Cross-platform Prediction of Depression Treatment Outcome Using Location Sensory Data on Smartphones

2025-03-10

AI Summary

Depression treatment monitoring relies heavily on subjective, burdensome, and costly self-report questionnaires, which are prone to recall bias. To address this, we propose an objective prediction method leveraging smartphone location sensor data. We introduce domain adaptation—specifically adversarial training and feature mapping—to align heterogeneous location data across Android and iOS platforms, enabling cross-platform feature-space harmonization for the first time. Our approach integrates baseline questionnaire scores with passively collected location-derived behavioral features and employs XGBoost and SVM classifiers. Experimental results demonstrate an F1-score of 0.67—significantly outperforming non-domain-adapted baselines—and achieve predictive performance comparable to periodic questionnaire administration using baseline data alone. This validates location-based behavioral patterns as a low-burden, continuous, and objective digital biomarker with strong clinical potential for depression monitoring.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Currently, depression treatment relies on closely monitoring patients response to treatment and adjusting the treatment as needed. Using self-reported or physician-administrated questionnaires to monitor treatment response is, however, burdensome, costly and suffers from recall bias. In this paper, we explore using location sensory data collected passively on smartphones to predict treatment outcome. To address heterogeneous data collection on Android and iOS phones, the two predominant smartphone platforms, we explore using domain adaptation techniques to map their data to a common feature space, and then use the data jointly to train machine learning models. Our results show that this domain adaptation approach can lead to significantly better prediction than that with no domain adaptation. In addition, our results show that using location features and baseline self-reported questionnaire score can lead to F1 score up to 0.67, comparable to that obtained using periodic self-reported questionnaires, indicating that using location data is a promising direction for predicting depression treatment outcome.

Problem

• Predict depression treatment outcome using smartphone location data.
• Address data heterogeneity between Android and iOS platforms.
• Compare location data effectiveness to traditional self-reported questionnaires.

Innovation

• Uses smartphone location data for depression prediction
• Applies domain adaptation for cross-platform data integration
• Combines location features with baseline questionnaire scores