Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study
| Autor: | Nicholas J. Wareham, Aiden R. Doherty, Rob Gillions, Malcolm H. Granat, Dan Jackson, Tim Peakman, Stephen J. Preece, Christoper G. Owen, Michael I. Trenell, Vincent T. van Hees, Nils Y. Hammerla, Thomas E. White, Patrick Olivier, Thomas Plötz, Soren Brage, S.M. Sheard |
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| Přispěvatelé: | White, Thomas [0000-0001-8456-0803], Brage, Soren [0000-0002-1265-7355], Wareham, Nicholas [0000-0003-1422-2993], Apollo - University of Cambridge Repository |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Gerontology
Male Wrist Joint Time Factors lcsh:Medicine Wrist Accelerometer Biochemistry Proxy (climate) 0302 clinical medicine Mathematical and Statistical Techniques Accelerometry Medicine and Health Sciences Public and Occupational Health Public Health Surveillance 030212 general & internal medicine lcsh:Science 10. No inequality Musculoskeletal System Biological Specimen Banks education.field_of_study Multidisciplinary Data Processing Physics Confounding Classical Mechanics Middle Aged Biobank 3. Good health Arms medicine.anatomical_structure Physical Sciences Engineering and Technology Female Analysis of variance Seasons Anatomy Information Technology Statistics (Mathematics) Research Article Computer and Information Sciences Evening Population Acceleration Bioenergetics Research and Analysis Methods 03 medical and health sciences medicine Humans Statistical Methods education Exercise Aged Analysis of Variance business.industry lcsh:R Limbs (Anatomy) Biology and Life Sciences 030229 sport sciences Physical Activity United Kingdom Age Groups People and Places lcsh:Q Population Groupings Electronics Accelerometers business Mathematics Demography |
| Zdroj: | PLoS ONE PLoS ONE, Vol 12, Iss 2, p e0169649 (2017) |
| ISSN: | 1932-6203 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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