Passive data do not improve prediction or detection of alcohol consumption beyond temporal patterns in major...

Major Depressive Disorder (MDD) is one of the most prevalent psychological disorders and frequently co-occurs with alcohol use disorders, increasing the risk of functional impairment. Monitoring alcohol use during depression treatment is therefore critical for early intervention. Passively collected data via devices like smartphones and smartwatches, offers a low-burden method for monitoring behavior in real time. This study investigated whether deep learning models trained on passively collected data (i.e., accelerometer, heart rate, respiratory rate, screen usage, and GPS data) could detect and predict alcohol use in individuals with MDD. Data were collected from 300 clinically depressed individuals who were enrolled in the Tracking Depression Study, a 90-day longitudinal study. Participants self-reported their alcohol use every week by completing the Timeline FollowBack. We trained models to predict same-day and next-day alcohol use. To validate these models, we split the data by participant, so that predictions were made on individuals who were not included in the training set. The models achieved moderate performance (mean AUC = 0.67 for both prediction tasks) when capturing both interindividual (between-person) and intraindividual (within-person) variability. Similar performances were observed when evaluating the model exclusively on predicting intraindividual variability (AUCs = 0.69 same-day, 0.68 next-day). However, model performance remained comparable to a baseline using only the day of week as predictor. These findings suggest that much of the predictive signal derives from temporal patterns. This indicates that interventions aligned with such temporal cues may already be effective, and that the added value of our model appears limited.