Computational network models for forecasting and control of mental health trajectories in digital applications

Abstract Ecological momentary assessments (EMA) have transformed mobile mental health by capturing real-time fluctuations in psychological states and behavior. While forecasting future states from EMA data is crucial for adaptive interventions, most current approaches to modeling the underlying psychological mechanisms rely on linear assumptions. These include common network based methods such as vector autoregression (VAR) or Kalman filtering, which assume fixed and proportional relationships among variables. However, a growing body of evidence suggests that psychological dynamics exhibit nonlinear properties raising concerns about the adequacy of linear models for both interpretation and prediction. Here, we leverage three independent 40-day micro-randomized trials (N=145) to benchmark a spectrum of models—from naïve baselines and linear network models to autoregressive Transformers and nonlinear state-space models (SSMs) built on piecewise-linear recurrent neural networks (PLRNNs). PLRNNs provided the most accurate forecasts, including predictions of how individuals responded to interventions. Beyond superior forecasting, the PLRNN’s latent-network structure allowed us to simulate how changes in individual psychological states spread through the system. This revealed interpretable patterns of influence—highlighting central network nodes like sad or down as high-impact intervention targets based on their strong ripple effects. Critically, performance remained robust under real-time retraining constraints and varying data completeness, underscoring the practical viability of nonlinear SSMs in deployed mobile mental health systems. Our results establish PLRNN-based forecasting as a powerful, interpretable foundation for real-time, model-predictive control of digital mental health.