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How to use machine learning and fuzzy cognitive maps to test hypothetical scenarios in health behavior change interventions: a case study on fruit intake
Department of Health Promotion, CAPHRI, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands.
Department of Computer Science & Software Engineering, Miami University, Oxford, OH, USA.
KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, RPL.ORCID-id: 0000-0002-3432-6151
Department of Health Promotion, CAPHRI, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands.
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2023 (engelsk)Inngår i: BMC Public Health, E-ISSN 1471-2458, Vol. 23, nr 1, artikkel-id 2478Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Background: Intervention planners use logic models to design evidence-based health behavior interventions. Logic models that capture the complexity of health behavior necessitate additional computational techniques to inform decisions with respect to the design of interventions. Objective: Using empirical data from a real intervention, the present paper demonstrates how machine learning can be used together with fuzzy cognitive maps to assist in designing health behavior change interventions. Methods: A modified Real Coded Genetic algorithm was applied on longitudinal data from a real intervention study. The dataset contained information about 15 determinants of fruit intake among 257 adults in the Netherlands. Fuzzy cognitive maps were used to analyze the effect of two hypothetical intervention scenarios designed by domain experts. Results: Simulations showed that the specified hypothetical interventions would have small impact on fruit intake. The results are consistent with the empirical evidence used in this paper. Conclusions: Machine learning together with fuzzy cognitive maps can assist in building health behavior interventions with complex logic models. The testing of hypothetical scenarios may help interventionists finetune the intervention components thus increasing their potential effectiveness.

sted, utgiver, år, opplag, sider
Springer Nature , 2023. Vol. 23, nr 1, artikkel-id 2478
Emneord [en]
Complex interventions, Fuzzy cognitive maps, Genetic algorithms, Machine learning
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Identifikatorer
URN: urn:nbn:se:kth:diva-341603DOI: 10.1186/s12889-023-17367-zISI: 001123738400002PubMedID: 38082297Scopus ID: 2-s2.0-85179365439OAI: oai:DiVA.org:kth-341603DiVA, id: diva2:1822618
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QC 20231227

Tilgjengelig fra: 2023-12-27 Laget: 2023-12-27 Sist oppdatert: 2024-02-29bibliografisk kontrollert

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