Key result
A refined approach to athlete screening and monitoring using historical and real-time data is advocated to support precise, context-driven decisions for injury prevention.
Why the study?
Traditional sports injury prevention and performance systems rely on infrequent assessments and binary classifications of injury that fail to reflect the complexity of athlete health.
Advocates for a refined approach to athlete screening and monitoring using historical and real-time data for precise, context-driven decisions in injury prevention.
Supports personalized athlete monitoring; leaves open prospective validation before practice adoption.
Injury prevention and performance optimisation in sports are evolving from generic strategies to more personalised, timely and data-informed approaches. Traditional systems often rely on infrequent assessments and binary classifications of injury, which fail to reflect the complexity of athlete health. This perspective advocates for a refined approach to athlete screening and monitoring that captures historical and real-time data to support precise, context-driven decisions. We differentiate between screening as a point-in-time evaluation of underlying risk and monitoring as the continuous assessment of an athlete's changing condition. The key principles of this approach include using valid, outcome-specific measures, timely data collection close to decision points and seamless integration into daily practice. By moving beyond simplistic metrics and incorporating advanced analytical techniques, this approach offers a more nuanced and proactive way to manage athlete health. It bridges the gap between research and real-world application, supporting better decision-making and long-term performance outcomes.
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Jiménez et al. (2025) conducted a review in Athlete injury prevention. Personalised, timely and data-informed athlete screening and monitoring vs. Traditional systems relying on infrequent assessments was evaluated. A refined approach to athlete screening and monitoring using historical and real-time data is advocated to support precise, context-driven decisions for injury prevention.
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