 0.03 0.09

0.09 0.12 0.10 0.32c

0.12 0.13 0.06 0.05

Subscale of the Eating Disorder Inventory-I (Garner et al., 1983). P ¼ 0.09. c P ¼ 0.02. b

n for all correlations Males 20 Females 51

Fear

Disgust

20 50

19 49

EDE-Qa score Global score Males Females

0.47c 0.21

0.31 0.12

0.13 0.05

Restraint Males Females

0.16 0.26

0.40 0.15

0.29 0.01

Weight concern Males Females

0.56c 0.27b

0.25 0.15

0.06 0.02

Shape concern Males Females

0.43b 0.21

0.21 0.17

0.90 0.05

Eating concern Males Females

0.55c 0.24b

0.22 0.35c

Spearman rho (rs)

0.07 0.02

a Eating Disorder Examination Questionnaire subscale and global scores (Beglin & Fairburn, 1992). b Po0.10, see text. c Po0.05, see text.


There were no statistically significant correlations between BMI and any emotive response of those aged 12–14 years or those aged 15–17 years. In young adults (X18 years) there was a negative correlation between disgust and BMI (n ¼ 71, rs ¼ 0.42, Po0.001) and a trend for a negative correlation between BMI and a fear response (n ¼ 73, rs ¼ 0.20, P ¼ 0.043). These correlations were present but did not reach significance in males (disgust, n ¼ 19, rs ¼ 0.37, P ¼ 0.06; fear, n ¼ 73, rs ¼ 0.51, P ¼ 0.011). Only disgust correlated with BMI in adult females (disgust, n ¼ 49, rs ¼ 0.38, P ¼ 0.004). Effects of school type The effect of type of school was tested with univariate analysis of variance on transformed data, controlling for age and gender of students. There were no significant differences or statistical trends in emotive response or body dissatisfaction scores between those in high/moderate fee versus low fee schools. The only differences found in single gender versus co-educational schools were that single gender school students had greater body image dissatisfaction (F ¼ 9.5, P ¼ 0.002) and a trend to a lower fear emotive response (F ¼ 4.1, P ¼ 0.043). The highest levels of body image dissatisfaction were found in the single gender girls’ schools (n ¼ 223, median 10, I.Q. range 5–15). Discussion The present study found a decrease in emotive responses to food with increasing age and significant gender differences in emotive responses in all participants, whereby males had more ‘happy’ levels of response, but when analyzed by age group this was statistically significant only in the 15–17 year-olds. Very young (o12-years males) had a weaker disgust response, and showed a trend to have a weaker disgust response in young adults. However, between gender differences were inconsistent across age groups for level of a fear response. Taken together, men had the more positive emotive, but not necessarily less negative emotions towards foods. In addition, in young adult men there were trends for lower levels of eating concerns (as reflected by the global EDE-Q and its eating weight and shape concern subscale scores) to be associated with a greater positive emotive response to the food images. Men are less likely to suffer eating disorders than women (Carlat & Camargo, 1991; Woodside et al., 2001) but little is known about why men are less at risk. We speculate that the more positive emotional response to foods found in this study may be a protective factor for disordered eating in men. However, these differences did not reach significance across all age groups, and the findings need to be cognizant of the limitations of smaller numbers of men in the adult group and the cross-sectional design. The findings need to be further investigated in a prospective longitudinal study to investigate whether these gender and age group

107

differences in emotive responses to food are mediators or merely proxy indicators of broader developmental factors that may be relevant to the greater risk in females for onset of eating problems in adolescent years. Despite the strong relationships between BMI, age and body dissatisfaction, there were few associations between BMI and emotive responses, or between body dissatisfaction and emotive responses in men and in younger females. In young adult females, a trend for a correlation between a fear response to food and body dissatisfaction was found, along with higher levels of fear towards foods being associated with eating concerns. This supported our first hypothesis and previous studies such as Ellison et al. (1998) and Harvey et al. (2002), namely women with eating disorders are more likely to have fear towards foods. However, there were also trends towards a greater positive response to food and higher weight and eating concerns in young adult women. Weight and shape concerns and other eating disorder features such as binge eating are also more prevalent in those of higher BMI (e.g. Darby et al., 2007). Thus, we predicted that people with higher BMI might as well have a greater negative emotive response to images of food, which may for example evoke fear of weight gain in people who are already sensitized to concerns about their weight. However, we found that a higher BMI was associated with a lower disgust emotive response in young adult women, which did not reach statistical significance in young adult males. There was also a trend towards a similarly lower fear response in young adults of higher BMI. This was thus contrary to expectations and not supportive of the Davey et al. (1998) finding of a relationship between a heightened disgust response and eating disorder symptoms in young adult women or the studies of Troop et al. (2000, 2002) in people with eating disorders. We thus had contradictory findings in the present study of both positive and negative emotive responses being associated with eating disorder symptoms in young adult women. These were all statistical trends, so must be interpreted with caution and further studies are needed to explore emotive relationships towards food in women. A possible explanation of this may be that emotive responses are more conflicted or inconsistent in women as foods that may be generally considered enjoyable (for example foods high in sugars and/or fats) may be the same foods that arouse concerns about weight and shape because of their high energy content, and these concerns are higher in young women than young men. This is however speculative and further studies analyzing the types of food images, for example high energy versus low energy foods, and a qualitative exploration of the emotive responses would also aid in understanding these results. Finally, when controlling for age and gender there were few differences on the basis of type of school, excepting that single gender school students had greater body image dissatisfaction and a trend to a lower fear emotive response. The trend may be statistical artifact, but the


C. McNamara et al. / Appetite 50 (2008) 102–109

finding of higher dissatisfaction in single gender schools, and particularly high levels in the single gender girls’ schools, is consistent with previous research, although to our knowledge this has not been reported in samples across both male and female single gender schools. Dyer and Tiggemann (1996) found a higher rate of disordered eating and nomination of a thinner ideal figure in girls at a high fee single gender school compared to adolescent girls at a high fee co-educational school in Australia. A later and larger study from this group (Tiggemann, 2001) failed to replicate the first findings, however they found adolescent girls (mean age 16.1 years) in single gender schools had a greater emphasis on achievement. Other studies in females have also have mixed results, with one reporting that female university students from single gender boarding and day schools have higher EDI scores than contemporaries from co-educational schools (Limbert, 2001) but a New Zealand study did not differentiate in eating disorder behaviors on the basis of gender mix of schools (Fear, Bulik, & Sullivan, 1996). These studies differ from the present study in that we included boys and a wider age range of children and adolescents, and our findings need to be replicated in a similar sample of both genders and similarly wide age range. Our main finding, however, was that schools were more alike than different in students’ emotive responses to foods. The study has a number of limitations. We did not obtain comprehensive socio-demographic details from participants. However, in Australia schools are classified into high and low fee structures that reflect the socioeconomic status of parents, and we had a wide range of such school types. As well, the participating university has students from broader social and economic backgrounds than the national average. To control for possible ethnic and cultural differences ideally the study would also have been conducted in a single State. In addition, ethical constraints precluded assessment of eating disorder symptoms in the school children and the requirement for parental consent in younger children may have biased the sample towards those where familial interest in the topic is higher. A further limitation is the relatively smaller number of adult males. However, excepting for BMI, there was a low rate of missing data. Finally, excepting the four neutral images, we only assessed images of foods and further research would extend the findings by including other types of emotive images as in the study by Stormark and Torkildsen (2004). In conclusion, these gender and age group differences in emotive responses to food may be indicators of broader developmental factors of relevance to the emergence of eating problems in adolescent years. Further explorative qualitative research and prospective studies would assist in understanding how age and eating disorder features relate to emotions and foods and contribute to either predisposition, or as suggested by Stormark and Torkildsen (2004) maintenance of eating disorder symptoms. Further direct studies of a range of both positive and negative emotive

responses to food in people with eating disorders would also be of interest as such negative emotions may impede engagement in therapies where the focus is on ‘normalizing’ eating patterns and exposure to a range of foods that may be emotionally aversive.

Acknowledgments We thank the following former University of Adelaide medical students: N. Radhakrishna, K. Ariffen, Hazlina Md-Noh, Anthony Todd, Kate Douglas, Joanne Wood, Aimee Reilly, Angela Okunga, Won-Kyung Sung, Jane Rosser, Karen Stewert, Alana Toscano and Joanne Chew. We thank the staff and students of the participating schools in South Australia and James Cook University. The study was funded in part from a University of Adelaide Small Grants scheme.

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ARTICLE IN PRESS


Research report

Is the desire to eat familiar and unfamiliar meat products influenced by the emotions expressed on eaters’ faces? S. Rousseta,, P. Schlichb, A. Chatonniera, L. Barthomeufc, S. Droit-Voletc a

Institut National de la Recherche Agronomique, UMR 1019, Centre de Recherche en Nutrition Humaine d’Auvergne, 58 rue Montalembert, 63009 Clermont-Ferrand, France b Centre Europeén des Sciences du Gouˆt, 15 rue Hugues Picardet, 21000 Dijon, France c Laboratoire de Psychologie Sociale et Cognitive, UMR 6024 CNRS, 34 Avenue Carnot, 63000 Clermont-Ferrand, France Received 12 October 2006; received in revised form 1 June 2007; accepted 1 June 2007

Abstract The aim of the present study was to test if the social context represented by eaters’ faces expressing emotions can modulate the desire to eat meat, especially for unfamiliar meat products. Forty-four young men and women were presented with two series of photographs. The first series (non-social context) was composed of eight meat pictures, four unfamiliar and four familiar. The second series (social context) consisted of the same pictures presented with eaters expressing three different emotions: disgust, pleasure or neutrality. For every picture, the participants were asked to estimate the intensity of their desire to eat the meat product viewed on the picture. Results showed that meat desire depended on interactions between product familiarity, social context and the participant’s gender. In the non-social context, the men liked the familiar meat products more than the women, whereas their desire to eat unfamiliar meat products was similar. Compared to the non-social context, viewing another person eating with a neutral and a happy facial expression increased the desire to eat. Furthermore, the increase in the desire to eat meat associated with happy faces was greater for the unfamiliar than for the familiar meat products in men, and greater for the familiar than for the unfamiliar meats in women. In the presence of disgusted faces, the desire to eat meat remained constant for unfamiliar products in all participants whereas it only decreased for familiar products in men. r 2007 Elsevier Ltd. All rights reserved. Keywords: Eating; Emotion; Meat; Faces; Familiarity

Introduction The sensory aspects of foods elicit emotional reactions of pleasure or disgust that are often considered as major reasons for the choice and the preference of foods (Eertmans, Baeyens, & Van den Bergh, 2001; Meiselman & Rivlin, 1986; Rozin, 1990). Dislike of a food thus leads to its rejection. However, according to Rozin and Fallon (1980), three psychologically meaningful categories explain food rejection: distaste, danger and disgust. Distaste is produced by the dislike of the appearance, taste or smell and/or texture of food. Danger is associated with the harmful consequences of ingestion. Disgust is generated on Corresponding author.

E-mail addresses: [email protected] (S. Rousset), [email protected] (S. Droit-Volet). 0195-6663/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.appet.2007.06.005

both ideational grounds and the distaste produced by contamination or by the intake of inedible foodstuffs. Food avoidance, a related but different topic concerns the process of rejection of unfamiliar food. This is related to neophobia, the reluctance to try unfamiliar foods, and the willingness to taste familiar rather than unfamiliar foods (Pliner & Hobden, 1992; Pliner, Latheenmaki, & Tuorila, 1998). Among the causes of neophobia are the fear of experiencing a bad taste and/or of being poisoned, as well as the fear of new experiences, more closely related to personality traits. Regardless of the case, reactions to unfamiliar animal foods, particularly meats, would also be mediated by the strong basic emotion of disgust (Pliner & Pelchat, 1991). To decrease neophobia, the authors proposed either to force exposure to an unfamiliar food in order to confirm or not the expectation of unpalatability (Pliner, 1982; Pliner & Hobden, 1992; Zajonc, 1968;

ARTICLE IN PRESS S. Rousset et al. / Appetite 50 (2008) 110–119

Zajonc, Markus, & Wilson, 1974), to associate this food with pleasant or familiar tastes by conditioning, or to provide information about these foods (Martins, Pelchat, & Pliner, 1997). To date, however, the possibility of decreasing or increasing neophobia in the social context by the presence of another person or other people expressing different emotions towards the unfamiliar food products has been rarely empirically studied. Since Lewin (1958), study in the power of the social context has been demonstrated on the change in eating habits, notably concerning disliked foods such as beef heart, sweetbread and kidneys. This author tested the influence of two types of intervention (individual and group) on housewives. In the individual case, only 3% of the women as opposed to 32% in the group case actually tried these meats. Although several factors explained these results, Lewin emphasized the influence of the social group and the difficulties of individuals to differentiate themselves too much from the group. As he said, the individual is likely to change only if the group changes (Lewin & Grabbe, 1945). The effect of social influence in the domain of eating is now well known (for a review see Herman, Roth, and Polivy (2003)). Notably, modelling studies suggested that the presence of others may facilitate or inhibit intake, depending on how much the model(s) eat(s). For example, Rosenthal and McSweeney (1979) showed that when a model ate either 40 or 10 crackers, participants ate more when paired with a high-consumption confederate than when paired with a low consumption confederate. The presence of others influences not only the amount of food eaten, but also the choice of the consumed food, as shown by Birch (1980). In her experiment, a ‘target’ child preferred vegetables A to B, while all of the other children at the same table preferred vegetables B. During four successive meals, the two vegetables were offered. When the target child serves himself first, he generally chooses vegetable A (in 80% of the cases). On the other hand, when he serves himself last, he tends to imitate the others and chooses vegetable B in 67% of the cases. Birch did not draw any conclusions from the respective effects of simple exposure to the vegetables, the emotional context of the meal or the mimicking of behaviour. As regards the impact of modelling on willingness to eat novel foods, Harper and Sanders (1975) showed that more children were willing to taste a novel food if the adult model tasted it first than if it was only offered. Moreover, when adult participants were exposed to eater models who ostensibly chose novel foods among familiar and unfamiliar foods, participants were more often inclined to choose novel foods than when they viewed models choose familiar foods (Hobden & Pliner, 1995). In their works, Birch (1980), and Harper and Sanders (1975) did not control the influence of emotions expressed by the children’s or adults’ faces on the target child’s willingness to taste the vegetables or the novel food. Conversely, Hobden and Pliner (1995) obtained their results while showing models who expressed

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neutral faces during the choice and tasting of food products. In non-specific nutrition-related domains of research, it is well known that the perception of emotions expressed by faces plays an important role on our own emotions (Adolphs, Damasio, Tranel, Cooper, & Damasio, 2000; Decety & Chaminade, 2003; Gallese, 2003). Indeed, ‘emotional contagion’ results from conscious appraisal, but also from more automatic mechanisms inaccessible to awareness, such as the automatic imitation of facial expressions in others (Hatfield, Cacioppo, & Rapson, 1994). The recent embodiment theories of cognition hold that the encoding of incoming emotional information involves an internal simulation of the perceived entities (e.g., imitation of the facial expression) with all of its physiological implications such as matching of emotional state (see Barsalou, Nidenthal, Barbey, & Ruppert, 2003; Niedenthal, Barsalou, Winkielman, Krauth-Gruber, & Ric, 2005, for a review). For example, in an fMRI study, Phillips et al. (1998) showed that the perception of facial expression of disgust produced activation of the anterior insula, directly involved in the physiological reaction of disgust. In the same way, Adolphs, Tranel, and Damasio (2003) reported on patients with damage affecting the insula who clearly showed selective impairment in recognising facial expressions of disgust and in their sensitivity to disgust. Since the discovery of the mirror-neuron system (Gallese, 2005), there is now evidence that observing or just imagining a person in a particular emotional state automatically arouses this state with all of the somatic and associated sensory-motor responses (Craig, 2002; Rizzolati, Fadiga, Fogassi, & Gallese, 2002; Singer et al. 2004). In short, the different studies described above suggest the important power of social context on the amount of food eaten and on the evaluation of liking or disliking food products, especially in the case of rejection of unfamiliar food and, more particularly, unfamiliar meat. The aim of the present study was thus to investigate the possibility of changing the participants’ desire to eat a food product by the presence of other people expressing and arousing different specified emotions. More precisely, we tested how emotional facial expressions (pleasure, neutrality and disgust) affect the participants’ desire to eat four familiar and four unfamiliar meat products. We expected that eaters’ faces expressing pleasure would increase the desire to eat meat, compared to those expressing neutrality and disgust, and inversely for the faces expressing disgust. Considering the rejection of novel food, we hypothesized that the participants should have less desire to eat unfamiliar than familiar products. Furthermore it might be possible that the change in desire to eat would be greater for unfamiliar than for familiar meat products because participants did not have any previous sensory experience with these new foods that would influence the importance of emotional context in the changes in neophobia. Finally due to an in-group advantage (Durrett & Levin, 2005), i.e.,


S. Rousset et al. / Appetite 50 (2008) 110–119

the tendency to imitate more members of our own group, we also assumed that the gender of the eaters might influence the responses of the participants. Method Participants In December 2004, a convenience sample was recruited in Clermont-Ferrand (France) through advertising in the local newspapers, TV and displays in stores (supermarkets). Among the subjects who phoned, 95% agreed to participate in the experiment. Eighty-eight young people, 44 men and 44 women, were recruited outside of our laboratory, with a mean age of 24.5 (SD 2.9). The mean body mass index (BMI) for men and women was 22.5 (SD 2.9) and 20.1 (SD 2.4), respectively. Participants were not receiving medical treatment for any progressive illness. They received a payment of h20 as compensation. Materials and their validation Pictures The stimulus set consisted of eight pictures of meat products and 18 of the emotional facial expressions of models (three men and three women expressing pleasure, neutrality and disgust). All the pictures were taken with a digital camera (Canon Power Shot G3, 4.0 Mega pixels, objective lens 7.2–28.8 mm 1:2.0–3.0) with a high-angle shot in order to show large objects in detail. These photographs were selected from a larger set of photographs on the basis of two pre-tests, one for the meat products and the other for the emotional expressions of the models. For each pre-test, additional participants were recruited as explained below. Pre-test of pictures of meat products Two categories of meat products were tested: unfamiliar versus familiar meat products. Four unfamiliar beef products were prepared by a technical centre (Association de De´veloppement de l’Institut de la Viande): ‘dry ham’,

‘cooked sausage with vegetables’, ‘cooked sausage with 5% fat’, and another ‘cooked sausage with 15% fat’. Four familiar pork products were purchased in a supermarket. They were bacon, dry sausage, cooked sausage and pistachio galantine. These food products were displayed on a simple white plate placed on a table covered with a beige tablecloth (Fig. 1). These eight photographs had been previously assessed by 24 participants with a mean age of 26 (SD ¼ 4, 13 women and 11 men). Each participant rated each photograph on perceived familiarity and classified meat products into one of the three meat types (fresh meat, processed pork and offal). Familiarity was assessed on a seven-point scale (from 0, ‘‘not familiar at all’’, to 6, ‘‘extremely familiar’’). The ANOVA (effect of products nested according to the category: unfamiliar vs. familiar products on the ratings of familiarity) shows that the familiarity scores of the four familiar meat products were higher than those of unfamiliar meat products: 4.3 vs. 2.1, p ¼ 0.03. Moreover, the familiar and unfamiliar meat products were classified by 93% and 75% of participants as processed meat, respectively. Pre-test of pictures of facial expressions Ten models, five men (mean age: 24) and five women (mean age: 25) with a standard body mass index between 19 and 25 were recruited for the photographs. Sixty photographs of these ten subjects showing three different emotional expressions—neutrality, disgust and pleasure— were taken twice under standard conditions (Fig. 2). These photographs were then evaluated by 21 additional participants (ten men and 11 women, mean age: 25 (SD ¼ 3). Photographs were presented in random order to the participants. They rated each face on the basis of nine emotional expressions: anger, disgust, fear, sadness, guilt, surprise, interest, satisfaction and pleasure (Ekman, Levenson, & Friesen, 1983). The term ‘without emotion’ was also added to the nine emotional expressions to illustrate neutrality. For each emotion, the participants responded on a seven-point scale (0, ‘‘the face does not express this emotion’’, to 6, ‘‘the face strongly expresses this emotion’’).

Fig. 1. Examples of familiar and unfamiliar meat products.


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Fig. 2. Examples of faces expressing pleasure, neutrality and disgust used for the validation of expression.

3

M2D1W2D1 M4D1M2D2 W5D1 M4D2 M3D1W3D2 W2D2 M3D2W3D1 W4D2 W1D2M1D2 W1D1 M1D1

2

DIM2 28.76 %

1

disgust

W5D2 W4D1 M5D1

0 -1

W4P2 W3P1 W1P1 W2P2 M3P1 M3P2 W5P1M1P1W2P1 M5P1M4P1 M2P1W3P2M1P2 pleasure M2P2M4P2 W4P1

anger surprise fear guilty sad

W1P2 satisfaction M5P2

interest

W5P2

M5D2 M3N1 W1N2

-2

M2N2 M2N1 W5N2 W4N2 M3N2 W3N1 M4N2 M1N2 neutrality M1N1 W4N1 W3N2 M4N1 W5N1 W1N1 W2N2 W2N1 M5N1 M5N2

-3 -4 -5

-4

-3

-2

-1

0

1

2

3

4

5

6

DIM1 65.10% Covariance Biplot-PCs and Loadings*4 Fig. 3. Location of the ten preliminary eater models with the three facial expressions in the emotional space (principal plot of PCA). The first two characters indicate the gender (M: man and W: for woman) and the model code (no. 1, 2, 3, 4 or 5). The third character, D, N and P, indicates that the model expresses disgust, neutrality or pleasure. The last number shows the photograph replicate.

A principal component analysis (PCA) computed on the covariance matrix was carried out with the mean intensities of emotional words for each photograph. The first two axes of the PCA accounted for 94% of total inertia, i.e., the information generated by all of the data (Fig. 3). The right part of the first horizontal axis was explained by ‘‘satisfaction’’, ‘‘pleasure’’ and ‘‘interest’’, which were opposed to ‘‘fear’’, ‘‘sadness’’ and ‘‘guilt’’ (the left part of the first axis). The second vertical axis opposed ‘‘disgust’’ and ‘‘anger’’ at the top, to ‘‘neutrality’’ at the bottom. ‘‘Surprise’’ was poorly represented on the plot. In that emotional space, three distinct groups of faces appeared. The faces expressing pleasure were located on the right, those expressing neutrality at the bottom, and those

showing disgust in the top left corner. This result clearly showed that the participants easily identified the three types of emotions mimicked by the models. Certain emotional face pictures (W1P2, M5P2, W5P2, W1N2, M5D2, M3N1, W5D2, W4D1, M5D1, W1D1 and M1D1) located between the groups were less clearly identified. The results of the analyses of variance (eater’s face nested according to gender) for each emotion confirmed that there were significant differences in pleasure and disgust scores given to the 20 faces expressing pleasure and disgust, F(18, 400) ¼ 5.1, po0.0001 and F(18, 400) ¼ 20.4, po0.0001 for pleasure and disgust, respectively. However, there was little difference in the 20 neutral scores distinguishing neutral faces, F(18, 400) ¼ 1.5, p ¼ 0.09. The gender of the


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photographed eater did not have a significant effect on the evaluation of disgust, F(1, 18) ¼ 0.02, p ¼ 0.89, pleasure F(1, 18) ¼ 0.97, p ¼ 0.34 or neutral expression, F(1, 18) ¼ 0.13, p ¼ 0.72. Table 1 shows the scores of the faces that best expressed disgust, neutrality and pleasure, and that did not differ between themselves in each emotional category. We therefore eliminated M5 because of its lower score of expressiveness of disgust (2.1), W4 because its disgust score was slightly lower than those of the other subjects (4.3), and M3 and W5 because their pleasure scores (3.9 and 4.5) were lower than those of the others. After the six eaters were selected on the basis of their validated emotional expressions (Fig. 2), photographs of their body seated at a table in front of a plate containing one of the eight meat contents were taken and subsequently attached to their heads using Photoshop (Fig. 4).

Table 1 Intensity of emotions perceived from the six eaters that best expressed disgust, neutrality and pleasure Emotional expressions Pleasure

Neutrality

Disgust

Mean

SD

Mean

SD

Mean

SD

Selected eater Mena M1 M2 M4

5.1 4.1 4.3

1.6 1.7 1.6

3.7 3.0 3.6

1.7 1.9 2.0

4.7 5.4 5.2

1.1 1.0 1.0

Womena W1 W2 W3

4.9 5.3 5.3

1.5 1.3 1.0

3.8 3.8 3.7

2.1 2.2 2.1

5.1 5.3 4.9

1.4 0.8 1.1

a

The first two characters indicate the sex (M for men and W for women) and the code (no. 1, 2, 3 or 4) of the selected eater.

Procedure Subjects other than those used in the pre-tests participated in one session at the laboratory, each between 11:00 am and 2:00 pm or between 6:00 pm and 8:00 pm. They were only instructed that they had to watch meat pictures and to assess their desire to eat the products shown on the picture. Each participant was presented with two series of pictures on a computer, the order of the series being counterbalanced across subjects. The first series was composed of the eight meat products (four familiar and four unfamiliar). The second series included 48 pictures, i.e., two eaters (one man and one women chosen from among the six selected eaters and not always the same, depending on the participants) for the three emotional expressions (disgust, neutrality and pleasure), and this for the eight meat products. Within each series, pictures were presented in random order. For each picture, the participants assessed the intensity of their eating desire on a vertical and non-structured scale (from the bottom, ‘‘I have no desire to eat’’, to the top ‘‘I have a great desire to eat’’). Scores varied between 0 and 10. Both the pictures and the eating desire scale were presented on the same screen. After the participant scored his/her evaluation, the next screen appeared. Statistical analyses We used three models of analysis of variance (repeated measurements GLM procedure of SAS), considering the successive evaluations of meat pictures by a given subject as repeated measurements. The first one was performed on the data of the first series of pictures (meat products in a non-social context). It was a 2*2 ANOVA run on the eating desire scale value (from 0 to 10) with participant’s gender as a between-subject factor, familiarity (familiar vs. unfamiliar) as a within-subject factor, and with their interaction. The four different meat products were

Fig. 4. Examples of a male model with expressions of pleasure, neutrality and disgust towards bacon.


considered as replicates within each familiarity level. The following three analyses (2*2*2 ANOVAs) were carried out on the data with the same two factors (participant’s gender, familiarity), and an additional within-subject factor, i.e., the two-picture series: meat presented in the non-social and a one-out-of-three social context (neutral, pleasure or disgust expressions). This was done in order to test the effect of facial expressions in each emotional condition. In this analysis, the scores given to the pictures showing male and female eater models were averaged. In order to better understand interactions, we used other ANOVA models. For example, we performed an ANOVA with context and familiarity as within-subject factors and gender as a between-subjects factor, for each type of product (familiar vs. unfamiliar). Moreover, for each gender and level of familiarity, we carried out a repeated measures one-way ANOVA to determine the effect of context. The last fourway ANOVA (3*2*2*2) aimed to compare the three emotional facial expressions and assessed the impact of eater model and participant’s gender on meat eating desire, as well as product familiarity and their interactions. Results Influence of participant’s gender and product familiarity on the desire to eat meat in a non-social context The repeated measures analysis of variance showed two main effects: product familiarity, F(1, 86) ¼ 91.2, po0. 0001, indicating that the familiar products elicited more desire to eat than the unfamiliar products (the first two bars in Fig. 5a and b). However, there was also a main effect of the participant’s gender, F(1, 86) ¼ 3.8, po0.05, as well as a significant familiarity gender interaction, F(1, 86) ¼ 4.0, po0.05. This interaction indicated that the gender difference in the desire to eat meat products was greater for the familiar meat products, F(1, 86) ¼ 6.0, po0.01, than for the unfamiliar products, F(1, 86) ¼ 0.9, po0.4 (Fig. 5a and b). Thus, men wanted to eat meat products more than women, but only in the case of familiar foods. Comparison of the non-social and social context on the desire to eat meat Non-social vs. social contexts: neutral faces Repeated measures analysis of variance showed two marginally significant effects of the participant’s gender and of the context without significant interaction between these two factors, F(1, 86) ¼ 3.6, po0.06, F(1, 86) ¼ 3.3, po0.07 and F(1, 86) ¼ 0.15, po0.7, respectively. Moreover, the context familiarity and the context participant’s gender familiarity interactions were significant, F(1, 86) ¼ 5.6, po0.01, F(1, 86) ¼ 16.3, po0.0001, respectively. To analyse this significant three-way interaction, we ran two-way ANOVAs with context and participant’s gender as factors for each product type taken separately.

