Dual trajectory of insomnia and depressive symptoms in women from early pregnancy to 6 months postpartum: a prospective cohort study

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BACKGROUND: Perinatal insomnia and depression significantly impact maternal-infant health, but their co-developing trajectories are poorly understood. This study examines their heterogeneous progression, interrelationships, and predictive factors across the perinatal period. METHODS: This was part of a mother-infant cohort study conducted in the obstetrics outpatient clinic of a tertiary hospital in Wuhan, Hubei Province. Pregnant women were enrolled (N = 1034) at early pregnancy (< 14 weeks) from July 2022 to September 2023. The perinatal depressive symptoms, insomnia severity, anxiety symptoms, and social capital were reassessed at 5-time points from enrollment (T0) to 6 months postpartum using the Edinburgh postnatal depression scale, the Insomnia Severity Index, the Pregnancy-related Anxiety Questionnaire, and the Personal Social Capital Scale 16, respectively. The follow-up time points were 16-20 weeks of gestation (T1), 28-36 weeks of gestation (T2), six weeks postpartum (T3) and six months postpartum (T4), respectively. Group-based trajectory modelling and binary logistic regression modelling were used to analyze the data (n = 436). RESULTS: We identified three trajectories for perinatal insomnia and depression symptoms. Insomnia: no insomnia (27.7%), subclinical (54.5%), clinical (17.8%). Depression: low-stable (38.7%), moderate-stable (43.9%), high-improving (17.4%). The dual trajectory analysis revealed significant co-occurrence patterns between insomnia and depression trajectories (p < 0.001). Members of the high-improving depression group were more likely to have clinical insomnia trajectories. Baseline ISI ≥ 8, EPDS ≥ 10, and PRAQ ≥ 24 predicted the higher trajectories of perinatal insomnia and depressive symptoms (all p < 0.05). CONCLUSIONS: Perinatal insomnia and depression follow three distinct but interrelated trajectories, identifiable through early screening. Risk-stratified interventions should consider their co-occurrence patterns to optimize outcomes.