Are parent‐reported sleep logs essential? A comparison of three approaches to guide open source accelerometry‐based nocturnal sleep processing in children

Author:

Burkart Sarah1ORCID,Beets Michael W.1,Pfledderer Christopher D.23ORCID,von Klinggraeff Lauren1ORCID,Zhu Xuanxuan4,St. Laurent Christine W.5ORCID,van Hees Vincent T.6ORCID,Armstrong Bridget1ORCID,Weaver R. Glenn1ORCID,Adams Elizabeth L.1ORCID

Affiliation:

1. Department of Exercise Science, Arnold School of Public Health University of South Carolina Columbia South Carolina USA

2. University of Texas Health Science Center (UTHealth) at Houston, School of Public Health in Austin Austin Texas USA

3. Michael and Susan Dell Center for Healthy Living UTHealth School of Public Health in Austin Austin Texas USA

4. Department of Epidemiology and Biostatistics, Arnold School of Public Health University of South Carolina Columbia South Carolina USA

5. Department of Psychological and Brain Sciences University of Massachusetts Amherst Amherst Massachusetts USA

6. Accelting Almere The Netherlands

Abstract

SummaryWe examined the comparability of children's nocturnal sleep estimates using accelerometry data, processed with and without a sleep log. In a secondary analysis, we evaluated factors associated with disagreement between processing approaches. Children (n = 722, age 5–12 years) wore a wrist‐based accelerometer for 14 days during Autumn 2020, Spring 2021, and/or Summer 2021. Outcomes included sleep period, duration, wake after sleep onset (WASO), and timing (onset, midpoint, waketime). Parents completed surveys including children's nightly bed/wake time. Data were processed with parent‐reported bed/wake time (sleep log), the Heuristic algorithm looking at Distribution of Change in Z‐Angle (HDCZA) algorithm (no log), and an 8 p.m.–8 a.m. window (generic log) using the R‐package ‘GGIR’ (version 2.6‐4). Mean/absolute bias and limits of agreement were calculated and visualised with Bland–Altman plots. Associations between child, home, and survey characteristics and disagreement were examined with tobit regression. Just over half of nights demonstrated no difference in sleep period between sleep log and no log approaches. Among all nights, the sleep log approach produced longer sleep periods (9.3 min; absolute mean bias [AMB] = 28.0 min), shorter duration (1.4 min; AMB = 14.0 min), greater WASO (11.0 min; AMB = 15.4 min), and earlier onset (13.4 min; AMB = 17.4 min), midpoint (8.8 min; AMB = 15.3 min), and waketime (3.9 min; AMB = 14.8 min) than no log. Factors associated with discrepancies included smartphone ownership, bedroom screens, nontraditional parent work schedule, and completion on weekend/summer nights (range = 0.4–10.2 min). The generic log resulted in greater AMB among sleep outcomes. Small mean differences were observed between nights with and without a sleep log. Discrepancies existed on weekends, in summer, and for children with smartphones and screens in the bedroom.

Funder

Eunice Kennedy Shriver National Institute of Child Health and Human Development

National Heart, Lung, and Blood Institute

National Institute of Diabetes and Digestive and Kidney Diseases

National Institute of General Medical Sciences

Publisher

Wiley

Subject

Behavioral Neuroscience,Cognitive Neuroscience,General Medicine

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