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Identifying adults' valid waking wear time by automated estimation in activPAL data collected with a 24 h wear protocol

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posted on 21.08.2017, 10:23 by Elisabeth A. H. Winkler, Danielle H. Bodicoat, Genevieve N. Healy, Kishan Bakrania, Thomas Yates, Neville Owen, David W. Dunstan, Charlotte L. Edwardson
The activPAL monitor, often worn 24 h d^−1, provides accurate classification of sitting/reclining posture. Without validated automated methods, diaries—burdensome to participants and researchers—are commonly used to ensure measures of sedentary behaviour exclude sleep and monitor non-wear. We developed, for use with 24 h wear protocols in adults, an automated approach to classify activity bouts recorded in activPAL 'Events' files as 'sleep'/non-wear (or not) and on a valid day (or not). The approach excludes long periods without posture change/movement, adjacent low-active periods, and days with minimal movement and wear based on a simple algorithm. The algorithm was developed in one population (STAND study; overweight/obese adults 18–40 years) then evaluated in AusDiab 2011/12 participants (n  =  741, 44% men, aged  >35 years, mean  ±  SD 58.5  ±  10.4 years) who wore the activPAL3™ (7 d, 24 h d^−1 protocol). Algorithm agreement with a monitor-corrected diary method (usual practice) was tested in terms of the classification of each second as waking wear (Kappa; κ) and the average daily waking wear time, on valid days. The algorithm showed 'almost perfect' agreement (κ  >  0.8) for 88% of participants, with a median kappa of 0.94. Agreement varied significantly (p  <  0.05, two-tailed) by age (worsens with age) but not by gender. On average, estimated wear time was approximately 0.5 h d^−1 higher than by the diary method, with 95% limits of agreement of approximately this amount  ±2 h d^−1. In free-living data from Australian adults, a simple algorithm developed in a different population showed 'almost perfect' agreement with the diary method for most individuals (88%). For several purposes (e.g. with wear standardisation), adopting a low burden, automated approach would be expected to have little impact on data quality. The accuracy for total waking wear time was less and algorithm thresholds may require adjustments for older populations.


The research was supported by the National Institute for Health Research (NIHR) Diet, Lifestyle & Physical Activity Biomedical Research Unit based at University Hospitals of Leicester and Loughborough University, the National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care—East Midlands (NIHR CLAHRC—EM) and the Leicester Clinical Trials Unit, United Kingdom. The views expressed are those of the authors and not necessarily those of the National Health Service, the NIHR or the Department of Health. Elisabeth Winkler was supported by a National Health and Medical Research Council (NHMRC) of Australia Centre for Research Excellence Grant on Sitting Time and Chronic Disease Prevention—Measurement, Mechanisms and Interventions (#1057608). Genevieve Healy was supported by a NHMRC Career Development (#1086029) Fellowship. David Dunstan was supported by an NHMRC Senior Research Fellowship (#1078360) and by the Victorian Government's Operational Infrastructure Support Program. Neville Owen was supported by a NHMRC Program Grant (#569940), NHMRC Centre for Research Excellence Grant (#1057608), a NHMRC Senior Principal Research Fellowship (#1003960) and by the Victorian Government's Operational Infrastructure Support Program.



Physiological Measurement, 2016, 37 (10), pp. 1653-1668 (16)

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