sports medicine Volume 41 Issue 1 January 2011

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Sports Med 2011; 41 (1): 1-15 0112-1642/11/0001-0001/$49.95/0

LEADING ARTICLE

ª 2011 Adis Data Information BV. All rights reserved.

Circulating Androgens in Women Exercise-Induced Changes Carina Enea,1 Nathalie Boisseau,1 Marie Agne`s Fargeas-Gluck,1 Ve´ronique Diaz1,2 and Benoit Dugue´ 1 1 Universite´ de Poitiers, Laboratoire des Adaptations Physiologiques aux Activite´s Physiques (EA3813), Faculte´ des Sciences du Sport, Poitiers, France 2 Service d’Exploration Fonctionnelle Respiratoire et Physiologie de l’Exercice, Pavillon Beauchant, Centre Hospitalier Universitaire (CHU) de Poitiers, Poitiers, France

Abstract

Physical exercise is known to strongly stimulate the endocrine system in both sexes. Among these hormones, androgens (e.g. testosterone, androstenedione, dehydroepiandrosterone) play key roles in the reproductive system, muscle growth and the prevention of bone loss. In female athletes, excessive physical exercise may lead to disorders, including delay in the onset of puberty, amenorrhoea and premature osteoporosis. The free and total fractions of circulating androgens vary in response to acute and chronic exercise/ training (depending on the type), but the physiological role of these changes is not completely understood. Although it is commonly accepted that only the free fraction of steroids has a biological action, this hypothesis has recently been challenged. Indeed, a change in the total fraction of androgen concentration may have a significant impact on cells (inducing genomic or nongenomic signalling). The purpose of this review, therefore, is to visit the exercise-induced changes in androgen concentrations and emphasize their potential effects on female physiology. Despite some discrepancies in the published studies (generally due to differences in the types and intensities of the exercises studied, in the hormonal status of the group of women investigated and in the methods for androgen determination), exercise is globally able to induce an increase in circulating androgens. This can be observed after both resistance and endurance acute exercises. For chronic exercise/training, the picture is definitely less clear and there are even circumstances where exercise leads to a decrease of circulating androgens. We suggest that those changes have significant impact on female physiology and physical performance.

1. Introduction Although produced in small amounts in females, androgens may have direct and crucial effects on physiology (e.g. bone density, muscle growth and erythrocyte production). However, these effects are regularly ignored in women, especially in female athletes. Androgens are pre-

cursors for endogenous estrogen synthesis and play a key role in female physiology. In athletes, excessive physical exercise may lead to disorders, including delay in the onset of puberty, amenorrhoea and premature osteoporosis. The free, bound and total fractions of circulating androgens may be influenced by a myriad of biological factors, such as the menstrual cycle phase, oral contraceptive

Enea et al.

2

intake, age, diet, and acute or chronic physical activity/training.[1] Steroid hormones, such as androgens, are predominantly bound to sex hormone binding globulin (SHBG) or albumin. The free fraction of steroid hormones represents a small portion of the total pool (free testosterone corresponding to 1–3% of total testosterone in women) and is often considered the biologically active fraction. Interestingly, the currently favoured view postulates that free and albumin-bound sex steroid hormones are available for diffusion out of capillaries and into the cells, where they initiate appropriate responses.[2] However, it has recently been suggested that supra-molecular complexes present on membranes (e.g. megalin and cubilin) may play a significant role in the internalization of proteinbound steroids.[3,4] Although the picture is not entirely clear, circulating bound hormone may have a significant biological role and the physiological impact of stimuli that alter those concentrations must be considered. Among these stimuli, physical exercise strongly influences the endocrine system, elevating the blood concentration of several hormones. Since the free hormone hypothesis has recently been challenged, changes in circulating androgens should be revisited, considering the potential influence of the total pool of androgens. This is especially important for subjects presenting low circulating concentrations of androgens such as elderly people, children and women. Therefore, this review aims to present new insights into the relationship between androgens and physical exercise in women. Particular attention is paid to (i) the origins of androgens in females; (ii) the biological actions of these androgens; and (iii) the effects of acute physical exercise and training on the concentrations of circulating androgens. 2. Androgen Origins in Females In both sexes, androgens are produced by reproductive organs and adrenals. The most important androgen secreted is testosterone; in the eugonadal man, the Leydig cells in the testes produce ~95% of the testosterone in the body. ª 2011 Adis Data Information BV. All rights reserved.

The adrenal glands (in both sexes) and the ovaries produce very little testosterone but secrete weaker androgens. In particular, dehydroepiandrosterone (DHEA; and its sulfo-conjugate) secreted by adrenals, and androstenedione secreted by adrenals and ovaries are of physiological importance in the women, not least because they can undergo peripheral conversion to more potent androgens, for example to testosterone and 5adihydrotestosterone (DHT). Androgen synthesis is catalyzed by several enzymes. To understand the pathways of androgenesis, it is important to bear in mind that the zonation of the adrenal glands and ovaries reflects the spatial distribution and expression of enzymes involved in steroidogenesis.[5] Two critical cytochrome P450 (CYP) enzymes are involved in androgen biosynthesis: (i) P450 side-chain cleavage (P450scc); and (ii) P450c17 (figure 1), both of which belong to the CYP gene superfamily. P450c17 catalyses 17a-hydroxylation and 17,20 bond cleavage (17,20 lyase).[6] The first and rate-limiting step in the biosynthesis of all steroid hormones is the conversion of cholesterol to pregnenolone by P450scc. Once pregnenolone is produced from cholesterol, it may undergo hydroxylation by 17a-hydroxysteroid dehydrogenase to 17a-hydroxypregnenolone, or it may be converted to progesterone by 3b-hydroxysteroid dehydrogenase. Both pregnenolone and progesterone may be 17a-hydroxylated to 17a-hydroxypregnenolone or 17a-hydroxyprogesterone, respectively. These steroids may undergo scission of C17,20 carbon bonds by P450c17 to yield DHEA and androstenedione.[7] Over the course of a woman’s lifetime, the contributions of ovaries and adrenal glands to the total androgen pool varies considerably. In premenopausal women, the ovaries and adrenals each contribute half of the total testosterone and androstenedione productions, whereas more than 90% of the DHEA-sulfate (DHEA-s) is thought to originate from the adrenals.[8] There is considerable controversy regarding whether the ovaries contribute to androgen production after menopause. In a recent study involving 684 postmenopausal women, the concentrations of total and bioavailable testosterone were 40% lower in females Sports Med 2011; 41 (1)

Androgens and Exercise in Women

3

Cholesterol P450scc

SULTs 17,20 lyase

17α-hydroxylase Pregnenolone

17α-hydroxypregnenolone

DHEA

3β-HSD

3β-HSD 17α-hydroxylase

Progesterone

DHEA-s STS

17,20 lyase 17α-hydroxyprogesterone

Androstenedione 17β-HSD Testosterone

Fig. 1. Pathways of androgen synthesis. Cholesterol is converted to pregnenolone by cytochrome P450 (CYP) side-chain cleavage (P450scc). CYP P450c17 subsequently mediates both steroid 17a-hydroxylase and 17,20-lyase activities. First, the enzyme catalyses hydroxylation of pregnenolone and progesterone at the C17 position, yielding 17a-hydroxypregnenolone and 17a-hydroxyprogesterone. Second, it cleaves the C17-C20 bond of either 17a-hydroxypregnenolone or 17a-hydroxyprogesterone to form dehydroepiandrosterone (DHEA) or androstenedione, respectively. DHEA and DHEA-sulfate (DHEA-s) freely and continuously interconvert via hydroxysteroid sulfotransferases (SULTs) and steroid sulfatase (STS). Only desulfated DHEA can be converted to a sex steroid downstream in the pathway. HSD = hydroxysteroid dehydrogenase.

with bilateral oophorectomy than in those with intact ovaries. These results, which were confirmed by two additional studies, demonstrate that the postmenopausal ovary remains a critical source of androgen after the menopausal transition. Controversially, it has been shown that intra-ovarian testosterone and androstenedione concentrations are negligible in postmenopausal ovarian tissues[9] due to the absence of steroidogenic enzymes in the majority of ovaries of postmenopausal women.[10,11] However, the expression of steroidogenic enzymes may also occur in various tissues including skeletal muscle, bone, brain and cartilage as it has recently been described in rats.[12-14]

2.2 Adrenal Androgen Synthesis

As in the ovaries, zonation of the adrenal glands reflects the spatial distribution and expression of enzymes involved in steroidogenesis.[5] Indeed, DHEA and DHEA-s are produced in the zona reticularis of the adrenals, although androstenedione is synthesized exclusively in the zona fasciculate. The production of these androgens is mainly stimulated by adrenocorticotrophic hormone (ACTH).[18-20] However, the presence of receptors for human chorionic gonadotropin,[21] insulin-like growth factors[22,23] and prolactin[24] in the adrenal cortex suggests that they may be involved in controlling adrenal steroidogenesis.

2.1 Ovarian Androgens Synthesis

The two-cell/two-gonadotropin theory describes steroidogenesis in the ovaries (figure 2).[15,16] In the ovaries, theca cells are primarily responsible for androgen production. Androgens are synthesized from cholesterol in the theca cellular compartment. The synthesis pathway is stimulated by luteinizing hormone and also by intraovary paracrine and autocrine mechanisms.[17] These androgens diffuse into the avascular granulosa cell compartment and, when stimulated by follicle stimulating hormone, undergo aromatization to estrogens via aromatase (P450c19). ª 2011 Adis Data Information BV. All rights reserved.

2.3 Biological Actions of Androgens at Cellular Level: Genomic and Non-Genomic Signalling

Our current knowledge of androgen metabolism is largely based on measurements of these hormones and their metabolites in blood. However, their intracellular actions are still poorly understood, particularly in females. The classic dogma regarding steroid hormone entry into target cells is the free hormone hypothesis. This hypothesis states that, by virtue of its small size and lipid solubility, only the ‘free’ steroid hormone, which has escaped the grasp of its Sports Med 2011; 41 (1)

Enea et al.

4

LH

FSH

Theca cell

Granulosa cell

17β-estradiol

Cholesterol

17β-HSD 1

P450scc P450c17

P450c17 17α-hydroxypregnenolone

Pregnenolone

3β-HSD

3β-HSD P450c17 Progesterone

Estrone

DHEA 3β-HSD

P450 arom

P450 arom

P450c17 17α-hydroxyprogesterone

Androstenedione

Androstenedione

17β-HSD 5 Testosterone

Testosterone

Basement lamina Fig. 2. The two-cell, two-gonadotropin hypothesis of ovarian steroidogenesis regulation. Androgens synthesized by the theca cell diffuse into the avascular granulosa cell compartment. Following stimulation from follicle stimulating hormone (FSH), androstenedione and testosterone are aromatized to estrone and estradiol, respectively. DHEA = dehydroepiandrosterone; HSD = hydroxysteroid dehydrogenase; LH = luteinizing hormone; P450scc = cytochrome P450 (CYP) side-chain cleavage; P450arom = CYP P450 aromatase.

circulating serum binding protein, can traverse the plasma membrane of target cells. As previously mentioned in the introduction, this view is now challenged. Until recently, it was believed that steroid hormones enter cells by non-specific diffusion after dissociation from their carrier proteins. However, specialized cell types that require large amounts of steroids for maintenance of normal function are likely to require additional mechanisms for specific and efficient uptake of steroid hormones. The biological actions of androgens once inside the cell are mediated by the androgen receptor (AR), a ligand-dependent transcription factor that belongs to the nuclear receptor superfamily.[25] These AR complexes interact with various factors (e.g. co-activators or co-repressors) to modulate transcription of androgen target genes via binding to specific DNA sequences.[26] Over the past 2 decades, it has become increasingly apparent that androgens may also regulate cellular activity via a more rapid non-genomic mechanism involving membrane receptors and/or ª 2011 Adis Data Information BV. All rights reserved.

cytosolic receptors.[27] These steroid receptors are able to activate intracellular signalling molecules, such as the mitogen-activated protein kinase 1 (MAPK1; also known as ERK2), by transcriptionindependent mechanisms.[28] These effects are commonly observed within seconds to minutes and are considered to be too rapid to involve changes in transcription and protein synthesis. Sex steroids have also been shown to elicit cellular responses in a rapid fashion, even when bound to SHBG.[29] The classical view of seeing the binding of androgens to carriers as providing a reservoir of inactive circulating compounds and regulating the amount of free hormones available for diffusion into tissues may not be correct. The existence of surface binding sites for androgen hormone carriers on tissues suggests alternative pathways for targeting protein-bound androgens to distinct cell types. Therefore, the changes, for instance, in SHBG should not only be seen as modulating the bioavailability of steroid hormones, as SHBGsteroid complex may induce a direct biological effect through membrane receptor binding. Sports Med 2011; 41 (1)


Moreover, recent works suggested that supramolecular complexes present on membranes (e.g. megalin and cubilin) may play a significant role in the internalization of protein-bound steroids.[3,4] Megalin is responsible for the retrieval of lipidsoluble vitamins and hormones in the kidney and also in other tissues that express this receptor. It seems that the megalin uptake pathway is used by steroid-dependent cells to actively acquire androgens and estrogens.[30] Megalin has been shown to be expressed (and most of the time co-expressed with cubilin) in many epithelial tissues (ileum, renal proximal tissues, glomeruli, mammary gland, uterus, and also in the inner ear neural tube and lung airway) and has very recently been described in thyroid and skin tissues (epidermis, hair follicle, sebaceous glands, sweat glands, arrector pili muscles) and blood vessels.[31] Therefore, although the picture is not clear, circulating bound hormone may have a significant biological role, and the physiological impact of stimuli that alter those concentrations must be considered.[30] The regulation of such kinds of receptors is largely unknown. However, it has previously been shown that estradiol was able to stimulate the cubilin-megalin binding activity in rats, whereas, different kinds of physical and psychic stress tended to decrease it.[32] Bile acids and similar structures have also been shown to regulate the expression of these proteins.[33,34] 2.4 Physiological Actions of Androgens in Women

Although the role of androgens in female physiology has not been well established, several recent clinical trials have indicated that testosterone supplementation at physiological doses in androgen-deficient women induces improvements in psychological well-being, sexual function, bone mineral density and lean body mass.[35-37] These physiological effects may be critical for athletic performance. For example, androgens are known to have direct anabolic effects on skeletal muscle. In eugonadal males, it has been shown that testosterone increases lean body mass and decreases fat mass in a dose- and concentration-dependent fashion.[38] This anabolic effect does not differ ª 2011 Adis Data Information BV. All rights reserved.

5

significantly between males and females in animal studies.[39] However, the effect of testosterone supplementation in women with serum androgen concentrations within the health-related reference interval has not been studied. Recently, in a study involving 329 healthy postmenopausal women, a positive correlation was found between bioavailable testosterone concentrations in blood and lean body mass, supporting the results observed in males.[40] The action of testosterone on muscle involves multiple mechanisms, including induction of protein synthesis,[41] recruitment of satellite cells[42,43] and stimulation of pluripotent mesenchymal cell differentiation along the myogenic lineage, thereby inhibiting differentiation along the adipogenic lineage.[44] By direct androgenic activity and transformation into estrogens, androgens may also modulate the bone-remodelling cycle. In a longitudinal study, Slemenda et al.[45] showed that lower androgen concentrations in blood predicted subsequent bone loss in premenopausal, perimenopausal and postmenopausal women. Several other studies have provided evidence for higher bone mineral density in women with excess endogenous androgens (for example, those with hirsutism or polycystic ovary syndrome [PCOS] compared with age-matched controls).[46,47] Limitations of the PCOS/hirsute model of androgen excess are the potential confounding differences in body mass index, body composition and menstrual irregularities that may affect skeletal homeostasis independent of the androgen excess. It is difficult to control for these factors in the case-control design of these studies. However, Rickenlund et al.,[48] observed that ‘hyperandrogenic’ athletes had higher bone mineral density than athletes with androgen concentrations within the healthrelated reference interval. The authors suggested that essential hyperandrogenism observed in this case could be an alternative mechanism underlying oligomenorrhoea or amenorrhoea in some female athletes and may imply a significant advantage for physical performance. The beneficial effects of certain sports on female bone mineralization have been reported by several authors,[49,50] and physical activity is considered to have a positive influence on bone density by Sports Med 2011; 41 (1)

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6

many others.[51,52] However, the potential interaction of physical exercise with hormonal stimuli, such as androgen action, is unknown and requires further study. It has been shown that AR knockout mice exhibited higher bone turnover with increased bone resorption.[53] In these mice, training/running significantly reduced bone turnover and prevented cancellous bone loss.[54] 3. Acute Exercise Effects 3.1 Endurance Exercise

Most of the studies performed on this topic have examined the effect of endurance exercise on blood androgen concentrations (tables I and II). The plasma concentrations of total testosterone, free testosterone and androstenedione have been observed to increase after prolonged endurance exercise in laboratory conditions[58,61-64] and field contexts.[55,56] Recently, a study from our laboratory has shown similar effects of prolonged exercise in young women of differing hormonal status (age range 18–30 years), including eumenorrheic women and women using oral contraceptives.[57] No significant exercise-induced changes related to the menstrual cycle were observed. The concentration of those analytes in blood returns to the baseline levels within hours of exercise cessation.[55,56] The mechanisms responsible for these increases immediately after physical exercise appear to be related to a decrease in metabolic clearance of these compounds. Indeed, hepatic plasma flow dramatically decreases during prolonged exercise.[63] Therefore, the exerciseinduced elevation of testosterone and androstenedione may be due to a reduction in the rate of destruction of these compounds in plasma.[65] Moreover, factors leading to haemoconcentration, including dehydration, sweating and hydrostatic pressure, may contribute to the observed exercise-induced increase of blood testosterone and androstenedione.[1,57,66] Such changes could affect target tissues. In premenopausal women, acute exercise induces an increase in blood DHEA-s and DHEA concentrations.[55,56,60] Similar effects have been reported in postmenopausal women after 30 minª 2011 Adis Data Information BV. All rights reserved.

utes of treadmill exercise performed at 80% of the maximal oxygen uptake.[59] Estrogen replacement therapy in postmenopausal women has been shown to enhance exercise-induced increases in DHEA, but not in DHEA-s, suggesting that estrogen may increase the adrenocortical sensitivity and/or the responsiveness to exercise.[59] Surprisingly, some authors observed an increase in DHEA-s only in trained premenopausal women.[67,68] The underlying mechanism for the exercise-elicited DHEA-s increase appears to be more complex than a simple reduction through plasma clearance, such as that suggested for testosterone and androstenedione. Keizer et al.,[67] attributed the elevation of DHEA-s to an increased secretion rate, since the enhancement in the circulating concentration of this hormone was significantly greater than the plasma volume reduction due to exercise. Exercise-induced increases in adrenal androgen concentrations have been attributed to an increased rate of secretion from the adrenal cortex in response to ACTH stimulation.[59,69] Exercise is a form of stress. The intensity and duration of this stress may differentially affect several factors that influence ACTH secretion. Inder et al.[70] suggested that hypothalamopituitary adrenal axis activation during exercise may involve hypothalamic secretion of both corticotrophin-releasing hormone (CRH) and arginine-vasopressin (AVP). High-intensity exercises have been shown to promote AVP release,[71] whereas, prolonged-duration exercises mainly induce CRH release. In women, DHEA-s is exclusively synthesized in the adrenal cortex, primarily in response to ACTH.[72,73] Non-ACTH regulators, such as angiotensins, gonadotropins and prolactin, may also regulate DHEA-s production.[74] Prolactin[75,76] and angiotensin concentrations[77-79] are known to increase during prolonged exercise, along with a decrease in the gonadotropin serum concentration.[80] Prolactin release from the anterior pituitary gland may, to some extent, occur in response to both increasing core body temperature with exercise[55,81-83] and passive heating.[84,85] However, further investigations are needed to confirm the links between prolactin and DHEA-s secretion. Sports Med 2011; 41 (1)

Population studieda

DHEAb

DHEA-sb

6 premenopausal women

›

2

30-km race (intensity between . 60% and 85% of VO2max)

5 women with varying gynaecological status

›

›

DHEA-s remained elevated for 2 h after exercise although DHEA returned to the basal concentrations in the 2 h after the race

56

Prolonged exercise to exhaustion at 75% . of VO2max

7 untrained eumenorrheic women; 9 untrained women using oral contraceptives; 12 trained women using oral contraceptives

›

NR

DHEA-s was increased in oral contraceptive users to a lesser extent than in eumenorrheic women

57

15 min consecutive work loads at 60%, 70% and 80% . of VO2max to exhaustion

13 eumenorrheic women, untrained 8 highly trained women

NR NR

› 2

30 min of treadmill exercise . (80% of VO2max)

Postmenopausal women: 7 HRT group; 9 untreated group

›

›

HRT enhances DHEA, but not DHEA-s, in response to exercise

59

Incremental treadmill exercise test to exhaustion

7 adolescent women runners (14–16 y)

›

›

No age-matched controls

60

3 sets of 8 exercises at 10 RM intensity

16 cross-trained premenopausal women

2

NR

No age-matched controls. The sessions took place during the early luteal phase (14–21 d) of each subject’s menstrual cycle in both types of exercise

61

40 min of cycling at 75% of HRmax

16 cross-trained premenopausal women

2

NR

3 sets of 8 exercises at 10 RM intensity.

30 cross-trained women (aged 19–69 y)

›

NR


62


30 cross-trained women (aged 19–69 y)

2

NR


62

a

Comments

Reference 55

58

61

Description of the studied subjects (no. and age). Control subjects were age-matched unless stated otherwise. Ten RM strength is the greatest amount of weight that an individual can lift only ten times for a specific exercise.

b

Concentrations in subjects are relative to control or basal values. . HRmax = maximal heart rate; HRT = hormone replacement therapy; NR = not reported; RM = repetition maximum; VO2max = maximal oxygen uptake; › indicates increase; 2 indicates no difference.

7

Sports Med 2011; 41 (1)

Acute exercise 16-km race



Table I. Summary of the influence of exercise on dehydroepiandrosterone (DHEA) and DHEA-sulfate (DHEA-s) concentrations

8


Table II. Summary of the influence of exercise on total testosterone (T), free T and androstenedione (A) concentrations Population studieda

Total Tb

Free Tb

Ab

Comments

Reference

6 premenopausal women

›

NR

›

Exercise intensity was not controlled

55

Prolonged exercise to . exhaustion at 75% of VO2max

7 untrained eumenorrheic women; 9 untrained women using oral contraceptives; 12 trained women using oral contraceptives

›

NR

NR

9 women

›

NR

NR

Marathon run (intensity between 60% and 85% . of VO2max)

5 women of varying gynaecological status

›

NR

›

15 min consecutive work loads at 60%, 70% and . 80% of VO2max to exhaustion

13 eumenorrheic women, untrained and 8 highly trained women (marathon runners)

›

NR

›

In the marathon runner, plasma T and A concentrations were increased significantly more (p < 0.05) at comparable work loads and exercise durations than in the untrained

58

Exercise on cycle ergometer

13 short-distance runners

›

NR

NR

Significant increase observed immediately after exercise. T concentrations returned to baseline s after 90 min of recovery

64


16 cross-trained premenopausal women (33 – 8 y)

›

NR

NR

All exercise sessions took place during the early luteal phase (14–21 d) of each subject’s menstrual cycle in both types of exercise

61


16 cross-trained premenopausal women (33 – 8 y)

2

NR

NR

61


30 recreationally trained women (19–69 y)

›

NR

NR

62


30 recreationally trained women (19–69 y)

›

NR

NR

62

. 2 h at 70% of VO2max

a b

57

No significant increase until 120 min of exercise

63

56


Concentrations in subjects are relative to control or basal values. . HRmax = maximal heart rate; NR = not reported; RM = repetition maximum; VO2max = maximal oxygen uptake; › indicates increase; 2 indicates no difference.

