Personalization Matters: Benchmarking ML, DL and LLMs for Mental Health Forecasting from Smartphone Sensing Data

Smartphone sensing offers a scalable, unobtrusive window into daily behavioral patterns linked to mental health. While prior work focuses on detection — identifying current conditions — forecasting future mental health states enables proactive, Just-in-Time Adaptive Interventions before crises emerge. We present the first benchmarking study comparing traditional machine learning (ML), deep learning (DL), and large language models (LLMs) for mental health forecasting using the College Experience Sensing dataset — the most extensive longitudinal passive sensing dataset on college student mental health to date. Our evaluation spans modeling paradigm, LLM adaptation strategy, and personalization, yielding three key findings: DL models, particularly Transformer, achieve the strongest overall performance, while LLMs exhibit a reasoning-prediction gap; few-shot consistently outperforms zero-shot and supervised fine-tuning (SFT); and personalization substantially improves forecasting. These findings lay groundwork for next-generation, human-centered adaptive systems supporting early mental health intervention. Our code is available at https://github.com/KaiDF/Study_MentalHealth_Forecasting.