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For the familiar products, we observed a significant context x gender interaction, F(1, 86) ¼ 4.7, po0.05. This interaction was due to the gender effect obtained in the non-social context, F(1, 86) ¼ 6.0, po0.01, but not in the neutral social context, F(1, 86) ¼ 1.8, po0.2 (the first four bars in Fig. 5a). Taking each gender separately and considering familiar products, the desire of men to eat in the presence of a neutral face did not significantly differ from the desire to eat in the non-social context, F(1, 43) ¼ 2.4, po0.2 (the first and third bars in Fig. 5a). The same result was observed in women, F(1, 43) ¼ 2.5, po0.2 (the second and fourth bars in Fig. 5a). For the unfamiliar products, only the social context effect was significant, F(1, 86) ¼ 8.5, po0.001. Regardless of the participants’ gender, the desire to eat unfamiliar meats was higher when presented in the neutral social context than in the non-social context (the first four bars in Fig. 5b). Non-social versus social contexts: faces expressing pleasure The main effects of social context and product familiarity were highly significant, and that of participant’s gender just significant: F(1, 86) ¼ 14.4, po0.001, F(1, 86) ¼ 14.4, po0.001 and F(1, 86) ¼ 4.0, po0.05, respectively. However, the context familiarity interaction, as well as the context familiarity participant’s gender interaction were also significant: F(1, 86) ¼ 3.95, po0.05, F(1, 86) ¼ 14.9, po0.001. When the products were familiar, men liked them more than women, as seen above in the non-social condition, F(1, 86) ¼ 6.0, po0.01, but this gender difference disappeared when presented in a pleasant social context, F(1, 86) ¼ 2.4, po0.2 (the first two bars and the fifth and sixth bars in Fig. 5a). For men, the context did not influence the desire score, F(1, 43) ¼ 0.15, po0.7 (the first and fifth bars in Fig. 5a), whereas for women, the pleasant faces increased the scores compared to the non-social context, F(1, 43) ¼ 7.46, po0.01 (the second and sixth bars in Fig. 5a). Conversely, when the products were unfamiliar, there was no significant gender difference in the non-social context, F(1, 86) ¼ 1.8, po0.2 (the first two bars in Fig. 5b). However, the gender difference appeared when meats were presented in a pleasant social context, F(1, 86) ¼ 4.0, po0.05 (the fifth and sixth bars in Fig. 5b). Moreover, the changes in desire to eat unfamiliar meat products in the presence of other people expressing pleasure compared to the non-social context was greater in men than in women: F(1, 43) ¼ 15.8, po0.001 and F(1, 43) ¼ 4.5, po0.05, respectively. Non-social versus social contexts: faces expressing disgust The main effects of participant’s gender, social context and their interaction were not significant: F(1, 86) ¼ 2.8, po0.1, F(1, 86) ¼ 0.1, po0.8 and F(1, 86) ¼ 0.9, po0.4, respectively. However, the context familiarity and context familiarity participant’s gender interactions were highly significant: F(1, 86) ¼ 9.3, po0.01 and F(1, 86) ¼ 12.3, po0.001. For familiar products, we observed


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Men 7

Women

6

Desire score

5 4 3 2 1 0 Non social

Neutrality

Pleasure

Disgust

Context 7

6 Men Women

Desire score

5

4

3

2

1

0 Non social

Neutrality

Pleasure

Disgust

Context Fig. 5. Effect of the neutral, pleasant and disgust social contexts compared to the non-social context on the desire to eat (a) familiar meats and (b) unfamiliar meats in male and female participants. (Mean7Confidence Interval: 95%). – – – Line showing men’s desire to eat in non social context. – – – Line showing women’s desire to eat in non social context.

a significant gender context interaction. As for neutrality and pleasure, there was a significant difference between men and women in the non-social context, with women having less desire to eat meat in this latter condition, but not when meats were presented in a disgust social context, F(1, 86) ¼ 1.2, po0.3 (the seventh and eighth bars in Fig. 5a). This was due to the decreased desire of men to eat familiar meat in the presence of disgusted faces, compared to the non-social context, F(1, 43) ¼ 9.8, po0.01, whereas the women maintained their lower desire to eat meat, F(1, 43) ¼ 0.17, po0.7. In contrast, no effect of gender, context or their interaction was significant in the case of the

unfamiliar products. Thus, for the unfamiliar products, the desire to eat meat remained low both in and not in the presence of faces expressing disgust. Comparison of the three emotional facial expressions on desire to eat meat in the social context The main effects of emotional facial expressions (neutrality, pleasure and disgust) and of product familiarity were highly significant on the desire to eat, F(2, 172) ¼ 13.3, po0.001 and F(1, 86) ¼ 78.5, po0.0001, but the participant’s gender and eater’s gender had no major effect. No two-way, three-way or four-way interactions


were significant except the familiarity eater’s gender interaction, F(1, 86) ¼ 5.6, po0.05. Thus, in the social context, regardless of the facial emotion perceived, the familiar meat products were preferred to unfamiliar meat products (means 4.8 vs. 3.1). As expected, the two-by-two face comparison showed that the disgusted faces produced lower eating desire scores than those expressing pleasure (means 3.7 vs. 4.2), F(1, 86) ¼ 14.6, po0.001 or neutrality (means 3.7 vs. 3.9), F(1, 86) ¼ 11.1, po0.001. In contrast, the faces expressing pleasure increased the level of eating desire, compared to neutral facial expressions (means 4.2 vs. 3.9), F(1, 86) ¼ 11.8, po0.001. Moreover, the significant familiarity eater’s gender interaction indicated that regardless of the participants’ gender, unfamiliar meat products received a higher eating desire score when a male eater was shown (means 3.2 vs. 3.0), F(1, 86) ¼ 8.77, p ¼ 0.004, whereas no difference between male and female eaters was observed for the familiar products (means 4.8 vs. 4.8), F(1, 86) ¼ 0.03, p ¼ 0.86. Discussion In the non-social context, the present study showed that the desire to eat meat was greater in men than in women in the specific case of familiar meat products. This lower desire in women might result from either disgust towards sensory characteristics of meat or be related to health, weight and/or ethical concerns (Audebert, Deiss, & Rousset, 2006; Kubberød, Ueland, Roodbotten, Westad, & Risvik, 2002; Lea & Worsley, 2002; Rousset, Deiss, Juillard, Schlich, & Droit-Volet, 2005). Indeed, some women reported disliking meat for its red colour, presence of blood and the flesh that reminded them of dead animals (Kubberød, Dingstad, Ueland, & Risvik, 2006; Lupton, 1996; Mooney & Walbourn, 2001). However, our study revealed that when the meat products were unfamiliar, the desire to eat meat also decreased in men who generally liked meat, to the point that there was no longer any observable difference between women and men in their eating desire. This result was consistent with those showing that lower liking is often observable for unfamiliar foods and particularly unfamiliar meat products (Pliner & Hobden, 1992; Pliner & Pelchat, 1991; Pliner et al., 1998). It is of particular interest that our study suggested that the perception of another person’s face modified the desire to eat meat in both men and women. The present study revealed that the observation of an unknown eater, even if expressing a neutral emotion, increased the desire to eat meat, and this to a greater extent for unfamiliar meat products. Watching another person eating was thus sufficient to increase the desire to eat. This result is consistent with the modelling studies in which the model’s behaviour (who ate more or less) influenced the amount of food eaten by the participant who imitated the model’s behaviour, independent of whether or not the participant was on a diet or hungry (Goldman, Herman, & Polivy, 1991; Rosenthal & McSweeney, 1979). Although our study

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deals with the desire to eat and not with the amount of food eaten, it provided results consistent those of studies showing the widely observed inclination of individuals to eat in the same way as others did. According to Leary, Tchividijian, and Kratxberger (1994), social intake norms may be a strong determinant of eater’s behaviour, probably stronger than physiological signals (hunger, satiety). However, as stated by Herman et al. (2003), the modelling effects remain a mystery. The main contribution of our study was to demonstrate that not only the presence of the eater models but the emotion that they expressed affected the participants’ desire to eat. Indeed, the participants wanted to eat the meat products more when they viewed the models with expressions of pleasure than with neutral expressions. Conversely, they had less desire to eat meat products when the models showed expressions of disgust rather than neutral ones. Thus, the pleasure faces could have suggested to the participants that the food tasted good, and the disgusted faces could have suggested that the food tasted bad. Consciousness of such information should increase or decrease individuals’ willingness to taste foods. However, as explained in the introduction, a more automatic and less conscious process could also be involved. According to embodiment theory, the observation of emotions expressed by other people’s faces induced the physiological reactions associated with this emotion in the individuals (Barsalou et al., 2003; Niedenthal et al., 2005). As suggested by Dimberg (1982, 1990), the perception of positive and negative facial expressions induces spontaneous mimicry that in turn produces the corresponding emotional state and influences the emotional attitude towards the context. Thus, via an imitation process, the facial expressions of others play an important role on our own emotions (Adolphs et al., 2000; Dećety & Chaminade, 2003; Gallese, 2003). Further investigations using electromyography methods are now required in order to test whether eater models’ facial expressions induce facial reactions in the participants. Such a study might prove that the emotion perceived in other people modulates the emotion elicited by the food products themselves. Our study also suggested that the influence of facial expression on the desire to eat depended on the type of food products and on the participant’s gender. In the case of familiar meat products, compared to the eating desire assessed in the non-social context, our results revealed that positive emotion increased the desire to eat in the subjects expressing a low eating desire (i.e., women), and the negative emotion decreased this desire in the subjects expressing a high eating desire (i.e., men). However, the desire level in the presence of pleasant faces did not change when the subjects (i.e., men) already liked the products. In the same way, the desire level in the presence of disgusted faces did not change when the subjects (i.e., women) disliked them. Regarding the desire of eating unfamiliar products in the non-social context, the scores were similarly low in men


S. Rousset et al. / Appetite 50 (2008) 110–119

and women (lower than 3 out of 10). In the social context of faces expressing pleasure, the scores given by men and women were improved. However, men increased their scores more than women. Thus, in a non-familiar situation of unfamiliar food, men were more influenced by positive facial information than women were. This raises the question of the stronger inclination of men to follow the group than women in the case of unfamiliar food products. On the one hand, Klesges, Bartsch, Norwood, Kautzman, and Haugrud (1984) found that social facilitation of eating (increase in eating in the presence of others) is generally stronger in men than in women. Moreover, one study by Hobden and Pliner (1995) showed that when participants had to choose between a novel and a familiar food product, men chose novel foods more often than women, which is consistent with our data. The behaviour of men towards unfamiliar food may also be associated with their lower risk perception in general. Men are less afraid, assess most risks lower and worry about them less than women (Fagerli & Wandel, 1999; Williams & Hammitt, 2001). Another plausible reason might be that men may be more prone to test unfamiliar meat because they like meat in general. In contrast, women were more influenced by positive emotions for familiar rather than non-familiar meat products. Because they did not like meat a lot, they might have had little desire to try unfamiliar meat. Surprisingly enough, the present study also suggested that the social influence on the desire to eat meat products varied as a function of the gender of the model observed. Indeed, regardless of the participant’s gender, unfamiliar meat products were judged as more desirable when the other person eating these products was a man instead of a woman. Few studies of social learning in humans have considered the roles played by the gender of the individual (Choleris & Kavaliers, 1999). As regards human eating behaviour, Mory, Chaiken, and Pliner (1987) showed that participants ate less when the eating companion was of the opposite sex. Moreover, Pliner and Chaiken (1990) showed that women ate less when the eating companion was a man and when he was most desirable. Their behaviour may be attributed to an attempt to convey an impression of femininity. However, no study has yet focused on the gender effect on desire/consumption of an unfamiliar food. In the case of animals, Kawamura (1959) studied the transmission of unfamiliar behaviours in Japanese monkeys, which suggested the existence of social constraints on the channelling of socially mediated learning. When a unfamiliar behaviour was invented by an adult male and subsequently acquired by the dominant male, its transmission through the group was faster. In conclusion, our study showed the influence of social context on desire to eat meat products. Moreover, this influence differed according to the familiarity of the concerned food, the desire to eat meat products in a nonsocial context and the participant’s gender. Men were more easily influenced than women by the social context of pleasure in the case of unfamiliar meats, for which the

desire score was low in a non-social context. In contrast, the social influence was stronger for women on their desire to eat familiar rather than unfamiliar meats. Thus, it might be easier to improve their desire to eat meat by using social influence rather than by creating unfamiliar meat products. Future research should seek to determine the effect of facial expressions on the desire to eat a wider range of foods such as palatable versus less palatable food products. Further experiments should be done to compare the impact of emotion and social context not only on the desire to eat, but also on food liking in relation to consumption. Acknowledgments We are grateful to Benjamin Charpy for his valuable photographic work, to E. Juillard for her technical assistance, and to Stephanie Meritet and Alexandra Tabouji-Beji (graduate students in ‘Statistics and data treatment’) for the statistical analyses. This work was supported by two grants, one from the Regional Council of Burgundy and INRA DADP, and the second from the Agence Nationale de la Recherche (ANR Blan06-2-145908 FaceExpress). References Adolphs, R., Damasio, H., Tranel, D., Cooper, G., & Damasio, A. R. (2000). A role for somatosensory cortices in the visual recognition of emotion revealed by three dimensional lesion mapping. Journal of Neuroscience, 20, 2683–2690. Adolphs, R., Tranel, D., & Damasio, A. R. (2003). Dissociable neural systems for recognizing emotions. Brain and Cognition, 52, 61–69. Audebert, O., Deiss, V., & Rousset, S. (2006). Hedonism as a predictor of attitudes of young French women towards meat. Appetite, 46, 239–247. Barsalou, L., Nidenthal, P., Barbey, K., & Ruppert, J. (2003). Social embodiment. In B. H. Ross (Ed.), The Psychology of Learning and Motivation: Advances in Research and Theory, vol. 43 (pp. 43–92). San Diego, CA: Academic Press. Birch, L. L. (1980). Effects of peer models’ food choices and eating behaviors on preschoolers’ food preferences. Child Development, 51(2), 489–496. Choleris, E., & Kavaliers, M. (1999). Social learning in animals: gender differences and neurobiologiacal analysis. Pharmacology Biochemistry & Behavior, 64(4), 767–776. Craig, A. D. (2002). How do you feel? Interoception: The sense of the physiological condition of the body. Nature Reviews Neuroscience, 3(8), 655–666. Decety, J., & Chaminade, T. (2003). Neural correlates of feeling sympathy. Neuropsychologia Special Issue on Social Cognition, 41, 127–138. Dimberg, U. (1982). Facial reactions to facial expressions. Psychophysiology, 19, 643–647. Dimberg, U. (1990). Facial electromyography and emotional reactions. Psychophysiology, 27, 481–494. Durrett, R., & Levin, S. A. (2005). Can stable social groups be maintained by homophilus imitation alone? Journal of Economic Behavior & Organization, 57, 267–286. Ekman, P., Levenson, R. W., & Friesen, W. V. (1983). Autonomic nervous system activity distinguishes among emotions. Science, 221, 1208–1210. Eertmans, A., Baeyens, F., & Van den Bergh, O. (2001). Food likes and their relatives importance in human eating behaviour: Review and

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ARTICLE IN PRESS


Research Report

The role of implicit wanting in relation to explicit liking and wanting for food: Implications for appetite control Graham Finlaysona,, Neil Kingb, John Blundellc a

Division of Psychology, Glasgow Caledonian University, Glasgow, Lanarkshire G4 0BA, UK Institute of Health and Biomedical Innovation, Human Movement Studies, Queensland University of Technology, QLD 4059, Australia c Biopsychology Group, Institute of Psychological Sciences, University of Leeds, Leeds, West Yorkshire LS2 9JT, UK

b

Received 19 February 2007; received in revised form 8 June 2007; accepted 11 June 2007

Abstract Eating is an action open to awareness by the individual; however, it cannot be claimed that processes that control the expression of eating habits are necessarily explicit. This distinction between implicit and explicit processes may enhance understanding of the expression of food reward (particularly the concepts of liking and wanting [Berridge, K. C., & Robinson, T. E. (2003). Parsing reward. Trends in Neurosciences, 26, 507–513] and its importance for human appetite control [Finlayson, G. S., King, N. A., & Blundell, J. E. (2007b). Liking vs. wanting food: Importance for human appetite control and weight regulation. Neuroscience and Biobehavioural Reviews, in press]. The present study investigated the effect of meal-induced satiation on implicit and explicit processes of liking (L) and wanting (W) by developing a computer-based procedure to measure L and W in hungry and satiated states. Explicit measures were derived from analogue ratings whilst an implicit W measure was derived from reaction time in a forced-choice procedure, which also identified food preferences. Seventy subjects (21.870.9 years, BMI: 22.270.5 kg/m2) completed the procedure before and immediately following consumption of a savoury test meal. Satiation caused explicit ratings of L and W to decrease in all food categories (po0.01); but with a more marked decrease for savoury foods compared with sweet foods (po0.01). Implicit W was increased for sweet categories (po0.01), but not for savoury. Implicit and explicit measures of L and W independently correlated with preference for sweet foods. This study provides support that implicit and explicit processes of food reward can be simultaneously measured and dissociated using a test meal. Adjustments in hunger were linked to changes in explicit L and W in a manner consistent with sensory specific satiety, while a relationship between hunger and implicit W was absent. We suggest that implicit W is not systematically downregulated by the physiological consequences of food consumption in the same way as hunger and therefore may be largely independent of homoeostatic processes influencing intake. r 2007 Elsevier Ltd. All rights reserved. Keywords: Implicit; Liking; Wanting; Preference; Food reward; Hedonic; Homoeostatic; Appetite control

Introduction Traditionally, homoeostatic and hedonic influences over eating have been treated separately both through experimentation and theory development. However, recent considerations of homoeostatic and non-homoeostatic determinants of eating (Berthoud, 2004, 2006; Blundell & Finlayson, 2004) imply that such systems interact in the Corresponding author.

E-mail address: graham.fi[email protected] (G. Finlayson). 0195-6663/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.appet.2007.06.007

overall expression of appetite. Since eating is a behavioural act carried out through the voluntary skeletal musculature, it is often believed that eating is invariably under voluntary conscious control. Despite the fact that eating is an action open to awareness by the individual, it cannot be claimed that processes that control the expression of eating habits are necessarily explicit. Furthermore, introspective accounts of explicit processes are often subject to misinterpretation and cross-contamination (Berridge, 1996; Booth, 1987). An individual may have awareness of the act of eating per se whilst remaining unaware of the processes

ARTICLE IN PRESS G. Finlayson et al. / Appetite 50 (2008) 120–127

that determine the expression of appetite and the pattern of eating. It is, of course, obvious that a person cannot be aware of the (implicit) changes in neuropeptides, hormones or other physiological processes that help to determine the initiation, termination and topography of eating. Moreover, in considering just the psychological mechanisms that influence eating, we assume that both implicit and explicit processes will operate. Verplanken (Verplanken, 2006; Verplanken & Aarts, 1999) argued that the formation of habits should be constructed as a psychological process with automatic (implicit) features that operate somewhat independently to the explicit experience of a repeated behaviour. Using a 12-item measure derived from the conception of habit as an implicit process (self-report habit index; Verplanken & Orbell, 2003), Verplanken (2006) demonstrated that the consumption of unhealthy snack food during a week was better accounted for by their measure of habit than by behavioural frequency of snack consumption during the previous week. Recently, implicit and explicit processes of reward and their involvement in the regulation of feeding behaviour have been examined in more detail. Through the examination of specific neural substrates in the brain, Berridge and colleagues argue that distinct processes of affective ‘‘liking’’ and motivational ‘‘wanting’’ for food can influence behaviour without explicit awareness of their underlying cause, i.e. changes in hedonic feelings (explicit liking) or the intent or desire to consume a specific food (explicit wanting) (Berridge & Robinson, 2003; Finlayson, King, & Blundell, 2007b). To test the influence of implicit processes on measurable aspects of feeding behaviour, Winkielman, Berridge, and Wilbarger (2005) subliminally presented subjects with emotionally valenced picture stimuli (16 ms exposure to positive or negative facial expressions) which were subsequently masked with neutral faces (400 ms exposure). Interestingly, this implicit affective priming technique had no effect on the subject’s explicit liking ratings recorded online, but interacted with thirst levels such that positive facial expressions increased the serving size and consumption of a beverage in thirsty subjects, while negative facial expressions inhibited drinking, relative to neutral primes. In a recent review, Mela (2006) explored the liking/ wanting distinction of food reward in human appetite and suggested that implicit measures of wanting (e.g. behavioural tasks, physiological correlates, etc.) are more valuable assays of obese/lean differences in food reward and food preferences than explicit liking or wanting responses. For example, Saelens and Epstein (1996) assessed the reinforcing value of food using a slotmachine-like progressive ratio computer task. In this paradigm, subjects’ commitment to the task was rewarded with points that could be exchanged for palatable snacks or allotments of time that could be spent playing a fun computer game. The reinforcing value of the palatable food was calibrated as the willingness to work for amounts of the food relative to the time playing the game. In a study

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comparing obese and lean subjects, the authors found that ratings of explicit liking for snack food items—including the most preferred item used in the progressive ratio task— did not differ between the groups; however, the obese subjects were found to work harder (i.e. greater implicit wanting) for food relative to playing the game, and this corresponded to the amount subsequently consumed. These findings may also demonstrate important differences in implicit and explicit processes as mediating variables in food consumption. Although explicit liking for foods as they are ingested may be involved in establishing their reinforcing value, it is possible for implicit processes to play a more significant role in maintaining consumption (e.g. Berridge, 1996; Robinson & Berridge, 1993) and this could facilitate over-consumption in people at risk of weight gain. To further examine the significance of dual liking and wanting components of food reward for appetite control, we previously reported the development of a novel computer-based procedure to allow the separate and concurrent assessments of explicit and implicit liking and wanting for the same target stimuli while preventing crosscontamination of these measures (Finlayson, King, & Blundell, 2007a). This was achieved by using a ‘‘forced choice’’ behavioural measure of implicit wanting in addition to explicit subjective measures of liking and wanting for photographic food stimuli varying in fat content and taste. The present study reports a key modification to our previous operation of implicit wanting in order to improve its validity. In line with other laboratories successful use of reaction time as an indicator of implicit processes (for example, the Implicit Association Test; Greenwald, McGhee, & Schwartz, 1998), implicit wanting was operationalised as the reaction time of each pair trial decision. Thus, the speed with which one stimulus is chosen in preference to its alternative provides a quantifiable measure which may relate to the implicit wanting for that food item. We were interested to explore whether this new measure could convey information about the degree (on a continuous, scaled unit of measurement) to which the chosen stimuli is wanted relative to its alternative. Furthermore, mean reaction time for each food category (distinguished according to fat content and taste) can give an indication of whether motivation is generally increasing or decreasing independent of the other food categories. Therefore, the aim of this study was to extend our previous exploratory investigation into the effect of manipulating hunger state—by meal-induced satiation— on novel laboratory measures of implicit and explicit liking and wanting processes. By measuring explicit liking (subjective feelings of orosensory pleasure), explicit wanting (subjective feelings of intent or desire), implicit wanting (motivational expression of reward attribution) and relative preference (forced-choice) for a range of food stimuli in hungry and satiated states, changes in these processes were assessed and compared.


G. Finlayson et al. / Appetite 50 (2008) 120–127

Methods Subjects Subjects were recruited from the staff and student population of the University of Leeds using posters, email and a database of previous study volunteers. Thirty males and forty females aged 18–45 years (mean ¼ 21.870.85) were included after an initial screening process to exclude those who were taking medication, actively losing weight, or reported a history of eating or psychological disorders. Subjects were familiarised with the study procedures, and told that they would be participating in a study to investigate mechanisms of human appetite, before giving their written consent. Of the 70 participants tested, data from 7 were not included in the final analysis because experimental procedures were not adhered to correctly. Data are presented for the remaining 63 subjects (25 male and 38 female) who completed all parts of the study. Based on reported height and weight, subjects’ BMI ranged from underweight to obese (mean ¼ 22.270.51 kg/m2; range ¼ 14.8–39.6 kg/m2). All subjects were instructed about the procedures before giving their written consent. Measures Explicit subjective sensations Subjective appetite sensations and ratings of explicit liking and wanting for the test meal were recorded using 100-mm visual analogue scales (VAS) anchored at each end with the statements ‘‘extremely’’ and ‘‘not at all’’. Subjects were required to rate their subjective sensations on three scales combined with the questions: How hungry do you feel now?; How full do you feel now?; and How much food could you eat now? Two scales corresponding to the explicit measures used in the liking & wanting computer procedure (see below) were used to assess the test meal after an initial taste, and again when the subject had eaten to fullness. Test meal The meal consisted of a commercial brand of cheese and tomato pizza (280 kcal/100 g; 18% protein, 41% carbohydrate, 41% fat) and water, supplied ad libitum. Other than its palatable and distinct sensory properties, no particular criteria were applied to the selection of this meal since the manipulation was designed primarily to facilitate the transition from a hungry to a satiated state. Energy intake was calculated by weighing the food before and after consumption (to the nearest 0.1 g) and with reference to the manufacturer’s energy values. Liking and wanting computer procedure The original liking and wanting procedure (Finlayson et al., 2007a) comprised two tasks designed to assess (1) explicit liking and wanting, followed immediately by (2) implicit wanting for the same target stimuli. Subsequently,

an important development to this procedure was to integrate the separate task elements in order to fully randomise explicit and implicit trials. Experiment generator software (E-prime v1.1.4) was used to integrate the single stimulus trials for the liking task with the paired stimuli trials for the wanting task. The integrated software was also programmed to centre the cursor between each trial to produce more consistent response times, and different question prompts were presented in contrasting colours to encourage discrimination. Food stimuli presented in the procedure were selected based on two key dimensions associated with loss of appetite control and over-consumption: the fat content and taste properties of foods. Stimuli were presented on a 17-in flat-screen monitor and measured 150 100 mm2. The food items were selected from a database of photographic stimuli and sorted according to their fat content and taste properties into one of four separate categories: high fat savoury (HFSA); low fat savoury (LFSA); high fat sweet (HFSW); and low fat sweet (LFSW). Each category was represented by five different foods; hence a total of 20 different food stimuli were presented in the procedure (see Table 1). Explicit liking and wanting trials The aim of the explicit task was to obtain introspective hedonic measures for the same stimuli used in the implicit wanting task. Therefore, each food stimulus was assessed independently using VAS. The explicit computer task trials consisted of 20 food stimuli presented one at a time and rated according to a 100-mm VAS anchored at each end by the statements ‘‘not at all’’ and ‘‘extremely’’. Subjects were prompted with the statements ‘‘How pleasant would it be to experience a mouthful of this food now?’’ and ‘‘How much do you want some of this food now?’’ In particular, the liking question was constructed to reflect the pleasure of the experience of tasting a mouthful of the given food to avoid eliciting a more general evaluation to do with properties inherent to the food itself (see Finlayson et al., 2007a, p. 40). The VAS was presented on-screen beneath each food stimulus and subjects used the mouse to move a centred cursor along the line to indicate their response. When a rating was made, the procedure automatically cycled to the Table 1 Photographic food stimuli used in liking and wanting computer task (grouped by food category) HFSA

LFSA

HFSW

LFSW

Salted peanuts Mixed olives Salted crisps Swiss cheese French fries

Savoury biscuits Spaghetti in sauce Pilau rice Boiled potatoes Bread roll

Blueberry muffin Milk chocolate Jam doughnut Cream cake Shortbread

Jelly (jello) Marshmallows Popcorn Jelly sweets (candies) Fruit salad

HFSA, high fat savoury; LFSA, low fat savoury; HFSW, high fat sweet; LFSW, low fat sweet.


next stimulus trial. Responses on the software were recorded online and mean ratings for each food category (HFSA, LFSA, etc.) were automatically computed. Implicit wanting and relative preference trials Implicit wanting was measured by a behavioural ‘‘forced choice’’ methodology. In this task, a food stimulus from one of the four food categories was paired with one stimulus from the remaining categories to form a series of 150 trials in which the subjects were given the standardised instruction to select the food they ‘‘most want to eat now’’. In addition to recording the frequency of selections made in each category (with a possible range of 0–75) which may reveal a relative preference, reaction time (in milliseconds) of each choice was also measured. By covertly recording reaction time, subjects remained unaware of implicit changes in their behaviour on the task, while remaining free to determine the direction of their choices. In this measure, the motivated behavioural response independent of the explicit awareness of its incentive value was the key variable. Data from the forced-choice task—including frequency of choice for each food category and reaction time of choice—were recorded online for later calculation of the means. Procedure The study conformed to a simple repeated measures design, with explicit and implicit measures for each food category compared immediately before and after the test meal intervention. Subjects attended the research unit for one lunchtime visit commencing at approximately 12:30 p.m., having not consumed any food for at least 3 h. Subjects verified when they had last eaten, and completed a screening questionnaire presented on the computer. Pre-satiation measures using the liking and wanting procedure were completed, followed by presentation of the test meal. Subjects were given the instruction to eat until they were ‘‘comfortably full’’ to ensure that the measures were carried out in two quite different states (a state of strong hunger, then a state of satiety). Postsatiation measures immediately followed consumption of the meal. Subjects were navigated through the procedure at all times by written instructions presented on-screen. Written and verbal debriefing was given prior to subjects leaving the unit. Data analyses Data were analysed using SPSS 14.0 for Windows. Data from the liking and wanting procedure collected using E-Prime (v.1.1.4.4.) were exported to MS Excel via E-DataAid. MS Excel was used to calculate the variables for export to SPSS. Subjective sensation VAS taken during the test meal were analysed by paired samples t-test and ratings of explicit liking and wanting for the test meal were correlated. To compare changes in liking and wanting for

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the four food categories of the computer procedure, across the two satiety conditions, parametric data were examined using 4 2 repeated measures ANOVA for each dependent variable (explicit liking, explicit wanting and implicit wanting). The food stimuli categories (HFSA, LFSA, HFSW and LFSW) and treatment conditions (pre-meal/ hungry state and post-meal/satiated state) were treated as within subjects factors. Trends in the relative preference measure were assessed descriptively. Delta scores for each measure of the computer procedure were correlated for each food category. Results Subjective sensations and test meal intervention Energy intake and subjective responses to the test meal were analysed to check that it had produced satiation in participants. Males consumed on average 1439 kcal (780) compared to 942 kcal (746) in females [t(61) ¼ 5.76, po0.01]. As anticipated, the test meal caused a significant decrease in hunger (mean7SEM: 7272 mm) and prospective consumption (6572 mm) and an increase in fullness (7173 mm) [t(62) ¼ 33.2, po0.01; t(62) ¼ 27.3, po0.01; and t(62) ¼ 29.9, po0.01, respectively]. Explicit ratings of the test meal at the first mouthful and at the end of the meal revealed significant reductions in explicit liking (5973 mm) [t(62) ¼ 19.22, po0.01] and explicit wanting (7472 mm) [t(62) ¼ 31.21, po0.01]. Explicit ratings of liking and wanting were significantly correlated at both time points [t1: r(63) ¼ 0.60, po0.01; t2: r(63) ¼ 0.40, po0.01] and the magnitude of change in these variables also correlated [t1–t2: r(63) ¼ 0.38, po0.01]. Liking and wanting computer procedure Data from the computer procedure were used to calculate mean ratings of explicit liking, explicit wanting, implicit wanting (choice reaction time), and relative preference (frequency of choice) for each food category. These data are shown in Table 2. Explicit liking As shown in Table 2, ratings of explicit liking declined for all food categories over time [F(1,62) ¼ 179.27, po0.01]. A time category interaction was also apparent [F(3,186) ¼ 48.43, po0.01] with a greater reduction in liking for savoury stimuli relative to sweet stimuli. Explicit wanting Ratings of explicit wanting were very similar to the liking data. Hence, main effects of time [F(1,62) ¼ 188.11, po0.01] and time category [F(3,186) ¼ 51.93, po0.01] were observed. Mean scores for explicit wanting were highly correlated to explicit liking in terms of change across the meal (see Table 3).


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Table 2 Mean (7SEM) computer task outputs for food categories pre- (time 1) and post- (time 2) test meal

HFSA LFSA HFSW LFSW

Explicit liking (mm)

Explicit wanting (mm)

Implicit wanting (ms)

Frequency of choice (count)

t1

t1

t1

t1

57.4 61.6 61.1 53.8

t2 (2.0) (1.7) (2.1) (1.7)

24.2 20.6 38.6 43.5

(2.1) (1.9) (3.3) (2.8)

56.0 59.7 56.9 49.5

t2 (2.0) (1.8) (2.2) (2.0)

20.8 17.1 34.5 39.3

(1.9) (1.7) (3.1) (2.8)

1911.5 1929.4 1978.6 2076.4

t2 (97.8) (95.6) (89.0) (104.8)

1733.2 1854.9 1514.2 1405.1

(82.1) (91.9) (76.9) (69.1)

38.7 43.2 39.9 28.2

t2 (1.7) (1.8) (1.9) (1.5)

26.8 20.3 46.8 56.2

(1.5) (1.5) (2.2) (1.3)

Means shown for high fat savoury (HFSA), high fat sweet (HFSW), low fat savoury (LFSA), and low fat sweet (LFSW) stimulus categories.

Table 3 Pearson’s correlation analysis of software outputs for each food category HFSA

Explicit liking Explicit wanting Frequency of choice Implicit wanting

LFSA

Explicit liking

Explicit wanting

Frequency of choice

Implicit wanting

Explicit liking

Explicit wanting

Frequency of choice

Implicit wanting

–

0.83** –

0.23 ns 0.27* –

0.22 ns 0.17 ns 0.15 ns

–

0.86 –

0.23 ns 0.32* –

0.13 ns 0.2 ns 0.33*

0.86** –

0.56** 0.43** –

0.19 ns 0.18 ns 0.31*

HFSW Explicit liking Explicit wanting Frequency of choice Implicit wanting

–

LFSW 0.92** –

0.43** 0.40** –

0.09 ns 0.19 ns 0.37** –

–

–

Values shown for high fat savoury (HFSA), high fat sweet (HFSW), low fat savoury (LFSA), and low fat sweet (LFSW) stimuli categories.*po0.05; **po0.01.