Enea et al.

Sports Med 2011; 41 (1)

Acute exercise 16-km race



Concentrations in subjects are relative to control or basal values.

a

b

In addition, prolonged exercise is known to increase lymphocyte proliferation,[86,87] which may stimulate DHEA production through direct cellular contact with adrenal zona reticularis cells.[88] 3.2 Resistance Exercise

F = female; HR = heart rate; M = male; NR = not reported; RM = repetition maximum; › indicates increase; 2 indicates no difference.

› 2 10 untrained young women (18.9 – 0.3 y) 8 wk resistance training (3 sets at 10 RM intensity)

73

2 2 7 adolescent women runners (14–16 y) 7 wk endurance training

The DHEA-s increase positively correlated with the muscle mass increase during training

2 times per wk 2 NR 10 postmenopausal women (64 – 3 y) 21 wk resistance training

60

90 During the 6-mo training period, the 1 RM values increased in both groups 2 2 11 middle-aged women (39 – 3 y) and 10 elderly women (67 – 3 y) 6 mo resistance training


95 One set of 8–12 repetitions, 10 exercises, 3 times per wk NR 2 6 mo resistance training

17 M and F (70–79 y)

96

95

Reference Comments DHEA-sb

NR

DHEAb

16 M and F (70–79 y)

2

Population studieda Training

6 mo endurance training

Table III. Summary of the influence of training on dehydroepiandrosterone (DHEA) and DHEA-sulfate (DHEA-s) concentrations

9

75–85% of HR reserve for 35–45 min, 3 times per wk


Resistance exercise induces changes in the concentrations of testosterone and DHEA in different ways. Most studies have shown no significant changes in testosterone concentrations in young or elderly women;[61,89-91] however, a few studies have reported a significant increase in testosterone after a short-term bout of resistance exercise (tables I and II).[92,93] Recent human studies have shown that a single bout of exercise was able to induce steroidogenesis in skeletal muscle.[94] 17b-Hydroxysteroid dehydrogenase (HSD) type 3 appears to be especially important in this process. There is little information available on the short-term responses of DHEA and DHEA-s to resistance exercise in females. Furthermore, there are reported discrepancies in the changes in DHEA and DHEA-s after this type of exercise. Some studies have reported no change in DHEA or DHEA-s in premenopausal women[61] or middle-aged/elderly women,[90] while Copeland et al.[62] observed that a resistance exercise session led to significantly increased DHEA serum concentrations in women aged 19–69 years. 4. Effect of Chronic Exercise/Training The current literature is inconclusive regarding the effects of training on blood androgen concentrations in females. Discrepancies in existing studies may be due to differences in subject populations (e.g. sex, age of the subjects) and experimental design (e.g. initial training of the subjects, training methods) [tables III and IV]. 4.1 Endurance Exercise Training

Young women who regularly exercise exhibit lower basal plasma testosterone concentrations than sedentary age-matched controls.[99-101] These results are consistent with those of one of our recent Sports Med 2011; 41 (1)

Enea et al.

Concentrations in subjects are relative to control or basal values.

F = female; HRmax = maximal heart rate; M = male; MS = multiple set; NR = not reported; SSC = single-set circuit; › indicates increase; fl indicates decrease; 2 indicates no difference.

b

99 60–75% of HRmax for 45 min, 5 times per wk NR fl 87 postmenopausal women (50–79 y) 12 mo endurance training

fl

2 2 10 postmenopausal women (64 – 3 y) 21 wk resistance training


NR NR › 34 untrained women: 12 low-volume SSC; 12 periodized high-volume MS; 10 non-exercising controls 24 wk resistance training

NR

NR 2 2 10 elderly women (67 – 3 y)


a

96

98 SSC: 1 set of 8–12 repetitions to muscular failure 3 d per wk; MS: 2–4 sets of 3–15 repetitions 4 d per wk

90 NR 2 2 11 middle-aged women (39 – 3 y) 6 mo resistance training

Serum T in the total group of women correlated with the individual changes recorded in strength during the training

Reference Comments

3 lower-limb exercises for the quadriceps and femoris muscle group (squat, leg press, knee extension) were performed 2 times per wk NR NR › 8 untrained M and F 8 wk resistance training

Free Tb

Ab Total Tb Population studieda Training

Table IV. Summary of the influence of training on total testosterone (T), free T and androstenedione (A) concentrations

97

10

studies, which indicated that endurance-trained women using oral contraceptives have lower basal testosterone concentrations than strength-trained (judoists) and sedentary age-matched control subjects.[57] Some studies have reported an increase in resting testosterone concentrations with training,[55,60,102] although others have reported no change.[68,103] Discrepancies in study design, differences in the groups of volunteers and difficulty in singling out confounding factors, such as fatigue, may be responsible for the discrepancies in the results of those studies. In overweight postmenopausal women, exercise training may decrease the circulating concentrations of free and total testosterone.[99] Interestingly, the mechanisms leading to the decrease in testosterone appear to be related to weight loss of the participants. As adipose tissue is a source of androgens,[104] reduction in this type of tissue may lead to decreased androgen concentrations. Moreover, changes in aromatase activity in adipocytes in this context may be in response to such a decrease. Young women engaged in endurance training may exhibit decreases in their resting plasma DHEA-s[101] and salivary DHEA concentrations.[105] The mechanism responsible for such decreases may be related to the observed decrease in ACTH after an endurance training programme.[101] However, some reports have shown increased basal blood DHEA concentrations in young female athletes.[106-109] Interestingly, in those studies, elevated basal ACTH was also observed after endurance training (tables III and IV).[106-108] 4.2 Resistance Exercise Training

A significant increase in resting testosterone concentrations in young women was observed after 12 weeks of resistance training (tables III and IV).[97,98] The magnitude of the increase appears to be related to the training volume;[98] however, some authors did not find such an enhancement.[90,110-112] In the studies that reported no enhancement, the length of the training and the age of the individuals may have affected the outcomes. Sports Med 2011; 41 (1)


Concerning the basal changes in adrenal androgen concentrations in females following resistance training, the age of the volunteers is of crucial importance. In young women, it has been shown that 8 weeks of resistance training leads to an increase in the resting serum DHEA-s concentration.[73] However, in middle-aged and older women who engaged in regular and moderate intensity training, no such change was detected.[90,95,96] This finding is consistent with the fact that aging is associated with adrenal androgen deficiency.[113,114] This observation may explain the absence of change in middle-aged women in response to physical training.[96] However, different intensities of the training regimens used in those studies may also have affected the change or absence of change in DHEA and DHEA-s concentrations (tables III and IV). 5. Conclusion We present a comprehensive review of physical exercise-induced changes in the concentration of circulating androgens in women. While it is well established that circulating androgens increase in response to an acute bout of endurance exercise, studies are contradictory regarding the effect of resistance exercise on these hormone concentrations. Discrepancies in study design, differences in the sex hormone concentrations in female volunteers (which included amenorrheics, eumenorrheics and oral contraceptive users) and the difficulty in isolating factors such as fatigue, may have contributed to the discrepancies between study results and conclusions. The physiological effects of the changes in circulating androgens (free, bound and total fraction) due to chronic physical exercise/training should be further investigated. One area of interest concerns the use of ‘supra-molecular complexes’ to internalize protein bound steroids and whether these supra-molecular complexes are affected by different factors (e.g. exercise, nutrition). The emergence of direct cell-based bioassays that now have the ability to collectively measure total circulating bioactivity with transduction assays should also be useful to estimate in a specimen the amount of steroids that have a real physiological impact. It ª 2011 Adis Data Information BV. All rights reserved.

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would also be of great importance to develop specific studies where the genomic and the nongenomic induced changes due to exercise could be distinguished. Acknowledgements The authors thank the Word Anti-Doping Agency for their recent financial support. The authors have no conflicts of interest to declare that are directly relevant to the content of this article.

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Correspondence: Professor Benoit Dugue´, Laboratoire des Adaptations Physiologiques aux Activite´s Physiques (EA3813), Faculte´ des Sciences du Sport, Universite´ de Poitiers, 8 alleé Jean Monnet, 86000, Poitiers, France. E-mail: [email protected]

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REVIEW ARTICLE


Developing Maximal Neuromuscular Power Part 1 – Biological Basis of Maximal Power Production Prue Cormie,1 Michael R. McGuigan2,3 and Robert U. Newton1 1 School of Exercise, Biomedical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia 2 New Zealand Academy of Sport North Island, Auckland, New Zealand 3 Institute of Sport and Recreation Research New Zealand, Auckland University of Technology, Auckland, New Zealand

Contents Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Muscle Mechanics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Force-Velocity Relationship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Length-Tension Relationship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Type of Muscle Action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.1 Time Available to Develop Force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.2 Storage and Utilization of Elastic Energy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.3 Interactions of Contractile and Elastic Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.4 Potentiation of Contractile and Elastic Filaments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.5 Stretch Reflexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.6 Effect of Training on Stretch-Shortening Cycle Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Morphological Factors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Muscle Fibre Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Muscle Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Cross-Sectional Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Fascicle Length . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Pennation Angle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Tendon Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Neural Factors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Motor Unit Recruitment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Firing Frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Motor Unit Synchronization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Inter-Muscular Coordination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Activation of Synergists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Co-Activation of Antagonists. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Muscle Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Abstract

17 18 18 19 20 20 20 20 21 21 22 22 22 23 23 24 25 25 26 26 27 28 29 29 30 30 30

This series of reviews focuses on the most important neuromuscular function in many sport performances, the ability to generate maximal muscular power. Part 1 focuses on the factors that affect maximal power

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production, while part 2, which will follow in a forthcoming edition of Sports Medicine, explores the practical application of these findings by reviewing the scientific literature relevant to the development of training programmes that most effectively enhance maximal power production. The ability of the neuromuscular system to generate maximal power is affected by a range of interrelated factors. Maximal muscular power is defined and limited by the force-velocity relationship and affected by the length-tension relationship. The ability to generate maximal power is influenced by the type of muscle action involved and, in particular, the time available to develop force, storage and utilization of elastic energy, interactions of contractile and elastic elements, potentiation of contractile and elastic filaments as well as stretch reflexes. Furthermore, maximal power production is influenced by morphological factors including fibre type contribution to whole muscle area, muscle architectural features and tendon properties as well as neural factors including motor unit recruitment, firing frequency, synchronization and intermuscular coordination. In addition, acute changes in the muscle environment (i.e. alterations resulting from fatigue, changes in hormone milieu and muscle temperature) impact the ability to generate maximal power. Resistance training has been shown to impact each of these neuromuscular factors in quite specific ways. Therefore, an understanding of the biological basis of maximal power production is essential for developing training programmes that effectively enhance maximal power production in the human.

Maximal power describes the highest level of power (work/time) achieved in muscular contractions.[1] From an applied perspective, maximal power represents the greatest instantaneous power during a single movement performed with the goal of producing maximal velocity at takeoff, release or impact.[2,3] This encompasses generic movements such as sprinting, jumping, changing direction, throwing, kicking and striking and therefore applies to the vast majority of sports. Empirical evidence supported by previous research has shown that superior ability to generate maximal power typically results in enhanced athletic performance.[2-6] A series of interrelated neuromuscular factors contribute to maximal power production. These factors, as well as any evidence of adaptations to these factors following training, will be discussed in part 1 of this review. Part 2, which will follow in a forthcoming edition of Sports Medicine, will explore the scientific literature relevant to the development of training programmes that most effectively improve maximal power production in dynamic athletic movements. ª 2011 Adis Data Information BV. All rights reserved.

The search for scientific literature relevant to this review was performed using US National Library of Medicine (PubMed), MEDLINE and SportDiscus databases and the terms ‘maximal power’ and ‘muscular power’. Relevant literature was also sourced from searches of related articles arising from the reference list of those obtained from the database searches. The studies reviewed examined factors that could potentially influence the production of maximal muscular power. 1. Muscle Mechanics 1.1 Force-Velocity Relationship

The force-velocity relationship represents a characteristic property of muscle that dictates its power production capacities. Various levels of organization have been used to study the relationship including molecular and single-cell levels, whole muscle and multi-muscle movements, as well as single and multi-joint movements.[7-13] Regardless of the approach, the characteristic hyperbola (figure 1) can be used to describe the Sports Med 2011; 41 (1)

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Velocity Power

Velocity/ Vmax and power/Pmax

1.0

0 0

1.0 Force/Fmax

Fig. 1. The force-velocity and force-power relationships for concentric contractions of skeletal muscle. Force, velocity and power are normalized to the maximum isometric force (Fmax), maximum velocity of shortening (Vmax) and maximum power output (Pmax), respectively.

inverse relationship between the force and velocity during concentric muscle contraction.[14] As the velocity of concentric muscle action is increased, less force is capable of being generated during that contraction. This is true for a given muscle or muscle group activated at a constant level as is due to actin-myosin cross-bridge cycling. Specifically, because it takes a fixed amount of time for cross-bridges to attach and detach, the total number of cross-bridges attached decreases with increasing velocity of muscle shortening. Due to the fact that the amount of force generated by a muscle depends on the number of attached crossbridges, force production decreases as the velocity of the contraction increases and power, therefore, is maximized at a combination of submaximal force and velocity values.[15] Although the force-velocity relationship was first defined using isolated frog sartorius muscle,[14] all human movements are similarly limited by this fundamental property of muscles.[7,8,10-12,16,17] Maximal muscular power is therefore determined by the parameters of the force-velocity relationship: maximal isometric force (Fmax), maximal velocity of shortening (Vmax) and the degree of curvature (defined by a/Fmax or b/Vmax). Improvements in maximal power output of a muscle can be achieved through increasing Fmax or Vmax and/or ª 2011 Adis Data Information BV. All rights reserved.

decreasing the degree of curvature. Measurements of the force-velocity relationship during movements in vivo (more accurately termed loadvelocity or torque-angular velocity relationship but referred to as force-velocity relationship throughout to prevent confusion) are complicated by mixed fibre composition,[16,18,19] architectural characteristics,[20,21] anatomical joint configuration[16] and levels of neural activation.[7,21-24] Despite these limitations, examination of the forcevelocity relationship during such movements quantifies the ability of the intact neuromuscular system to function under various loading conditions. This information is essential in understanding maximal power production during human movements. 1.2 Length-Tension Relationship

The ability of skeletal muscle to generate force is critically dependent on sarcomere length.[25-27] The greatest potential for force production on activation of the cross-bridge cycle exists when the sarcomere length provides for optimal overlap between the actin and myosin filaments (described as the ‘optimal length’). At this length, cross-bridge interaction is maximal, which allows for the greatest levels of active tension development.[25-27] Force production is impaired when sarcomere lengths are shortened below the optimal length due to overlap of the actin filaments from opposite ends of the sarcomere and the compression of the myosin filament as it comes in contact with the Z-disk.[15] Stretching a sarcomere beyond the optimal length also reduces the force production capacity. At longer lengths, cross-bridge interaction is decreased as a result of less overlap between actin and myosin filaments.[25-27] In vivo research has demonstrated that resting muscle lengths are generally slightly shorter than the optimal length[28] and, therefore, muscular force may be increased with a slight stretch prior to activation. While muscular power is defined by the force-velocity relationship, the length-tension relationship influences the ability of muscle fibres to develop force and, therefore, plays an important role in maximal muscular power production. Sports Med 2011; 41 (1)

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1.3 Type of Muscle Action

The ability of muscle to generate maximal power is influenced by the type of action involved; eccentric or concentric contractions as well as actions involving the combination of eccentric, isometric and/or concentric contractions.[29] Muscle function required in natural human movement rarely calls for the use of these muscle actions in isolation. The successive combination of eccentric and concentric actions forms the most common type of muscle function and is termed the stretchshortening cycle (SSC).[29,30] When a muscle fibre is activated, stretched, then immediately shortened, the force and power generated during the concentric action is greater than a concentric-only contraction.[31,32] Therefore, maximal muscular power is superior in movements involving a SSC.[17,33-40] While there is a consensus within the literature regarding the potentiating effect of a SSC on performance, the mechanisms responsible for improved performance during SSC movements are an issue of debate amongst researchers. 1.3.1 Time Available to Develop Force

One of the proposed mechanisms driving the superior maximal power output observed during SSC compared with concentric-only movements is based on the fact that it takes time for muscle to generate force (due to time constraints imposed by stimulation, excitation and contraction dynamics[41]). The eccentric action during a SSC movement allows time for the agonist muscles to develop considerable force prior to the concentric contraction. In contrast, the concentric contraction starts as soon as force development (beyond that which is required to maintain a static position) begins in concentric-only movements. An alternate view of this same principle is that SSC contractions have enhanced power generation capability due to the greater distance over which force can be developed compared with concentriconly movements (i.e. based on the work-energy relationship). Hence, force during the concentric phase is greater in SSC movements, subsequently resulting in superior performance.[42-46] However, power output was observed to be higher in a SSC movement compared with a concentric-only moveª 2011 Adis Data Information BV. All rights reserved.

ment immediately preceded by a maximal isometric action,[47] indicating that the time available to develop force is not the only factor contributing to enhance muscular power. 1.3.2 Storage and Utilization of Elastic Energy

The most generally reported mechanism believed to drive the SSC-induced enhancement of maximal power is the storage and utilization of elastic energy.[48] When an active muscle-tendon unit (MTU) is stretched, mechanical work is absorbed by the MTU and this work can be stored in part as potential energy in the series elastic component (SEC; includes fibre cross-bridges, aponeurosis and tendon).[31,34,49] It is believed that some of this potential energy can then be used to increase the mechanical energy and positive work during the following concentric contraction.[17,31,33,34,36,49] This recoil of the SEC is thought to contribute to the increased force at the beginning of the concentric phase in SSC movements and ultimately to enhanced maximal power production.[17,31,33,34,36,49] 1.3.3 Interactions of Contractile and Elastic Elements

In SSC movements, the interactions between the contractile and elastic elements play an important role in enhancing maximal performance. Tendinous recoil has been shown to influence the contribution of the contractile component of work produced during SSC movements.[50-52] Higher force at the beginning of the concentric phase during SSC movements results in greater tendinous lengthening with less fascicle lengthening.[53-57] As the concentric contraction progresses, the muscle fibre contracts at a nearly constant length (i.e. isometric), while the rapid shortening of the MTU largely depends on the shortening of the tendinous structure.[53-57] In contrast, while some tendinous displacement does occur, the majority of the MTU length change during concentric-only movements is due to fascicle shortening.[54] The minimal displacement of muscle fibres during the concentric phase of SSC movements is believed to be caused by the catapult action of the tendinous structures (i.e. lengthening-shortening behaviour).[58] Sports Med 2011; 41 (1)


These interactions may influence performance in three distinct ways. First, elastic energy would be stored predominantly in the tendinous structures and therefore can be utilized with minimal dissipation via the tendon recoil during the concentric phase.[58,59] Second, the minimal displacement of muscle fibres during SSC movements means that they operate closer to their optimal length and, based on the length-tension relationship, can therefore produce more force.[53,55,56] Finally, while the net shortening velocity of the MTU is high, fascicle length change occurs at relatively slow velocities. Thus, fascicles are able to generate high forces according to the forcevelocity relationship.[60] Therefore, during SSC movements, the contractile element acts as a force generator producing high forces at relatively low shortening velocities, while the tendinous structures act as an energy re-distributor and power amplifier.[60] The interaction of these components is vital in SSC movements because it allows for the muscle-tendon complex to generate superior maximal power output. 1.3.4 Potentiation of Contractile and Elastic Filaments

The potentiation of the actin-myosin crossbridges is another mechanism thought to contribute to the SSC-induced enhancement in maximal power output.[34,47,50,61] In tetanized isolated muscle and single muscle fibres, an active stretch has been observed to enhance work output of the contractile machinery during subsequent shortening,[32,62-64] a finding supported by in vivo studies involving intact muscle-tendon complexes.[34,47,61] This potentiating effect is thought to be due to enhanced force production per crossbridge rather than an increase in the number of active cross-bridges.[62,64] Woledge and Curtin[65] proposed that strained cross-bridges are detached in a state that permits them to re-attach more rapidly than cross-bridges not exposed to a prestretch. While suggestions have also been made that some cross-bridges may be left in a highly strained state after the stretch, it is not currently known precisely how the force per cross-bridge is enhanced.[61] Despite the convincing in vitro evidence, the extent to which the potentiation of the ª 2011 Adis Data Information BV. All rights reserved.

