Volume 33 - Supplement (Cardiometabolic)
J Transl Med Res. 2026, 33 - Supplement (Cardiometabolic): 25-47 |
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Research code: مقاله مروری
Ethics code: مقاله مروری
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Hooshmand-Moghadam B, Gaeini A A. Smart exercise therapy in the management of chronic diseases: A review of physiological mechanisms and Artificial intelligenceai-based personalized interventions. J Transl Med Res. 2026; 33 :25-47
URL: http://journal.bums.ac.ir/article-1-3566-en.html
URL: http://journal.bums.ac.ir/article-1-3566-en.html
1- Department of Exercise Physiology, Faculty of Sport Sciences, Ferdowsi University of Mashhad, Mashhad, Iran , b.hooshmand@um.ac.ir
2- Department of Exercise Physiology, Faculty of Sports and Health Sciences, University of Tehran, Tehran, Iran
2- Department of Exercise Physiology, Faculty of Sports and Health Sciences, University of Tehran, Tehran, Iran
Abstract: (53 Views)
Background and Aims: Chronic diseases remain a major challenge for global health systems, and conventional therapeutic approaches, particularly exercise therapy, are often insufficient to address the complex and evolving needs of patients. In recent years, the integration of exercise physiology with smart technologies and artificial intelligence (AI) algorithms has opened new horizons for designing precise and personalized exercise interventions. This analytical narrative review, adopting an integrative approach, aimed to elucidate the physiological mechanisms underlying exercise therapy and examine the role of AI in optimizing and personalizing this process.
Methods: A systematic search of major scientific databases was conducted. Of more than 500 screened articles, 112 eligible studies were included in the final analysis. Data were categorized and synthesized into three major themes using thematic analysis and conceptual integration.
Results: Exercise training improves physiological adaptations in patients with chronic diseases through metabolic, cardiovascular, and neurohormonal pathways. Wearable technologies, physiological sensors, and real-time feedback systems enable continuous monitoring of these adaptations. Furthermore, machine learning algorithms and big data–driven predictive models can identify individual exercise response patterns and facilitate the development of adaptive exercise programs tailored to patients' real-world conditions.
Conclusion: This review demonstrates that integrating exercise therapy with AI opens a new frontier in precision medicine. The future of exercise therapy in the AI era lies in personalized interventions and the integration of biological, behavioral, and environmental data. This emerging paradigm has the potential to transform exercise physiology from a rehabilitation tool into a decision-support discipline within precision medicine.
Methods: A systematic search of major scientific databases was conducted. Of more than 500 screened articles, 112 eligible studies were included in the final analysis. Data were categorized and synthesized into three major themes using thematic analysis and conceptual integration.
Results: Exercise training improves physiological adaptations in patients with chronic diseases through metabolic, cardiovascular, and neurohormonal pathways. Wearable technologies, physiological sensors, and real-time feedback systems enable continuous monitoring of these adaptations. Furthermore, machine learning algorithms and big data–driven predictive models can identify individual exercise response patterns and facilitate the development of adaptive exercise programs tailored to patients' real-world conditions.
Conclusion: This review demonstrates that integrating exercise therapy with AI opens a new frontier in precision medicine. The future of exercise therapy in the AI era lies in personalized interventions and the integration of biological, behavioral, and environmental data. This emerging paradigm has the potential to transform exercise physiology from a rehabilitation tool into a decision-support discipline within precision medicine.
Keywords: Chronic diseases, Exercise physiology, Machine learning, Metabolic and neuro-hormonal mechanisms, Personalized interventions, Smart exercise therapy, Wearable technologies
Type of Study: Review |
Subject:
Sports Medicine
Received: 2025/11/25 | Accepted: 2026/03/30 | ePublished: 2026/03/30
Received: 2025/11/25 | Accepted: 2026/03/30 | ePublished: 2026/03/30
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