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

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 id...

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Published in:British journal of sports medicine 2011-04, Vol.45 (4), p.238-244
Main Authors: Myer, Gregory D, Ford, Kevin R, Hewett, Timothy E
Format: Article
Language:English
Subjects:
Algorithms
Anterior Cruciate Ligament Injuries
Athletes
Athletic Injuries - physiopathology
Athletic Injuries - prevention & control
Biomechanical Phenomena
Body Mass Index
Female
Females
Freeware
Humans
Image Processing, Computer-Assisted
Kinematics
Knee
Knee Injuries - physiopathology
Knee Injuries - prevention & control
Ligaments
Muscle Strength - physiology
Photography
Quadriceps Muscle - physiology
Range of Motion, Articular
Risk Factors
Software
Sports injuries
Sports medicine
Task Performance and Analysis
Tibia - anatomy & histology
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cites cdi_FETCH-LOGICAL-b590t-10e5610289035f024046926891977a29365bc63d4ef34fafcfaa33cea1911da13
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container_title British journal of sports medicine
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Hewett, Timothy E
description 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-to-hamstrings 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.
doi_str_mv 10.1136/bjsm.2010.072843
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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-to-hamstrings 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.</description><identifier>ISSN: 0306-3674</identifier><identifier>ISSN: 1473-0480</identifier><identifier>EISSN: 1473-0480</identifier><identifier>DOI: 10.1136/bjsm.2010.072843</identifier><identifier>PMID: 21081640</identifier><language>eng</language><publisher>England: BMJ Publishing Group Ltd and British Association of Sport and Exercise Medicine</publisher><subject>Algorithms ; Anterior Cruciate Ligament Injuries ; Athletes ; Athletic Injuries - physiopathology ; Athletic Injuries - prevention &amp; control ; Biomechanical Phenomena ; Body Mass Index ; Female ; Females ; Freeware ; Humans ; Image Processing, Computer-Assisted ; Kinematics ; Knee ; Knee Injuries - physiopathology ; Knee Injuries - prevention &amp; control ; Ligaments ; Muscle Strength - physiology ; Photography ; Quadriceps Muscle - physiology ; Range of Motion, Articular ; Risk Factors ; Software ; Sports injuries ; Sports medicine ; Task Performance and Analysis ; Tibia - anatomy &amp; histology</subject><ispartof>British journal of sports medicine, 2011-04, Vol.45 (4), p.238-244</ispartof><rights>Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions</rights><rights>Copyright: 2011 Published by the BMJ Publishing Group Limited. 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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-to-hamstrings ratio. It predicts high KAMs in female athletes with high sensitivity (77%) and specificity (71%). 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source BMJ_英国医学会期刊
subjects Algorithms
Anterior Cruciate Ligament Injuries
Athletes
Athletic Injuries - physiopathology
Athletic Injuries - prevention & control
Biomechanical Phenomena
Body Mass Index
Female
Females
Freeware
Humans
Image Processing, Computer-Assisted
Kinematics
Knee
Knee Injuries - physiopathology
Knee Injuries - prevention & control
Ligaments
Muscle Strength - physiology
Photography
Quadriceps Muscle - physiology
Range of Motion, Articular
Risk Factors
Software
Sports injuries
Sports medicine
Task Performance and Analysis
Tibia - anatomy & histology
title New method to identify athletes at high risk of ACL injury using clinic-based measurements and freeware computer analysis
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