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contractile filaments influences in vivo SSC performance has been questioned.[66] In vivo observations of isometric (rather than lengthening) action of muscle fascicles during a stretch[54,55] cast doubt on the possible contribution of force potentiation to enhanced SSC performance in vivo. Additionally, the potentiation of elastic filaments such as titin and/or nebulin has been proposed as another possible mechanism contributing to enhanced force production following an active stretch.[67-70] It has been theorized that an active stretch may be associated with a calciumdependent increase in titin stiffness, which in turn contributes to enhanced force production compared with a non-activated stretch.[67-70] However, a recent investigation suggests that enhanced force production in the absence of actin-myosin overlap cannot be explained by calcium-induced stiffening of titin and proposes cross-bridge force-dependent titin-actin interactions to be responsible for non actin-myosin-based force enhancement observed following an active stretch.[71] Indeed, further research is required to establish if, and to what extent, potentiation of contractile and elastic filaments occurs during SSC movements in vivo as well as the relative contribution of this effect to maximal muscular power. 1.3.5 Stretch Reflexes

Another mechanism proposed to contribute to the enhanced maximal power output during SSC movements is the activation of spinal reflexes. The forced lengthening of the MTU during the eccentric phase of SSC movements causes a mechanical deformation of the muscle spindles, which activates reflex mechanisms (stretch reflexes of a-motoneurons).[72] The stretch reflex subsequently increases muscle stimulation, resulting in increased contraction force during the concentric phase and ultimately contributes to enhanced maximal power output.[37,39,73-78] Despite some reservations, the consensus within the literature appears to be that SSC movements do evoke a stretch reflex of sufficient magnitude to contribute to the increase in muscular force during the concentric phase.[37,39,48,73-75,77] Therefore, the development of maximal power during SSC movements Sports Med 2011; 41 (1)

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may be influenced in some degree by the activation of stretch reflexes. 1.3.6 Effect of Training on Stretch-Shortening Cycle Function

The beneficial effects of resistance training on SSC performance has been well documented.[79-84] However, to date, no conclusive evidence exists identifying how the aforementioned mechanisms contributing to enhanced SSC performance are affected by training. Several speculative theories exist but further research is required to identify the adaptations driving training-induced improvements in SSC performance. 2. Morphological Factors The ability to generate maximal power during a movement is dictated by the contractile capacity of the muscles involved. The contractile capacity of muscle is influenced by a series of morphological factors but primarily its fibre type composition and architectural features. Additionally, the properties of tendon influence the function of the contractile elements within the MTU and therefore impact maximal power production. 2.1 Muscle Fibre Type

Due to the unique characteristics of each fibre type, the force-velocity properties of a muscle are determined by the fibre type contribution to whole muscle area.[8,12] Type II fibres have a greater capacity to generate power per unit crosssectional area (CSA).[8,12,19,85-87] In an investigation of single fibres from the vastus lateralis, peak power per unit CSA was observed to be 5- and 10-fold greater in type IIa and IIx fibres, respectively, when compared with type I fibres.[87] However, these contractile properties were measured as sub-physiological temperatures (15C) and thus may not reflect function in vivo.[88] Examination of results of studies using closer to in vivo muscle temperatures suggest that the differences in peak power per unit CSA are smaller than those observed at lower temperatures. In a study specifically addressing this issue, the propelling velocity of actin filaments by myosin from human muscle fibres was only 2-fold greater with ª 2011 Adis Data Information BV. All rights reserved.

type IIx versus type I myosin when measured at 35C, compared with a 7.5-fold difference at 15C.[88] In a rare study measuring the contractile properties of intact human muscle fibres at 37C, bundles of type II fibres were found to have a 3-fold greater Vmax and a 4-fold greater maximum power output (Pmax) than bundles of type I fibres.[19] The differences in peak power per unit CSA are due to differences in specific force (i.e. Fmax/CSA), Vmax and the curvature of the forcevelocity curve amongst the fibre types.[13,15,19,87] Using single fibre preparations, type II fibres have been observed to have significantly greater specific force than type I fibres.[13,87,89] Similar findings have been observed in whole skeletal muscle investigations (i.e. muscles composed mainly of type II fibres vs mainly type I fibres) although this is a somewhat controversial area in muscle physiology.[15] However, differences in Vmax are theorized to have a much more pronounced influence on the difference in Pmax values between fibre types.[15] Type II fibres are characterized by high sarcoplasmic reticulum and myofibrillar adenosine triphosphatase (ATPase) activities, and correspondingly high Vmax and short contraction time/ twitch duration (i.e. the heads of type II myosin isoforms split ATPase approximately 600 times/ second vs approximately 300 times/second for type I myosin isoforms).[90-94] This allows for a short cross-bridge cycle time and, therefore, the ability to develop force rapidly. In contrast, type I fibres display comparatively low ATPase activity and Vmax with long contraction times/twitch durations.[90-94] For example, Vmax has been shown to vary from approximately 0.8 fibre lengths/second in type I fibres to approximately 3.5 fibre lengths/second and 5.6 fibre lengths/second in type IIa and IIx fibres, respectively[86,95,96] (note these investigations used sub-physiological temperatures and thus may not reflect function in vivo[88]). When this literature involving single fibre preparations is collated, a continuum of Vmax (relative to fibre length) and Pmax (relative to CSA) for the fibre types is evident as follows IIx > IIa > I. Furthermore, investigations of bundles of fibres reported a greater a/Fmax ratio in type II versus type I fibres, indicating a greater degree of curvature of the force-velocity curve, and thus lower power output, for type I fibres.[19,94] Sports Med 2011; 41 (1)


Therefore, the maximal power output of a muscle is influenced by its fibre type composition. Muscles with a high percentage of type II fibres display greater Pmax in comparison to muscles with a high percentage of type I fibres.[8,12,97] However, future research is required in order to determine the magnitude of differences in Pmax as well as Vmax between fibre types and subtypes at physiological temperatures in intact fibres. Cross-sectional comparisons have revealed that elite strength-power athletes have predominately type II fibres, whereas elite endurance athletes display a predominance of type I fibres.[98,99] While approximately 45% of the variance in muscle fibre type is believed to be associated with inherited factors,[100] findings of fibre type transformations from type I to II (and vice versa) after periods of intense training[101-105] and detraining[106,107] indicate plasticity in fibre type composition based on environmental conditions.[100] However, transformations between type I and II fibres have been debated throughout the literature and further research is required to understand the precise conditions under which they occur.[15] Additionally, resistance training has been shown to elicit transformations in myosin heavy chain gene expression within type I and II fibres. Transformations in type II subtypes have occurred following strength training whereby type IIx isoforms are reduced at the expense of an increase in the expression of type IIa isoforms.[87,108-110] When a muscle is chronically stressed with high loading requirements, it is theorized that the contractile protein properties are shifted to a more economical cross-bridge cycling system (i.e. increased oxidative capacity allowing for sustained power output over a longer period).[111,112] This shift in type II subtypes may be detrimental to Pmax but is compensated for by the preferential hypertrophy of type II fibres following strength training (discussed further in section 2.2.1). Interestingly, a period of detraining following strength training has been observed to evoke an ‘overshoot’ in type IIx composition that is markedly higher than values observed prior to the strength training.[110] However, the influence of ballistic power training on possible myosin heavy chain isoform shifts is unclear, with conflicting reports of strong trends towards transforª 2011 Adis Data Information BV. All rights reserved.

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mations from type IIx to IIa[86,113] and no such changes following training.[114,115] Further research is required to elucidate exactly how muscle fibre subtypes respond to ballistic power training. It is important to note that even if transformations between muscle fibre types and/or subtypes did occur, the contribution to improving maximal muscular power would be relatively small compared with alterations in other morphological properties (i.e. CSA or architectural characteristics).[15] Additionally, contractile properties can also improve following training without apparent changes in fibre type or subtype proportions. For example, Malisoux and colleagues[86,116] reported increases in Vmax of all fibre types following plyometric training as well as improvements in several functional performance measures despite an increase in type IIa at the expense of IIx. Further research is necessary to determine the degree of training-induced adaptations in contractile properties evident across the fibre types and subtypes at physiological temperatures. 2.2 Muscle Architecture 2.2.1 Cross-Sectional Area

The maximal force generated by a single muscle fibre is directly proportional to its CSA, irrespective of the fibre type.[1,18,117-119] Due to the fact that power is heavily influenced by Fmax, a muscle fibre with greater CSA can therefore generate higher Pmax.[16,86,87,120] A comparison of single muscle fibres between sedentary men and men involved with regular resistance training for 7.6 – 1.6 years highlights these findings.[120] The resistance-trained men had significantly greater CSA, Fmax and Pmax for type I and type II fibres compared with the sedentary men. However, the differences between the groups were no longer evident when Fmax was normalized to CSA and Pmax was normalized to fibre volume (which accounts for differences in both fibre CSA and length).[120] Evidence from single fibre studies is supported by research demonstrating that maximal voluntary isometric force is proportional to whole-muscle CSA.[121-123] For example, using CT scans to assess muscle CSA, Maughan and associates[123] reported significantly higher Fmax in muscles with Sports Med 2011; 41 (1)

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greater CSA. The Fmax-to-CSA ratio was not significantly different between experienced strengthtrained subjects and untrained controls, suggesting that variation in CSA accounted for the majority of the differences seen in Fmax.[123] Strong relationships have also been reported between knee extension Fmax and quadriceps CSA in both men (r = 0.71) and women (r = 0.76).[124,125] However, it is important to note that not all of the variation in whole-muscle Fmax can be explained solely by variation in muscle CSA.[126] Factors such as neural drive,[127-129] fibre-type composition,[130] pennation angle[131] and the lever system through which Fmax is measured[132] may also contribute. In response to training, changes to Fmax of single muscle fibres are proportional to changes in fibre CSA.[96,120,133] Increases in fibre CSA are brought about through increases in the size and number of myofibrils within the muscle fibre.[111,134,135] These hypertrophic adaptations occur in both type I and II muscle fibres in response to heavy strength training but to a greater degree in type II fibres.[109,136-141] Extensive research has established that heavy strength training is a very effective stimulus for eliciting a hypertrophic response in muscle.[87,109,112,124,129,131,142,143] Traininginduced increases in CSA or Fmax are typically accompanied by improvements in maximal muscular power.[10,16,84,86,87] However, much of this research involved relatively untrained subjects with low to moderate strength levels, in which improvements in muscular function are easily invoked. Increases in CSA following heavy strength training of stronger/more trained individuals are expected to be lower and take longer.[128] Therefore, the possible influence of increased CSA on muscular power is theorized to diminish as the training age of the athlete increases. Furthermore, the degree of muscle hypertrophy is highly dependent on the type of training and the specific programme variables (i.e. intensity, volume and frequency).[144] The relatively lighter loads used during ballistic power training are typically too small to elicit the necessary mechanical stimulus required to initiate a significant hypertrophic response.[144-150] However, observations of hypertrophic responses following plyometric training[86,151,152] indicate that further research is necessary to determine the imª 2011 Adis Data Information BV. All rights reserved.

portant variables in plyometric and/or ballistic training that may elicit an increase in CSA (i.e. significant eccentric component to plyometrics, volume or time under tension, etc.). Consequently, increases in maximal muscular power mediated by improved CSA are achieved primarily through heavy strength training and, typically, not (or markedly less) in response to specific power training. 2.2.2 Fascicle Length

While sarcomere Vmax differs quite significantly between various fibre types, the Vmax of a muscle fibre is proportional to its length (assuming a constant level of activation).[16,18,118,153-155] For example, if a sarcomere shortens at two fibre lengths per second, a fibre containing ten sarcomeres in series would have a greater Vmax than a fibre containing five sarcomeres in series (i.e. 20 vs 10 fibre lengths/second). Due to the fact that power is heavily influenced by Vmax, a longer muscle fibre can therefore generate higher Pmax.[16,18,118,153] Correlational studies have reported significant relationships between fascicle length of vastus lateralis and gastrocnemius lateralis and 100 m sprint time in both men and women (r = -0.43 to -0.57).[156,157] Furthermore, cross-sectional investigations have revealed the fascicle lengths of the vastus lateralis, gastrocnemius medialis and gastrocnemius lateralis to be significantly longer in sprinters compared with long-distance runners and untrained controls.[158] However, it is unclear if these observations are a result of genetic predisposition or if fascicle lengthening is an adaptation to the modalities of training commonly used by sprinters (i.e. high-intensity sprint training and high-intensity strength/power training). Regardless of the origin of this architectural difference, these data indicate the importance of relatively longer fascicle lengths to rapid force-generation and maximal power production during dynamic movements. The adaptive response of fibre length following training is not well understood. Animal models have been used to investigate fibre length change following various training interventions but have returned inconclusive results.[159-161] Fascicle length in humans has been measured as an indicator of fibre length but the current literature Sports Med 2011; 41 (1)


offers little additional insight into the influence of training on fibre length. Training studies have reported fascicle length to increase in response to resistance training with heavy loads,[142,162-164] resistance training with light loads,[165] as well as in subjects who ceased strength training and performed jump and sprint training.[142] In contrast, an effective heavy strength training programme of the elbow extensors had no effect on fascicle length of the triceps brachii,[166] a finding supported by similar studies involving the lower body musculature.[167,168] While some of these changes were coupled with improvements in performance, it is unknown exactly how the changes in fascicle length affected muscle Vmax or Pmax. Further research is required to elucidate the most effective training stimulus for longitudinal growth of muscle fibres. Furthermore, while the addition of sarcomeres in series is theorized to occur through similar pathways as the addition of sarcomeres in parallel, factors determining which type of muscle growth occurs are unknown (the interested reader should refer to Blazevich and Sharp[169] for a more detailed discussion). 2.2.3 Pennation Angle

The pennation angle of a muscle, defined as the angle between the muscle’s fascicles and the line of action,[155,170,171] has important physiological effects on the force-velocity relationship and thus Pmax. As pennation angle increases, more sarcomeres can be arranged in parallel (i.e. more contractile tissue can attach to a given area of an aponeurosis or tendon) and the muscle can therefore produce more force.[154,172] Additionally, an increased pennation angle allows for muscle fibres to shorten less for a given tendon displacement due to the rotation of pennate muscle fibres during contraction.[173] This increases the likelihood that a fibre with a greater pennation angle operates closer to its optimum length and, based on the lengthtension relationship, is able to generate more force.[173] These factors act to increase Fmax and, therefore, pennation angle influences the maximal power output generated by a muscle. However, greater pennation angles are also associated with slower contraction velocities and thus, increasing a muscle’s pennation angle may negatively imª 2011 Adis Data Information BV. All rights reserved.

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pact Vmax.[155] Despite this, the increase in Fmax is theorized to have substantially greater impact on maximal power than increases to Vmax brought about through an increase in pennation angle.[16] Pennation angle is commonly thought to increase in response to heavy strength training and decrease in response to sprint training. These theories are based on observations of population differences whereby bodybuilders displayed greater pennation angles and CSA than untrained subjects,[174] and highly trained sprinters possessed smaller pennation angles than both lesser trained sprinters[157] and untrained controls.[156] Further support for possible adaptability of pennation angle to heavy strength training stemmed from the significant relationships between muscle thickness (indicative of CSA) and pennation angle in the triceps brachii (r = 0.81), vastus laterals (r = 0.61) and gastrocnemius medialis (r = 0.56) of over 700 people with various training backgrounds.[175] These observations were corroborated by studies involving training interventions in which heavy strength training significantly increased pennation angle,[131,166] while sprint/jump training significantly decreased pennation angle.[142] Increases in pennation angle following heavy strength training were accompanied by increased CSA and Fmax[131,166] resulting in enhanced Pmax.[107,110] However, other longitudinal studies have failed to establish pennation angle changes in response to heavy strength training in previously trained[176] and untrained[167,168] people. While the effectiveness of the training protocols implemented and the reliability of the techniques used may have prevented pennation angle changes being discovered, these findings highlight that the effects of heavy strength training on pennation angle are not clearly understood. Furthermore, it is unknown if ballistic power training and other training modalities elicit changes in pennation angle or if changes are influenced by the training status of the subject. 2.3 Tendon Properties

As previously discussed in section 1.3.3, fascicle behaviour is affected by interactions between the contractile and elastic elements of the Sports Med 2011; 41 (1)

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MTU.[53-57] The intrinsic compliance of tendon impacts these interactions (i.e. affects the amount of fascicle displacement) and, because a muscle’s ability to generate force is both velocity and length dependent, the level of tendon compliance can influence maximal muscular power. Few data currently exist regarding the potential adaptability of tendon compliance in response to exercise[177,178] and the cross-sectional data to date have revealed mixed results.[179,180] Kubo and colleagues[179] reported a negative relationship between sprint performance and tendon compliance (r = -0.757) indicating that greater compliance is beneficial for sprint performance. In contrast, Bojsen-Møller and associates[180] observed knee extensor rate-of-force development (RFD) to relate positively to stiffness of the vastus lateralis tendon-aponeurosis (r = 0.55), suggesting that less compliance is associated with enhanced muscular performance. Thus, further research is essential in order to determine the specific influence of tendon compliance on maximal power production as to whether this tendon property is amendable to exercise. 3. Neural Factors The ability to generate maximal power during a movement is not only governed by the muscles morphology, but also by the ability of the nervous system to appropriately activate the muscles involved. The nervous system controls the activation of muscles primarily through changes in motor unit recruitment, firing frequency and synchronization as well as inter-muscular coordination. 3.1 Motor Unit Recruitment

The force produced by a muscle is related to the number and type of motor units recruited. Motor units are recruited in a systematic order during graded, voluntary contractions of increasing force according to the size principle.[181,182] Relatively small a-motoneurons that innervate type I fibres are initially activated at low force levels while progressively larger a-motoneurons that activate type IIa and IIx fibres are typically activated after the slow-twitch motor units at ª 2011 Adis Data Information BV. All rights reserved.

higher thresholds of force.[181-183] The size principle is the general rule of recruitment not only for slow, graded contractions but also for isometric[184] and ballistic contractions.[185,186] However, compared with slow, graded contractions, the threshold of motor unit recruitment is typically lower during ballistic movements due to the rapid force escalation to high levels.[186,187] The maximum force capabilities of a motor unit has been estimated to vary by up to 50 times.[188] Thus, the force capable of being generated during a movement is affected by which motor units are recruited. During contractions typically required for maximal power production, recruitment of high-threshold motor units is very beneficial to force production as they innervate a relatively large number of high RFD/force-producing muscle fibres.[189] Therefore, the ability to rapidly recruit high-threshold motor units influences maximal muscular power. There are three common theories of adaptation in motor unit recruitment that may occur in response to training. It is hypothesized that training may result in increased motor unit recruitment, preferential recruitment of high-threshold motor units and/or lowering of the thresholds of motor unit recruitment.[128,190] All of these possible adaptations would act to increase agonist activation resulting in increased tension development by the muscle and consequently improved power output. Observations of increased electromyography (EMG) amplitude following training suggests that a possible adaptation associated with enhanced muscular power may be an increase in the level of motor unit recruitment.[128] However, current techniques are unable to definitively establish whether or not training elicits a true increase in motor unit recruitment as this would require the identification of previously uninvolved motor units that are recruited after training. Methodologies have been implemented to gain an indication of possible training-induced changes to the level of motor unit activation (which encompasses recruitment and firing frequency). These techniques involve the comparison of force produced during a maximal voluntary contraction (MVC) and either a maximal Sports Med 2011; 41 (1)


tetanic muscle stimulation, or a supra-maximal stimulus applied to the nerve of a muscle engaged in a MVC (i.e. interpolated twitch technique).[73,191-195] In both of these cases, the stimulus can cause a significant difference in force production between the voluntary and stimulated contractions if all motor units have not been recruited voluntarily (or the firing frequency of the recruited motor units is submaximal, as discussed in section 3.2). Results from early investigations indicated that despite considerable inter-subject variability, full voluntary activation was possible in a variety of muscles during single joint, isometric contractions in untrained but well motivated individuals.[73,128,191-194] Consequently, it was difficult to attribute training-induced increases in EMG to changes in the level of motor unit recruitment. However, advancements in techniques have allowed for more sensitive measurements, which have revealed levels of voluntary activation to range from 85% to 95% of maximum capacity in the quadriceps femoris and 95–100% in a range of other muscles.[195] Despite these differences and the theory that untrained individuals may not be able to consistently recruit the highest threshold motor units, resistance training studies involving healthy adults indicate that maximal voluntary activation does not increase following training.[196-203] It is important to note, however, that these longitudinal studies may have been impaired by use of less sensitive techniques than what are now available, the use of non-specific isometric tests to evaluate the effects of dynamic training, and the small window for improvement in some of the muscles assessed.[195] Furthermore, voluntary activation during maximal dynamic contractions has been shown to be 88–90%, significantly lower than voluntary activation during maximal isometric contraction (95.2%).[204] It may therefore be possible that training results in improved voluntary activation during dynamic movements and especially in more complex, multi-joint sportspecific movements. If future research was to demonstrate this, increased motor unit recruitment (or firing frequency) may in fact contribute to training-induced improvements in maximal muscular power. ª 2011 Adis Data Information BV. All rights reserved.

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The preferential recruitment of high-threshold motor units following training is a somewhat common theory of neural adaptation.[2,205,206] While few exceptions to the size principle exist, it has been theorized that well trained athletes may be able to activate high-threshold motor units in place of low-threshold motor units during ballistic movements in an attempt to enhance maximal muscular power.[2,206] This theory stems from selective recruitment of high-threshold motor units observed during very rapid stereotyped movements in the cat[207] as well as during eccentric[208,209] or electrically induced contractions[210,211] in humans. In one of the only studies to assess this theory, van Cutsem and co-workers[187] observed the orderly motor unit recruitment of the size principle to be preserved during both slow ramp and ballistic contractions following ballistic power training. However, this same study observed that motor units were recruited at lower thresholds after training during ballistic contractions.[187] The post-training recruitment thresholds underwent a significant shift to lower percentages of MVC than those observed during ballistic contractions at baseline and in comparison with a non-training control group. The earlier activation was reported to be likely to contribute to the observed significant increase in the speed of voluntary ballistic contraction.[187] Therefore, increases in maximal power output following training may be due in some part to lower recruitment thresholds during ballistic contractions. While preferential recruitment of type II fibres remains a possibility, the current evidence for it occurring in response to exercise in humans is not convincing. It is important to note that a motor unit is trained in direct proportion to its recruitment,[111] so movements that require the recruitment of high-threshold units must be incorporated into the training programme for changes in recruitment to have an impact on performance. 3.2 Firing Frequency

The motor unit firing frequency represents the rate of neural impulses transmitted from the a-motoneuron to the muscle fibres. The firing frequency of a motor unit can impact the ability of a Sports Med 2011; 41 (1)

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muscle fibre to generate force in two ways. First, increasing the firing frequency enhances the magnitude of force generated during a contraction. It has been estimated that the force of contraction may increase by 300–1500% when the firing frequency of a motor unit is increased from its minimum to maximum rate.[188] Second, motor unit firing frequency impacts the RFD of muscle contraction. During ballistic contractions motor units have been reported to begin firing at very high frequencies followed by a rapid decline.[212] The high initial firing frequency, which is believed to be associated with an increase in the number of doublet discharges,[187,213] results in increased RFD, even if only maintained for a very short period of time.[214] Therefore, by influencing the force and RFD of muscle contraction, motor unit firing frequency plays a role in the development of maximal muscular power. Training-induced enhancement of maximum motor unit firing frequency has been proposed as a possible mechanism driving improvements in neuromuscular performance.[215] A cross-sectional examination reported that weightlifters displayed greater maximum motor unit firing frequency during a MVC of the quadriceps compared with untrained controls,[216] thus indicating that training may increase the maximal firing frequency of motor units. As discussed in section 3.1, most resistance training studies involving healthy adults indicate that voluntary activation (which gives an indication of both motor unit recruitment and firing frequency) does not increase following training.[196-203] However, more recent research involving intramuscular EMG has reported training-induced increases in motor unit firing frequency during maximal contractions.[187,217,218] These observations were made following strength training during maximal isometric contractions of the abductor digiti minimi[217] and vastus lateralis[219] as well as during ballistic contractions in the tibialis anterior following ballistic power training.[187] In the two strength-training studies, rapid and pronounced improvements occurred in maximal firing frequency between subsequent testing sessions prior to training, which mirrored improvements in maximal force.[217,218] Maximal firing frequency remained elevated following vasª 2011 Adis Data Information BV. All rights reserved.

tus lateralis training[218] but returned to values similar to those observed at baseline in the abductor digiti minimi after training.[217] van Cutsem and co-workers[187] observed an increase in maximal motor unit firing frequency following 12 weeks of ballistic power training as well as enhanced maximal force and RFD values. These results suggest that increases in maximal motor unit firing frequency may contribute to improved force and power generation especially in the early phases of training. Perhaps a more important consideration for improved athletic performance is the possible training-induced adaptations to the pattern of motor unit firing frequency and the subsequent impact on RFD. Compared with long-distance runners and untrained controls, Saplinskas et al.[220] observed sprinters to have the highest motor unit firing frequency during the onset of rapid isometric dorsiflexion. This observation was supported by an intervention study that reported the peak firing frequency at the onset of ballistic contraction to increase following ballistic training.[187] Furthermore, these higher firing frequencies were maintained for longer throughout the contraction after training.[187] Additionally, the authors reported a training-induced increase in the percentage of doublet discharges (i.e. a motor unit firing two consecutive discharges in a 5 ms or less interval) at the onset of a ballistic contraction that were reported to contribute to increases in RFD and time to peak force during ballistic contractions.[187] Therefore, ballistic power training may prompt adaptations to the pattern of motor unit firing frequency that contributes to enhanced maximal power production. 3.3 Motor Unit Synchronization

Motor unit synchronization occurs when two or more motor units are activated concurrently more frequently than expected for independent random processes.[221] Although it is yet to be convincingly demonstrated, synchronization has commonly been hypothesized to augment force production and positively influence RFD.[127,222] Furthermore, synchronization is theorized to be a nervous system adaptation that assists with the Sports Med 2011; 41 (1)


coactivation of numerous different muscles in order to enhance RFD.[223,224] The manner in which synchronization may influence force or RFD is not readily apparent. No difference in force production has been observed between asynchronous and synchronous motor unit activation at frequencies similar to those observed in MVC and asynchronous discharges of action potentials has been shown to result in greater force production at submaximal firing frequencies.[225,226] Furthermore, voluntary contractions have been shown to produce greater RFD than evoked tetanic contractions in which all motor units are stimulated to fire concurrently.[214] However, synchronization may actually be one of the strategies for inter-muscular coordination and therefore could impact force and/or RFD during complex, multi-joint movements as opposed to isolated, single-joint movements where synchronization does not appear to have a significant impact. It has been hypothesized that synchronization between muscles may be a strategy to simplify and coordinate the activity of muscles in control of mechanically unstable joints (e.g. the medial and lateral vasti muscles and the patellofemoral joint),[224] which would allow for greater transmission of muscular power in complex movements. Therefore, further investigation is required in order to determine if motor unit synchronization contributes to enhanced maximal power production especially during complex multi-joint movements. Observations from cross-sectional comparisons have led to the theory that motor unit synchronization may improve as a result of training. Using surface EMG, Milner-Brown et al.[221] observed recreational weightlifters to display greater motor unit synchronization in the hand muscles than untrained subjects. This observation was corroborated by Semmler and Nordstrom[227] who, using techniques that measured motor unit discharges directly, demonstrated motor unit synchronization to be significantly greater in strength-trained subjects than both musicians and untrained subjects. In one of the only intervention studies examining motor unit synchronization, Milner-Brown et al.[221] reported a significant improvement in motor unit synchronization (measured by surface EMG) following 6 weeks of MVC ª 2011 Adis Data Information BV. All rights reserved.

