Computational network models for forecasting and control of mental health trajectories in digital applications
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".