Fully automated waist-worn accelerometer algorithm for detecting children's sleep-period time separate from 24-h physical activity or sedentary behaviors

Catrine Tudor-Locke, Tiago V. Barreira, John M. Schuna, Emily F. Mire, Peter T. Katzmarzyk

Research output: Contribution to journalArticlepeer-review

118 Scopus citations

Abstract

Analysis of 24-h waist-worn accelerometer data for physical activity and sedentary behavior requires that sleep-period time (from sleep onset to the end of sleep, including all sleep epochs and wakefulness after onset) is first identified. To identify sleep-period time in children in this study, we evaluated the validity of a published automated algorithm that requires nonaccelerometer bed- and wake-time inputs, relative to a criterion expert visual analysis of minute-by-minute waist-worn accelerometer data, and validated a refined fully automated algorithm. Thirty grade 4 schoolchildren (50% girls) provided 24-h waist-worn accelerometry data. Expert visual inspection (criterion), a published algorithm (Algorithm 1), and 2 additional automated refinements (Algorithm 2, which draws on the instrument's inclinometer function, and Algorithm 3, which focuses on bedtime and wake time points) were applied to a standardized 24-h time block. Paired t tests were used to evaluate differences in mean sleep time (expert criterion minus algorithm estimate). Compared with the criterion, Algorithm 1 and Algorithm 2 significantly overestimated sleep time by 43 min and 90 min, respectively. Algorithm 3 produced the smallest mean difference (2 min), and was not significantly different from the criterion. Relative to expert visual inspection, our automated Algorithm 3 produced an estimate that was precise and within expected values for similarly aged children. This fully automated algorithm for 24-h waist-worn accelerometer data will facilitate the separation of sleep time from sedentary behavior and physical activity of all intensities during the remainder of the day.

Original languageEnglish (US)
Pages (from-to)53-57
Number of pages5
JournalApplied Physiology, Nutrition and Metabolism
Volume39
Issue number1
DOIs
StatePublished - Jan 8 2014
Externally publishedYes

Keywords

  • Measurement
  • Physical activity
  • Sedentary behavior
  • Youth

ASJC Scopus subject areas

  • Endocrinology, Diabetes and Metabolism
  • Physiology
  • Nutrition and Dietetics
  • Physiology (medical)

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