Predictors of Overuse Shoulder Injuries in Collegiate Volleyball Athletes Laura Friess, MS, ATC; Scott L Bruce, MS, ATC; Gary Wilkerson, EdD, ATC 1. 1.Kugler A, Kruger-Franke M, Reininge S, Trouillie H, Rosemeyer B. Muscular imbalance and shoulder pain in volleyball attackers. Br J Sports Med. 1996;30: Rauh MJ, Macera CA, Ming J, Wiksten DL. Subsequent injury patterns in girls’ high school sports. JAT. 2007;42: Bonza JE, Fields SK, Yard EE, Comstock RD. Shoulder injuries among United States high school athletes during the and school years. JAT. 2009;44(1): Rokiko AS, Jobe FW, Pink MM, Perry J, Brault J. Electromyographic analysis of shoulder function during the volleyball serve and spike. J Shoulder and Elbow Surgery. 1998;7: Wang HK, Cochrane T. A descriptive epidemiological study of shoulder injury in top level English male volleyball players. Int J Sports Med. 2001;22: NCAA Division I volleyball athletes Age 19 ± 1 years; height: ± 5.7 cm; weight: 71.8 ± 8.5 kg 10 reported no previous history of shoulder injury; 3 reported a previous history of shoulder injury Pre-season assessments: Subjective Shoulder Dysfunction (SSD; Shoulder Pain And Disability Index) Back Extension Hold (BEH; Modified Behring-Sorenson test) (Fig. 1a) Trunk Flexion Hold (TFH) (Fig. 2a) Side-Plank Hold (SPH) (Fig. 3a) Only non-traumatic shoulder injuries were included in this study Injury was defined as Requiring an evaluation by an athletic trainer Resulted in ≥ 2 consecutive days of treatment OR restricted/lost participation Repetitive overhead activity is associated with a high prevalence of shoulder injury 1-3 Overhead biomechanics are similar for baseball, softball, and volleyball 4 Pitch count limits are widely used to control exposure to high stress loads in baseball Arm swing limits are not recognized as an injury prevention measure in volleyball Contributors to shoulder pain among volleyball players include: 5 Long training history Repetitive spiking Hitting the ball while airborne (no foot to ground surface contact) Study purpose: to develop a clinical prediction rule that quantifies risk for overuse shoulder injuries among female volleyball players. BACKGROUND & PURPOSE Predictive Factor SensitivitySpecificity+ LR TFH < 139 s SPH < 39.5 s High Exp BEH < s ≥3 Factors Table 1. Total Positive Predictive Factors SPADI total score quantified both pain and disability Arm swing defined as any forceful overhead contact with a volleyball (including attacks/spikes & serves) Game data (attacks/spikes and serves) for each player were derived from statistics posted on the team’s website Practice session arm swing data was estimated from direct observation (arm swing count for randomly selected players) Arm swings were not counted during pre-practice or pre-game warm-up activities Receiving operating characteristic (ROC) analysis was used to determine cut-point for predictor variable dichotomization Estimated arm swing count (ASC), SSD, BEH, TFH, & SPH (Figures 1b, 2b, & 3b) Categorical predictor variables included in analysis Exposure (Exp): Starter or Non-Starter Position (Pos): Hitter/Non-Hitter Dependent variable Injured versus Non-Injured Dichotomized predictor variables were evaluated separately for sensitivity, specificity, and positive likelihood ratio (+LR) Each possible combination of predictors was compared to identify the best prediction model on the basis of maximum +LR PROCEDURE & DATA ANALYSIS PARTICIPANT CHARACTERISTICS & METHODS Figures 1a & b. Back Extension Hold Test RESULTS Lo TFH, Lo SPH, Hi Exp, and Lo BEH identified as the best injury predictors (Table 1) Variables excluded from the prediction model were Hi ASC (+LR = 1.43), Pos-Hit (+LR = 1.43), and Hi SSD (invalid +LR = ,due to inadequate number of cases) Athletes with ≥ 3 positive factors were 5X more likely to sustain an overuse shoulder injury than athletes with < 3 positive factors DISCUSSION This study demonstrates that lack of core muscle endurance was a predictor of shoulder dysfunction in our cohort of volleyball athletes Contrary to expectation, estimated ASC did not provide as much predictive value as a simple dichotomization of exposure (Starter or Non-starter) The relatively small number of cases available for analysis limits confidence that the prediction model is valid for other cohorts (10 cases per prediction factor considered optimal) Multi-center research is needed to validate each component of the 4-factor prediction model Figures 2a & b. Trunk Flexion Hold Test Fig. 1b. BEH ROC Cut point = <142.5 Fig. 1a Fig. 2b. TFH ROC Cut point = <139 s Fig. 3b. SPH ROC Cut point = <39.5 s Figures 3a & b. Side-Plank Hold Test Total Positive Predictive Factors Figure 4. Total Positive Predictive Factors Cut point =  3 Factors Fig. 3a Fig. 2a REFERENCES