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training of the hand muscles. However, the validity of using surface EMG to assess motor unit synchronization has been questioned.[228] Therefore, further research is required to elucidate if changes to motor unit synchronization occur in response to training. 3.4 Inter-Muscular Coordination

Inter-muscular coordination describes the appropriate activation (both magnitude and timing) of agonist, synergist and antagonist muscles during a movement. For highly effective and efficient movement, agonist activation needs to be supplemented by increased synergist activity and decreased co-contraction of the antagonists.[190] The coordinated activation of these muscles is required to generate the greatest possible force in the direction of movement.[190] ‘Triple extension’ (i.e. extension of the hips, knees and plantar flexion of the ankles) of the lower limbs typical of jumping and sprinting involves quite complex interaction of uni- and multi-articulate musculotendinous units performing various actions. It is only with precise timing and level of activation and relaxation of the agonists, synergists and antagonists that power flow through the kinetic chain will be optimized, impulse on the ground maximized and, thus, performance in terms of takeoff velocity maximized. Therefore, the ability to generate maximal power output during athletic movements is considerably influenced by inter-muscular coordination. 3.4.1 Activation of Synergists

Synergists play a role in maximal power production and it is possible that improved activation and/or coordination of synergist muscles could contribute to enhanced performance. While there is much evidence of task-specific synergist coordination, little information is available monitoring possible changes to synergist activity brought about by training. While untrained people have been shown to activate agonists quite effectively,[191-193] it is theorized that enhanced activation and/or coordination of synergist muscles may contribute to performance improvements following training and are associated with Sports Med 2011; 41 (1)

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the superior performance of trained individuals.[229] Furthermore, adaptations in synergist muscles may help explain the increases in force production observed independent of increased neural activation of the agonists, especially during the early phases of training. Additional research is required to clarify the nature of adaptations in synergists and the relative contribution to enhancing performance. 3.4.2 Co-Activation of Antagonists

The magnitude of antagonist co-activation is dependent on various factors including the type of contraction,[230] load, velocity and precision[231] of the movement as well as its range of motion.[232] Antagonist co-activation is counterproductive to movements in which maximal force must be generated due to the fact that the co-activation would produce torque about the joint acting in the opposite direction of the desired movement.[233-235] There is also evidence that co-activation may impair the full activation of agonist muscles through reciprocal inhibition.[236] However, antagonist coactivation is beneficial in coordinating movements and maintaining joint stability during actions, especially those ballistic in nature. Despite these advantages, excessive antagonist co-activation may negatively influence the ability to perform movements with maximal power. It is hypothesized that training-induced improvements in performance are influenced to some degree by a decrease in antagonist co-activation. Comparisons of individuals with different training backgrounds have rendered inconclusive results and intervention studies have reported conflicting evidence of adaptations to antagonist coactivation. Hence, the possible training-induced adaptations in antagonist co-activation and subsequent impact on performance, remains unclear. Antagonist co-activation has been reported to be prominent during ballistic movements[237] and, therefore, the potential to reduce co-activation in such movements following training is relatively greater. Furthermore, the level of antagonist coactivation may be much greater during dynamic, multi-joint movements than during the single-joint, isometric movements commonly researched. Although these areas have not yet been investigated, it is theorized that a reduction in antagonist coª 2011 Adis Data Information BV. All rights reserved.

activation during such complex movements would contribute to improvements in maximal power following training.[143] 4. Muscle Environment Acute changes in the muscle environment (i.e. alterations resulting from fatigue, changes in hormone milieu and muscle temperature) impact muscular performance and therefore the ability to generate maximal power. During fatigue, numerous muscle properties are altered including ionic changes on the action potential, extracellular and intracellular ions as well as intracellular metabolites (the interested reader should refer to recent comprehensive reviews of this topic[238,239]). Each of these alterations negatively affects maximal muscular power through impairing the force generation and/or the velocity of shortening during contractions.[238,239] Furthermore, recent evidence suggests that the combination of factors co-existing during fatigue in vivo result in even greater impairment than what has been observed for fatigue factors individually.[240] While the influence of endocrine factors on adaptational mechanisms in muscle and the resulting enhancement in muscular function have been well reviewed,[241,242] acute hormonal changes may potentially impact the ability to generate maximal muscular power immediately. Recent evidence indicating that treating bundle fibres with physiological concentrations of dihydrotestosterone increases specific force and phosphorylation of myosin light chains of type II fibres, suggests that changes in androgenic hormone concentrations in the blood may acutely impact maximal muscular power.[243] Additionally, alterations in muscle temperature also influence maximal power production as it has been shown that Pmax, Vmax, Fmax as well as RFD decrease with a decrease in muscle temperature[244-246] (for extensive reviews of this topic please refer to[247-249]). 5. Conclusion Maximal muscular power is influenced by a wide variety of neuromuscular factors including muscle fibre composition, cross-sectional area, Sports Med 2011; 41 (1)


fascicle length, pennation angle and tendon compliance as well as motor unit recruitment, firing frequency, synchronization and inter-muscular coordination. Maximal power is also affected by the type of muscle action involved and, in particular, the time available to develop force, storage and utilization of elastic energy, interactions of contractile and elastic elements, potentiation of contractile and elastic filaments as well as stretch reflexes. Furthermore, acute changes in the muscle environment (i.e. alterations resulting from fatigue, changes in hormone milieu and muscle temperature) impact the ability to generate maximal power. Development of effective training programmes that enhance maximal muscle power must involve consideration of these factors and the manner in which they respond to training. Acknowledgements The authors have no potential conflicts of interest to disclose and no funding was received for this review.

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225. Lind AR, Petrofsky JS. Isometric tension from rotary stimulation of fast and slow cat muscle. Muscle Nerve 1978; 1: 213-8 226. Rack PM, Westbury DR. The effects of length and stimulus rate on tension in the isometric cat soleus muscle. J Physiol 1969; 204: 443-60 227. Semmler JG, Nordstrom MA. Motor unit discharge and force tremor in skill- and strength-trained individuals. Exp Brain Res 1998; 119 (1): 27-38 228. Yue G, Fuglevand AJ, Nordstrom MA, et al. Limitations of the surface electromyography technique for estimating motor unit synchronization. Biol Cybern 1995; 73 (3): 223-33 229. Behm DG. Neuromuscular implications and applications of resistance training. J Strength Cond Res 1995; 9 (4): 264-74 230. Ostering LR, Hamill J, Corcos DM, et al. EMG patterns accompanying isokinetic exercise under varying speed and sequencing conditions. Am J Phys Med 1984; 63: 289-97 231. Gordon J, Ghez C. EMG patterns in antagonist muscles during isometric contractions in man: relation to response dynamics. Exp Brain Res 1984; 55: 167-71 232. Karst G, Hazan Z. Antagonist muscle activity during forearm movements under varying kinematic and loading conditions. Exp Brain Res 1987; 67: 391-401 233. Baratta R, Solomonow M, Zhou BH, et al. Muscular coactivation: the role of the antagonist musculature in maintaining knee stability. Am J Sports Med 1988; 16 (2): 113-22 234. Aagaard P, Simonsen EB, Andersen JL, et al. Antagonist muscle coactivation during isokinetic knee extension. Scand J Med Sci Sports 2000; 10 (2): 58-67 235. Kellis E, Baltzopoulos V. The effects of antagonist moment on the resultant knee joint moment during isokinetic testing of the knee extensors. Eur J Appl Physiol Occup Physiol 1997; 76 (3): 253-9 236. Milner TE, Cloutier C, Leger AB, et al. Inability to activate muscles maximally during cocontraction and the effect of joint stiffness. Exp Brain Res 1995; 107: 293-305 237. Carpentier A, Duchateau J, Hainaut K. Velocity-dependent muscle strategy during plantarflexion in humans. J Electromyogr Kinesiol 1996; 6: 1-11 238. Fitts RH. The cross-bridge cycle and skeletal muscle fatigue. J Appl Physiol 2008; 104 (2): 551-8 239. Allen DG, Lamb GD, Westerblad H. Skeletal muscle fatigue: cellular mecanisms. Physiol Rev 2008; 88 (1): 287-332 240. Karatzaferi C, Franks-Skiba K, Cooke R. Inhibition of shortening velocity of skinned skeletal muscle fibers in conditions that mimic fatigue. Am J Physiol Regul Integr Comp Physiol 2008; 294: R948-55 241. Kraemer WJ, Ratamess NA. Hormonal responses and adaptations to resistance exercise and training. Sports Med 2005; 35 (4): 339-61 242. Kraemer WJ, Ratamess NA. Endocrine responses and adaptations to strength and power training. In: Komi PV, editor. Strength and power in sport. Oxford: Blackwell Scientific Publications, 1992 243. Hamdi MM, Mutungi G. Dihydrotestosterone activates the MAPK pathway and modulates maximum isometric

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force through the EGF receptor in isolated intact mouse skeletal muscle fibres. J Physiol 2010; 588 (3): 511-25 244. De Ruiter CJ, Jones DA, Sargeant AJ, et al. Temperature effect on the rates of isometric force development and relaxation in the fresh and fatigued human adductor pollicis muscle. Exp Physiol 1999; 84: 1137-50 245. Ranatunga KW. Temperature-dependence of shortening velocity and rate of isometric tension development in rat skeletal muscle. J Physiol 1985; 329: 465-83 246. De Ruiter CJ, De Haan A. Temperature effect on the force/velocity relationship of the fresh and fatigued human adductor pollicis muscle. Pflu¨gers Arch 2000; 440: 163-70


247. Rall JA, Woledge RC. Influence of temperature on mechanics and energetics of muscle contraction. Am J Physiol 1990; 259: R197-203 248. Bennett AF. Thermal dependence of muscle function. Am J Physiol 1984; 247: R217-29 249. Ferretti G. Cold and muscle performance. Int J Sports Med 1992; 13 Suppl. 1: 185S-92S

Correspondence: Dr Prue Cormie, School of Exercise and Biomedical Health Sciences, Edith Cowan University, 270 Joondalup Drive, Joondalup, WA 6027, Australia. E-mail: [email protected]

Sports Med 2011; 41 (1)

REVIEW ARTICLE

Sports Med 2011; 41 (1): 39-57 0112-1642/11/0001-0039/$49.95/0


b2-Agonists and Physical Performance A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Babette M. Pluim,1 Olivier de Hon,2 J. Bart Staal,3,4 Jacqueline Limpens,5,6 Harm Kuipers,7 Shelley E. Overbeek,8 Aeilko H. Zwinderman9 and Rob J.P.M. Scholten6,9 1 Royal Netherlands Lawn Tennis Association (KNLTB), Amersfoort, the Netherlands 2 Anti-Doping Authority the Netherlands, Capelle aan den IJsel, the Netherlands 3 Department of Epidemiology and Caphri Research School, Maastricht University, Maastricht, the Netherlands 4 Scientific Institute for the Quality of Healthcare, Radboud University Medical Centre, Nijmegen, the Netherlands 5 Medical Library, Academic Medical Center, Amsterdam, the Netherlands 6 The Dutch Cochrane Centre, Amsterdam, the Netherlands 7 Department of Movement Sciences, Nutrition and Toxicology Research Institute Maastricht, (NUTRIM), University of Maastricht, Maastricht, the Netherlands 8 Medical Centre Alkmaar, Alkmaar, the Netherlands 9 Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, Amsterdam, the Netherlands

Contents Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Literature Search Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Criteria for Considering Studies for this Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Types of Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 Types of Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.3 Types of Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.4 Types of Outcome Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Search Methods for Identification of Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Data Collection and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Selection of Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Data Extraction and Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.3 Assessment of Risk of Bias in Included Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.4 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.5 Subgroup Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.6 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Search Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Description of Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Assessment of Risk of Bias in Included Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Effect of Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Inhaled b2-Agonists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Subgroup Analysis of Inhaled b2-Agonists. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

40 41 41 41 41 41 42 42 42 43 43 43 43 47 47 47 47 47 47 47 49 49 50

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3.4.3 Systemic b2-Agonists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.4 Subgroup Analysis of Systemic b2-Agonists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Principal Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Strength and Weaknesses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Abstract

50 50 50 50 53 55

Inhaled b2-agonists are commonly used as bronchodilators in the treatment of asthma. Their use in athletes, however, is restricted by anti-doping regulations. Controversies remain as to whether healthy elite athletes who use bronchodilators may gain a competitive advantage. The aim of this systematic review and meta-analysis is to assess the effects of inhaled and systemic b2-agonists on physical performance in healthy, nonasthmatic subjects. To this end, MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials (CENTRAL) were searched up to August 2009. Reference lists were searched for additional relevant studies. The search criteria were for randomized controlled trials examining the effect of inhaled or systemic b2-agonists on physical performance in healthy, nonasthmatic subjects. Two authors independently performed the selection of studies, data extraction and risk of bias assessment. Parallel-group and crossover trials were analysed separately. Mean difference (MD) and 95% confidence intervals were calculated for continuous data and, where possible, data were pooled using a fixed effects model. Twenty-six studies involving 403 participants (age range 7–30 years) be compared inhaled b2-agonists with placebo. No significant effect could . detected for inhaled b2-agonists on maximal oxygen consumption (VO2max) [MD -0.14 mL kg-1 min-1;.95% CI -1.07, 0.78; 16 studies], endurance time to exhaustion at 105–110% VO2max (MD -1.5 s; 95% CI -15.6, 12.6; four studies), 20-km time trial duration (MD -4.4 s; 95% CI -23.5, 14.7; two studies), peak power (MD -0.14 W kg-1; 95% CI -0.54, 0.27; four studies) and total work during a 30-second Wingate test (MD 0.80 J kg-1; 95% CI -2.44, 4.05; five studies). Thirteen studies involving 172 participants (age range 7–22 years) compared systemic b2-agonists with placebo, with 12 studies involving oral and one study involving intravenous salbutamol. A significant effect was at 80–85% detected for systemic b2-agonists on endurance time to exhaustion . . VO2max (MD 402 s; 95% CI 34, 770; two studies), but not for VO2max (placebo -1 min-1, one study), 42.5 – 1.7 mL kg-1 min-1, salbutamol . 42.1 – 2.9 mL kg endurance time to exhaustion at 70%. VO2max (MD 400 s; 95% CI -408, 1208; one study) or power output at 90% VO2max (placebo 234.9 – 16 W, salbutamol 235.5 – 18.1 W, one study). A significant effect was shown for systemic b2-agonists on peak power (MD 0.91 W kg-1; 95% CI 0.25, 1.57; four studies), but not on total work (MD 7.8 J kg-1; 95% CI -3.3, 18.9; four studies) during a 30-second Wingate test. There were no randomized controlled trials assessing the effects of systemic formoterol, salmeterol or terbutaline on physical performance. In conclusion, no significant effects were detected for inhaled b2-agonists on endurance, strength or sprint performance in healthy athletes. There is some evidence indicating that systemic b2-agonists may have a positive effect on physical performance in healthy subjects, but the evidence base is weak.


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b2-Agonists and Physical Performance

1. Introduction Inhaled b2-agonists are commonly used bronchodilators and are essential as reliever therapy in the management of asthma.[1] Elite athletes have an increased risk of asthma and exercise-induced bronchoconstriction compared with the general population.[2-11] In sports, the use of inhaled b2-agonists in the treatment of asthmatic athletes is restricted by anti-doping regulations. This is based on the assumption that b2-agonists have the potential to improve physical performance, resulting in an unfair competitive advantage when taken by healthy athletes. The origins of these rules can be traced back to 1972, when inhaled salbutamol was prohibited for the first time at the Olympic Games in Munich, Germany.[12] Since then, inhaled b2-agonists have alternately been allowed and prohibited at the Olympic Games. Their status has switched from prohibited (1972–5) to permitted with notification before the event (1976–83; 1993–2000), permitted with retrospective notification (1984–5), fully permitted (1986–92), and prohibited without a therapeutic use exemption (2001–9).[12] In January 2010, the rules of the World AntiDoping Agency (WADA) changed again and the use of all b2-agonists was prohibited in athletes (both in and out of competition), except for salbutamol and salmeterol by inhalation, which required a declaration of use.[13] In January 2011, the requirement to submit a declaration of use was lifted for salbutamol and salmeterol and these are now permitted by inhalation.[14] Urinary salbutamol concentration is not allowed to exceed 1000 ng mL-1.[14] However, the performance enhancing effects of b2-agonists have been questioned. Kindermann[15] reviewed 19 randomized, placebo-controlled trials, and concluded that the performance enhancing effect of inhaled formoterol, salbutamol, salmeterol and terbutaline could not be proven, whereas, oral administration of salbutamol seemed to improve muscle strength and endurance performance. Backer et al.[16] argued that it was debatable whether or not b2-agonists enhance performance and that they should therefore not be permitted


41

for use by athletes without objective signs of asthma. The authors suggested that systemic use would probably enhance performance. In the International Olympic Committee (IOC) consensus statement of January 2008, it was suggested that inhaled b2-agonists are not considered to enhance performance, but that oral salbutamol increases strength.[17] Finally, in a review by the Joint Task Force of the European Respiratory Society and the European Academy of Allergy and Clinical Immunology, it was concluded that neither inhaled nor systemic b2-agonists improve physical performance in healthy athletes.[1] To resolve this issue, we conducted a systematic review and meta-analysis of randomized controlled trials with the aim of assessing the effect of inhaled and systemic b2-agonists on physical performance in healthy, non-asthmatic athletes. 2. Literature Search Methodology 2.1 Criteria for Considering Studies for this Review 2.1.1 Types of Studies

We considered all the randomized controlled studies that addressed the effect of b2-agonists on physical performance. 2.1.2 Types of Participants

We included studies with healthy subjects (all ages, male and female). We looked for studies on elite athletes, recreational athletes and non-athletic participants. We sought to document the level and intensity of sports participation,. the training level (maximal oxygen consumption [VO2max]) and the type of sport at inclusion. Athletes were considered to be highly trained if they had a . VO2max above 55 mL kg-1 min-1 (females) or 60 mL kg-1 min-1 (males). Studies that included some or all participants with diseases such as asthma, chronic obstructive pulmonary disease and cardiovascular disease, were excluded, as were studies in which participants used other medications (except for oral contraceptives in women), unless the results of healthy subjects were presented separately.

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Table I. Assessment of risk of bias[19] Criteria/definition

Comments

Method of randomization adequate

A random (unpredictable) assignment sequence. Examples of adequate methods are the use of computer-generated random number tables and the use of sealed opaque envelopes. Methods of allocation using date of birth, date of admission, hospital numbers or alternation were not regarded as appropriate

Concealment of treatment allocation

Assignment generated by an independent person not responsible for determining the eligibility of the participants. This person has no information about the persons included in the trial and has no influence on the assignment sequence or on the decision about eligibility of the participant. The use of coded canisters was also considered adequate

Blinding of subjects, researchers and outcome assessors

If there was enough information about the blinding of the various persons, a ‘yes’ was scored

Follow-up

Withdrawal rate described and acceptable: the number of participants who were included in the study but did not complete the observation period or were not included in the analysis, must be described and reasons given. If the percentage of withdrawals did not exceed 20% for short-term follow-up (1–6 weeks) and 30% for long-term follow-up (>6 weeks), and appeared not to lead to substantial bias, a ‘yes’ was scored

Intent-to-treat analysis

All randomized participants should have been reported/analysed in the group to which they were allocated by randomization for the most important moments of effect measurement (minus missing values), irrespective of non-compliance and co-interventions

Participants adequately tested for the absence of asthma[13]

In order to receive a ‘yes’, an objective measurement of airflow obstruction must have been used, such as a bronchodilator reversibility test (12% increase in FEV1) or a bronchial provocation test (test-specific decrease in FEV1). Both direct stimuli (metacholine, histamine) and indirect stimuli (exercise, eucapnic voluntary hyperventilation, mannitol, hypertonic saline) can be used. Peak flow measurements, lung function at rest and questionnaires on medical history or bronchial complaints are not considered adequate testing

FEV1 = forced expiratory volume in 1 second.

