ann. behav. med. (2008) 35:123–124 DOI 10.1007/s12160-007-9012-9

LETTER TO THE EDITOR

Exploring Physical Activity Preferences Abby C. King, Ph.D. & Sara Wilcox, Ph.D.

Published online: 12 February 2008 # The Society of Behavioral Medicine 2008

We read with interest the article by Beauchamp and colleagues [1] that explored preferences for exercising alone or in age-specific groups among a convenience sample of 947 Leeds, UK adults. (Individuals were approached in the city center by one of six trained research assistants and asked to respond to a series of questions about their “views about exercise” (p. 202). The article describes some interesting results concerning preferences for same-age exercise classes across the different age groups sampled. The authors compare their results with those from some of our work in the physical activity preference area [2]. Some of the interpretations that they have made about our work, however, are at odds with our own interpretations of our data. We believe that these differences merit discussion to reduce confusion in the field. A large number of domains underlie people’s preferences for physical activity participation. One domain consists of the presence or absence of an exercise instructor whose job is typically to structure an exercise class or group with respect to location, schedule, size, participant configuration, and content. The dichotomous preference question used in our work (i.e., “Which is more appealing to you, exercising in a group with an exercise leader or

A. C. King (*) Stanford Prevention Research Center, Dept. of Medicine, Stanford University School of Medicine, Hoover Pavilion, Room N229, 211 Quarry Road, Stanford, CA 94305–5705, USA e-mail: [email protected] S. Wilcox Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA

exercising on your own, with some instruction?”) has been aimed primarily at this structural domain. We have noted specifically as part of this work that this type of question does not address whether someone choosing to exercise outside of an instructor-led class format (i.e., “on your own”) is also choosing to exercise completely alone or with neighbors, friends, relatives, coworkers, or other people in his or her environment [2] (p. 395). This latter social domain (i.e., exercise undertaken alone or with others) is the domain being targeted by the authors in the current study. The authors rightfully note that the structural and social domains are overlapping yet distinct [1] (p. 205). Neither of these studies has, in our opinion, assessed both of these domains in a way that can meaningfully separate the structural from social aspects of exercise preferences. The authors cite our work in noting that “epidemiological research has suggested a move away from group-based exercise interventions amongst older adults and toward interventions that are directed at the individual level” [1] (p. 204). In fact, a major objective underlying our work has been to uncover the vast array of factors delineating subgroups of mid-life and older adults that may do best with different types of exercise interventions, including those, as noted in Wilcox et al. [2], who may prefer instructor-led exercise classes. Such investigations can help to set the stage for developing interventions that broaden the choices available to different subgroups of adults, including older adults, often hidden behind population means. It is also important not to equate home-based approaches with ‘individual’ interventions, given that homebased approaches can involve group- or neighborhood-level interventions. We believe that the physical activity intervention field, including studies of physical activity preferences that can

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be used to inform that field, can best be advanced through the following: –

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Observational research derived from population-based sampling [2, 3], given that without knowing what the denominator is and, by extension, who is being represented and who is not, interpretations related to generalizability of study results to the broader population are diminished. Inclusion, as the authors recommend, of a variety of preference domains to obtain a more comprehensive view of how such domains may combine, as well as the trade-offs attendant with different domain choices. Applications of experimental designs in testing systematically the impact of varying domains on actual physical activity participation levels [4–6]. While metaanalysis has, as noted in the authors’ article, become increasingly popular as a tool in attempting to evaluate the relative efficacy of different intervention parameters in this as well as other health behavior fields, the substantial heterogeneity of intervention studies included in a number of such investigations, in terms of subject samples, outcome measurement, and intervention content, length, dose, timing, delivery source, and format, render the interpretation of results from such investigations difficult at best. Only through direct experimental testing of the relative effects of differing structural and social domains can the importance of such domains in driving actual physical activity behavior change, both in relation to and independent of participant preferences, be fully evaluated. This issue has arisen in several of our previous RCTs when anecdotal reports of initial participant preferences did not generally coincide with how well participants did with different physical activity interventions. For example, in a previous randomized clinical trial [4], while the majority of individuals anecdotally reported preferring class-based exercise instruction before study randomization, those randomized to telephone-supervised home-based exercise had generally greater increases in physical activity participation rates across a 2-year period [4, 7, 8]. Similarly, in a recently completed randomized clinical trial comparing telephone-based exercise instruction delivered by humans vs computer, while the majority of individuals entering the study reported a preference for receiving physical activity advice from a human advisor, the two different

forms of instruction resulted in similar physical activity increases across 12 months [9]. These results suggest, consistent with social cognitive theory-based interpretations, that the utility of initial program preferences in predicting behavior change may be curtailed when individuals have limited experience with one or more of the interventions under investigation [10, 11]. In light of the increased prevalence of inactivity as people age, it is imperative that scientists in the field continue to strive to develop the range of intervention tools and strategies required to meet the diverse needs of all segments of our aging society.

References 1. Beauchamp MR, Carron AV, Mccutcheon S, Harper O. Older adults Preferences for exercising alone versus in groups considering contextual congruence. Annals Behav Med. 2007; 33(2): 200–6. 2. Wilcox S, King AC, Brassington G, Ahn D. Physical activity preferences of middle-aged and older adults: A community analysis. J Aging Phys Act. 1999; 7: 386–399. 3. King AC, Taylor CB, Haskell WL, De Busk RF. Identifying strategies for increasing employee physical activity levels: Findings from the Stanford/Lockheed exercise survey. Health Ed Q. 1990; 17: 269–285. 4. King AC, Haskell WL, Taylor CB, Kraemer HC, De Busk RF. Group- versus home-based exercise training in healthy older men and women: A community-based clinical trial. JAMA. 1991; 266: 1535–1542. 5. Dunn AL, Marcus BH, Kampert JB, et al. Comparison of lifestyle and structured Interventions to increase physical activity and cardiorespiratory fitness: A randomized trial. JAMA. 1999; 281: 327–334. 6. Perri MG, Martin AD, Leermakers EA, Sears SF, Notelovitz M. Effects of group- versus home-based exercise in the treatment of obesity. J Consult Clin Psychol. 1997; 65(2): 278–85. 7. King AC, Haskell WL, Young DR, Oka RK, Stefanick ML. Longterm effects of varying intensities and formats of physical activity on participation rates, fitness, and lipoproteins in men and women aged 50 to 65 years. Circulation. 1995; 91(10): 2596–604. 8. King AC, Kiernan M, Oman RF, et al. Can we identify who will adhere to long-term physical activity? Application of signal detection methodology as a potential aid to clinical decisionmaking. Health Psychol. 1997; 16: 380–389. 9. King AC, Friedman RM, Marcus B, et al. Ongoing physical activity advice by humans versus computers: The Community Health Advice by Telephone (CHAT) Trial. Health Psychol. 2007; 26(6): 718–727. 10. Bandura A. Social cognitive theory: An agentic perspective. Ann Rev Psychol. 2001; 52: 1–26. 11. Rothman AJ. Toward a theory-based analysis of behavioral maintenance. Health Psych. 2000; 19(1 Suppl): 64–9.