Erich J Petushek1, Edward T Cokely2, Paul Ward3, John J Durocher4, Sean J Wallace5, Gregory D Myer6. 1. School 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. 2. Department of Cognitive and Learning Sciences, Michigan Technological University, Houghton, Michigan, USA Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, Berlin, Germany. 3. School of Human and Health Sciences, University of Huddersfield, Huddersfield, UK. 4. Department of Biological Sciences, Michigan Technological University, Houghton, Michigan, USA. 5. Department of Computer Science, Illinois Institute of Technology, Chicago, Illinois, USA. 6. Division of Sports Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA Departments of Pediatrics and Orthopaedic Surgery, University of Cincinnati, Cincinnati, Ohio, USA The Sports Health and Performance Institute, OSU Sports Medicine, Ohio State University Medical Center, Columbus, Ohio, USA The Micheli Center for Sports Injury Prevention, Waltham, Massachusetts, USA.
Abstract
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.
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.
Authors: Nathan D Schilaty; Nathaniel A Bates; Sydney Kruisselbrink; Aaron J Krych; Timothy E Hewett Journal: Am J Sports Med Date: 2020-07-21 Impact factor: 6.202