Big Data in Sports Medicine and Exercise Science: Integrating Theory and Practice for Future Innovations




Athletic Performance, Biomechanics, Data Analytics, Deep Learning, Epidemiology, Injury prediction, Machine learning, No-contact injuries, Omics, Personalized Medicine, Proactive health, Proteomics, Rehabilitation, Sensors, training load, transcriptomics


Background: Big data has been successfully applied in medicine, offering remarkable advancements in patient care and health management. The rise of big data in sports medicine and exercise science is a pivotal development, offering fresh perspectives on optimizing athlete performance, preventing injuries, and managing non-communicable diseases (NCDs). This review explores the integration and the potential of big data in these areas, with a particular focus on wearables, genomics, and metabolomics.


Objectives: The objectives of this study were: (i) to present the current state of big data in sports medicine, including its applications and advancements in wearables, genomics, and metabolomics, and (ii) to outline the future prospects of big data in sports medicine and exercise science, emphasizing a call to action for ongoing research and interdisciplinary collaboration.


Methods: An extensive review of the existing literature and current practices in the application of big data in sports medicine was conducted. The review concentrated on the advancements and applications in the fields of wearables, genomics, and metabolomics, and their implications for sports science and health management.


Results: The application of big data has significantly influenced sports medicine, particularly through advanced wearable technologies for monitoring physiological parameters, genomic approaches for personalized athlete care, and metabolomics for in-depth analysis of metabolic responses to exercise. These developments have led to more individualized training and rehabilitation programs, effective injury prevention strategies, and better health management practices for both athletes and the general population.


Conclusion: The integration of big data into sports medicine signifies a substantial shift towards more data-driven and tailored approaches. This progression fosters interdisciplinary collaboration and opens new avenues for research in areas like biomechanics and transcriptomics. Leveraging these opportunities can lead to practical improvements in athlete care, health management, and a broader understanding of sports science, benefiting both the sports community and the wider public.

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How to Cite

Dergaa, I. ., & Chamari , K. . (2024). Big Data in Sports Medicine and Exercise Science: Integrating Theory and Practice for Future Innovations. Tunisian Journal of Sports Science and Medicine, 2(1), 1-13.

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