[[DP - Geolocation]] [[Mania]] [[Depression]] [[Paper - Classifying and clustering mood disorder patients using smartphone data from a feasibility study]] - # Passive Sensor Data Collected The mindLAMP app continuously gathered three types of passive smartphone data to capture daily behaviors and routines: - Geolocation: real-time GPS points to map movement patterns and travel radius - Accelerometer: device motion readings to estimate physical activity and infer sleep periods - Screen-state: on/off logs to measure total phone usage and detect routine breaks --- # Behavioral Features Derived Raw sensor streams were processed into four key metrics for each 24-hour period: - Home time: total hours spent at the primary location each day - Entropy: a quantified measure of how often and widely locations changed, indicating routine stability - Sleep duration: inferred from sustained periods of phone inactivity, aided by acceleration and screen data - Screen duration: sum of daily screen-on intervals, reflecting engagement and possible restlessness --- # How Mood Disorders Were Detected Researchers transformed these features into input variables for machine learning models: 1. Binary classification - Distinguished healthy controls from mood-disorder participants using a random forest model - Passive data alone achieved an AUC of 0.65 for control vs. non-control classification 2. Depression vs. bipolar classification - Logistic regression on passive data had limited performance (AUC = 0.52) - Adding active survey responses (PHQ-2, GAD-2) improved AUC to 0.62, highlighting the value of combining passive and active data 3. Unsupervised clustering - K-means grouped participants into four clusters based on all features - Moderate silhouette (0.46) and ARI (0.27) scores suggested behavioral patterns partly aligned with clinical diagnoses These methods show that passively collected smartphone metrics can offer a low-cost, scalable digital biomarker for mood disorders. Further validation with larger, richer datasets is needed before clinical deployment.