Can an automated sleep detection algorithm for waist-worn accelerometry replace sleep logs?
The purpose of this study was to test whether estimates of bedtime, wake time, and sleep period time (SPT) were comparable between an automated algorithm (ALG) applied to waist-worn accelerometry data and a sleep log (LOG) in an adult sample. A total of 104 participants were asked to
log evening bedtime and morning wake time and wear an ActiGraph wGT3X-BT accelerometer at their waist for 24 h/day for 7 consecutive days. Mean difference and mean absolute difference (MAD) were computed. Pearson correlations and dependent-sample t tests were used to compare LOG-based
and ALG-based sleep variables. Effect sizes were calculated for variables with significant mean differences. A total of 84 participants provided 2+ days of valid accelerometer and LOG data for a total of 368 days. There was no mean difference (p = 0.47) between LOG 472 ± 59 min
and ALG SPT 475 ± 66 min (MAD = 31 ± 23 min, r = 0.81). There was no significant mean difference between bedtime (2348 h and 2353 h for LOG and ALG, respectively; p = 0.14, MAD = 25 ± 21 min, r = 0.92). However, there was a significant mean difference
between LOG (0741 h) and ALG (0749 h) wake times (p = 0.01, d = 0.11, MAD = 24 ± 21 min, r = 0.92). The LOG and ALG data were highly correlated and relatively small differences were present. The significant mean difference in wake time might not be practically meaningful
(Cohen’s d = 0.11), making the ALG useful for sample estimates. MAD, which gives a better estimate of the expected differences at the individual level, also demonstrated good evidence supporting ALG individual estimates.
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Document Type: Research Article
Biology Department, Utica College, 1600 Burrstone Rd., Utica, NY 13505, USA.
School of Education, Syracuse University, 820 Comstock Ave., Syracuse, NY 13244, USA.
College of Public Health and Human Sciences, Oregon State University, 1500 SW Jefferson St., Corvallis, OR 97331, USA.
Pennington Biomedical Research Center, 6400 Perkins Rd., Baton Rouge, LA 70808, USA.
Department of Kinesiology, University of Massachusetts Amherst, 30 Eastman Ln., Amherst, MA 01002, USA.
January 1, 2018
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