Study on the Exercise Prescription Formulation Model Based on Operations Research_Vol. 2 No. 3 (JMHS 2024)_Journal of Medicine and Health Science (ISSN: 2959-0639)

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Study on the Exercise Prescription Formulation Model Based on Operations Research

DOI: https://doi.org/10.62517/jmhs.202405305

Author(s)

Yuqiong Lin1,#, Jing Zhuang2,#, Ying Zhang3,#, Xuecan Yang1,4, Laurent Peyrodie5, Jean-Marie Niang1,6, Zefeng Wang3,4,5,6,*

Affiliation(s)

1ASIR, Institute - Association of Intelligent Systems and Robotics, Paris, France 2Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, Zhejiang, China 3College of Engineering, College of Teacher Education, Huzhou University, Huzhou, Zhejiang, China 4IEIP, Institute of Education and Innovation in Paris, Paris, France 5ICL, Junia, Université Catholique de Lille, LITL, F-59000 Lille, France 6Sino-Congolese Foundation for Development, Brazzaville, Republic of the Congo *Corresponding Author. #These authors contributed equally to this work.

Abstract

This paper explores the integration of operations research (OR) methodologies into exercise prescription to enhance the precision and effectiveness of personalized exercise programs. Traditional exercise prescriptions often fail to consider the unique physiological and psychological factors influencing an individual's response to exercise. By leveraging OR techniques such as linear optimization, integer optimization, network optimization, and dynamic programming, this study aims to develop a robust framework for creating tailored exercise prescriptions that address these complexities. The research demonstrates that OR can optimize various components of exercise plans, including frequency, intensity, type, time, and progression, thereby improving health outcomes and adherence. The study outlines the principles of exercise prescription and the fundamental concepts of OR, providing a detailed discussion on how these methodologies can be applied to design effective exercise programs. Case studies and real-world applications highlight the practical benefits of OR in exercise prescription, showcasing improvements in operational efficiency, resource management, and patient care. Key findings indicate that integrating OR into exercise prescription allows for data-driven, evidence-based approaches that enhance the personalization and sustainability of exercise interventions. Challenges such as accurate data collection, continuous monitoring, and the complexity of implementing OR models in clinical settings are also addressed, emphasizing the need for further research and development. The practical significance of this study is profound, offering healthcare professionals a powerful tool to move beyond traditional exercise recommendations. By adopting OR methodologies, personalized exercise plans can be developed to meet individual health profiles, goals, and preferences, leading to better patient engagement and long-term health benefits. The findings support the potential of OR to transform exercise prescription practices, promoting more effective and efficient healthcare delivery. This paper concludes by suggesting future research directions to refine and expand the application of OR in exercise science, underscoring the importance of continued innovation and interdisciplinary collaboration.

Keywords

Operations Research; Exercise Prescription; Personalized Healthcare; Optimization Techniques; Health Outcomes

References

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