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

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.

A comparison of physical activity from Actigraph GT3X+ accelerometers worn on the dominant and non‐dominant wrist

“…As greater compliance rates will provide the researcher with activity data over more days and capture a greater proportion of that day, researchers will have more confidence that the data collected is representative of habitual PA. As will recent findings which showed that wristworn accelerometers is highly acceptable to participants with a median wear-time of 6Á9 days and the very high proportion of people (103 578 of 106 053 (98%)) in whom the data were of high quality and completeness (Doherty et al, 2017). These findings will provide confidence to researchers given the well-established relationships between physical activity and successful ageing (Dogra & Stathokostas, 2012;Almeida et al, 2014) and adiposity markers (Jensen et al, 2014).…”

“…Of the several different types of accelerometers available (Actical, Actiwatch, GENEActiv, Axivity, etc. Despite recent findings which question the classification accuracy of wrist accelerometry processing methods (Ellingson et al, 2017), the use of wrist-worn accelerometers to characterize activity patterns in large cohorts of individuals is now common (Doherty et al, 2017;Menai et al, 2017;NHANES, 2018). Historically accelerometers were worn on the waist to reflect whole body movement and thus energy expenditure but poor compliance and subsequent selection bias and misclassification (Troiano et al, 2014) has seen increased use of wrist-worn accelerometers to assess habitual PA. Wrist-worn accelerometers have also been validated against established measures of physical activity energy expenditure (van Hees et al, 2011;Hildebrand et al, 2014;White et al, 2016) and have shown to provide high (85%-97%) activity classification accuracies when using machine learning models (Zhang et al, 2012;Mannini et al, 2013).…”

“…Indeed, wrist-worn accelerometers are being used to assess PA in large population surveys such as the UK BioBank study (Doherty et al, 2017), the Whitehall II cohort (Sabia et al, 2014;Menai et al, 2017) and the US National Health and Nutrition Examination Survey (NHANES) (NHANES, 2018) which used the ActiGraph GT3X+ model in the 2011-2012 and 2013-2014 cycles. In these studies different methodologies have been applied where either the non-dominant (Sabia et al, 2015;Menai et al, 2017;NHANES, 2018) or dominant wrist (Doherty et al, 2017) has been used.…”

Accelerometer-derived physical activity in those with cardio-metabolic disease compared to healthy adults: a UK Biobank study of 52,556 participants

“…Raw accelerometer data were processed using GGIR V1.5-9 package (R Core Team, Vienna, Austria) (https://cran.rroject.org/web/packages/GGIR/index.html) [3]. We defined MVPA using a 100 milligravity (m g ) cut-off, based on laboratory findings [4].…”

Wearable Internet of Things - from human activity tracking to clinical integration

“…The physiological activity tracking data generated at home can also be used to improve the existing therapies and study recovery variations over a wider population. Motion sensors combined with temperature and pulse sensors will see a wider acceptance in public and clinical practice [2,10].…”