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Title: Identifying adults' valid waking wear time by automated estimation in activPAL data collected with a 24 h wear protocol
Authors: Winkler, Elisabeth A. H.
Bodicoat, Danielle H.
Healy, Genevieve N.
Bakrania, Kishan
Yates, Thomas
Owen, Neville
Dunstan, David W.
Edwardson, Charlotte L.
First Published: 21-Sep-2016
Publisher: IOP Publishing
Citation: Physiological Measurement, 2016, 37 (10), pp. 1653-1668 (16)
Abstract: 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.
DOI Link: 10.1088/0967-3334/37/10/1653
ISSN: 0967-3334
eISSN: 1361-6579
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © the authors, 2016. This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Appears in Collections:Published Articles, College of Medicine, Biological Sciences and Psychology

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