Implicit wanting (choice reaction time) Mean choice RT decreased (became faster) in all food categories. However, the decrease in RT for savoury foods was smaller compared to the decrease in RT for sweet foods. There was a main effect of time [F(1,62) ¼ 21.09, po0.01] and a time category interaction [F(3,186) ¼ 16.53, po0.01]; suggesting that sweet foods were being chosen faster than savoury foods following the meal. Relative preference (frequency of choice) The mean frequency of choice in each category indicated that choices in the savoury categories were decreasing with a corresponding increase in choices for sweet foods. There also appeared to be a slight decrease in choices for high fat category foods after the meal relative to a small increase in choices for low fat foods. Comparison of liking and wanting measures (mean D) Mean change in each measure of the computer procedure was calculated by subtracting scores at time 1 from scores at time 2. Using these delta scores, the measures were correlated for each food category (see Table 3). For the sweet categories, it was observed that the

explicit measures correlated positively [r(63) ¼ 0.50–0.92, po0.01] and in turn were correlated with relative preference [r(63) ¼ 0.40–0.56, po0.01]. Implicit wanting was positively correlated with the relative preference [r(63) ¼ 0.31, po0.05] indicating that a faster response time was associated with an increase in the frequency those categories were selected. A more complex relationship emerged for the savoury categories. For the HFSA category, explicit liking correlated with explicit wanting [r(63) ¼ 0.83, po0.01] and explicit wanting correlated with relative preference [r(63) ¼ 0.27, po0.05]. However, implicit wanting did not correlate with any other measure. The relationship between measures for the LFSA category was similar to HFSA in that explicit liking correlated with explicit wanting [r(63) ¼ 0.86, po0.01] and explicit wanting correlated with relative preference [r(63) ¼ 0.32, po0.05]. In this category however, explicit liking also correlated with relative preference [r(63) ¼ 0.33, po0.01]. Fig. 1 illustrates how the four dependent measures change from before to after the test meal for each category of stimuli. Ratings of explicit liking and wanting decreased for savoury categories relative to sweet after the meal. Relative preference decreased for savoury categories and


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Fig. 1. Mean scores for liking and wanting computer task outputs shown for each food category under hungry and satiated conditions. Means shown for high fat savoury (HFSA), low fat savoury (LFSA), high fat sweet (HFSW) and low fat sweet (LFSW) stimuli categories. Standard error (SE) shown as vertical lines.

increased for sweet, and implicit wanting increased (became faster) for sweet categories relative to savoury. Discussion The aim of this study was to manipulate hunger state to examine its effect on explicit and implicit processes of liking and wanting measured using a novel computer procedure. In addition to causing a considerable reduction in hunger and increase in fullness, the test meal manipulation was likely to have induced sensory specific satiety (e.g. Rolls, Murzi, Yaxley, Thorpe, & Simpson, 1986) or habituation (e.g. Epstein, Rodefer, Wisniewski, & Caggiula, 1991) to its sensory properties due to its unvaried, uniform sensory composition. Indeed, satiety induced by eating the savoury test meal caused explicit ratings of liking and wanting to decrease in all food categories; but with a more marked decrease for savoury foods compared with sweet foods. Implicit wanting was increased for sweet categories, but not for savoury categories as demonstrated by a decrease in choice RT for sweet stimuli. Relative preference decreased for savoury and increased for sweet categories. Comparison of changes observed in the outcome measures Mean change in explicit liking and wanting were highly correlated for all categories of food stimuli. One interpretation is that explicit liking and wanting were affected similarly by the test meal manipulation. However, introspective ratings are vulnerable to cross-contamination where distinct sets of underlying processes may be interpreted as a single—more general—variable which is

only subsequently partitioned cognitively into the required domains (Booth, 1987). Therefore, it is also possible that the subjective reporting of explicit processes may have been derived from a single evaluatory process, or that subjects found it difficult to make this distinction explicitly. This uncertainty makes the interpretation of multiple explicit measures of subjective sensations problematic. The inclusion of an implicit, instrumental measure in the liking and wanting computer procedure to assess the same food stimuli was therefore an important development to prevent cross-contamination issues in the measurement of underlying processes. Mean change in relative preference was found to correlate with explicit wanting for all food categories, and with explicit liking for sweet categories. Relative preference was also positively associated with implicit wanting for all categories except HFSA. It is interesting that relative preference was largely associated with both explicit and implicit measures, but that no direct relationship existed between implicit wanting and explicit liking or wanting. Indeed, implicit wanting did not correlate with any of the explicit ratings, but was correlated to relative preference. This suggests that when a category was selected more frequently, it was also chosen more rapidly, but independent to any change in explicit evaluation of the stimuli. By requiring a choice between two target food stimuli, the procedure was by definition measuring relative behavioural preference. Therefore, one interpretation is that while potentially containing an element of liking, the major process driving each choice was likely to be the momentary motivation or wanting for the chosen food relative to its alternative over successive pair trials (e.g. Finlayson et al., 2007a). However, the findings of the


G. Finlayson et al. / Appetite 50 (2008) 120–127

present study provide support for the conceptualisation of preference (in terms of a behavioural outcome) as containing implicit and explicit elements of liking and wanting. From these preliminary findings we propose that the explicit measures may be tapping a different process to the implicit measure of wanting, whereas relative preference may contain elements of both these discernable processes. In the present study, explicit liking and wanting for an array of food stimuli decreased following ad libitum consumption of a savoury test meal. This effect was observed regardless of the sensory or macronutrient properties of the stimuli, and would suggest that the foods became less pleasurable as a consequence of them becoming less useful (i.e. the presence of an alliesthesialike effect; Cabanac, 1989). These data also contribute to the literature documenting sensory specific satiety (e.g. Rolls, 1999). The test meal had savoury and fatty taste properties and prolonged exposure to these gustatory elements can be linked to greater decrease in liking for savoury stimuli relative to sweet. Interestingly, the test meal produced a cross-modal effect on explicit liking, as none of the food stimuli were tasted and the test meal food did not feature as one of the 20 food stimuli presented in the computer procedure. Therefore, exposure to the specific properties of a given food is not required to observe a sensory specific decrease in liking for that food. The implicit wanting measure revealed faster reaction times for sweet foods following the test meal, whereas for savoury foods, no significant change was observed. Using reaction time as an indicator of implicit wanting for a food category relative to its alternatives, these findings suggest that sweet foods (and not savoury foods) were wanted more following the test meal. In other words, subjects responded faster to stimuli with novel (uneaten) taste properties but no change in response occurred for stimuli with the same generic taste properties. These data may reveal more about the sensory specific satiety phenomenon and its role in appetite. It is thought that novel stimuli can delay the development of sensory specific satiety causing food to elicit more pleasure and therefore be consumed for longer (Hetherington, Foster, Newman, Anderson, & Norton, 2006). An alternative mechanism for this effect is (dis)habituation caused by exposure to salient and discriminable stimuli that can reinstate both responding and hedonic evaluation of a food that has become monotonous (Epstein et al., 1991; Temple et al., 2006). However, neither of these explanations can sufficiently account for the reaction time outcome in the present study. Rather, it is possible that during food consumption, two processes were in operation. The first component reflected the pleasure elicited by the test meal which declined after the subject habituated to its stimulatory properties. The second component tracked the motivational significance of contrasting (discriminable) stimuli which increased after the pleasure of the given stimulus had declined. This demonstrates that implicit wanting can be dissociated from explicit liking and wanting, and that implicit and explicit

processes appear to have separate intervening roles in the expression of food preference. We would argue that the relative changes in choice reaction time for each food category may provide a distinct profile of implicit wanting that carries theoretical significance. However, a limitation to reaction time tasks in human experimental studies is that subjects tend to respond faster to stimuli (regardless of their properties) when performing the task for a second time. This learning effect (probably due to subjects becoming familiar with the requirements of the task) was evident in the present data. Nevertheless, previous exposure to the task was unlikely to affect the direction of subjects’ choices, nor was it likely to affect the reaction time of decisions made within an administration of the task. However, to address this issue, future studies could conduct test procedures on different days in order to counterbalance across treatment conditions. Furthermore, the inclusion of a control session in which no test meal is consumed between treatment conditions would also help to rule out practice effects. Alternatively, choice reaction time data can be most effectively used (as with the present study) to infer shifts in wanting over time where a clear divergence of one or more food categories relative to the other categories has occurred. Implications for appetite control The explicit measurement of liking and wanting are subject to cross-contamination, however this confound can be circumvented by measuring one—and ideally both— processes implicitly. The dissociation between implicit wanting and explicit liking and wanting demonstrated in the present study suggests that two (at least partially) independent processes were being measured. By measuring changes in implicit wanting and explicit liking associated with the consumption of a test meal, an interesting difference in the relationship between these processes with hunger was apparent. Adjustments in hunger were linked to changes in explicit liking and wanting in a manner consistent with alliesthesia and/or sensory specific satiety, while a relationship between hunger and implicit wanting was absent. This may seem counterintuitive since it may be supposed that hunger and wanting could share a common motivating capacity. However, we have demonstrated that implicit wanting can be unaltered or even enhanced for specific foods while hunger and explicit liking and wanting are observed to decrease. Thus, it is suggested that implicit wanting is not systematically downregulated by the physiological consequences of food consumption in the same way as hunger and therefore implicit wanting may be largely independent of homoeostatic processes influencing intake. Because of this, implicit wanting could be considered as an independent risk factor for overconsumption—one that is not normally tapped into by the usual methodologies used to study appetite control. Certainly, it can be expected that processes of wanting would act in concert with liking and with hunger to


contribute to consumption above physiological need. For example, recent research is exploring the concept of ‘‘hedonic hunger’’ as a mediating state between perceived deprivation of pleasure and non-homoeostatic consumption (Lowe & Levine 2005; Lowe & Butryn, 2007). It seems plausible that the theoretical notion of implicit wanting is consistent with the hedonic hunger concept (e.g. see Finlayson et al., 2007b). Hence, implicit wanting can be viewed as a more free-running process to explicit liking and wanting, consistent with a neurochemical substrate that is insensitive to homoeostatic changes in appetite while reactive to environmental triggers. Conclusion This study provides support that implicit and explicit processes of food reward can be simultaneously measured and dissociated using a test meal. The study engineered the opportunity to observe an uncoupling in implicit and explicit processes by observing them across a meal. Explicit liking and wanting were found to decrease for all food stimuli, and more so for foods with similar taste properties to the food consumed. Implicit wanting was found to increase for food stimuli with novel taste properties. These data indicate that the refined liking and wanting computer procedure has tapped two separate psychological processes that contribute independently to food preference, with implications for appetite control. References Berridge, K. C. (1996). Food reward: Brain substrates of wanting and liking. Neuroscience and Biobehavioural Reviews, 20, 1–25. Berridge, K. C., & Robinson, T. E. (2003). Parsing reward. Trends in Neurosciences, 26, 507–513. Berthoud, H. R. (2004). Neural control of appetite: Cross-talk between homeostatic and non-homeostatic systems. Appetite, 43, 315–317. Berthoud, H. R. (2006). Homeostatic and non-homeostatic pathways involved in the control of food intake and energy balance. Obesity, 14, S197–S200. Blundell, J. E., & Finlayson, G. S. (2004). Is susceptibility to weight gain characterised by homeostatic or hedonic risk factors for overconsumption? Physiology and Behaviour, 82, 21–25. Booth, D. A. (1987). Objective measurement of determinants of food acceptance: Sensory, physiological and psychosocial. In J. Solms, D. A. Booth, R. M. Pangbom, & O. Raunhardt (Eds.), Food acceptance and nutrition (pp. 1–27). London: Academic Press.

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Cabanac, M. (1989). Maximization of pleasure, the answer to a conflict of motivations. Comptes Rendus de l’Academie des Sciences. Serie III, 309, 397–402. Epstein, L. H., Rodefer, J. S., Wisniewski, L., & Caggiula, A. R. (1991). Habituation and dishabituation of human salivary response. Physiology and Behaviour, 51, 945–950. Finlayson, G. S., King, N. A., & Blundell, J. E. (2007a). Is it possible to dissociate ‘liking’ and ‘wanting’ for foods in humans? A novel experimental procedure. Physiology and Behaviour, 90, 36–42. Finlayson, G. S., King, N. A., & Blundell, J. E. (2007b). Liking vs. wanting food: Importance for human appetite control and weight regulation. Neuroscience and Biobehavioural Reviews, in press. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Hetherington, M. M., Foster, R., Newman, T., Anderson, A. S., & Norton, G. (2006). Understanding variety: Tasting different foods delays satiation. Physiology and Behaviour, 87, 263–271. Lowe, M. R., & Levine, A. S. (2005). Eating motives and the controversy over dieting: Eating less than needed versus less than wanted. Obesity Research, 13, 797–806. Lowe, M. R., & Butryn, M. R. (2007). Hedonic hunger: a new dimension of appetite? Physiology and Behaviour, 91, 432–439. Mela, D. J. (2006). Eating for pleasure or just wanting to eat? Reconsidering sensory hedonic responses as a driver of obesity. Appetite, 47, 10–17. Robinson, T. E., & Berridge, K. C. (1993). Addiction. Annual Review of Psychology, 54, 25–53. Rolls, E. T. (1999). The brain and emotion. Oxford: Oxford University Press. Rolls, E. T., Murzi, E., Yaxley, S., Thorpe, S. J., & Simpson, S. J. (1986). Sensory-specific satiety: Food specific reduction in responsiveness of ventral forebrain neurons after feeding in the monkey. Brain Research, 368, 79–86. Saelens, B. E., & Epstein, L. H. (1996). Reinforcing value of food in obese and non-obese women. Appetite, 27, 41–50. Temple, J. L., Kent, K. M., Giacomelli, A. M., Paluch, R. A., Roemmich, J. N., & Epstein, L. H. (2006). Habituation and recovery of salvation and motivated responding for food in children. Appetite, 45, 280–284. Verplanken, B. (2006). Beyond frequency: Habit as mental construct. The British Journal of Social Psychology, 45, 639–656. Verplanken, B., & Aarts, H. (1999). Habit, attitude, and planned behaviour: Is habit an empty construct or an interesting case of goal-directed automaticity? European Review of Social Psychology, 10, 101–134. Verplanken, B., & Orbell, S. (2003). Reflections on past behaviour: A selfreport index of habit strength. Journal of Applied Social Psychology, 33, 1313–1330. Winkielman, P., Berridge, K. C., & Wilbarger, J. L. (2005). Unconscious affective reactions to masked happy versus angry faces influence consumption behavior and judgements of value. Personality and Social Psychology Bulletin, 31, 121–135.

ARTICLE IN PRESS


Research Report

Deconstructing the vanilla milkshake: The dominant effect of sucrose on self-administration of nutrient–flavor mixtures Amy M. Naleidb, Jeffrey W. Grimmc, David A. Kesslere, Alfred J. Sipolsd, Sepideh Aliakbaria, Jennifer L. Bennettb, Jason Wellsc, Dianne P. Figlewicza,b, a VA Puget Sound Health Care System (151), Seattle, WA, USA Department of Psychiatry & Behavioral Sciences, University of Washington, Seattle, WA, USA c Department of Psychology, Western Washington University, Bellingham, WA, USA d Institute of Experimental and Clinical Medicine, University of Latvia, Riga, Latvia e Department of Pediatrics, University of California at San Francisco, CA, USA

b

Received 12 April 2007; received in revised form 14 May 2007; accepted 12 June 2007

Abstract Rats and humans avidly consume flavored foods that contain sucrose and fat, presumably due to their rewarding qualities. In this study, we hypothesized that the complex mixture of corn oil, sucrose, and flavor is more reinforcing than any of these components alone. We observed a concentration-dependent increase in reinforcers of sucrose solutions received (0%, 3%, 6.25%, and 12.5%) in both fixed ratio and progressive ratio procedures, but with equicaloric corn oil solutions (0%, 1.4%, 2.8%, and 5.6%) this finding was replicated only in the fixed ratio procedure. Likewise, addition of 1.4% oil to 3% or 12.5% sucrose increased fixed ratio, but not progressive ratio, reinforcers received relative to those of sucrose alone. Finally, addition of 3% vanilla flavoring did not change self-administration of 3% sucrose or 3% sucrose+1.4% oil solutions. These data suggest that, calorie-for-calorie, sucrose is the dominant reinforcing component of novel foods that contain a mixture of fat, sucrose, and flavor. r 2007 Elsevier Ltd. All rights reserved. Keywords: Self-administration; Sucrose; Fat; Reinforcement; Macronutrients; Flavor

Introduction What makes us drink a vanilla milkshake? Is it the sugar, the fat, the flavor, or the unique combination of all three? Rewarding properties of foods are partially attributed to their fat and carbohydrate content (Levine, Kotz, & Gosnell, 2003). Increases in the content of these nutrients in the modern food supply may contribute to the obesity problem the developed world is experiencing (Drewnowski, 2003) and therefore it is important that we understand their impact on our intake and our desire to obtain and consume them. Although some milkshake-like beverages are used as a meal replacement for weight loss (Drewnowski & Bellisle, Corresponding author. Metabolism/Endocrinology (151), VA Puget Sound Health Care System, 1660 So. Columbian Way, Seattle, WA 98108, USA. E-mail address: [email protected] (D.P. Figlewicz).


2007), sodas and other high-calorie beverages are consumed in addition to regular meals, and studies show that consumers do not fully compensate for the additional calories, leading to weight gain (Raben, Vasilaras, Moller, & Astrup, 2002; Tordoff & Alleva, 1990). In addition to their abundance and relative low cost (Drewnowski & Bellisle, 2007), these beverages are also easy to consume, requiring no food preparation or even chewing. One needs expend very little work to consume these calories. Many studies indicate that fat and carbohydrates (particularly sugars) are rewarding in humans and in animals (Levine et al., 2003; Sclafani, 2004). However, fat and sugar are rarely, if ever, consumed in isolation from other nutrients or flavors and most palatable foods are not mixtures of nutrients alone, but have some flavor added to them. This can be studied using rats working for delivery of nutrient solutions. For example, we have observed in our laboratory that rats consistently consume more, and work

ARTICLE IN PRESS A.M. Naleid et al. / Appetite 50 (2008) 128–138

harder for, chocolate Ensure than they do for pure sucrose solutions (unpublished data). Ensure differs from sucrose solutions in both macronutrient composition and oral qualities (i.e. texture) that resemble a milkshake. We became curious about which component(s) of complex highly palatable liquid foods evoke the most motivation in the rats. Nutrients and their rewarding properties have been extensively studied in animal models. Sucrose and fat have both been shown independently to be rewarding and motivating in several paradigms, including self-administration (Freed & Green, 1998; Grimm, Fyall, & Osincup, 2005), conditioned place preference (Figlewicz et al., 2004; Figlewicz, Higgins, Ng-Evans, & Havel, 2001), conditioned flavor preferences (Ackroff, Rozental, & Sclafani, 2004; Sclafani, Nissenbaum, & Ackroff, 1994) and intake tests (Corwin, 2004; Zhang & Kelley, 1997). In the present study, we aimed to determine whether fat, sucrose, a mixture, or a flavored mix is most reinforcing, using the self-administration procedure. Flavor preferences can be conditioned by intra-gastric infusion of either sweet or fat solutions. This finding suggests that macronutrient nutritive qualities alone can be positively associated with oral experiences of flavor (Sclafani, 2004). Other studies show that the nutrients’ pre-absorptive properties contribute to consumption, including some showing rats’ positive responses to non-nutritive fats (Ackroff, Vigorito, & Sclafani, 1990; Elizalde & Sclafani, 1990). While these studies demonstrate that there are various ways in which individual macronutrients confer reward, they do not explain which part of a mixture of macronutrients is most reinforcing. This kind of analysis requires systematic dismantling of a rewarding solution into its components. In such a study using bottle-choice tests, Kimura, Okada, Endo, and Fujimoto (2003) showed that rats prefer a mix of sucrose and fat in a liquid diet, relative to either sucrose or fat alone. In this study, rats habituated to an evaporated milk-based diet were subjected to one- and two-bottle tests of evaporated milk solutions enriched with corn oil only, sucrose only, or a mixture of corn oil and sucrose. In both one- and two-bottle intake tests, rats consumed significantly more of the fat+sucrose solution than of the sucrose-only solution. When the fat-only solution was presented with the sucrose-only solution, the sucrose was preferred. This study and others indicate that fat alone is less preferred than sucrose, but that it has a facilitative effect on ad libitum intake when added to sucrose (Takeda, Imaizumi, & Fushiki, 2000). No studies have yet tested whether flavor added to the mixture makes a solution more rewarding, although this may seem intuitively obvious. In the Kimura study above, nutrients were added to milk, which may have confounded results, considering the sugars, fats and flavors inherent to milk. Furthermore, it seems possible that milk resembles mothers’ milk closely enough for rats that it recalls reward experienced in early life. In order to completely differentiate between responses to fat, sucrose, and flavor, we used emulsions of corn oil, sucrose and/or vanilla flavoring

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in water, rather than in milk. This design allowed us to test the relative reinforcing properties of each individual macronutrient, rather than the complex combination of milk plus nutrient. We also wanted to determine the amount of work fully sated animals were willing to expend in order to obtain fat, sucrose, a mix, or a flavored mix. This is a different measure than mere consumption of each nutrient. Selfadministration chambers, in which rats press a lever to obtain a small amount of reinforcer, allow measurement of the degree to which they will work for such a reinforcer. Using fixed ratio (FR) reinforcement (FR1: one sucrose reinforcer for each lever press), we were able to ascertain the amount of each solution animals consumed. Using a progressive ratio (PR) schedule, in which each successive sucrose reinforcer requires more lever presses than the previous one, we determined the degree of motivation rats expressed for each solution. The PR schedule is thought to be the best way to determine the relative reinforcing strength of different reinforcers (Arnold & Roberts, 1997; Hodos, 1961; Richardson & Roberts, 1996). Note that we will use the term ‘‘motivation’’ here to refer to the amount of work animals will expend for a limited amount of reinforcer, as in our PR procedure. See reviews by Wise and Hoffman (1992) and Arnold and Roberts (1997) for expanded discussion of the concepts of reward, reinforcement, and motivation. In our attempt to ‘‘deconstruct the vanilla milkshake’’, we hypothesized that a combination of fat and sucrose (in the form of emulsified corn oil and sucrose in water) would elicit more self-administration responding than either nutrient alone. In addition, we hypothesized that adding flavor to the mix would make the solution even more rewarding. Using standard self-administration chambers, we analyzed FR and PR responding for a series of solutions containing combinations of sucrose, corn oil, and vanilla. Methods Subjects Subjects were male Albino rats (350–450 g) from Simonsen (Gilroy, CA). Rats were maintained on water and chow ad libitum at all times. They were maintained on a 12:12 h light–dark cycle with lights on at 6 AM. All procedures performed on the rats followed the NIH guidelines for animal care, and were approved by the Animal Care and Use Sub-Committee of the Research and Development Committee at the VA Puget Sound Health Care System or the WWU Animal Care and Use Committee. Each subject only underwent one round of training and testing, as described below. Apparatus Med Associates (Georgia, VT) self-administration chambers, controlled by a Med Associates integrator system,


A.M. Naleid et al. / Appetite 50 (2008) 128–138

had two levers, but only one lever (an active, retractable lever) activated the infusion pump. Presses on the other lever (an inactive, stationary lever) were also recorded. For all experiments, the number of presses on the inactive lever was very low (less than 10 presses/session) and no experimental manipulation altered inactive lever pressing. The sucrose solution was delivered into a liquid drop receptacle for oral consumption.

effect on intake compared to water (Sipols, Stuber, Klein, Higgins, & Figlewicz, 2000). Separate groups of rats were used for each nutrient, but within each nutrient, concentration was the randomized repeated measure. Thus, 15 rats were given all four concentrations of sucrose, and 23 rats were given all four concentrations of oil. In this experiment, animals in the oil condition were trained to press for 4.7% oil, rather than 10% sucrose.

General procedures

Experiment 2

Procedures were based upon our published methodology (11,17). The procedural sequence included: autoshaping; FR training (10 days followed by day off); experimental FR testing; PR training (10 days followed by day off); and experimental PR testing, using the PR algorithm of Richardson and Roberts (1997). This algorithm requires 1, 2, 4, 6, 9, 12, 16, 20, 28, 36, 48, 63, 83, 110, 145, 191, 251, 331, 437, 575, 759, 999, 999 (etc.) lever presses for succeeding reinforcer deliveries within a session (Richardson & Roberts, 1996). Rats were trained to self-administer 10% sucrose (0.6 ml reinforcer) delivered into a liquid drop receptacle. FR training was conducted during one 1-h session per day for 10 days under a continuous reinforcement schedule (FR1: each lever press was reinforced), with a maximum possible of 50 sucrose reinforcers delivered per session. Each session began with the insertion of the active lever and the illumination of a white house light that remained on for the entire session. A 5-s tone (2900 Hz, 20 dB above background)+light (7.5 W white light above the active lever) discrete compound cue accompanied each reinforcer delivery. Responses during reinforcer delivery (10 s) and the subsequent 10 s were recorded but had no consequences. This effectively served as a 20-s ‘‘time out’’. PR training was similar to FR training, but was held for 3 h each day and reinforcers were increasingly difficult to obtain, according to the schedule described above. In addition, the reinforcer volume was 0.4 ml and the lever was retracted for the duration of reinforcer delivery. There was no time out beyond the duration of the reinforcer delivery (6.2 s). PR sessions ended after 30 min of no active lever press responding, at which point the house light was turned off and the active lever retracted. Here, we report number of presses on the active lever (active presses) and number of droplets received (reinforcers).

Next, we sought to determine whether animals would work harder for sucrose if oil were added to it. The sucrose/ oil combinations were: 0% sucrose+0% oil; 0% sucrose+1.4% oil; 3% sucrose+0% oil; 3% sucrose+1.4% oil; 12.5% sucrose+0% oil; and 12.5% sucrose+1.4% oil. These combinations tested whether the response to the lowest or highest concentrations of sucrose could be enhanced by the addition of oil. Twenty-four naı¨ ve rats were included in this study. A follow-up to this study, in 16 naı¨ ve animals, compared work for 3% sucrose with either 1.4% oil or 5.6% oil, to determine if the oil effect was concentration dependent. Doses were given in a randomized, repeated-measures design.

Experimental procedures Experiment 1 We first tested animals’ willingness to work for various dilutions of sucrose or corn oil, matched for caloric density. We tested FR and PR responding for 0%, 3%, 6.25%, and 12.5% sucrose, and for 0%, 1.4%, 2.8%, and 5.6% corn oil. Oil was emulsified in water with 7.5% Tween-80, which was added to all solutions, including controls. As described previously, data from our lab indicate that Tween has no

Experiment 3 To test the hypothesis that flavor would enhance the reinforcing properties of a sucrose or sucrose+oil solution, we added 3% alcohol-free vanilla flavoring (Durkee, ACH Food Companies) to each solution. This concentration was chosen based on initial taste preference tests conducted with separate animals. These animals preferred sweetened 3% vanilla over 1.5% vanilla, and preferred the 3% vanilla over a similarly diluted sweetened chocolate flavoring (Torani Syrups, www.torani.com), leading us to choose vanilla as our test flavor. As in Experiment 1, Tween-80 was used to emulsify the oil and was part of the vehicle for all solutions. The combinations were: vehicle alone; 3% sucrose alone; 3% sucrose+3% vanilla; 3% sucrose+1.4% oil; and 3% sucrose+3% vanilla+1.4% oil. As above, subjects received each combination in a randomized, repeated-measures design. Thirty naı¨ ve rats were included. In a follow-up experiment, we tested whether a lower concentration (1.5%) of an all-natural vanilla (wwwnaturesflavors.com; ingredients are water, guar gum, and flavor) flavoring would be more rewarding than the 3% concentration of the artificial vanilla. We observed identical results when we repeated this experiment with the natural vanilla. For example, PR active presses for sucrose+oil were 81.779.1 without vanilla and 83.5710.5 with vanilla [paired t-test, p ¼ 0.85]. Statistical analyses Number of reinforcers and active lever responses were analyzed using a repeated-measures analysis of variance


(ANOVA), with each animal receiving each combination of sucrose, oil, and/or vanilla. Fisher’s post-hoc analysis (significant differences indicated by different colors on graphs) and paired t-tests (Tables 1 and 2) were used to determine differences between treatments. Group data are presented as the mean7SEM in the text and figures. Significance is defined as pp0.05.

Experiment 1 In our initial concentration-response test for sucrose, we observed concentration-dependent responding for sucrose in both the FR and PR procedures, but only for oil in the FR procedure. There was no significant effect of sucrose concentration on FR active presses [F(14,42) ¼ 0.86, p ¼ 0.47], but we did find a main effect of concentration on reinforcers [F(14,42) ¼ 16.13, po0.0001] (Fig. 1, left panel). We observed a significant effect of corn oil concentration for both FR active presses [F(22,66) ¼ 4.24, p ¼ 0.008] and for reinforcers [F(22,66) ¼ 6.67, p ¼ 0.0005] (Fig. 1, right panel). We similarly observed a main effect of concentration on PR active presses for sucrose [F(14,42) ¼ 7.59, p ¼ 0.0004] as well as for PR sucrose reinforcers [F(14,42) ¼ 6.69, p ¼ 0.0009] (Fig. 2, left panel). However, we observed no effect of oil concentration on PR active presses [F(22,66) ¼ 0.29, p ¼ 0.83] or reinforcers [F(22,66) ¼ 0.28, p ¼ 0.84] (Fig. 2, right panel). Table 1 Experiment 2: Does adding oil increase work for sucrose? Sucrose concentration (%)

% Change with 1.4% oil added

Paired t-test p-value

Fixed ratio Active presses 0 3 12.5

+6.9 +17.8 +16.1

0.54 0.02 0.01

+7.6 +26.8 +11.3

0.49 0.001 0.04

19.9 +2.5 7.5

0.11 0.89 0.50

9.4 4.3 3.8

0.03 0.63 0.30

Rewards received 0 3 12.5 Progressive ratio Active presses 0 3 12.5 Rewards received 0 3 12.5

Table 2 Experiment 3: Does adding vanilla increase work for sucrose or sucrose/oil solutions? Sucrose+oil combination (%)

% Change with 3% vanilla

Paired t-test p-value

Fixed ratio Active presses Sucrose Sucrose+oil

12.8 1.0

0.10 0.88

12.1 3.6

0.11 0.56

8.5 +4.9

0.33 0.60

0 4.9

0.90 0.27

Rewards received Sucrose Sucrose+oil

Results

Bold values indicate a significant change in active presses or rewards received from the corresponding oil-free solution. Student’s t-test determined difference between conditions, which represent responding from all animals in the study (n ¼ 24).

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Progressive ratio Active presses Sucrose Sucrose+oil Rewards received Sucrose Sucrose+oil

Student’s t-test determined difference between conditions, which represent responding from all animals in the study (n ¼ 30).

Experiment 2 This experiment was designed to determine whether a combination of oil and sucrose is more rewarding or motivating than either nutrient alone. While oil alone did not increase responding over the control solution, it did increase responding for sucrose. We found a significant overall effect of nutrient for active lever presses in the FR procedure [F(23,115) ¼ 12.2, po0.0001] (Fig. 3, top). Oil alone did not increase responding above control levels [p ¼ 0.54]. However, both the 3% and 12.5% concentrations of sucrose alone increased responding on the active lever by about 37% [po0.001 for both, compared to control]. The 12.5% concentration of sucrose did not increase lever presses above those for 3% sucrose. Oil combined with either sucrose concentration increased active lever presses by about 17% above responding with the sucrose concentration alone [po0.05 for both, above both sucrose concentrations]. The oil/sucrose combinations increased pressing by about 60% above control conditions [po0.0001 for both combinations compared to control]. See Table 1 for paired t-tests comparing addition of oil to sucrose for FR active lever presses. We also observed a significant overall effect of nutrient for number of reinforcers received in the FR procedure [F(23,115) ¼ 32.7, po0.0001] (Fig. 3, bottom). Again, oil alone had no effect on reinforcers received [p ¼ 0.49]. The 3% sucrose solution increased reinforcers by 58% [po0.0001] compared to control; the 12.5% solution increased reinforcers by 82% [po0.0001]. In addition, the 12.5% solution increased reinforcers by 16% above the 3% solution [p ¼ 0.02]. Addition of 1.4% oil to the 3% sucrose further increased reinforcers by 27% [p ¼ 0.0003], and addition of oil to 12.5% sucrose further increased reinforcers by 11%



Fig. 1. Concentration-dependent increase in FR consumption of sucrose or oil. Left panel: Top, FR active presses for sucrose. Bottom, sucrose reinforcers received. Right panel: Top, FR active presses for oil. Bottom, oil reinforcers received. Within each graph, each bar color indicates significant difference (po0.05) from bars of other colors. Bars represent means7SEM; repeated-measures, n ¼ 15 in sucrose condition, n ¼ 23 in oil condition.

[p ¼ 0.04]. Table 1 further demonstrates the facilitative effect of oil on FR pressing for sucrose. In the PR procedure, we again observed a significant overall effect of nutrient on active lever presses [F(23,115) ¼ 10.2, po0.0001] (Fig. 4, top). Oil alone had no effect on pressing [p ¼ 0.35]. The 3% sucrose increased pressing by 42% [p ¼ 0.48]; 12.5% sucrose increased pressing by 103% above control [po0.0001] and by 43% above 3% sucrose pressing [p ¼ 0.005]. Adding oil to either of these sucrose concentrations did not increase pressing above that obtained with sucrose alone. See Table 1 for paired t-tests comparing addition of oil to sucrose for PR active lever presses. There was likewise a significant overall effect of nutrient on reinforcers received in the PR procedure [F(23,115) ¼ 14.7, po0.0001] (Fig. 4, bottom). The 3% sucrose increased reinforcers by 12% [p ¼ 0.02]. The 12.5% sucrose increased reinforcers by 30% above control [po0.0001] and by 14% above the 3% concentration [p ¼ 0.003]. As for active lever presses, addition of oil to either of these concentrations did not change number of reinforcers received from that of the sucrose concentration

alone. See Table 1 for paired t-tests comparing addition of oil to sucrose for PR reinforcers received. The PR portion of this experiment thus demonstrated that the addition of a sweet component to a fat solution increases its motivational value, but addition of fat to an already motivating sweet solution does not enhance its value. Finally, to test whether an even higher concentration of oil would increase responding more than our original 1.4% concentration, we compared FR and PR active lever presses and reinforcers received for 3% sucrose+1.4% oil and 3% sucrose and 5.6% oil. We saw no differences between these two oil+sucrose concentrations (data not shown). Experiment 3 Addition of vanilla did not increase responding for oil, sucrose, or a combination of the two. We found a significant overall effect of solution on FR active lever presses [F(29,116) ¼ 6.25, p ¼ 0.0001] (Fig. 5, top). The 3% sucrose alone increased pressing by 36% above control


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Fig. 2. Concentration-dependent increase in PR motivation for sucrose but not oil. Left panel: Top, PR active presses for sucrose. Bottom, sucrose reinforcers received. Right panel: Top, PR active presses for oil. Bottom, oil reinforcers received. Within each graph, each bar color indicates significant difference (po0.05) from bars of other colors. Bars represent means7SEM; repeated-measures, n ¼ 15 in sucrose condition, n ¼ 23 in oil condition.

[p ¼ 0.0007]. Addition of vanilla to sucrose had no effect on active presses. Addition of oil to sucrose, without vanilla, did not increase lever presses compared to sucrose alone [p ¼ 0.52], although the increase above control was maintained. Addition of vanilla to the oil+sucrose solution did not increase FR pressing [p ¼ 0.89]. There was also a significant overall effect of solution components on number of reinforcers received in the FR procedure [F(29,116) ¼ 22.4, po0.0001] (Fig. 5, bottom). Sucrose alone increased reinforcers by 84% [po0.0001]. Vanilla did not increase reinforcers for sucrose [p ¼ 0.11]. As we observed in Experiment 2, oil did increase reinforcers when added to sucrose [p ¼ 0.04]. Addition of vanilla to the oil+sucrose combination did not increase reinforcers received [p ¼ 0.56], when compared to oil+sucrose alone. However, there was a significant increase in reinforcers received in the oil+sucrose+vanilla condition compared to the sucrose+vanilla combination alone [p ¼ 0.002], suggesting that, while not rewarding itself, vanilla did not inhibit pressing for already-rewarding solutions. See Table 2 for paired t-tests comparing addition of vanilla to sucrose or sucrose+oil for FR active presses and reinforcers.

