The relationship between machine-learning-derived sleep parameters and behavior problems in 3- and 5-year-old children: results from the CHILD Cohort study

Abstract

Study Objectives Machine learning (ML) may provide insights into the underlying sleep stages of accelerometer-assessed sleep duration. We examined associations between ML-sleep patterns and behavior problems among preschool children. Methods Children from the CHILD Cohort Edmonton site with actigraphy and behavior data at 3-years (n = 330) and 5-years (n = 304) were included. Parent-reported behavior problems were assessed by the Child Behavior Checklist. The Hidden Markov Model (HMM) classification method was used for ML analysis of the accelerometer sleep period. The average time each participant spent in each HMM-derived sleep state was expressed in hours per day. We analyzed associations between sleep and behavior problems stratified by children with and without sleep-disordered breathing (SDB). Results Four hidden sleep states were identified at 3 years and six hidden sleep states at 5 years using HMM. The first sleep state identified for both ages (HMM-0) had zero counts (no movement). The remaining hidden states were merged together (HMM-mov). Children spent an average of 8.2 ± 1.2 h/day in HMM-0 and 2.6 ± 0.8 h/day in HMM-mov at 3 years. At age 5, children spent an average of 8.2 ± 0.9 h/day in HMM-0 and 1.9 ± 0.7 h/day in HMM-mov. Among SDB children, each hour in HMM-0 was associated with 0.79-point reduced externalizing behavior problems (95% CI −1.4, −0.12; p < 0.05), and a 1.27-point lower internalizing behavior problems (95% CI −2.02, −0.53; p < 0.01). Conclusions ML-sleep states were not associated with behavior problems in the general population of children. Children with SDB who had greater sleep duration without movement had lower behavioral problems. The ML-sleep states require validation with polysomnography.

Associations of Sleep-Related Outcomes with Behavioral and Emotional Functioning in Children with Overweight/Obesity

“…Despite evidence using device-assessed sleep behaviors is limited, the few previous studies observed that device-estimated sleep duration was related to behavioral J o u r n a l P r e -p r o o f functioning in children [15][16][17]40]. However, our findings are in agreement with those from Hammam et al [14], supporting that total sleep duration was not a predictor of behaviors problems among preschool children. Guerlich et al [17] found that longer device-assessed sleep duration was not associated with externalizing problems in 8-yearold children.…”

“…Device-assessed sleep duration has shown inconsistent associations with behavioral problems in children [14][15][16][17]. For example, longer sleep duration was associated with lower risk of internalizing, but not externalizing problems in 8-year-old children [17].…”

Measuring Behavior Problems in Children from Low-Income Families: A Rasch Analysis of the Child Behavior Checklist for Ages 1½–5

“…These assumptions are essential in a rigorous IRT analysis. Given the emerging interest among empirical studies in reporting behavior problems at these two subscale levels (e.g., Hammam, et al, 2020; Quiñones-Camacho, et al, 2021; Rodrigues et al, 2022; Steenhoff et al, 2021), a more comprehensive IRT study on the externalizing and internalizing subscale scores is needed, To our knowledge, this is the first study applying IRT analysis to examine the psychometric properties of the CBCL/1½ – 5 among preschool children, particularly those who are from low-income population.…”