2.1.3 Types of Interventions

For any short- or long-acting inhaled or systemic (oral or intravenous) b2-agonist, the use could be single (once) or administered at multiple occasions (1 or more days to weeks). Studies with clenbuterol were excluded, as this drug has been defined as an anabolic agent and not a b2-agonist by the WADA.[14] 2.1.4 Types of Outcome Measures

The types of outcome measures in the studies were. as follows: 1. VO2max in L min-1 or mmol L kg-1 min-1 determined with a maximal exercise test on a treadmill or cycle ergometer. If information . was provided on peak power output only, VO2max was estimated using the equation according to Arts and Kuipers.[18] 2. Endurance time(s) to exhaustion during. an exercise test at a predetermined percentage of VO2max. 3. Duration time(s) of a time trial, in which a certain distance has to be covered or a certain amount of work has to be delivered.


4. Peak power (W or W kg-1), average power (W or W kg-1), or total work (J or J kg-1) during a Wingate test. 5. The one-repetition maximum (1RM, kg) or concentric peak torque (Nm s-1 or Nm kg-1 s-1) during an isokinetic strength test of any muscle group. Studies that did not provide quantitative results for at least one of these physical performance variables were excluded.

2.2 Search Methods for Identification of Studies

One of the authors (JL), a medical librarian with experience in conducting searches for systematic reviews, undertook a systematic search of the electronic databases MEDLINE (1950 to August 2009), EMBASE (1980 to August 2009) and Cochrane Central Register of Controlled Trials (CENTRAL) [to August 2009] to identify controlled clinical trials on b2-agonists in healthy individuals. No language or any other restricSports Med 2011; 41 (1)


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tions were applied. The search included an iterative process to refine the search strategy through adding search terms as new relevant citations were identified. We downloaded all references identified into Reference Manager software (version 11.0). The search strategy consisted of free-text words and subject headings (MeSH, SH) related to the intervention (b-agonists, including individual agents) and the population (i.e. athlete*, sport*), the outcome (i.e. doping, endurance, muscle strength, wingate*) or sport-specific journals (i.e. sport*.jw.). In MEDLINE and EMBASE, the search was combined with broad RCT filters, developed for Cochrane Systematic Reviews of interventions. We searched reference lists of identified randomized controlled trials for additional studies or relevant reviews. 2.3 Data Collection and Analysis 2.3.1 Selection of Studies

Two authors independently evaluated studies for inclusion (BMP and OdH). In case of dis-

agreement, further discussion was undertaken to achieve consensus. 2.3.2 Data Extraction and Management

The same two authors (BMP and OdH) independently extracted data. Information was extracted from each study for the following characteristics: design, participants (sample size, sex, age [mean – SD and/or median – range]), type. and level of sports participation, training level (VO2max), intervention (brand, type, dose, study duration) and outcome (type of outcome analysis, outcomes analysed). Data were extracted for each of the outcomes considered by the review. Data were entered by one reviewer (BMP) and double checked on a separate occasion by a second person (OdH). 2.3.3 Assessment of Risk of Bias in Included Studies

The risk of bias was independently assessed and scored by the two reviewers (BMP and OdH) using the methodological criteria listed in table I.[19] In addition to the specific domains recommended for Cochrane reviews, we added whether participants

Literature search

373 records identified

324 articles excluded based on title and/or abstract

49 articles requested for full assessment

8 additional articles found by searching reference lists of papers 57 articles available for full assessment

39 articles were included in the final analysis

1 article excluded because the intervention did not match our inclusion criteria; 5 articles excluded because they were not related to sports performance but to unloading; 12 articles excluded because they did not provide quantitative results for at least one of the five pre-determined physical performance variables

Fig. 1. Flow chart for search results.


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Table II. Characteristics of included inhaled (26 studies) and systemic (13 studies) b2-agonists studies Study, year

Design

Study population no. of subjects; sex; age [y] – SD

activity and performance level

Inhaled studies

Interventiona

Primary outcome measures

3-way crossover

10; M; 23.3 – 3.2

Physically active, 5.9 – 2.0 h wk-1

Salbutamol 200 mg, 800 mg

Quadriceps muscle strength during maximal voluntary contraction and femoral nerve magnetic stimulation before and after (i) a maximal incremental cycling test; and (ii) 50 maximal isometric one-leg extensions

Sporer et al.,[40] 2008

4-way crossover

30; M; 29 – 6

Salbutamol 200 mg, 400 mg, 800 mg

Mean power (W) and duration of 20-km time trial

Tjorhom et al.,[41] 2007

Crossover

23; M; 29.2 – 4.5

Formoterol 18 mg

Riiser et al.,[42] 2006

Crossover

20; M; 29.2 – 4.4

State level cyclists and . triathletes, VO2max 67.1 – 4.3 mL kg-1 min-1 . Endurance athletes, VO2max 60.6 – 3.8 mL kg-1 min-1 . Endurance athletes, VO2max 61.1 mL kg-1 min-1

Running time to exhaustion at -20C at . 107% VO2max . VO2max, running time to exhaustion in . hypobaric conditions at 107% VO2max

Van Baak et al.,[43] 2004

Crossover

16; M; 23 – 3

Salbutamol 800 mg

Stewart et al.,[44] 2002

3-way crossover

10; M; 26.2 – 2.8

Goubault et al.,[45] 2001

3-way crossover

12; M; 23 – 2

Carlsen et al.,[46] 2001

Crossover

24; M; 25.0 – 2.8

Regional cyclists and . triathletes, VO2max 69.9 – 6.8 mL kg-1 min-1 . Highly trained athletes, VO2max 65.6 – 2.4 mL kg-1 min-1 . Trained athletes, VO2max 57.9 – 5.1 mL kg-1 min-1 . Competitive athletes, VO2max 67.4 – 5.0 mL kg-1 min-1

Sue-Chu et al.,[47] 1999

Crossover

8; M; 23 – 4

Highly trained cross-country . skiers, VO2max >70 mL kg-1 min-1

Salmeterol 50 mg

Sandsund et al.,[48] 1998

Crossover

8; M; 25.1 – 3.6

Highly trained cross-country . skiers, VO2max 75.3 – 5.0 mL kg-1 min-1

Salbutamol 400 mg

Larsson et al.,[49] 1997

Single-blind crossover

20; M; 24 – 6

Terbutaline 3 mg

Carlsen et al.,[50] 1997

3-way crossover

18; M; 22.9 – 6.3

Elite cyclists, skiers, runners, . VO2max 70.8 (68.2–73.3) mL kg-1 min-1 . Runners, VO2max >63.1 mL kg-1 min-1

Formoterol 18 mg

Formoterol 12 mg, salbutamol 400 mg Salbutamol 200 mg, 800 mg Formoterol 9 mg

Salbutamol 800 mg, salmeterol 50 mg

Cycling time to exhaustion at 75% Wmax

. VO2max, 30-s Wingate test . Cycling time to exhaustion at 85% VO2max . VO2max, running time to exhaustion at 105% . VO2max . . VO2max, running time at 90–80% VO2max and then incremental to exhaustion at -15C . . VO2max, running time at 50–95% VO2max and then incremental to exhaustion at -15C and 23C . VO2max, running time to exhaustion during maximal exercise test at -10C . VO2max, running time to exhaustion during maximal exercise test Continued next page

Pluim et al.

Sports Med 2011; 41 (1)

Decorte et al.,[39] 2008

Study, year

Design



Crossover

11; M; 24.6 – 3.7

Crossover

15; M; 25 – 4

Elite cyclists, US Cycling Federation category I and II . Well trained cyclists, VO2max -1 62.8 – 7.0 mL kg min-1

Morton et al.,[53] 1996

Crossover

16; M; 23.3 – 3.5

Lemmer et al.,[54] 1995 Heir and Stemshaug,[55] 1995

Crossover

14; M; 22.6 – 3.7

Crossover

17; M; range 18–30

Unnithan et al.,[56] 1994

Single-blind crossover

10; M; 10.4 – 0.5

Fleck et al.,[57] 1993 Morton et al.,[58] 1993 Signorile et al.,[59] 1992 Meeuwisse et al.,[60] 1992

Crossover

21; M; 23.8 – 5.0

Crossover

McDowell et al.,[51] 1997 Norris et al.,[52] 1996

High performance cyclists, triathletes, cycling Australia grade A or B Elite cyclists, US Cycling Federation category I and II Highly trained skiers, runners, . orienteers, VO2max >70 mL kg-1 min-1 . School boys, VO2max 55.3 – 5.8 mL kg-1 min-1

Interventiona


Salmeterol 42 mg

30-s Wingate test

Salbutamol 400 mg

. VO2max, 60-s Wingate test, 20-km time trial duration

Salmeterol 50 mg

10-s and 30-s Wingate test; isokinetic strength knee flexion and extension

Albuterol 360 mg

30-s Wingate test

Salbutamol 50 mg kg-1

. VO2max, running time to exhaustion at 110% . VO2max

Terbutaline 500 mg

. VO2peak, total running time, submaximal running economy

Albuterol 360 mg

Wmax

17; M; 22 – 4

Elite cyclists, US Cycling Federation category I and II Athletes (power events)

Salbutamol 200 mg

Crossover

15; 8 M, 7 F; 18–33

Healthy subjects

Albuterol 360 mg

10-s Wingate test; isokinetic strength knee flexors and extensors Four 15-s Wingate tests

Crossover

7; M; 23.6 – 4

. Highly trained cyclists, VO2max >60 mL kg-1 min-1

Salbutamol 200 mg

Morton et al.,[61] 1992

Crossover

17; 16 M, 1 F; 21.6 – 4

High performance runners, . VO2max 75.3 – 6.8 mL kg-1 min-1

Salmeterol 50 mg

Gong et al.,[62] 1988

Crossover

15; 14 M, 1 F; 23 – 5

Endurance cyclists, triathletes, . VO2max 61 – 4 mL kg-1 min-1

Albuterol 180 mg

Booth et al.,[63] 1988

Crossover

10; F; 21.6 – 7.25

McKenzie et al.,[64] 1983

Parallel design

. Trained cyclists, VO2max 50.1 – 4.9 mL kg-1 min-1

Salbutamol 100 mg

. VO2max, 30-s Wingate test, timed sprint to ex. haustion after 45 min exercise at 70% VO2max . VO2max, running time to exhaustion during maximal exercise test, 10-s and 30-s Wingate test . VO2max after 60-min submaximal exercise and exhaustive final sprint; exposure to ozone or filtered air Wmax . VO2max

4; M; 25.0 – 7.7

Highly trained track and field athletes 64.0 – 2.2 mL kg-1 min-1

Salbutamol 800 mg

5; M; 27.0 – 9.6

64.7 – 4.4 mL kg-1 min-1

Placebo

5; F; 24.4 – 8.6

59.4 – 12.1 mL kg-1 min-1

Salbutamol 800 mg

5; F; 26.0 – 13.4

61.9 – 8.7 mL kg-1 min-1

Placebo Continued next page

45

Sports Med 2011; 41 (1)



Table II. Contd

46


Table II. Contd Study, year

Design



Interventiona


Systemic studies Crossover

12; F; 22.3 – 3.1

Athletics, weight-lifting, cycling, 1–3 times per wk

Salbutamol 4 mg

30-s Wingate test

Le Panse et al.,[66] 2006a

Crossover

14; F; 20.9 – 1.1 (recreational athletes), 23 – 2.4 (sedentary subjects)

7 recreational athletes, 2–3 times per wk, 7 sedentary subjects

Salbutamol 12 mg day-1 for 4 wk

30-s Wingate test

Le Panse et al.,[67] 2006b

Crossover

14; F; 22 – 1.7

7 recreational athletes, 7 sedentary subjects


Le Panse et al.,[68] 2005

Crossover

15; M; 29.1 – 6.2 (strength-trained athletes), 30.5 – 6.9 (sedentary subjects)

8 strength-trained athletes, 7 sedentary subjects


30-s Wingate test

Collomp et al.,[69] 2005

Crossover

13; M; 31.2 – 5.8

Sedentary subjects and recreational weight-lifters, 1–3 times per wk

Salbutamol 4 mg

30-s Wingate test

Caruso et al.,[70] 2005

Parallel design

22; M; 18–22

Healthy men


Collomp et al.,[71] 2002

Crossover

8; M; 26 – 5.9

. Normally active, VO2max 54.4 – 2.2 mL kg-1 min-1

Isokinetic strength elbow and knee flexors and extensors . Cycling time to exhaustion at 90% VO2max

Collomp et al.,[72] 2000a

Crossover

9; M; 24.6 – 3.9

Moderately trained: cycling, running, ball games

Salbutamol 6 mg

Cycling time to exhaustion at 80–85% . VO2max

Collomp et al.,[73] 2000b

Crossover

8; M; 23.4 – 2.3

Recreational runners and cyclists, 3–5 times per wk


Cycling time to exhaustion at 80–85% . VO2max

Van Baak et al.,[74] 2000

Crossover

16; M; 23.3 – 2.1

Healthy volunteers, twice per wk: track and field, fitness, hockey, soccer, cycling

Salbutamol 4 mg

Cycling time to exhaustion during at 70% . VO2max, isokinetic leg strength

Caruso et al.,[75] 1995

Parallel design

22; 13 M; 21.4 – 3.3: 9 F; 21.4 – 1.8

Sedentary subjects and recreational athletes, fitness 7 days from normal activities); group 2 – high-energy crash circumstances

Injured skiers/snowboarders with a completed ski patrol accident report for a nonhead/non-neck injury but who did not belong in either group 1 or group 2 cases

Only partially in terms of As above identifying high-energy crashes. Assumption is that because they did not have head/neck injuries then they did not sustain an impact to those regions

Skiers/snowboarders, Norway[38]

Injured skier/snowboarder who was treated by, or consulted with, the ski patrol or first-aid room staff after an incident in the skiing area during skiing or lift transport

Random sample of noninjured skiers/snowboarders as they entered the bottom of the main ski lift at each resort

No

As above, but could include skiers despite injury, if impact descriptors known

Skiers/snowboarders, western USA[31]

Injured skiers/snowboarders with an injury to the head/face or neck reported by ski patrol, associated with a fall or collision

Injured skiers/snowboarders with injuries below the neck reported by ski patrol, associated with a fall or collision

No

Skier/snowboarder matched for age, experience, ski area, sustained impact to the head, and impacted object descriptor


Sports Med 2011; 41 (1)

Biomechanical Selection of Epidemiological Controls

fully comparable to the cases, who all (by definition) sustained an impact injury to the head. 6. Discussion There is no doubt that well designed and conducted case-control studies can provide valuable information about sports injury risk and the likely protective capacity of preventive measures.[1] From a study design and analysis point of view, major challenges need to be considered in future sports injury case-control studies and this requires a new standard approach that includes biomechanical considerations. The definition of cases and controls and the processes/rationale for control selection based on biomechanical considerations must be included upfront in the planning stages of all sports injury studies, as has previously been suggested in other injury contexts.[22,23] We have argued that this new approach towards the selection of controls for sports injury case-control studies is needed as standard practice. In doing so, this will extend the traditional epidemiological concepts for case selection with an appreciation of the underlying, biomechanical mechanisms underpinning injury causation thereby ensuring that exposure to injury risk is more appropriately quantified. It will still be important, however, to ensure that over-matching of cases and controls does not occur, as this could reduce the power of the study to identify significant risk or protective effects when they do exist. This will need to be considered on a case-by-case basis in light of the research question/s to be answered in any study, as increasing levels of matching enable more specific questions to be answered. This paper has presented key findings from a review of the peer-reviewed literature from 1985–2008, which identified 32 studies reporting methodological principles relating to the design and analysis of sport injury case-control studies. Although a definition of recruited cases and controls was given in almost all studies, matching of controls to cases was not common. Moreover, adjustment of confounders at the analysis stage was also uncommon. Appropriate definition of cases and selection of controls is vital to ensuring the validity of caseª 2011 Adis Data Information BV. All rights reserved.

69

control studies and conclusions drawn from them. Theoretical, epidemiologically focused frameworks for the selection of controls, such as a three-part review published in the leading international epidemiological methodology journal,[67-69] stress that all case-control studies should be designed to reduce the following three key biases: (i) selection bias – so that comparisons should be made within the study base; (ii) confounding bias – comparisons of the effects of exposure level on risk should not be distorted by the effects of other factors; and (iii) information bias – any errors in exposure measurement must be non-differential in both cases and controls (to avoid bias in one group but not another). Clear threats to case-control studies being bias free can also exist if the cases and controls are not fully comparable in terms of key exposures of interest. Theoretically, the control group provides an estimate of the prevalence of the study exposure in the population from which the cases arose. From an epidemiological perspective, random sampling from the population base, where controls are chosen independently of characteristics of the cases, is the simplest strategy. However, controls should also be representative of those individuals who would have been selected as cases had they developed the outcome. For this reason, it is critical that sports injury case-control researchers use caution when selecting samples of cases and controls and provide clear definitions of the outcome/s being studied and the likely biomechanical determinants of injury risk/exposure. 7. Conclusion To provide necessary guidance for the optimal selection of controls in case-control studies of intervention to prevent sports-related impact injury, this article has outlined a new case-control selection strategy that reflects the importance of biomechanical considerations to ensure that controls are selected based on the presence of the same global injury mechanism as the cases. There is no doubt that adopting this method in practice will add additional steps, both ethically and logistically, for injury researchers, and some of these steps will be challenging. However, the additional Sports Med 2011; 41 (1)

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information describing the injury event (impacted object, body region/s struck, velocity at time of collision and impact velocity) could be collected through specifically designed questionnaires and, with increasing sophistication, using video analysis of injury producing or possible events in both cases and controls.[70,71] This argues the need for strong multidisciplinary approaches to be adopted in all sports injury studies, especially as evidence is translated from the laboratory to the implementation of safety devices and other interventions in the real world context of sport, to actual on-field sports behaviours in relation to these interventions and, finally, through to the setting of safety policies and formal procedures.[3] Future studies should emphasize the interface between epidemiological and biomechanical concepts of selecting appropriate controls for accurate case-control design. This is consistent with a recent conclusion[72] that sports injury researchers need to understand differences between causes, risk factors and confounders in relation to injury risk. To summarize, the general biomechanical principles that should apply to the selection of controls in future case-control studies are as follows: 1. The global injury mechanism[20] or event should be considered. Each control must have been exposed to the same global injury mechanism as the case (e.g. head impact, inverted fall, fall onto outstretched arm, step and cut on floor of similar friction). 2. The intrinsic (individual) risk factors should also be considered in either selecting the controls (e.g. matching) or in the analysis phase (e.g. age, sex, skill level, anatomical and physiological factors, fitness) unless they represent the intervention. These factors are important as they reflect underlying biomechanical characteristics. Finally, whilst this paper has focused on casecontrol studies, the same considerations for control selection apply to other study designs such as matched cohort studies[73] or case-crossover studies.[8] Acknowledgements Professor Caroline Finch was supported by a National Health and Medical Research Council (NHMRC), Principal Research Fellowship. Dr Shahid Ullah was supported by an


Injury Trauma and Rehabilitation Research Fellowship funded through a (NHMRC) Capacity Building Grant in Population Health. Peter Richardson assisted with the original literature searches and retrieval of published articles for the review. The authors have no conflicts of interest that are directly relevant to the content of this review.

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56. Dirx M, Bouter LM, de Geus GH. Aetiology of handball injuries: a case-control study. Br J Sports Med 1992; 26 (3): 121-4 57. Myburgh KH, Hutchins J, Fataar AB, et al. Low bone density is an etiologic factor for stress fractures in athletes. Ann Intern Med 1990; 113 (10): 754-9 58. Bouter LM, Knipschild PG, Volovics A. Binding function in relation to injury risk in downhill skiing. Am J Sports Med 1989 Mar-Apr; 17 (2): 226-33 59. Fung Y. The application of biomechanics to the understanding of injury and healing. In: Nahum A, Melvin J, editors. Accidental injury: biomechanics and prevention. New York: Springer-Verlag, 1993: 1-11 60. Janda DH. The prevention of baseball and softball injuries. Clin Orthop Rel Res 2003; (409): 20-8 61. McIntosh A, McCrory P, Finch C. Performance enhanced headgear: a scientific approach to the development of protective headgear. Br J Sports Med 2004; 34 (1): 46-9 62. Dempsey AR, Lloyd D, Elliott B, et al. The effect of technique change on knee loads during sidestep cutting. Med Sci Sports Exerc 2007; 39 (10): 1765-73 63. Hewett T, Ford K, Myer G. Anterior cruciate ligament injuries in female athletes: part 2. A meta-analysis of neuromuscular interventions aimed at injury prevention. Am J Sports Med 2006; 34 (3): 490-8 64. McIntosh A, McCrory P. Impact energy attenuation performance of football headgear. Br J Sports Med 2000; 34 (10): 337-41 65. Hoshizaki TB, Brien SE. The science and design of head protection in sport. Neurosurgery 2004; 55 (4): 956-67


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Correspondence: Professor Caroline F. Finch, Accident Research Centre, Monash University, Clayton, VIC 3800, Australia. E-mail: [email protected]

Sports Med 2011; 41 (1)

Sports Med 2011; 41 (1): 73-86 0112-1642/11/0001-0073/$49.95/0

REVIEW ARTICLE


Sex Hormone Effects on Physical Activity Levels Why Doesn’t Jane Run as Much as Dick? Robert S. Bowen,1,2 Michael J. Turner1 and J. Timothy Lightfoot1,3 1 Department of Kinesiology, University of North Carolina Charlotte, Charlotte, North Carolina, USA 2 Human Nutrition, Foods and Exercise, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA 3 Department of Health and Kinesiology, Texas A&M University, College Station, Texas, USA

Contents Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Brief Overview of the Biochemistry of Sex Hormones. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Do the Sex Hormones Regulate Physical Activity Levels in Humans? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Are Females and Males Differentially Active? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Is there a Difference between Activity Levels Before and After Menopause? . . . . . . . . . . . . . . . . 2.3 Does Physical Activity Change Across the Menstrual Cycle? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Do Hormone Replacement Therapies or Pharmacological Interventions Alter Activity Levels? . . 2.4.1 In Females. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 In Males . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Are Research Design Issues the Reason for Species Differences in Sex Hormone Effects in Rodents and Humans? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Appropriateness of Rodents as Models of Human Physiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Independent versus Dependent Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Use of Surveys to Determine Physical Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Mode of Hormone Therapy Used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Abstract

73 75 75 77 77 78 78 78 80 81 81 82 82 83 84

The relationship between physical activity levels and disease rates has become an important health-related concern in the developed world. Heart disease, certain cancers and obesity persist at epidemic rates in the US and Western Europe. Increased physical activity levels have been shown to reduce the occurrence of many chronic diseases leading to reductions in the burden on the healthcare system. Activity levels in humans are affected by many cultural and environmental factors; nevertheless, current research points to a strong biological input with potential genetic, neurological and endocrinological origins. Of unique interest, the sex hormones appear to have a very strong influence on activity levels. The current animal literature suggests that females tend to be more active than males due to biological pathways of estrogenic origin. The majority of human epidemiological and anthropological data, on the contrary, suggest women are less active than men in spite of this inherent activity-increasing mechanism.

