sports medicine Volume 39 Issue 12 December 2009

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Sports Med 2009; 39 (12): 977-979 0112-1642/09/0012-0977/$49.95/0

ACKNOWLEDGEMENT

ª 2009 Adis Data Information BV. All rights reserved.

Dear Reader As we reach the final issue of the year for Sports Medicine, we hope that you have found the articles published throughout 2009 to be both interesting and informative. The editor and publishing staff have appreciated the high quality of content contributed to the journal this year and the forward programme for 2010 looks set to build on these standards as we continue to provide you with the very best of drug safety research and opinion. We were delighted this year to launch our new interactive online platform, AdisOnline, which we hope will help you navigate our content. The platform includes many new features including access to featured articles and collections, ability to see the most viewed and e-mailed articles across all titles and provides many personalization features. The platform also allows exporting of figures to PowerPoint. The high quality of a number of our journal titles was further recognized in the new ISI impact factors for 2008. The impact factor of Clinical Drug Investigation increased to 1.3 (an increase of over 100%), Molecular Diagnosis and Therapy increased to 2.14 (an increase of 54%), Drugs increased to 4.13 and PharmacoEconomics increased to 2.8. Clinical Pharmacokinetics also registered an increase in its impact factor. The impact factor of Drug Safety held steady for another year a 3.537, compared to 3.536 last year, illustrating the consistent importance to the literature of the articles the journal publishes. Adis has been providing quality content to healthcare professionals for nearly 40 years. This year saw several of our titles celebrate major milestones: Sports Medicine (25 years), Clinical Drug Investigation (20 years), CNS Drugs and BioDrugs (15 years), and Pediatric Drugs (10 years). In 2010, the American Journal of Clinical Dermatology will celebrate its 10-year anniversary. In 2011, many more of our titles have major anniversaries. Last, but not the least, we would like to say a big thank you to all the authors who have contributed articles to Sports Medicine in the last 12 months. Without their hard work and diligence we would not have been able to publish the journal. The quality of published articles reflects also the significant time and effort dedicated by the peer reviewers who ensure that we continue to publish content of the highest possible standard. In addition to the members of our Honorary Editorial Board, we would like to thank the following individuals who acted as referees for articles published in Sports Medicine in 2009: Chris R. Abbiss, Australia Nidhal B. Abdelkrim, Tunisia Edmund O. Acevedo, USA Lucia Alejandro, Spain R. McNeill Alexander, UK J.B. Allen, USA

Fernanda Amicarelli, Italy Duarte Araujo, Portugal Chris I. Ardern, Canada Elizabeth A. Arendt, USA Per Aspenberg, Sweden Robert J. Aughey, Australia

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Stephen P. Bailey, USA James C. Baldi, New Zealand Carla Bann, USA Adrian G. Barnett, Australia Thomas J. Barstow, USA Peter P. Bartsch, Germany Fabien A. Basset, Canada David G. Behm, Canada Ralph Beneke, UK David Bentley, Australia Catherine S. Berkey, USA Alfred Bernard, Belgium Tomasz M. Bielecki, Poland Veronique L. Billat, France Alexander Boldyrev, Russian Federation Marc Bonnefoy, France Katarina T. Borer, USA Heather Bowles, USA Helen Brown, Australia Lee E. Brown, USA Marybeth Brown, USA Nick Brown, Australia Wendy Brown, Australia Johannes Brug, the Netherlands Jonathan D. Buckley, Australia Louise M. Burke, Australia Dale J. Butterwick, Canada Chris Button, New Zealand Jeffrey Bytomski, USA Jose A.L. Calbet, Spain Robert C. Cantu, USA Michael R. Carmont, UK James B. Carter, Canada Alison Carver, Australia Douglas Casa, USA Carmen Castaneda-Sceppa, USA George D. Chloros, USA Sally A. Clark, Australia Vernon G. Coffey, Australia Damian Coleman, UK David O. Conant-Norville, USA Kirsten Corder, UK Aaron J. Coutts, Australia Kay L. Cox, Australia Lynette L. Craft, USA Elizabeth V. Cyarto, Australia Brian Dawson, Australia Laura C. Decoster, USA J. Scott Delaney, Canada Michaela C. Devries, Canada Gurpreet Dhaliwal, USA P.E. di Prampero, Italy Mary K. Dinger, USA David D. Docherty, Canada Scott Drawer, UK Barry B. Drust, UK Jose A. Duarte, Portugal G. Duncan, USA James S. Duncan, New Zealand Andrea L. Dunn, USA Conrad P. Earnest, USA Tammie R. Ebert, Australia Eric Eils, Germany Mahmoud S. El-Sayed, UK


Carolyn A. Emery, Canada Luigi Fattorini, Italy Bo Fernhall, USA Caroline F. Finch, Australia Marie T. Flores, Chile Brian C. Focht, USA Daniel T.-P. Fong, China Carl C. Foster, USA Jonathon Fowles, Canada Belinda J. Gabbe, Australia Olivier Galy, New Caledonia Theodore T. Ganley, USA H. Gaulrapp, Germany J. Parry Gerber, USA Nathan N. Gibbs, Australia Carmen Gomez-Cabrera, Spain Christopher J. Gore, Australia Eric D.B. Goulet, Canada Michael T. Gross, USA Bernard B. Gutin, USA Joseph Hamill, USA Karyn L. Hamilton, USA Per M. Haram, Norway Marcus H. Heitger, New Zealand Jay Hertel, USA Timothy Hewett, USA William R. Hiatt, USA Lisa Hodgson, UK Jan Hoff, Norway Peter Hofmann, Austria Wildor Hollmann, Germany Tricia J. Hubbard, USA Clare Hume, Australia Veronica K. Jamnik, Canada Tero A. Jarvinen, Finland Lisa B. Johnston, USA Fawzi Kadi, Sweden Stavros K. Kakkos, Greece Jyotsna N. Kalavar, USA James D. Kang, USA Simon Kemp, UK Robert W. Kenefick, USA John P. Kirwan, USA William J. Kraemer, USA Jesper Krogh, Denmark David E. Laaksonen, Finland Ylva T. Lagerros, Sweden Paul B. Laursen, Australia Peter le Rossignol, Australia Tzai-Li Li, Taiwan Martin Lindstrom, Sweden Marius Locke, Canada Jim Macintyre, USA Clare MacMahon, Australia Nicola N. Maffulli, UK Peter Magyari, USA Nele Mahieu, Belgium Andrew A. Maiorana, Australia Gerald A. Malanga, USA Frank E. Marino, Australia Derek Marks, USA Joseph C. Maroon, USA Robert N. Marshall, New Zealand Lester Mayers, USA

Sports Med 2009; 39 (12)

Acknowledgement

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Brendon McDermott, USA Michael R. McGuigan, Australia Terry McMorris, UK Robert G. McMurray, USA Lars R. McNaughton, UK Christa Meisinger, Germany Antti A. Mero, Finland Michael C. Meyers, USA Adrian W. Midgley, UK Edward Miech, USA A. Lynn Millar, USA Allan Mishra, USA Michael Molloy, Ireland Stefan P. Mortensen, Denmark Richard Hugh Morton, New Zealand Christian Munthe, Sweden Nanette Mutrie, UK Andrew T. Nathanson, USA Karl M. Newell, USA Jeanne F. Nichols, USA Tim D. Noakes, South Africa E.G. Noble, Canada Maria Nordlund, Sweden Lars Norgren, Sweden Kevin I. Norton, Australia John J. Orchard, Australia Francesco F. Orio, Italy Sergej M. Ostojic, Yugoslavia Nicole M. Panhuyzen-Goedkoop, the Netherlands Gaynor Parfitt, UK Brian B. Parr, USA Timothy E. Paterick, USA Helene Pavlov, USA Stephane Perrey, France Danny M. Pincivero, USA Jennifer A. Pintar, USA Kenneth H. Pitetti, USA David C. Poole, USA Ermanno Rampinini, Italy Dan K. Ramsey, USA Dennis N. Ranalli, USA Christopher Randolph, USA Thomas Reilly, UK Caroline R. Richardson, USA James H. Rimmer, USA Kai Roecker, Germany Christer G. Rolf, UK Dori E. Rosenberg, USA Thomas W. Rowland, USA

Kristin L. Sainani, USA Philo U. Saunders, Australia Timothy Scheett, USA Ernest Schilders, UK Jean-Paul Schmid, Switzerland E. Todd Schroeder, USA Andrew L. Sherman, USA Richard Shuttleworth, Australia Arthur J. Siegel, USA Ronald J. Sigal, Canada Holly J. Silvers, USA Indrani Sinha-Hikim, USA Sarianna Sipila, Finland Evelyne P. Soriano, Brazil Tim Spalding, UK Alan St Clair Gibson, UK Joseph W. Starnes, USA Darren J. Stefanyshyn, Canada David J. Stensel, UK Emma Stevenson, UK Jeffrey R. Stout, USA Eric J. Strauss, USA James Stray-Gundersen, USA Alasdair G. Sutherland, UK Jeroen Swart, South Africa Janet L. Taylor, Australia Richard D. Telford, Australia Gershon Tenenbaum, USA Peter M. Tiidus, Canada Ian R. Tofler, USA Tarkan Tuli, Germany Maria L. Urso, USA Jaci Van Heest, USA Geoffrey M. Verrall, Australia Niels B.J. Vollaard, UK Serge P. von Duvillard, USA Darren E.R. Warburton, Canada Gordon L. Warren, USA Wendy Watson, Australia Jon P. Wehrlin, Switzerland Joseph P. Weir, USA Randall L. Wilber, USA Mark Williams, UK Jonathan E. Wingo, USA Kate Woolf-May, UK Maurice R. Yeadon, UK Warren B. Young, Australia Bohdanna T. Zazulak, USA Rebecca A. Zifchock, USA

We look forward to your continued support in 2010 and to bringing you first-class content from around the globe. With best wishes from the staff of Sports Medicine and all at Adis, a Wolters Kluwer business.


