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

Autor:	Irandoust K; Department of Sport Sciences, Imam Khomeini International University, Qazvin, Iran., Parsakia K; Department of Psychology and Counseling, KMAN Research Institute, Richmond Hill, ON, Canada., Estifa A; Department of Sport Sciences, Imam Khomeini International University, Qazvin, Iran., Zoormand G; Department of Physical Education, Huanggang Normal University, Huanggang, China., Knechtle B; Medbase St. Gallen Am Vadianplatz, St. Gallen, Switzerland., Rosemann T; Institute of Primary Care, University of Zürich, Zürich, Switzerland., Weiss K; Institute of Primary Care, University of Zürich, Zürich, Switzerland., Taheri M; Department of Cognitive and Behavioural Sciences in Sport, Faculty of Sport Science and Health, University of Tehran, Tehran, Iran.
Jazyk:	angličtina
Zdroj:	Frontiers in nutrition [Front Nutr] 2024 Aug 07; Vol. 11, pp. 1390751. Date of Electronic Publication: 2024 Aug 07 (Print Publication: 2024).
DOI:	10.3389/fnut.2024.1390751
Abstrakt:	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 interventions on individuals with eating disorders, initially diagnosed using The Eating Disorder Diagnostic Scale (EDDS). The interventions were: (1) Counseling, exercise, and dietary regime, (2) Aerobic exercises with dietary regime, (3) Walking and dietary regime, (4) Exercise with a flexible diet, and (5) Exercises through online programs and applications. Out of 955 enrolled participants, 706 completed the study, which measured Body Fat Percentage (BFP), Waist-Hip Ratio (WHR), Fasting Blood Sugar (FBS), Low-Density Lipoprotein (LDL) Cholesterol, Total Cholesterol (CHO), Weight, and Triglycerides (TG) at baseline, during, and at the end of the intervention. Random Forest and Gradient Boosting Regressors, following feature engineering, were used to analyze the data, focusing on the interventions' long-term effectiveness on health outcomes related to eating disorders. Results: Feature engineering with Random Forest and Gradient Boosting Regressors, respectively, reached an accuracy of 85 and 89%, then 89 and 90% after dataset balancing. The interventions were ranked based on predicted effectiveness: counseling with exercise and dietary regime, aerobic exercises with dietary regime, walking with dietary regime, exercise with a flexible diet, and exercises through online programs. Conclusion: The results show that Machine Learning (ML) models effectively predicted the long-term effectiveness of lifestyle interventions. The current study suggests a significant potential for tailored health strategies. This emphasizes the most effective interventions for individuals with eating disorders. According to the results, it can also be suggested to expand demographics and geographic locations of participants, longer study duration, exploring advanced machine learning techniques, and including psychological and social adherence factors. Ultimately, these results can guide healthcare providers and policymakers in creating targeted lifestyle intervention strategies, emphasizing personalized health plans, and leveraging machine learning for predictive healthcare solutions. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2024 Irandoust, Parsakia, Estifa, Zoormand, Knechtle, Rosemann, Weiss and Taheri.)
Databáze:	MEDLINE
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