Digital twins in sports performance: an overview of reviews of technologies, application domains, and computational frameworks
DOI:
https://doi.org/10.47197/retos.v83.119619Keywords:
Artificial Intelligence, Biomechanical Analysis, Digital Twins, Machine Learning, Sports PerformanceAbstract
Introduction: Digital twins represent a multidisciplinary opportunity for precision medicine and sports performance optimization; however, there is still a lack of a comprehensive synthesis of the methodologies, enabling technologies, and conceptual frameworks applied in these fields.
Objective: To review the state of the art of digital twin applications in health and sport from 2009 to 2025, identifying key technologies, application domains, and major research gaps.
Methodology: A review of reviews was conducted following the PRISMA 2020 guidelines and the PRIOR Statement. Twenty-one studies were included (12 systematic reviews, 6 scoping reviews, 2 narrative reviews, and 1 meta-review). Methodological quality was assessed using AMSTAR-2.
Results: The main technologies identified were conceptual frameworks (28%), artificial intelligence and machine learning (24%), neuromusculoskeletal modeling (20%), wearable devices and IoT (20%), and motion capture systems (16%). The most frequent application domains were sports performance (32%), injury prevention (20%), rehabilitation (20%), general health (16%), and occupational ergonomics (12%). Methodological quality was heterogeneous: 28% of the reviews showed moderate confidence, while 72% presented low or critically low quality.
Conclusions: The findings reveal technological fragmentation and a gap between computational development and clinical validation. Although digital twins demonstrate high clinical and engineering potential, their implementation requires greater prospective validation, standardization of metrics, and robust evaluation frameworks.
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