Sports Med 2009; 39 (12)

Sports Med 2009; 39 (12): 981-993 0112-1642/09/0012-0981/$49.95/0

CURRENT OPINION


Why Do Pedometers Work? A Reflection upon the Factors Related to Successfully Increasing Physical Activity Catrine Tudor-Locke1 and Lesley Lutes2 1 Walking Behaviour Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA 2 Department of Psychology, East Carolina University, Greenville, North Carolina, USA

Abstract

The results of two recent independent meta-analyses focused on pedometerbased programmes conclude that they work; that is, they are effective. Specifically, physical activity increases while blood pressure and weight decrease as a result of participating in a pedometer-based intervention. An improved understanding of the unique measurement and motivational properties of pedometers as behaviour-change tools will assist researchers and practitioners to maximize benefits. In an effort to begin to outline why pedometers work, for whom, and under what conditions, the purpose of this current opinion article is to explore the published literature (drawing heavily from those studies previously identified in published meta-analyses and our own work in this area) to identify factors related to using pedometers to increase physical activity. In particular it is important to: (i) gain a better understanding of the activitypromoting characteristics of pedometers; (ii) determine effective elements of pedometer-based programming; and (iii) identify participants who engage in, and benefit most from, such programming. Pedometers are most sensitive to walking behaviours, which is consistent with public health and clinical approaches to increasing physical activity. Specifically, they offer an affordable and accessible technology that is simplistic in output, low-literacy friendly, and immediately understandable to end-users. Support materials are becoming readily available for researchers and practitioners in terms of expected (normative or benchmark) values, patterns of change, indices to aid screening and interpretation, and measurement protocols. Pedometer-based programme theory is now being articulated and tested, and the critical elements necessary to shape a successful programme are becoming more clearly defined. More research is needed, however, to compare the effectiveness of self-selected individualized goals with tailored goals (based on a specified baseline characteristic, for example), standardized goals (e.g. percentage-based increments) and pre-set uniformly administered goals (i.e. a volume total of 10 000 steps/ day or an incremental total of 2000 extra steps/day for everyone). Since most studies of pedometer-based programmes have been of relatively short duration, it is unknown to what extent observed changes are sustainable or whether it is possible to continue to accrue benefits over long-term adherence. Peer delivery of treatment has the potential for enabling wider and less costly dissemination, although this has not been directly evaluated. In addition, the majority of pedometer-based programme participants to date have been women,

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suggesting that more research is needed on men and how they react to this form of physical activity intervention. Increases in steps/day have been negatively correlated with baseline values, indicating that those with lower baseline steps/day stand to make the greatest relative incremental increases in physical activity behaviour. A clearly articulated programme theory is lacking in most interventions. A clearer understanding is needed of what programme features, including the nature of goal-setting, are necessary for optimal participant success. Additionally, we need a better profile of the participant who benefits most, and/or requires additional or alternative strategies to succeed in their personal behaviour-change attempts. Continued efforts to refine answers regarding what works well for whom under what conditions will foster evidencebased applications of pedometer-based programmes.

Physical activity assessment has benefited from the rapid expansion of body-worn technologies, including accelerometers and pedometers, which have greatly advanced researchers’, practitioners’ and lay people’s interest in quantifying, and ability to quantify, physical activity patterns and volumes. Of the two instruments, however, the pedometer has been used more frequently as a motivational tool imbedded in an intervention programme. As testament to the growing interest in pedometer-based programming, two recent metaanalyses examined such programmes’ efficacy in terms of increasing walking behaviours.[1,2] Specifically, Richardson et al.[1] summarized findings from nine randomized and controlled pedometerbased programmes and found that participants increased their activity by 1800–4500 steps/day and lost a modest amount of weight (on the order of approximately 0.05 kg/week) over the course of interventions lasting from 4 weeks to 1 year (median duration 16 weeks). Bravata et al.[2] considered both randomized, controlled studies and observational studies, and reported similar changes: pedometer users increased their physical activity by approximately 2100–2500 steps/day and decreased their body mass index (BMI) by 0.38 kg/m2. The pedometer-based interventions described in the published meta-analyses[1,2] offer inspiration, but few alone can serve as useful programme templates due to a lack of a clearly articulated detailed programme or intervention theory.[3] Theorybased health behaviour-change programmes are believed to be more effective than those that do not ª 2009 Adis Data Information BV. All rights reserved.

use theory; unfortunately, a recent review of theorybased programming indicated only 35.7% of health behaviour-change programmes published between 2000 and 2005 even mentioned theory.[4] A detailed programme theory is necessary to organize and explain what happens in a programme and why. It is initially informed by the existing programmerelated literature and clinical experience.[3] Therefore, in an effort to begin to outline why pedometers work, for whom, and under what conditions, the purpose of this current opinion article is to reflect upon characteristics of pedometers, pedometer-based programming, and the participants who engage in such programming. Such a discourse is necessary to better understand theoretical mechanisms in an effort to refine and replicate optimal programming templates. 1. Characteristics of Pedometers 1.1 Measurement Mechanism

Pedometers are generally designed to be most sensitive to detecting ambulatory activity, and this is a ‘good thing’ in terms of measuring and motivating walking behaviours. Of all types of physical activity, walking is most commonly encouraged.[5] It is the most commonly reported form of leisuretime physical activity,[6] and it is also a functional component of shopping, transportation and walking the dog, to name but a few examples of other forms of walking behaviours.[7] Pedometers offer a simple estimate of physical activity volume Sports Med 2009; 39 (12)

Why Do Pedometers Work?

in terms of steps taken. This uncomplicated and straightforward output is a direct indicator of movement as a result of behavioural choices. Traditional pedometers detect steps by using a horizontal, spring-suspended lever arm which moves up and down as a result of vertical accelerations of the hip. A step is recorded when a vertical acceleration above the manufacturer-designed force sensitivity threshold of the pedometer (e.g. 0.35 g for the Yamax pedometer [Yamax Corp., Tokyo, Japan]) deflects the lever arm sufficiently to complete an electronic circuit. The electronic circuitry within a pedometer is designed to accumulate steps and continually display this updated information on a digital screen. A force sensitivity threshold is an important pedometer characteristic, regardless of its underlying measurement mechanism, since it is necessary to censor out ‘non-steps’ (e.g. inevitable jostling during car driving[8]). The sensitivity/specificity trade-off, however, results in a loss of recorded low acceleration steps, typical of slower paces (e.g. steps taken while standing in line at the grocery store). Since health promotion efforts have focused on the benefits of brisk walking, or that of at least moderate intensity, this censoring feature should not be problematic in most populations, and in fact can be interpreted as an attribute as it conveniently pushes the participant to focus more on detectable walking behaviours. Unfortunately, specific pedometer force sensitivity thresholds for detecting steps can vary widely between available instruments[9] and are only as consistent between instruments of the same brand as factory quality control efforts impose. This unfortunate circumstance undermines the consistency of operationally defining a step, and impairs our ability to compare step outputs across populations and studies. At this time there is no conversion factor available to ‘correct’ step outputs from different instruments. However, a number of pedometer brand-to-brand comparisons have been conducted,[10-12] which have identified researchquality pedometers. It is important to clearly declare here that the effectiveness of pedometerbased programming is prefaced on the use of valid and reliable instruments, like the Yamax brand pedometers, for example. The effectiveness of ª 2009 Adis Data Information BV. All rights reserved.

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lesser instruments is questionable and potentially detrimental to behaviour-change efforts.[13] Since pedometers are typically designed to be most sensitive to vertical accelerations at the hip, it almost goes without saying that pedometers (and waist-worn accelerometers) do not detect nonambulatory activities, including swimming, cycling and weight training. The prevalence of participation in these activities is quite low. Only 5.8% of adults report swimming, 11% report cycling and 8.6% report weight-training when asked about activities performed over the past 30 days.[14] Fortunately, these types of activities are salient and therefore more easily recalled, so a combination of pedometer and self-report should suffice to capture them.[15] Adding ‘bonus steps’ to daily pedometerdetermined steps for performance of these types of non-ambulatory activities (e.g. 200 extra steps for every 10 minutes of active time) is a strategy that may work well on the individual level for interventions, but appears not to be necessary for population level analyses.[16] Traditional pedometers are also not designed to detect intensity of activity. Furthermore, pedometer attachments that rotate the instrument off the vertical plane will impair measurement function. For example, in obese individuals it is possible that a pedometer will be tilted off the vertical axis in manufacturer-recommended attachment sites (i.e. typically at the waist, centered over the right knee), resulting in compromised detection of lower force steps.[17,18] To solve this problem, there are instruments that offer improved precision in obese individuals[17] (see below), with the caveat that these might result in the sensitivity/specificity trade-off mentioned above. However, some researchers have reported that pedometers can be moved about on the waist band (e.g. placed on the mid-axillary line or in line with the posterior thigh where it is less likely to be tilted) without compromising measurement properties.[18,19] Specifically, we have had success with teaching participants how to attach the pedometer so that it is not tilted, and to monitor the accuracy of their pedometers on a daily basis with a simple 20-step test, adjusting placement as necessary.[19] Emerging technologies include a piezoelectric accelerometer mechanism that generates a sine Sports Med 2009; 39 (12)

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wave corresponding to vertical accelerations at the hip during walking and running. A count of the sine waves is interpreted as steps taken. Some of these instruments can also provide outputs related to the intensity of these steps. An added feature is an on-board memory function that recalls previous days’ data. The NL-1000 (New-Lifestyles Inc., Lees Summit, MO, USA), NL-2000(New-Lifestyles Inc.), Kenz Lifecorder EX (Suzenken Co., Ltd, Nagoya, Japan) and the Omron HJ 720ITC (Omron Corp., Kyoto, Japan) are examples. The latter two instruments can also transfer data directly to a computer by way of a USB cable, facilitating data management requirements. Although these added features are useful to researchers, the evaluation of their impact on individual behaviour is limited. For example, although the measurement mechanism of the piezoelectric pedometers does provide a more precise estimate of steps taken in obese individuals,[17] the published meta-analysis demonstrated pedometer effectiveness (even in obese individuals) without necessarily using this technology. 1.2 Acceptability to End-Users

Although a wide variety of commercial pedometers are available, most are small, unobtrusive in their attachment on the body (typically clipping directly to a waist band), and inexpensive (approximately $US20–$US50; year of costing 2009). As such, pedometers offer an accessible technology that is simplistic in output, low-literacy friendly and immediately understandable to end-users. Their output is personalized, since each individual can be equipped with their own pedometer. Furthermore, with consistent wear, pedometers are plausibly effective for longer term monitoring and behaviour change, much like a wristwatch can be used for self-monitoring behaviours that are timedependent throughout the day. Focus groups conducted following completion of a pedometerbased programme revealed that pedometers are well accepted and are considered to be highly useful goal-setting tools, capable of immediately increasing personal awareness of physical activity levels, and providing sources of readily available visual feedback.[19-21] Although pedometer manuª 2009 Adis Data Information BV. All rights reserved.

