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 Hewett

Research output: Contribution to journalArticle

67 Citations (Scopus)

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 - 2011
Externally publishedYes

Fingerprint

Athletes
Knee
Public Sector
Articular Range of Motion
Mechanics
Tibia
Biomechanical Phenomena
Software
Sensitivity and Specificity
Anterior Cruciate Ligament Injuries

ASJC Scopus subject areas

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

Cite this

New method to identify athletes at high risk of ACL injury using clinic-based measurements and freeware computer analysis. / Myer, Gregory D.; Ford, Kevin R.; Hewett, Timothy.

In: British Journal of Sports Medicine, Vol. 45, No. 4, 2011, p. 238-244.

Research output: Contribution to journalArticle

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