PocketPPD: Screening for Postpartum Depression Risk Using Passive Smartphone Sensing
Human-Computer InteractionComputers and Society
Summary
The authors studied a new way to detect postpartum depression (PPD) by using data from smartphones without needing users to actively input information. They created PocketPPD, which monitors changes in behavior and routines to identify PPD risk in new mothers. In their study with 61 women, using smartphone data alone detected PPD with decent accuracy, and combining this with self-reported information improved results further. They also found that changes in morning and late-night routines were important signs influenced by factors like the baby's age and whether the mother worked. Their work suggests a promising, low-effort way to keep track of mothers' mental health after childbirth.
Authors
Jia Tang, Helinyi Peng, Akihito Taya, Kaoru Sezaki, Daisuke Nishi, Anind K Dey, Yuuki Nishiyama
Abstract
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.