Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study
Activity counts measure attention, not model quality. Missing counts are unknown. Publication status follows the linked source; a listed venue alone does not confirm peer review.
Doherty and colleagues describe how seven-day, 100 Hz wrist accelerometry was collected and processed for more than 100,000 UK Biobank participants, including calibration, gravity and noise removal, and non-wear detection. Of 103,712 datasets received, 96,600 participants (93.3%) provided valid physical activity data, with a median wear time of 6.9 days. Overall activity (vector magnitude) was 7.5% lower per decade of age and varied strongly by time of day, while weekday/weekend and seasonal differences were small.
- Signals: Accelerometer
- Result: 103,712 datasets were received (44.8% response), with a median wear-time of 6.9 days (IQR:6.5-7.0).
- Result: 96,600 participants (93.3%) provided valid data for physical activity analyses.
- Venue
- PLOS ONE
- Published
- 2017
- arXiv
- Not reported
- Signals
- Not reported
- Form factor
- Not reported
- Devices
- Subjects
- 100,000 participants
- Code
- Not reported
- Added
- Oct 15, 2026
Abstract
BackgroundPhysical 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.MethodsParticipants 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.Results103,712 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.ConclusionsIt 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.
Verbatim fromEurope PMC· Licence: cc by
How it works
- Cohort
- 100,000 participants
Devices· 1
Illustrations by form factor; product photos are added as licences clear.
Datasets used· 1
Cite
@article{dohertyukbwristaccelerom2017,
title = {{Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study}},
author = {Aiden Doherty and Dan Jackson and Nils Hammerla and Thomas Plötz and Patrick Olivier and Malcolm H. Granat and Tom White and Vincent T. van Hees and Michael I. Trenell and Christoper G. Owen and Stephen J. Preece and Rob Gillions et al.},
year = {2017},
journal = {PLOS ONE},
doi = {10.1371/journal.pone.0169649}
}History
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