Theoretical Bases of Health and Injury Management

About this course

This course introduces the scientific foundations of sports injury causation, workload modelling, and the emerging role of artificial intelligence in injury prevention. Learners will explore how injury mechanisms have evolved from simple monocausal views to multifactorial, dynamic, and systems-based approaches that reflect real athlete complexity. Through clear explanations and practical football examples, the course covers essential concepts such as intrinsic and extrinsic risk factors, the dynamic recursive model, the workload–injury relationship, and modern systems thinking used in elite performance environments. The second part of the course examines precision workload metrics, including acute: chronic workload ratios, high-speed running thresholds, acceleration load, sleep quality monitoring, and contextual load management. Finally, learners discover how AI and machine learning are transforming injury prediction, from multi-modal data integrations to SVM models, decision trees, Bayesian networks, individualised risk profiles, and early-warning systems used in elite clubs. By the end of this course, learners will understand the biological, physiological, and data-driven mechanisms behind injuries, and how modern AI-enhanced monitoring strategies reduce injury risk, optimize performance, and enhance player availability.

Course objectives

• examine intrinsic and extrinsic risk factors for injuries
• learn about dynamic systems thinking in athlete performance
• utilize modern metrics for monitoring athlete workload
• understand AI's role in injury prevention strategies