PocketPPD: Screening for Postpartum Depression Risk Using Passive Smartphone Sensing

Jia Tang, Helinyi Peng, Akihito Taya, Kaoru Sezaki, Daisuke Nishi, Anind K Dey, Yuuki Nishiyama

Postpartum depression (PPD) is a serious perinatal mental health condition affecting approximately 20% of new mothers worldwide. Common screening approaches for PPD, such as self-report questionnaires and active digital logs, rely heavily on user input and thus impose a substantial burden on participants, limiting their feasibility for long-term use. Recent passive mobile sensing (PMS) approaches have enabled low-burden detection of depressive symptoms using machine learning methods with multi-modal sensor data from off-the-shelf mobile devices including smartphones. However, the postpartum period entails distinct behavioral patterns, raising uncertainty about whether sensing-based indicators for general depression and mental disorders generalize to PPD. To address this gap, we propose PocketPPD, a PMS-based PPD screening method that detects PPD risk using maternal contextual features, such as disruptions in behavioral rhythms and shifts in stability, collected through a smartphone. In our exploratory four-week feasibility study with 61 postpartum women, the PMS-only model achieved an AUC of 0.75, while the best-performing model, integrating PMS-oriented data and self-report features, achieved an AUC of 0.83. Moreover, we find that morning and late-night routine volatility ranks among the top digital biomarkers, dynamically moderated by maternal contexts such as infant developmental stage and employment status. This work provides empirical evidence for low-burden PPD risk screening and our findings lay the groundwork for continuous perinatal mental health monitoring.