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.
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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
A Self-Paced Walk Test for Individual Calibration of Heart Rate to Energy Expenditure
Kate Westgate, Tomas I. Gonzales, Stefanie Hollidge, Tim Lindsay, Nick Wareham, and Søren Brage
Introduction: Estimating free-living physical activity (PA) with continuous heart rate (HR) monitoring is challenging due to individual variation in the relationship between HR and energy expenditure. This variation can be captured through individual calibration with graded exercise tests, but structured tests with prescribed load require medical screening and are not always feasible in population settings. We present and evaluate an individual calibration method using HR response to a less demanding self-paced walk test. Methods: Six hundred and forty-three participants from the Fenland Study (Cambridgeshire, the United Kingdom) completed a 200-m self-paced walk test, a treadmill test, and 1 week of continuous HR and accelerometry monitoring. Mixed-effects regression was used to derive a walk test calibration model from HR response to the walk using treadmill-based parameters as criterion. Free-living PA estimates from the calibration model were compared with treadmill-calibrated and non-exercise-calibrated estimates. Results: Walk calibration captured 57% of the variance in the HR–energy expenditure relationship determined by the treadmill test. Applying walk calibration to data from free-living yielded similar PA estimates to those using treadmill calibration (52.7 vs. 52.0 kJ·kg−1·day−1; mean difference: 0.7 kJ·kg−1·day−1, 95% confidence interval [−0.0, 1.5]) and high correlation (r = .89). Individual differences were observed (root mean square error: 10.0 kJ·kg−1·day−1; 95% limits of agreement: −20.6, 19.1 kJ·kg−1·day−1). Walk calibration improved precision by 29% compared with nonexercise group calibration (root mean square error: 14.0 kJ·kg−1·day−1; 95% limits of agreement: −30.4, 24.5 kJ·kg−1·day−1). Conclusions: A 200-m self-paced walk test captures between-individual variation in the HR–energy expenditure relationship and facilitates estimation of free-living PA in population settings.
Associations of Objectively Measured Physical Activity and Sedentary Time With Arterial Stiffness in Pre-Pubertal Children
Eero A. Haapala, Juuso Väistö, Aapo Veijalainen, Niina Lintu, Petri Wiklund, Kate Westgate, Ulf Ekelund, Virpi Lindi, Soren Brage, and Timo A. Lakka
Purpose:
To investigate the relationships of objectively measured physical activity (PA) and sedentary time (ST) to arterial stiffness in prepubertal children.
Method:
Altogether 136 children (57 boys, 79 girls) aged 6–8-years participated in the study. Stiffness index (SI) was assessed by pulse contour analysis based on photoplethysmography. ST, light PA, moderate PA, and vigorous PA were assessed using combined acceleration and heart rate monitoring. We investigated the associations of ST (<1.5METs) and time spent in intensity level of PA above 2–7METs in min/d with SI using linear regression analysis. We studied the optimal duration and intensity of PA to identify children being in the highest quarter of SI using Receiver Operating Characteristics curves.
Results:
Moderate PA, vigorous PA, and cumulative time spent in PA above 3 (β=–0.279, p = .002), 4 (β =–0.341, P<0.001), 5 (β =–0.349, P<0.001), 6 (β =–0.312, P<0.001), and 7 (β =–0.254, p = .005) METs were inversely associated with SI after adjustment for age, sex, and monitor wear time. The cutoffs for identifying children being in the highest quarter of SI <68 min/d for PA exceeding 5 METs and <26 min/d for PA exceeding 6 METs.
Conclusion:
Lower levels of PA exceeding 3–6 METs were related to higher arterial stiffness in children.
Physical Behaviors and Their Association With Adiposity in Men and Women From a Low-Resourced African Setting
Amy E. Mendham, Julia H. Goedecke, Nyuyki Clement Kufe, Melikhaya Soboyisi, Antonia Smith, Kate Westgate, Soren Brage, and Lisa K. Micklesfield
Background : We first explored the associations between physical behaviors and total and regional adiposity. Second, we examined how reallocating time in different physical behaviors was associated with total body fat mass in men and women from a low-income South African setting. Methods : This cross-sectional study included a sample of 692 participants (384 men and 308 women) aged 41–72 years. Physical behaviors were measured using integrated hip and thigh accelerometry to estimate total movement volume and time spent in sleeping, sitting/lying, standing, light physical activity, and moderate to vigorous physical activity (MVPA). Total body fat mass and regional adiposity were measured using dual-energy X-ray absorptiometry. Results : The associations between total movement volume and measures of regional obesity were mediated by total body adiposity. In men, reallocating 30 minutes of sitting/lying to 30 minutes of MVPA was associated with 1.0% lower fat mass. In women, reallocation of 30 minutes of sitting/lying to MVPA and 30 minutes of standing to MVPA were associated with a 0.3% and 1.4% lower fat mass, respectively. Conclusions : Although the association between physical behaviors and fat mass differed between men and women, the overall public health message is similar; reallocating sedentary time to MVPA is associated with a reduction in fat mass in both men and women.
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.