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The purpose of this study is to review the current literature regarding the control of physical activity levels by the sex hormones in humans. Using the natural transitional phases of the aging endocrine system, natural periodicity of the menstrual cycle and pharmacological/hormone replacement therapy as variable experimental stages, some authors have been able to provide some information regarding the existence of an inherent activity-increasing mechanism in humans. In brief, activity levels during life stages prior to and after menopause do not significantly differ, despite the vast changes in sex hormone levels and function. Sex hormone differences throughout a regular menstrual cycle do not appear to influence activity levels in humans either; an effect that is pronounced in the female rodent. The use of hormone replacement therapies provides researchers with more systematic controls over hormone modulation in human subjects; however, this benefit comes with additional confounding variables, mostly due to disease or other states of malfunction. Despite the addition of these confounding factors, minor changes to the activity pattern have been observed in women, especially during the initial administration of the therapy. Observations are yet to be made in male subjects during replacement therapy. In general, some evidence exists suggesting that a biological mechanism extending from the sex hormones influences activity in humans. Unfortunately, despite a small number of investigative reports, the paucity of human research investigating how the sex hormones affect activity levels in humans prevents conclusive delineation of the mechanisms involved. Future research in this unique sub-field of endocrinology and exercise science utilizing more appropriate research protocols and effective techniques will provide definitive evidence of such mechanisms.

It is axiomatic that the daily accumulation of physical activity will offset many different health conditions and will lead to increased quality and quantity of life. However, there are stark differences in the amount of regular physical activity that human males and females complete with little scientific explanation for these differences. While the regulation of physical activity has long been thought of as voluntary and/or influenced solely by environmental factors, there is a growing body of evidence suggesting that physical activity, defined as movement that is not forced upon the individual, is at least partially regulated by biological factors.[1-11] These biological regulating factors may take many forms, including an increase or decrease of various physiological substances or structures, and/or genetics that may fundamentally alter receptor/protein interaction in the intact organism.[6,7,9] While beyond the scope of this review, extensive animal literature has shown that sex of the individual significantly ª 2011 Adis Data Information BV. All rights reserved.

influences physical activity patterns by working through various sex-related hormonal pathways.[12] Biologically, sex hormones play a large role in regulating various physiological parameters; thus, they have naturally been the subjects of investigations trying to elucidate the possible roles they play in regulating physical activity in animals. However, currently, the role that sex hormones play in regulating human activity has been largely unexplored, probably due to the unappreciated role that biological factors play in regulating ‘voluntary’ activity. The increasing rate of cardiovascular and other hypokinetic diseases in women[13] make understanding the mechanisms regulating physical activity in human females critical in the context of the health-related goals of our society. Thus, the focus of this article is to review the literature investigating the effect of and the possible physiological mechanisms through which the various sex hormones regulate physical activity in humans. Sports Med 2011; 41 (1)

Sex Hormone Effects on Physical Activity Levels

For the purpose of this review, physical activity level will be considered as the total amount of activity an individual accomplishes during the course of a day that not only includes formal exercise, but movement that is associated with activities of daily living (e.g. stair climbing, yard work, etc.). This operational definition includes estimates of physical activity level based on energy expenditure levels that are corrected for body mass. Whereas males of most species generally have larger masses than females, presenting energy expenditure data as physical activity levels without correcting for mass biases the estimates towards males, regardless of total daily activity. The literature for this review was identified using standard literary search techniques. The PubMed database was searched across all years covered within the database using multiple search terms including: ‘sex or gender difference’, ‘sex steroids’, ‘sex hormones’, ‘physical activity’, ‘activity’, ‘physical activity levels’, ‘locomotion’, or ‘exercise’. The uncovered literature was then evaluated and only studies investigating the effects of sex on activity in which activity was treated as the dependent variable were accepted for review. Furthermore, articles investigating the extrinsic and/or environmental influences on activity were not included in the present review. Thus, the current review focuses on the effect that sex has on physical activity levels in humans and is not intended to be an exhaustive review of the regulatory mechanism underlying general physical activity levels in humans. 1. Brief Overview of the Biochemistry of Sex Hormones The biochemistry of sex hormones is well understood and is summarized in figure 1. Sex hormones primarily consist of androgens (testosterone) and estrogens (estradiol). Both are primarily derived from progestins (progesterone) or from pregnenolone via dehydroepiandrosterone, androstenedione and androstenediol, which are formed from cholesterol upon stimulation by adrenocorticotropic hormone. While the primary sex hormones differ between males (testosterone) and females (estrogen) as do the primary sites of synthesis ª 2011 Adis Data Information BV. All rights reserved.

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(testes for male; ovaries for female), quantities of testosterone and estrogen occur in both sexes. Testosterone is also an intermediate substance in the formation of estrogen in both males and females. Through an aromatization process using the aromatase enzyme complex, testosterone is converted to 17b-estradiol; this conversion is not reversible. In males, while some testosterone is converted to estrogen, the majority of testosterone is converted into dihydrotestosterone, which cannot be aromatized into estrogen. While testosterone concentration exhibits minor variations on a daily basis in healthy adult males (»6–10 ng mL-1), estrogen and progesterone exhibit cyclical peaks in females, with estrogen peaking at approximately 200–300 pg mL-1 at day 12 of the menstrual cycle (during the ‘follicular’ phase) then drops to approximately 100–150 pg mL-1 during the early ‘luteal’/late follicular transition. A progesterone peak of 8–10 ng mL-1 occurs at approximately day 20 of the cycle and is coupled with a concurrent rise in estrogen to approximately 150–200 pg mL-1 (during the luteal phase). The hormonal fluctuations evident in females are characteristic of healthy adult females and are not typically seen in prepubescent girls or menopausal women.

2. Do the Sex Hormones Regulate Physical Activity Levels in Humans? The available animal literature (see Lightfoot[12] for a current review) strongly suggests the presence of a physical activity regulating mechanism centered around sex hormone physiology. In rodents, wheel running is reduced after surgical/pharmacological gonadectomy and is increased after hormones are reintroduced via capsules or injections. The necessary ethical limitations of research in human subjects have limited the use of such experimental manipulations, leading to a reduced understanding of the biological mechanisms present in humans contributing to physical activity regulation. Through use of natural (due to sex, aging and the menstrual cycle) and artificial (pharmacological and hormone replacement therapies) changes in clinical populations, some research has been compiled to evaluate the extent Sports Med 2011; 41 (1)

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HO

Cholesterol

CH3 C

CH 3 C

O

O

O OH

HO

HO

Pregnenolone

HO

17α-OH-Pregnenolone

CH3 C

Dehydroepiandrosterone

CH3 O

C

O

O

OH

O

OH

OH

Aromatase O

O

Progesterone

O

HO

HO

Androstenedione

17α-OH-Progesterone Progestins

Estriol

Estrone

OH

OH

HO

Testosterone

17β-Estradiol

Estrogens

Androgens Fig. 1. Basic pathways for sex hormone biochemistry in mammalian species. The general sex hormone classes are grouped in dashed boxes. Double-headed arrows indicate reversible reactions; single-headed arrows indicate one-way reactions.

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O


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to which sex hormones regulate activity in the human population.

Males Females 70

2.1 Are Females and Males Differentially Active?

60


Adults (%)

50 40 30 20 10

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ta

se be

07

(o

20

01 BR

FS

S

20 S FS BR

01 20 S FS

)

t) gh ei rw ve

(o

20 S FS

BR BR

H

AN

ES

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(to

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If sex hormones play a role in the regulation of daily activity patterns, it is appropriate to hypothesize that male and female activity patterns would differ, especially given the cyclical nature of sex hormones in females. The extensive animal literature[12] shows that female rodents, in general, are more active on a daily basis than male rodents, regardless of the measurement used. Interestingly, the majority of literature investigating this question in humans shows that human females, whether child, adolescent or adult, are less active on a daily basis than males. Pate and colleagues,[14] in an extensive review of physical activity in adolescents, showed that female children and adolescents were generally less active than males. This trend continues into adulthood; figure 2 represents findings from the 2001[15,16] and 2007[17] US Behavioral Risk Factor Surveillance Survey (BRFSS). In 2001, over 200 000 individuals were surveyed and showed that a greater percentage of males met physical activity recommendations for moderate and/or vigorous exercise than adult females. The 2001 survey was limited to exercise activity, and thus was limited in its ability to identify energy expenditure extending from tasks of daily living. The 2007 dataset was expanded to include over 400 000 subjects and additional questions were added to better identify activities of daily living habits. The 2007 survey identified an even larger activity gap between the sexes in normal weight, overweight and obese adults. Recent accelerometer data (figure 2) from Troiano et al.[18] from the 2003–4 National Health and Nutrition Examination Survey (NHANES) show the same qualitative pattern (i.e. males were slightly more active than females) but compared with the BRFSS data, show a significantly lower number of individuals that actually complete moderate activity. The same male-female differential activity pattern has also been observed in the non-technical Old Order Amish culture[19] as well as in several hunter/gatherer cultures (figure 3 shows the Nunõa and Tamang being notable ex-

Fig. 2. Percentage of US adults meeting moderate/vigorous activity recommendations. The total data include all subjects, overweight data include subjects with a body mass index (BMI) between 25.0 and 29.9 kg/m2, and obese data include subjects with a BMI ‡30.0 kg/m2. Data from US 2001[15,16] and 2007[17] Behavioral Risk Factor Surveillance Survey (BRFSS) and the 2003–4[18] National Health and Nutrition Examination Survey (NHANES). BRFSS data are presented as the whole dataset[15,17] and subdivided by weight status.[16]

ceptions), indicating that, in general, regardless of culture, males are more active than females.[20,21] Therefore, in contrast to the well established rodent literature, the majority of the available literature suggests that human males are more active than human females.[12] The causes of differential activity levels in humans are not entirely known; however, measurement of activity levels surrounding naturally or artificially induced changes in sex hormone concentrations may enhance our understanding of the sexually differentiated activity pattern. 2.2 Is there a Difference between Activity Levels Before and After Menopause?

The transition in female humans at menopause allows activity to be compared under two very different hormonal conditions. Prior to menopause, Sports Med 2011; 41 (1)

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Males Females 8 7

Activity (h • day −1)

6

complicates this comparison. Thus, while the literature is limited, the use of both cross-sectional and longitudinal studies measuring activity levels preand postmenopause is certainly warranted. 2.3 Does Physical Activity Change Across the Menstrual Cycle?

5 4 3 2 1 0 Nuñao

Kaul

Lufa

Ju/'hoansi Tamang

Hunter/gather culture Fig. 3. Hours per day of moderate/vigorous activity in hunter/ gatherer cultures. Data for the Nunõa, Kaul, Lufa and Ju/’hoansi populations are from Leslie et al.[20] and data for the Tamang population are from Panter-Brick.[21] Note: the Ju/’hoansi people are referred to as !Kung in the article[20] from which these data were taken. This terminology refers to the language spoken by the Ju/’hoansi people and is now considered offensive and derogatory.[22]

the sex hormones circulate in higher concentrations compared with the concentrations experienced after menopause. Unfortunately, the scientific literature has yet to address this question directly or in sufficient detail. In one indirect study, Dorn et al.[23] investigated breast cancer risk in pre- and postmenopausal women with and without breast cancer. As a component of this analysis, the researchers asked the subjects to recall their strenuous physical activity patterns during the previous 2 years. As a result of their illness, the patients with breast cancer likely had activity patterns that were deviant from normal and therefore were not considered for this review. Unfortunately, statistical comparisons were not made between the two control groups to directly evaluate pre- and postmenopause activity levels; however, these subjects estimated their yearly strenuous physical activity to be 77.3 – 127.8 h y-1 in women prior to menopause and 68.4 – 176.7 h y-1 in women after menopause.[23] While it is tempting to infer a reduction in activity following the decline in hormone concentration after menopause, the considerable variation in the activity measurement and the varying ages of the subjects significantly


Examination of activity patterns in eumenorrheic women provides a hormonal concentration milieu that is unique. Tworoger and colleagues[24] reported the results of a carefully crafted correlation study that used data from 565 premenopausal women (aged 33–52 years) who were originally surveyed in the Nurses’ Health Study II. The investigators measured a wide variety of sex hormones in blood samples taken during the luteal and follicular phases of the menstrual cycle in woman who varied in the amount of physical activity preformed during a typical week as gathered through self-report questionnaires. These authors noted that while in general there were no strong associations between physical activity and testosterone, progesterone or follicular estradiol levels, there was an inverse association (p = 0.05) between activity and luteal estrogen levels. Unfortunately, interpretation of any relationship between activity levels and estrogen levels in this study is difficult because body fat levels were lower in those women who were more active. Given that adipose tissue can increase estrogen levels,[25] the lower body fat levels of the active women could have confounded the negative relationship between activity levels and estrogen levels in this study. 2.4 Do Hormone Replacement Therapies or Pharmacological Interventions Alter Activity Levels? 2.4.1 In Females

As noted in section 2, there is extensive rodent literature suggesting that estrogen in particular, but also testosterone,[12] increases activity levels via activation of the estrogen a-receptor (ERa) pathway leading to downstream regulation of other physiological structures, potentially in the brain. While limited data exist to answer the question of whether this is also true in humans, the studies that have been done have addressed Sports Med 2011; 41 (1)


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the effect of hormone replacement therapy (HRT) on physical activity in women. Using a correlation study design, Andersen et al.[26] observed that in the NHANES III data, women who had never used HRT reported higher levels of inactivity (40%) compared with those women who had used HRT (28.5%), suggesting that women who had not used HRT (decreased estrogen) were more likely to be sedentary (decreased activity) when compared with those women that had used HRT. While supporting the hypothesis

of sex hormone effects on activity levels, a major limitation of the NHANES III data was that it surveyed leisure time activity alone without regard for activities of daily living. Thus, any women who did not report any leisure time activity were classified as sedentary/inactive in spite of potential vigorous activity completed as part of their daily lives. Another research design issue with these data was the lack of control for the type of HRT or delivery method used (e.g. oral or transdermal; see section 3.4 for further consideration). Baseline E2 E2 + P

b

a 15 000 Activity level (kJ • 24 h−1)

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70−80 Age of subjects (y)

Baseline 12 mo 24 mo 36 mo

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125 PASE total score

Never used HRT (n = 90)

100 75 50 25 0 Placebo

Low-dose E2 HRT used

Fig. 4. The effect of hormone replacement therapy (HRT) on physical activity: (a) shows the lack of difference in physical activity estimates amongst the HRT and non-HRT groups (data from Redberg et al.[27]); (b) shows the lack of an effect of HRT (both estrogen [E2] and E2 plus progesterone [E2 + P] treatments) on estimated activity levels in younger menopausal (aged 45–55 years) and older menopausal (aged 70–80 years) subjects (data from Anderson et al.[28]); (c) shows the lack of a difference in estimated physical activity between low-dose E2 and placebo over a 36-month period (data from Kenny et al.[29]). Mets = metabolic equivalents; PAL = physical activity level; PASE = Physical Activity Scale in the Elderly; * indicates a significant difference from placebo treatment at same time period.


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Redberg et al.[27] stratified 248 post-menopausal women based on their HRT use and interviewed them regarding their physical activity, with physical activity levels estimated as metabolic equivalent hours per week (figure 4a). The physical activity data were analyzed across the following four different groups (each subject was included in one or more of the experimental groups): those women currently receiving HRT (n = 108) versus those not currently receiving HRT (n = 140) and those women who were currently or had been receiving HRT (n = 158) versus those who had never used HRT (n = 90). A total of 94% of the HRT subjects (n = 101) used oral estrogen therapy, while transdermal estrogen was used in 6% of the subjects (n = 7). Of the group currently using HRT, 48% were receiving combined estrogen/ progestin supplementation with the composition of the HRT for the remainder of the subjects not noted. Redberg and colleagues[27] reported large variability in physical activity in each group (figure 4a) with no significant differences between any of the groups, thus concluding that activity was not affected by HRT status. Anderson et al.[28] conducted a repeated measures trial on younger post-menopausal (45–55 years old, n = 18) and older post-menopausal (70–80 years old, n = 15) women investigating the effect of transdermally applied estrogen and estrogen plus vaginally applied progesterone supplementation on a variety of energy balance measures with a 2-month washout period between treatments. This study was unique in that the estrogen and progesterone treatments were carefully titrated to result in near physiological circulating levels of both estrogen (76.89 – 4.43 to 80.50 – 5.43 pg mL-1) and progesterone (7.09 – 0.91 ng mL-1). Furthermore, the subjects were encouraged not to lose weight while in the experimental programme. Figure 4b shows that Anderson and colleagues[28] observed no differences in physical activity level as measured by survey techniques across the treatments, thus supporting the lack of an effect of HRT on activity levels. Kenny and colleagues[29] completed a 3-year, double blind, placebo-controlled trial investigating the effect of estrogen therapy on various muscle and physical functions in 167 older post-


menopausal women (average age at baseline »74 years). The women were randomly assigned to either an estrogen treatment group (0.25 mg oral 17b-estradiol) or a placebo group (figure 4c). Physical activity was estimated at the beginning of the study and then once per year for the 3-year trial using the Physical Activity Scale in the Elderly (PASE). Kenny et al.[29] reported that while the baseline activity scores were different between the groups, the rate of decrease in activity during the 3 years was the same in all of the groups. Poehlman et al.[30] published a purported 6-year controlled longitudinal study of HRT and energy balance that supposedly showed an increase in leisure time activity with HRT. Unfortunately, this article was subsequently retracted from the literature when the author admitted to fabricating the data in the study.[31-33] 2.4.2 In Males

Two possible avenues could be used to artificially manipulate sex hormone levels in males in order to evaluate effects on activity. In one approach, the effects of antiandrogens (e.g. flutamide), which are often prescribed to individuals at risk of or suffering from prostate cancer, as well as those suffering from a severe sexual disorder, on activity could be considered. However, there have been no published investigations on the effects of antiandrogens on physical activity. A second avenue would be to consider the effects of suprapharmacological doses of androgens on activity. While there are anecdotal statements of hyperactivity in individuals taking suprapharmacological doses of androgens,[34] there have been no studies that directly or indirectly measure physical activity levels in these subjects and possible changes with androgen supplementation. There is a large body of literature concerning aggression with androgen supplementation in both men and women. Unfortunately, using the data on androgen supplementation and aggression to infer possible linkages between androgen supplementation and physical activity levels is difficult. This is a result of the possible confounding effects of the various psychological states occurring with suprapharmacological doses of androgens and the uncertainty regarding gonadal sufficiency of the subjects. Sports Med 2011; 41 (1)


The human data available regarding the sex hormone effects on activity are sparse and that which exists only addresses female subjects. While the female correlation study data are split, with one study showing a positive relationship between lack of HRT and inactivity,[26] and one showing no relationship,[24] all of the available prospective studies[27-29] do not support the contention that estrogen administration alters physical activity. These studies are strong from the standpoint that in each case, total daily physical activity levels were estimated as opposed to estimation of just leisure time activities as was the case with the NHANES III data.[26] However, definitive conclusions are difficult to reach due to several possible confounding research design factors. 3. Are Research Design Issues the Reason for Species Differences in Sex Hormone Effects in Rodents and Humans? Animals, especially rodents, have been used to model phenotypes or physiological phenomenon in humans with reasonable success. However, as has been discussed previously, the animal data show conclusively that sex hormones, specifically estrogen, exert powerful effects on daily activity, while the limited human data suggest that sex hormones play no role in the biological regulation of activity. Thus, the species difference in sex hormone effects on activity requires the consideration of several factors, including (i) appropriateness of the animal model; (ii) how dependency is assigned to variables; (iii) whether rodent modes of activity directly translate to the measurement of activity in humans; and (iv) how the hormones are replaced or introduced to the organism. These factors help to outline the limitations of animal to human translation in physical activity level research and provide a foundation for future investigations in both humans and animal models. 3.1 Appropriateness of Rodents as Models of Human Physiology

Any consideration of species differences, especially differences between rodents and human models, must begin with questioning the approª 2011 Adis Data Information BV. All rights reserved.

