A control theoretic approach to evaluate and inform ecological momentary interventions
Objectives Ecological 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...
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| Main Authors: | , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
December 2024
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| In: |
International journal of methods in psychiatric research
Year: 2024, Volume: 33, Issue: 4, Pages: 1-10 |
| ISSN: | 1557-0657 |
| DOI: | 10.1002/mpr.70001 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1002/mpr.70001 Verlag, kostenfrei, Volltext: http://onlinelibrary.wiley.com/doi/abs/10.1002/mpr.70001 
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| Author Notes: | Janik Fechtelpeter, Christian Rauschenberg, Hamidreza Jalalabadi, Benjamin Boecking, Therese van Amelsvoort, Ulrich Reininghaus, Daniel Durstewitz, Georgia Koppe |
| Summary: | Objectives Ecological 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. Methods We 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. Results We 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. Conclusions Combined 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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| Item Description: | Erstmals veröffentlicht: 22. Oktober 2024 Gesehen am 08.04.2025 |
| Physical Description: | Online Resource |
| ISSN: | 1557-0657 |
| DOI: | 10.1002/mpr.70001 |