facturers offer an array of value-added features (delayed reset buttons, multiple day memories, ‘talking’ pedometers, etc.) and outputs (estimates of distance walked, energy expended, time in activity of at least moderate intensity, etc.), it has been our observed experience of the participants with whom we have worked that most are comfortable with a simple output of steps taken and a single reset button. Furthermore, mathematical manipulations (accomplished by an on-board pedometer microprocessing feature or resulting from post-data processing) of the simple step output to extrapolate distance walked (based on inputted stride length or height) and/or energy expended (based on sex, age, mass or a selection of these) result in diminishing accuracy.[22] The cumulative and readily available visual feedback is a pedometer characteristic that warrants further discussion. The cumulative nature of the counted steps provides a constant and changing barometer reflecting personal behaviour choices as they occur in real-time. Unlike instruments that may require downloading and/or other types of processing prior to personal access, traditional pedometers offer instantaneous awareness to the end-user that can then be directly acted upon. That being said, emerging technologies are capable of providing both immediate feedback and more complex data analysis after download. Used for baseline behavioural assessment, the immediate feedback provides the user with a readily digestible estimate of personal physical activity level, which can inform decisions about behaviour change. Used as part of a guided and repetitive self-monitoring, feedback and goalsetting process, the pedometer provides up-tothe-minute information that can spur and hone behaviour choices. 1.3 Availability of Related ‘Software’ Facilitating Use

We have previously likened pedometers to computer hardware (e.g. keyboards, monitors, disks, etc.).[23] However, without the accompanying software (e.g. expected values, standardized protocols, indices to interpret change, data management rules, reporting procedures, etc.) their use is limited. We are now benefiting from the Sports Med 2009; 39 (12)


independent and collective efforts of a growing number of researchers and practitioners who continue to publish quality work related to the impact of pedometer-based programmes on activity. Cases in point are the two meta-analyses previously mentioned[1,2] and a third,[24] all of which provide vital expected (normative or benchmark) values, necessary for interpreting change and comparison purposes, and variance estimates required for determining sample size. Similar data are now available for young populations.[25] Patterns of change[26,27] have also been published, which are useful for facilitating local implementation and evaluation. Efforts have also been made to identify practical indices reflective of public health guidelines,[28] to classify hierarchical levels of physical activity,[29,30] and to link threshold values with outcomes of interest including BMI[31,32] and body fat percentage.[33] Practical guidance for measurement protocols and procedures has been published[34] and recently expanded to young populations.[35] These rich and growing publicly available resources are invaluable to facilitate optimal use of pedometers in physical activity interventions. 2. Characteristics of Pedometer-Based Physical Activity Interventions 2.1 Nothing Quite Like a Good Theory

It is important to reiterate that pedometers are simple tools and that successful interventions are ultimately based on empirically validated treatments that are informed by good theory.[3] This includes identifying and clearly articulating key components or activities that must be present in a programme; this in turn is informed by existing behavioural theories, models and accepted techniques. Behavioural and social scientists interested in physical activity have employed a number of theories and models originally developed for other behavioural applications (e.g. addiction and smoking cessation), often in combination, to better understand this unique behaviour and to design successful interventions. These theories and models generally include: (i) classical learning theory; (ii) the health belief model; (iii) the transtheoretical model; (iv) relapse prevention; (v) social ª 2009 Adis Data Information BV. All rights reserved.

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cognitive theory; (vi) theory of reasoned action and planned behaviour; (vii) social support; (viii) selfregulation theory; and (ix) ecological approaches.[36] Table I catalogues the theoretically based models/ techniques and intervention goal algorithms of pedometer-based interventions identified in the recent meta-analyses.[1,2] At present only a handful of these interventions[26,58,61,63] have explicitly acknowledged using a recognized health behaviourchange theory to guide programme design and delivery. We know of only one that has presented its programme theory in a logic model (i.e. an accepted tool used to illustrate programme components, outcomes and linkages between them).[3] For example, drawing primarily from social cognitive theory and the transtheoretical model, we previously identified the critical inputs underlying the First Step Program – a pedometer-based daily physical activity intervention originally developed for individuals with type 2 diabetes mellitus.[3] These critical inputs included: (i) individualized programming (e.g. self-selected incremental goals); (ii) flexibility in structure of regimen; (iii) activity that is of moderate intensity and focused specifically on walking behaviours; (iv) acceptable self-monitoring and feedback tools; and (v) follow-up contact. In addition, a facilitated programme was selected to guide participants through decision balance techniques early in the behaviour-change process, and self-contracts were imbedded as a regular part of an incremental and individualized goal-setting process. Relapse prevention and planning was incorporated during the behaviour-change period.[64] Social cognitive theory[65] dictates that the constructs of self-efficacy and social support are important mediating variables in programmes designed to increase physical activity behaviours. Since the most influential source of self-efficacy is performance accomplishment or mastery, it follows that opportunities to directly experience physical activity (i.e. go for a walk) are integral to good programme design. Brief group walks incorporated into the First Step Program were useful for increasing personal awareness of steps taken in a specific time frame (necessary for selecting informed incremental goals) and also provided opportunity for socialization.[21] Social Sports Med 2009; 39 (12)

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Table I. Theoretical models/techniques and intervention goal algorithms of pedometer-based interventions identified in recent meta-analyses[1,2] Target population

Explicitly identified theoretical model (in bold type) or technique

Intervention goal algorithm

Araiza et al.,[37] 2006

Adults with type II diabetes mellitus

None identified

Achieve 10 000 steps/day

de Block et al.,[38] 2006

Adults with COPD

Motivational interviewing

To › lifestyle PA

Butler and Dwyer,[39] 2004

Sedentary adults

None identified

To › PA 30 min (~3000 steps) daily during wk 1 and 2, › to 40 min (~4000 steps) during wk 3 and 4

Chan et al.,[26] 2004

Sedentary adult employees

Social cognitive theory and transtheoretical model

To › PA weekly

Croteau,[40] 2004

Adult employees

Goal setting based on baseline step counts

If 10 000; if 8000–10 000, then increased 5% every 2 wk until >10 000; if >10 000 at baseline, then maintain

Eastep et al.,[41] 2004

Healthy adults

Feedback

Centre-based exercise class and recommendations to increase daily PA through walking

Engel and Lindner,[42] 2006

Adults with type II diabetes

Coaching (including problemsolving, education, self-efficacy, goal-setting, and social support

If healthy older adult step goals = 6000–8500, if older adult with disabilities and chronic illness, step goals = 3500–5500

Hultquist et al.,[43] 2005

Sedentary non-smoking adult women

Self-monitoring


Izawa et al.,[44] 2005

Adults with a history of myocardial infarction

Self-efficacy theory and transtheoretical model

Centre-based cardiac rehabilitation programme – no goals specified

Jensen et al.,[45] 2004

Obese older adult women

Goal-setting and self-monitoring

› daily steps by 5000 and additional goals if warranted

Kilmer et al.,[46] 2005

Adults with neuromuscular diseases

Self-monitoring

› daily steps by 25% compared with baseline

Koulouri et al.,[47] 2006

Healthy adults

None identified

› daily steps by 2000 compared with baseline

Lindberg,[48] 2000

Sedentary healthy adults

Self-monitoring and motivational messages


Moreau et al.,[49] 2001

Adult postmenopausal women

Self-monitoring and goal setting

› distance by 1.4 km above their baseline wk 1, › 0.5 each time until the desired walking of › to 3.0 km/day by wk 3

Ransdell et al.,[50] 2004

Multi-generational women in families

Self-monitoring

› duration and volume of activity by 10% every 2 weeks and to › lifestyle-oriented PA

Schneider et al.,[51] 2006

Overweight and obese adults

Self-monitoring

Achieve 7000 steps/day for wk 1, 8000 steps/day for wk 2, 9000 steps/day for week 3, and 10 000 steps/day thereafter

Sidman et al.,[52] 2004

Adult sedentary women

Goal-setting

Achieve 10 000 steps/day or to › daily steps by 1000–3000

Stovitz et al.,[53] 2005

Healthy adults

Goal-setting

› daily steps by 400 each week

Sugiura et al.,[54] 2002

Adult menopausal women

Goal-setting and centre-based exercise

› daily steps by 2000–3000 in addition to the exercise class

Swartz et al.,[55] 2003

Overweight inactive women

Self-monitoring

Achieve 10 000 steps/day Continued next page

Tudor-Locke & Lutes

Sports Med 2009; 39 (12)

Source, year

› daily steps by 2000 compared with baseline Adults from a single state Wyatt et al.,[62] 2004

Self-monitoring

African American breast cancer survivors Wilson et al.,[61] 2005


support can take many forms beyond professional follow-up contact and includes personal support networks encompassing peers, family and friends. Activating personal support networks offers a prospect of establishing ongoing support that extends well beyond formal programme structures, and provides a natural opportunity to foster sustainability of behaviour change.[3,19]

2.2 The Importance and Nature of a Goal

COPD = chronic obstructive pulmonary disease; PA = physical activity; › indicates increase.

