Biology of Sport
eISSN: 2083-1862
ISSN: 0860-021X
Biology of Sport
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abstract:
Review paper

Digital twins in sports science: applications for performance enhancement, injury prevention, and rehabilitation through advanced big data analytics and deep learning methodologies – a comprehensive narrative review

Afef Sédiri
1
,
Sabri Barbaria
1
,
Halil Ibrahim Ceylan
2
,
Andrea De Giorgio
3
,
Luca Puce
4
,
Valentina Stefanica
5
,
Nicola Luigi Bragazzi
6
,
Ismail Dergaa
7
,
Hanene Boussi Rahmouni
1

  1. Laboratory of Biophysics and Medical Technologies, LR13ES07 (BTM), University of Tunis Elmanar, Higher Institute of Medical Technologies of Tunis (ISTMT), Tunis, Tunisia
  2. Physical Education and Sports Teaching Department, Faculty of Sports Sciences, Atatürk University, Erzurum 25240, Turkey
  3. Artificial Engineering, Naples 80121, Italy
  4. Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, 16132 Genoa, Italy
  5. Faculty of Sciences, Physical Education and Informatics, National University of Science and Technology Politehnica Bucharest, Pitesti University Center, Pitesti, Romania
  6. Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
  7. High Institute of Sport and Physical Education of Ksar Said, University of Manouba, Mannouba 2010, Tunisia
Biol Sport. 2026;43:1271–1291
DOI: https://doi.org/10.5114/biolsport.2026.161106
Online publish date: 2026/04/20
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Digital twin (DT) technology, combined with advanced computational methodologies, represents a paradigm shift in sports science. DTs generate virtual athlete replicas through real-time data integration and predictive analytics. While computational capabilities and data acquisition have advanced rapidly, DT applications in sports remain fragmented, warranting systematic synthesis. This narrative review examines the applications of DT in sports, with a focus on big data analytics and deep learning for performance enhancement, injury prevention, and rehabilitation. A narrative review was conducted using publications from 2018 to 2025 across multiple databases. Eligible studies applied DT frameworks, Artificial Intelligence (AI), including machine learning algorithms and deep neural networks, or big data analytics in elite and amateur sport contexts, with a focus on football as a case study. Extracted data focused on technological approaches, clinical outcomes, and practical applications. DT applications cluster into three domains: (1) performance enhancement via biomechanical modelling (convolutional or recurrent neural networks); (2) injury prevention using ensemble learning and predictive risk models; and (3) rehabilitation optimization through multimodal sensors and virtual reality. Key examples include cycling telemetry, computer vision for technique correction, and real-time musculoskeletal monitoring. The integration of generative AI and the Internet of Things further enhances predictive accuracy and decision-making. DTs offer significant potential for proactive athlete management. Widespread adoption requires standardized protocols, clinical validation, and robust ethical frameworks for data privacy. Successful integration supports data-driven training, individualized recovery, and enhanced athlete welfare.
keywords:

Artificial intelligence, Big data analytics, Biomechanics, Convolutional neural networks, Deep learning, Digital technology, Injury prevention, Machine learning, Rehabilitation, Sports medicine, Virtual reality

  
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