
AI-Guided Mood Plan Yields 55% Depression Remission in Trial
UC San Diego researchers used two weeks of smartwatch data and EMA mood logs to train a personalized machine‑learning model that identifies each participant’s top mood drivers and pairs them with tailored, remote coaching to create an individualized Mood Augmentation Plan (iMAP). Over six weeks, 55% of participants showed depression remission on PHQ-9, anxiety decreased 36%, and benefits persisted for three months post-intervention, suggesting a scalable, data‑driven approach to personalized depression care.













