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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., (© Sleep Research Society 2020. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.)

Study Objectives: To examine the association between the age of onset and duration of parent-reported symptoms of sleep-disordered breathing (SDB) and behavioral problems at age 2., Methods: Parent-reported SDB symptoms were assessed quarterly between 3 months and 2 years among 583 Canadian Healthy Infant Longitudinal Development Edmonton-site participants. Parent-reported SDB symptoms were clustered into phenotypes using group-based trajectory analysis based on age of onset and duration of symptoms. Home-based polysomnography (PSG) was completed at 1 year. The Child Behavior Checklist preschool-version (Mean T-score 50, standard deviation 10 points) assessed total, externalizing (attention), and internalizing (anxiety, depression) behaviors at 2 years., Results: Four phenotypes were identified: no SDB (64.7%), early-onset SDB (15.7%, peak symptoms at 9 months), late-onset (14.2%, peak symptoms at 18 months), and persistent SDB symptoms (5.3%, peak symptoms from 3 through 24 months). Persistent SDB (9.5 points, 95% CI 1.7, 17.2; p = .02) predicted the greatest magnitude of effect of total behavior problems, compared with children without SDB. Children with early-onset SDB (3.5 points, 95% CI 1.6, 5.4; p ≤ .001) and late-onset SDB (6.1 points 95% CI 4.0, 8.3; p ≤ .001) had increased total behavioral problems than children without SDB to 2 years. Additional analyses showed that the SDB phenotypes' trajectories were important for internalizing but not for externalizing behavior problems. There were no significant associations between home-PSG and parent-reported behavior problems., Conclusions: Findings suggest that the age of onset and duration of parent-reported SDB symptoms prior to age 2 have adverse consequences for overall behavior problems., (© Sleep Research Society 2017. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.)