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priateness of using rodents to investigate human health questions. A variety of scientific organizations recognize the usefulness of rodents in modelling human physiology, especially with less than 1% of the mouse genome differing significantly from the human genome.[35] Specifically, rodents are used frequently as models of human sex hormone physiology; in 2006 and 2007 the National Library of Medicine listed 881 articles that were published concerning mice and reproductive hormones. For example, rodent models have been used extensively to document the role of the estrogen receptors ERa and ERb in human cardiovascular physiology.[36] It is reasonable to suggest that biological data collected in rodents can be applied to human physiology; however, the multifactorial nature of human behavioural traits complicates the translation between species. Environmental factors affect human and rodent behavioural traits differently; therefore, the extension of results from rodents to humans requires extensive caution. Evidence of an animal decision-making capacity based on a free will directive is limited, but the existence of such a mechanism in humans may impose extensive complexity on the outward expression of a behavioural trait. The decision to be physically active or physically inactive may invariably result from decisions made in response to the free will input, thus, the lack of a free will directive in animals may explain the differences in volume of activity performed between high activity rodents and low active humans. However, the existence of free will is insufficient to explain why an apparent bias exists between human males and females. It is difficult to impute that a sex bias affects free will directives in humans; that is to say, that one sex is more proficient at utilizing or abiding by their free will directive. A more likely explanation is that extrinsic variables, free will included, are subject to pressures exerted by biological inputs, which may override the effects of the extrinsic stimuli. Role dynamics and societal structure may also influence behavioural characteristics in humans differently then in rodents. In the study by Bassett et al.[19] variability in the amount of activity performed between the sexes may be explained Sports Med 2011; 41 (1)

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by the volume or the workload that the typical male- and female-associated tasks require. Males performed more labour-intensive activities in this cultural group and thus the appearance of the male activity bias is a byproduct of societal workload expectancies rather than the sex hormones. Uniquely, however, a bias for higher activity levels in males exists in indigenous people groups known to follow societal organizational principles quite different from Western society. The Ju/’hoansi exhibit a similar male activity bias to that observed in Western cultures, but follow egalitarian principles.[22] The male bias is not present in the overall activity levels of the egalitarian Tamang population; however, corrections for basal metabolic rate or activity intensity (i.e. outdoor subsistence activities elevate energy demand compared with indoor activities and lead to a higher energy expenditure in the individuals performing the outdoor activities) instill a male bias in this population.[21] Clearly, human complexity, extending from cultural, environmental and emotional stimuli, is an important consideration when detailing the effectiveness of an animal model to represent behavioural traits in humans. From the perspective of physical activity, it appears that the effects of these extrinsic factors are likely to be limited. Furthermore, the increasing rate of physical inactivity in the US, despite a plethora of literature examining the effects of extrinsic factors on human activity levels, provide additional impetus that the biological regulation of activity levels remain of intransient importance. Indeed, differences exist between the modelled and actual response, but the discrepancies in the animal and available human literature may exist because of methodological rather than model representation issues. 3.2 Independent versus Dependent Variables

Another factor that confounds the determination of whether sex hormones play a causative role in the regulation of daily activity in humans is experimental design issues, especially the assignment of independent/dependent variable relationships. While the animal literature has primarily studied the effect of sex hormones (i.e. independent variable) on physical activity (i.e. dependent variable), the ª 2011 Adis Data Information BV. All rights reserved.

majority of research in the human literature has approached physical activity as the independent variable, with sex hormone levels treated as dependent variables. This approach is somewhat understandable given the belief that activity levels were controlled by voluntary directives and not the product of biological factors, and, thus, could not be affected by biological manipulation. With mounting evidence that ‘voluntary’ physical activity levels may actually be driven to a significant extent by biological/genetic factors,[6,7,9,37] it becomes important to understand the factors, such as sex hormones, that regulate daily activity. The understanding that there are discrete biological/genetic factors that drive ‘voluntary’ physical activity calls for further carefully controlled studies (in addition to the three studies currently available[27-29]), where activity level is the ‘dependent variable’ with hormone level/supplementation as the ‘independent variable’. This approach would allow further understanding in the human model of the role of sex hormones – much as has been done in the animal literature. 3.3 Use of Surveys to Determine Physical Activity

The use of surveys or questionnaires to estimate physical activity levels in all of the current human studies limits conclusions. Numerous reviews (e.g. see Shephard[38]) have observed that survey estimations of physical activity generally overestimate actual activity levels. This was reinforced recently by Troiano and colleagues[18] when they measured daily activity in a large population using accelerometers and found only a small percentage (3.5% – 0.3%) of adults completed moderate activity on a daily basis compared with other survey estimates of adult activity levels (e.g. 45.4% – 0.2% in Macera et al.[15]). As noted in an extensive review of the reliability, validity and sensitivity of physical activity questionnaires,[38] correlation coefficients between measurement of physical activity (e.g. doublylabelled water or accelerometers) and questionnaire results vary widely with few rising above 0.60 and with the questionnaires generally overestimating activity levels. Specifically, two factors that often lead to the least valid estimates of activity are the Sports Med 2011; 41 (1)


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length of time separating the recall from the activity and individuals who have lower levels of activity.[38] It is difficult to determine the possible effect of these two confounding factors on the conclusions of the currently available prospective HRT studies,[28,29] given that none listed the recall period that was used in their studies, even though the instruments used by Kenny et al.[29] and Anderson et al.[28] were designed to estimate daily activity based on a 7-day recall period. Therefore, to eliminate the possible confounds of using survey/questionnaire approaches that grossly overestimate daily activity, the use of direct measures of activity, such as was done in the NHANES 03–04 study,[18] would considerably strengthen the investigation of sex hormone effects on daily activity levels.

tern of sex hormone concentration over the course of a 24-hour period. The use of transdermal administration provides a more stable plasma concentration of the hormone, but to this point, only one of the prospective studies[28] used transdermal administration and verified constant physiological concentrations of the sex hormones in their subjects. Additionally, other than the Anderson et al.[28] study, no study has delineated the type of estrogen/ progesterone used in prospectively assessing the effect of sex hormones on activity. Both the ERa and ERb preferentially bind to certain estrogenic substrates. As noted in section 2.4.1, the ERa pathway has been shown to be the primary pathway involved in physical activity regulation. With different estrogens and progestins used in common HRT formulations[39] and the hypothesized activity inducing actions of the ERa pathway,[12] it is possible that different HRT formulations produce different physical activity responses solely due to the lack of a compound with the necessary ability to bind to the ERa. Thus, while the limited human data suggest that female activity patterns are not influenced by sex hormones, given the methodological limitations present, this conclusion is tentative at best and misleading at worst. Future studies controlling

3.4 Mode of Hormone Therapy Used

The method of hormone delivery can significantly influence the circulating concentrations of sex hormone.[39] For example, plasma concentrations of orally delivered hormone reaches peak physiological levels approximately 3 hours after administration and then declines rapidly,[39] exposing the individual to a non-physiological pat-

Physical activity level Sex Biological (−) Progesterone

Central

(+) Estrogen

Peripheral

(+) Dopamine (+?) NP-Y (?) Serotonin

(?) Other factors

(+) GLUT-4

(+) Testosterone

Genetic regulation Fig. 5. Hypothesized avenues of biological regulation resulting in differential controls of physical activity in male and female humans. Doubleheaded arrows indicate potential two-way influences. GLUT-4 = glucose transporter type 4; NP-Y = neuropeptide Y; ? indicates effects that are uncertain due to minimal or conflicting research.


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the type and concentration of hormone replacement, the use of research designs that recognize that activity levels are influenced by biological factors, and the direct measurement of daily activity will provide further information to support or refute this hypothesis. However, while extremely speculative, if we assume that the current human data are representative of the human activity response to sex hormones, the question of why there is a species difference in the activity patterns of female rodents versus humans remains enigmatic. 4. Summary and Conclusions There is no doubt that increasing physical activity levels in adult populations, both nationally and internationally, should be a health-related priority, and the fact that general levels of physical activity in both adults and children are less than optimal (e.g. see Pate et al.[14] and Troiano et al.[18]) is troubling. Decades of activity promotion and research have not produced a noticeable reduction in the number of inactive individuals. Thus, these data present a clarion call for a greater understanding of the underlying mechanisms regulating voluntary daily activity and the roles that biological/genetic factors play in activity regulation (see figure 5 for a hypothetical schematic of the biological regulation of activity). The plethora of epidemiological evidence that suggests that human females, in most cases, are less active than males, along with increasing rates of cardiovascular disease in women,[13] provides additional impetus for understanding all factors involved in activity regulation. In spite of the strong animal literature that suggests a probable physiological mechanism of activity regulation by sex hormones,[12] human females are still generally less active than males. Thus, the question becomes and remains as to whether the environmental/cultural influences on physical activity override the increased physical activity drive inherent in women. While there is some literature suggesting differential environmental, cultural and psychological effects on activity levels between the sexes, there is no literature available investigating the interaction of these factors and biological effects ª 2011 Adis Data Information BV. All rights reserved.

on activity. Further study is warranted that specifically sets hormone concentration as an independent variable. Ideally, experimental modulation of hormone levels in a wider variety of individuals is necessary to completely understand the influence that hormone levels have on activity. Such studies should capitalize on current therapeutic intervention used by medical professionals to manage endocrine-related diseases and disorders, including birth control administration, testosterone treatments and HRT. The naturally occurring variation of hormone levels in both males and females throughout the life cycle, during distinct physiological conditions such as the luteal and follicular phases of the menstrual cycle, and between individuals may also add appropriate and measurable variability that allows independence to be assigned to hormone concentration. Additionally, the more reliable estimation of activity achieved through use of accelerometers and other quantifiable forms of locomotion measurement should be utilized in place of traditional survey methods. The clinical implications of expanding this research are potentially far reaching. Currently, a large body of literature exists regarding how extrinsic and environmental factors influence physical activity levels in humans. Unfortunately, beneficial alterations to health indicators are minimal, and physical inactivity and related conditions remain disproportionately high. Thus, physical activity management strategies and research agendas that focus solely on environmental or extrinsic factors remains shortsighted. The overall goal of physical activity-sex hormone interaction research should be to further delineate biological pathways and identify enzymes/proteins/ biomolecules involved in upregulating activity levels in humans. In doing so, the medical community may in fact find ways in which to capitalize on these biomolecules and pathways leading to the promotion of physical activity and increased beneficial health outcomes in the general population. Acknowledgements The authors would like to thank the editing and proofreading comments of the Kinesiology Writing Accountability

Sports Med 2011; 41 (1)


Klatch: Drs T. Hubbard, S. Tsivitse and M. Cordova, as well as suggestions for studies and verbiage to include in this review by A.M. Knab. This review was supported by funding from NIH NIAMS RO1AR050085. The authors have no conflicts of interest that are directly relevant to the content of this article.

References 1. Festing MF. Wheel activity in 26 strains of mouse. Lab Anim 1977 Oct; 11 (4): 257-8 2. Joosen AM, Gielen M, Vlietinck R, et al. Genetic analysis of physical activity in twins. Am J Clin Nutr 2005 Dec; 82 (6): 1253-9 3. Kaprio J, Koskenvuo M, Sarna S. Cigarette smoking, use of alcohol, and leisure-time physical activity among samesexed adult male twins. Twin research 3: epidemiological and clinical studies. Prog Clin Biol Res 1981; 69 (Pt C): 37-46 4. Lauderdale DS, Fabsitz R, Meyer JM, et al. Familial determinants of moderate and intense physical activity: a twin study. Med Sci Sports Exerc 1997 Aug; 29 (8): 1062-8 5. Lerman I, Harrison BC, Freeman K, et al. Genetic variability in forced and voluntary endurance exercise performance in seven inbred mouse strains. J Appl Physiol 2002 Jun; 92 (6): 2245-55 6. Lightfoot JT, Turner MJ, Daves M, et al. Genetic influence on daily wheel running activity level. Physiol Genomics 2004 Nov 17; 19 (3): 270-6 7. Lightfoot JT, Turner MJ, Pomp D, et al. Quantitative trait loci for physical activity traits in mice. Physiol Genomics 2008 Feb 19; 32 (3): 401-8 8. Perusse L, Tremblay A, Leblanc C, et al. Genetic and environmental influences on level of habitual physical activity and exercise participation. Am J Epidemiol 1989 May; 129 (5): 1012-22 9. Stubbe JH, Boomsma DI, De Geus EJ. Sports participation during adolescence: a shift from environmental to genetic factors. Med Sci Sports Exerc 2005 Apr; 37 (4): 563-70 10. Stubbe JH, Boomsma DI, Vink JM, et al. Genetic influences on exercise participation in 37 051 twin pairs from seven countries. PLoS One 2006; 1: e22 11. Turner MJ, Kleeberger SR, Lightfoot JT. Influence of genetic background on daily running-wheel activity differs with aging. Physiol Genomics 2005 Jun 16; 22 (1): 76-85 12. Lightfoot JT. Sex hormones’ regulation of rodent physical activity: a review. Int J Biol Sci 2008; 4 (3): 126-32 13. Ford ES, Capewell S. Coronary heart disease mortality among young adults in the U.S. from 1980 through 2002: concealed leveling of mortality rates. J Am Coll Cardiol 2007 Nov 27; 50 (22): 2128-32 14. Pate RR, Long BJ, Heath G. Descriptive epidemiology of physical activity in adolescents. Pediatr Exerc Sci 1994; 6 (4): 434-47 15. Macera CA, Ham SA, Yore MM, et al. Prevalence of physical activity in the United States: behavioral risk factor surveillance system, 2001. Prev Chronic Dis 2005 Apr; 2 (2): A17 16. McCracken M, Jiles R, Blanck HM. Health behaviors of the young adult U.S. population: behavioral risk factor surveillance system, 2003. Prev Chronic Dis 2007 Apr; 4 (2): A25 17. Centers for Disease Control and Prevention (CDC). Prevalence of self-reported physically active adults: United States, 2007. MMWR Morb Mortal Wkly Rep 2008 Dec 5; 57 (48): 1297-300


85

18. Troiano RP, Berrigan D, Dodd KW, et al. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc 2008 Jan; 40 (1): 181-8 19. Bassett DR, Schneider PL, Huntington GE. Physical activity in an old order Amish community. Med Sci Sports Exerc 2004 Jan; 36 (1): 79-85 20. Leslie PW, Bindon JR, Baker PT. Caloric requirements of human populations: a model. Hum Ecol 1984; 12 (2): 137-62 21. Panter-Brick C. Seasonal and sex variation in physical activity levels among agro-pastoralists in Nepal. Am J Phys Anthropol 1996 May; 100 (1): 7-21 22. Lee R. The Dobe Ju/’Hoansi (case studies in cultural anthropology). 3rd ed. Belmont (CA): Wadsworth Publishing, 2003 23. Dorn J, Vena J, Brasure J, et al. Lifetime physical activity and breast cancer risk in pre- and postmenopausal women. Med Sci Sports Exerc 2003 Feb; 35 (2): 278-85 24. Tworoger SS, Missmer SA, Eliassen AH, et al. Physical activity and inactivity in relation to sex hormone, prolactin, and insulin-like growth factor concentrations in premenopausal women-exercise and premenopausal hormones. Cancer Causes Control 2007 Sep; 18 (7): 743-52 25. Yang K, Khalil MW, Strutt BJ, et al. 11 Beta-hydroxysteroid dehydrogenase 1 activity and gene expression in human adipose stromal cells: effect on aromatase activity. J Steroid Biochem Mol Biol 1997 Feb; 60 (3-4): 247-53 26. Andersen RE, Crespo CJ, Franckowiak SC, et al. Leisuretime activity among older U.S. women in relation to hormone-replacement-therapy initiation. J Aging Phys Act 2003; 11 (1): 82-9 27. Redberg RF, Nishino M, McElhinney DB, et al. Long-term estrogen replacement therapy is associated with improved exercise capacity in postmenopausal women without known coronary artery disease. Am Heart J 2000 Apr; 139 (4): 739-44 28. Anderson EJ, Lavoie HB, Strauss CC, et al. Body composition and energy balance: lack of effect of shortterm hormone replacement in postmenopausal women. Metabolism 2001 Mar; 50 (3): 265-9 29. Kenny AM, Kleppinger A, Wang Y, et al. Effects of ultralow-dose estrogen therapy on muscle and physical function in older women. J Am Geriatr Soc 2005 Nov; 53 (11): 1973-7 30. Poehlman ET, Toth MJ, Gardner AW. Changes in energy balance and body composition at menopause: a controlled longitudinal study. Ann Intern Med 1995 Nov 1; 123 (9): 673-5 31. Sox HC. Notice of retraction: final resolution. Ann Intern Med 2005 May 3; 142 (9): 798 32. Poehlman ET. Notice of retraction: final resolution. Ann Intern Med 2005 May 3; 142 (9): 798 33. Sox HC. Notice of retraction. Ann Intern Med 2003 Oct 21; 139 (8): 702 34. Salvador A, Moya-Albiol L, Martinez-Sanchis S, et al. Lack of effects of anabolic-androgenic steroids on locomotor activity in intact male mice. Percept Mot Skills 1999 Feb; 88 (1): 319-28 35. Waterston RH, Lindblad-Toh K, Birney E, et al. Initial sequencing and comparative analysis of the mouse genome. Nature 2002 Dec 5; 420 (6915): 520-62

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36. Arias-Loza PA, Jazbutyte V, Fritzemeier KH, et al. Functional effects and molecular mechanisms of subtypeselective ERalpha and ERbeta agonists in the cardiovascular system. Ernst Schering Found Symp Proc 2006; (1): 87-106 37. Simonen RL, Rankinen T, Perusse L, et al. Genome-wide linkage scan for physical activity levels in the Quebec family study. Med Sci Sports Exerc 2003 Aug; 35 (8): 1355-9 38. Shephard RJ. Limits to the measurement of habitual physical activity by questionnaires. Br J Sports Med 2003 Jun; 37 (3): 197-206


Bowen et al.

39. Turgeon JL, Carr MC, Maki PM, et al. Complex actions of sex steroids in adipose tissue, the cardiovascular system, and brain: insights from basic science and clinical studies. Endocr Rev 2006 Oct; 27 (6): 575-605

Correspondence: Dr Robert S. Bowen, Integrated Life Sciences Building, Human Nutrition, Foods and Exercise, Virginia Polytechnic Institute and State University, 1981 Kraft Drive (0913), Blacksburg, VA 24060, USA. E-mail: [email protected]

Sports Med 2011; 41 (1)

CORRESPONDENCE

Sports Med 2011; 41 (1): 87-90 0112-1642/11/0001-0087/$49.95/0


No Evidence for the Relative Age Effect in Professional Women’s Sports The relative age effect (RAE) refers ‘‘both to the immediate participation and long-term attainment constraints in sports, occurring as a result of chronological age and associated physical differences as well as selection practices in annual agegrouped cohorts.’’[1] In one of the early attempts to comprehensively assess RAE, Musch and Grondin[2] reviewed the existing research and concluded that children born shortly before the cutoff date for annual age grouping in sport were indeed less likely to be involved in sports and therefore would be unlikely to evolve into professional athletes later in life. This phenomenon was strongly evident in sports such as hockey in Canada[3] and soccer in the UK,[4] while it was not apparent in other sports (e.g. gymnastics and American football). A recent and extensive meta-analysis published in Sports Medicine by Stephen Cobley and his colleagues[1] found that the RAE was evident across different age categories (although declining for the cohort of subjects aged ‡18 years), the skill levels (although less noticeable in the elite category) and was prevalent across many different sports (e.g. hockey, soccer and basketball). It was also determined that RAE was present for women’s sports, although its magnitude was smaller than for men. Both of these summary studies[1-2] lamented the paucity of research in the realm of women’s sports and encouraged further investigation (only 2% of the sample in the meta-analysis were female athletes). We conducted a targeted archival exploration in response to this relative lack of empirical data.[5] We focused on elite female athletes in three of the most highly competitive sports across two continents, North America and Europe. We obtained the largest sample size of female professional

athletes to date (n = 444) and compared it with past studies of RAE in elite women’s sports.[6,7] The leagues selected were chosen due to the relatively high salaries their players command, the intense competition on roster spots (partially due to the collegiate feeder system) and the prestige they espouse (the Danish Women’s Handball League [GuldBageren Ligaen] was founded in 1935 and, barring one season, is still operating successfully, without interruption, to this day). This study utilized the internet as its primary source of data gathering. Information was obtained from online rosters for teams within the Women’s National Basketball Association (WNBA) league (n = 146) during the 2009 season, the Women’s United Soccer Association (WUSA) professional soccer league (n = 104) for rosters during the 2002–3 season (the league is now defunct) and the professional GuldBageren Ligaen (n = 194) for the 2009–10 season. Each player’s day, month and year of birth was collected and tabulated. Their month of birth was then recoded into quartiles, starting with the month of September. We compared the obtained data with the equal distribution of birth dates across the four quartiles as expected based on no RAE influence (the null hypothesis). The data were also compared in half-year periods. There was no RAE in basketball, soccer or handball. In the latter league, there was a significantly greater probability of becoming a professional if the player was born in the last quartile (in stark contradiction to the RAE). There were also no significant differences in half-season analyses in any of the sports. Combined, these results suggest that there is no RAE in elite women’s sports. When repeated with only native (excluding foreign-born) players included in each league and with August as the alternative first month of age grouping, these analyses yielded identical results. With the growing popularity of women’s sports, this finding is important to consider. One possible explanation for it is that there is intense competition in youth sports in the early ages and only limited positions available, which characterizes some male sports, and this is considered one of the prime reasons behind the RAE and does

88

Letter to the Editor

not reach a sufficient level even among the most competitive women’s sports to produce RAE. Also, the earlier maturation of girls and the lower variability of the maturity status in girls in comparison to boys[8] can both explain the insignificant results. Moreover, in the GuldBageren Ligaen, professional players were more likely to be born in the last quartile. This finding stands in contrast to the RAE. In order to understand the roots of this effect, a longitudinal research design should be conducted. One possible account is resiliency: while for those young athletes who are not as physically strong in the beginning but remain in the sport are more likely to persist, the rest, once they lose their physical advantage, wither away gradually. This finding is aligned with the general trend of reduction in magnitude of RAE with age progression.[1] Future research should focus on longitudinal designs that would allow separating those variables, shedding more light on the differences between the sexes. What are the practical implications of the results? Past research[2] has advocated elaborate solutions to counter the RAE, such as determining groups based on biological age or variation of cutoff dates within the competition year. Based on the current results, sports organizations that nurture female athletes into taking their first steps in sports, and who focus primarily on producing top calibre performers, should not be concerned with the adverse impact of RAE. Nadav Goldschmied University of San Diego, San Diego, California, USA

Acknowledgements No funding was used to assist in the preparation of this letter. The author has no conflicts of interest that are directly relevant to the content of this letter.

References 1. Cobley S, Baker J, Wattie N, et al. Annual age-grouping and athlete development. Sports Med 2009; 39 (3): 235-56 2. Musch J, Grondin S. Unequal competition as an impediment to personal development: a review of the relative age effect in sport. Dev Rev 2001; 21 (2): 147-67 3. Barnsley RH, Thompson AH. Birthdate and success in minor hockey: the key to the NHL. Can J Behav Sci 1988; 20: 167-76


4. Dudink A. Birth date and sporting success [abstract]. Nature 1994; 368: 592 5. Dyer S, Raphaeli M, Goldschmied N. No evidence for the relative age effect in professional women sports [abstract no. 1225]. 118th American Psychological Association Annual Meeting; 2010 Aug 12; San Diego (CA) 6. Edgar S, O’Donoghue P. Season of birth distribution of elite tennis players. J Sport Sci 2005; 23 (10): 1013-20 7. Wattie N, Baker J, Cobley S, et al. A historical examination of relative age effects in Canadian hockey players. Int J Sport Psychol 2007; 38: 178-86 8. Baxter-Jones A, Helms P, Maffuli N, et al. Growth and development of male gymnasts, swimmers, soccer and tennis players: a longitudinal study. Ann Hum Biol 1995; 22 (5): 381-94

Authors’ Reply Relative Age Effects in Female Contexts We thank Dr Goldschmied[1] for his letter to the editor and welcome his findings, which contribute to our understanding of Relative Age Effects (RAEs). Dr Goldschmied’s data do seem to differ from hypothetical expectations (i.e. relatively older participants being overrepresented[2]), offering new insight into what now seems an increasingly complex effect. When compared with existing knowledge of male and female samples in other team sport contexts (e.g. adult female Canadian ice hockey,[3] junior representative basketball[4]), Dr Goldschmied’s observations offer contrasts by showing (i) no RAEs in basketball and soccer; and (ii) the relatively younger participants being overrepresented in handball. Whilst understanding that limited detail can be placed within a letter, prior to further discussion it would be prudent to check a few points on Goldschmied’s methods and data analysis. We assume that a common cut-off date (i.e. September) was applied throughout the relevant stages of sport participation and development, which is important because this is typically where RAEs originate. There is a suggestion that a different cut-off was applied (i.e. August) in the parallel analysis conducted with ‘local players’. It would Sports Med 2011; 41 (1)


be useful to know more about what this extended analysis included, but more importantly here, to understand why an alternative cut-off date was used as it differs to that applied for the other samples. It would also be worth considering whether or not any such cut-off dates have changed over the past 10–20 years (e.g. handball). Such changes could have influenced the current adultelite relative age player profiles. As described in previous research,[5,6] changed cut-off dates modify subsequent participation and attainment profiles. These checks will all help to discount alternative explanations for why the relatively younger were overrepresented in the context of elite handball and, at the same time, generate further confidence in the reported findings. ‘Why?’ is the fundamental question all RAE researchers ask when they encounter a disproportionate representation, or the occurrence of an enduring inequality based on being relatively older.[7] Similarly, we ask this in relation to the absence of RAEs in female US basketball and soccer, as well as the overrepresentation of relatively younger players in Danish handball (both of which may be contrary to expectations). Helpfully, Goldschmied offers plausible explanations. A reduced depth of competition, which would support universal enrolment, could account for the non-existence of RAEs in female basketball and soccer. Here, the logic is that involvement rarely reflects strict selection for few places. There is some evidence to support this notion. For example, historically RAEs were not apparent in elite women’s ice hockey.[3] However, this situation may be changing and more recent observations of an increasingly popular, more competitive game and league structure[8] suggest RAEs could have evolved in parallel with these developments. At the core of the depth of competition hypothesis appears be the issue of access. If provision (such as local school and community clubs) exceeds demand for participation, there is less of a need for some form of selection (a process that generates RAEs). RAEs may surface when participation demand exceeds local provision capacities. Dr Goldschmied suggests that the absence of RAEs in basketball and soccer could also be attributed to the earlier maturation of girls, along ª 2011 Adis Data Information BV. All rights reserved.

