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Injury Risk Estimation Expertise: Assessing the ACL Injury Risk Estimation Quiz
Erich J Petushek1, Edward T Cokely2, Paul Ward3
1School of Human and Health Sciences, University of Huddersfield, Huddersfield, UK Department of Cognitive and Learning Sciences, Michigan Technological University, Houghton, Michigan, USA erich.petushek@gmail.com.
Background:
Available methods for screening anterior cruciate ligament (ACL) injury risk are effective but limited in application as they generally rely on expensive and time-consuming biomechanical movement analysis. A potentially efficient alternative to biomechanical screening is skilled movement analysis via visual inspection (ie, having experts estimate injury risk factors based on observations of athletes' movements).
Purpose:
To develop a brief, valid psychometric assessment of ACL injury risk factor estimation skill: the ACL Injury Risk Estimation Quiz (ACL-IQ).
Study Design:
Cohort study (diagnosis); Level of evidence, 3.
Methods:
A total of 660 individuals participated in various stages of the study, including athletes, physicians, physical therapists, athletic trainers, exercise science researchers/students, and members of the general public in the United States. The ACL-IQ was fully computerized and made available online (www.ACL-IQ.org). Item sampling/reduction, reliability analysis, cross-validation, and convergent/discriminant validity analyses were conducted to refine the efficiency and validity of the assessment.
Results:
Psychometric optimization techniques identified a short (mean time, 2 min 24 s), robust, 5-item assessment with high reliability (test-retest: r = 0.90) and high test sensitivity (average difference of exercise science professionals vs general population: Cohen d = 2). Exercise science professionals and individuals from the general population scored 74% and 53% correct, respectively. Convergent and discriminant validity was demonstrated. Scores on the ACL-IQ were best predicted by ACL knowledge and specific judgment strategies (ie, cue use) and were largely unrelated to domain-general spatial/decision-making ability, personality, or other demographic variables. Overall, 23% of the total sample (40% of exercise science professionals; 6% of general population) performed better than or equal to the ACL nomogram.
Conclusion:
This study presents the results of a systematic approach to assess individual differences in ACL injury risk factor estimation skill; the assessment approach is efficient (ie, it can be completed in <3 min) and psychometrically robust. The results provide evidence that some individuals have the ability to visually estimate ACL injury risk factors more accurately than other instrument-based ACL risk estimation methods (ie, ACL nomogram). The ACL-IQ provides the foundation for assessing the efficacy of observational ACL injury risk factor assessment (ie, does simple skilled visual inspection reduce ACL injuries?). The ACL-IQ can also be used to increase our understanding of the perceptual-cognitive mechanisms underlying injury risk assessment expertise, which can be leveraged to accelerate learning and improve performance.