Predicting and comparing the long-term impact of lifestyle interventions on individuals with eating disorders - a machine learning evaluation

Aug 5, 2026

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The study evaluates and predicts the long-term effectiveness of five lifestyle interventions for people with eating disorders using machine learning. Participants were diagnosed using the Eating Disorder Diagnostic Scale and measures of body composition, metabolic and lipid outcomes were collected at baseline, during, and at intervention end. Random Forest and Gradient Boosting models, after feature engineering and dataset balancing, achieved high predictive accuracy. Predicted effectiveness ranked counseling with exercise and diet highest, followed by aerobic exercise with diet, walking with diet, flexible diet exercise, and online program-based exercise.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Objective

Methods

Results

Conclusion

Zurich Open Repository and Archive University of Zurich University Library Strickhofstrasse 39 CH-8057 Zurich [www.zora.uzh.ch](www.zora.uzh.ch) Year: 2024 Predicting and comparing the long-term impact of lifestyle interventions on individuals with eating disorders in active population: a machine learning evaluation Irandoust, Khadijeh ; Parsakia, Kamdin ; Estifa, Ali ; Zoormand, Gholamreza ; Knechtle, Beat ; Rosemann, Thomas ; Weiss, Katja ; Taheri, Morteza DOI: [https://doi.org/10.3389/fnut.2024.1390751](https://doi.org/10.3389/fnut.2024.1390751) Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: [https://doi.org/10.5167/uzh-262952](https://doi.org/10.5167/uzh-262952) Journal Article Published Version The following work is licensed under a Creative Commons: Attribution 4.0 International (CC BY 4.0) License. Originally published at: Irandoust, Khadijeh; Parsakia, Kamdin; Estifa, Ali; Zoormand, Gholamreza; Knechtle, Beat; Rosemann, Thomas; Weiss, Katja; Taheri, Morteza (2024) . Predicting and comparing the long-term impact of lifestyle interventionson individuals with eating disorders in active population: a machine learning evaluation. Frontiers in Nutrition, 11:1390751 . DOI: [https://doi.org/10.3389/fnut.2024.1390751](https://doi.org/10.3389/fnut.2024.1390751) OPEN ACCESS EDITED BY Alessandra Pokrajac-Bulian, University of Rijeka, Croatia REVIEWED BY Alexandre Puttick, Bern University of Applied Sciences, Switzerland Petra Anić, University of Rijeka, Croatia Yu Wang, Sichuan University, China *CORRESPONDENCE Khadijeh Irandoust [irandoust@ikiu.ac.ir](irandoust@ikiu.ac.ir)[ ](irandoust@ikiu.ac.ir)Beat Knechtle beat. knechtle@hispeed.ch RECEIVED 23 February 2024 ACCEPTED 15 July 2024 PUBLISHED 07 August 2024 CITATION Irandoust K, Parsakia K, Estifa A, Zoormand G, Knechtle B, Rosemann T, Weiss K and Taheri M (2024) Predicting and comparing the long-term impact of lifestyle interventions on individuals with eating disorders in active population: a machine learning evaluation. Front. Nutr. 11:1390751 . doi: 10.3389/fnut.2024.1390751 COPYRIGHT © 2024 Irandoust, Parsakia, Estifa, Zoormand, Knechtle, Rosemann, Weiss and Taheri. This isan open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. TYPE Original Research PUBLISHED 07 August 2024 DOI 10.3389/fnut.2024.1390751 Predicting and comparing the long-term impact of lifestyle interventions on individuals with eating disorders in active population: a machine learning evaluation Khadijeh Irandoust 1*, Kamdin Parsakia 2, Ali Estifa 1, Gholamreza Zoormand3, Beat Knechtle4*, Thomas Rosemann 5, Katja Weiss 5 and Morteza Taheri 6 1 Department of Sport Sciences, Imam Khomeini International University, Qazvin, Iran, 2 Department of Psychology and Counseling, KMAN Research Institute, Richmond Hill, ON, Canada, 3 Department of Physical Education, Huanggang Normal University, Huanggang, China, 4 Medbase St. Gallen Am Vadianplatz, St. Gallen, Switzerland, 5 Institute of Primary Care, University of Zürich, Zürich, Switzerland, 6 Department of Cognitive and Behavioural Sciences in Sport, Faculty of Sport Science and Health, University of Tehran, Tehran, Iran Objective: This study aims to evaluate and predict the long-term effectiveness of five lifestyle interventions for individuals with eating disorders using machine learning techniques. Methods: This study, conducted at Dr. Irandoust’s Health Center at Qazvin from August 2021 to August 2023, aimed to evaluate the effects of five lifestyle intervention

FAQs

What is the main objective of the study?

To evaluate and predict the long-term effectiveness of five lifestyle interventions for individuals with eating disorders using machine learning techniques.

Which interventions were compared in the study?

The interventions were counseling with exercise and dietary regime; aerobic exercise with dietary regime; walking with dietary regime; exercise with a flexible diet; and exercises through online programs and applications.

What machine learning models were used and how accurate were they?

Random Forest and Gradient Boosting Regressors were used. Accuracy reached about 85% and 89%, and then about 89% and 90% after dataset balancing.

Aug 5, 2026

Upload by Maeve