89

with a lowered variability of maturity status during adolescence. Indeed, there are more marked differences in physical performance among adolescent boys according to early onset or late maturation.[9] For girls, this relationship appears less consistent and/or influential on particular performance measurements.[10] This highlights the potential for performance indicators to be less confounded by variable maturation, and for selection to be less biased by the markers of differential development. Where a sport, or sporting context, values qualities other than physical dominance, RAEs may be less prevalent or even totally absent. Psychological resilience is offered as an explanation for the overrepresentation of the relatively younger players in Danish handball. This certainly has intuitive appeal if the context of Danish handball is similar to other sport development systems (e.g. those seen in Germany).[11] Here, the mechanism seems to be that relatively younger players, even though they are more likely to be later maturing, maintain participation and development despite regular exposure to developmentally related disadvantages. As a result, these individuals may acquire/develop a set of psychological and/or performance skills that help them to circumvent the RAE maturation-selection problem, and that eventually may overcome those who were initially selected for maturational differences. The withdrawal of those superseded athletes, combined with refined robust psychological coping profiles of the adherers, may ultimately provide the adherers with delayed advantages in selection,[12] performance and career longevity.[11] While each of these mechanisms is plausible, others should still be considered. Importantly, this kind of conjecture highlights the limitations of cross-sectional studies of the RAE phenomenon. As we have argued elsewhere,[13] RAE studies need to move beyond atheoretical descriptions in different contexts. We now need hypothesisdriven research that can help to develop a comprehensive model of RAEs in sport. Where RAEs are present, regardless of sex, there is a need to not only understand the underpinning mechanisms, but also trial and evaluate intervention studies aimed at resolving their presence. Just as we anticipate Sports Med 2011; 41 (1)

90

that RAEs are context specific, we expect this of the interventions too, although this remains to be confirmed. Thus, the underpinning implementation science will help the RAE field to positively influence youth participation and elite performance development. To conclude, we would like to briefly discuss the implications and generalizability of the present findings. Dr Goldschmied suggests that the absence of adverse RAEs, at least for the female sport contexts examined and their respective governing bodies, means that they should not be of direct concern. However, when referring to the handball data, we should consider why a disproportionate representation in adulthood is more acceptable when it is based, possibly, on a psychological mechanism, as opposed to a maturation-selection mechanism. That said, we also urge caution in transferring present findings to other female sport settings, particularly when such data are not available for assessment. We should keep in mind that RAEs are prevalent in certain female sport contexts, and many further contexts have yet to be examined. With possible increases in female participation rates and evolving talent development systems, RAEs could feature in the future. Such possibilities re-emphasize the value and necessity of applied research with local community settings, sport governing bodies and their sport development systems. Stephen Cobley,1 Nick Wattie,1 Joseph Baker 2 and Jim McKenna1 1

Carnegie Research Institute, Leeds Metropolitan University, Leeds, West Yorkshire, UK 2 School of Kinesiology and Health Science, York University, Toronto, Ontario, Canada



Acknowledgements No funding was used to assist in the preparation of this letter. The authors have no conflicts of interest that are directly relevant to the content of this letter.

References 1. Goldschmied N. No evidence for the relative age effect in professional women sports [letter]. Sports Med 41 (1): 87-8 2. Cobley S, Baker J, Wattie N, et al. Annual age-grouping and athlete development. Sports Med 2009; 39 (3): 235-56 3. Wattie N, Baker J, Cobley S, et al. A historical examination of relative age effects in Canadian hockey players. Int J Sport Psychol 2007; 38: 178-86 4. Hoare D. Birthdate and basketball success: is there a relative age effect? Proceedings of the Pre-Olympic Congress: Sports Medicine and Physical Education International Congress on Sports Science; 2000 Sep 7-13; Brisbane (QLD) 5. Musch J, Hay R. The relative age effect in soccer: cross-cultural evidence for a systematic discrimination against children born late in the competition year. Sociol Sport J 1999; 16: 54-64 6. Helsen WF, Starkes JL, Van Winckel J. Effect of a change in selection year on success in male soccer players. Am J Hum Biol 2000; 12: 729-35 7. Till K, Cobley S, Wattie N, et al. The prevalence, influential factors and mechanisms of relative age effects in UK Rugby League. Scand J Med Sci Sports 2010; 20 (2): 320-9 8. Weir PL, Smith KL, Paterson C, et al. Canadian women’s ice hockey: evidence of a relative age effect. Tal Dev Excel 2010; 2 (2): 209-17 9. Sherar LB, Baxter-Jones ADG, Faulkner RA, et al. Does physical maturity and birth date predict talent in male youth ice hockey players? J Sports Sci 2007; 25 (8): 879-86 10. Malina RM, Bouchard C, Bar-Or O. Growth, maturation and physical activity. 2nd ed. Champaign (IL): Human Kinetics, 2004 11. Schorer J, Cobley S, Bu¨sch D, et al. Influences of competition level, gender, player nationality, career stage and playing position on relative age effects. Scand J Med Sci Sports 2009; 19 (5): 720-30 12. Baker J, Logan AJ. Developmental contexts and sporting success: birth date and birthplace effects in national hockey league draftees 2000-2005. Br J Sports Med 2007; 41: 515-7 13. Wattie N, Cobley S, Baker J. Towards a unified understanding of relative age effects. J Sports Sci 2008; 26 (13): 1403-9

Sports Med 2011; 41 (1)

CORRESPONDENCE

Sports Med 2011; 41 (1): 87-90 0112-1642/11/0001-0087/$49.95/0


No Evidence for the Relative Age Effect in Professional Women’s Sports The relative age effect (RAE) refers ‘‘both to the immediate participation and long-term attainment constraints in sports, occurring as a result of chronological age and associated physical differences as well as selection practices in annual agegrouped cohorts.’’[1] In one of the early attempts to comprehensively assess RAE, Musch and Grondin[2] reviewed the existing research and concluded that children born shortly before the cutoff date for annual age grouping in sport were indeed less likely to be involved in sports and therefore would be unlikely to evolve into professional athletes later in life. This phenomenon was strongly evident in sports such as hockey in Canada[3] and soccer in the UK,[4] while it was not apparent in other sports (e.g. gymnastics and American football). A recent and extensive meta-analysis published in Sports Medicine by Stephen Cobley and his colleagues[1] found that the RAE was evident across different age categories (although declining for the cohort of subjects aged ‡18 years), the skill levels (although less noticeable in the elite category) and was prevalent across many different sports (e.g. hockey, soccer and basketball). It was also determined that RAE was present for women’s sports, although its magnitude was smaller than for men. Both of these summary studies[1-2] lamented the paucity of research in the realm of women’s sports and encouraged further investigation (only 2% of the sample in the meta-analysis were female athletes). We conducted a targeted archival exploration in response to this relative lack of empirical data.[5] We focused on elite female athletes in three of the most highly competitive sports across two continents, North America and Europe. We obtained the largest sample size of female professional

athletes to date (n = 444) and compared it with past studies of RAE in elite women’s sports.[6,7] The leagues selected were chosen due to the relatively high salaries their players command, the intense competition on roster spots (partially due to the collegiate feeder system) and the prestige they espouse (the Danish Women’s Handball League [GuldBageren Ligaen] was founded in 1935 and, barring one season, is still operating successfully, without interruption, to this day). This study utilized the internet as its primary source of data gathering. Information was obtained from online rosters for teams within the Women’s National Basketball Association (WNBA) league (n = 146) during the 2009 season, the Women’s United Soccer Association (WUSA) professional soccer league (n = 104) for rosters during the 2002–3 season (the league is now defunct) and the professional GuldBageren Ligaen (n = 194) for the 2009–10 season. Each player’s day, month and year of birth was collected and tabulated. Their month of birth was then recoded into quartiles, starting with the month of September. We compared the obtained data with the equal distribution of birth dates across the four quartiles as expected based on no RAE influence (the null hypothesis). The data were also compared in half-year periods. There was no RAE in basketball, soccer or handball. In the latter league, there was a significantly greater probability of becoming a professional if the player was born in the last quartile (in stark contradiction to the RAE). There were also no significant differences in half-season analyses in any of the sports. Combined, these results suggest that there is no RAE in elite women’s sports. When repeated with only native (excluding foreign-born) players included in each league and with August as the alternative first month of age grouping, these analyses yielded identical results. With the growing popularity of women’s sports, this finding is important to consider. One possible explanation for it is that there is intense competition in youth sports in the early ages and only limited positions available, which characterizes some male sports, and this is considered one of the prime reasons behind the RAE and does

88


not reach a sufficient level even among the most competitive women’s sports to produce RAE. Also, the earlier maturation of girls and the lower variability of the maturity status in girls in comparison to boys[8] can both explain the insignificant results. Moreover, in the GuldBageren Ligaen, professional players were more likely to be born in the last quartile. This finding stands in contrast to the RAE. In order to understand the roots of this effect, a longitudinal research design should be conducted. One possible account is resiliency: while for those young athletes who are not as physically strong in the beginning but remain in the sport are more likely to persist, the rest, once they lose their physical advantage, wither away gradually. This finding is aligned with the general trend of reduction in magnitude of RAE with age progression.[1] Future research should focus on longitudinal designs that would allow separating those variables, shedding more light on the differences between the sexes. What are the practical implications of the results? Past research[2] has advocated elaborate solutions to counter the RAE, such as determining groups based on biological age or variation of cutoff dates within the competition year. Based on the current results, sports organizations that nurture female athletes into taking their first steps in sports, and who focus primarily on producing top calibre performers, should not be concerned with the adverse impact of RAE. Nadav Goldschmied University of San Diego, San Diego, California, USA

Acknowledgements No funding was used to assist in the preparation of this letter. The author has no conflicts of interest that are directly relevant to the content of this letter.

References 1. Cobley S, Baker J, Wattie N, et al. Annual age-grouping and athlete development. Sports Med 2009; 39 (3): 235-56 2. Musch J, Grondin S. Unequal competition as an impediment to personal development: a review of the relative age effect in sport. Dev Rev 2001; 21 (2): 147-67 3. Barnsley RH, Thompson AH. Birthdate and success in minor hockey: the key to the NHL. Can J Behav Sci 1988; 20: 167-76


4. Dudink A. Birth date and sporting success [abstract]. Nature 1994; 368: 592 5. Dyer S, Raphaeli M, Goldschmied N. No evidence for the relative age effect in professional women sports [abstract no. 1225]. 118th American Psychological Association Annual Meeting; 2010 Aug 12; San Diego (CA) 6. Edgar S, O’Donoghue P. Season of birth distribution of elite tennis players. J Sport Sci 2005; 23 (10): 1013-20 7. Wattie N, Baker J, Cobley S, et al. A historical examination of relative age effects in Canadian hockey players. Int J Sport Psychol 2007; 38: 178-86 8. Baxter-Jones A, Helms P, Maffuli N, et al. Growth and development of male gymnasts, swimmers, soccer and tennis players: a longitudinal study. Ann Hum Biol 1995; 22 (5): 381-94

Authors’ Reply Relative Age Effects in Female Contexts We thank Dr Goldschmied[1] for his letter to the editor and welcome his findings, which contribute to our understanding of Relative Age Effects (RAEs). Dr Goldschmied’s data do seem to differ from hypothetical expectations (i.e. relatively older participants being overrepresented[2]), offering new insight into what now seems an increasingly complex effect. When compared with existing knowledge of male and female samples in other team sport contexts (e.g. adult female Canadian ice hockey,[3] junior representative basketball[4]), Dr Goldschmied’s observations offer contrasts by showing (i) no RAEs in basketball and soccer; and (ii) the relatively younger participants being overrepresented in handball. Whilst understanding that limited detail can be placed within a letter, prior to further discussion it would be prudent to check a few points on Goldschmied’s methods and data analysis. We assume that a common cut-off date (i.e. September) was applied throughout the relevant stages of sport participation and development, which is important because this is typically where RAEs originate. There is a suggestion that a different cut-off was applied (i.e. August) in the parallel analysis conducted with ‘local players’. It would Sports Med 2011; 41 (1)


be useful to know more about what this extended analysis included, but more importantly here, to understand why an alternative cut-off date was used as it differs to that applied for the other samples. It would also be worth considering whether or not any such cut-off dates have changed over the past 10–20 years (e.g. handball). Such changes could have influenced the current adultelite relative age player profiles. As described in previous research,[5,6] changed cut-off dates modify subsequent participation and attainment profiles. These checks will all help to discount alternative explanations for why the relatively younger were overrepresented in the context of elite handball and, at the same time, generate further confidence in the reported findings. ‘Why?’ is the fundamental question all RAE researchers ask when they encounter a disproportionate representation, or the occurrence of an enduring inequality based on being relatively older.[7] Similarly, we ask this in relation to the absence of RAEs in female US basketball and soccer, as well as the overrepresentation of relatively younger players in Danish handball (both of which may be contrary to expectations). Helpfully, Goldschmied offers plausible explanations. A reduced depth of competition, which would support universal enrolment, could account for the non-existence of RAEs in female basketball and soccer. Here, the logic is that involvement rarely reflects strict selection for few places. There is some evidence to support this notion. For example, historically RAEs were not apparent in elite women’s ice hockey.[3] However, this situation may be changing and more recent observations of an increasingly popular, more competitive game and league structure[8] suggest RAEs could have evolved in parallel with these developments. At the core of the depth of competition hypothesis appears be the issue of access. If provision (such as local school and community clubs) exceeds demand for participation, there is less of a need for some form of selection (a process that generates RAEs). RAEs may surface when participation demand exceeds local provision capacities. Dr Goldschmied suggests that the absence of RAEs in basketball and soccer could also be attributed to the earlier maturation of girls, along ª 2011 Adis Data Information BV. All rights reserved.

89

with a lowered variability of maturity status during adolescence. Indeed, there are more marked differences in physical performance among adolescent boys according to early onset or late maturation.[9] For girls, this relationship appears less consistent and/or influential on particular performance measurements.[10] This highlights the potential for performance indicators to be less confounded by variable maturation, and for selection to be less biased by the markers of differential development. Where a sport, or sporting context, values qualities other than physical dominance, RAEs may be less prevalent or even totally absent. Psychological resilience is offered as an explanation for the overrepresentation of the relatively younger players in Danish handball. This certainly has intuitive appeal if the context of Danish handball is similar to other sport development systems (e.g. those seen in Germany).[11] Here, the mechanism seems to be that relatively younger players, even though they are more likely to be later maturing, maintain participation and development despite regular exposure to developmentally related disadvantages. As a result, these individuals may acquire/develop a set of psychological and/or performance skills that help them to circumvent the RAE maturation-selection problem, and that eventually may overcome those who were initially selected for maturational differences. The withdrawal of those superseded athletes, combined with refined robust psychological coping profiles of the adherers, may ultimately provide the adherers with delayed advantages in selection,[12] performance and career longevity.[11] While each of these mechanisms is plausible, others should still be considered. Importantly, this kind of conjecture highlights the limitations of cross-sectional studies of the RAE phenomenon. As we have argued elsewhere,[13] RAE studies need to move beyond atheoretical descriptions in different contexts. We now need hypothesisdriven research that can help to develop a comprehensive model of RAEs in sport. Where RAEs are present, regardless of sex, there is a need to not only understand the underpinning mechanisms, but also trial and evaluate intervention studies aimed at resolving their presence. Just as we anticipate Sports Med 2011; 41 (1)

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that RAEs are context specific, we expect this of the interventions too, although this remains to be confirmed. Thus, the underpinning implementation science will help the RAE field to positively influence youth participation and elite performance development. To conclude, we would like to briefly discuss the implications and generalizability of the present findings. Dr Goldschmied suggests that the absence of adverse RAEs, at least for the female sport contexts examined and their respective governing bodies, means that they should not be of direct concern. However, when referring to the handball data, we should consider why a disproportionate representation in adulthood is more acceptable when it is based, possibly, on a psychological mechanism, as opposed to a maturation-selection mechanism. That said, we also urge caution in transferring present findings to other female sport settings, particularly when such data are not available for assessment. We should keep in mind that RAEs are prevalent in certain female sport contexts, and many further contexts have yet to be examined. With possible increases in female participation rates and evolving talent development systems, RAEs could feature in the future. Such possibilities re-emphasize the value and necessity of applied research with local community settings, sport governing bodies and their sport development systems. Stephen Cobley,1 Nick Wattie,1 Joseph Baker 2 and Jim McKenna1 1

Carnegie Research Institute, Leeds Metropolitan University, Leeds, West Yorkshire, UK 2 School of Kinesiology and Health Science, York University, Toronto, Ontario, Canada



Acknowledgements No funding was used to assist in the preparation of this letter. The authors have no conflicts of interest that are directly relevant to the content of this letter.

References 1. Goldschmied N. No evidence for the relative age effect in professional women sports [letter]. Sports Med 41 (1): 87-8 2. Cobley S, Baker J, Wattie N, et al. Annual age-grouping and athlete development. Sports Med 2009; 39 (3): 235-56 3. Wattie N, Baker J, Cobley S, et al. A historical examination of relative age effects in Canadian hockey players. Int J Sport Psychol 2007; 38: 178-86 4. Hoare D. Birthdate and basketball success: is there a relative age effect? Proceedings of the Pre-Olympic Congress: Sports Medicine and Physical Education International Congress on Sports Science; 2000 Sep 7-13; Brisbane (QLD) 5. Musch J, Hay R. The relative age effect in soccer: cross-cultural evidence for a systematic discrimination against children born late in the competition year. Sociol Sport J 1999; 16: 54-64 6. Helsen WF, Starkes JL, Van Winckel J. Effect of a change in selection year on success in male soccer players. Am J Hum Biol 2000; 12: 729-35 7. Till K, Cobley S, Wattie N, et al. The prevalence, influential factors and mechanisms of relative age effects in UK Rugby League. Scand J Med Sci Sports 2010; 20 (2): 320-9 8. Weir PL, Smith KL, Paterson C, et al. Canadian women’s ice hockey: evidence of a relative age effect. Tal Dev Excel 2010; 2 (2): 209-17 9. Sherar LB, Baxter-Jones ADG, Faulkner RA, et al. Does physical maturity and birth date predict talent in male youth ice hockey players? J Sports Sci 2007; 25 (8): 879-86 10. Malina RM, Bouchard C, Bar-Or O. Growth, maturation and physical activity. 2nd ed. Champaign (IL): Human Kinetics, 2004 11. Schorer J, Cobley S, Bu¨sch D, et al. Influences of competition level, gender, player nationality, career stage and playing position on relative age effects. Scand J Med Sci Sports 2009; 19 (5): 720-30 12. Baker J, Logan AJ. Developmental contexts and sporting success: birth date and birthplace effects in national hockey league draftees 2000-2005. Br J Sports Med 2007; 41: 515-7 13. Wattie N, Cobley S, Baker J. Towards a unified understanding of relative age effects. J Sports Sci 2008; 26 (13): 1403-9

Sports Med 2011; 41 (1)

sports medicine Volume 41 Issue 1 January 2011

sports medicine Volume 41 Issue 9 September 2011

sports medicine Volume 41 Issue 4 April 2011

sports medicine Volume 41 Issue 7 July 2011

sports medicine Volume 41 Issue 10 October 2011

sports medicine Volume 41 Issue 5 May 2011

sports medicine Volume 41 Issue 2 February 2011

sports medicine Volume 41 Issue 8 August 2011

sports medicine Volume 41 Issue 3 March 2011

sports medicine Volume 41 Issue 6 June 2011

sports medicine Volume 39 Issue 1 January 2008

sports medicine Volume 40 Issue 1 January 2010

Trends in Cognitive Sciences. January, 2011 Volume 15, Issue 1

2DArtist Issue 061 January 2011 issue 61

Physiotherapy, Volume 97, Issue 1 (2011)

Nature Reviews Immunology 2011-1 (January 2011)

Radical Philosophy Issue: 165 - January February 2011

L'Espresso - Issue 4, 27 January 2011

sports medicine Volume 40 Issue 10 October 2010

sports medicine Volume 39 Issue 8 August 2009

sports medicine Volume 39 Issue 12 December 2009

sports medicine Volume 39 Issue 5 May 2009

sports medicine Volume 40 Issue 8 August 2010

sports medicine Volume 39 Issue 10 October 2009

sports medicine Volume 39 Issue 7 July 2009

sports medicine Volume 40 Issue 2 February 2010

sports medicine Volume 38 Issue 10 October 2008

sports medicine Volume 39 Issue 4 April 2009

sports medicine Volume 40 Issue 12 December 2010

sports medicine Volume 40 Issue 3 March 2010

sports medicine Volume 38 Issue 12 December 2008

sports medicine Volume 41 Issue 1 January 2011

sports medicine Volume 41 Issue 9 September 2011

sports medicine Volume 41 Issue 4 April 2011

sports medicine Volume 41 Issue 7 July 2011

sports medicine Volume 41 Issue 10 October 2011

sports medicine Volume 41 Issue 5 May 2011

sports medicine Volume 41 Issue 2 February 2011

sports medicine Volume 41 Issue 8 August 2011

sports medicine Volume 41 Issue 3 March 2011

sports medicine Volume 41 Issue 6 June 2011

sports medicine Volume 39 Issue 1 January 2008

sports medicine Volume 40 Issue 1 January 2010

Trends in Cognitive Sciences. January, 2011 Volume 15, Issue 1

2DArtist Issue 061 January 2011 issue 61

Physiotherapy, Volume 97, Issue 1 (2011)

Nature Reviews Immunology 2011-1 (January 2011)

Radical Philosophy Issue: 165 - January February 2011

L'Espresso - Issue 4, 27 January 2011

sports medicine Volume 40 Issue 10 October 2010

sports medicine Volume 39 Issue 8 August 2009

sports medicine Volume 39 Issue 12 December 2009

sports medicine Volume 39 Issue 5 May 2009

sports medicine Volume 40 Issue 8 August 2010

sports medicine Volume 39 Issue 10 October 2009

sports medicine Volume 39 Issue 7 July 2009

sports medicine Volume 40 Issue 2 February 2010

sports medicine Volume 38 Issue 10 October 2008

sports medicine Volume 39 Issue 4 April 2009

sports medicine Volume 40 Issue 12 December 2010

sports medicine Volume 40 Issue 3 March 2010

sports medicine Volume 38 Issue 12 December 2008

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