Artificial Intelligence-Based Training Load Monitoring and Injury Prevention in Youth Athletes: A Sports Science Perspective

Abstract

Objectives: This study aimed to examine the effectiveness of Artificial Intelligence (AI)-based monitoring systems in optimizing training load management and reducing injury risk among youth athletes.

Materials and Methods: This study used a mixed-method approach by combining quantitative data obtained from wearable devices with qualitative information collected from coaches and sports science practitioners. The quantitative component focused on training load, fatigue indicators, and injury-risk signals, while the qualitative component explored the practical use of AI-based systems in training decision-making.

Results: The findings indicate that AI-based monitoring systems can provide more accurate and real-time analysis of athlete workload. These systems support the early detection of fatigue, excessive training load, and potential injury-risk factors. In addition, AI-based monitoring helps coaches design more individualized training programs based on the physiological and biomechanical characteristics of each athlete.

Conclusions: AI-based training load monitoring has the potential to improve performance management and reduce injury risk in youth athletes. Its use can support safer, more personalized, and evidence-based training practices in youth sports development. Future studies are recommended to examine long-term implementation, data privacy, and ethical considerations in the use of AI in sports training.