Validity of an automated algorithm to identify waking and in-bed wear time in hip-worn accelerometer data collected with a 24 h wear protocol in young adults
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Researchers are increasingly using 24 h accelerometer wear protocols. No automated method has been published that accurately distinguishes 'waking' wear time from other data ('in-bed', non-wear, invalid days) in young adults. This study examined the validity of an automated algorithm developed to achieve this for hip-worn Actigraph GT3X + 60 s epoch data. We compared the algorithm against a referent method ('into-bed' and 'out-of-bed' times visually identified by two independent raters) and benchmarked against two published algorithms. All methods used the same non-wear rules. The development sample (n = 11) and validation sample (n = 95) were Australian young adults from the Raine pregnancy cohort (54% female), all aged approximately 22 years. The agreement with Rater 1 in each minute's classification (yes/no) of waking wear time was examined as kappa (κ), limited to valid days (≥10 h waking wear time per day) according to the algorithm and Rater 1. Bland–Altman methods assessed agreement in daily totals of waking wear and in-bed wear time. Excellent agreement (κ > 0.75) was obtained between the raters for 80% of participants (median κ = 0.94). The algorithm showed excellent agreement with Rater 1 (κ > 0.75) for 89% of participants and poor agreement (κ < 0.40) for 1%. In this sample, the algorithm (median κ = 0.86) performed better than algorithms validated in children (median κ = 0.77) and adolescents (median κ = 0.66). The mean difference (95% limits of agreement) between Rater 1 and the algorithm was 7 (−220, 234) min d−1 for waking wear time on valid days and −41 (−309, 228) min d−1 for in-bed wear time. In this population, the automated algorithm's validity for identifying waking wear time was mostly good, not worse than inter-rater agreement, and better than the evaluated published alternatives. However, the algorithm requires improvement to better identify in-bed wear time.
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Validity of an automated algorithm to identify waking and in-bed wear time in hip-worn accelerometer data collected with a 24 h wear protocol in young adultsMcVeigh, Joanne; Winkler, E.; Healy, Genevieve; Slater, J.; Eastwood, Peter; Straker, Leon (2016)Researchers are increasingly using 24 h accelerometer wear protocols. No automated method has been published that accurately distinguishes 'waking' wear time from other data ('in-bed', non-wear, invalid days) in young ...
Identifying adults' valid waking wear time by automated estimation in activPAL data collected with a 24 h wear protocolWinkler, E.; Bodicoat, D.; Healy, Genevieve; Bakrania, K.; Yates, T.; Owen, N.; Dunstan, D.; Edwardson, C. (2016)© 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 ...
Winkler, E.; Gardiner, P.; Clark, B.; Matthews, C.; Owen, N.; Healy, Genevieve (2012)Purpose: The authors evaluated the accuracy of three automated accelerometer wear-time estimation algorithms against self-report. Direct effects on sedentary time (<100 cpm) and indirect effects on moderate-to-vigorous ...