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

## TL;DR

This paper explores how advanced computational models can predict and help manage mental health states in real time using data from daily life tracking.

## Contribution

The study introduces and validates nonlinear state-space models for accurate and interpretable forecasting of mental health dynamics.

## Key findings

- Nonlinear PLRNN models outperformed others in forecasting mental health states and responses to interventions.
- The models revealed interpretable psychological network structures with key influence points like 'relaxed' states.
- Simulated perturbations using these models linked network structure directly to intervention planning.

## Abstract

Ecological momentary assessment (EMA) enables fine-grained tracking of affective and behavioral states in daily life. Accurately forecasting these states and their responses to interventions can guide adaptive mental health strategies. Network-based models are commonly used to capture such psychological dynamics, but most existing approaches make linear assumptions, and are rarely evaluated on forecasting performance. More flexible nonlinear models could better match evidence that psychological processes unfold in nonlinear, context-dependent ways and may offer superior predictive accuracy, but their internal dynamics are typically less interpretable. Here, we benchmarked a spectrum of models across three 40 day micro-randomized trials (N = 145), spanning linear network models, nonlinear state-space models (SSMs) based on piecewise-linear recurrent neural networks (PLRNNs), and Transformers. Three key findings emerged. First, PLRNNs provided the most accurate forecasts of spontaneous and intervention-driven EMA dynamics. Second, their latent-network structure yielded psychologically interpretable connectivity patterns, identifying affective nodes such as relaxed as high-impact influence points. Third, the inferred dynamics allowed simulating future perturbations, establishing a direct link between psychological network structure, forecasting, and intervention planning. Model performance remained robust under real-time retraining and incomplete data, indicating that nonlinear SSMs offer a practical and interpretable foundation for real-time control in digital mental health.