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https://www.selleckchem.com/products/dubs-in-1.html 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 behaviour problems among preschool children. Methods Children from the CHILD Cohort Edmonton site with actigraphy and behaviour data at three-years (n=330) and five-years (n=304) were included. Parent-reported behaviour 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/day. We analyzed associations between sleep and behaviour problems stratified by children with and without sleep-disordered breathing (SDB). Results Four hidden sleep states were identified at three years and six hidden sleep states at five 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 hours/day in HMM-0 and 2.6±0.8 hours/day in HMM-mov at three years. At age five, children spent an average of 8.2±0.9 hours/day in HMM-0 and 1.9±0.7 hours/day in HMM-mov. Among SDB children, each hour in HMM-0 was associated with 0.79-point reduced externalizing behaviour problems (95%CI -1.4, -0.12; p less then 0.05), and a 1.27-point lower internalizing behaviour problems (95%; -2.02, -0.53; p less then 0.01). Conclusions ML-sleep states were not associated with behaviour problems in general-population of children. Children with SDB who had greater sleep duration without movement had lower behavioural problems. The ML-sleep states require validation with polysomnography.Study objectives This field study a) assessed sleep quality of sailors on United States Navy (USN) ships while underway, b) investigated whether the Pitt

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