The relationship between machine-learning-derived sleep parameters and behavior problems in 3- and 5-year-old children: results from the CHILD Cohort study
| Autor: | Valerie Carson, Piushkumar J. Mandhane, Jeffrey R. Brook, Malcolm R. Sears, Dorna Sadeghi, Victor E. Ezeugwu, Joyce Chikuma, Diana L. Lefebvre, Nevin Hammam, Allan B. Becker, Charmaine van Eeden, Sukhpreet K Tamana, Theo J. Moraes, Stuart E. Turvey, Padmaja Subbarao |
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| Rok vydání: | 2020 |
| Předmět: |
Population
Child Behavior Disorders Polysomnography Machine learning computer.software_genre Cohort Studies Machine Learning 03 medical and health sciences 0302 clinical medicine Physiology (medical) Humans Medicine 030212 general & internal medicine Child Child Behavior Checklist education Problem Behavior Sleep Stages education.field_of_study medicine.diagnostic_test business.industry Actigraphy Child Preschool Cohort Neurology (clinical) Sleep (system call) Artificial intelligence Sleep business computer 030217 neurology & neurosurgery Cohort study |
| Zdroj: | Sleep. 43 |
| ISSN: | 1550-9109 0161-8105 |
| DOI: | 10.1093/sleep/zsaa117 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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