In the PR procedure, we again saw a significant overall effect of solution on active presses [F(29,116) ¼ 2.82, p ¼ 0.03] (Fig. 6, top). Sucrose alone increased presses by 37% [p ¼ 0.002]. Vanilla did not significantly increase responding when added to sucrose [p ¼ 0.33]. When oil or oil+vanilla were added to sucrose, no increase was seen in PR active presses. Finally, in the PR procedure, while we did see a significant overall effect of solution on reinforcers received [F(29,116) ¼ 3.7, p ¼ 0.007] (Fig. 6, bottom), this was entirely due to sucrose. Sucrose induced an increase in reinforcers received by 15% compared to control [p ¼ 0.002], and this number of reinforcers was received under all other conditions. See Table 2 for paired t-tests comparing addition of vanilla to sucrose or sucrose+oil for PR active presses and reinforcers received. Discussion In this study we tested the hypothesis that it is the unique combination of flavor, fat, and sucrose that makes nutrient mixtures, such as a milkshake, so rewarding. In fully sated rats, we evaluated self-administration of increasing



Fig. 3. FR consumption of sucrose+oil combinations. Upper panel: FR active presses. Lower panel: Reinforcers received. Within each graph, each bar color indicates significant difference (po0.05) from bars of other colors. Bars represent means7SEM; repeated-measures, n ¼ 24.

Fig. 4. PR motivation for sucrose+oil combinations. Upper panel: PR active presses. Lower panel: Reinforcers received. Within each graph, each bar color indicates significant difference (po0.05) from bars of other colors. Bars represent means7SEM; repeated-measures, n ¼ 24.

concentrations of sucrose or oil alone, the addition of oil to sucrose solutions, and the addition of vanilla flavoring to solutions of sucrose or sucrose+oil. We hypothesized that

addition of oil would lead to increased responding for sucrose. We also expected that the addition of flavor would increase consumption above that of nutrients alone.


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Fig. 5. FR consumption of sucrose+oil+vanilla combinations. Upper panel: FR active presses. Lower panel: Reinforcers received. Within each graph, each bar color indicates significant difference (po0.05) from bars of other colors. Bars represent means7SEM; repeated-measures, n ¼ 30.

Fig. 6. PR motivation for sucrose+oil+vanilla combinations. Upper panel: PR active presses. Lower panel: Reinforcers received. Within each graph, each bar color indicates significant difference (po0.05) from bars of other colors. Bars represent means7SEM; repeated-measures, n ¼ 30.



In fact, we observed that sucrose was the strongest determinant of nutrient solution self-administration. In the first experiment, all concentrations of sucrose elicited more FR and PR presses than oil. We chose concentrations of sucrose or corn oil that were calorically matched, so that self-administration would reflect reward value independent of caloric value. We observed a significant increase in FR reinforcers received as sucrose concentration increased. Others have also shown that rats will self-administer more sucrose reinforcers as concentration increases (Slawecki, Samson, & Hodge, 1997). We expected to see a similar concentration-dependent increase in FR lever presses; however, we observed an unusually high rate of pressing for vehicle in this experiment. These presses must have occurred during the time out, since they are not reflected in the reinforcers received. It should be noted here that in all of our experiments, animals were trained for ten days each of FR and PR pressing for 10% sucrose (or 4.7% oil for the oil group in Experiment 1), followed by testing with various concentrations of sucrose, oil, or control, in a randomized pattern. During testing, those receiving vehicle, oil, or 3% sucrose were then pressing for something other than what they had trained to press for. Therefore, we may have observed a burst of extinction responding when rats were presented with these solutions. Furthermore, the light and tone that served as secondary reinforcers were consistently present, which would have increased likelihood of responding even in the absence of the primary reinforcer. Nonetheless, sucrose proved to be more reinforcing than oil, which elicited half as many presses, even at the highest concentration tested. FR presses and reinforcers were significantly elevated above control levels for this high concentration of oil, however. PR lever pressing and reinforcers were elevated at the highest concentration of sucrose, but there was no increase in PR performance for oil. In Experiment 2, we hypothesized that adding fat to sucrose would increase its reinforcing properties. This was confirmed in the FR procedure, with the low concentration of oil increasing presses and reinforcers of both 3% and 12.5% sucrose. However, similar to the results of Experiment 1, oil did not increase responding in the PR procedure. The observation that rats will press for oil in the FR but not the PR procedure suggests that oil may be rewarding enough for increased consumption, but adding an incremental cost to each oil reinforcer diminishes its motivational value. In support of this idea, Arnold and Roberts (1997) suggest that FR1 responding can be considered equivalent to the rate of consumption, while PR responding is a better measure of the relative reinforcing efficacy of different drug doses or, in our case, sucrose and oil concentrations. Another consideration is that the PR schedule chosen in this laboratory might have been too stringent for weak reinforcers such as dilute corn oil. The first reported PR schedule, described by Hodos (1961), was a ratio increased by an additive increment of two for a reinforcer of dilute sweetened milk. Yet, our

stringent schedule is not too much work for chocolate Ensure, and is still effective for demonstrating differences in motivation for dilute sucrose. That it is too stringent only for dilute oil further illustrates the relatively higher reinforcing value of equicaloric sucrose compared to oil. Data from the first two experiments indicate that, when matched for caloric density, the two reinforcing nutrients in a milkshake, fat and sucrose, are not equally powerful. This is an important finding, since increased consumption is one direct cause of weight gain, regardless of motivation. If, as has been discussed thoroughly in nutrition literature (Blundell, Burley, Cotton, & Lawton, 1993; Blundell & MacDiarmid, 1997; Gerstein, Woodward-Lopez, Evans, Kelsey, & Drewnowski, 2004; Johnstone, Stubbs, & Harbron, 1996; Stubbs, Ferres, & Horgan, 2000; Westerterp, 2006), fat is less satiating than other macronutrients, then its presence in the human diet could be the reason for over-consumption of vanilla milkshakes (among other fine foods). These data support the findings of Kimura et al. (2003) who showed that fat alone does not enhance intake of condensed milk, but that it does facilitate intake of sucrose in the same vehicle. Another study measuring consumption of emulsions in addition to chow in the home cage showed that more sucrose+fat solution was consumed than either sucrose or fat alone (Lucas & Sclafani, 1990). Our study now demonstrates the high relative reinforcing value of sucrose compared to oil. This is consistent with our previous report of performance in a 5-min lickrate task by rats. In that study, rats had 5 min to lick a series of equicaloric solutions of sucrose and corn oil (including the concentrations used here, as well as higher concentrations). This is a behavioral paradigm in which post-ingestive influences are minimal or absent, and is thought to reflect hedonic valuation of the solutions. We observed that lick rates for sucrose were higher than those for oil. Further, increasing oil concentration as high as 16% did not result in increased licking of oil solutions (Sipols et al., 2000). In the present study, a non-reinforcing concentration of oil did increase pressing for sucrose, suggesting that the presence of even a low amount of fat in foods makes them more rewarding. Our final question was whether addition of a flavor increased the reinforcing qualities of sucrose or sucrose+ fat solutions. In this experiment, sucrose and fat seemed to be the only reinforcing factors. Vanilla did not increase responding. Oil did increase the number of reinforcers received in the FR procedure, in a manner similar to what we observed in Experiment 2, although only FR reinforcers, not active presses, were significantly elevated above those for sucrose alone. Our main question in this experiment—whether vanilla increased responding— yielded a clear, negative, answer. Vanilla neither enhanced nor attenuated responding for sucrose+fat, and this was the case even for a lower concentration of an all-natural vanilla (data not shown). Previous studies have shown that rats and humans have different preferences for polycose versus sucrose (Feigin, Sclafani, & Sunday, 1987), and


although sugars are detected by different receptors than are flavors, it seems reasonable to assume that rats may have different preference for vanilla than do humans. The rats had been exposed once to the vanilla solution before the study, to eliminate neophobia, but they had not had positive associative experiences with it, as have, for example, humans who enjoy vanilla. Our finding that vanilla does not alter consumption confirms that of Archer, Brown, Rayner, Stubbs, and Mercer (2006), who tested rats’ consumption of four flavors of Ensure and found no difference in intake or body weight gain over 2 or 3 weeks. A human-subjects counterpart to these studies was conducted by Essed, van Staveren, Kok, and de Graaf (2007), in which flavor and monosodium glutamate (MSG) were added to meals provided to elderly patients in a nursing home. The authors observed no increase in intake by the groups who received flavoring or MSG added to their food over 16 weeks. Other studies (Mathey, Siebelink, de Graaf, & Van Staveren, 2001; Schiffman and Warwick, 1993) have shown that flavor enhancement does increase intake in elderly subjects, but attribute their original diminished intake to a decline in flavor perception. Since our rats presumably have intact flavor perception, these studies may not be comparable. In an unpublished study by our group, we observed that rats prefer sucrose+vanilla in a two-bottle test, but do not consume more sucrose as a result of flavoring. These studies suggest that flavor alone is not an appetite stimulant to rats. We are currently exploring the role of experience and positive association in conferring reward value to flavors such as vanilla. From all of these studies the relative value of sucrose is quite clear. For rats, this is the most important component of the nutrient mixtures we tested. Nevertheless, there is something in the composition of chocolate Ensure that compels our rats to work very hard. Two aspects of palatability that our experiments did not test are texture and past association. Ensure may offer both of these to rats, recalling the texture and bonding experience of milk while nursing. Our deconstruction did not attempt to replicate these factors. Questions about the relative roles of macronutrients in food reward warrant future investigation. For example, many products targeted to dieters substitute sugar for fat calories in low-fat foods. Behavioral economic analysis tells us that this is an acceptable solution, since palatable alternatives are able to lure subjects away from a high-fat option (Freed & Green, 1998). Based on some of the present findings, this may be effective in minimizing caloric intake, since the addition of fat prolonged consumption in the FR1 test. However, based on our other findings, sucrose is the strongest reinforcer present in the solutions, and sucrose is often the substituted nutrient in low-fat human food. If the obesity problem results from excessive desire for palatable foods, then our results suggest that substituting sucrose for fat would not lead to a decrease in their consumption. We also must acknowledge that our methods are not specific enough to account for the pre- and

137

post-ingestive reward effects of each solution. For example, as we increase the concentration of each solution, we simultaneously increase the reward value as well as the degree of satiation. These factors both affect self-administration. Therefore, this analysis is a first attempt at describing the reinforcing value of each nutrient, but remains limited in its ability to draw conclusions about psychological mechanisms involved in the intake of each nutrient. Clearly, the reasons for human over-consumption of high-calorie foods are complex. The present results suggest that, while fat is rewarding and problematic for a healthy diet, an equivalent number of calories of sucrose is more motivating; therefore, the sucrose content of our foods must be considered as we try to limit our consumption.

Acknowledgments The authors thank Richard L. Martin, III, for his help in the execution of these studies. This work was supported by Contract 4442sc (DFL) and 4443sc (JWG), from the University of California at San Francisco. This publication was made possible through support from the Dean’s Office of the School of Medicine, University of California at San Francisco, and the Resnick Family Foundation, and through the Exploratory Center for Obesity Research at the University of Washington, Seattle, NIH Grant P20RR020774. Dianne Figlewicz Lattemann is supported by NIH DK40963 and the Department of Veterans Affairs (Research Career Scientist Award). Dr. Naleid is supported by the University of Washington NIH F32 DA02031501A2 and NIH Training Grant T32-AA007455. Dr. Sipols is supported by Latvian Council of Science Grant 04.1116. Dr. Grimm is supported by NIH/NIDA Grant R15 DA016285 and Western Washington University.

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Mathey, M. F., Siebelink, E., de Graaf, C., & Van Staveren, W. A. (2001). Flavor enhancement of food improves dietary intake and nutritional status of elderly nursing home residents. Journal of Gerontology A—Biological Science and Medical Science, 56(4), M200–M205. Raben, A., Vasilaras, T. H., Moller, A. C., & Astrup, A. (2002). Sucrose compared with artificial sweeteners: Different effects on ad libitum food intake and body weight after 10 wk of supplementation in overweight subjects. American Journal of Clinical Nutrition, 76(4), 721–729. Richardson, N. R., & Roberts, D. C. (1996). Progressive ratio schedules in drug self-administration studies in rats: A method to evaluate reinforcing efficacy. Journal of Neuroscience Methods, 66(1), 1–11. Schiffman, S. S., & Warwick, Z. S. (1993). Effect of flavor enhancement of foods for the elderly on nutritional status: Food intake, biochemical indices, and anthropometric measures. Physiology & Behavior, 53(2), 395–402. Sclafani, A. (2004). Oral and postoral determinants of food reward. Physiology & Behavior, 81(5), 773–779. Sclafani, A., Nissenbaum, J. W., & Ackroff, K. (1994). Learned preferences for real-fed and sham-fed polycose in rats: Interaction of taste, postingestive reinforcement, and satiety. Physiology & Behavior, 56(2), 331–337. Sipols, A. J., Stuber, G. D., Klein, S. N., Higgins, M. S., & Figlewicz, D. P. (2000). Insulin and raclopride combine to decrease short-term intake of sucrose solutions. Peptides, 21(9), 1361–1367. Slawecki, C. J., Samson, H. H., & Hodge, C. W. (1997). Differential changes in sucrose/ethanol and sucrose maintained responding by independently altering ethanol or sucrose concentration. Alcohol— Clinical and Experimental Research, 21(2), 250–260. Stubbs, J., Ferres, S., & Horgan, G. (2000). Energy density of foods: Effects on energy intake. Critical Reviews in Food Science and Nutrition, 40(6), 481–515. Takeda, M., Imaizumi, M., & Fushiki, T. (2000). Preference for vegetable oils in the two-bottle choice test in mice. Life Sciences, 67(2), 197–204. Tordoff, M. G., & Alleva, A. M. (1990). Effect of drinking soda sweetened with aspartame or high-fructose corn syrup on food intake and body weight. American Journal of Clinical Nutrition, 51(6), 963–969. Westerterp, K. R. (2006). Perception, passive overfeeding and energy metabolism. Physiology & Behavior, 89(1), 62–65. Wise, R. A., & Hoffman, D. C. (1992). Localization of drug reward mechanisms by intracranial injections. Synapse, 10, 247–263. Zhang, M., & Kelley, A. E. (1997). Opiate agonists microinjected into the nucleus accumbens enhance sucrose drinking in rats. Psychopharmacology (Berlin), 132(4), 350–360.

ARTICLE IN PRESS


Research Report

Decreasing dislike for sour and bitter in children and adults Elizabeth D. Capaldi, Gregory J. Privitera Department of Psychology, Arizona State University, 950 S. McAllister Street, Tempe, AZ 85287, USA Received 10 April 2007; received in revised form 12 June 2007; accepted 16 June 2007

Abstract Strategies for preference conditioning were investigated in children (Experiment 1) and adults (Experiment 2). In Experiment 1, subjects were 49 children, between the ages of 2 and 5 years old. The results showed that initially mixing grapefruit juice with the sweet taste of sucrose increased liking for the sour taste of grapefruit juice (without the added sucrose) and that this increased liking persisted weeks later. In Experiment 2, subjects were 62 undergraduates with a median age of 18 years. Consistent with Experiment 1, the results showed that initially mixing broccoli or cauliflower with the sweet taste of sugar increased liking for the bitter taste of these vegetables. The results are discussed in terms of decreasing dislike for fruits and vegetables. r 2007 Elsevier Ltd. All rights reserved. Keywords: Flavor reinforcement; Preferences; Liking; Fruits; Juices; Vegetables

Introduction At birth human infants reject bitter and sour tastes while favoring sweet tastes such as sucrose (Desor, Maller, & Andrews, 1975; Kajiura, Cowart, & Beauchamp, 1992; Lipsitt & Behl, 1990). Although the initial affective reaction to the basic tastes (including sour and bitter) is genetically mediated, this response can be modified through experience (Capaldi, 1996; Capaldi & Hunter, 1994; Capaldi, Hunter, & Lyn, 1997). Conditioning with these basic tastes is one way to modify liking (Holman, 1975). For example, flavor– flavor conditioning occurs when a conditioned stimulus (CS; e.g., an odor extract) is mixed with an unconditioned stimulus (US; e.g., an innately liked taste, such as sweetness). Flavor–nutrient conditioning occurs when a CS is mixed with as caloric US (e.g., sucrose; Lyn & Capaldi, 1994). Both preference conditioning strategies increase the liking for the CS even when the US is subsequently removed, although most research in this area has focused on animals (Capaldi, Campbell, Sheffer, & Bradford, 1987; Fanselow & Birk, 1982; Holman, 1975). Human studies have shown that undergraduates receiving several pairings of flavored tea (CS) with sucrose (US) Corresponding author.

E-mail address: [email protected] (G.J. Privitera). 0195-6663/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.appet.2007.06.008

will increase their preference for the flavor of a sweetened tea, even when it is no longer sweetened (Zellner, Rozin, Aron, & Kulish, 1983). More recent work by Stevenson and colleagues has shown that odors paired with sweet tastes are not only more liked, they are rated as smelling sweeter (Stevenson, Boakes, & Prescott, 1998; Stevenson, Prescott, & Boakes, 1995). Whether the results from these studies were mediated by flavor–flavor or flavor–nutrient learning cannot be determined since sugary solutions that contain nutrients are both flavorful and caloric. The present studies asked whether these preference conditioning procedures will increase the pleasantness or liking for foods, such as fruit juice (Experiment 1) and vegetables (Experiment 2). In Experiment 1, we tested whether mixing sucrose with the sour taste of grapefruit juice will increase pleasantness ratings for the juice. Pairing a CS flavor with sugar effectively facilitates long-term preferences for flavors that are subsequently unsweetened in rat studies. We were interested in whether this would also facilitate longterm preferences for grapefruit juice with children. Experiment 1 Method Subjects: Subjects were 63 children between the ages of 2 and 5 years old. Thirty-five children were recruited from


E.D. Capaldi, G.J. Privitera / Appetite 50 (2008) 139–145

Baby Gator Educational Research Center and 28 children were recruited from a KinderCare Learning Center. Both centers were located on the University of Florida campus and the majority of the children’s parents were either employed by or were students of the university. Of the 63 subjects 25 were girls and 38 were boys. Each child had to have a parental consent form signed by their parents in order to participate. Thirteen children were dropped because they were either absent during the baseline condition or they were absent all through the first test period. One child was dropped from the study because he was allergic to citrus. As a result the data for only 49 children was analyzed. Materials and apparatus: All baseline and testing solutions were presented in 5 oz Dixie paper cups (James River Corporation, Del Rio, Texas). Training solutions were presented in 5 oz Solo plastic cups (Solo Cup Company, Highland Park, Illinois). Baseline and testing solutions were cherry kool aid (Kraft General Foods, White Plains, NY; wt/vol) mixed with 10.61% sucrose (Publix Supermarket brand; wt/vol) and concentrated Bluebird grapefruit juice (Citrus World Inc., Lakes Wales, FL; vol/vol). For the kool aid one packet of cherry kool aid was mixed with 2000 ml of distilled water plus 10.61% sucrose (which is the percentage of sucrose found in the concentrated grapefruit juice). For the grapefruit juice, one part grapefruit juice (1000 ml) was mixed with four parts distilled water (4000 ml). Red food coloring (McCormick’s brand) was added to the grapefruit juice, so that it would appear differently than when it was presented during training. The red food coloring changed the color of the grapefruit juice to pink. The training solutions were concentrated Bluebird grapefruit juice mixed with 20% sucrose and concentrated Bluebird grapefruit juice mixed with 30% sucrose. The grapefruit juice was mixed the same as in baseline and testing. In order to add the concentrations of sucrose we first determined that the grapefruit juice already contained 10.61% sucrose; so, 9.39% sucrose was added to the grapefruit juice to get a 20% concentration of sucrose and 19.39% sucrose was added to get a 30% concentration of sucrose. Scoring: A rating scale with five pictures was used (SAM: Self-Assessment Manikin, Bradley & Lang, 1994; see Fig. 1). This pictorial scale has been used to measure affective reactions to various stimuli, such as emotion. In the present study the scale was used to measure liking and disliking. The teachers (who administered the experiment) asked each subject to rate each solution (grapefruit juice or

kool aid) by having him or her point to one of the five pictures. The teachers recorded the responses on a scale from 1 to 5 (where 1 meant ‘‘really liked’’, 2 meant ‘‘liked’’, 3 meant ‘‘not liked nor disliked’’, 4 meant ‘‘disliked’’ and 5 meant ‘‘really disliked’’). For example, if the subject pointed to the picture with the happy face, then the teacher gave a rating of 1 for that subject; if the subject pointed to the middle picture (the face with no expression), then the teacher gave a rating of 3 for that subject; and if the subject pointed to the picture with the sad face, then the teacher gave a rating of 5 for that subject. Procedure Debriefing: As previously noted, the teachers at each center administered the solutions during the baseline, training and testing conditions. There were four teachers from the Baby Gator Center and four teachers from the KinderCare Center. Teachers were responsible for their own subjects. All of the teachers were instructed on how to administer each solution and how to record the responses on the rating sheets that were provided. The teachers were instructed to offer the solutions to each subject each day. In no way were they to force a subject to drink any of the solutions. They were instructed to present the solutions non-contingently (i.e., independent of any foods or activities). All of the teachers presented the solutions individually, rather than as a group, because of the potential risk of peer influence on rating preference for each solution. All of the teachers usually gave the solutions between 10:30 a.m. to 12:00 p.m. or 3:00 p.m. to 4:00 p.m. The teachers were given a rating sheet for each subject to record the responses. They were allowed to explain the five picture rating scale to any subject who did not understand the rating scale. If this was necessary they described how this was done in the comment section for that subject. After a subject drank each solution the teachers asked whether he or she liked it or not. They were instructed not to use words or facial expressions which might influence a subject’s decision. An example of an improper question would be, ‘‘Isn’t this juice delicious?’’ while a proper question would be, ‘‘What did you think of the juice today?’’ Ratings for each solution were recorded and whether a subject tasted the solution or not (scored ‘‘y’’ if yes, scored ‘‘d’’ if a child drank the entire solution, or scored ‘‘refused’’ if a subject refused to drink any of the solution) was recorded as well. Any remarks that the teachers felt were important were also written in the comment section for each subject.

Fig. 1. The Self-Assessment Manikin [SAM] (Bradley & Lang, 1994).

ARTICLE IN PRESS E.D. Capaldi, G.J. Privitera / Appetite 50 (2008) 139–145

Baseline: To minimize any potential exposure effects prior to training, the baseline condition lasted only 1 day. On this day each subject was presented with a 4 oz cup of kool aid and a 4 oz cup of plain grapefruit juice. To determine if the assessment scale (i.e., SAM) was valid an already well-liked drink was rated, in this case kool aid. Kool aid was also used as a control to determine whether training with the grapefruit juice mixed in sucrose would affect liking for the taste of plain grapefruit juice. We expected the ratings for kool aid to remain the same from baseline to test. If the reaction changed from baseline to test for grapefruit juice but not for kool aid, then we took that as evidence that the results were due to the training manipulations. Each teacher allowed half of their subjects to try the grapefruit juice first and the remaining half were allowed to try the kool aid first. Ratings for both solutions were recorded for each child. Training: The training condition lasted 20 days and began the day after the baseline condition ended. During the training condition each subject received only one solution. Each subject was randomly assigned to one of two groups: Group Grapefruit 20 or Group Grapefruit 30. Subjects in Group Grapefruit 20 received a 4 oz cup of grapefruit juice mixed with 20% sucrose and subjects in Group Grapefruit 30 received a 4 oz cup of grapefruit juice mixed with 30% sucrose. We used two concentrations of sucrose (the US) since the effectiveness of an US as a reinforcer is dependent on its concentration in animal studies. We assumed the same might be true with humans and wanted to be sure we used a strong enough concentration to obtain conditioning. Two different cups were used to differentiate each group: solutions in blue cups were given to the subjects in Group Grapefruit 20 and solutions in white cups given to the subjects in Group Grapefruit 30. Both the subjects and the teachers were blind to which solutions were mixed with which concentration of sucrose as well as what concentrations of sucrose were used. Ratings of the two types of grapefruit juice were recorded daily for each child. Testing: The first test, Test 1, began after the last day of training. The procedure for testing was identical to the procedure used during the baseline condition. The only difference was that testing lasted for 5 days. Ratings for both the kool aid and the plain grapefruit juice were recorded daily for each child. A follow-up test, Test 2, was conducted two weeks after the first test ended. This test was conducted to determine whether the effects of training would persist over time. This test was identical to the first test and also lasted for 5 days. Data analyses: An analysis of variance (ANOVA) for baseline included gender, groups (Grapefruit 20 or Grapefruit 30) and reaction (liked or disliked) as the between factors and fluid (kool aid or unsweetened grapefruit juice) as the within factor. An ANOVA for testing included condition (baseline or test) as an additional within factor.

141

Results Baseline: Child SAM ratings for kool aid and grapefruit juice varied by group, F(1,45) ¼ 59.98, po.001. Newman–Keuls tests showed that there were no group differences in SAM ratings for kool aid (p4.74), but a significant difference in SAM ratings for grapefruit juice (po.05). The mean ratings for plain grapefruit juice and kool aid were 3.00 and 1.36 for Group Grapefruit 20 and 2.67 and 1.79 for Group Grapefruit 30, respectively. Since mean ratings for grapefruit juice varied quite a bit, the groups were broken down into whether grapefruit juice was liked or disliked, and the overall error term from the original AVNOA was used as the denominator for all subsequent ANOVA tests. There were 22 children who rated grapefruit juice as ‘‘really liked’’ or ‘‘liked’’ (a rating of 1 or 2). Of these 22 children 10 were in Group Grapefruit 20 and 12 were in Group Grapefruit 30. There were 27 children who rated the grapefruit juice as ‘‘not liked nor disliked’’, ‘‘disliked’’ and ‘‘really disliked’’ (a rating of 3, 4 or 5). Of these 27 children 15 were from Group Grapefruit 20 and 12 were from Group Grapefruit 30. In general, when subjects liked grapefruit juice they rated it as being liked slightly higher than kool aid. However, when subjects disliked grapefruit juice the kool aid was rated higher. Training: During training, those subjects that had disliked unsweetened grapefruit juice in baseline now liked sweetened grapefruit juice in training regardless of sucrose concentration, F(1,47) ¼ 5.46, po.03; those subjects that had liked unsweetened grapefruit juice in baseline now reported slightly lower ratings (i.e., increased liking) for sweetened grapefruit juice, although this did not reach statistical significance, Fo1. As shown in Fig. 2, only children initially reporting a dislike for the grapefruit juice showed a significant change in mean SAM ratings from baseline to training (and test). Testing: As shown in Fig. 2, mean SAM ratings for grapefruit juice decreased (i.e., increased liking) from baseline to Tests 1 and 2 for subjects who disliked grapefruit juice in baseline (not for subjects who liked grapefruit juice in baseline). This was supported by the significant three-way Condition Fluid Reaction interaction, F(1,47) ¼ 4.25, po.05. Subsequent pairwise analyses showed that subjects who disliked unsweetened grapefruit juice in baseline now liked it in test, F(1,25) ¼ 15.50, po.01. For children who initially liked unsweetened juice, however, there was no significant change in liking, F(1,20) ¼ 2.5, p4.05. This asymmetry is consistent with our hypothesis, and is inconsistent with changes being due to regression to the mean. As expected, there was also a significant main effect of fluid, F(1,47) ¼ 19.55, po.001. Subsequent Newman– Keuls tests showed that kool aid was rated on average as being more liked than grapefruit juice (po.05). Finally, consistent with what we would expect, ratings for kool aid remained the same from baseline to test, F(1,47) ¼ 3.75,


142

increasing liking for initially disliked foods is more difficult and can many times produce the opposite effect (Birch, Birch, Marlin, & Kramer, 1982; Birch & Fisher, 1996). Also in the present study mere exposure did not change the liking for kool aid. Experiment 2 investigated whether these results could be replicated in an adult sample with bitter tasting vegetables and included a mere exposure group.

5

Mean SAM Ratings

4

3

Experiment 2 2

Disiked Before Training

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0 Baseline

Training

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Conditions Fig. 2. Mean SAM ratings in baseline, training, Tests 1 and 2 for grapefruit juice whether it was liked or disliked in baseline. Vertical lines represent standard error of the mean (SEM).

p4.05, and this did not vary by group. Thus, mere exposure did not change liking for kool-aid. No additional effects were significant in either test (Fo1). Discussion The results of this experiment show that mixing grapefruit juice with sucrose can reduce the initial dislike for the taste of grapefruit juice, even when the sucrose is later removed from the juice. As a result of adding sucrose to the juice, children who rated unsweetened grapefruit juice as being neutral or not liked subsequently rated unsweetened grapefruit juice as being liked. The initial affective reaction to the sour taste of grapefruit juice reflected in the baseline ratings was now changed by the experience during training. The results show that adding sucrose to grapefruit juice is one way to increase the liking for the taste of plain grapefruit juice. They also indicate that if a child already likes the taste of plain grapefruit juice, then sweetening it will not enhance liking. An alternative explanation may be that the results were due to regression toward the mean in that those who liked grapefruit juice decreased liking and those who disliked grapefruit juice increased liking over trials. While this may have contributed to the findings, only the latter effect was significant, which is most consistent with a preference conditioning explanation. Also, although a mere exposure control was not included in the present design, it should be noted that mere exposure with disliked foods or flavors is not a very effective procedure to begin with. For example, while mere exposure to foods that children are willing to consume does increase liking over trials,

Experiment 2 tested whether pairings with sucrose could also be useful in decreasing dislike for bitter tasting vegetables in a sample of college students. College students were used as subjects to extend the generality of the findings in Experiment 1 from children to college students and from grapefruit juice to vegetables. A within-subjects mere exposure control group was included in the experiment. Subjects: A sample of 32 undergraduates (14 males and 18 females) enrolled in an Introductory Psychology class at the University of Florida was included in this study. The median age of the subject pool was 18.0 years (age range: 18–23) and the mean height for subjects was 68 in. The mean Body Mass Index (BMI) score was 21.9, ranging from 18.2 to 23.7 and showed no reliable differences in restraint by group assignments, as assessed by a modified version of the restraint scale (Herman & Polivy, 1975; see procedures section). All subjects had no physician or doctor diagnosed food allergies, medical conditions, or dietary restrictions due to pregnancy, diabetes, or a previous physical illness. Subjects were instructed on the sign-up sheet not to eat any foods within 2 h of the study. Only those who met all requirements for this experiment were included in the data analyses. Materials and apparatus: All baseline, training, and test foods were served on 4 in paper plates. The foods used were broccoli and cauliflower (Publix Supermarket brand). Some of these vegetables were briefly (for a period of ten seconds) dipped into 2000 ml of distilled water plus 20% sucrose during training. No food coloring was added to the sweetened water; no foods were sweetened during baseline or test. Cold water was served in 16 oz cups and more water was made available upon request. Scoring: Subjects were asked to rate each food on three nine-point scales, the endpoints for the first being 1 ¼ extremely pleasant and 9 ¼ extremely unpleasant; for the second, 1 ¼ extreme sweetness and 9 ¼ absence of sweetness; and for the third, 1 ¼ extreme bitterness and 9 ¼ absence of bitterness. Subjects were instructed to eat all of each food in front of them and to rate it on the scales, although it was emphasized that subjects were not required to eat any foods they did not wish to consume voluntarily. All subjects ate at least half of each food portion on each day of the experiment, which we assumed was sufficient for reliable daily ratings of the experimental foods used.