Progressive step goals (not described

Postmenopausal African American women Williams et al.,[60] 2005

Health belief model

› daily step goals compared with baseline by taking into consideration the 10 000 steps/day general recommendation and the participant’s self-selected targeted value

Older male and female adults with coronary artery disease VanWormer et al.,[59] 2004

Behavioural contracting and selfmonitoring

None described

Adults with type II diabetes Tudor-Locke et al.,[58] 2004

None identified

› daily steps by 3000 compared with baseline

Adult employees Thomas and Williams,[57] 2006

Social cognitive theory and transtheoretical model

› daily steps each week with an ultimate goal of 10 000 steps/day

Older adults with knee osteoarthritis Talbot et al.,[56] 2003

Goal-setting and self-monitoring

› 10% compared with baseline every 4 wks for an overall goal of 30% above baseline steps/day

Target population

Goal-setting, self-monitoring and feedback

987

Source, year

Table I. Contd

Explicitly identified theoretical model (in bold type) or technique

Intervention goal algorithm


Since pedometers are ‘personal wear’ items, which reflect individual behaviours, they fit very well into a programme of self-monitoring, personalized feedback, and self-selected incremental goal-setting. Programmes that have encouraged increased physical activity in the absence of a goal have shown no significant improvements in steps/day compared with those with increases of ‡2000 steps/day in programmes that have promoted the use of the 10 000 steps/day goal or other goals (although few studies have evaluated alternative goals, which limits conclusions specifically about the ultimate magnitude of the goal and its efficacy).[2] Although it is tempting to adopt an ‘across the board’ prescription of a specified total or incremental number of steps/day, individualized programming is more personally relevant, easily adjusted as needed, and is likely to be well endured by typically sedentary individuals (the most likely target of a pedometer-based physical activity intervention). Individualized programming that uniquely engages and responds to an individual and encourages self-selection of a personal goal (e.g. the First Step Program) is not quite the same thing as a tailored intervention, which may include automatic responses and messaging created to address the needs of a group of individuals defined, for example, on baseline steps/ day. However, such an approach may be both efficient and effective. For example, in three recent studies,[66-68] participants uploaded or manually entered their daily step counts over the Internet and received pre-set tailored feedback regarding goal-setting based on automatic algorithms. All Sports Med 2009; 39 (12)

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studies showed significant improvements in steps/ day over the course of the intervention. An alternative approach has been to promote percentage-based goals.[46,50] The concern with strictly implementing this approach is that the absolute incremental number of steps will be higher with higher baselines, presenting greater and possibly insurmountable challenges (and perhaps under-challenging those with lower baselines). Since there is little research that has studied alternative goals or goal-setting strategies, more is needed comparing the effectiveness of: (i) self-selected goals; (ii) goals tailored to individuals defined by a baseline characteristic; (iii) standardized goals (e.g. percentage-based increments); and (iv) pre-set uniformly administered goals (i.e. a volume total like 10 000 steps/day or an incremental total like 2000 steps/day for everyone). It may very well be that setting and working towards any goal that represents an increase over baseline values is of much greater importance than the manner in which it is set. 2.3 Self-Monitoring

As discussed above (sections 1.2 and 2.2), the pedometer is an acceptable self-monitoring and feedback tool. However, its power is enhanced if it is coupled with some formal process of recording daily values (e.g. on a simple calendar) as a strategy to help reinforce activity behaviours.[19,21] Metaanalysis results[2] have indicated that participants in pedometer-based programmes who recorded their daily step count increased their activity by approximately 2000–3200 steps/day over baseline. This is significantly greater than those who were not required to record their data (mean change 832 steps/day). Participants have reported that the act of recording (i.e. writing down)[19,20,40] pedometer data over the course of days on a simple paper calendar provides additional visual feedback of progress, illuminates personal behaviour patterns of interest (e.g. weekend vs weekday steps), and produces a tangible record of personal success. Requiring participants to submit their data (e.g. in person, by mail or by electronic means) also provides a sense of accountability.[20] Follow-up contact is a form of social support and is considered ª 2009 Adis Data Information BV. All rights reserved.

important to continued motivation. It has taken the form of telephone calls,[21] postcards,[58] emails[20,40] and face-to-face interactions[26] – to name a few examples. The future brings the promise of increased availability of commercially interactive websites and purchasable software to be used in tandem with objectively monitored physical activity entered either manually[69] or by instrument download.[66,67] 2.4 Strategies

Since pedometers capture a cumulative count of steps taken throughout the day, ultimate flexibility in structure of a personalized regimen is assured; almost limitless options are available to accrue daily steps. Croteau[70] catalogued strategies reported by participants engaged in a pedometer-based programme: 64.7% walked to a meeting or workrelated errand, 50% walked after work, 35.5% walked before work, 47.1% walked at lunch, 32.4% walked on the weekend, 32.4% walked while travelling, 32.4% walked with the dog, and 29.4% walked to a destination (e.g. work/store). In addition, 50% of participants reported parking further away, 23.5% preferentially used the stairs rather than an elevator, and 52.9% performed other cardiovascular activity. Participation in sports and exercise has been shown to produce consistently higher steps/day over 1 continuous year of monitoring.[71] Programme participants have reported adopting personal strategies of taking a ‘day off’ without guilt (secure in the knowledge that they could alter their behaviour on subsequent days), and ‘banking steps’ in anticipation of a sedentary day.[19] 2.5 Delivery Options

Little research has been conducted on the possible role that interventionists’ characteristics might play in moderating participants’ attempts at behaviour-change in pedometer-based programmes. Interventionists’ characteristics include such aspects as the programme deliverer’s similarity to programme recipients, personal attitudes and physical activity behaviours, comfort with the intervention, and training.[3] We have recently demonstrated that a pedometer-based programme delivered either by a peer leader (e.g. an individual with type 2 diabetes who had previously completed Sports Med 2009; 39 (12)


the same programme) or a professional (e.g. a nurse or dietician)[72] elicited similarly favourable changes in steps/day, weight, waist girth, resting heart rate, and blood pressure. Peer delivery has the potential for enabling wider and less costly dissemination, although this has not been directly evaluated. Another delivery option that is rapidly gaining popularity is by computer. Initial reviews of physical activity interventions[73,74] delivered in this manner have shown variable success of tailored interventions delivered by computer. However, more recent studies of computer-delivered, theorybased interventions that have also incorporated a pedometer have shown clear and consistent success (i.e. an increase of 1300–2000 steps/day over baseline values).[66-68] Since most studies of pedometer-based programmes have been of relatively short duration, it is unknown to what extent observed changes are sustainable or whether it is possible to continue to accrue benefits over long-term adherence. Clearly, the optimal length of an intervention is unknown. As stated above, Richardson et al.[1] catalogued pedometer-based interventions lasting from 4 weeks to 1 year. They reported a strong negative relationship between study duration and resulting weight loss, indicating that benefits are potentiated with prolonged adherence. Chan and Tudor-Locke[75] reported that participants who completed the First Step Program (i.e. wore the pedometer for at least 9 weeks and completed a survey at 12 weeks) reported higher steps/day at 12 weeks (approximately 12 000 steps/day) and at the 1-year follow-up (approximately 11 000 steps/day) compared with baseline values (approximately 7800 steps/day). The First Step Program documented an immediate increase in steps/day that peaked at the end of formal contact, and although it deteriorated somewhat over time, still remained elevated compared with baseline values.[27,58] It is possible that by implementing ‘booster sessions’ (extended but infrequent contact), researchers and practitioners might be able to facilitate prolonged behaviour change. However, we do not yet know the optimal pattern of contact necessary to sustain adherence. ª 2009 Adis Data Information BV. All rights reserved.

989

3. Characteristics of Participants The study of the characteristics of pedometerbased programme participants has been primarily limited to a descriptive nature. The majority of pedometer-based programme participants to date have been women (73% of programme participants from studies included in the Richardson metaanalysis were women[1]), who took between 4700 to 7000 steps/day (i.e. sedentary or low active[29,30]) and were overweight at baseline. However, these characteristics may also reflect recruitment strategies to some extent. There is some evidence to suggest that pedometers may be appealing only for short-term behaviour monitoring in men.[76] An exploratory analysis of factors related to pedometer-based programme adherence and completion revealed that those most likely to complete the programme were overweight or obese class I (i.e. with a BMI between 30 and 35 kg/m2).[77] The authors speculated that the higher attrition in normal weight individuals potentially suggests a personal sense of programme irrelevance. For those with greater levels of obesity (i.e. class II and class III), higher attrition might have indicated a more overwhelming sense of challenge. The authors also observed that attrition was characterized by lower initial incremental changes in steps/day and subsequent regression towards baseline values. Increases in steps/day have been negatively correlated (r = -0.368) with baseline values, indicating that those with lower baseline steps/day stand to make the greatest relative incremental increases in physical activity behaviour.[26] However, there was no noted correlation between increases in steps/day and baseline BMI values. The meta-analysis conducted by Bravata et al.[2] of both randomized controlled trials and observational studies of pedometer-based programmes indicated that sex, BMI and race/ethnicity were not significant predictors of increased activity. In terms of anticipated changes in health outcomes as a result of increased steps/day, we recently reported that individual responses vary widely.[72] Specifically, we noted that although First Step Program participants’ waist girth decreased by 1.5–1.7 cm on average, the range of change was widely variable (-16 cm to +10 cm). Sports Med 2009; 39 (12)

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Table II. Factors related to successfully increasing physical activity in pedometer-based programmes Characteristics of pedometers

Characteristics of the programme

Characteristics of participants

Most sensitive to ambulatory activity Simple estimate of physical activity volume expressed as steps/day Displays accumulated steps taken Able to censor out ‘non-steps’ Acceptable to end-users Affordable, valid and reliable instruments available Small and unobtrusive, typically attached to the waist band Accessible technology, low-literacy friendly, immediately understandable Offers readily available and personalized visual feedback Useful self-monitoring, goal-setting, and feedback tools Immediately increases awareness of physical activity levels Increasingly available resource materials to support measurement and motivation efforts

Need to clearly articulate underlying programme theory Minimally, a programme of selfmonitoring, incremental goal-setting, and personalized feedback Fundamental importance of a goal, possibly 10 000 steps/day, possibly selfselected, but little alternative research has been conducted Flexibility in structure of a personalized regimen Need to record and submit daily values Follow-up contact as one form of social support Opportunities to build self-efficacy Activating personal support networks Peer delivery is effective and may enable dissemination Optimal programme duration and pattern of contact unknown to support sustainability

Majority of participants have been women; may reflect recruitment strategies to some extent May be only appealing for short-term behaviour monitoring in men Those most likely to complete are overweight and obese class I Attrition indicated by lower initial incremental changes in steps/day, regression to baseline values Individual responses vary Little is known about who benefits most

More research is necessary to identify who benefits most from pedometer-based programming in order to target such interventions more appropriately.

evidence-based applications of pedometer-based programmes. Acknowledgements

4. Conclusions The results of the two recent meta-analyses[1,2] focused on pedometer-based programmes conclude that they work. An improved understanding of the unique properties of pedometers as behaviour-change tools will assist researchers and practitioners to maximize these attributes. A clearer understanding is also needed of what programme features, including the nature of goal-setting, are necessary for optimal participant success. Finally, we need a better profile of the participant who benefits most, and/or requires additional or alternative strategies to succeed in their personal behaviour-change attempts. It is premature to offer an optimal programme template; however, we have compiled a summary of the factors related to successfully increasing physical activity in pedometer-based programmes in table II. Continued efforts to refine answers to what works well for whom and under what conditions will foster ª 2009 Adis Data Information BV. All rights reserved.

No sources of funding were used to assist in the preparation of this article. Dr Tudor-Locke receives royalties from the sale of a self-help book focused on using pedometers to increase physical activity. The authors have no other conflicts of interest that are directly relevant to the content of this article.

