: A new model for injury prediction and prevention. Study Design Prospective cohort Prospective cohort Retrospective cohort/quasi-experimental Participants Firefighter trainees (n = 108) Male elite emergency task force (ETF) officers (n = 53) Male firefighters (n = 408), mean age 41.8 years; female
Mark B. Andersen and Jean M. Williams
A theoretical model of stress and athletic injury is presented. The purpose of this paper is to propose a framework for the prediction and prevention of stress-related injuries that includes cognitive, physiological, attentional, behavioral, intrapersonal, social, and stress history variables. Development of the model grew from a synthesis of the stress-illness, stress-accident, and stress-injury literatures. The model and its resulting hypotheses offer a framework for many avenues of research into the nature of injury and reduction of injury risk. Other advantages of the model are that it addresses possible mechanisms behind the stress-injury relationship and suggests several specific interventions that may help diminish the likelihood of injury. The model also has the potential of being applied to the investigation of injury and accident occurrence in general.
Erich J. Petushek, Edward T. Cokely, Paul Ward and Gregory D. Myer
Instrument-based biomechanical movement analysis is an effective injury screening method but relies on expensive equipment and time-consuming analysis. Screening methods that rely on visual inspection and perceptual skill for prognosticating injury risk provide an alternative approach that can significantly reduce cost and time. However, substantial individual differences exist in skill when estimating injury risk performance via observation. The underlying perceptual-cognitive mechanisms of injury risk identification were explored to better understand the nature of this skill and provide a foundation for improving performance. Quantitative structural and process modeling of risk estimation indicated that superior performance was largely mediated by specific strategies and skills (e.g., irrelevant information reduction), and independent of domain-general cognitive abilities (e.g., mental rotation, general decision skill). These cognitive models suggest that injury prediction expertise (i.e., ACL-IQ) is a trainable skill, and provide a foundation for future research and applications in training, decision support, and ultimately clinical screening investigations.
Kirk Krumrei, Molly Flanagan, Josh Bruner and Chris Durall
Injuries are somewhat commonplace in highly active populations. One strategy for reducing injuries is to identify individuals with an elevated injury risk before participation so that remediative interventions can be provided. Preparticipation screenings have traditionally entailed strength and flexibility measures thought to be indicative of inflated injury risk. Some researchers, however, have suggested that functional movements/tasks should be assessed to help identify individuals with a high risk of future injury. One assessment tool used for this purpose is the Functional Movement Screen (FMS). The FMS generates a numeric score based on performance attributes during 7 dynamic tasks; this score is purported to reflect future injury risk. Expanding interest in the FMS has led researchers to investigate how accurately it can identify individuals with an increased risk of injury.
Focused Clinical Question:
Can the Functional Movement Screen accurately identify highly active individuals with an elevated risk of injury?
Jonathan M. Williams, Michael Gara and Carol Clark
Balance is important for injury prediction, rehabilitation, 1 and prevention. 2 However, clinic-based balance measurement is often constrained to subjective judgment or task duration. This fails to determine the quality of balance performance and lacks detailed objectivity necessary for
Marcus J. Colby, Brian Dawson, Peter Peeling, Jarryd Heasman, Brent Rogalski, Michael K. Drew and Jordan Stares
decisions, practitioners may further validate these metrics for injury prediction in prospective seasons. Fourth, the HRSs defined here were taken from previous literature and case studies specific to this cohort. Future research should examine the HRSs specific to a particular sport. Finally, this modeling
Brandon M. Ness, Kory Zimney and William E. Schweinle
Injury risk factors and relevant assessments have been identified in women’s soccer athletes. Other tests assess fitness (eg, the Gauntlet Test [GT]). However, little empirical support exists for the utility of the GT to predict time loss injury.
To examine the GT as a predictor of injury in intercollegiate Division I female soccer athletes.
Retrospective, nonexperimental descriptive cohort study.
College athletic facilities.
71 female Division I soccer athletes (age 19.6 ± 1.24 y, BMI 23.0 ± 2.19).
Main Outcome Measures:
GT, demographic, and injury data were collected over 3 consecutive seasons. GT trials were administered by coaching staff each preseason. Participation in team-based activities (practices, matches) was restricted until a successful GT trial. Soccer-related injuries that resulted in time loss from participation were recorded.
71 subjects met the inclusion criteria, with 12 lower body time loss injuries sustained. Logistic regression models indicated that with each unsuccessful GT attempt, the odds of sustaining an injury increased by a factor of 3.5 (P < .02). The Youden index was 2 GT trials for success, at which sensitivity = .92 and specificity = .46. For successive GT trials before success (1, 2, or 3), the predicted probabilities for injury were .063, .194, and .463, respectively.
The GT appears to be a convenient and predictive screen for potential lowerbody injuries among female soccer athletes in this cohort. Further investigation into the appropriate application of the GT for injury prediction is warranted given the scope of this study.
Mattias Eckerman, Kjell Svensson, Gunnar Edman and Marie Alricsson
absence than the primary injury. 1 Traditionally, literature is largely focused on physical, biomechanical, and physiological aspects of injury prediction. 5 However, the role of psychological risk factors regarding sports injuries has been studied over the last few decades. 6 To address possible
Mahsa Jafari, Vahid Zolaktaf and Gholamali Ghasemi
pattern as directed,” a score of 2 means “a perform pattern with compensation,” a score of 1 means “unable to perform the pattern,” and a score of 0 means “pain with pattern regardless of quality.” The composite or total score of FMS ranges from 0 to 21. For injury prediction, most studies reported that
Lydia R. Vollavanh, Kathleen M. O’Day, Elizabeth M. Koehling, James M. May, Katherine M. Breedlove, Evan L. Breedlove, Eric A. Nauman, Debbie A. Bradney, J. Eric Goff and Thomas G. Bowman
. However, we believe our data is an important first step in understanding impact mechanism in men’s lacrosse. Furthermore, our results are consistent with previous findings by Talavage et al, 6 Bazarian et al, 42 and Guskiewicz et al 43 that suggest head injury prediction cannot only take a single