Subjects were randomly assigned to two groups: Group Broccoli (n ¼ 15; seven males and eight females) and Group Cauliflower (n ¼ 17; seven males and 10 females). Group names reflected which vegetable would be sweetened in training. Experimental sessions were run each day in the afternoon between 11:00 am and 1:00 pm for 5 days. There were 3–5 subjects run in each session, and subjects were seated individually in one of the five cubicles in the room. On Day 1 (baseline), subjects first signed an Informed Consent form and were told that they would be tasting and rating foods and filling out questionnaires. After signing the form, subjects were then given a plate containing one stalk (weighing an average of 14 g) of unsweetened broccoli and cauliflower and asked to consume these foods and rate them for pleasantness, sweetness, and bitterness. After rating these foods, subjects were dismissed and reminded to come back the next day. Days 2–4 were training days. On each day, half the subjects in the study received one stalk of unsweetened broccoli and one stalk of sweetened cauliflower (Group Cauliflower); for the other half, the broccoli was sweetened and the cauliflower was unsweetened (Group Broccoli). Subjects were again asked to rate each food on the three scales each day. Subjects were also instructed to consume the unsweetened vegetable first each day to avoid any confound of taste. After consuming these foods each day, subjects were dismissed with a reminder to come back the next day. Day 5 was a testing day. On this final day, all subjects received unsweetened broccoli and unsweetened cauliflower to determine if sweetening the vegetables during training had increased preferences for these bitter tasting vegetables. After consuming and rating these test foods, subjects were given debriefing sheets and dismissed. Data analyses: An ANOVA for baseline included gender, groups (Broccoli and Cauliflower) and rating (1–9 for pleasantness, sweetness, and bitterness). An ANOVA for training included days (1–3) and taste (sweetened vs. unsweetened) as an additional factor. An ANOVA for testing included condition (baseline vs. test) as the additional factor. Results Baseline (1 day): The mean pleasantness ratings for unsweetened broccoli and cauliflower were 5.88 and 7.33 for Group Broccoli and 6.46 and 7.23 for Group Cauliflower, respectively. Overall ratings for these vegetables did not vary by group (p4.50) or gender (p4.79). While, mean pleasantness ratings during baseline indicated that cauliflower was more disliked than broccoli in both groups, F(1,28) ¼ 6.74, po.02, all subjects reported disliking these bitter tasting vegetables in baseline. Since there was no group difference in pleasantness ratings in Experiment 2, subjects were not separated into high and low pleasantness groups (as in Experiment 1). All groups reported similar

ratings for low sweetness (mean rating was 8.57) and high bitterness (mean rating was 2.94) of the unsweetened vegetables. Training (3 days): Broccoli and cauliflower were rated as more pleasant over days, F(1,28) ¼ 5.74, po.03, and (as expected) this increase compared to baseline was greatest for vegetables that were sweetened (po.01). As shown in Fig. 3, sweetening the vegetable during training improved pleasantness ratings of the vegetables more so than mere exposure to those vegetables unsweetened. There was also a significant main effect of days, F(1,28) ¼ 13.66, po.002. Newman–Keuls tests (po.04) showed that the vegetables were rated as less pleasant on Day 1 (mean rating was 5.96) compared to Day 3 (mean rating was 5.11), regardless of whether the vegetables were sweetened. Pleasantness ratings did not vary by group or gender. As expected, measures of sweetness and bitterness depended on whether the vegetables were sweetened, F(1,140) ¼ 82.23, po.001, and ratings did not vary by day (p4.12). Newman–Keuls tests showed that sweetened vegetables were rated as significantly sweeter than unsweetened vegetables (po.001); unsweetened vegetables were rated as significantly more bitter than sweetened vegetables (po.001). No additional effects were significant. Testing (1 day): The mean pleasantness ratings for unsweetened vegetables significantly increased from baseline to test, F(1,28) ¼ 8.88, po.001, but only for vegetables that were sweetened in training (po.01). As shown in Fig. 4, subjects rated the unsweetened vegetables in test as more pleasant only if these vegetables were sweetened in training. Also, vegetables that were sweetened during training (flavor preference learning) were rated as significantly more pleasant compared to these same vegetables

Vegetable Sweetened

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6 5 4 3 2 1 0 BL

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Baseline (BL) and Days Fig. 3. Mean pleasantness ratings during baseline and during each day of training (sweetened vs. unsweetened vegetables). All vegetables were unsweetened during baseline. Vertical lines represent SEM.


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highly resistant to extinction would need to be further tested with humans. At present, these findings are the first to demonstrate that pleasantness ratings for an innately disliked taste (such as sour and bitter) can be reduced using preference conditioning strategies with children and adults. They also indicate the possibility that one effective strategy parents can use to increase liking for disliked juice (such as grapefruit juice) and vegetables (such as cauliflower and broccoli) would be to mix them with tastes their children prefer. Such a strategy would only need to be employed for a relatively short period of time to be effective.

2

Acknowledgment

1 0 Baseline and Test Fig. 4. Mean pleasantness ratings for unsweetened vegetables during test. Vertical lines represent SEM.

that remained unsweetened during training (mere exposure learning). Thus, while there was a general tendency for increased pleasantness over days, the flavor reinforcement learning strategy was significantly more effective than mere exposure at decreasing dislike for these bitter-tasting vegetables. No additional effects were significant.

General discussion The results from Experiment 2 demonstrate that sweetening vegetables on only three occasions was sufficient to increase pleasantness ratings for these vegetables, even when they were subsequently unsweetened. Mere exposure was not sufficient to produce this change in pleasantness ratings. Also, special care was taken to ensure that hunger and fullness states were similar in each group by only including subjects who followed the instructions for eating prior to the experimental procedures. Whether these increased ratings reflect a transfer of affect (an inherent shift in liking) or conditioned responding (liking that is dependent on further associations with sweetness) cannot be presently deciphered. A conditioned responding explanation would uniquely predict that these ratings for pleasantness would extinguish quickly, especially considering the short duration of training in the present experiment (i.e., 3 days). Therefore, it would be worthwhile to consider how resistant these findings would be to extinction (i.e., testing) in future experiments. Animal research suggests that preference conditioning produces a transfer of affect, rather than a cognitive expectancy of receiving some reinforcement (Capaldi, 1996). Indeed, recent work by Stevenson and colleagues has shown that odors paired with sweet tastes are rated as sweeter long after the sweet taste is removed (Stevenson, Boakes, & Wilson, 2000). Whether ratings of pleasantness are also

This research was supported by the Florida Citrus Foundation. References Birch, L. L., Birch, D., Marlin, D., & Kramer, L. (1982). Effects of instrumental eating on children’s food preferences. Appetite, 3, 125–134. Birch, L. L., & Fisher, J. A. (1996). The role of early experience in the development of children’s eating behavior. In E. D. Capaldi (Ed.), Why we eat what we eat: The psychology of eating (pp. 113–141). Washington, DC: American Psychological Association. Bradley, M. M., & Lang, P. J. (1994). Measuring emotion: The SelfAssessment Manikin and the semantic differential. Journal of Behavior Therapy & Experimental Psychiatry, 25, 49–59. Capaldi, E. D. (1996). Conditioned food preferences. In E. D. Capaldi (Ed.), Why we eat what we eat: The psychology of eating (pp. 53–80). Washington, DC: American Psychological Association. Capaldi, E. D., Campbell, D. H., Sheffer, J. D., & Bradford, J. P. (1987). Conditioned flavor preferences based on delayed caloric consequences. Journal of Experimental Psychology: Animal Behavior Process, 13, 150–155. Capaldi, E. D., & Hunter, M. J. (1994). Taste and odor in conditioned flavor preference learning. Animal Learning & Behavior, 22, 355–365. Capaldi, E. D., Hunter, M. J., & Lyn, S. A. (1997). Conditioning with taste as the CS in conditioned flavor preference learning. Animal Learning & Behavior, 25(4), 427–436. Desor, J. A., Maller, O., & Andrews, K. (1975). Ingestive responses of human newborns to salty, sour and bitter stimuli. Journal of Comparative and Physiological Psychology, 89, 966–970. Fanselow, M., & Birk, J. (1982). Flavor–flavor associations induce hedonic shifts in taste preference. Animal Learning & Behavior, 10, 223–228. Herman, C. P., & Polivy, L. (1975). Anxiety, restraint and eating behavior. Journal of Abnormal Psychology, 84, 666–672. Holman, E. W. (1975). Immediate and delayed reinforcers for flavor preferences in rats. Animal Learning & Behavior, 6, 91–100. Kajiura, H., Cowart, B. J., & Beauchamp, G. K. (1992). Early developmental changes in bitter taste responses in human infants. Developmental Psychobiology, 25, 375–386. Lipsitt, L. P., & Behl, G. (1990). Taste-mediated differences in the sucking behavior of human newborns. In E. D. Capaldi, & T. L. Powley (Eds.), Taste, experience, and feeding (pp. 75–93). Washington, DC: American Psychological Association. Lyn, S. A., & Capaldi, E. D. (1994). Robust conditioned flavor preferences with a sensory preconditioning procedure. Psychonomic Bulletin & Review, 1(4), 491–493. Stevenson, R. J., Boakes, R. A., & Prescott, J. (1998). Changes in odor sweetness resulting from implicit learning of a simultaneous

ARTICLE IN PRESS E.D. Capaldi, G.J. Privitera / Appetite 50 (2008) 139–145 odor–sweetness association: An example of learned synesthesia. Learning and Motivation, 29, 113–132. Stevenson, R. J., Boakes, R. A., & Wilson, J. P. (2000). Resistance to extinction of conditioned odour perceptions: Evaluative conditioning is not unique. Journal of Experimental Psychology: Learning, Memory, & Cognition, 26, 423–440.

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Stevenson, R. J., Prescott, J., & Boakes, R. A. (1995). The acquisition of taste properties by odors. Learning and Motivation, 26, 433–455. Zellner, D. A., Rozin, P., Aron, M., & Kulish, D. (1983). Conditioned enhancement of human’s liking for flavors by pairing with sweetness. Learning and Motivation, 14, 338–350.

ARTICLE IN PRESS


Research Report

Quest for social safety in imported foods in China: Gatekeeper perceptions John Knighta,, Hongzhi Gaoa, Tony Garrettb, Ken Deansa a

Marketing Department, Otago School of Business, University of Otago, P.O. Box 56, Dunedin, New Zealand b Marketing Department of the Korea University, Seoul, Republic of Korea Received 16 April 2007; received in revised form 11 June 2007; accepted 19 June 2007

Abstract Concerns about food safety, and mistrust of food production systems within China, result in imported food products generally enjoying a higher reputation than locally produced food products. Findings from interviews of gatekeepers are discussed in relation to conspicuous consumption, social trust, and the symbolic value of foreign brands and production. The issue of social safety emerges as a dominant consideration in determining product choice. Culturally bound constructs are integrated with price-perceived value constructs in order to build a comprehensive model of social risk avoidance applicable to food channel gatekeepers in China. r 2007 Elsevier Ltd. All rights reserved. Keywords: China; Distribution channel gatekeepers; Food choice; Social trust; Social safety

Introduction Few studies have investigated country-of-origin (COO) effects on Mainland Chinese consumer perceptions of foreign products (Ahmed & d’Astous, 1999; Wang, Siu, & Hui, 2004; Zhang, 1996). ‘‘In the early years after the inception of the open-door policy, most PRC consumers generally associated foreign products with such concepts as sophistication, modernity, novelty, and faddishness’’ (Zhou & Hui, 2003, p. 37). Furthermore, there has been a perception that foreign products have been of higher quality. ‘‘When imports work better, Chinese tend to accept the fact and show a favourable attitude. This is also a reflection of Chinese ‘sense of righteousness’. Tired of the low quality of domestic goods for decades, Chinese consumers regard imported products as more prestigious and higher class than domestic products’’ (Gong, 2003, p. 376). Other recent work indicates that Chinese consumers now express generally favourable attitudes Corresponding author.

E-mail addresses: [email protected] (J. Knight), [email protected] (H. Gao), [email protected] (T. Garrett), [email protected] (K. Deans). 0195-6663/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.appet.2007.06.010

towards Chinese-owned grocery brands (Kwok, Uncles, & Huang, 2005), and growing evidence indicates that, with improved quality of locally produced products, consumer preference for foreign goods has diminished; this ‘‘reflects the increasing sophistication of some Chinese consumers’’ (Cui & Liu, 2001, p. 97). According to Schlevot (2000, p. 54) ‘‘Gallup polls indicate that Chinese consumers previously favoured Japanese and US brands but now prefer Chinese brands. Eighty per cent of consumers favour local brand names, and 79 per cent prefer products labelled ‘‘Made in China’’ over goods produced abroad.’’ However, care needs to be taken in interpreting such data because these aggregate figures for the overall population may not reflect preferences of affluent consumer market segments—who imported products might be expected to target. Conceptual development relating to COO effects has in the main relied on data collected in Western countries. As recently pointed out (Burgess & Steenkamp, 2006, p. 337), ‘‘it is paramount for the future of marketing science and practice that we conduct more research in so-called emerging markets (EMs). EMs present significant departures from the assumptions of theories developed in the Western world that challenge our conventional wisdom.’’

ARTICLE IN PRESS J. Knight et al. / Appetite 50 (2008) 146–157

Heslop and Papadopoulos (1993) consider that ‘‘the product image dimension of the country involvedyserves directly as a status symbol’’ (Heslop & Papadopoulos, 1993, p. 71). This aspect seems particularly relevant to the Chinese market in view of the importance of status and prestige in Chinese culture and the symbolic value inherent in foreign products (Wei, 1997; Zhou & Hui, 2003). However, Zhou and Hui (2003) have argued that the symbolic value (exotic appeal) of foreign products has become faded as foreign companies concentrate on utilitarian attributes and adapting the image of their products for local consumption (Zhou & Hui, 2003). They advocate a strategy of ‘‘position nonlocally; act locally’’ for foreign products in the PRC (p. 53), in order to sustain the foreign image. The importance of status and prestige in Chinese society is related to another important cultural concept, mianzi, literally meaning ‘‘face’’ in English. Mianzi refers to a person’s social position or prestige in Chinese society (Hwang, 1987). A person’s mianzi can be derived from: a socially ascribed status such as family background; wealth, knowledge, and ability; one’s position and status within an organisation; and social connections (Ho, 1976). Among these factors, a person’s network of social connections (guanxi) plays the major role in evaluating a person’s social status and mianzi (Hwang, 1987).

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Research questions The aim of this study was to determine key factors that influence from where gatekeepers in China source imported products. What are the elements of risk that are most salient to gatekeepers in regard to food imports? How important is COO in purchase decision-making by gatekeepers in the food distribution channel in China? How is COO seen in relation to other extrinsic cues? Does it act as a proxy for quality and/or trustworthiness? What is the symbolic value of particular COOs or countries of brand? Methodology Semi-structured in-depth interviews with 20 companies in six main commercial centres in the Peoples’ Republic of China, including Hong Kong, provided the basis for this exploratory study. Thirteen of these contacts were provided by the authors’ national trade commissioners based in Shanghai, Guangzhou, and Beijing. The remaining seven were contacted by the researchers via personal contacts and referrals from other respondents. The characteristics of the interviewees, and their geographical locations, are shown in Table 1. Purposive sampling was used to provide considerable diversity among the participants so that an overall view

Table 1 Profile of key informants, companies, and organisations in sample Company

Interviewee

Sector

Location

Activity

A B C

Executive chef General Manager General Manager

Hospitality Food retail Wine

Shanghai Shanghai Shanghai

D E

Dairy products Food retail

Shanghai Shanghai

Fruit

Shenzhen

G H I J K

Marketing Manager Asia Regional Manager Chairman Sales Manager General Manager Executive chef Assistant Manager General Manager Managing Director

Five-star hotel, International hotel chain Supermarket, specialising in imported foods Spanish importing company, imports wines from numerous countries Importer and distributor of NZ dairy products Multinational, one of largest retailers in the world, annual revenue in excess of US$200 billion Importer, distributor, fruit from many countries

Meat Hospitality Food retail Seafood Food service

Shenzhen Shenzhen Guangzhou Guangzhou Beijing

L

Customer Manager

Food retail

Beijing

M

Sales Department Manager Procuring Manager General Manager Category Manager Category Manager Deputy Purchasing Manager General Sales Manager Director of Sales and Marketing

Food import and manufacture Food retail Food service Food retail Food retail Food retail

Beijing

Importer, distributor, imports meat from several countries Five-star hotel Supermarket, Chinese-owned Importer, distributor Importer—grand agent company, representing leading international brands Supermarket, one of largest supermarket chains in the world, turnover in excess of US$100 billion Manufacturer and importer of Western-style foods

Changchun Changchun Hong Kong Hong Kong Hong Kong

Supermarket, Western-style Supermarket, Supermarket, Supermarket,

Meat importer

Hong Kong

Supplier to supermarkets, Hong Kong-owned

Food and wine

Hong Kong

Supplier to supermarkets, Hong Kong-owned

F

N O P Q R S T

provincial, Chinese-owned cafe one of top 5 in Hong Kong, foreign-owned also one of top 5 in Hong Kong, foreign-owned one of top 5, Mainland Chinese-owned


J. Knight et al. / Appetite 50 (2008) 146–157

could be distilled from these diverse sources (Denzin & Lincoln, 2005; Patton, 1990). Eight respondents were decision makers in supermarket chains, four domestically owned and four multinational. Nine were importers and distributors in diverse sectors, including wine, meat, dairy products, packaged goods, fruit, and seafood. Two were executive chefs in five-star hotels, and one was the owner of a western-style food service provider. The identity of informants has been protected from disclosure.

Table 2 Topics used to guide interviews

In-depth interviews

Personal interviews were conducted with key informants of these companies. In-depth personal interviews result in a richness of material that is unlikely to emerge from a mailed out questionnaire, and uncover a diversity of responses, whether or not these have been anticipated by the enquirer (Merton, Fiske, & Kendall, 1956, p. 12). Interviews were conducted at the premises of the respondents in all except four interviews. Two were conducted at offices of trade commissioners and two took place in hotel facilities. Interviews ranged from 60 to 120 min, and in most cases were conducted entirely in Mandarin, the native language of one of the authors. An interview template loosely guided the major areas of enquiry (Denzin & Lincoln, 2005; Kvale, 1996; McCracken, 1988; Merton et al., 1956). The interviewer aimed to establish rapport with the respondents and to build a ‘‘conversation-like dialogue rather than asking questions that impose categorical frameworks on informants’ understanding and experiences’’ (Arnould & Wallendorf, 1994, p. 492). Questions were open-ended to enable determination of what was most salient to respondents: ‘‘Once respondents have been brought within sight of the topic, they must be allowed to ‘go’ wherever they wish’’ (McCracken, 1988, p. 40). An example of the type of question asked is: ‘‘When you consider importing products from different countries, what are the factors that you consider important?’’ The main topic areas covered are listed in Table 2, although some of the topics are dealt with elsewhere (Gao & Knight, 2007). Steps were taken to minimise the risk of social desirability bias (Schuman & Presser, 1981), ‘‘the basic human tendency to present oneself in the best possible light’’ (Fisher, 1993, p. 303). Indirect questioning, a projective technique in which respondents are asked to provide opinions from the perspective of another person or group, was used wherever possible. This mode of questioning encourages respondents to ‘‘describe their own feelings from behind a fac- ade of impersonality’’ (Simon & Simon, 1975, p. 586). The importance of mianzi in Chinese culture is recognised as intensifying the likelihood of social desirability interfering with qualitative data collection, particularly when a foreign visitor accompanied the interviewer. A guiding principle in Chinese culture is: ‘‘honour the hierarchy first, your vision of truth second’’ (Gao, Ting-Toomey, & Gudykunst, 1996, p. 91).

Problems encountered with food sourced from particular countries Issues considered when choosing source countries Reasons for deciding against purchasing from particular countries Aspects of food quality that influence purchasing decisions Quality issues that most influence your industrial customers Extent to which products retain country brand in channel Aspects of country of origin that influence your customers’ choice Extent to which country image influences purchase decisions, by industrial buyers and/or consumers Examples of countries that customers (industrial and/or end consumers) consider produce high-quality food Factors determining perceptions of quality of food from particular countries Trustworthiness Importance of hygiene standards Quality control, grading Freedom from chemical residues, hormones Organic production Pasture feeding versus feedlot Environmental conditions, population density, industrial pollution Employment conditions, social aspects, political aspects Genetic modification-relevance

Analysis Tape recorded interviews were transcribed and translated into English, and then coded on the transcripts using different text colours and marginal keywords (Coffey & Atkinson, 1996; Seidel & Kelle, 1995). Excerpts were labelled with multiple codes where needed. According to Seidel and Kelle (1995) this process has three components: (a) noticing relevant phenomena; (b) collecting examples of those phenomena, and (c) analysing those phenomena in order to find commonalities, differences, patterns and structures. According to Spiggle (1994, p. 97) ‘‘interpretation occurs as a gestalt shift and represents a synthetic, holistic, and illuminating grasp of meaning, as in deciphering a code.’’ In our case, we immersed ourselves in writings about Chinese cultural behaviour and thinking (Bond, 1996; Fukuyama, 1995; Gong, 2003; Lau, 1992; Yau, 1988) in order to distill meaning out of excerpts. The data derived from this iterative process were then subjected to intense scrutiny by each author in order to interpret and seek underlying meaning. Cresswell and Miller (2000) refer to qualitative researchers using a ‘‘lens’’ determined by their own personal assumptions, beliefs and biases when interpreting qualitative data. In this case the ‘‘western’’ and ‘‘Chinese’’ lenses used by different authors provided diverse interpretations, which were brought together to provide a consensus interpretation. This culmination ‘‘brings the dragon to life by painting its eyes’’—an apt Chinese expression. Results and discussion In view of the open-ended mode of questioning used in the interviews, the order of responses provides an


indication of what is top-of-mind for gatekeepers in considering their purchasing decisions. Perceptions of price and value A strong emphasis on purchase value was a recurring theme and, as might be expected, price was mentioned frequently as the major determinant of purchase decisions. For example, a meat importer (G) indicated: Mainly price influences our purchase decisionyThe market for imported beef has been decreasing. The main challenge for imported beef is competition from local suppliers. The local beef is very cheap. Price sensitivity of Chinese consumers was emphasised by some. Others qualified the importance of price, by indicating that it was not low price by itself that was the key determinant, but purchase value—something deeply ingrained in Confucian philosophy and Chinese culture (Tse, 1996, p. 359). A seafood importer (J) associated value with certain countries: We normally choose countries that provide better quality seafood with lower prices. A major difference was seen between the views of wholesalers, catering for the retail market, and decision makers in the hospitality sector. An executive chef of a fivestar hotel (A) in Shanghai explained that, although price is an important factor in decision-making, it must never be used as short-term justification for sacrificing quality in hotel restaurants: Actually price is a big factor for us. Of course quality is the biggest factor at the end of the day because quality is going to bring people back. This view was mirrored by an executive chef from another five-star hotel (H), this time in Shenzhen. For him the local beef—while cheap—was of totally unacceptable quality for his purposes: In my restaurant y I buy my own food from Australia or the US, especially meat. I never use meat processed locally here. The meat here is just so terrible and miserable. In accordance with our use of indirect questioning as a device for minimising the risk of social desirability bias, respondents reflected on how consumers respond in regard to price; presumably, their own buying decisions are determined by how they see their consumers responding. Interesting reflections were provided regarding changing attitudes depending on income levels and age cohorts of consumers. The manager of a provincial supermarket (N) explained it thus: As you know, people’s attitudes towards price vary according to their income levels. People who are above 40 years old in China are quite concerned about the

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priceyPeople who are younger than 30 years are not thinking about the price much once they perceive the products that interest them have good quality, are nice looking and have acceptable functions. Attitudes are changing as consumers become more affluent. According to respondent K (an importer and brand agent): In China more and more people are concerned with quality more than price. However, this trend is not universal. A supermarket manager (L) observed that consumers are torn between on the one hand paying higher prices in nice shopping environments, which might confer feelings of self-worth and prestige, and on the other hand saving money by shopping in more crowded environments where prices are lower. A herd mentality is likely to cause consumers to equate obvious popularity of a crowded supermarket with ‘‘value for money’’: Sometimes there seems a conflict here. People want to save their ‘‘face’’ by shopping in a big and spacious store. On the other hand, people also want to buy cheap goods from the crowded stores. Most likely they would choose the latter. According to this respondent (L), this conflict is important to understand when planning store layout and deciding on positioning strategy. Gong (2003, p. 375) refers to the Chinese saying ‘‘never make a purchase until you have compared three shops’’; this typical searching behaviour of Chinese consumers is fully compatible with the Confucian emphasis on prudence and risk-avoidance, but also with the high level of concern with the social consequences of a purchase. ‘‘Hardworking and thrifty (sic) are highly praised traditional virtues in China. To pay more for a product than necessary is considered shameful and a waste of money. On the other hand, the Chinese also believe that cheap products are never good’’ (Gong, 2003, p. 376). Avoiding social risk With regard to wine, an importer (C) reported prevalence of a strong price–quality relationship, which can be related to the conspicuous nature of wine consumption: Another safe thing would be the price y if it’s expensive, they think it’s OKy Even if the French wine is expensive, the person may think ‘‘I’d better choose the expensive oney’’ The safety of choosing the expensive option relates more to social safety, than to perception that an expensive wine will necessarily be of better quality. The expensive option will ‘‘save face’’ in the eyes of a guest to whom the wine is to be served, and will avoid the social risk of giving a gift downgraded as ‘‘low class’’ in the eyes of the recipient.


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Respondent (C) referred to Chinese consumers typically having little knowledge about wine, a relatively new introduction to the Chinese market:

Although people have more concerns with their health now, these concerns are still limited. Once the food has good taste, people will accept it.

I think Chinese have got better noses than tastes, because they are more used to the tea. Their noses are quite good. But their tastes, sometimes because of the GanBei thing, drink the glass and see the bottom up. Sometimes they do funny things, mixing white wines with ice on the glass. They put ice directly into a white wine glass. It dilutes the taste because of too much water.

Another supermarket manager (B) believed that with packaged goods, the quality of the packaging conveyed an important message about quality—and in particular about safety in regards to hygiene and chemical contamination:

This lack of familiarity and knowledge provides added reason for relying on price as an indicator of quality in order to avoid social risk. Conspicuous consumption of a prestigious product such as wine is a way to show off one’s position in one’s social network in Chinese society (Schu¨tte & Ciarlante, 1998), thus requiring great care in order to preserve and enhance that position. The emphasis on avoiding social risk regarding wine purchase can be seen also in regard to purchasing bottled water, as reported by respondent K (an importer and brand agent): People buy imported water for its image. When Chinese people get foreign friends to come home, of course they will serve Pellegrino, not because it is expensive, but because of respect. The purpose of the purchase (gift or own consumption) strongly influences purchasing behaviour (Sun & Collins, 2006; Tse, 1996, p. 362), as reflected in this comment from a supermarket manager (N): One typical reason for some consumers to buy imported products is to give them away as gifts even though they cannot afford to consume the imported products themselves. This respondent indicated that young people will frequently buy expensive imported products to give to parents and other older relatives in order to demonstrate filial piety—an important Confucian value (Ho, 1996). Buying imported products reduces the complexity of evaluating a gift in the eyes of the recipient and avoids the associated social risk for the giver. Quality attributes Taste was mentioned by several respondents as being the key determinant of quality—much more important than concerns about the way in which food has been produced. Respondent G considered that grain-fed beef is preferred to grass-fed beef because it tastes better. Subsequent questioning revealed that issues to do with feedlot farming, and whether or not the grain was genetically modified were of little interest to Chinese consumers. According to a supermarket manager (I), concern with taste still outweighs concern for health:

Good packaging means the products are produced by the big companies, and with guarantee of quality. If the packaging is poor, people may think the product is not good quality. People worry that these inferior products will make people ill, or have chemical contamination. Brand as symbol of quality and prestige Brands have become hugely important in the Chinese marketplace in recent years (Schlevot, 2000), and the Chinese have been termed ‘‘the world’s most loyal customers’’ (Crow 1937 quoted in Yau, 1988). This can be interpreted as reflecting the Confucian desire for continuity and past-time orientation (Yau, 1988). An importer and manufacturer (M) expressed it this way: People tend to buy the famous brand for products they are not familiar with. Consumers are really concerned with the brand, especially famous brand, so they have more confidence that the product has good quality. The equating of brand with ‘‘fame’’ indicates the importance of status and prestige, and confidence that such a purchase will not cause loss of face (Schu¨tte & Ciarlante, 1998). ‘‘The motivation to use name-brand products as symbols to underscore social distance from others is most pronounced’’ (Tse, 1996, p. 361). Respondent D indicated that an international brand such as Nestle´ instils confidence, even when consumers know that Nestle´ products are locally produced: People all know Nestle´ is locally produced, but they also know it is an international brand. When you ask where this brand is from, they will answer you they don’t care. Consumers differentiate between locally made and imported variants of the same foreign-branded product, according to a supermarket manager (B): Joint venture-made products have a much lower price than the original products. On my shelves I have both imported and local Coca Cola side by side, but with a five-fold difference in price. Furthermore, some foreign brands have been in the Chinese market so long that they are now regarded largely as domestic brands. For example, in the words of a dairy products distributor (D): Kraft products are perceived as local products. They have to sell their products at lower prices.


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Respondent B raised an interesting point in relation to the prevalence of fake products in the Chinese market:

In China normally the meat is inspected by the government before being put on the market, but it is hard to ensure 100% safety of meat.

Brand means symbol of status and quality. For example, in Xingyanglu market (where fake products carrying well-known brands are widely available), products with a good brand will easily sell compared to products without brands.

More evidence of general low trust in local producers and government monitoring systems is reflected in the following from supermarket manager N:

Even when consumers know there is a high chance of the product being counterfeit, a ‘‘good brand’’ will raise status and aid sale of such a product. ‘‘Imported’’ as halo brand ‘‘Imported’’ status of food products clearly confers symbolic value on products, as reflected in the following quote from a supermarket manager (I): Chinese people feel that consumption of imported products reflects their social status, and at least makes them feel good about themselves. It is believed that only high-class people can consume imported products. High trust placed in the quality of imported products reduces the uncertainty and complexity of evaluating products in a low ‘‘social trust’’ country like China (Fukuyama, 1995). Expressed to us by another supermarket manager (N) was a general perception that food products from outside of China are of higher quality than domestic products: Quite a lot of consumers still have prejudice regarding locally made products, and tend to think they are not as good as the products from the original country of the brand. The main reasons for this view are the worry about the use of material, and operation of equipment, and the production process. A dairy foods distributor (D) referred to widespread mistrust of domestic processes downgrading the perception of products imported in bulk and then packaged in China: People don’t care about which country imported products come from, but they do care whether they are imported or not. Local packaging will downgrade the imported product in the eyes of Chinese consumers. Chinese consumers think that if a product is labelled with ‘imported’, it should be fully imported, rather than half imported. A respondent from the food service sector (K) made the very interesting point that imported foods have passed stringent quarantine inspection whereas locally produced products have not. This in itself provides a pragmatic reason for having greater trust in imported products. This view of widespread low trust in locally produced food was supported by a supermarket manager (B):

When there was a rumour about mad cow disease in Europe and the US, market demand for beef, either imported or local, decreased tremendously. Because of malfunctioning of normal food safety monitoring mechanisms, the government had to start labelling the food with ‘this beef is safe to consume’ to keep the market’s confidence over beef products. Trust in products from developed countries Imported products from developed countries enjoy a reputation for quality that is not shared by products from less-developed countries. According to a fruit importer (F): People tend to think that products from developed countries are always good, are given high praise. However, in the case of Japan, although the country is renowned for quality products, consumer animosity might still dissuade older generations from buying Japanese products, as found by others (Klein, Ettenson, & Morris, 1998). A manufacturer and importer (M) put it this way: We never target Japanese products because of culture conflicts. A lot of Chinese still hate the Japanese for what they did in the Colonial time. Many Japanese companies came to our company, but we never responded to them. Developed countries are recognised as having more sophisticated traceability systems in food production. A manufacturer and importer (M) mentioned France as a country that he understood had instituted such sophisticated systems: A pig is registered after birth, and has archives from birth through to death. People could trace to any stage of this animal’s life if there was a problem found. A supermarket manager (B) felt that once the distinction is drawn between ‘‘developed’’ and ‘‘less developed’’, little difference is then attributed to specific countries: For products from developed countries, consumers tend to believe they are good y They don’t really care if they are from the US, Canada, or Germany. Perceptions of quality overlap with perceptions of safety. A fruit importer (F) commented: New Zealand is a developed country, and people feel safe to consume food products from New Zealand.


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In contrast to these respondents, a supermarket manager (I) thought it all came down to characteristics of the product: Thailand? Not much difference for food products. Once the imported products have good taste, people don’t care about the status of the countries the products come from. For example, abalone from the Philippines sells very well in this store. An interesting perspective from the manager of a different supermarket (B) was that a scandal in a developed country would be seen as a ‘‘one off’’ and readily forgiven whereas a scandal in a less-developed country would be seen as part of a pattern: If there are some scandals in Japan, they may be perceived as incidental and unpredicted events, but if they happened in Thailand, people may think this is naturally happening, systematic, and general. Capitalising on the generally favourable perception of imported products, particularly from more-developed countries, use of false COO stickers to increase the status and therefore price of locally grown produce appears to be quite widespread, as reported by the executive chef of a five-star hotel (A): Some people just buy local fruit and put stickers on them, and sell them as imported fruit. I believe the government is trying to discourage this. People put stickers on, add value, and increase the profit. Of course, their target market is not the top-end supermarket and not the five-star hotels. Reliance on trust in retailers Mistrust arising from the number of counterfeit products on sale, and difficulty separating fact from fiction in the marketplace, force customers to rely on their trust of retail outlets. Here, the reputation of the retailer overcomes uncertainty which consumers might otherwise have regarding the provenance of branded products. A supermarket manager (L) expressed it this way: They trust bigger hypermarkets. We have more than 50 stores in China. We have strict control systems to assure the quality of products placed on our shelves. But if consumers go to small stores, they may ask themselves ‘Are these genuine? Is it safe to buy them?’ In small stores consumers have a lot of risk. In the bigger stores like ours, consumers know that, even if there are problems with goods they buy in the store, we will fix them. According to another supermarket manager (B), legal redress might even be possible: Normally street markets are dirty and middle income customers don’t like to go there. Although the vegetables may be the same between supermarkets and

open markets, there are more guarantees from the supermarkets. At least if there are quality problems, they can sue the supermarkets y People who shop in supermarkets believe supermarkets are safer places. This suggestion of suing a supermarket is an interesting contrast to the traditional Confucian concept of Yuan (Leung, 1996), which leads consumers to attribute failure of a product or service to fate rather than to the producer or supplier. Traditionally, this has led to a great reluctance to complain about products that do not meet with expectations (Yau, 1988), together with a need to avoid confrontation, which runs counter to the Confucian value of harmony (Tse, 1996). The quotes from channel members presented here suggest that Chinese consumers may be becoming more assertive in regard to consumer rights and returning of products, and/or complaining about poor quality goods. Low social trust China, along with many other countries in Asia, may well be largely inured to food-related risks since they are part and parcel of daily living. Therefore, factors, which weigh heavily in Western countries regarding scepticism towards food imported from certain countries, might not be of similar importance in less-developed countries. However, China has been designated a ‘‘low-social trust’’ society in which ‘‘there is a very strong inclination on the part of Chinese to trust only people related to them, and conversely to distrust people outside their family and kinship group’’ (Fukuyama, 1995, p. 75). This can be related to China’s long history of being a centralised feudalist society, resulting in a strong preference for familism (Fukuyama, 1995, p. 361). Such feelings of mistrust towards outsiders appear to be largely confined to people within China’s borders, and do not typically extend to foreigners. This leads to mistrust of locally produced food products compared with foreign products. As Slovic (1999, p. 49) points out ‘‘distrust, once initiated, tends to reinforce and perpetuate distrust’’. According to a fruit importer (F): Consumers know that a lot of chemicals are used to grow fruit in China y I have a child—I always go to Hong Kong to buy imported milk. I don’t believe in milk produced locally. This mistrust has some justification, given the number of food-related scandals in China in recent years (Wang, 2005; Wu, 2005), and in particular to scandals relating to sale of fake milk powder (Liu, 2005). According to Gong (2003, p. 379), ‘‘Despite their generally low expectations, Chinese consumers hold very high expectations about foreign, especially western, products.’’ However, several of the recent food scandals have involved domestically produced foreign branded products including KFC, Heinz and Nestle´ (Wu, 2005).


