Why the study?
Gender and sex disparities in anterior cruciate ligament injury rates are commonly estimated using athlete-exposures, but the limitations of this construct for cross-gender comparisons need conceptual evaluation.
The use of athlete-exposures to compare injury rates by gender/sex may be biased due to differences in training-to-match ratios and team sizes, highlighting the need for finer-grained data collection.
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Athlete-exposure metrics warrant caution in ACL gender comparisons; leaves open need for granular exposure data in future studies.
High rates of anterior cruciate ligament (ACL) injuries in girls' and women's sports have garnered significant attention from researchers, sport organisations and the media. Gender/sex disparities in ACL injury rates are often estimated using the construct of athlete-exposures (AEs), a widely used measure of exposure time in sports science and epidemiology that is defined as one athlete participating in one practice or competition. In this narrative review, we explain the limitations of AEs as a measure of exposure time and develop a series of conceptual critiques regarding the use of AEs for the purposes of comparing injury rates by gender/sex. We show that the differing training-to-match ratio and average team size between women and men-rooted in persistent gendered inequities in sports participation and professionalisation-may jeopardise the validity of using AEs for cross-gender comparisons and skew gender/sex disparities in ACL injury rates. To avoid bias, we invite researchers interested in gender/sex disparities in injury rates to collect finer-grained data including individual-level AEs disaggregated by training and competition, as well as to appropriately control for team size and training-to-match ratio at the data analysis stage. Any quantitative comparisons of injury rates should also thoroughly contextualise the limitations of AEs, including their inability to capture the potential qualitative differences between women's and men's training and sporting environments that may influence injury rates.
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Danielsen et al. (2024) studied this question.
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