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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 adults
Journal article   Peer reviewed

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

Joanne A. McVeigh, Elisabeth A. H. Winkler, Genevieve N. Healy, James Slater, Peter R. Eastwood and Leon M. Straker
Physiological measurement, Vol.37(10), pp.1636-1652
2016
DOI: https://doi.org/10.1088/0967-3334/37/10/1636
PMID: 27652717
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Abstract

algorithm measurement young adults physical activity sedentary behaviour
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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Source: InCites

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11 Record Views
38 Times Cited - Web of Science

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Collaboration types
Domestic collaboration
Citation topics
1 Clinical & Life Sciences
1.44 Nutrition & Dietetics
1.44.103 Physical Activity
Web Of Science research areas
Biophysics
Engineering, Biomedical
Physiology
ESI research areas
Biology & Biochemistry
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