Association Between Questionnaire- and Accelerometer-Assessed Physical Activity: The Role of Sociodemographic Factors

American Journal of Epidemiology © The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted reuse, distribution, and reproduction in anymedium, provided the original work is properly cited. Vol. 179, No. 6 DOI: 10.1093/aje/kwt330 Advance Access publication: February 4, 2014 Practice of Epidemiology Association Between Questionnaire- and Accelerometer-Assessed Physical Activity: The Role of Sociodemographic Factors Séverine Sabia*, Vincent T. van Hees*, Martin J. Shipley, Michael I. Trenell, Gareth HaggerJohnson, Alexis Elbaz, Mika Kivimaki, and Archana Singh-Manoux * Correspondence to Dr. Séverine Sabia, Department of Epidemiology and Public Health, University College London, 1-19 Torrington Place, London WC1E 6BT, United Kingdom (e-mail: ); or Dr. Vincent T. van Hees, MoveLab—Physical Activity and Exercise Research, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne NE2 4HH, United Kingdom (e-mail: ). The correlation between objective and self-reported measures of physical activity varies between studies. We examined this association and whether it differed by demographic factors or socioeconomic status (SES). Data were from 3,975 Whitehall II (United Kingdom, 2012–2013) participants aged 60–83 years, who completed a physical activity questionnaire and wore an accelerometer on their wrist for 9 days. There was a moderate correlation between questionnaire- and accelerometer-assessed physical activity (Spearman’s r = 0.33, 95% confidence interval: 0.30, 0.36). The correlations were higher in high-SES groups than in low-SES groups (P ’s = 0.02), as defined by education (r = 0.38 vs. r = 0.30) or occupational position (r = 0.37 vs. r = 0.29), but did not differ by age, sex, or marital status. Of the self-reported physical activity, 68.3% came from mild activities, 25% from moderate activities, and only 6.7% from vigorous activities, but their correlations with accelerometer-assessed total physical activity were comparable (range of r ’s, 0.21–0.25). Self-reported physical activity from more energetic activities was more strongly associated with accelerometer data (for sports, r = 0.22; for gardening, r = 0.16; for housework, r = 0.09). High-SES persons reported more energetic activities, producing stronger accelerometer associations in these groups. Future studies should identify the aspects of physical activity that are most critical for health; this involves better understanding of the instruments being used. accelerometry; cohort studies; elderly; epidemiologic methods; physical activity; questionnaires Abbreviations: CI, confidence interval; MET, metabolic equivalent; SES, socioeconomic status. might affect the association, although their role remains unclear (4, 8–15). Our aim in the present study was to examine whether the correlation between questionnaire-assessed and accelerometerassessed physical activity differed by sociodemographic factors in a large British cohort. In addition, we assessed the potential influence of level and type of physical activity reported. Physical inactivity has a deleterious effect on health; it is estimated that a 25% decrease in its prevalence would prevent over 1.3 million deaths worldwide every year (1). However, these estimates are imprecise, as much of the evidence comes from self-reported data on physical activity (2). The Spearman correlation (r) between objectively measured physical activity (e.g., accelerometry, doubly labeled water, heart rate monitoring) and activity measured via questionnaire varies between studies and ranges from −0.71 to 0.96 (3–5), but it is typically low to moderate (mean across studies: r = 0.37; standard deviation, 0.25) (4). The reasons for this inconsistency are poorly understood. Differences in the measurement instruments used (4, 6, 7) and the sociodemographic characteristics of study populations (such as age, sex, and education) METHODS Study population Data were drawn from the Whitehall II Study, a United Kingdom cohort study of 10,308 persons (67% men) aged 781 Am J Epidemiol. 2014;179(6):781–790 Initially submitted September 27, 2013; accepted for publication December 4, 2013. 782 Sabia et al. 35–55 years that was established in 1985–1988 (16). Participants gave written informed consent, and the University College London ethics committee approved the study protocol. Since the study’s inception, sociodemographic, behavioral, and health-related factors, including self-reported physical activity, have been assessed approximately every 5 years (1985–1988, 1991–1993, 1997–1999, 2002–2004, 2007– 2009 and 2012–2013). Accelerometry measurements were added to the study during the 2012–2013 wave of data collection for participants seen at the central London clinic and those living in the southeastern regions of England, who were screened at home. Questionnaire-based assessment of physical activity Accelerometer-assessed physical activity For accelerometer assessment of physical activity, a wristworn triaxial accelerometer (GeneActiv; Activinsights Ltd., Cambs, United Kingdom) was used, and participants were Sociodemographic factors Demographic variables included age, sex, ethnicity (white, South Asian, black, or other), and marital status (married/cohabiting, single, widowed, or divorced/separated). Results for participants reporting “other” ethnicity were not analyzed because of small numbers in this category (n = 31). Am J Epidemiol. 2014;179(6):781–790 For questionnaire assessment of physical activity, we used a modified version of a previously validated questionnaire, the Minnesota Leisure Time Physical Activity Questionnaire (17, 18). The questionnaire instructions stated, “We would like to know about your activities at work and in your free time that involve physical activity.” It included 20 items on the amount of time spent in the following activities: walking, sports (cycling, soccer, golf, swimming, and 2 open-ended questions on “other sports”), gardening (weeding, mowing, and 1 open-ended question on “other gardening activities”), housework (carrying heavy shopping items, cooking, hanging out washing, and 2 open-ended questions on “other housework”), and do-it-yourself activity (building, modifying, or repairing something without the aid of experts or professionals, such as manual car-washing, painting, or decorating, and 1 open-ended question on “other do-it-yourself activity”), as well as 2 open-ended questions on “other activities.” For each item, participants were required to take into account activity patterns over the past 4 weeks to give an indication of their usual activity and to provide the total number of hours spent in that activity per week (19). For each activity, including open-end (...truncated)