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Clinical correlates to laboratory measures for use in non-contact anterior cruciate
ligament injury risk prediction algorithm
Gregory D. Myera,b,c,⁎, Kevin R. Forda,b,d, Jane Khourya,e, Paul Succopf, Timothy E. Hewetta,b,g,h,i,j
aCincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
bSports Medicine Biodynamics Center and Human Performance Laboratory, Cincinnati, Ohio, USA
cRocky Mountain University of Health Professions, Provo, Utah, USA
dDepartment of Pediatrics, University of Cincinnati, USA
eDivision of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
fDepartment of Environmental Health, University of Cincinnati, Cincinnati, Ohio, USA
gDepartment of Pediatrics, College of Medicine, University of Cincinnati, USA
hDepartment of Orthopaedic Surgery, College of Medicine, University of Cincinnati, USA
iDepartment of Biomedical Engineering, College of Medicine, University of Cincinnati, USA
jDepartment of Rehabilitation Sciences, College of Medicine, University of Cincinnati, USA
a b s t r a c ta r t i c l e i n f o
Received 1 February 2010
Accepted 29 April 2010
Biomechanics correlated to increased ACL
ACL injury risk factors
Targeted neuromuscular training
ACL injury prevention
Background: Prospective measures of high knee abduction moment during landing identify female athletes at
high risk for non-contact anterior cruciate ligament injury. Biomechanical laboratory measurements predict
high knee abduction moment landing mechanics with high sensitivity (85%) and specificity (93%). The
purpose of this study was to identify correlates to laboratory-based predictors of high knee abduction
moment for use in a clinic-based anterior cruciate ligament injury risk prediction algorithm. The hypothesis
was that clinically obtainable correlates derived from the highly predictive laboratory-based models would
demonstrate high accuracy to determine high knee abduction moment status.
Methods: Female basketball and soccer players (N=744) were tested for anthropometrics, strength and
landing biomechanics. Pearson correlation was used to identify clinically feasible correlates and logistic
regression to obtain optimal models for high knee abduction moment prediction.
Findings: Clinical correlates to laboratory-based measures were identified and predicted high knee abduction
moment status with 73% sensitivity and 70% specificity. The clinic-based prediction algorithm, including
(Odds Ratio: 95% confidence interval) knee valgus motion (1.43:1.30–1.59 cm), knee flexion range of motion
(0.98:0.96–1.01°), body mass (1.04:1.02–1.06 kg), tibia length (1.38:1.25–1.52 cm) and quadriceps to
hamstring ratio (1.70:1.06–2.70) predicted high knee abduction moment status with C statistic 0.81.
Interpretation: The combined correlates of increased knee valgus motion, knee flexion range of motion, body
mass, tibia length and quadriceps to hamstrings ratio predict high knee abduction moment status in female
athletes with high sensitivity and specificity.
Clinical Relevance: Utilization of clinically obtainable correlates with the prediction algorithm facilitates high
non-contact anterior cruciate ligament injury risk athletes' entry into appropriate interventions with the
greatest potential to prevent injury.
© 2010 Elsevier Ltd. All rights reserved.
Female athletes are reported to be four to six times more likely
than males to sustain a sports related non-contact anterior cruciate
ligament (ACL) injury (Arendt and Dick, 1995; Malone et al., 1993).
Several investigators have demonstrated that female athletes exhibit
high knee abduction moment (KAM) related landing mechanics more
often than males during landing and pivoting movements (Ford et al.,
2003, 2006;Malinzaketal.,2001;Hewettet al.,2004, 2006b;Chappell
et al., 2002; McLean et al., 2004a; Kernozek et al., 2005; Zeller et al.,
2003; Pappas et al., 2007). These altered neuromuscular strategies or
decreased neuromuscular control of the lower extremity during the
executionof sports movements may underlie the increased risk of ACL
injury in female athletes (Ford et al., 2003, 2005; Hewett et al., 2005;
McLean et al., 2004b; Chappell et al., 2002; Myer et al., 2006b).
