Fully automated waist-worn accelerometer algorithm for detecting children’s sleep-period time separate from 24-h physical activity or sedentary behaviors
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.
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The algorithm provides more accurate sleep duration estimates than previous algorithms (9,10).
“This new algorithm captures total sleep time from sleep onset to the end of sleep, including all epochs and wakefulness after onset, and provides more accurate sleep duration estimates than previous algorithms (9, 10).”
From Are children like werewolves? Full moon and its association with sleep and activity behaviors in an international sample of children (2016) · cites this paper as [10]
The new sleep algorithm produces more precise estimates of sleep duration than previous algorithms.
“This new algorithm produces more precise estimates of sleep duration than previous algorithms and captures total sleep time from sleep onset to the end of sleep, including all epochs and wakefulness after onset [24, 25].”
From Proportion of children meeting recommendations for 24-hour movement guidelines and associations with adiposity in a 12-country study (2016) · cites this paper as [25]
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- Authors
- Tudor-Locke, C., Barreira, T. V., Schuna, J. M., Mire, E. F., & Katzmarzyk, P. T.
- Affiliations
- Pennington Biomedical Research Center (US)
- Venue
- Applied Physiology Nutrition and Metabolism
- Publisher
- Canadian Science Publishing
- Published
- 2014-01
- Volume
- 39
- Issue
- 1
- Pages
- 53-57
- ISSN
- 1715-5312 (ISSN-L)
- 1715-5320
- OpenAlex
- W1882457022
- Cited by
- 285
- References
- 45
- Citations by year
- 2026: 24 · 2025: 26 · 2024: 24 · 2023: 19 · 2022: 22 · 2021: 20 · 2020: 28 · 2019: 20 · 2018: 24 · 2017: 22 · 2016: 19 · 2015: 23 · 2014: 13 · 2013: 1
- FWCI
- 12.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Applied Physiology, Nutrition, and Metabolism (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Coca-Cola Foundation
- Topics
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- children, physical activity, sedentary behavior, sleep onset, algorithm validation, inclinometer
- MeSH terms
- Algorithms, Automation, Child, Female, Humans, Male, Motor Activity, Sleep, Time Factors, Sedentary Behavior, Accelerometry
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