Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study.

Article English OPEN
Doherty, A ; Jackson, D ; Hammerla, N ; 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: volume 12 12, issue 2 (issn: 1932-6203 1932-6203, eissn: 1932-6203)
  • Related identifiers: doi: 10.1371/journal.pone.0169649, doi: 10.17863/CAM.8837, pmc: PMC5287488
  • Subject: 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 | Age Groups | Physical Activity | Biochemistry | 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. The UK Biobank Activity Project and the collection of activity data from participants was funded by the Wellcome Trust (https://wellcome.ac.uk/) and the Medical Research Council (http://www.mrc.ac.uk/). The analysis was supported by the British Heart Foundation Centre of Research Excellence at Oxford (http://www.cardioscience.ox.ac.uk/bhf-centre-of-research-excellence) [grant number RE/13/1/30181 to AD], the Li Ka Shing Foundation (http://www.lksf.org/) [to AD], the UK Medical Research Council (http://www.mrc.ac.uk/) [grant numbers MC_UU_12015/1 and MC_UU_12015/3 to NW and SB], the RCUK Digital Economy Research Hub on Social Inclusion through the Digital Economy (SiDE) (http://www.rcuk.ac.uk/) [EP/G066019/1 to NH], the EPSRC Centre for Doctoral Training in Digital Civics (https://www.epsrc.ac.uk/)[EP/L016176/1 to DJ], and the National Institute for Health Research (http://www.nihr.ac.uk/) [SRF-2011-04-017 to MIT]. The MRC and Wellcome Trust played a key role in the decision to establish UK Biobank, and the accelerometer data collection. No funding bodies had any role in the analysis, decision to publish, or preparation of the manuscript.
  • References (22)
    22 references, page 1 of 3

    1. Lee I­M, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. Effect of physical inactivity on major non­communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380: 219-29. doi: 10.1016/S0140­6736(12)61031­9. pmid:22818936 View Article PubMed/NCBI Google Scholar

    2. Chief Medical Officers of England, Scotland, Wales, and Northern Ireland. Start Active, Stay Active: A report on physical activity for health from the four home countries. U.K. Department of Health; 2011.

    3. Steene­Johannessen J, Anderssen SA, van Der Ploeg HP, Hendriksen IJM, Donnelly AE, Brage S, et al. Are Self­report Measures Able to Define Individuals as Physically Active or Inactive? Med Sci Sport Exerc. 2016;48. Available: http://journals.lww.com/acsmmsse/Fulltext/2016/02000/Are_Self_report_Measures_Able_to_Define.8.aspx View Article PubMed/NCBI Google Scholar

    4. Strath SJ, Kaminsky LA, Ainsworth BE, Ekelund U, Freedson PS, Gary RA, et al. Guide to the assessment of physical activity: Clinical and research applications: a scientific statement from the American Heart Association. Circulation. 2013;128: 2259-79. doi: 10.1161/01.cir.0000435708.67487.da. pmid:24126387 View Article PubMed/NCBI Google Scholar

    5. Wijndaele K, Westgate K, Stephens SK, Blair SN, Bull FC, Chastin SF, et al. Utilization and Harmonization of Adult Accelerometry Data: Review and Expert Consensus. Med Sci Sports Exerc. 2015 View Article PubMed/NCBI Google Scholar

    6. Craig R, Mindell J, Hirani V. Health Survey for England-2008: Physical activity and fitness. National Centre for Social Research; 2008.

    7. Sartini C, Wannamethee SG, Iliffe S, Morris RW, Ash S, Lennon L, et al. Diurnal patterns of objectively measured physical activity and sedentary behaviour in older men. BMC Public Health. 2015;15: 609. doi: 10.1186/s12889­015­1976­y. pmid:26141209 View Article PubMed/NCBI Google Scholar

    8. Berkemeyer K, Wijndaele K, White T, Cooper AJM, Luben R, Westgate K, et al. The descriptive epidemiology of accelerometer­measured physical activity in older adults. Int J Behav Nutr Phys Act. BioMed Central; 2016;13: 2. doi: 10.1186/s12966­015­0316­z. pmid:26739758 View Article PubMed/NCBI Google Scholar

    9. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sport Exerc. 2008;40: 181-188. View Article PubMed/NCBI Google Scholar

    10. Colley RC, Garriguet D, Janssen I, Craig CL, Clarke J, Tremblay MS. Physical activity of Canadian adults: accelerometer results from the 2007 to 2009 Canadian Health Measures Survey. Heal reports. 2011;22: 7-14. Available: http://europepmc.org/abstract/med/21510585 View Article PubMed/NCBI Google Scholar

  • Related Research Results (5)
  • Software (2)
  • Metrics
    No metrics available