A recent scandal involving Haagen-Dazs illustrates just how vulnerable a foreign brand can be if strict control is not maintained over local production in the PRC (Chen, 2005, p. 4): ‘‘Shocked Chinese consumers find it hard to fathom—why would such a reputable foreign brand produce ice-cream cakes, without a sanitation permit, in an ordinary apartment with a rubbish bin in its centre and next to a toilet?yIt has created a serious crisis of confidence among Chinese customers, raising a question of whether the food General Mills produces in China stamped with the Haagen-Dazs brand is safe.’’ Country-of-origin In many product categories, Chinese consumers are unable to determine where products originate, particularly when the product is an ingredient used for further processing or are used in the food service sector. According to a respondent in this sector (O): Consumers don’t know the country. They can’t tell the quality of cheeses by eating the pizza. In addition, the tastes of cheese can’t be easily differentiated. They don’t care where the western-style food is originally from. The executive chef of a five-star hotel commented on the inconvenience of having to change the menu if COO was designated: I don’t personally think this (country image) is a selling point. To change the menu in a five-star hotel is very expensive because all menus are shown in the rooms. I got burned this year with US beef banned here this year. We have to reprint all menus because we named US beef. It is so expensive. In this regard, there is no major difference from the situation in developed countries, where COO gets lost as soon as imported raw materials are used in the food service sector, or become incorporated into manufactured products. But even in regard to retail food products, the association with country image of source countries appears weak in China. A fruit importer (F) expressed it this way: Country image? I don’t think there is a relationship. We are in business, so we only think about this from business perspective. Because fruit from New Zealand are good quality, we can sell them. It is very simple. An obvious exception can be seen from the comments of respondent C in regard to wine. This is a product sector where COO is highly integrated into perceptions of the product: I think the first top of the mind will be the country origin. First France always. French is the best wine in the world because it used to be. But still French wines are perceived to be the best wines. They have entered the mind of the Chinese that French wine is the best wine.

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It’s very difficult to change the perception. They know French wine. That will be one safe choice. Once again, this perception of ‘‘safe’’ is all about avoiding social risk, the risk of losing face through making a purchase of a product that others may see as ‘‘low class’’. As pointed out by Tse (1996, p. 360), ‘‘the need to distinguish oneself from others and one group from other groups is especially strong in Chinese societies. Consumption is a readily available tool to manage such social intricacies.’’ In certain quite specific categories, particular countries have become associated with particular products in the minds of consumers—and especially in the minds of gatekeepers. According to one supermarket manager (B): For New Zealand the kiwifruit is a good choice. The quality is the best. New Zealand kiwifruit is believed to be the best everywhere in the world, not only in China. This is quite ironic, given that the plant that gave rise to modern ‘‘kiwifruit’’ originally came from China. Once modern greatly improved cultivars were developed through selective breeding, the name ‘‘kiwifruit’’ was coined as a marketing tool in export markets. New Zealand kiwifruit occupy a premium niche in the Chinese market, and this status confers social safety on the purchaser. We found it interesting that sheets of stickers claiming New Zealand origin were readily available for placing on fruit originating domestically or elsewhere. According to another supermarket manager (L): Sunkist is the best orange. People have this perception because they are imported from the US There are likely to be two elements to this perception of ‘‘best’’: first, the product can be expected to be of best quality in terms of product attributes such as taste, juiciness, consistency, freedom from chemical contamination and spray residues etc; and second, that brand can be most likely to convey positive symbolic meaning when used either as gifts or for self-consumption (Sun & Collins, 2006). According to a Hong Kong supermarket manager (P): Sunkist has been very acceptable by HK customers. They may think first of all about Sunkist. They may think about Sunkist coming from California, or US. They may prefer Sunkist if the price is same as other origins, such as South Africa, Australia or other places from US. This seems to indicate it is perception of the Sunkist brand, rather than the country it comes from, that provides the perceived value. Interestingly, respondent (L) did not consider that the country of domicile of the owners of a supermarket chain had any bearing on why consumers would choose to shop at that store:


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It is not the case that some consumers go to Lotus (Thai-owned supermarket chain) because they like Thailand. You know, most customers have low loyalty in selecting where to shop. If they find one product is sold in Carrefour (French-owned supermarket chain) at 1000 yuan one day and find the same product sold in Lotus at 999 yuan, they will go to Lotus for the one yuan difference. A distributor of dairy products (D) considered that trusted brands were often not associated in the minds of consumers with the country from which they originate: People don’t associate Nestle´ with Switzerland—they never think about that. My mom goes to the supermarket to buy cheese and butter, she doesn’t really know about New Zealand cheese or European cheese. She only tastes once, and may think this is her flavour, and it becomes her favourite, and she will continue buying it. She doesn’t really care which country the brand comes from. Familiarity with the characteristics of different source countries for products appears to be limited, and productcountry image appears to be of relatively low importance in this market. This is not unexpected, given that the Chinese economy was closed for a long period and foreign food products have only been imported on a large scale in recent years. Furthermore, only a small percentage of the population have travelled outside of China. As a result of these combined factors, consumers’ knowledge about foreign products and their association with COO is still limited (Gong, 2003). This lack of detailed knowledge restricts the capability of most Chinese consumers to apply COO as a cue. Furthermore, many foreign companies entering China have paid scant attention to the need for heavy promotion in order to establish their brand and COO as famous for a particular product. Country associations in food service An interesting perspective on the relevance of association with a particular country was provided by respondents in the food service sector, who drew a distinction between dećor associated with a particular foreign country and the origin of the food served there. According to the proprietor of a western-style cafe´ (O): For a cafe´ affiliated with a particular country, the key is the decoration style of the cafe´. The most important thing is to differentiate from Chinese cafes by the unique decoration in relation to a particular country. In similar vein, according to the executive chef of a fivestar hotel (H), the foreign country name was used to create ambience: Our cafe´’s Italian name is just used to attract people’s attention. The cafe´ has Italian styled decoration. That is all. This name has nothing to do with the food.

Conclusions Concerns about food safety and mistrust of food production systems within China result in imported food products generally enjoying a higher reputation with food distribution channel members than do locally produced food products. If locally produced products carry famous international brands, then such products enjoy a reputation higher than purely domestic products but generally considerably lower than wholly imported food products. Mistrust of domestic production and regulatory systems can be seen to originate from low social trust prevailing in China. This is manifested in a strong inclination to trust only people related to oneself, and conversely to mistrust people outside one’s family and kinship group (Fukuyama, 1995). This widespread mistrust of people and companies leads in turn to mistrust of domestic food products, and this mistrust has become amplified by a succession of food scandals. This low level of social trust is generally confined to within China’s borders, and there appears to be a perception that imported products in general are safer. In part, this is because imported products have to pass through a series of safety inspections on entering China. However, the designation ‘safer’ has two distinct meanings: ‘safer’ from a food safety perspective, and also safer from the perspective of avoiding social risk—risk of losing face through purchasing a less-prestigious product destined for conspicuous consumption (Zhou & Hui, 2003). Products from developed countries are seen as being of higher quality than those from less developed countries. In this regard, our findings support previous work indicating a general preference (even in less-developed countries) for products from more-developed countries (Agbonifoh & Elimimian, 1999; Batra, Ramaswamy, Steenkamp, & Ramachander, 2000; Hulland, Todino, & Lecraw, 1996; Wang & Lamb, 1983). However, provided appearance is acceptable and there is a price advantage, products from less-developed countries also gain good acceptance in the Chinese market. Apart from level of development, source countries do not appear to be greatly discriminated between except in specialised categories such as wine, where the name of the source country is inextricably connected with the image (and therefore symbolic value and social safety) of the product. COO associations are limited and specific to particular food categories e.g. ‘New Zealand’ with ‘kiwifruit’. Brands are an important indicator of both social and physical safety in China. However, the high prevalence of counterfeit products puts international brands and halo brands, such as the designation ‘‘imported’’ or the country name, at risk. This factor has led to widespread mistrust of small retail outlets and street markets, and correspondingly enhanced trust in supermarkets—particularly foreignowned chains. We acknowledge there are some potential differences between Hong Kong and Mainland Chinese gatekeepers in


perceptions of imported products. Hong Kong consumers have obviously had much longer and greater exposure to products of foreign origin. Compared with their Mainland counterparts, Hong Kong gatekeepers reported that their consumers appear more knowledgeable about international brands and more able to associate foreign brands with specific quality attributes. However, once allowance is made for these differences attributable to greater or lesser experience of imported products, the culturally based views of social risk and social safety appeared very similar. Conceptual implications The conceptual contribution of this study is to place factors influencing purchasing decisions of Chinese gatekeepers in the wider context of how they perceive value and how they minimise risk. To achieve this, it is necessary to integrate conceptual models derived from the COO literature with models derived from pricing/customer perception theory. Fig. 1 integrates the factors identified as important in the Chinese context into Monroe’s PricePerceived Value model (Monroe, 2003). In particular, we have incorporated Confucian philosophical values (hierarchy, harmony), and Chinese cultural values and behaviours (guanxi: face and favour) (Bond, 1996; Hwang, 1987). These interact with brand and other attributes to confer status, and prestige, reduce social risk and thus provide social safety. We present social safety as the central construct underlying gatekeeper purchasing behaviour in

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China, and show how diverse facets contribute to this construct. Managerial implications The most important implication is that well-known foreign brands still have great value in the Chinese market, seem likely to continue to do so, and should be jealously protected and promoted. In a market where mistrust of domestically produced food products is widespread, and fuelled by a succession of scandals, foreign companies need to weigh very carefully the wisdom of manufacturing locally in order to reduce costs. As suggested by Zhou and Hui (2003), foreign products need to be positioned as nonlocal in order to sustain the foreign image. This is necessary both for perceptions of food safety and of social safety. For prestigious products destined for conspicuous consumption, foreign production (including packaging) should be used to enhance social safety. If dual language labelling is used or required, then care should be taken to prominently label the product as foreign produced. Premium pricing should be used to enhance the status and trust in safety (both social and physical) and quality. If local production is to be used, then the Haagen Dazs episode referred to above provides a cautionary message concerning the need for strict control over production standards. The widespread occurrence of counterfeit products in the PRC gives rise to a need to control distribution carefully to ensure sale only through well-respected outlets, since trust

Fig. 1. Contextual representation of factors influencing purchase of imported food products by Chinese distribution channel gatekeepers. (Adapted from Monroe’s Price-Perceived Value model. Hollow arrows represent inhibitory effects.)


J. Knight et al. / Appetite 50 (2008) 146–157

in the product is intertwined with trust in the retailer. If possible, labels which are difficult to copy should be used. Chinese distributors need to actively monitor use of the brand and ensure authorities act against counterfeits. Failure to achieve this could lead to rapid erosion of brand value. Limitations and future research The national origin of the researchers, with the use of national Trade Commissioners in recruiting many of the interviewees, could have led respondents to focus too much on that country’s products. However, we took steps to broaden the scope to other countries where possible. This was facilitated by the fact that the primary interviewer is Chinese with Mandarin as his native language. It is acknowledged that this is a relatively small convenience sample, and that care must be taken in extrapolating results to the wider population of food channel members in such a large country. However, the commercial centres chosen extended from Hong Kong to Changchun in the north-east, and included Shanghai and Beijing. Future work needs to extend this exploratory study to a more broadly representative sample of food channel members throughout the main commercial centres of China. Acknowledgements This research has been supported by a grant from the New Zealand Agricultural and Marketing Research and Development Trust. References Agbonifoh, B., & Elimimian, J. (1999). Attitudes of developing countries towards ‘country-of-origin’ products in an era of multiple brands. Journal of International Consumer Marketing, 11(4), 97–116. Ahmed, S., & d’Astous, A. (1999). Product-country images in Canada and in the People’s Republic of China. Journal of International Consumer Marketing, 11(1), 5–22. Arnould, E., & Wallendorf, M. (1994). Market-oriented ethnography: Interpretation building and marketing strategy formulation. Journal of Marketing Research, 31(4), 484–505. Batra, R., Ramaswamy, A., Steenkamp, J.-B. E. M., & Ramachander, S. (2000). Effects of brand local/non-local origin on consumer attitudes in developing countries. Journal of Consumer Psychology, 9(2), 83–95. Bond, M. (1996). The handbook of Chinese psychology. Hong Kong: Oxford University Press. Burgess, S., & Steenkamp, J.-B. E. M. (2006). Marketing renaissance: How research in emerging markets advances marketing science and practice. International Journal of Research in Marketing, 23, 337–356. Chen, Q. (2005). Customers’ trust in brand name melts away. China Daily (North American ed., p. 4), June 28, 2005. Coffey, A., & Atkinson, P. (1996). Making sense of qualitative data: Complementary research strategies. Thousand Oaks: Sage. Cresswell, J., & Miller, D. (2000). Determining validity in qualitative inquiry. Theory into Practice, 39(3), 124–131. Cui, G., & Liu, Q. (2001). Executive insights: Emerging market segments in a transitional economy: A study of urban consumers in China. Journal of International Marketing, 9(1), 84–106.

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Research Report

Modulation of biting procedures induced by the sensory evaluation of cheese hardness with different definitions Haruka Dan, Fumiyo Hayakawa, Kaoru Kohyama National Food Research Institute, 2-1-12 Kannondai, Tsukuba 305-8642, Japan Received 24 October 2006; received in revised form 29 March 2007; accepted 22 June 2007

Abstract This study seeks to clarify the bite process in individuals who assess cheese hardness to confirm the relationships among hardness definitions, their causative bite procedures, targeted intra-oral mechanical events, and judgment. Subjects were required to bite and evaluate samples using two different definitions of hardness. We measured an intra-oral bite time–force profile using a tactile pressuremeasurement system with a sheet sensor unobtrusively inserted into the subject’s mouth along with a sample. The first bite profile was an exploratory procedure adjusted to optimize perception of the designated textural attribute. From the temporal modification of the bite profile, we could estimate the bite parameter that was targeted as the sensory information for a particular texture attribute. We examined inter-definition and inter-subject variations in the relationship between hardness judgment and its sensory source estimated from the bite procedure. We demonstrated that the bite measurement could elucidate both the bite procedures and sensory information for hardness evaluation. Different definitions induced different bite procedures that resulted in a change in the sensory signal. The definition also affected inter-subject variability in bite procedures and sensory–judgment correspondence. r 2007 Elsevier Ltd. All rights reserved. Keywords: Sensory assessment; Judgment; Paired-comparison; First bite; Bite force; Fracture; Multiple-point sheet sensor (MSS); Instrumental test.

Introduction Sensory evaluation of food texture is achieved by utilizing sensations perceived during oral processing of food, as early as the first bite. In order to sense the textural characteristics of any food, mechanical action such as biting has to occur during eating (Marshall, 1993). The bite profile, especially its temporal characteristics, reflects biting action voluntarily controlled by the human. In addition to its aspect as movement history, bite profile is a source of sensory information for texture perception. We have demonstrated that the first bite force is affected by the mechanical properties of the sample (Dan & Kohyama, 2007; Dan, Okuhara, & Kohyama, 2003; Dan, Watanabe, Dan, & Kohyama, 2003). Change of bite force is transmitted by the periodontal mechanoreceptors, which encode information about the temporal, spatial, and intensive aspects of tooth loads (Johnsen & Trulsson, Corresponding author.

E-mail address: [email protected] (K. Kohyama). 0195-6663/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.appet.2007.06.009

2005; Trulsson, 2006). Therefore, from the bite time–force profile during the subject’s biting we can obtain (1) how the subject bites on the sample (bite procedure), and (2) what the subject senses from the sample (sensory information based on the sample properties). We have found that the first bite is voluntarily modified by subjects according to the requirements of the sensory evaluation (Dan & Kohyama, 2007; Dan, Okamoto, Wada, Dan, & Kohyama, in press). The temporal characteristics of biting behavior are markedly changed if subjects are requested to judge the intra-oral fracture force of a cheese sample: the initial fracture is temporally elongated when the subjects evaluate it. It is reported that food panelists adapt their oral movements to optimize the sensations required to assess the target attribute (de Wijk, Wulfert, & Prinz, 2006; Mioche & Martin, 1998). Similarly, the bite profile is also modified by the requirement to evaluate a specific textural attribute, besides the mechanical properties of the food. The modulated bite profile can be explained as an exploratory procedure (Lederman & Klatzky, 1987, 1993) optimized for encoding the sensory

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properties designated in a sensory evaluation (Dan et al., in press). To encode the fracture force information for evaluation, the fracture event as the exploratory procedure was necessarily elongated, and the relationship between the procedure and the targeted sensory information was common across all subjects. In addition, the relationship between the targeted information and judgment was robust across the subjects. Using bite time–force profile measurement during the sensory evaluation of the textural property allowed us to ascertain which sensory properties were explored and used for the judgment. Sensory evaluation studies of food texture have sought to identify relationships between causal mechanical events and perceived attributes. However, the exact physical determinants of each textural attribute have remained elusive. The bite measurement during the sensory evaluation clarifies the internal process of the sensory panel, which is typically treated as a black box. When several sensory panelists evaluate a certain textual property, it is important to know which evaluation procedure each panelist uses, what sensory information they obtain for their judgments, and whether the whole process is common across the panelists, for the scientific interpretation of the sensory result. The evaluation procedure itself changes, depending on the targeted textural property. The commonality of the evaluation procedures across panelists may also change depending on the textural property or its definition. The evaluation procedure may vary among panelists, or it may be common across panelists if different definitions are designated for the same textural property (e.g. hardness). The objective of our study was to reveal the effects of definition on biting procedures and resulting sensory characteristics during sensory evaluation of cheese hardness. Hardness is one of the most important attributes of food texture, including that of cheese. However, a longstanding argument in the field of sensory evaluation involves exactly what is being perceived as hardness when a specimen is compressed between the molar teeth (Szczesniak, 1987). Several definitions differ from the ISO definition (mechanical textural attribute relating to the force required to achieve a given deformation or penetration of a product) but are commonly used in sensory evaluation studies. It is not clear whether the use of different definitions causes a discrepancy in evaluation results. We used the following two definitions for cheese hardness: the force required to penetrate (bite into) the sample with the molar teeth (H1 definition) (Bryant, Ustunol, & Steffe, 1995; Dan & Kohyama, 2007; Gunasekaran & Ak, 2003; Hort & Le Grys, 2000; Lee, Imoto, & Rha, 1978), and the force required to bite completely through (fully compress) the sample (H2 definition) (Barrangou, Drake, Daubert, & Foegeding, 2006; Meullenet, Carpenter, Lyon, & Lyon, 1997; Meullenet, Finney, & Gaud, 2002; Munoz, 1986). The possible mechanical event(s), bite parameters for the target intra-oral event(s), and the relationship between the bite parameters and judgments for these definitions are assumed to be as follows.

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The targeted intra-oral mechanical event for the H1 definition has been identified in previous papers (Dan & Kohyama, 2007; Dan et al., in press), and re-analyzed data is presented in this paper for comparison with the H2 definition. The H1 definition is related to the initial fracture of the sample surface. As an exploratory procedure, temporal elongation of the intra-oral initial fracture event was observed among the subjects. The sensory cues for the evaluation estimated from the exploratory procedure were the peak bite force or impulse (time integral of force) during the initial fracture event. These bite parameters were confirmed as sensory cues for the H1 texture attributes, since the correspondence between the bite force/impulse of the first peak and the judgment was significant (Dan et al., in press). For evaluation with the H2 definition, subjects were asked to evaluate the force required to bite completely through the sample. This definition, rather than the H1 definition, is commonly used in sensory assessments of food texture. With the H1 definition, it is relatively easy to identify the mechanical counterpart. In contrast, the H2 definition does not refer to any particular mechanical event; thus, the targeted mechanical event is controversial (Meullenet et al., 1997; Moiny, Meullenet, & Xiong, 2002). Cheese basically exhibits ductile fracture. It requires continuous force loading until it is completely bisected (Luyten, van Vliet, & Walstra, 1991). Thus, a series of mechanical events (e.g. elastic and plastic deformation, initial fracture, and subsequent compression up to toothto-tooth contact) were targeted as a whole. Temporal elongation of these events (and thus elongation of the total biting duration) was treated as an exploratory procedure of these successive events. The targeted bite parameters were the impulses during the overall biting process (i.e. the time integrals of bite force during several mechanical events). The impulse during the overall biting process was also presumed as a targeted bite parameter for the H2 definition, based on data in Meullenet and Gandhapuneni (2006), in which the total energy (i.e. the area under the force deformation curve for the uniaxial compression test) was the instrumental parameter most significantly correlated with the sensory result of H2 hardness, not the fracture force or fracture energy. The impulse as the sensory cue for H2 judgment can be verified if correspondence between the impulse during the overall biting process and the judgment is significant for all subjects, as well as the exploratory behavior for the overall biting process. By examining the behavioral and sensory aspects of sensory evaluation processes for each subject, we demonstrated that the evaluation procedure was changed by the designated definition, even for the same texture attribute (hardness). In addition, the variability of exploratory procedures among the subjects differed between these two definitions; the inter-subject variation was larger with the H2 definition than with the H1 definition, with which all the subjects performed similar exploratory procedures. Different exploratory procedures indicated that the


160

targeted sensory information was dissimilar across the subjects. The resulting judgment was based on the various sensory cues, making the sensory evaluation less controlled. We concluded that the bite measurement was an objective source of evaluation procedures and resulting sensory signals, linking intra-oral mechanical events and texture perception (Wilkinson, Dijksterhuis, & Minekus, 2000). Methods Cheese samples and their instrumental properties Four different cheese products (samples W, X, Y, and Z) were used in this study, as in previous reports (Dan & Kohyama, 2007). The samples were stored at 4 1C and then cut into rectangular pieces (20 mm in length and width, 10 mm in thickness). The cut samples were stored in an airtight container and left at room temperature (20 1C) for an hour before evaluation. The compression test was designed for instrumental determination of the samples’ mechanical properties. The fracture force (N), measured using a universal testing machine (Instron 5542, Canton, MA, USA) at a crosshead speed (test speed) of 8 mm/s, is presented in Table 1. Additional information about the cheese samples and the instrumentally determined properties have been reported elsewhere (Dan & Kohyama, 2007). Subjects Nine healthy individuals (four females and five males, average age 30 years) with normal dentition participated in the study. The study was approved by the Institutional Ethics Committee of the National Food Research Institute. After a full explanation of the procedure, written informed consent was obtained from each individual. Procedure Normal biting without judgment task (control task) The subjects were asked to bite cheese specimens normally with the molars on their habitual chewing side as a control task, without any attempt to evaluate them Table 1 Force (N) at initial fracture measured in an instrumental compression test ANOVA

Fracture force (N)

F

sample W

sample X

sample Y

sample Z

232.48

25.9 a

37.8 b

36.8 b

44.8 c

The average of five specimens is provided for each sample (W, X, Y, and Z). An analysis of variance (ANOVA) revealed a significant sample effect (po0.001). Post hoc multiple comparisons were carried out by Tukey’s HSD test. The different letters (a, b, c) for each parameter indicate that statistical differences (po0.05) were observed between the samples.

(Dan & Kohyama, 2007). Six specimens of each cheese sample were given to all subjects in a pseudo-randomized order. Biting with judgment task The subjects were requested to bite cheese specimens in order to evaluate their textural attributes. A paired comparison task was conducted as a sensory evaluation of the textural attribute ‘‘hardness’’ by the two definitions (H1 definition, the force required to penetrate (bite into) the sample with the molar teeth; and H2 definition, the force required to bite completely through (fully compress) the sample). To help the subjects fully understand, these definitions (translated into Japanese) were presented to them prior to the judgment sessions. The word ‘‘hardness’’ itself was not used, in order to encourage subjects to rely on the mechanical aspects of term definitions rather than any potential pre-existing interpretation of a familiar term (Barrangou et al., 2006), as well as to avoid possible confusion. Judgments by H1 and H2 definitions were conducted in separate sessions. In each session, the subjects were requested to bite the samples with the molars of their habitual chewing side in the optimal way and in a consistent manner throughout the session, so as to perceive the designated textural attribute. Twelve possible combinations of cheese samples (WX, XY, YZ, WY, XZ, WZ, and their reverse sequence) were prepared for each subject in each session. Two samples in combination were presented in sequential order. After biting the pair, the subjects selected the sample that was superior in the defined textural attribute. Twelve pairs were given in pseudo-randomized order to all subjects. Bite measurement The biting time–force profile was measured during the control and judgment tasks by a sheet sensor (MSCAN3, Nitta Corp., Osaka, Japan), as previously reported (Dan, Azuma, Hayakawa, & Kohyama, 2005; Dan & Kohyama, 2007). Each cheese specimen was placed in the center of the sensing area. The sensor and the specimen on it were inserted between the subject’s upper and lower molars, with no contact with the sample. The subjects bit the sample once, then pulled out the sensor and spat out the specimen if it had remained in their mouth. The subjects closed their eyes during the session so that they could not visually distinguish the samples. Data processing and analysis Sensory assessment Examining the judgments from each subject by a Friedman analysis for the pairwise ranking test (Meilgaard, Civille, & Carr, 1999) enabled us to arrange the samples on a scale of intensity of H1/H2 attributes and provide a numerical assessment of the differences between samples. We could also determine the significance of such differences by applying Tukey’s HSD as a multiple comparison


procedure. The one sample pair that did not yield a significant difference (pair XY, see Results for details) in this analysis was not used in the following bite analysis. Biting data Two regions were set for the bite time–force curve obtained from each trial (Fig. 1) (Dan & Kohyama, 2007). Stage 1 was from the onset of biting to the local minimum between the first and second peaks; stage 2 was thereafter to the end of biting. The duration of each stage was then determined as T1 and T2. The total biting duration (T) was defined as T1+T2. Force parameters F1 and F2 were subsequently selected as the maximum forces at the first and second stages. The greater of F1 and F2 was determined to be the maximum force during biting (Fmax). Finally, the impulse parameters, denoted as I1 and I2, were designated as the force integrated over time for stages 1 and 2. The total biting impulse (I) was defined as I1+I2. Within-subject analysis of bite parameters The effects of bite conditions (normal biting, biting with H1 judgment, biting with H2 judgment) were examined for each subject. All bite parameters were compared between the control and H1/H2 judgment tasks to examine any behavioral modifications for judgment tasks. The Kruskal–Wallis H test and post hoc Mann–Whitney U test (with Force (N) Stage 1

Stage 2 Peak 2

F2 Peak 1 F1

161

Bonferroni correction) were performed. The Kruskal–Wallis H test was also used to examine the effects of sample difference for each subject. We next assessed the within-subject agreement between the bite force/impulse parameter and the judgment from the judgment tasks. For example, the agreement ratio for ten pairs was set to 10/10 ¼ 1 when all samples rated as superior by the subject were also determined to be greater in bite parameter. The bite–judgment agreement was statistically examined against 0.5 (by-chance level) using a binomial test for each selected bite parameter.

Results Analysis of bite time–force profiles of judgment tasks Bite time–force profiles of the control and H1 and H2 judgment tasks were obtained to identify bite modifications during the sensory evaluation. Fig. 2 presents typical examples of bite profiles during the control and H1 and H2 judgment tasks for a single subject. The biting behavior changed with changing task conditions. Both the time and force components of the bite curve were affected by the task conditions. Elongation of the stage 1 duration (T1) and decrease of the first and second peak forces (F1 and F2) were prominent features of the H1 judgment task. The H2 judgment task produced elongation of the total biting duration and a decrease in F1, but these were not as pronounced as in the H1 judgment task. Effects of the sample difference on each bite parameter were also observed, but these effects were similar irrespective of task differences. We address the effects of task differences on each bite parameter in the following subsections (details of the sample effects have been reported in Dan and Kohyama (2007)).

Local Minimum

30 control H1 judgment

25 I1

I2

H2 judgment

Time (s) T1

T2 T

Fig. 1. Time–force curve obtained from a bite measurement. Stages and peaks are defined. Bite parameters are also indicated. Stage 1 was from the onset of biting to the local minimum between the first and second peaks; stage 2 was thereafter to the end of biting. The duration of each stage was then determined as T1 and T2. The total biting duration (T) was defined as T1+T2. Force parameters F1 and F2 were subsequently selected as the maximum forces at the first and second stages. The greater of F1 and F2 was determined to be the maximum force during biting (Fmax). Finally, the impulse parameters, denoted as I1 and I2, were designated as the force integrated over time for stages 1 and 2. The total biting impulse (I) was defined as I1+I2.

Force (N)

20

15

10

5

0

0

0.5

1

1.5

2

Time (s) Fig. 2. Typical examples of bite time–force profiles during control and judgment tasks. One of the subjects bit sample X in the control and two (H1 and H2) judgment tasks.


162

Determination of explored bite stages based on temporal elongation

hypothesized behavioral strategy, were observed across all subjects. These exploratory procedures indicated that the sensory cues for H1 judgment were on the first peak. In the H2 judgment task, T1 elongation was observed in most of the subjects, but significant elongation was observed for only five of them. The T1 elongation for H2 judgment was not as prominent as for H1. Five subjects exhibited significant elongation in T2, but significant shortening was observed for one subject. Elongation of T was significant for seven subjects, while shortening was significant for one subject. The significant elongation of T indicated that elongation of the overall biting duration, and thus the overall biting stage, was a possible source of sensory information for H2 judgment for these seven subjects. However, effects of H2 judgment were not common across all subjects as in the H1 judgment. Between-subject variation in exploratory procedures indicated that resulting sensory signals for the H2 judgment also varied across all subjects. Fig. 4a presents sample effects on time parameters. The sample effect on T1 was not significant for five subjects

The time components of the bite curve were examined to determine which biting stages were explored as information sources for H1 and H2 judgments. T1, T2, and T were compared between normal and H1/H2 judgment tasks to examine whether the bite procedure differed in a taskdependent manner. We hypothesized that the primary procedure that the subjects would utilize would be to change their biting speed and consequently change their biting duration. Thus, stages that were elongated under judgment tasks were treated as targeted biting stages. Fig. 3 presents temporal modifications of biting stages in judgment tasks for each subject. The H1 task effects were significant (po0.05) in all the subjects for T1, which was significantly elongated. In contrast, T2 was shortened in all the subjects. The shortening of T2 was significant for six subjects. T1 elongation and T2 shortening resulted in elongation of the total biting duration T. Elongation of the first peak and shortening of the second peak, which was the

ID01 H1 control

ID02 ns

*

H2

ns

0

#

H1 control

*

ns

0

*

H1 control

2.0

ns

*

ID07

#

*

H2

* *

*

0 1 2 3 4 5 6 7 8 Time (s)

* 0

*

#

*

H2

*

*

*

*

0.5 1.0 1.5 2.0 2.5 Time (s)

#

*

H2

*

*

0.5 1.0 1.5 2.0 2.5 Time (s)

ns

0

* *

*

0.5 1.0 1.5 2.0 2.5 Time (s)

ID09

H1 control

*

H2

ns

0

*

0.5 1.0 1.5 2.0 2.5 Time (s)

H1 control

ID08

H1 control

ns

* 0

*

ID06

H1 control

*

#

*

H2

ns

ns

0.5 1.0 1.5 Time (s)

ID05

H1 control

0

ns

*

H2

#

0.3 0.6 0.9 1.2 1.5 Time (s)

ID04

H2

ID03

#

ns

*

0.5 1.0 1.5 Time (s)

H1 control H2

* 2.0

#

* ns

*

*

*

0 0.2 0.4 0.6 0.8 1.0 1.2 Time (s)

Mean of 9 subjects T1 T2 T

+

H1 control

#

*

H2

* 0

ns

*

*

0.5 1.0 1.5 2.0 2.5 Time (s)

Fig. 3. Temporal modifications of biting stages in judgment tasks for each subject. Time parameters (T1, T2, T) were compared between the control and H1/H2 judgment tasks to examine the behavioral modifications on them. Kruskal–Wallis H test and post hoc Mann–Whitney U test (with Bonferroni correction) were performed. Mean across the nine subjects is also presented. After the data of trials on the same sample were averaged for the control and two judgment tasks for each subject, the effect of the task difference was analyzed using the sign test (with Bonferroni correction). Significant (po0.05) elongation of H1/H2 tasks in comparison with control condition is indicated by *, whereas significant (po0.05) shortening of H1/H2 tasks in comparison with control condition is indicated by #. ns: not significant.


