As smartphone and wearable device ownership increase, interest in their utility to monitor physical activity has risen concurrently. Numerous examples of the application of wearables in clinical and epidemiological research settings already exist. However, whether these devices are all suitable for physical activity surveillance is open for debate. In this commentary, we respond to a commentary by Mair et al. () and discuss four key issues specifically relevant to surveillance that we believe need to be tackled before consumer wearables can be considered for this measurement purpose: representative sampling, representative wear time, validity and reliability, and compatibility between devices. A recurring theme is how to deal with systematic biases by demographic groups. We suggest some potential solutions to the issues of concern such as providing individuals with standardized devices, considering summary metrics of physical activity less prone to wear time biases, and the development of a framework to harmonize estimates between device types and their inbuilt algorithms. We encourage collaborative efforts from researchers and consumer wearable manufacturers in this area. In the meantime, we caution against the use of consumer wearable device data for inference of population-level activity without the consideration of these issues.
Tessa Strain, Katrien Wijndaele, Matthew Pearce, and Søren Brage
Ignacio Perez-Pozuelo, Thomas White, Kate Westgate, Katrien Wijndaele, Nicholas J. Wareham, and Soren Brage
Background: Wrist-worn accelerometry is the commonest objective method for measuring physical activity in large-scale epidemiological studies. Research-grade devices capture raw triaxial acceleration which, in addition to quantifying movement, facilitates assessment of orientation relative to gravity. No population-based study has yet described the interrelationship and variation of these features by time and personal characteristics. Methods: 2,043 United Kingdom adults (35–65 years) wore an accelerometer on the non-dominant wrist and a chest-mounted combined heart-rate-and-movement sensor for 7 days free-living. From raw (60 Hz) wrist acceleration, we derived movement (non-gravity acceleration) and pitch and roll (forearm) angles relative to gravity. We inferred physical activity energy expenditure (PAEE) from combined sensing and sedentary time from approximate horizontal arm angle coupled with low movement. Results: Movement differences by time-of-day and day-of-week were associated with forearm angles; more movement in downward forearm positions. Mean (SD) movement was similar between sexes ∼31 (42) mg, despite higher PAEE in men. Women spent longer with the forearm pitched >0°, above horizontal (53% vs 36%), and less time at <0° (37% vs 53%). Diurnal pitch was 2.5–5° above and 0–7.5°below horizontal during night and daytime, respectively; corresponding roll angles were ∼0° (hand flat) and ∼20° (thumb-up). Differences were more pronounced in younger participants. All diurnal profiles indicated later wake-times on weekends. Daytime pitch was closer to horizontal on weekdays; roll was similar. Sedentary time was higher (17 vs 15 hours/day) in obese vs normal-weight individuals. Conclusions: More movement occurred in forearm positions below horizontal, commensurate with activities including walking. Findings suggest time-specific population differences in behaviors by age, sex, and BMI.