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

    Access Status
    Open access via publisher
    Authors
    Winkler, E.
    Bodicoat, D.
    Healy, Genevieve
    Bakrania, K.
    Yates, T.
    Owen, N.
    Dunstan, D.
    Edwardson, C.
    Date
    2016
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Winkler, E. and Bodicoat, D. and Healy, G. and Bakrania, K. and Yates, T. and Owen, N. and Dunstan, D. et al. 2016. Identifying adults' valid waking wear time by automated estimation in activPAL data collected with a 24 h wear protocol. Physiological Measurement. 37 (10): pp. 1653-1668.
    Source Title
    Physiological Measurement
    DOI
    10.1088/0967-3334/37/10/1653
    ISSN
    0967-3334
    School
    School of Physiotherapy and Exercise Science
    URI
    http://hdl.handle.net/20.500.11937/9395
    Collection
    • Curtin Research Publications
    Abstract

    © 2016 Institute of Physics and Engineering in Medicine.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; ?) and the average daily waking wear time, on valid days. The algorithm showed 'almost perfect' agreement (? > 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.

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