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Correspondence: Dr Catrine Tudor-Locke, Director, Walking Behaviour Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA 70808, USA. E-mail: [email protected]

Sports Med 2009; 39 (12)

Sports Med 2009; 39 (12): 995-1009 0112-1642/09/0012-0995/$49.95/0

REVIEW ARTICLE


Encouraging Walking for Transport and Physical Activity in Children and Adolescents How Important is the Built Environment? Billie Giles-Corti,1 Sally F. Kelty,1 Stephen R. Zubrick2 and Karen P. Villanueva1 1 Centre for the Built Environment and Health, School of Population Health, University of Western Australia, Crawley, Western Australia, Australia 2 Centre for Developmental Health, Curtin University of Technology and Telethon Institute for Child Health Research, West Perth, Western Australia, Australia

Contents Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 995 1. Built Environment (BE) Influences on the Physical Activity (PA) of Children and Young People. . . . . . 997 2. BE Factors that Influence Walking in Children and Young People. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 997 2.1 Destinations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 997 2.2 Neighbourhood Walkability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 998 2.3 Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 998 3. BE Factors Associated with PA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1000 3.1 Destinations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1000 3.2 Neighbourhood Walkability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1001 3.3 Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1001 3.4 Neighbourhood Aesthetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1001 4. Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1002 4.1 What are the Implications? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1003 4.2 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1005 5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1005

Abstract

In the post-World War II era, there have been dramatic changes to the environment that appear to be having a detrimental impact on the lifestyles and incidental physical activities of young people. These changes are not trivial and have the potential to influence not only physical health, but also mental health and child development. However, the evidence of the impact of the built environment on physical activity to date is inconsistent. This review examines the evidence on the association between the built environment and walking for transport as well as physical activity generally, with a focus on methodological issues that may explain inconsistencies in the literature to date. It appears that many studies fail to measure behaviour-specific environmental correlates, and insufficient attention is being given to differences according to the age of study participants. Higher levels of out-of-school-hours physical activity and walking appear to be significantly associated with higher levels of urban density and

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neighbourhoods with mixed-use planning, especially for older children and adolescents. Proximate recreational facilities also appear to predict young people’s level of physical activity. However, there are inconsistencies in the literature involving studies with younger children. Independent mobility increases with age. For younger children, the impact of the built environment is influenced by the decision-making of parents as the gatekeepers of their behaviour. Cross-cultural differences may also be present and are worthy of greater exploration. As children develop and are given more independent mobility, it appears that the way neighbourhoods are designed – particularly in terms of proximity and connectivity to local destinations, including schools and shopping centres, and the presence of footpaths – becomes a determinant of whether children are able, and are permitted by their parents, to walk and use destinations locally. If older children and adolescents are to enjoy health and developmental benefits of independent mobility, a key priority must be in reducing exposure to traffic and in increasing surveillance on streets (i.e. ‘eyes-onthe-street’) through neighbourhood and building design, by encouraging others to walk locally, and by discouraging motor vehicle use in favour of walking and cycling. Parents need to be assured that the rights and safety of pedestrians (and cyclists) – particularly child pedestrians and cyclists – are paramount if we are to turn around our ‘child-free streets’, now so prevalent in contemporary Australian and US cities. There remains a need for more age- and sex-specific research using behaviour- and context-specific measures, with a view to building a more consistent evidence base to inform future environmental interventions.

Increasing physical activity is one key strategy in curbing the alarming increases witnessed over the past 30 years in childhood and adolescent obesity and preventable chronic diseases in this young population.[1,2] Physically active children are at reduced risk of developing chronic diseases[3,4] and have enhanced psychological and emotional wellbeing.[5,6] Even children with chronic diseases function better if they are physically active.[7] Although youth-specific physical activity (PA) guidelines recommend at least 60 minutes of daily moderate- to vigorous-intensity physical activities (MVPA),[4,8] a sizeable number of young people do not engage in sufficient MVPA on a regular daily basis.[9] Given the evidence, focusing attention on children and young people is justified.[10] It is now generally well accepted that changes in the environment are contributing to the obesity epidemic by impacting upon three key behaviours: sedentariness, PA and overeating.[11-13] As Kelty et al.[14] observe, before World War II, habitual active travel, active play, incidental activity, physically demanding work and household chores were ª 2009 Adis Data Information BV. All rights reserved.

integral to daily life, work and play. However, technological advances have dramatically reduced incidental PA. For example, driving is now the preferred and most prevalent mode of transport in most developed countries.[15] Numerous laboursaving devices are readily available in households and workplaces.[14] In many Western nations, especially in Australia, workers now work longer hours than their parents and grandparents and are increasingly employed in more sedentary jobs.[16] Popular leisure-time activities, especially for children and adolescents, have also become more sedentary, e.g. electronic gaming.[9,17] In contemporary society a sedentary lifestyle has become the norm, hence the need to actively encourage more PA, especially active play, incidental PA and transport-related walking or cycling.[5] The idea of optimizing environments to provide healthful choices and to facilitate behaviour change is not new.[18] Yet in a recent systematic review of PA interventions targeting youth, Van Sluijs and colleagues[19] found that most well-designed intervention studies undertaken to date were Sports Med 2009; 39 (12)

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knowledge-based interventions, with little evidence that they were effective. Rather their review confirmed lessons from tobacco control,[20] that effective interventions targeting youth need to be comprehensive and multifaceted: (i) educating about and promoting PA; (ii) targeting gatekeepers of healthful behaviours (i.e. parents, teachers); and (iii) providing opportunities to be active in the various settings regularly frequented by youth, i.e. school, home and neighbourhoods.[21] Until recently, PA research has focused on understanding individual and social environmental correlates of behaviour. Studies on the impact of the built environment (BE) on the PA of youth are relatively new, although a growing body of evidence is now emerging.[22-25] In this invited review we examine the evidence on the association between the BE and walking for transport as well as PA. The BE is defined as ‘‘the neighborhoods, roads, buildings, food sources, and recreational facilities in which people live, work, are educated, eat and play’’.[26] This review does not seek to replicate previous reviews of the literature, but rather focuses on methodological issues that may explain inconsistencies in the literature to date. Where there does appear to be consistent evidence, we go on to consider actions that could be taken by practitioners and policy-makers to create a supportive BE that encourages more PA in youth. Finally, we consider areas for future research. The presentation of the literature is structured around Pikora and colleagues’ Systematic Pedestrian and Cycling Environmental Scan (SPACES) framework of environmental determinants of walking in adults.[27] However, no evidence was available for some features of the framework, which are therefore not considered in the text. 1. Built Environment (BE) Influences on the Physical Activity (PA) of Children and Young People There is now a growing body of evidence showing that the BE has a significant influence on the active lifestyle choices of adults, particularly walking for transport.[12,28] Although our knowledge about children and adolescents is more limited, a body of evidence is developing.[14,22,24-26] ª 2009 Adis Data Information BV. All rights reserved.

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While positive associations have been identified,[14,22,24] there are conflicting evidence and conclusions being drawn about the potential impact of the BE on young people’s behaviour,[14,22,24-26] depending on age and sex.[29] In an earlier paper, we argued that this relationship was behaviour- and context-specific.[30] Many previous studies and some reviews have failed to consider behaviourspecific correlates. Thus, in this review, we consider behaviour-specific evidence of environmental correlates and seek to highlight age and sex differences that may explain inconsistencies in research findings to date. 2. BE Factors that Influence Walking in Children and Young People Active transport (AT) includes travel by foot, bicycle and other non-motorized vehicles.[31] Increasing AT has been identified as one strategy that could increase community PA levels[31-33] as well as producing environmental[34,35] and social benefits.[36] Evidence from both Australia and the UK estimates that approximately 20% of car trips made during weekday morning rush-hour periods are short journeys made by parents dropping children at school.[37,38] Increasing daily activities such as walking to school and doing errands with or for parents have been identified as realistic strategies for increasing PA.[33,39] Yet there is consistent evidence that AT among children and adolescents has declined in the last two decades.[40-44] A reduction in active trips to school appears to be the major contributor to this decline. A number of factors are implicated in why children and adolescents are engaging in lower levels of AT,[23] including a range of demographic[43,45-48] and parental factors.[23] However, as argued by Andersen[49] and others,[50] unquestionably the BE plays a critical role by influencing opportunities for, and the safety of, AT. These BE factors are considered below. 2.1 Destinations

Destinations refer to the commercial and recreational land uses found in neighbourhoods, e.g. the parks, sports centres, shops, cinemas or public Sports Med 2009; 39 (12)

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services contained in a geographical area. In adults, there is considerable evidence that both walking for transport and recreation is more likely if destinations are present,[12] and recent evidence suggests that the variety and mix of destinations nearby is important.[51] Research on young people mirrors these findings, with proximity to relevant destinations also being critical, as outlined below. Time and distance are key factors that influence whether children or adolescents walk or cycle for transport to school or other destinations.[25,38,41,52-55] Nevertheless, few studies have defined a ‘walkable distance’ for children.[56] Timperio et al.[54] showed that, after adjustment, children living within 800 m of their school were 5–10 times more likely to commute actively to school. This was consistent with parents of these children reporting that 1.6 km (roundtrip) was a walkable distance for their children.[57] Similarly, in the US, McMillan[58] found that children were more likely to engage in AT to school if the school was within 1 km of their home. Although there is increasing evidence that distance to school and other destinations influences levels of walking or cycling,[50] many of the childhood AT studies failed to control for distance to destinations.[45,59] A US study by Falb and colleagues[60] estimated that only 1–51% of children lived within a ‘safe and reasonable’ walking distance from school, which in this study was defined as at least 1 mile (1.6 km) from the school along the street network along streets with a posted traffic speed of 4 weeks) would result in a positive doping test is yet to be determined, particularly as improvements in performance are often reported after 8 weeks of supplementation but not after 4 weeks.[58,61]

In vivo

Central nervous system

Blood-brain barrier GHRH

Autonomic nervous system

Hypothalamus

Pituitary

↑ GH

Neuroendocrine system

↑ Sympathetic activity

Liver

MAPK pathway

↓ URTI[24]

Proteasome inhibition Trend towards an ↑ type IIA fibre area[64]

↑ Fat free mass[63,65]

↑ TNFr1 cleaved by TACE ↑ sTNFr1[44]

↑ Explosive power[57,58,59]

↑ Circulating amino acid concentration[73]

Ex vivo BC + PBMC

↑ Salivary IgA[68,74]

↑ Vigour ↓ Fatigue[49] ↑ Endogenous IGF-1[56,68,80]

↑ Parasympathetic activity

↑ IFN-γ, IL-10 and IL-2[41] MAPK pathway Jun NH2− terminal kinase inhibition

Prevents LPSinduced IL-6 release[41]

↑ Strength[62] Fig. 1. Potential pathways of action for reported changes associated with bovine colostrum (BC) supplementation. Dashed circles denote significant changes observed in previous literature. Dashed arrows represent possible interactions. GH = growth hormone; GHRH = gonadotrophin hormone-releasing hormone; IFN = interferon; IGF = insulin-like growth factor; IL = interleukin; LPS = lipopolysaccharide; MAPK = mitogenactivated protein kinase; PBMC = peripheral blood mononuclear cells; sTNFr1 = soluble tumour necrosis factor receptor 1; TACE = tumour necrosis a-converting enzyme; URTI = upper respiratory tract infection; › indicates increase; fl indicates decrease.


