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Open access

Diurnal Profiles of Physical Activity and Postures Derived From Wrist-Worn Accelerometry in UK Adults

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.

Open access

Network Harmonization of Physical Activity Variables Through Indirect Validation

Matthew Pearce, Tom R.P. Bishop, Stephen Sharp, Kate Westgate, Michelle Venables, Nicholas J. Wareham, and Søren Brage

Harmonization of data for pooled analysis relies on the principle of inferential equivalence between variables from different sources. Ideally, this is achieved using models of the direct relationship with gold standard criterion measures, but the necessary validation study data are often unavailable. This study examines an alternative method of network harmonization using indirect models. Starting methods were self-report or accelerometry, from which we derived indirect models of relationships with doubly labelled water (DLW)-based physical activity energy expenditure (PAEE) using sets of two bridge equations via one of three intermediate measures. Coefficients and performance of indirect models were compared to corresponding direct models (linear regression of DLW-based PAEE on starting methods). Indirect model beta coefficients were attenuated compared to direct model betas (10%–63%), narrowing the range of PAEE values; attenuation was greater when bridge equations were weak. Directly and indirectly harmonized models had similar error variance but most indirectly derived values were biased at group-level. Correlations with DLW-based PAEE were identical after harmonization using continuous linear but not categorical models. Wrist acceleration harmonized to DLW-based PAEE via combined accelerometry and heart rate sensing had the lowest error variance (24.5%) and non-significant mean bias 0.9 (95%CI: −1.6; 3.4) kJ·day−1·kg−1. Associations between PAEE and BMI were similar for directly and indirectly harmonized values, but most fell outside the confidence interval of the criterion PAEE-to-BMI association. Indirect models can be used for harmonization. Performance depends on the measurement properties of original data, variance explained by available bridge equations, and similarity of population characteristics.