Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study.
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.
Big Data Institute, Nuffield Department of Population Health, BHF Centre of Research Excellence, University of Oxford, Oxford, United Kingdom.|Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
Aiden
http://orcid.org/0000-0003-1840-0451
A
Doherty
Open Lab, Newcastle University, Newcastle, United Kingdom.
Dan
D
Jackson
Open Lab, Newcastle University, Newcastle, United Kingdom.
Nils
N
Hammerla
Open Lab, Newcastle University, Newcastle, United Kingdom.
Thomas
http://orcid.org/0000-0002-1243-7563
T
Plötz
Open Lab, Newcastle University, Newcastle, United Kingdom.
Patrick
http://orcid.org/0000-0003-2841-7580
P
Olivier
School of Health Sciences, University of Salford, Manchester, United Kingdom.
Malcolm H
MH
Granat
MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
Tom
http://orcid.org/0000-0001-8456-0803
T
White
MoveLab, Institute of Cellular Medicine, Newcastle University, Newcastle, United Kingdom.
Vincent T
VT
van Hees
MoveLab, Institute of Cellular Medicine, Newcastle University, Newcastle, United Kingdom.
Michael I
MI
Trenell
Population Health Research Institute, St George's University of London, London, United Kingdom.
Christoper G
CG
Owen
School of Health Sciences, University of Salford, Manchester, United Kingdom.
Stephen J
SJ
Preece
UK Biobank, Stockport, United Kingdom.
Rob
http://orcid.org/0000-0003-0914-8370
R
Gillions
UK Biobank, Stockport, United Kingdom.
Simon
http://orcid.org/0000-0002-8925-8280
S
Sheard
UK Biobank, Stockport, United Kingdom.
Tim
T
Peakman
MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
Soren
S
Brage
MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
Nicholas J
http://orcid.org/0000-0003-1422-2993
NJ
Wareham
D061725:Accelerometry; D000368:Aged; D018070:Biological Specimen Banks; D015444:Exercise; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D062486:Public Health Surveillance; D012621:Seasons; D013997:Time Factors; D006113:United Kingdom; D014955:Wrist Joint / Q000502:physiology*