New method to identify athletes at high risk of ACL injury using clinic-based measurements and freeware computer analysis

Gregory D. Myer, Kevin R. Ford, Timothy E. Hewett

Research output: Contribution to journalArticle

70 Scopus citations

Abstract

Background: High knee abduction moment (KAM) landing mechanics, measured in the biomechanics laboratory, can successfully identify female athletes at increased risk for anterior cruciate ligament (ACL) injury. Methods: The authors validated a simpler, clinic-based ACL injury prediction algorithm to identify female athletes with high KAM measures. The validated ACL injury prediction algorithm employs the clinically obtainable measures of knee valgus motion, knee flexion range of motion, body mass, tibia length and quadriceps-tohamstrings ratio. It predicts high KAMs in female athletes with high sensitivity (77%) and specificity (71%). Conclusion: This report outlines the techniques for this ACL injury prediction algorithm using clinic-based measurements and computer analyses that require only freely available public domain software.

Original languageEnglish (US)
Pages (from-to)238-244
Number of pages7
JournalBritish journal of sports medicine
Volume45
Issue number4
DOIs
StatePublished - Jul 19 2011

ASJC Scopus subject areas

  • Orthopedics and Sports Medicine
  • Physical Therapy, Sports Therapy and Rehabilitation

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