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Experts in Sport: Misconceptions within sports injury research

How do misconceptions within sports injury research affect research findings and their application within the landscape of applied sport science?

In this episode of the Experts in Sport podcast, host Dr Stuart McErlain-Naylor is joined by guest Dr Judd Kalkhoven to discuss common misconceptions within the scope of sports injury research, as well as the nature of causal relationships within injury risk reduction and associated interventions.

References are made to exploratory research, its applicability to statistical and/or causal relationships, and whether this influences research findings and their interpretation. It is also considered whether the intended future application (e.g., injury risk reduction intervention) should influence the way in which research is conducted, posing ethical issues within this area.

The difference between association and causation is discussed, along with applying underlying causal logic to research, and a little bit of myth-busting. Issues with measurement are referenced, demonstrating an inability to measure appropriate variables in relation to injury.

Questions are raised around problem solving in a lab, and transitioning lab findings to a field setting and the accuracy of measurements such as mechanical loading placed on tissues. Misconceptions around the nature of science and how it leads to practical outcomes are debated, informing decision-making across the field of sports injury research.

Emphasis is placed on the importance of theory-driven research and adopting a causal framework to identify critical variables to measure and utilise, rather than submitting to the labour-intensive task of measuring all variables (described as a ‘hit and hope’ approach within sports science).

Dr Judd Kalkhoven is an Associate Lecturer at Western Sydney University. He completed a PhD at University of Technology Sydney, focusing on the complexities of the muscle-tendon unit and athletic injury. Judd is currently researching how to use causal models to predict athletic injuries in real-world settings and to obtain causal effects that can guide interventions for preventing these injuries.

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