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Doherty, A; Hammerla, N; Jackson, D; Plötz, T; Olivier, P; Granat, MH; White, T; van Hees, VT; Trenell, MI; Owen, CG; Preece, SJ; Gillions, R; Sheard, S; Peakman, T; Brage, S; Wareham, NJ (2017)
Publisher: Public Library of Science
Journal: PLoS One
Languages: English
Types: Article
Subjects: Electronics, Research Article, Information Technology, Anatomy, Mathematics, Classical Mechanics, Engineering and Technology, Mathematical and Statistical Techniques, Wrist, Population Groupings, Acceleration, Physical Sciences, Analysis of Variance, Bioenergetics, People and Places, Public and Occupational Health, Physics, Statistics (Mathematics), Biology and Life Sciences, Computer and Information Sciences, Data Processing, Research and Analysis Methods, Musculoskeletal System, Medicine, Age Groups, Physical Activity, Q, R, Biochemistry, Science, Accelerometers, Arms, Medicine and Health Sciences, Limbs (Anatomy), Statistical Methods

Background

Physical activity has not been objectively measured in prospective cohorts with sufficiently large numbers to reliably detect associations with multiple health outcomes. Technological advances now make this possible. We describe the methods used to collect and analyse accelerometer measured physical activity in over 100,000 participants of the UK Biobank study, and report variation by age, sex, day, time of day, and season.

Methods

Participants were approached by email to wear a wrist-worn accelerometer for seven days that was posted to them. Physical activity information was extracted from 100Hz raw triaxial acceleration data after calibration, removal of gravity and sensor noise, and identification of wear / non-wear episodes. We report age- and sex-specific wear-time compliance and accelerometer measured physical activity, overall and by hour-of-day, week-weekend day and season.

Results

103,712 datasets were received (44.8% response), with a median wear-time of 6.9 days (IQR:6.5–7.0). 96,600 participants (93.3%) provided valid data for physical activity analyses. Vector magnitude, a proxy for overall physical activity, was 7.5% (2.35mg) lower per decade of age (Cohen’s d = 0.9). Women had a higher vector magnitude than men, apart from those aged 45-54yrs. There were major differences in vector magnitude by time of day (d = 0.66). Vector magnitude differences between week and weekend days (d = 0.12 for men, d = 0.09 for women) and between seasons (d = 0.27 for men, d = 0.15 for women) were small.

Conclusions

It is feasible to collect and analyse objective physical activity data in large studies. The summary measure of overall physical activity is lower in older participants and age-related differences in activity are most prominent in the afternoon and evening. This work lays the foundation for studies of physical activity and its health consequences. Our summary variables are part of the UK Biobank dataset and can be used by researchers as exposures, confounding factors or outcome variables in future analyses.

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