Biobank Accelerometer Analysis Tool
A tool for large scale analysis of wearable sensor data to extract health-relevant metrics on activity levels, step counts, circadian rhythm, sedentary time, and more. The tool has been used in studies involving the UK Biobank accelerometer dataset as well as the China Kadoorie Biobank accelerometer dataset, enabling fine-grained analysis of 24h physical behaviour patterns at the population scale.
Availability
This is openly available via an academic use license (link), while a license would need to be negotiated for commercial applications.
Location
Nuffield Department of Population Health
Contact Aiden Doherty or Shing Chan
Publications
Doherty et al (2017) Large scale population assessment of physical activity using wrist worn accelerometers: the UK Biobank study. PLOS ONE 12(2):e0169649
Willetts et al (2018) Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants. Scientific Reports. 8(1):7961
Walmsley et al (2021) Reallocating time from device-measured sleep, sedentary behaviour or light physical activity to moderate-to-vigorous physical activity is associated with lower cardiovascular disease risk. British Journal of Sports Medicine doi: 10.1136/bjsports-2021-104050