ReviewBiology of sport2026
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.
Review in Biology of sport, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Artificial intelligence interaction is not equivalent to social interaction: Rethinking artificial intelligence-mediated digital exposure in early childhood.Pediatric investigation · 2026Review
- A Physics-Based Digital Twin for Trail Running Race Performance Prediction: A Proof-of-Concept Study.Sensors (Basel, Switzerland) · 2026Article
- Machine learning prediction of ACL loading during the wide lunge: a multifactorial coupling analysis based on kinematic and electromyographic signals.Frontiers in bioengineering and biotechnology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.