163

Examination of force and impulse parameters as the sensory cue of hardness judgment Time (s)

2

T2 T1

1

0

W X

Y Z

control

W X Y Z H1 judgment

W X Y Z H2 judgment

Force (N)

60

40

F2 F1 20

0

W X

Y Z

control

W X Y Z H1 judgment

W X

Y Z H2 judgment

Impulse (N s)

40 30

I2 I1

20 10 0

W X

Y Z

control

W X Y Z H1 judgment

W X

Y Z H2 judgment

Fig. 4. Comparison between the control and two judgment tasks: (a) time parameters; (b) force parameters; (c) impulse parameters.

Table 2 Individual analysis for sample effects on bite parameters

T1 T2 T F1 F2 Fmax I1 I2 I

ID01

ID02

ID03

ID04

ID05

ID06

ID07

ID08

ID09

** ns ns *** ns ns *** ns ns

ns *** ns *** ns * *** ns **

** ** * *** ** *** *** ns ***

ns ns ns *** * ns *** ns ***

ns ns ns *** ns ns *** ns ns

*** ns *** *** ns *** *** ns ***

ns ns ns *** ns ns *** ns *

*** Ns Ns *** Ns Ns *** Ns **

ns ns ns *** ns *** *** ns ***

The sample effect was statistically tested for each subject using Kruskal–Wallis H test. * po0.05; ** po0.01; *** po0.001. ns: not significant.

(Table 2). Only two subjects exhibited significant sample effects on T2 and T parameters. Although sample effects were inevitable in the temporal feature of the bite profile (Dan & Kohyama, 2007), the effects of the task conditions were robust; thus, sample effects became relatively small.

The above section determined the effects of evaluation procedures induced by the two definitions of hardness. Bite action produced sensory information that was subsequently used for judgments. As candidates of sensory signals for H1/H2 judgment, we extracted six parameters (F1, F2, and Fmax were force-related, while I1, I2, and I were impulse-related) from the bite profile. The effects of task and sample differences were examined for each of these parameters. The significant sample differences indicated that the parameter can include the sample information and thus be used as the sensory cue for judgment. Fig. 4b and c presents sample effects on force and impulse parameters. Effects of sample difference on F1 and I1 were highly significant (po0.001) for all the subjects (Table 2). Not all subjects exhibited significant effects on parameters F2, Fmax, and I. No subject demonstrated significant sample effects on I2. Parameters F1 and I1 could be sensory signals for hardness judgment. Several subjects could also use the other parameters (F2, Fmax, or I); but I2 was less likely as the sensory signal, as it could not be used to distinguish the samples (Table 2). Fig. 4b and c also presents task effects on force and impulse parameters. Effects of task difference on F2 and Fmax were highly significant (po0.001) for all the subjects (Table 3). The other parameters exhibited significance for several (but not all) subjects. The task effects on the force and impulse parameters were considered as byproducts of modification in the bite procedure. In the H1 task, the biting procedure for attenuating the second peak caused the second peak force (F2) to be lower than F1. Task effects on the other parameters were explained as rheological side effects of bite procedure modulation on the viscoelastic cheese samples. Details have been reported elsewhere (Dan & Kohyama, 2007). Agreement between judgment and bite parameters in explored stages The time parameters that revealed significant elongation in the judgment tasks indicated the exploratory procedure in that stage and thus the importance of that stage for Table 3 Individual analysis for task effects on bite parameters

F1 F2 Fmax I1 I2 I

ID01

ID02

ID03

ID04

ID05

ID06

ID07

ID08

ID09

ns *** *** ** *** ***

** *** *** *** ** ns

* *** *** ns ns ns

*** *** *** *** *** **

*** *** *** *** *** ***

ns *** *** * *** *

** *** *** *** *** ***

* *** *** ns *** ***

** *** *** *** ** ns

The task effect was statistically tested for each subject, using Kruskal– Wallis H test. *po0.05, **po0.01, ***po0.001. ns: not significant.


164

judgment. T1 exhibited significant elongation in the H1 judgment task. Thus, the force and impulse parameters in these stages (Fig. 4b and c) may be sensory cues for H1 judgment. T experienced significant elongation in the H2 judgment task in most subjects, indicating that the impulse parameter for all the biting stages (Fig. 4c) may be a major sensory cue for H2 judgment. The agreement between the sample judged by the subject as superior and the sample determined to be greater in above-bite parameters was examined for each pair and subject, to determine whether these parameters, estimated from bite procedure (temporal elongation), act as sensory cues. Table 4 presents the ratios of agreed pairs for each subject for H1 and H2 judgment tasks. All subjects exhibited significant agreement between their judgment and F1 in the H1 task. Thus, F1, which was the intra-oral fracture force of the sample surface, could be a sensory cue for H1 judgment. All subjects exhibited significant sample effects on this parameter (Table 2), confirming that F1 can be a common carrier of hardness information. Several subjects (but not all subjects as with F1) exhibited significant agreement between Fmax, I1, or I and their judgments. In contrast, no subject showed significant agreement between F2/I2 and their judgments. Therefore, the second biting stage was not important in the H1 judgment task. No subject reveals significant agreement with any single parameter in the H2 task (Table 4). Only a single subject (ID02) demonstrated significant agreement with I. Five of nine subjects (ID02, 04, 05, 06, and 08) revealed significant agreement to I1 or F1, indicating that they used the tactile

H2

Subject F1

F2

Fmax I1

I2

I

F1

F2

Fmax I1

I2

I

ID01 ID02 ID03 ID04 ID05 ID06 ID07 ID08 ID09 Mean

0.4 0.5 0.4 0.3 0.8 0.6 0.4 0.8 0.3 0.50

0.4 0.7 1.0* 0.9* 0.8 1.0* 0.9* 0.9* 0.8 0.82

0.6 0.3 0.3 0.3 0.6 0.7 0.2 0.5 0.3 0.42

0.8 0.8 1.0* 0.9* 0.8 0.9* 0.8 1.0* 0.8 0.87y

0.7 1.0* 0.8 0.8 1.0* 0.9* 0.8 0.9* 0.8 0.86y

0.9* 0.9* 0.8 0.5 0.5 0.7 0.6 0.9* 0.6 0.71

0.9* 0.9* 0.8 0.5 0.5 0.5 0.6 0.9* 0.6 0.69

0.9* 0.8 0.4 0.5 0.5 0.5 0.7 0.7 0.5 0.61

0.8 0.9* 0.8 0.6 0.6 0.7 0.7 0.8 0.6 0.72

0.9* 1.0* 1.0* 0.9* 0.9* 1.0* 0.9* 1.0* 0.9* 0.94y

0.9* 0.8 1.0* 0.8 0.7 1.0* 0.8 0.8 0.9* 0.86y

H1 judgment

small

* YX

Z

57

81

105

* H2 judgment

small

large

*

W

YX

Z

60

81 82

101

large

Fig. 5. Rank sum scales for H1 and H2 judgment tasks. Samples W, X, Y, and Z are located on the scales for each task. Tukey’s HSD was used as a multiple comparison procedure. Significant differences between the samples are indicated by * for po0.05.

information at the first peak as a judgment cue. Three subjects (ID01, 02, and 08) demonstrated significant agreement to Fmax and may have used the maximum force experienced during the first bite as a cue. One subject (ID01) demonstrated significant agreement with F2 and I2; this subject may have used the second peak as a sensory cue. However, the judgments of three subjects (ID03, 07, 09) did not significantly agree to any of the biting parameters we examined. Unlike the H1 judgment, the sensory cues for H2 judgment as well as the biting procedure included large inter-subject variability. No bite parameter acted as a common sensory cue for H2 judgment. In addition, several subjects did not show significant agreement between any bite parameters and judgment; this result implied that their judgments were not based on the single consistent sensory signal, or any of the intra-oral properties enabled to determine in this study. Separate analysis of subjects’ judgments as sensory results

Table 4 Bite–judgment agreement in sample pairs H1

* W

0.7 0.9* 0.8 0.9* 1.0* 1.0* 0.8 0.9* 0.7 0.86y

Orders of bite parameters within the paired samples were compared with the subjects’ judgments for that pair. The bite–judgment agreement was determined as the ratio of the pair for which the chosen sample was greater in bite parameter for each subject and parameter. The bite–judgment agreement for each subject was statistically examined against 0.5 (bychance level) using a binomial test (*po0.05). The mean of the bite–judgment agreement was calculated for each parameter. The intersubject average and 95% confidence interval were calculated for each parameter. The parameters for which the lower limits of the 95% confidence interval were above 0.75 are designated by y.

The judgment data could be analyzed separately using a Friedman analysis for the pairwise ranking test (Meilgaard et al., 1999), which is the typical method in sensory evaluation. The results for the H1 and H2 judgment tasks are presented in Fig. 5 on a rank sum scale of large vs. small. The rank sum orders of the samples (WoYXoZ) were the same for both definitions. Significant differences between samples W and Y, and X and Z were observed for both conditions. This sample order was the same as the instrument fracture force (Table 1). Thus, the sensory results alone led us to consider that both were compatible definitions for the fracture force evaluation, in spite of the intrinsic intra- and inter-subject variability of the evaluation procedures in H2 judgment.

Discussion This study’s objective was to examine the bite procedure during the sensory evaluation of different hardness definitions, the correspondence between the intra-oral


sensation and judgment, and their intra- and inter-subject variability. We confirmed the relationship between hardness definitions and their exploratory procedures for each subject, using direct bite measurement while the subjects actually evaluated the specified texture definition. We obtained subsequent sensory information as well as the exploratory procedure for targeted texture from the bite time–force profile. The targeted sensory information could be estimated from the exploratory procedures. Agreement between the explored sensory information with judgment provided sufficient evidence for the sensory information as the intra-oral cue for the hardness judgment. We concluded that (1) different definitions of hardness induced different exploratory procedures, (2) inter-subject and inter-definition differences existed for targeted intra-oral sensory signals, and (3) consistencies among bite procedure, targeted sensory signal, and judgment depended on the definition. This study demonstrated that bite procedure and resulting sensory information differed within a subject, depending on the texture definition given (inter-definition difference). The results of sensory evaluations were similar for both the H1 and H2 definitions (Fig. 5). However, the similarity of sensory results between H1 and H2 definitions did not ensure similarity in the evaluation process (i.e. bite procedure, resulting sensory information, judgment, and their relationship) for the two definitions. Additionally, prominent inter-subject differences were observed in these processes in the H2 judgment task, while the processes were estimated to be relatively homogeneous across all subjects for the H1 judgment task. Elongation of the first peak in the H1 judgment task was a common exploratory strategy, and the force at the first peak was the information typically used for H1 judgment by all subjects. H1 is defined as the force required to penetrate (bite into) a sample with the molar teeth (Bryant et al., 1995; Gunasekaran & Ak, 2003; Hort & Le Grys, 2000; Lee et al., 1978). This definition is mechanical-property-oriented rather than sensory-oriented. The intra-oral mechanical source was expected to be the initial fracture of the sample surface, at least by researchers of food rheology. In fact, the H1 definition of hardness led the subjects (nonrheologists) to focus on the first peak force with the exploratory procedure of the first peak, to acquire and store tactile information regarding the peak force, and to retrieve the stored information to make a judgment. These processes were relatively stable across different sample pairs. Therefore, the H1 definition is more suitable for obtaining a less ambiguous result for hardness evaluation when equating the sensation from the intra-oral fracture force to hardness. The H2 definition of hardness, the force required to bite completely through a sample with molars, was defined by Munoz (1986) after Szczesniak’s familiar definition of hardness, ‘‘the force necessary to attain a given deformation’’ (Szczesniak, Brandt, & Friedman, 1963). The H2

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definition of hardness induced elongation of the total biting duration as an almost common strategy for all subjects. However, the presumed biting parameter (I), which was estimated from both the H2 definition and behavioral modulation, was not acquired, stored, or retrieved for subsequent judgment by most of the subjects. It is considered that I was not used for the hardness judgment because of difficulties in encoding biting parameter I (i.e. impulse for a series of different intra-oral mechanical events) as an integrated mental representation. Thus, alternatively, half of the subjects used the information of the first peak force for the H2 judgment. Another subject used the tactile features of the second peak or the maximum force during the bite process. The results of a few subjects (ID03, 07, 09) can be explained by one of the following three possibilities: (1) they used different sensory signals, depending on the samples (intra-subject inconsistency), for H2 judgment; (2) they used an unknown information source, unmeasured in this study, for H2 judgment; or (3) while they performed well in the H1 judgment task, they could not return an H2 judgment based on their sensory information. Regardless of the cause, it is clear that the H2 definition of hardness led to varying and uncertain internal processes for all the subjects (Table 4), in addition to discrepancies between definition and intention. This finding was similar to Booth et al.’s result, in which one or more mechanical characteristics were used to assess a designated texture by some subjects and not by others, possibly based on inter-subject differences in biting movements, tooth morphology, the resulting sensory inputs, and the meaning of the descriptive word (Booth, Earl, & Mobini, 2003). The H2 definition is commonly used for cheese hardness and may be suitable to describe broadly what we perceive as ‘‘hard’’. However, its ambiguity leads the subjects to interpret its meaning differently and thus to focus on different mechanical aspects of the food sample by utilizing different biting procedures. Subjects use tactile properties, aroused from physical contact between the teeth and the food sample, for texture evaluation. Evaluative biting of a sample is a mechanical fracture event in terms of the sample, while it represents complex sensorimotor and cognitive processes for the subjects. The biting process as a mechanical event is affected by the physical properties of the sample (Dan & Kohyama, 2007), whereas the biting process as exploratory behavior is affected by the task demands (i.e. evaluation of a specific texture). The biting profile, which includes both the bottom-up tactile information of the sample properties and the topdown motor effects of task instruction, enables us to confirm the intra-oral mechanical event that is critical for a specific texture evaluation. Monitoring the biting profile, together with the overt judgments of the subjects, is a feasible approach to confirming the relationships between the perceived texture and the causative mechanical properties of the foodstuffs.


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Acknowledgment This study was supported by the Program for Promotion of Basic Research Activities for Innovative Biosciences (PROBRAIN). References Barrangou, L. M., Drake, M. A., Daubert, C. R., & Foegeding, E. A. (2006). Sensory texture related to large-strain rheological properties of agar/glycerol gels as a model food. Journal of Texture Studies, 37, 241–262. Booth, D. A., Earl, T., & Mobini, S. (2003). Perceptual channels for the texture of a food. Appetite, 40, 69–76. Bryant, A., Ustunol, Z., Steffe, J. (1995). Texture of Cheddar cheese as influenced by fat reduction. Journal of Food Science, 60, 1216–1219, 1236. Dan, H., Azuma, T., Hayakawa, F., & Kohyama, K. (2005). Measurement of bite force variables related to human discrimination of leftright hardness differences of silicone rubber samples placed between the incisors. Archives of Oral Biology, 50, 517–526. Dan, H., & Kohyama, K. (2007). Interactive relationship between the mechanical properties of food and the human response during the first bite. Archives of Oral Biology, 52, 455–464. Dan, H., Okamoto, M., Wada, Y., Dan, I., & Kohyama, K. First bite for hardness judgment as haptic exploratory procedure. Physiology and Behavior, in press. Dan, H., Okuhara, K., & Kohyama, K. (2003). Discrimination of cucumber cultivars using a multiple-point sheet sensor to measure biting force. Journal of the Science of Food and Agriculture, 83, 1320–1326. Dan, H., Watanabe, H., Dan, I., & Kohyama, K. (2003). Effects of textural changes in cooked apples on the human bite, and instrumental tests. Journal of Texture Studies, 34, 499–514. de Wijk, R. A., Wulfert, F., & Prinz, J. F. (2006). Oral processing assessed by M-mode ultrasound imaging varies with food attribute. Physiology and Behavior, 89, 15–21. Gunasekaran, S., & Ak, M. M. (2003). Cheese rheology and texture. Boca Raton: CRC Press, Inc. Hort, J., & Le Grys, G. (2000). Rheological models of Cheddar cheese texture and their application to maturation. Journal of Texture Studies, 31, 1–24. Johnsen, S. E., & Trulsson, M. (2005). Encoding of amplitude and rate of tooth loads by human periodontal afferents from premolar and molar teeth. Journal of Neurophysiology, 93, 1889–1897.

Lederman, S. J., & Klatzky, R. L. (1987). Hand movements: A window into haptic object recognition. Cognitive Psychology, 19, 342–368. Lederman, S. J., & Klatzky, R. L. (1993). Extracting object properties through haptic exploration. Acta Psychologica, 84, 29–40. Lee, C.-H., Imoto, E. M., & Rha, C. (1978). Evaluation of cheese texture. Journal of Food Science, 43, 1600–1605. Luyten, H., van Vliet, T., & Walstra, P. (1991). Characterization of the consistency of Gouda cheese: Fracture properties. Netherlands Milk and Dairy Journal, 45, 55–80. Marshall, R. J. (1993). Physicochemical properties accounting for cheese texture. Acta Psychologica, 84, 69–77. Meilgaard, M., Civille, G. V., & Carr, B. T. (1999). Sensory evaluation techniques (3rd ed). Boca Raton: CRC Press, Inc. Meullenet, J.-F., Carpenter, J. A., Lyon, B. G., & Lyon, C. E. (1997). Bicyclical instrument for assessing texture profile parameters and its relationship to sensory evaluation of texture. Journal of Texture Studies, 28, 101–118. Meullenet, J.-F., Finney, M. L., & Gaud, M. (2002). Measurement of biting velocities, and predetermined and individual crosshead speed instrumental imitative tests for predicting cheese hardness. Journal of Texture Studies, 33, 45–58. Meullenet, J.-F., & Gandhapuneni, R. K. (2006). Development of the BITE Master II and its application to the study of cheese hardness. Physiology and Behavior, 89, 39–43. Mioche, L., & Martin, J.-F. (1998). Training and sensory judgment effects on mastication as studied by electromyography. Journal of Food Science, 63, 1–5. Moiny, V., Meullenet, J.-F., & Xiong, R. (2002). Uniaxial compression of Cheddar cheese at various loading rates and its correlation to sensory texture profiles. Journal of Texture Studies, 33, 237–254. Munoz, A. M. (1986). Development and application of texture reference scales. Journal of Sensory Studies, 1, 55–83. Szczesniak, A. S. (1987). Correlating sensory with instrumental texture measurements—An overview of recent developments. Journal of Texture Studies, 18, 1–15. Szczesniak, A. S., Brandt, M. A., & Friedman, H. H. (1963). Development of standard rating scales for mechanical parameters of texture and correlation between the objective and the sensory methods of texture evaluation. Journal of Food Science, 28, 397–403. Trulsson, M. (2006). Sensory—motor function of human periodontal mechanoreceptors. Journal of Oral Rehabilitation, 33, 262–273. Wilkinson, C., Dijksterhuis, G. B., & Minekus, M. (2000). From food structure to texture. Trends in Food Science and Technology, 11, 442–450.

ARTICLE IN PRESS


Research Report

Engaging with healthy eating discourse(s): Ways of knowing about food and health in three ethnocultural groups in Canada Svetlana Ristovski-Slijepcevica,, Gwen E. Chapmana, Brenda L. Beaganb a

Food, Nutrition and Health, Faculty of Land and Food Systems, University of British Columbia, 2205 East Mall, Vancouver, BC, Canada V6T-1Z4 b School of Occupational Therapy, Room 207, Forrest Building, Dalhousie University, Halifax, NS, Canada B3H-3J5 Received 21 February 2007; received in revised form 20 June 2007; accepted 2 July 2007

Abstract The aim of this study was to increase our understanding of how people make sense of healthy eating discourses by exploring the ‘ways of knowing’ about healthy eating among members of three different ethnocultural groups in Canada: African Nova Scotians, Punjabi British Columbians and Canadian-born European Nova Scotians and British Columbians. Data for this paper come from in-depth, individual interviews with 105 adults where they described their experiences, interpretations, and reasoning used in learning and deciding what to believe and/or reject about healthy eating. Between and within ethnocultural group differences in how people come to know and use practices about healthy eating were examined as they were represented through three broad healthy eating discourses: cultural/ traditional, mainstream and complementary/ethical. The discourses represented different ways to interpret the food–health relationship and make sense of the evidence about healthy eating in the everyday experience. Engagement with different discourses led participants to undertake different practices upon themselves in the name of healthy eating. We suggest that each of the discourses has a significant contribution to make in a dialogue about how healthy eating, as part of health and well-being, should be conceptualized by a society. r 2007 Elsevier Ltd. All rights reserved. Keywords: Healthy eating; Culture; Qualitative; Foucault; Discourse; Technologies of the self

Introduction A population that is well-nourished is healthier, more productive, contributes to lower health care and social costs, and enjoys better quality of life (Health Canada, 1996). Because of the role of nutrition in promoting and maintaining health, messages about healthy eating have become widespread, occurring at various levels in society. Canada’s Food Guide and the USDA’s Food Pyramid are two examples of such messages—pictorial guides designed to promote a pattern of healthful eating. However, despite the wide availability of information, a gap between healthy eating messages and people’s actual eating practices

Corresponding author.

E-mail addresses: [email protected] (S. Ristovski-Slijepcevic), [email protected] (G.E. Chapman), [email protected] (B.L. Beagan). 0195-6663/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.appet.2007.07.001

persists (Dixon, Cronin, & Kebs-Smith, 2001; Jacobs Starkey, Johnson-Down, & Gray-Donald, 2001). Recent research exploring food choice processes suggests that this gap can partially be explained by the multiple factors shaping food choice. Food decisions are not based solely on people’s health and nutrition beliefs, but involve financial, social, lifestyle, environmental and family issues (Furst, Connors, Bisogni, Sobal, & Falk, 1996; Wetter et al., 2001). But other research looking specifically at health/nutrition beliefs suggests that these beliefs themselves need to be more fully explored. Overall, people generally consider healthy eating as important (Patterson, Satia, Kristal, Neuhouser, & Drewnowski, 2001). For example, in a 2001 Canadian survey, 92% of women and 85% of men said that nutrition was an important consideration for them when choosing food (Health Canada, 2002). But there are also indications that people may be confused by or are rejecting the plethora of nutrition and health messages available today (Goldberg, 1992; Patterson et al., 2001). Scepticism towards such


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messages appears to be higher in men than women, the younger and older than the middle aged, and those with lower socioeconomic status. In response to evidence of confusion and scepticism, institutions providing nutrition guidance have attempted to improve their efforts to educate people about how to eat healthier. Both the USDA Pyramid and Health Canada’s Food Guide have recently been reviewed and revised (Health Canada, 2004, 2007; USDA, 2007). But a food guide, while an important resource, is only one among a plethora of information sources about healthy eating currently available to people. Even though health professionals are perceived to have the highest expertise (Hiddink, Hautvast, van Woerkum, & Fieren, 1997; Holgado, Martinez-Gonzalez, De Irala-Estevez, Gibney, Kearney, & Martinez, 2000), it is often friends and family (Ankeny, Oakland, & Terry, 1991; Hiddink et al., 1997), media sources such as books/magazines, newspaper, TV/ radio (Ankeny et al., 1991; Hiddink et al., 1997; Holgado et al., 2000; Medeiros, Russell, & Shipp, 1991) and more recently the Internet (Goldberg, 2000) that people turn to for information about healthy eating. Sources used vary to an extent based on country of residence (Holgado et al., 2000; Lappalainen, Kearney, & Gibney, 1998) as well as the existence of an illness (Ankeny et al., 1991). The available literature about people’s interpretations of healthy eating has shown that people are quite familiar with the concept and its basic assumptions. Although they may not specifically refer to official nutritional guidelines as information sources, people often use basic messages from guidelines such as decreasing high-fat foods and increasing fruits and vegetables (Health Canada, 2003; Keane & Willetts, 1996; Paquette, 2005). But beyond the general guidelines, people may define healthy eating in different ways (Chapman & Beagan, 2003; Falk, Sobal, Bisogni, Connors, & Devine, 2001; Keane & Willets, 1996; Povey, Conner, Sparks, James, & Shepherd, 1998) by, for example, expressing different orientations to healthy eating (Chapman & Beagan, 2003), or speaking of healthy eating in either general (‘‘eating the right sort of food at the right time’’) or specific terms (proportions of protein, grains, vegetables, and carbohydrates that should be consumed) (Keane & Willets, 1996). That people interpret healthy eating in different ways may thus be another clue to answering the question of why the discrepancy between healthy eating messages and behaviour remains. While it has been acknowledged that the diversity in interpretations may be due the broad nature of the concept ‘healthy eating’ (Povey et al., 1998), as well as differences in interpretations about food- and healthrelated issues across age/lifestage (Patterson et al., 2001; Van Dillen, Hiddink, Koelen, de Graaf, & van Woerkum, 2004), gender (Fagerli & Wandel, 1999; Patterson et al., 2001; Rozin, Fischler, Imada, Sarubin, & Wrzesniewski, 1999), social class (Coveney, 2005; Patterson et al., 2001), and national/cultural differences (Lappalainen et al., 1998; Rozin et al., 1999), most research on healthy eating has focused at the individual level.

The emphasis on individual beliefs about healthy eating is problematic because, while everyday decisions about food are individual acts, they are also reflective of societal norms about modes of being. These norms are conveyed through discourses—patterned systems of language and practices about phenomena through which individuals come to understand themselves (Foucault, 1972; Lupton, 1996; McNay, 1994). Discourses provide a ‘language’ for conveying meanings and practices in society. Meanings and practices; however, are socially and historically situated and change with time and context (Brandt & Rozin, 1997; Coveney, 2000; Rotberg, 2000; Rosen, 1993). Within Western culture, for example, current social representations of health and illness are predominantly commonsense versions of scientific theories practiced at the level of everyday discourse (Moscovici, 2001). Norms about being healthy are represented through discourses of risk that focus on promoting health and preventing illness by the monitoring and modification of risk factors (Castel, 1991). While promoting individual free choice with concurrent responsibility for one’s health (Beck-Gernsheim, 2000; Petersen, 1996), the appropriate site for action is the individual who is expected to adopt a self-regulating, calculating, and prudent attitude towards prevention and risk (Petersen, 1997). Similarly, notions of healthy eating can be seen as representations conveyed through discourses, where nutritional guidelines act as an official discourse for explaining the relationships between healthy eating, health and wellbeing in Western societies. People are expected to learn and enact the messages by incorporating these discourses into the practicalities of everyday food practices, for example in the form of diet regimes for the family (Coveney, 2000; Petersen, 1996). However, this is by no means the only way to view these relationships. Throughout time, societies have developed complex ways of explaining health and illness (many with longer histories of health and illness representations than the current Western one) drawing on different ways to conceptualize health, with discourses often encompassing more integrated relationships between the individual and the environment (Airhihenbuwa, 1995; Jovchelovitch & Gervais, 1999). Despite this, the majority of healthy eating research has drawn on Caucasian participants and has neglected to include an examination that reflects the cultural and immigration patterns in Western societies. While some food research on particular sub-cultural groups does exist, synchronic studies examining sub-cultural variations within a society have remained largely unexamined (Sobal, 1998). Although it is increasingly apparent that different populations have diverse views of the role of food in health and well-being, there is a paucity of knowledge about how various people make sense of discourses about healthy eating and how they incorporate this knowledge into their everyday experiences. Theoretically, Foucault’s work on how people come to know about themselves, particularly his work on the ‘technologies of the self’

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(Foucault, 1988; McNay, 1994), provides a useful point of departure for answering such questions. In an attempt to outline the ‘‘different ways in our culture that humans develop knowledge about themselves’’ (Foucault, 1988, pp. 17–18), Foucault saw ‘technologies of the self,’ as a set of practices: permitting individuals to effect by their own means or with the help of others a certain number of operations on their own bodies and souls, thoughts, conduct, and way of being, so as to transform themselves in order to attain a certain sense of happiness, purity, wisdom, perfection, or immortality (Foucault, 1988, p. 18). Rather than viewing human practices from the perspective of their being the result of coercive forces, ‘technologies of the self’ are most concerned with how individuals act upon themselves ‘‘as an exercise of the self on the self, by which one attempts to develop and transform oneself, and to attain to a certain mode of being’’ (Foucault, 1996, p. 433). Foucault demonstrated the social and historical contingency of the particular ways we do so by showing how an understanding of ‘the concern for the self’ changed from the time of the Greco-Roman period to the one of early Christianity. The concern for the self in the Greco-Roman period was understood as the ‘‘progressive consideration of self, or mastery over oneselfy through the acquisition and assimilation of truth’’ (Foucault, 1988, p. 35), whereby one would seek authority in order to improve and develop oneself as a person without giving up one’s autonomy. Practices of the self in the early Christian period were changed to resemble self-denial and obedience via ‘‘a certain renunciation of the self and of reality’’ (Foucault, 1988, p. 35) where one’s autonomy was to be relinquished to authority. In an attempt to make sense of the different ways that people engage with healthy eating discourses, our aim in this paper, therefore, is to explore the ways that people from three different ethnocultural groups in Canada draw on, interpret and use healthy eating discourses. What we have broadly termed ‘ways of knowing’ about healthy eating are people’s discussions of experiences, interpretations, and reasoning used in learning and deciding what to believe, reject, and/or act upon regarding healthy eating. We use Foucault’s conceptualization of the ‘technologies of the self’ to interpret healthy eating knowledges and practices as embodied forms of discourses about healthy eating. We also focus on the varied social and cultural influences that shape the ways that people draw from, interpret and use healthy eating discourses within the context of their everyday lives. Methods This paper is based on data collected with adult participants whose families participated in a qualitative study of family food decision-making conducted in Halifax and Vancouver. The two cities are situated on the opposite

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coasts of Canada, Halifax in the East Coast province of Nova Scotia (NS) with a population of nearly 400,000 and Vancouver in the West Coast province of British Columbia (BC) with a population of over 2.2 million (Statistics Canada, 2001). Nova Scotia was first occupied by Europeans in 1604; the vast majority of Nova Scotians today are of British, Irish or French heritage. British Columbia was first settled by Europeans in the 1860s; Vancouver is one of Canada’s most ethnically diverse cities with a large proportion of recent immigrants, particularly from Asia and South Asia. Apart from the dominant ethnocultural group in Halifax (European NS) and Vancouver (European BC), two minority groups were included: African NS in Halifax and Punjabi BC in Vancouver. Both groups represent a sizeable community in their respective places, and offer differing perspectives on culture and history of immigration and integration in Canada. African NS have a much longer presence in Canada, imbued with a history of oppression, and lead a more isolated way of life in Halifax. In contrast, Punjabi BC’s immigration to Canada is more recent, but the community, while remaining distinct, is larger and more integrated in Vancouver’s society. In this paper, we differentiate among four groups of participants from three ethnocultural backgrounds: 26 African NS (19 females and 7 males) and 20 European NS (13 females and 7 males) in Halifax, as well as 34 Punjabi BC (23 females and 11 males) and 25 European BC (17 females and 8 males) in Vancouver. For reasons that include recruitment criteria (families with at least one woman aged 25–55 years, no specific requirement of male partners to be interviewed), the nature of households (households with no male partners), as well as the potential for gendered interest in food-related issues, our sample consists of a larger number of women than men. Ethical approval was obtained from Research Ethics Boards at both Dalhousie University in Halifax and the University of British Columbia in Vancouver. Participant families were recruited through community-based organizations, community contacts and notices posted in public locations. Each participant read and signed an informed consent form at the onset of data collection. Qualitative methods were used to collect data as they are best suited for depicting the complexity of human behaviour within the context of where they occur (Denzin & Lincoln, 2000; Lincoln & Guba, 1985). Individual interviews were carried out in parallel ways in Halifax with families of African and European origin and in Vancouver with families of Punjabi and European origin. The research assistants collecting data with the African NS and Punjabi BC participants were members of those respective ethnocultural groups. The interviews were tape-recorded and transcribed verbatim. Summary memos about healthy eating interpretations were written for each participant. Atlas/Ti software was used in the initial stages of analysis to facilitate coding of the data. Codes were developed both at the onset of the study, as well as during the progression of data collection and



analysis. Overarching codes (e.g., ‘healthy eating,’ ‘ways of knowing,’ ‘culture’) as well as more specific codes (‘sources of info,’ ‘trust in info’ and ‘control/monitor’) were used to develop the thematic analysis in this paper. Our interpretations during analysis were guided by the assumption that human beings construct knowledge by inventing concepts, models, and schemes to make sense of experience, and modifying these constructions in light of new experiences. Because humans are social beings, all of this occurs against a backdrop of shared understandings and practices (Schwandt, 1994). Results In examining the data on participants’ discussions about healthy eating, three broad discourses became apparent, each with its own ‘ways of knowing’ about healthy eating. The three discourses are termed here as the ‘cultural/ traditional’, ‘mainstream’, and ‘complementary/ethical’ discourses. Participants’ ‘ways of knowing’ about healthy eating became apparent through their explanations of how they made sense of healthy eating by evaluating, drawing upon or resisting various healthy eating discourses, the sources from which they obtained information about healthy eating, as well as through their views about expertise and trust in information in healthy eating. The themes that run across each of the discourses are: the foods and/or preparation methods considered healthy, the food–health relationship (how do they see food contributing to health), making sense of the evidence in everyday life (how do they know what they know about healthy eating) and responses to/evaluations of healthy eating discourses. We use the plural ‘discourses’ rather than the singular ‘discourse’ for each of the groupings to promote the sense that participants in each grouping articulated similar rather than identical ways of expressing views about healthy eating, complicated by a sense of fluidity between discourses. Cultural/traditional healthy eating discourses: promoting the positive aspects of food Very few European BC, but a sizeable number of European NS, as well as the majority of older African NS and Punjabi BC and newer immigrant Punjabi BC tended to draw on what we are calling cultural/traditional discourses in describing healthy eating. These discourses incorporated accounts of cultural/traditional food choices that were considered to be healthy. While the food choices may or may not have actual cultural/traditional roots, they were perceived in that way by participants themselves. Each of the ethnocultural groups had particular foods that they associated with their own cultural/traditional background such as corn bread, curry chicken, and boiled dinner for African NS; the roti meal (roti bread+lentil dish+vegetable dish) for Punjabi BC; and boiled dinner, the meat–potatoes–vegetables meal for European NS.

