JMIR Publications

ABSTRACT

Background:

Postpartum maternal mental health (MMH) symptoms, including depression, anxiety, and childbirth-related posttraumatic stress disorder (CBPTSD), 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 early identification of infant sleep problems through machine learning remains understudied.

Objective:

This study aimed to examine whether postpartum MMH symptom profiles can predict infant sleep outcomes during the first year of life. The analysis focused on two clinically relevant sleep indicators: (1) nocturnal sleep duration and (2) night awakening frequency.

Methods:

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 three validated instruments: the Edinburgh Postnatal Depression Scale (EPDS), the Hospital Anxiety and Depression Scale (HADS), and the City Birth Trauma Scale (City BiTS). Infant sleep outcomes were assessed using the Brief Infant Sleep Questionnaire (BISQ). Six supervised machine learning algorithms were evaluated: logistic regression, random forest, support vector classifier (SVC), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and multilayer perceptron (MLP). Post-hoc feature importance analysis was conducted to identify the most influential predictors associated with each infant sleep outcome.

Results:

All models demonstrated high predictive performance. The best model achieved a precision-recall area under the curve (PR-AUC) of 0.92, F1 score of 0.84, and accuracy of 0.88 for predicting short nocturnal sleep duration. For frequent night awakenings, the top PR-AUC was 0.91, with an F1 score of 0.78 and accuracy of 0.85. Key predictors included maternal age and total scores from the EPDS, HADS-A, and City BiTS, with individual symptom items offering additional discriminative value.

Conclusions:

Machine learning models can accurately predict which infants are at risk for suboptimal sleep based on maternal mental health profiles, enabling personalized, responsive, and developmentally informed postpartum care that promotes long-term maternal and infant well-being.