PocketPPD: Screening for Postpartum Depression Risk Using Passive Smartphone Sensing

2026-07-19

AI Summary

Current postpartum depression screening relies heavily on active self-reporting, which imposes a significant burden and lacks sustainability, while the applicability of generic digital markers in postpartum populations remains unclear. This study presents the first validation of passive mobile sensing (PMS) via smartphones for feasible postpartum depression risk screening, leveraging multimodal sensor data to capture contextual behavioral features such as daily rhythm and stability, integrated with machine learning models. In a four-week study involving 61 postpartum women, a PMS-only model achieved an AUC of 0.75, which improved to 0.83 when combined with self-reported data. The research identified key digital biomarkers—including morning and late-night behavioral variability—and demonstrated their dynamic modulation by infant developmental stage and maternal employment status, offering a novel, low-burden paradigm for continuous perinatal mental health monitoring.

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.

Problem

• postpartum depression
• passive mobile sensing
• digital biomarkers
• maternal mental health
• behavioral rhythms

Innovation

• Passive Mobile Sensing
• Postpartum Depression
• Digital Biomarkers
• Behavioral Rhythms
• Smartphone Sensing