The accuracy of passive phone sensors in predicting daily mood

Background Smartphones provide a low‐cost and efficient means to collect population level data. Several small studies have shown promise in predicting mood variability from smartphone‐based sensor and usage data, but have not been generalized to nationally recruited samples. This study used passive smartphone data, demographic characteristics, and baseline depressive symptoms to predict prospective daily mood. Method Daily phone usage data were collected passively from 271 Android phone users participating in a fully remote randomized controlled trial of depression treatment (BRIGHTEN). Participants completed daily Patient Health Questionnaire‐2. A machine learning approach was used to predict daily mood for the entire sample and individual participants. Results Sample‐wide estimates showed a marginally significant association between physical mobility and self‐reported daily mood (B = –0.04, P 0.80) for 11.8% of participants. Conclusions Passive smartphone data with current features may not be suited for predicting daily mood at a population level because of the high degree of intra‐ and interindividual variation in phone usage patterns and daily mood ratings. Personalized models show encouraging early signs for predicting an individual's mood state changes, with GPS‐derived mobility being the top most important feature in the present sample.