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4. Combined Effects It is more than likely that BC supplementation influences numerous pathways within the human body given the varied components of colostrum. Figure 1 presents significant findings reported in humans following a period of BC supplementation and potential pathways that may be influenced by BC. It is important to note that the influence of BC on these pathways remains speculative; however, the intention is to present potential mechanisms for observed changes following BC supplementation and possible pathways of action to investigate in future work. 5. Conclusions BC contains a range of proteins, immune factors and hormones, which are homologous to the contents of human colostrum. The influence of BC on the growth and development of calves is well understood, but the influence of BC on adult human health is not. Whilst supplementation with BC may be increasing among athletes, in many instances conclusions from studies showing the effect of BC on exercise performance and recovery are equivocal. Data that show improvements in exercise performance and recovery, and changes in immune function during and following supplementation are limited. BC supplementation does not appear to influence body composition during a period of endurance training; however, the data suggest that supplementation is beneficial to exercise performance following consecutive days of high-intensity training (HIT) and to recovery in the days following HIT. Potential mechanisms that researchers have speculated may be responsible for observed improvements in exercise performance and immune surveillance following colostrum supplementation include increases in plasma concentrations of IGF-1,[56] improved intramuscular buffering capacity,[67] increases in lean body mass[63] and increases in salivary IgA concentrations.[74,80] Given that there are contradictory reports regarding the influence of BC on each of these parameters, the changes may be considered modest, but the cumulative effect on a ª 2009 Adis Data Information BV. All rights reserved.

range of parameters may result in improved performance and recovery. Repeated trials, using a well-defined BC product at a standard dosage and length of supplementation, and measuring a wide range of performance and immune parameters, are required to confirm this hypothesis. However, the interpretation of data from such trials will only be relevant once the metabolism and uptake of BC constituents from the gut has been elucidated and researchers are confident of the possible bioactive constituents that may contribute to improved exercise performance. Acknowledgements Cecilia Shing has previously received funding from Numico Research Australia to investigate the influence of colostrum supplementation on exercise performance. Lesley Stevenson was formerly employed by Numico Research Australia. Denise Hunter has no conflicts of interest that are directly relevant to the content of this review. No funding support was received for the preparation of this manuscript. The authors would like to thank Sonya Marshall of Bond University for input into early stage drafts of this manuscript.

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93. Rauprich AB, Hammon HM, Blum JW. Influence of feeding different amounts of first colostrum on metabolic, endocrine, and health status and on growth performance in neonatal calves. J Anim Sci 2000; 78: 896-908 94. Nieman DC. Is infection risk linked to exercise workload? Med Sci Sports Exerc 2000; 32: S406-11 95. Atlaoui D, Duclos M, Gouarne C, et al. The 24-h urinary cortisol/cortisone ratio for monitoring training in elite swimmers. Med Sci Sports Exerc 2004; 36: 218-24 96. Halson SL, Bridge MW, Meeusen R, et al. Time course of performance changes and fatigue markers during intensified training in trained cyclists. J Appl Physiol 2002; 93: 947-56 97. Halson SL, Lancaster GI, Jeukendrup AE, et al. Immunological responses to overreaching in cyclists. Med Sci Sports Exerc 2003; 35: 854-61 98. Fitzgerald L. Overtraining increases the susceptibility to infection. Int J Sports Med 1991; 12 Suppl. 1: S5-8 99. Gleeson M. Assessing immune function changes in exercise and diet intervention studies. Curr Opin Clin Nutr Metab Care 2005; 8: 511-5 100. McKune AJ, Smith LL, Semple SJ, et al. Immunoglobulin responses to a repeated bout of downhill running. Br J Sports Med 2006 Oct; 40: 844-9 101. McKune AJ, Smith LL, Semple SJ, et al. Influence of ultraendurance exercise on immunoglobulin isotypes and subclasses. Br J Sports Med 2005; 39: 665-70 102. McKune AJ, Smith LL, Semple SJ, et al. Changes in mucosal and humoral atopic-related markers and immunoglobulins in elite cyclists participating in the Vuelta a Espana. Int J Sports Med 2006; 27: 560-6 103. Biron CA. Cytokines in the generation of immune responses to, and resolution of, virus infection. Curr Opin Immunol 1994; 6: 530-8 104. Sethi SK, Bianco A, Allen JT, et al. Interferon-gamma (IFN-gamma) down-regulates the rhinovirus-induced expression of intercellular adhesion molecule-1 (ICAM-1) on human airway epithelial cells. Clin Exp Immunol 1997; 110: 362-9 105. Bachert C, van Kempen MJ, Hopken K, et al. Elevated levels of myeloperoxidase, pro-inflammatory cytokines and chemokines in naturally acquired upper respiratory tract infections. Eur Arch Otorhinolaryngol 2001; 258: 406-12 106. Carmichael MD, Davis JM, Murphy EA, et al. Role of brain IL-beta on fatigue following exercise-induced muscle damage. Am J Physiol Regul Integr Comp Physiol 2006; 291 (5): R1344-8 107. Lakier Smith L. Overtraining, excessive exercise, and altered immunity: is this a T helper-1 versus T helper-2 lymphocyte response? Sports Med 2003; 33: 347-64 108. Lambert GP, Broussard LJ, Mason BL, et al. Gastrointestinal permeability during exercise: effects of aspirin and energy-containing beverages. J Appl Physiol 2001; 90: 2075-80 109. Prosser C, Stelwagen K, Cummins R, et al. Reduction in heat-induced gastrointestinal hyperpermeability in rats by bovine colostrum and goat milk powders. J Appl Physiol 2004; 96: 650-4 110. Lim CL, Wilson G, Brown L, et al. Pre-existing inflammatory state compromises heat tolerance in rats exposed

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to heat stress. Am J Physiol Regul Integr Comp Physiol 2007; 292: R186-94 111. Buckley JD, Brinkworth GD, Southcott E, et al. Bovine colostrum and whey protein supplementation during running training increase intestinal permeability [abstract]. Asia Pac J Clin Nutr 2004; 13: S81 112. Caradonna L, Amati L, Magrone T, et al. Enteric bacteria, lipopolysaccharides and related cytokines in inflammatory bowel disease: biological and clinical significance. J Endotoxin Res 2000; 6: 205-14 113. Spiekermann GM, Finn PW, Ward ES, et al. Receptormediated immunoglobulin G transport across mucosal barriers in adult life: functional expression of FcRn in the mammalian lung. J Exp Med 2002; 196: 303-10


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114. Roos N, Mahe S, Benamouzig R, et al. 15N-labeled immunoglobulins from bovine colostrum are partially resistant to digestion in human intestine. J Nutr 1995; 125: 1238-44 115. Davis PF, Greenhill NS, Rowan AM, et al. The safety of New Zealand bovine colostrum: nutritional and physiological evaluation in rats. Food Chem Toxicol 2007; 45: 229-36

Correspondence: Dr Cecilia M. Shing, School of Human Life Sciences, Locked Bag 1320, University of Tasmania, Launceston, TAS 7250, Australia. E-mail: [email protected]

Sports Med 2009; 39 (12)

RESEARCH REVIEW

Sports Med 2009; 39 (12): 1055-1069 0112-1642/09/0012-1055/$49.95/0


Oligomenorrhoea in Exercising Women A Polycystic Ovarian Syndrome Phenotype or Distinct Entity? Susan Awdishu,1 Nancy I. Williams,2 Sheila E. Laredo3 and Mary Jane De Souza2 1 Women’s Exercise and Bone Health Laboratory, Graduate Department of Exercise Sciences, University of Toronto, Toronto, Ontario, Canada 2 Women’s Health and Exercise Laboratory, Department of Kinesiology, Penn State University, University Park, Pennsylvania, USA 3 Women’s College Research Institute, Women’s College Hospital, Toronto, Ontario, Canada

Abstract

To date, the predominant mechanism underlying menstrual disturbances in exercising women supports an underlying energy deficiency-related aetiology, in which a failure to compensate dietary intake for the energy cost of exercise suppresses reproductive function. Increasing evidence demonstrates that energy deficiency plays a causal role in the induction of amenorrhoea in exercising women, and consistent with this mechanism are findings of glucoregulatory perturbations such as low triiodothyronine, reduced insulin secretion and elevated cortisol, growth hormone and ghrelin levels. The menstrual disturbance that may differ in its energetic characteristics and, perhaps in its androgenic and ovarian steroid environment, is oligomenorrhoea. We conducted a systematic review of the literature to begin to understand whether oligomenorrhoea in exercising women is a mild subclinical phenotype of polycystic ovarian syndrome (PCOS) in which exercise is conferring beneficial effects in protecting women from the classic PCOS phenotype, or whether oligomenorrhoea is part of the spectrum of menstrual disturbances caused by an energy deficiency that is often reported in exercising women with menstrual disturbances. We included observational, randomized controlled trials and cross-sectional studies that reported clinical, hormonal and metabolic profiles in exercising women with amenorrhoea or oligomenorrhoea and in women with PCOS. Previous studies examining the underlying mechanisms and consequences of exercise-associated menstrual disturbances have grouped exercising amenorrhoeic and oligomenorrhoeic women into a single group, and have relied primarily on self-reported menstrual history. Although scarce, the data available to date suggest that hyperandrogenism, such as that observed in PCOS, may likely be associated with oligomenorrhoea in exercising women, and may not always represent hypothalamic inhibition secondary to an energy deficiency. It is critical to closely examine the metabolic and endocrine status of women with menstrual disturbances because the treatment strategies for energy deficiency-related menstrual disturbances differ from that of disturbances traceable to hyperandrogenaemia. Further investigation is necessary to explore whether different endocrine aetiologies underly menstrual disturbances, particularly oligomenorrhoea, in physically active women.

