Identifying Children’s Nocturnal Sleep Using 24-h Waist Accelerometry

BARREIRA, TIAGO V.; SCHUNA, JOHN M.; MIRE, EMILY F.; KATZMARZYK, PETER T.; CHAPUT, JEAN-PHILIPPE; LEDUC, GENEVIEVE; TUDOR-LOCKE, CATRINE

doi:10.1249/mss.0000000000000486

Cited by 164Sleep and related disordersPhysical Activity and HealthContext-Aware Activity Recognition Systems

Identifying Children’s Nocturnal Sleep Using 24-h Waist Accelerometry

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BARREIRA, TIAGO V., SCHUNA, JOHN M., MIRE, EMILY F., KATZMARZYK, PETER T., CHAPUT, JEAN-PHILIPPE, LEDUC, GENEVIEVE, TUDOR-LOCKE, CATRINE·2015·Medicine & Science in Sports & Exercise

DOI 10.1249/mss.0000000000000486

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Abstract

PURPOSE: The purposes of this study were 1) to add layers and features to a previously published fully automated algorithm designed to identify children's nocturnal sleep and to exclude episodes of nighttime nonwear/wakefulness and potentially misclassified daytime sleep episodes and 2) to validate this refined sleep algorithm (RSA) against sleep logs. METHODS: Forty-five fourth-grade school children (51% female) participants were asked to log evening bedtime and morning wake time and wear an ActiGraph GT3X+ (ActiGraph LLC, Pensacola, FL) accelerometer at their waist for seven consecutive days. Accelerometers were distributed through a single school participating in the Baton Rouge, USA, site of the International Study of Childhood Obesity, Lifestyle, and the Environment. We compared log-based variables of sleep period time (SPT), bedtime, and wake time to corresponding accelerometer-determined variables of total sleep episode time, sleep onset, and sleep offset estimated with the RSA. In addition, SPT and sleep onset estimated using standard procedures combining sleep logs and accelerometry (Log + Accel) were compared to the RSA-derived values. RESULTS: RSA total sleep episode time (540 ± 36 min) was significantly different from Log SPT (560 ± 24 min), P = 0.003, but not different from Log + Accel SPT (549 ± 24 min), P = 0.15. Significant and moderately high correlations were apparent between RSA-determined variables and those using the other methods (r = 0.61 to 0.74). There were no differences between RSA and Log + Accel estimates of sleep onset (P = 0.15) or RSA sleep offset and log wake time (P = 0.16). CONCLUSIONS: The RSA is a refinement of our previous algorithm, allowing researchers who use a 24-h waist-worn accelerometry protocol to distinguish children's nocturnal sleep (including night time wake episodes) from daytime activities.

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Nocturnal sleep duration was estimated using a recently validated algorithm for 24-h waist-worn accelerometers (9).
“Nocturnal sleep duration was estimated using 60-s epochs and using a recently validated algorithm for 24-h waist-worn accelerometers (9).”
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 [9]
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 [9]
Nocturnal sleep duration was estimated using a fully automated algorithm for 24-h waist-worn accelerometers that was recently validated for ISCOLE.
“Nocturnal sleep duration was estimated from the accelerometry data using 60-s epochs and a fully automated algorithm for 24-h waist-worn accelerometers that was recently validated for ISCOLE [24].”
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 [24]
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 [24]

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Authors: BARREIRA, T. V., SCHUNA, J. M., MIRE, E. F., KATZMARZYK, P. T., CHAPUT, J.-P., LEDUC, G., & TUDOR-LOCKE, C.
Affiliations: Pennington Biomedical Research Center (US)
Children's Hospital of Eastern Ontario (CA)
ORCID: BARREIRA, TIAGO V.
MIRE, EMILY F.
KATZMARZYK, PETER T.
CHAPUT, JEAN-PHILIPPE
TUDOR-LOCKE, CATRINE
Venue: Medicine & Science in Sports & Exercise
Publisher: Ovid Technologies (Wolters Kluwer Health)
Published: 2015-05
Volume: 47
Issue: 5
Pages: 937-943
ISSN: 0195-9131 (ISSN-L)
DOI: 10.1249/mss.0000000000000486
OpenAlex: W2334259804
Cited by: 163
References: 16
Citations by year: 2026: 6 · 2025: 7 · 2024: 7 · 2023: 8 · 2022: 13 · 2021: 7 · 2020: 14 · 2019: 16 · 2018: 20 · 2017: 17 · 2016: 23 · 2015: 23 · 2014: 2
FWCI: 30.06
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: Medicine & Science in Sports & Exercise (publishedVersion)
PubMed (publishedVersion)
Topics: Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
Health Sciences › Medicine › Physiology › Physical Activity and Health
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
Keywords: children, sleep onset, bedtime, wake time
MeSH terms: Activity Cycles, Algorithms, Child, Female, Humans, Male, Sleep, Accelerometry

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Identifying Children’s Nocturnal Sleep Using 24-h Waist Accelerometry

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