The purpose of screening in sports is to examine large populations of asymptomatic individuals aiming to predict who is at greatest risk of sustaining an injury. Ideally, once at-risk athletes are identified, scientists, clinicians, and coaches would address variables associated with risk by using behavior modification, changing training regime, or improving movement strategies (mitigate risk). This approach is logical and would be of great benefit to active individuals everywhere, but sports scientists cannot agree on whether it is possible. Some sports scientists have advocated the use of individual tests and measures that are associated with injury to stratify risk based on linear models and multiple regression methodology (Freckleton & Pizzari, 2013; Hewett, 2016). Critics of this approach have countered that no single test or measure nor any combination thereof has demonstrated strong enough sensitivity and specificity to predict injury and that, therefore, risk screening (high sensitivity) and injury prediction (high specificity) should be abandoned in favor of placing all players on every team on an injury prevention program (Bahr, 2016, 2016b). Still others have begun to wonder whether injury screening and prediction are too complex for the linear models that have been used in the past (van Dyk & Clarsen, 2017). It is also possible that a recursive model employing ongoing, frequent monitoring of athletes may address some of these shortcomings. In 2016, Bittencourt et al. (Bittencourt et al., 2016) advanced thinking in this area by recommending more frequent screening as well as proposing that injury might be predicted by ...
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", language="en", issn="1466-853X", doi="10.1016/j.ptsp.2019.10.010", url="http://dx.doi.org/10.1016/j.ptsp.2019.10.010" }