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Ovulatory cyclic reproductive function involves the successful recruitment and development of ovarian follicles, oocyte maturation and ovulation, and formation of a corpus luteum, which contributes to the necessary secretory changes of the endometrium should fertilization occur.[1-3] Precise signals that inhibit or stimulate the hypothalamus regulate these repetitive cycles.[1-3] Classic experiments by Knobil[1-3] demonstrate the key components of this neuroendocrine system, specifically the modulating effects of ovarian steroids on gonadotropin hormones and the requirement for pulsatile secretion of gonadotropin-releasing hormone into the pituitary portal system. A disruption of the hypothalamic-pituitary-gonadal axis can lead to a spectrum of menstrual cycle perturbations ranging from subtle (i.e. luteal phase defects) to more severe menstrual disturbances (i.e. amenorrhoea).[4] The spectrum of menstrual disturbances observed in exercising women is depicted in figure 1. In exercising women, the prevalence of menstrual disturbances is high, with anovulatory and luteal phase defects being the most common abnormality associated with physical activity and exercise.[5] A higher prevalence of menstrual disturbances has been observed in specific sports in which thinness offers a competitive advantage, such as gymnastics and cross-country running.[6,7] At the extreme end of the spectrum is amenorrhoea, defined as no menses for 3 or more consecutive months, which is associated with chronic estrogen deficiency and clinical sequelae such as decreased bone mineral density.[4] Reports of the prevalence of amenorrhoea in athletes range from 1% to 66%.[8-11] Oligomenorrhoea is defined by irregular and inconsistent menstrual cycles of 36–90 days in length,[4,11] and to date there are no definitive prevalence data available for oligomenorrhoea in exercising women. However, as we suggest in this review, the prevalence of oligomenorrhoea in exercising women may be confounded by the presentation of various phenotypes of polycystic ovarian syndrome (PCOS) in athletic women. Moreover, since oligomenorrhoea is difficult to study owing to the inherent irregularity of the cycles, investigators have often grouped this menstrual disturbance with ameª 2009 Adis Data Information BV. All rights reserved.

Awdishu et al.

norrhoea, presuming that these two conditions in the exercise environment are similar. A large body of evidence suggests that the mechanism responsible for menstrual disturbances in exercising women is consistent with an underlying energy deficiency.[12-17] Indeed, it is well documented that in exercising women, the failure to compensate dietary intake for the energy cost of exercise can have a profound suppressive effect on the reproductive axis.[12,14] Reproduction is a physiologically costly process that requires significant energy. When energy is scarce, metabolic fuel is repartitioned away from long-term processes such as growth, immune function and reproduction to processes necessary for immediate survival. The availability of oxidizable metabolic fuel (i.e. glucose, free fatty acids, ketones) has been shown in animal models to represent an important modulator of reproductive function.[18] Thus far, the specific mechanism whereby the energy status of the body is transmitted to the hypothalamus is unknown, although experiments in rodents, non-human primates and in humans have yielded results supporting the importance of numerous metabolic signals.[18,19] Increasing evidence demonstrates that energy deficiency plays a causal role in the induction of exercise-associated menstrual disturbances.[13,14] Consistent with an energy deficiency-related aetiology, metabolic alterations such as low triiodothyronine (TT3), insulin and leptin, and elevated cortisol, growth hormone and ghrelin levels have been observed in amenorrhoeic exercising women.[4,17,20,21] Of interest, many of the metabolic adjustments to energy deficiency reportedly vary in magnitude with the severity of menstrual perturbation observed.[15,22] Although irregular cycles of extended length, i.e. oligomenorrhoea, is a condition commonly included in the spectrum of perturbations of the menstrual cycle associated with exercise, some investigators have suggested that the reproductive and metabolic hormonal profiles of women displaying oligomenorrhoea is not consistent with that of other exercise-related menstrual abnormalities.[23] Specifically, it is proposed that essential hyperandrogenism is, more often than not, the primary mechanism underlying oligomenorrhoea in exercising women.[23] Sports Med 2009; 39 (12)

25

30

0

5

0

5

E1G (ng/mL)

0.35 0.30 0.25 0.20 0.15 0.10 0.05 0

10 15 20 25 30 35 Day of cycle

200 180 160 140 120 100 80 60 40 20 0

20 18 16 14 12 10 8 6 4 2 0 0

5

10 15 20 Day of cycle

LPD Ovulatory

25

PdG (ug/mL)

30

E1G ng/mL PdG ug/mL

LH (m/u/mL)

20 18 16 14 12 10 8 6 4 2 0

200 180 160 140 120 100 80 60 40 20 0

PdG (ng/mL)

E1G (ng/mL)

R003-1 Day vs R003-1 E1G ng/mL R003-1 Day vs R003-1 PdG ug/mL R003-1 Day vs R003-1 LH m/u/mL


25

E1G ng/mL PdG ug/mL 200 180 160 140 120 100 80 60 40 20 0

30

20 18 16 14 12 10 8 6 4 2 0 0

5


25

PdG (ng/mL)


E1G (ng/mL)

5

20 18 16 14 12 10 8 6 4 2 0

200 180 160 140 120 100 80 60 40 20 0

PdG (ng/mL)

0

PdG (ug/mL)

20 18 16 14 12 10 8 6 4 2 0

Day vs E1G ng/mL Day vs PdG ug/mL

E1G (ng/mL)

E1G (ng/mL)

200 180 160 140 120 100 80 60 40 20 0

Oligomenorrhoea in Exercising Women


E1G ng/mL PdG ug/mL

30

Oligomenorrhoiec

Anovulatory

Amenorrhoiec

Fig. 1. Spectrum of reproductive disturbances, ranging from ovulatory cycles, subtle presentations of luteal phase deficiency (LPD) and anovulatory cycles to the most severe menstrual disturbance, amenorrhoea. Data shown are depicted by daily estrone glucuronide (E1G), pregnanediol glucuronide (PdG) and luteinizing hormone (LH) concentrations.

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Hyperandrogenism, defined as elevated serum androgen levels and/or the clinical expression of the biological action of hyperandrogenism (i.e. hirsutism), is the most widely accepted principal feature of PCOS.[24,25] Affecting nearly 6–8% of the population,[26] PCOS is the most common endocrine disorder among women of reproductive age.[26,27] Currently, two primary sets of criteria for PCOS are in widespread use,[25,28] and are shown in table I. The first definition of PCOS was established by an expert panel convened by the National Institutes of Health (NIH) in 1990. The following major criteria were established for the NIH definition of PCOS in this order of importance: (i) hyperandrogenaemia or clinical evidence of hyperandrogenism; (ii) oligo-ovulation and (iii) exclusion of other commonly related disorders.[25] The second definition arose from an expert conference in Rotterdam sponsored by the European Society for Human Reproduction and Embryology and the American Society for Reproductive Medicine in 2003 referred to as the Rotterdam Criteria.[28] These criteria established that PCOS was apparent when at least two of the following features were present: (i) oligo- or anovulation, (ii) clinical and/or biochemical hyperandrogenaemia and (iii) polycystic ovaries on ultrasound with the exclusion of other commonly related disorders.[28] In a recent systematic review of literature to identify different phenotypes of Table I. Current definitions of polycystic ovarian syndrome arising from two expert conference proceedings sponsored by the National Institute of Child Health and Human Disease of the US National Institutes of Health (NIH) and the European Society for Human Reproduction and Embryology (ESHRE) and the American Society for Reproductive Medicine (ASRM) Criteria NIH Hyperandrogenism and/or hyperandrogenaemia Oligo-anovulation Exclusion of other known disorders ESHRE/ASRM Oligo- and/or anovulation Clinical and/or biochemical signs of hyperandrogenism Polycystic ovaries Exclusion of other known disorders


Awdishu et al.

PCOS,[24] the Androgen Excess Society (AES) Task Force concluded that PCOS is primarily a disorder of androgen excess. While it is recognized that there are subclinical phenotypes of PCOS without overt hyperandrogenism present, validation of these phenotypes is required. Considering the features of PCOS, including ovulatory dysfunction, hyperandrogenaemia, hirsutism and polycystic ovaries, the AES Task Force identified nine phenotypes that could be considered PCOS, and are shown in table II.[24] Whereas it has recently been proposed that PCOS is a disorder of androgen excess or hyperandrogenism,[24] oligomenorrhoea in exercising women has generally been considered a component of a spectrum of menstrual disturbances (see figure 1) in exercising women with characteristic metabolic perturbations secondary to an energy deficit.[29] Given the potential for oligomenorrhoea to be associated with hyperandrogenism, it may be that exercising women with oligomenorrhoea do not present with the typical energy deficiencyrelated aetiology of menstrual disturbances classically observed in exercising women. This misclassification has led to the acceptance that oligomenorrhoea in exercising women is due to an energy deficiency-related aetiology. Consequently, treatment has been aimed at correcting an energy deficiency by increasing energy intake and/or decreasing energy expenditure. Given the higher prevalence of oligomenorrhoea in certain sports where muscle mass offers a competitive advantage, it may be that elevated androgenic profiles observed in oligomenorrhoeic women are an inherited or PCOS-related trait rather than an exercise-induced trait. As such, it would be surmised that some women with PCOS may naturally self-select into the athletic environment.[7] This possibility warrants further research as the treatment for energy deficiency differs from that of PCOS in that it likely involves increased energy intake. This paper will review existing literature to address the similarities and differences between oligomenorrhoea induced in association with exercise and that associated with PCOS. Specifically, it will address whether oligomenorrhoea in exercising women may be more consistent with a clinical Sports Med 2009; 39 (12)


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Table II. Polycystic ovarian syndrome phenotypes based on the presence or absence of hyperandrogenism, hirsutism, oligo-anovulation and polycystic ovaries according to the Androgen Excess Society (AES) 2006 position statement Feature

Phenotype A

B

C

D

E

F

G

H

I

J

Hyperandrogenaemia

+

+

+

+

-

-

+

-

+

-

Hirsutism

+

+

-

-

+

+

+

+

-

-

Oligo-anovulation

+

+

+

+

+

+

-

-

-

+

Polycystic ovaries

+

-

+

-

+

-

+

+

+

+

AES 2006 criteria

+

+

+

+

+

+

+

+

+

+

- indicates absence; + indicates presence.