These participants deemed aspects of their eating patterns as children to be healthier that their current ones, one reason being the unavailability of junk or convenience foods in their youth: ‘‘We just didn’t have quick food. You know quick food was corn on the cob’’ (European NS woman, 42). Those using cultural/traditional healthy eating discourses also interpreted traditional and natural ways of producing food as more healthful: We grew up on a farm, so the eggs were from the chickens, if you had bread it was wheat bread, because we were wheat farmersy We always do vegetables in the garden. So it was just natural, the food that you had available, and that’s what you ate. My mom and grandma and my dad, that was just the way it was done and still is (European NS woman, 33). The more natural and simpler ways of producing food during childhood were also coupled with healthier preparation and consumption patterns in the home. The way food was made by mothers was considered more wholesome and natural, the non-use of sweeteners and other food additives making it healthier than current ways of eating. One African NS participant said of her mother’s ways of preparing porridge: My mom always had that hot porridgey my mother would just take it out of the pot like clay, and just throw it on the plate. The way they used to do it, because it was healthy for you, was to stick more to your bones. So, but now you got to make it with the brown sugar, cinnamon, and make it all nice and stuff (African NS woman, 59). In addition to preparation methods, the African NS woman’s reflection of her mother’s food preparation also illustrates the positive aspects of food contributing to health that were apparent in representations of these discourses. While the different ethnocultural groups drew on these discourses in culturally specific ways, they all spoke of food in relation to its positive effects on health. The ‘stick more to your bones’ phrase was particularly used by African NS to illustrate the healthfulness of food in terms of functionality, a healthy person being one who has some ‘meat’ on their body. Similar roles of food were discussed by Punjabi BC participants with healthy eating themes of the roti meal ‘providing strength’ as an attribute of health: I feel [roti meal] has a lot of vitamins and provides strength. It is very good. If one eats roti, they stay full for longer tooy Roti has a lot of strength in it. Dahl and subjee. They have a lot of strengths. Here they don’t make as much dahl, but in India, they make a lot at home and it is really good for you. It is very good for the health (Punjabi BC woman, 70). In the cultural/traditional discourses, healthy eating knowledge was learned from family and community


members, through knowledge that was seen as having accumulated over many generations. This knowledge, due to its long history became accepted as common sense, learned as part of everyday life: Well ever since I was a kid, down through the Black generation and whatever they always preach to you, eat as healthy as you can. It just stuck out in the back of your mind, in mine anyway, since I was a kid (African NS woman, 45). Similar ways of knowing about healthy eating by reference to learned common-sense notions of health were noted by Punjabi BC typically by observing others in the community: In India, old people used to drink less tea and eat more of these things and say that these are good (Punjabi BC woman, 62). Punjabi BC in particular made reference to how things have always been done, learning about common understandings through advice given by wise elders or shared ways of eating stretching back for generations. Due perhaps to the historically rooted nature of the knowledge, there was a tendency in this group to accept knowledge acquired about healthy eating in this way as given without much questioning, often responding to probes with ‘‘it’s been like this from the very beginning.’’ When asked about why they eat certain foods or how they knew about certain diet–health links, participants replied that: In India, from the very beginning, we eat roti only, so we are used to eating roti, vegetables and dahl (Punjabi BC woman, 35). In Canada, the knowledge African NS and Punjabi BC learned from family and community was supplemented by the expertise of health professionals advising them how to eat healthy even when participants did not understand why: I take [supplements] sometimesy because they are said to be goody the doctor asked me to eat them (Punjabi BC woman, 62). Often, because of language and literacy barriers, Punjabi media sources such as Indian programs on TV and radio were the sources that taught them about what doctors suggest they should eat. In addition to the cultural/traditional repertoires and experts’ advice about healthy eating, interpretations of healthy eating were also shaped by participants’ observations of the health-promoting effects of foods in everyday life—a kind of embodied way of knowing. One African NS man reflected that he does not get colds due to the foods he eats: [K]nock on wood, I’ve never been sicky Right now, right as we speak my household is sick. They all have colds right, and [my partner] has been sort down for that last week or more as well as the other people in my house. That cold doesn’t even touch me, and that’s because of how I eaty those spices [in Soul food], I tend to believe it’s the spices, because spices they flush out your system, and everything. So and everything I eaty has a hot spice on it, you know. And I tell those fellows and part of that when I first used to tell them that, they

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didn’t pay me no mind. Then my son got old enough where he will watch and see (African NS man, 39). Similarly, immigration to Canada provided Punjabi BC with another layer of analysis for interpreting healthy eating where participants were able to observe, compare, and reflexively critique eating practices in India and Canada. In comparison to the perceived healthfulness of the flat bread, lentils, and vegetables they eat on a daily basis, many Western foods such as pasta were viewed as less healthy and ‘‘too heavy’’ to digest. Western patterns of eating were also critiqued, often through observations of their own children and grandchildren in the family: [Children] go to work, then one goes swimming, to play, to get groceries, life is so busy and lots of responsibilities that they do not have the time to eaty [T]hey have a little bit of milk and a couple of cookies, how long is that going to sustain a person? [In India] if you’ve eaten two rotis, dahl, subjee, yogurt, and a glass of water it will last youy Here, kids don’t fill up on their food. That’s why they eat so frequently, they eat bread, banana, candy, I’m hungry, have pop, cookies, chips. Me, I don’t eat anything between my meals. I eat roti and I stay full until it’s time to eat again (Punjabi BC woman, 70). Further comparisons of contextual lifestyle differences in India and Canada that affected the way they organized their eating were used to make sense of healthy eating. Participants reflected on being able to eat more food in India because of the amount of physical work they used to do in the fields or in the house. In Canada, most elderly participants did not work outside the home and, therefore, made appropriate changes to eat less food and take daily walks to circumvent digestion issues and weight gain. These changes, however, were not done in the spirit of ‘risk prevention,’ but rather as a response to the natural and inevitable physical changes due to age: As my body is getting older, there are certain things that my body cannot toleratey So slowly I change my habits (Punjabi BC woman, 69). Through these learned knowledges about healthy eating— an accumulation of common sense cultural/traditional forms of eating, acceptance of experts’ advice and their own observations of the effects of food—participants found conflicting meanings in food that needed to be negotiated. For the most part, while changes such as reducing portion sizes or taking supplements occurred, foods eaten remained the same because participants could not see themselves avoiding the foods they were used to. Habitual ways of eating were infused with complex meanings about culture, identity and health that conflicted with other learned knowledge about healthy eating: The way I eat it reflects who I am. I like hot meals, you know I like a lot of Soul food, it reflects who I am and I’m not changing that for nobodyy my mom has Caribbean descent in her. So I like a lot of curry, curry



chicken, curry goat, curry ox taily I love all of those things and I cook all of those things. That’s who I am, and that’s my acquired taste (African NS man, 39).

of those hazards of always having French fries. So I avoid greasy things, I never buy pasta dishes, as much as I like pasta (European NS man, 47).

For this group of participants, therefore, perceived cultural/traditional food, preparation methods, and eating patterns were viewed as healthy ways of eating with particular emphasis being placed on the positive characteristics of food, which contribute to one’s health. Healthy eating knowledge was accumulated in complex ways over many generations through family and community relations, supplemented more recently with new knowledge from scientific expertise from health professionals, personal observations of the body and comparisons in ways of eating between India and Canada.

This kind of monitoring of food also went down to the level of nutrients, with further considerations of ‘risk factors’ such as one’s gender and life-stage. In contrast to participants who predominantly drew on the cultural/ traditional discourses and who were more accepting of the life-course processes of declining health, participants in this group exerted efforts to prevent some of these processes even when they could not see or feel them. For example, some women talked about taking into consideration factors such as being a woman, knowing about the risk of osteoporosis for women later in life, the negative effects of caffeine as well as the preventative effect of calcium in this relationship, and their efforts to consume calcium through food:

Mainstream healthy eating discourses: controlling the effects of food and nutrients Categorized under the mainstream healthy eating discourses are participants’ reflections about healthy eating based primarily on official nutritional guidelines: all four food groups, low in fat and high in fruit and vegetablesy more fibre (European NS woman, 46). Perhaps due to the pervasiveness of these messages in Canada and other Western societies, the mainstream healthy eating discourses were used by participants from all four groups and a wide range of socioeconomic statuses. However, with higher education and particularly for women, there was a tendency for descriptions of the mainstream healthy eating discourses to include not only the foods considered to be healthy, but also differentiation of the effects of particular nutrients in food as well as the amounts of each that should be consumed. Healthy eating was described with precision at a level of detail that often incorporated naming specific nutrients beyond what one could see with an eye: [Healthy eating means] making sure you get all the vitamins and nutrition that you need, like protein, vitamin C and E and all the nutritional value you can get from food and eating lots of fruits and vegetablesy I supplement with vitamin C, vitamin E, Gluteine, what else? ‘One-a-day’ (supplements) with vitamin B (European BC woman, 51). Rather than general themes such as the ‘strength-giving’ and ‘functionality’ of food, the themes in the mainstream healthy eating discourses were those of ‘controlling’ and ‘monitoring’ both food and nutrient intake. The use of phrases such as ‘‘I am trying to cut down’’, ‘‘I am watching’’ or ‘‘I am avoiding’’ a particular food or nutrient were well pronounced in these discourses with a sense that discipline is needed to achieve healthy food consumption. One participant, who often travelled due to work requirements, discussed his food choices when eating out by saying: I just stopped eating French fries at all, I like French fries but I just stopped ordering anything that includes them because they just pile on so many and it’s just one

I have two cups (of coffee) but it’s latte so it’s like only one cup of caffeine (laughs) and then I’ll have yogurty I am concerned. I want more calcium. You know, I realize having coffee and tea and all those kinds of things at my age I have to keep the calcium intake so that’s why the yogurt (European BC woman, 47). These participants were not only well versed in the current scientific evidence in particular nutritional areas of interest, but they also paid attention to the change in messages over time. Via these observations of the trends in the nutrition literature, participants’ food and nutrient decisions went through changes accordingly: I remember quite a few years ago people said beta carotene was the best thing and then now it’s being linked to certain forms of lung cancer and so, you know, too much of a good thing. You’ve got to watch what you take (European BC woman, 51). Many of these participants felt that healthy eating information was easily accessible and all around them. Their own knowledge thus far was a gradual process learned over time often beginning with their parents and/or past nutritional guidelines learned in school. The most common current sources of information for these participants were TV, newspapers, the Internet, as well as health professionals (e.g., dietitians they knew informally), family and friends. Doctors were not consulted for nutrition information, perhaps due to the accessibility of information elsewhere in everyday life. Men often credited their female partners as being more knowledgeable on nutrition issues, becoming sources of healthy eating information. Women in turn credited their general interest in food and nutrition and becoming mothers as prominent factors for paying close attention to healthy eating. Mainstream healthy eating discourses were also used by some African NS and Punjabi BC, particularly those with higher formal education and for the Punjabi BC, those who had lived in Canada longer. The mainstream ways of


knowing about healthy eating, however, were often contextualized in their own particular cultural ways of preparing food or culturally relevant health concerns. For Punjabi BC, for example, priorities were often about preparing cultural foods modified by mainstream notions of healthy eating: Our culture uses a lot of butter and margarine, you know, when they’re making their curries. It’s almost part of the base. I try to cut down on a lot of the stuff that I’m using, like cut the quantity down so, you know, instead of using a tablespoon I’ll try to use a teaspoon of it (Punjabi BC woman, 45). From this quote, it also becomes evident that mainstream healthy eating discourses were used to question cultural notions of healthy eating. While participants found it important to eat culturally appropriate foods, the new ways of eating learned in Canada generated a critique of certain culturally informed eating habits. The cultural significance of foods needed to be balanced with healthier practices learned from the mainstream healthy eating discourses. The two discourses were combined to interpret optimal ways of eating in an attempt for the two different notions of healthy eating to co-exist. Cultural notions of healthy eating were important for the continuity of traditional understandings of health through generations, as were new notions of healthy eating learned via mainstream discourses in Canada: I try to introduce my children to all kinds of food. They eat roti also, as it is healthy food and it is not junk food. And they will remain healthy; if we remained healthy then even they will stay healthy. Nothing has happened to us as we have been eating roti for so long, so even our kids will stay healthyy [but] we do not get all the nutrition from roti, so we eat food from different culturesy sometimes when we cook dahls, we lose its nutritional value as we cook it for a long time. When we cook it less, the vegetables are not overcooked, we get more nutrition (Punjabi BC woman, 37). Similarly, some African NS also felt that negotiations needed to be made between cultural and mainstream healthy eating because of community concerns with heart health. Like some of the Punjabi BC, these participants attributed unhealthy ways of preparing food to their own cultural traditions, rather than attributing poor health to Western influences: Yes, there is [a Black way of eating]. It’s not healthyy It’s a lot of deep-fried and fried foods and stuff like thaty I just want to incorporate a more healthier wayy [The Black way of eating is important] because food like cornbread and stuff like that and it seems like Black people are always—at any function they always have foody I don’t think it’s going to affect the culture if you stop—you know, we start eating a lot more

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healthier. I think it would prolong our lives. We have a high rate of heart disease (African NS man, 42). Therefore, due to the ubiquity and easy accessibility of messages, mainstream healthy eating discourses were prominent in discussions of participants from all three ethnocultural groups. Those of European descent in NS and BC, and particularly the women in these groups, used food as a way of managing health, trusting information about the effects of food and nutrients that went beyond what was observable in everyday life. While these participants may have been following cultural/traditional conceptualizations about healthy eating, their ways of eating fit easily with mainstream guidelines emerging and changing over time. In contrast, African NS and Punjabi BC used discourses to integrate two ways of eating—that of their culture/tradition and that of mainstream guidelines. Their process was thus more explicitly negotiated. Interestingly, while this process led to some questioning of their own cultural ways of eating, it did not seem to raise questions about mainstream ways of healthy eating for participants most closely aligned with the mainstream healthy eating discourses. Complementary/ethical healthy eating discourses: healthy eating in the larger food system In the complementary/ethical healthy eating discourses, participants emphasized complex interactions in the food system where, in addition to health values, food decisions encompassed explicit moral and ethical values. Participants with these discourses were primarily women from the European BC group, but a few European NS also drew on certain aspects of it. While there was a tendency for the well educated to have more to say about these issues, there were also those of lower socioeconomic status who made use of these discourses. Specific foods incorporated in complementary/ethical definitions of healthy eating varied to an extent. Some participants mentioned only vegetarian foods or food produced as a result of the ethical treatment of animals, while most mentioned local, natural, and organic foods as the way to healthy eating. Their discourses viewed decisions about healthy eating as part of larger food sustainability concerns and relationships with local people involved in the food process: Healthy eating—how would I define it? (pause) I think it’s eating food to nurture your spirit as well as your body and, you know, so it’s really being connected to where it comes from as well as where it’s going, like to feel good. Like when I eat Swiss chard that came from my friend’s garden up in Lytton and then, you know, I’m putting the balsamic vinegar on it that’s from the people in the Okanagan who make it with special stuff in it and apricots, and I eat that, I feel connected to the land that it grew in. Like I feel I’m getting—I can feel the iron go into my body because I know it’s really got



iron in it and it really has the things in it that I need (European BC woman, 52). As may be noted by the incorporation of iron in the discussion by this woman, these discourses were not necessarily in opposition to the overall messages the nutritional guidelines provided and in fact participants incorporated many of those messages in their food decisions. But they also included further concerns about food such as the politics of food and the trust they had in people producing and consuming food locally. The same woman continued: [I] used to choose to buy [fruit] from growers in the Okanagan who’d bring them up to a market here and then I’d buy it from them, and they grew it, and their family, and their parents planted the trees and I’m all happy, right? But they’re not there anymore though. Because you see, it’s very hard for those people to actually make a living bringing it here now because of the gas prices (European BC Woman, 52). Some participants questioned certain aspects of the mainstream healthy eating discourses such as the ethical development of food guidelines. Some believed that the meat and dairy food groups of the CFGHE were overemphasized despite knowledge that current production and consumption of meat and dairy runs contrary to environmental sustainability concerns. They believed these food groups are emphasized only because of the involvement of food industries in the development process of the guidelines: yCanada’s Food Guide, which I have a real issue with that because of the way that it was put together. The beef industry and the dairy industry spent, you know, millions of dollars lobbying in Ottaway that’s incredibly biasingy those industries have been very successful at kind of still staying on that national agenda in that way (European BC woman, 47). Instead of relying on health professionals, these participants considered themselves knowledgeable about very specific health issues through their own research. They depended on themselves for healthy eating information by actively searching out and evaluating information about healthy eating using a diverse set of resources: I read a lot of magazinesy online cookbooksy food writersy M.F.K. Fisher or Eve Johnsony newspapersy health columns or the food columnsy Margaret Visser (European BC woman, 47). Therefore, while both of the groups using the cultural/traditional and mainstream healthy eating discourses relied to some extent on health professionals as sources of expertise regarding healthy eating, those using complementary/ethical discourses questioned health professionals’ knowledge as applied to their own bodies and health. One woman said: My doctor, who I’ve had for 25 years, her and I have had many arguments over the years because she’s the—

what I call the older thing of, you know, the total worry about protein and she was always, ALWAYS, testing me for anemia and all that kind of things and then when I wasn’t eating meat when I was pregnant she was, like, mortified. And none of it’s ever panned out (European BC woman, 47). These participants believed that scientific knowledge needs to be integrated with personalized nutrition for each individual, paying particular attention to the individual’s knowledge of their own body: Well, it’s not really about the amount of nutrition. It’s about the amount of nutrients that you absorb. And that’s what I believe. I mean you can take lots of vitamins or whatever but if your body’s not absorbing them they won’t do you any good, so it’s really what works for one person is going to be a little different than what works for another. One person, their source of protein that’s more valuable to them may be in legumes. They may process that a lot better. Like for me, for example, I like fish but every once in a while it just doesn’t sit well with me. My body, you know, I’ve thrown up a few times and my body’s just kind of like, so obviously on those particular days, whatever, it wasn’t going down well so it wasn’t doing me much good. So I think it really depends on your system (European BC woman, 50). In summary, for participants using the complementary/ ethical healthy eating discourses, conceptualizations of healthy eating drew to an extent from mainstream definitions, but were also incorporative of discourses about the ethical aspects of food production and consumption. Therefore, healthy eating was seen not only as a collection of scientific evidence about the effects of food on health, but also as playing a role in providing answers to sustainability and ethical issues for the health and wellbeing of society. In this way, these participants viewed science and health professionals as having credibility only to the extent that nutritional and health knowledge could be incorporated with people’s own assessments of their body and health. They viewed themselves as capable of evaluating food and health issues, and took an ethical stance that individuals have a responsibility to safeguard their own well-being and the well-being of communities, food systems and the earth. Discussion When gaps are noted between healthy eating messages and people’s eating practices, one of the main assumptions by health professionals is that messages are not reaching the public and that education about the messages themselves or the processes by which the messages are communicated needs to be improved. Our study suggests the possibility of two other considerations for the disconnect between nutritional guidelines and people’s


ways of understanding and practicing healthy eating, one referring to the ways in which healthy eating is conceptualized in society and another referring to the ways in which healthy eating conceptualizations are enacted through food practices of the self. With regard to the first consideration, our findings present clear evidence of multiplicity of healthy eating discourses that people in two areas of Canada can currently draw upon. In addition to mainstream discourses, participants were able to also draw upon cultural/traditional and complementary/ethical discourses suggesting that official health-promotion messages need to be positioned in the larger historical and social context of everyday food experiences where forms of knowledge in addition to nutritional guidelines are recognized. While what is considered ‘traditional’ knowledge and what is considered ‘official’ knowledge concerning health changes over time, currently both Coveney (2000, 2005) and Lupton (1996) have noted the centrality of nutritional science in people’s understandings of food and health in contemporary Western societies, promoting the management of health via people’s individual responsibility and limited direct governance by health authorities. These discourses focus on the ability of people to self-regulate through monitoring and influencing the course of risk factors (Castel, 1991; Petersen, 1996, 1997). Healthy eating understandings as suggested by official discourses of healthy eating were part of the ways of knowing for most participants in our study through incorporation of knowledge from nutritional guidelines. For those using the mainstream discourses, rationality, reasoning and practices were based solely on scientific discourses about healthy eating, where participants not only incorporated knowledge about food practices but also about potential health risks that are invisible in everyday experience. In these discourses of healthy eating, it was understood that the management of health through healthy eating included individual responsibility not only for one’s observable food practices but also prevention of potential risks to one’s future health. In the context of the participants’ daily food practices; however, health professionals’ discourses about healthy eating provide only one of potentially many food and health discourses that they could draw upon to interpret healthy eating (Petersen, 1997). Some participants in our study also drew on the cultural/traditional and complementary/ethical discourses in addition to the mainstream healthy eating discourses. Similarly, in our earlier Vancouver study (Chapman & Beagan, 2003) with women, some of whom were breast cancer survivors, we found that the women drew on two additional broad perspectives about healthful eating in addition to the mainstream/health professional perspective where overall perspectives were related to how the women viewed the food–breast cancer relationship. Coveney (2005) also highlighted different forms of lay knowledge about food and health between parents living in high- and low-income suburbs in Adelaide, Australia. The additional discourses represent

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distinctive but no less rational ways than the mainstream to conceptualize healthy eating. For example, with the cultural/traditional understandings described here, healthy eating was conceptualized through values of culture and tradition, and was considered to be proven as valid through observations of the effects of food on health over centuries. The mode of thinking this group utilized was that of ‘orality’ where knowledge is conveyed in oral ways. In this mode of thinking, knowledge comes about over time through accumulation and combining of ideas, with little potential for conflict in the acquiring of new/different knowledges (Nayar, 2004). This mode of thinking may help explain why even though the participants who drew upon these discourses may not have identified with their doctor’s advice as much as they identify with healthy eating messages from their own culture, they were able to incorporate some messages from authorities easily into their food practices. Specific ethnocultural interpretations of healthy eating were utilized not only by the older and less acculturated participants, but were also drawn upon to an extent by some participants well versed in the mainstream discourses, suggesting that even with higher levels of acculturation, people still hold on to certain traditional ways of cooking and eating (Axelson, 1986; Satia-Abouta, Patterson, Neuhouser, & Elder, 2002). Many food-related changes cannot be explained by food availability or by change in socioeconomic status of individuals. The strong effect of cultural/traditional understandings beyond these determinants (Axelson, 1986) seems to remain even when it is acknowledged that some cultural/traditional food practices are nutritionally poor (Airhihenbuwa & Kumanyika, 1996). While the nutritional inadequacy of some cultural/ traditional foods may be a factor as to why certain ethnic groups have higher-than average risk and/or prevalence for developing diet-related chronic diseases, the opposite has also been found: acculturation to the Western diet has also been associated with decreased health (Satia-Abouta et al., 2002), while a stronger cultural identity, on the other hand, has been associated with healthier dietary behaviours (Bedaiko, Kwate, & Rucker, 2004). It may be important to promote cultural/traditional interpretations of healthy eating through the values and reflective critiques these discourses offer. For example, that the roti meal was perceived as healthier than many options in Canada by some Punjabi BC may illustrate a legitimate knowledge of the healthfulness of cultural/traditional food practices. Interpretations of healthy eating identified as cultural/ traditional in our study were not only grounded in perceived knowledges of past practices, but were also rational reasonings based on observations of the effects of food on health over time, comparisons between food and eating patterns of different social groups, as well as scientific evidence as presented by doctors’ advice. While coming from a different set of knowledges, a similar range of knowledges was also drawn on by participants in the complementary/ethical discourses. Their reflections about



healthy eating and health were more encompassing so that in addition to scientific evidence of the mainstream discourses, healthy eating included knowledges with respect to one’s own body, ethical consumption, and sustainable ways of eating. With regard to the second consideration for the disconnect between nutritional guidelines and people’s ways of understanding and practicing healthy eating, our findings also extend previous work on nutrition discourses by exploring the different ways they are enacted through food practices of the self. One view of the nature of practices may be that individuals are seen as having to conform to and undertake practices as offered by the discourses of governance. Another view may be that the relationship between discourses and individuals is more flexible and negotiable, with the possibility of people’s resistance to discourses (McNay, 1994). The form of governance favoured by people drawing on particular healthy eating discourses may be at least partially influenced by social and cultural worldviews of expertise in knowledge. Participants drawing on cultural/traditional healthy eating discourses seemed to believe in expert knowledge about healthy eating, a belief that was shared by those drawing on the mainstream healthy eating discourses. However, the approach taken towards influencing their own health via food was different, with those drawing on the cultural/traditional discourses showing more acceptance for the possibility of certain life-course processes such as aging and illness and limited monitoring of the self through food practices. In contrast, healthy eating for participants drawing on the mainstream discourses was approached through practices of the self that resembled the self-examination practices in the Christian era. Foucault used the analogy of the mill to describe the nature of self-examination practices required in this period. Thoughts are like grains, and consciousness is the mill store. It is our role as the miller to sort out amongst the grains those which are bad and those which can be admitted to the mill store to give good flour and good bread of our salvation (Foucault, 1988, p. 46). The analogy of the mill where the sorting of good grains gives good flour can easily be re-applied as the compliance in the sorting of nutrients to give good health in the mainstream healthy eating discourses. Participants drawing on the complementary/ethical healthy eating discourses saw themselves as responsible to participate in the construction of knowledge about their own health by finding and evaluating information about healthy eating. Similarly, while acknowledging expert advice, participants in the cultural/traditional discourses who critiqued Western eating patterns were also able to show agency in healthy eating through practices drawing on discourses different than the mainstream. These participants shared the Greco-Roman view of authority where the relationship of individuals with health professionals was founded on the ability of the health profes-

sionals to give good advice leading to a happy but still autonomous life (Foucault, 1988). The autonomy for making decisions about healthy eating where people are resisting being passive receivers of information, and instead actively seek out information about topics beyond the basic dietary guidelines has also been observed elsewhere (Van Dillen et al., 2004). These participants can thus be viewed as knowledgeable/experts of their own lives, perhaps even seen as offering knowledgeable resistance to official discourses about healthy eating. Whether the motivations behind these discourses are more to do with concerns about personal health or the health of the environment/society (Belasco, 2005) remains to be resolved, but both concerns seem to intersect in their critique of science’s inability to answer larger health and social problems (Belasco, 2005; Brown & Zavestoski, 2004). The above distinctions do not promote the view that mainstream healthy eating discourses were deemed unworthy or resisted in entirety; both cultural/traditional and complementary/ethical conceptualizations were in fact often combined with current mainstream conceptualizations of healthy eating, providing illustrations that different accounts about the relationship between food and health can co-exist, even if they seem contradictory (Coveney, 2005; Jovchelovitch & Gervais, 1999). The larger issue may lie in mainstream discourses’ lacking in consideration and incorporation of other ways of knowing healthy eating. Alternative discourses to health and wellbeing have previously been described as providing more meaningful conceptualizations of healthy eating beyond the focus of a person’s physiological health (Sointu, 2006) and individual responsibility as in mainstream health discourses (Petersen, 1997). Bottorff et al. (2001) have noted that the ways immigrant South Asian women express health concerns reflect broader cultural notions about being a woman and a sense of belonging to a cultural group. They argue that an individualistic philosophy and differentiation between physical and emotional problems represented in the biomedical model are inconsistent with South Asian worldviews of family and community involvement in health decisions and an integrative perspective of health issues. We would, therefore, suggest that even if they are seen to lack the sophisticated scientific knowledge of the mainstream discourses, the cultural/ traditional and the complementary/ethical discourses also provide valuable ways of perceiving health and well-being. Overall, the findings of this paper show how analyses of non-mainstream conceptualizations of healthy eating offer opportunities not only for appreciating different ways but also for contextualizing official understandings of healthy eating. Health behaviours need to be seen in the larger context of influences that contribute to ‘well-being’ that may be even broader than the current social determinants of health of concern. Findings from other research that certain ethnic groups with high risk factors (low socioeconomic status and educational achievements) have more positive health outcomes (e.g., lower mortality) than


expected point to the need to focus on the complex relationship between culture, behaviour and health (HayesBautista, 2003). The ways that people choose, prepare and eat their food need to be seen in the broader context of socially and culturally constructed ways of life with respect to family and community relationships (Hayes-Bautista, 2003), orientations to work, stress, and pleasure, so that the interdependence of socially and culturally influenced perceptions, behaviour, and health is considered. As people live, they learn, observe, and experience their lives, aware of the multitude of factors that encourage and/or constrain their food decisions. Clearly, the way in which individuals express their agency is the result of the complex interaction of a variety of factors, health-promotion norms about eating being one. The process of culturalizing health perspectives and practices affirms diversity in the way people construct their individual and collective realities within the possibilities of their living conditions. What is positive or negative cannot be based on a singular view (Airhihenbuwa, 1995); instead, different kinds of knowledges (e.g., scientific, local, and situated) should be engaged to creatively contribute to constructing health-promoting knowledges and well-being. Lay knowledges have a logic and rationality and it is important that we understand the social origins of their sense-making and the role they play in structuring worldviews (Coveney, 2005). Through the cultural/traditional and complementary/ethical healthy eating discourses, participants may help question whether the current taken-for-granted notions of health are indeed health-promoting. Dialogues between discourses are needed where different forms of knowledge interact to realize the optimal paths to health and well-being of the society as healthy eating is as much about the everyday as it is about the scientific. Acknowledgements We would like to thank our research assistants Lucki Kang, Andrea D’Sylva, and Carolyn Gill as well as the three anonymous reviewers for their insightful comments on an earlier draft of this paper. The study was funded by the Canadian Institutes of Health Research (CIHR). During the course of her studies, Svetlana RistovskiSlijepcevic was funded by doctoral fellowships from the Family Context of Food Decision Making study, the Vancouver Foundation’s BC Medical Services Fellowship, and University of British Columbia’s University Graduate Fellowship (UGF). References Airhihenbuwa, C. O. (1995). Health and culture: Beyond the Western paradigm. London, UK: SAGE Publications. Airhihenbuwa, C. O., & Kumanyika, S. (1996). Cultural aspects of African American eating patterns. Ethnicity & Health, 1, 145–260. Ankeny, K., Oakland, M. J., & Terry, R. D. (1991). Dietary fat: Sources of information used by men. Journal of the American Dietetic Association, 91, 1116–1117.

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Erratum to ‘‘Curt Richter: A life in the laboratory, Jay Schulkin (Ed.); Johns Hopkins University Press, Baltimore, Maryland, 2005, Cost US$49.95. ISBN 0-8018-8073-4.’’ [Appetite 45 (2005) 360–361] Gerard P. Smith Department of Psychiatry, Weill College of Medicine of Cornell University, New York-Presbyterian Hospital, Westchester Division, 21 Bloomingdale Road, White Plains, NY 10605, USA

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Appetite (2008) 180

Calendar of Events 17 January, 2008

The Columbia University Seminar on Appetitive Behavior, Faculty House, Columbia University, Food Craving, Marci Pelchat, Monell Chemical Senses Center.

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The Columbia University Seminar on Appetitive Behavior, Faculty House, Columbia University, Fructose, Karen Acroff, Brooklyn College.

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5 June, 2008

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