Prediction of Lower Extremity Injuries from Vertical Jump Kinetic Data in Collegiate Athletes

By Pontillo, M., Hines, S., Krochak, R., & Sennett, B.

March 1, 2019

“Prediction of Lower Extremity Injuries from Vertical Jump Kinetic Data in Collegiate Athletes”

Pontillo, M., Hines, S., Krochak, R., & Sennett, B. (2019). Prediction of Lower Extremity Injuries from Vertical Jump Kinetic Data in Collegiate Athletes. Accepted for Poster Presentation at American College of Sports Medicine Conference 2019.

Key takeaways:  

Predictors of Lower Extremity Injury

  • LOAD, EXPLODE, DRIVE (χ2= 14.6, df=4, p < 0.01)
  • EXPLODE (p = 0.02)

Predictors of ACL Injury

  • LOAD, EXPLODE, DRIVE (χ2= 13.92, df=3, p < 0.01)
  • LOAD, EXPLODE (p < 0.05)
     

Population: Kinetic data from vertical jump assessment as well as injury data were recorded for 234 injured athletes and 234 healthy control athletes during a 3 year span.

Summary

The questions covered:

  • Can we identify collegiate athletes who sustained a lower extremity injury based on force plate variables from a vertical jump task?
     

The purpose of this study was to identify which force plate variables from a vertical jump task could identify collegiate athletes who sustained a lower extremity injury.  The testing procedure consisted of each subject performing a series of 6 consecutive vertical jumps. 234 lower extremity injuries were identified. Subjects were matched by age, sex and sport. Vertical jump variables used were LOAD, EXPLODE and DRIVE, operationally defined as the average eccentric rate of force development, average concentric force, and concentric impulse, respectively. Logistic regression was used to determine if the battery of variables could predict whether or not an athlete would sustain a lower extremity injury. Additionally, athletes who sustained an ACL injury were identified, matched, and analyzed correspondingly.

LOAD, EXPLODE, and DRIVE, when entered into the regression equation, showed the ability to predict lower extremity injury, χ2 = 14.6, df=4, p < 0.01; with EXPLODE independently showing significant prediction at p = 0.02. LOAD, EXPLODE, and DRIVE also showed the ability to predict ACL injury, χ2 = 13.92, df = 3, p < 0.01, with LOAD and EXPLODE independently showing significant prediction at p < 0.05.

The force plate variables collected from vertical jumps were able to identify and predict athletes who sustained a lower extremity injury. Additionally, these variables were able to identify and predict athletes who sustained an ACL injury.

This abstract will be presented by the authors at the American College of Sports Medicine 2019 Annual Meeting.

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