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

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 s

SUBMITTER: Fechtelpeter J 

PROVIDER: S-EPMC12827976 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature