Smart sport ecosystems and cardiometabolic health: Designing of next-generation frameworks for health-oriented management

Hooshmand-Moghadam, Balal; Talebpour, Mahdi; Keshtidar, Mohammad

Volume 33 - Supplementary J Transl Med Res. 2026, 33 - Supplementary: 38-61 | Back to browse issues page

Research code: مقاله مروری

Ethics code: مقاله مروری

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Hooshmand-Moghadam B, Talebpour M, Keshtidar M. Smart sport ecosystems and cardiometabolic health: Designing of next-generation frameworks for health-oriented management. J Transl Med Res. 2026; 33 :38-61
URL: http://journal.bums.ac.ir/article-1-3563-en.html

Smart sport ecosystems and cardiometabolic health: Designing of next-generation frameworks for health-oriented management

Balal Hooshmand-Moghadam¹, Mahdi Talebpour¹, Mohammad Keshtidar ^*²

1- Department of Sport Management, Faculty of Sport Science, Ferdowsi University of Mashhad, Mashhad, Iran
2- Department of Sport Management, Faculty of Sport Science, Ferdowsi University of Mashhad, Mashhad, Iran , keshtidarr@um.ac.ir

Abstract: (1247 Views)

Cardiometabolic diseases (CMR) remain a major global health challenge, imposing significant economic and social burdens on healthcare systems. Although extensive evidence supports the role of exercise in mitigating cardiometabolic risk factors, critical gaps persist in the integration of clinical data, physiological monitoring, and digital technologies. This disconnect limits the design of effective and personalized interventions. This study aimed to evaluate the role of smart exercise ecosystems in cardiometabolic health management and propose an integrated framework for the next generation of interventions. Accordingly, a narrative analytical review approach was employed. Within this framework, key cardiometabolic health indicators, physiological mechanisms of targeted exercise, wearable technologies, and data-driven algorithms were analyzed. Additionally, a conceptual model for health-centered exercise ecosystems was developed. Findings indicated that the integration of evidence-based exercise, continuous physiological monitoring, and AI-driven predictive algorithms can reduce cardiometabolic risk, improve metabolic markers, and enhance overall quality of life. The proposed model integrates physiological, behavioral, and data-driven pathways, enabling personalized exercise prescriptions, remote monitoring, and evidence-based decision-making. This framework strengthens a health-centered paradigm in exercise, with significant clinical and managerial implications, including improved risk assessment, enhanced intervention effectiveness, and reduced healthcare costs. The results of this study provided a foundation for smart policy-making and the development of community-oriented health programs at both national and international levels.

Keywords: Artificial Intelligence, Cardiometabolic Risk Factors, Data Mining, Exercise, Health Services Administration, Monitoring, Physiologic, Wearable Electronic Devices

Full-Text [PDF 1004 kb] (819 Downloads)

Type of Study: Review | Subject: Health Information Technology
Received: 2025/11/20 | Accepted: 2026/03/14 | ePublished: 2026/03/6