{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T07:05:25Z","timestamp":1792479925255,"version":"4.5.0"},"reference-count":34,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Pediatr Parent"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Postpartum maternal mental health (MMH) symptoms, including depression, anxiety, and childbirth-related post-traumatic stress disorder, are known to influence infant sleep trajectories. While previous research has examined their individual and combined associations, the predictive utility of these MMH symptoms for the early identification of infant sleep problems through machine learning (ML) remains understudied.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to examine whether postpartum MMH measures can predict infant sleep outcomes during the first year of life. The analysis focused on 2 clinically relevant sleep indicators: (1) nocturnal sleep duration and (2) night awakening frequency.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n A total of 409 mother-infant dyads were included in the study. Predictor variables comprised postpartum MMH symptoms assessed between 3 and 12 months postpartum, along with sociodemographic characteristics of mothers and infants. MMH symptoms were measured using 3 validated instruments: the Edinburgh Postnatal Depression Scale, the Hospital Anxiety and Depression Scale, and the City Birth Trauma Scale. Infant sleep outcomes were assessed using the Brief Infant Sleep Questionnaire. Six supervised ML algorithms were evaluated: logistic regression, random forest, support vector classifier, extreme gradient boosting, Light Gradient Boosting Machine, and multilayer perceptron. Post hoc feature importance analyses were conducted to identify the most influential predictors associated with each infant sleep outcome.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n All models demonstrated high predictive performance. The best model achieved a precision-recall area under the curve of 0.92,\n F<\/jats:italic>\n 1<\/jats:sub>\n -score of 0.84, and accuracy of 0.88 for predicting short nocturnal sleep duration. For frequent night awakenings, the top precision-recall area under the curve was 0.91, with an\n F<\/jats:italic>\n 1<\/jats:sub>\n -score of 0.78 and accuracy of 0.85. Key predictors included maternal age and total scores from the Edinburgh Postnatal Depression Scale, Hospital Anxiety and Depression\u2013Anxiety subscale, and City Birth Trauma Scale, with individual symptom items offering additional discriminative value.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n ML models can accurately predict which infants are at risk for suboptimal sleep based on MMH measures, enabling personalized, responsive, and developmentally informed postpartum care that promotes long-term maternal and infant well-being.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/78937","type":"journal-article","created":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T21:42:43Z","timestamp":1770154963000},"page":"e78937-e78937","source":"Crossref","is-referenced-by-count":3,"title":["Predicting Infant Sleep Patterns From Postpartum Maternal Mental Health Measures: Machine Learning 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