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Sleep Med. 2019 Aug;60:197-201. doi: 10.1016/j.sleep.2019.04.006. Epub 2019 Apr 18.

Comparing measures of free-living sleep in school-aged children.

Author information

1
Arnold School of Public Heath, Department of Exercise Science, University of South Carolina, Columbia, SC, 29208, USA. Electronic address: brazendk@email.sc.edu.
2
Arnold School of Public Heath, Department of Exercise Science, University of South Carolina, Columbia, SC, 29208, USA.
3
Healthy Active Living and Obesity Research Group, Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, K1H 8L1, Canada.

Abstract

OBJECTIVE/BACKGROUND:

Recent technological advances and emerging commercially-available consumer-friendly sleep assessment products affords researchers with a host of tools to consider for capturing free-living sleep in children. The purpose of this study was to compare free-living sleep characteristics (duration and bed/wake times) across different measures in children.

METHODS:

Elementary school-aged children (N = 30, mean age 7.2 years, 63% boys, 87% non-Hispanic white) wore an ActiGraph GT9X Link© and Fitbit Charge HR© on the non-dominant wrist, with a Beddit 3 Sleep Monitor© affixed to their mattress for two consecutive weekend nights of free-living sleep. Parents completed a sleep log of bed and wake times. Absolute differences in bed and wake times were examined and Bland Altman plots assessed the level of agreement across sleep measures.

RESULTS:

Across the four sleep measures, total sleep time (TST) ranged from 458 min/night (ActiGraph GT9X Link©) to 613 min/night (Parent report). Mean bed and wake times ranged from 8:46PM to 9:03PM, and 6:52AM to 7:16AM, respectively. Pearson correlation coefficients were moderate between all four sleep measures (range r = 0.30-0.71). Bland-Altman plots indicated the highest level of agreement for TST was between Beddit 3 Sleep Monitor© and Fitbit Charge HR© (mean difference -11.7, limits of agreement: 119.0, -142.4 min).

CONCLUSIONS:

The findings from this study show a high level of agreement of when a child goes to sleep and wakes up across a variety of sleep measures; however, more work is needed to classify TST once the sleep period has commenced.

KEYWORDS:

Accelerometry; Child; Measures; Sleep; Technology

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