or subclinical phenotype of PCOS[30] or whether oligomenorrhoea is indeed part of the spectrum of energy deficiency-related menstrual disturbances classically observed in exercising women. 1. Methods An electronic search of the computerized database PubMed was performed for the period 1970–2008 using the search terms: oligomenorrhoea, functional hypothalamic amenorrhoea, athletic amenorrhoea, polycystic ovary syndrome, androgens, menstrual disturbance, hirsutism, energy deficiency and undernutrition. We included all published studies of randomized controlled trials, observational and prospective studies, and cross-sectional studies that assessed oligomenorrhoea in both exercising and non-exercising women. Because the definition of oligomenorrhoea, i.e. menstrual cycles that are irregular and lengthened (36–90 days), displays overlap with a definition of amenorrhoea that is used commonly, i.e. absence of menses for 90 days, we included amenorrhoea in our discussions of the hormonal characteristics of oligomenorrhoea. We excluded studies not published in English. 2. Results 2.1 Reproductive Profiles: Exercising Amenorrhoeic and Oligomenorrhoeic Women

Exercise-associated amenorrhoea has been classified as hypothalamic amenorrhoea as luteinizing hormone (LH) pulsatility is suppressed and a prepubertal pattern of release is typically ª 2009 Adis Data Information BV. All rights reserved.

exhibited.[4,16,22,31] Because many investigators have not incorporated sampling periods long enough to capture oligomenorrhoeic cycles, scarce data exist that examines or characterizes the reproductive hormonal profiles of this group. For the most part, investigators often group exercising amenorrhoeic and oligomenorrhoeic women into a single group, and have relied primarily on self-reported menstrual history.[32-35] More recently, Rickenlund and colleagues[36] confirmed that LH pulsatility was decreased in amenorrhoeic athletes, and identified that the suppression of LH pulsatility was not apparent in separately grouped oligomenorrhoeic athletes. The latter group of athletes displayed an average of five menstrual cycles in the last year. Participants were amenorrhoeic if they failed to menstruate for the last 3 months, oligomenorrhoeic if menses occurred at intervals exceeding 6 weeks and not more than nine times in the last year, and regular menstruating if menses occurred within an interval of 22–34 days. Thus, the oligomenorrhoeic athletes did not demonstrate a LH pulsatility pattern characteristic of functional hypothalamic amenorrhoea, as previously described in amenorrhoeic athletes.[20] Data also suggestive of a unique hypothalamic-pituitary status in oligomenorrhoeic athletes have been published by Constantini and Warren.[7] They observed a significantly increased serum LH and follicle-stimulating hormone (FSH), and an elevated LH : FSH ratio in swimmers with oligomenorrhoea, where oligomenorrhoea was defined as two consecutive menstrual cycles shorter than 21 days or longer than 45 days.[7] Taken together, these studies suggest that oligomenorrhoeic exercising women Sports Med 2009; 39 (12)

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do not demonstrate suppressed levels of gonadotropins, evidence that perhaps these athletes are distinct from amenorrhoeic athletes with hypothalamic dysfunction. Although studies in amenorrhoeic exercising women have confirmed that, in addition to suppression of LH and FSH, ovarian steroid secretion is compromised,[31,37] no data have been published that describe the ovarian steroid profiles of oligomenorrhoeic exercising women. To date, there are no profiles in the literature of the specific daily ovarian steroid excretion of oligomenorrhoeic cycles in exercising women. We have recently monitored the menstrual cycles of exercising women with oligomenorrhoeic cycles, where oligomenorrhoea was defined as menstrual intervals of 36–90 days. We examined the ovarian steroid profiles in nine oligomenorrhoeic women by assessing daily urinary concentrations of estrone-1-glucuronide and pregnanediol glucuronide over one or two entire cycles. We assessed the presence or absence of ovulation using previously published methods of analysing urinary hormone profiles,[38] and we calculated follicular and luteal phase lengths. We observed that approximately half of the cycles evaluated were ovulatory.[38] Herein, we present two characteristic examples of oligomenorrhoeic cycles in exercising women in figures 2a and 2b. 2.2 Reproductive Profiles: Women with Polycystic Ovarian Syndrome (PCOS)

Numerous investigators have reported that women with PCOS have elevated levels of LH with suppressed FSH levels and an elevated LH : FSH ratio (LH : FSH >2),[39-41] which is in sharp contrast to that observed in women with functional hypothalamic amenorrhoea (LH : FSH 6.5 has been utilized as a sensitive and specific indicator of hyperandrogenism.[78] Over time, these biochemical abnormalities may progress to clinical hyperandrogenism, i.e. hirsutism, acne, androgenic alopecia and chronic oligo-anovulation.[43] Increased serum levels of TT, freeT and DHEAS have been observed in all phenotypes of PCOS, with higher mean levels in women characterized by the classic PCOS phenotype, and intermediate levels in women with ovulatory PCOS.[57] It is well documented that the PCOS phenotypes that include hyperandrogenism are associated with more severe metabolic, endocrine and reproductive risks while the non-androgenic phenotype defined by the Rotterdam criteria Sports Med 2009; 39 (12)


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Table III. Studies reporting androgens among amenorrhoeic (functional hypothalamic and athletes) and oligomenorrhoeic (sedentary or athletes) women with polycystic ovarian syndrome (PCOS) Study, year

Measure

Amenorrhoeic

Laughlin et al.,[73] 1998

TT

ü

-

DHEAS

Laughlin and Yen,[17] 1996

TT

ü

-

Androstenedione

fl ü

-

FAI

Rickenlund et al.,[23] 2003

-

FAI TT Androstenedione

-

Sex-hormone-binding globulin

fl

TT: sex-hormone-binding globulin

ü

ü

›

TT

›

freeT

›

Androstenedione Rickenlund et al.,[36] 2004

Diurnal TT

Azziz et al.,[24] 2006

TT

PCOS

-

freeT Goodarzi and Azziz,[74] 2006

Oligomenorrhoeic

-

› ü

fl

ü

› ü

DHEAS

› ›

freeT

›

Wakat et al.,[35] 1982

FAI

ü

›

Chang et al.,[58] 2005

FAI

ü

›

Taponen et al.,[39] 2003

FAI

ü

›

Mathur et al.,[78] 1981

FAI

ü

›

freeT

›

Bioavailable testosterone

›

DHEAS = dehydroepiandrosterone sulphate; FAI = free androgen index; freeT = free testosterone; TT = total testosterone; ü indicates study group compared with regularly cycling controls; › indicates concentrations greater than regularly cycling controls; fl indicates concentrations less than regularly cycling controls; – indicates concentrations the same as regular cycling controls.

(oligo-anovulation + polycystic ovaries) is associated with neuroendocrine profiles similar to ovulatory controls. Women with polycystic ovaries, hyperandrogenism and oligomenorrhoea generally have the most adverse metabolic profiles in comparison with other PCOS phenotypes (i.e. polycystic ovaries and oligomenorrhoea, polycystic ovaries and hyperandrogenism or hyperandrogenism and oligomenorrhoea),[56,58-61,79] i.e. these women are the most insulin resistant, have the most abnormal lipid profiles and have the highest BMI.[56] Table III summarizes studies reporting androgens in amenorrhoeic and oligomenorrhoeic PCOS women. ª 2009 Adis Data Information BV. All rights reserved.

3. Conclusions For the most part, studies examining the underlying mechanisms and consequences of exercise-associated menstrual disturbances have grouped exercising amenorrhoeic and oligomenorrhoeic women together, with the majority of investigators’ studies establishing this based on self-reported menstrual history.[32-35] Investigators to date have not carefully examined the daily ovarian steroid profiles to accurately determine menstrual status and proceed with careful evaluation of hormonal and energetic factors in order to determine whether separate findings distinguish Sports Med 2009; 39 (12)

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oligomenorrhoeic and amenorrhoeic exercising women. This lack of detailed assessment has led to the general acceptance that oligomenorrhoea in exercising women is likely due to an energy deficiency-related aetiology. Consequently, treatment has been aimed at correcting an energy deficiency by increasing energy intake and/or decreasing energy expenditure. Oligomenorrhoea in exercising women is a menstrual disturbance that is under-investigated in the literature, likely attributable to the difficult nature of studying these inconsistent and irregular cycles. The exact underlying mechanism of this disturbance in exercising women may not always be related to an energy-deficient aetiology, as previously has been assumed, and further research is certainly warranted. Thus, oligomenorrhoea may not always represent an intermediate state in hypothalamic inhibition resulting from an energy deficiency; an alternate and likely possible explanation may be linked to hyperandrogenism. It is also critical to distinguish between oligomenorrhoeic and amenorrhoeic exercising women because the treatment strategy for amenorrhoea (increasing energy intake) likely differs from oligomenorrhoea (increasing energy expenditure and decreasing energy intake) as these variants likely represent unique underlying aetiologies. Further investigation into the hormonal environment may be necessary as the finding of elevated androgens could indicate PCOS and this may mandate appropriate treatments. We propose that investigators who assess women who have irregular cycles should specifically assess women for clinical and biochemical evidence of hyperandrogenism. We propose that the well validated and semi-quantitative Ferriman-Gallwey score[64] be utilized as a measure of hirsutism as well as a measure of the free androgens, and perhaps SHBG. Evidence from women with PCOS, and from our study of exercising women with oligomenorrhoea, suggests that low SHBG, which has good assay reproducibility, may be a useful marker as it is commonly associated with hyperandrogenism, and as it is also associated with insulin resistance which we propose underlies the predisposition to oligomenorrhoea in women who exercise. Women who have evidence of androgen ª 2009 Adis Data Information BV. All rights reserved.

excess or low SHBG should be considered at risk for a disorder other than energy deficiency associated with amenorrhoea in athletes. Moreover, in studies related to the Female Athlete Triad, investigators need to rule out hyperandrogenism in oligomenorrhoeic women, as this aetiology is not a component of the Female Athlete Triad. When oligomenorrhoea is present, a detailed history of menstrual status, exercise and diet history including history of stress fractures followed by clinical/laboratory tests to rule out other causes of menstrual disturbance (i.e. PCOS) is warranted. More studies are required to validate the underlying mechanism involved in this oligomenorrhoea in exercising women. Acknowledgements No sources of funding were used to assist in the preparation of this review. The authors have no conflicts of interest that are directly relevant to the content of this review.

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Correspondence: Dr Mary Jane De Souza, Department of Kinesiology, Penn State University, University Park, PA 16803 USA. E-mail: [email protected]

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