ArticleInternational journal of methods in psychiatric research2024
A control theoretic approach to evaluate and inform ecological momentary interventions.
Article in International journal of methods in psychiatric research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A machine learning-based ecological momentary intervention for mental health promotion in youth: a micro-randomized trial.Translational psychiatry · 2026Trial
- Re-engineering the disordered mind: clinical experimentation, dynamical systems, and AI for personalized psychiatry.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Review
- Computational network models for forecasting and control of mental health trajectories in digital applications.NPJ digital medicine · 2025Article
- Optimizing the frequency of ecological momentary assessments using signal processing.Psychological medicine · 2025Article
- Health-Promoting Effects and Everyday Experiences With a Mental Health App Using Ecological Momentary Assessments and AI-Based Ecological Momentary Interventions Among Young People: Qualitative Interview and Focus Group Study.JMIR mHealth and uHealth · 2025Article
- Article
- A control theoretic approach to evaluate and inform ecological momentary interventions.International journal of methods in psychiatric research · 2024Article
- Characterizing the dynamics, reactivity and controllability of moods in depression with a Kalman filter.PLoS computational biology · 2024Article
- Formalizing psychological interventions through network control theory.Scientific reports · 2023Article
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Authors and funding
8 authors.
Funding
Abstract
objectivesEcological momentary interventions (EMI) are digital mobile health interventions administered in an individual's daily life to improve mental health by tailoring intervention components to person and context. Experience sampling via ecological momentary assessments (EMA) furthermore provides dynamic contextual information on an individual's mental health state. We propose a personalized data-driven generic framework to select and evaluate EMI based on EMA.
methodsWe analyze EMA/EMI time-series from 10 individuals, published in a previous study. The EMA consist of multivariate psychological Likert scales. The EMI are mental health trainings presented on a smartphone. We model EMA as linear dynamical systems (DS) and EMI as perturbations. Using concepts from network control theory, we propose and evaluate three personalized data-driven intervention delivery strategies. Moreover, we study putative change mechanisms in response to interventions.
resultsWe identify promising intervention delivery strategies that outperform empirical strategies in simulation. We pinpoint interventions with a high positive impact on the network, at low energetic costs. Although mechanisms differ between individuals - demanding personalized solutions - the proposed strategies are generic and applicable to various real-world settings.
conclusionsCombined with knowledge from mental health experts, DS and control algorithms may provide powerful data-driven and personalized intervention delivery and evaluation strategies.
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