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Predictors of Overuse Shoulder Injuries in Collegiate Volleyball Athletes

Authors:

Abstract

Muscular imbalance and shoulder pain in volleyball attackers. analysis of shoulder function during the volleyball serve and spike. J Shoulder and Elbow Surgery. 1998;7:256-263. 5. Wang HK, Cochrane T. A descriptive epidemiological study of shoulder injury in top level English male volleyball players. Int J Sports Med. 2001;22:159-163. • 13 NCAA Division I volleyball athletes • Age 19 ± 1 years; height: 173.9 ± 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.
Predictors of Overuse Shoulder Injuries in Collegiate Volleyball Athletes
Laura Friess, MS, ATC; Scott L Bruce, MS, ATC; Gary Wilkerson, EdD, ATC
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;30:256-259.
2. Rauh MJ, Macera CA, Ming J, Wiksten DL. Subsequent injury patterns in girls’ high school sports. JAT. 2007;42:486-494.
3. Bonza JE, Fields SK, Yard EE, Comstock RD. Shoulder injuries among United States high school athletes during the 2005-2006 and
2006-2007 school years. JAT. 2009;44(1):76-83.
4. 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;7:256-263.
5. Wang HK, Cochrane T. A descriptive epidemiological study of shoulder injury in top level English male volleyball players. Int J
Sports Med. 2001;22:159-163.
13 NCAA Division I volleyball athletes
Age 19 ± 1 years; height: 173.9 ± 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 injury1-3
Overhead biomechanics are similar for baseball, softball, and volleyball4
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 Sensitivity Specificity + LR
TFH < 139 s 0.67 0.70 2.23
SPH < 39.5 s 0.67 0.60 1.67
High Exp 0.50 0.60 1.67
BEH < 142.5 s 1.00 0.30 1.43
≥3 Factors 1.00 .80 5.00
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
Figure 4. Total Positive Predictive Factors
Cut point =
3 Factors
Fig. 3a
Fig. 2a
REFERENCES
ResearchGate has not been able to resolve any citations for this publication.
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