A review of the application of digital phenotyping in predicting peripartum depressive symptoms.

Peripartum depression (PPD) affects ~12-25% of pregnant and postpartum women worldwide, yet routine screening often fails to capture real-time symptom changes. Digital phenotyping (DP), using data from digital devices such as text entries, sleep tracking, physical activity, social media behavior, and ecological momentary assessments, has been proposed as a complementary approach to support the prediction and early identification for PPD. This systematic review (PROSPERO: CRD42023461325) examined 14 studies published between 2014 and March 2025 that explored passive and active DP data across the antenatal and postnatal periods. Most studies employed observational designs and used the Edinburgh Postnatal Depression Scale as the primary outcome. Passive DP data related to sleep and circadian rhythms were frequently associated with depressive symptoms, whereas findings for physical activity were inconsistent. Active DP data, including language features from text entries, mood logs, semi-random ecological momentary assessments, and social media behavior, were often reported as informative, particularly when combined with personal history or self-reported measures. However, considerable variation across study designs, data sources, analytical approaches, and validation strategies limits direct comparison of findings and prevents causal interpretation. Overall, the evidence remains largely exploratory, and findings should be interpreted cautiously pending more rigorous validation.