Athletic Injury Research: Frameworks, Models and the Need for Causal Knowledge.

Area of Science:

• Sports Science and Medicine
• Epidemiology
• Biostatistics

Background:

• Applied sports science faces challenges in understanding athletic injury causality and prevention due to limited variable control.
• Randomized controlled trials are often impractical or unethical, leading to a heavy reliance on observational research.
• Current observational approaches often lack causal inference tools, hindering knowledge acquisition and effective injury prevention strategy development.

Purpose of the Study:

• To propose a novel model for investigating athletic injury aetiology and mechanisms.
• To present a framework for developing and evaluating athletic injury prevention strategies.
• To address the limitations of current research methodologies in sports science and medicine.

Main Methods:

• Utilizing causal diagrams, including frameworks, models, and causal directed acyclic graphs (DAGs).
• Guiding athletic injury research and prevention efforts through structured causal inference.
• Facilitating the investigation of specific causal links, mechanisms, and assumptions.

Main Results:

• The proposed causal diagram approach facilitates the investigation of specific causal links and mechanisms.
• It aids in translating lab-based research into applied sports settings.
• It guides causal inferences from applied research by establishing robust causal structures.

Conclusions:

• Causal diagrams offer a solution to challenges in athletic injury research, improving understanding of injury causality.
• This approach guides the development and adoption of relevant metrics, technologies, and prevention strategies.
• It encourages the establishment of strong theoretical foundations, minimizing research waste and improving scientific rigor.