A control theoretic approach to evaluate and inform ecological momentary interventions.
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| Title: | A control theoretic approach to evaluate and inform ecological momentary interventions. |
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| Authors: | Fechtelpeter, Janik (AUTHOR), Rauschenberg, Christian (AUTHOR), Jalalabadi, Hamidreza (AUTHOR), Boecking, Benjamin (AUTHOR), van Amelsvoort, Therese (AUTHOR), Reininghaus, Ulrich (AUTHOR), Durstewitz, Daniel (AUTHOR), Koppe, Georgia (AUTHOR) |
| Source: | International Journal of Methods in Psychiatric Research. Dec2024, Vol. 33 Issue 4, p1-10. 10p. |
| Subjects: | Ecological momentary assessments (Clinical psychology), Linear dynamical systems, Mental health policy, Psychometrics, Mobile health |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Methods in Psychiatric Research is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10498931 |
| DOI: | 10.1002/mpr.70001 |