Females often demonstrate knee landing alignments associated with
high KAM at the time of injury, in validation of these laboratory
findings (Olsen et al., 2004; Krosshaug et al., 2007; Boden et al., 2000).
In addition, prospective measures related to knee abduction moment
measured during drop vertical jump also predict ACL injury risk in
Clinical Biomechanics 25 (2010) 693–699
⁎ Corresponding author. Cincinnati Children's Hospital, 3333 Burnet Avenue,
MLC 10001, Cincinnati, OH 45229.
E-mail address: firstname.lastname@example.org (G.D. Myer).
0268-0033/$ – see front matter © 2010 Elsevier Ltd. All rights reserved.
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/clinbiomech
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young female athletes (Hewett et al., 2005) and in military cadets
(Padua et al., 2009).
Calculation of KAM, through inverse dynamics, requires complex
laboratory-based three-dimensional kinematic and kinetic measure-
ment techniques. However, a recent report has isolated biomechanical
during landing(Myer et al., in press-a). These biomechanical predictors
of KAM, which include increased knee abduction angle, increased
relative quadriceps recruitment and decreased knee flexion range of
motion (RoM), concomitant with increased tibia length and mass
normalized to body height that accompanies growth, are also measure-
ments that have all been related to increased risk of ACL injury in
previous prospective and retrospective epidemiological reports (Boden
et al., 2000; Uhorchak et al., 2003; Hewett et al., 2005; Padua et al.,
2009). Unfortunately, expensive biomechanical laboratories, with the
are required to acquire these measurements. This restricts thepotential
to perform athlete risk assessments on a large scale, in particular
limiting the potential to target high injury risk athletes with the
appropriate intervention strategies. If simpler assessment tools are
developed that can be administered in a clinic or field testing
environment, which are validated by the highly accurate laboratory-
based assessment, screening for ACL injury risk can be performed on a
potential correlates to laboratory-based predictors of high KAM for use
in clinic-based ACL injury risk prediction algorithm. The hypothesis
tested was that clinically obtainable correlates to measures used in the
highly predictive laboratory-based models would demonstrate high
accuracy in determination of high KAM status.
Between 2004 and 2008, all sixth through twelfth grade female
athlete participants in basketball and soccer were recruited from a
county public school district with five middle schools and three high
schools to participate in a prospective longitudinal study. The goal of
the study was to determine potential underlying mechanisms that
increase ACL injury risk. First time visits for 744 subjects' were
designated for inclusion into the current analyses. Subjects were
excluded (n=46) from the study if they did not complete
biomechanical testing or if errors were found in the calculation of
the dependent variable, KAM, leaving 698 subjects available for
inclusion in the analyses (mean: 1SD) (age 13.9: 2.4 years; height
159.3: 8.6 cm; body mass 54.0: 12.5 kg; percent in each maturational
status 17% pre-pubertal, 30% pubertal and 53% post-pubertal). For the
initial model development, 598 of the 698 eligible subjects were
randomly assigned to formulate the optimized prediction models. The
data from the remaining 100 subjects was retained for verification of
the optimized multivariable linear and logistic prediction models
from the 598 randomly selected girls.
Cincinnati Children's Hospital Medical Center and Rocky Mountain
University of Health Professions Institutional Review Boards approved
the data collection procedures and consent forms. Parental consent and
athlete assent were received prior to data collection. Subjects were
tested prior tothe start of their basketball or soccercompetitive season.
The testing consisted of a knee exam, medical history, maturational
estimates, dynamic strength and landing biomechanical analysis.
Body mass was measured on a calibrated physician scale. A static
standing trial was conducted prior to biomechanical testing in which
the subject was instructed to stand still with foot placement
standardized to the laboratory coordinate system. The static standing
trial was used to calculate segment lengths as the estimated distance
between the proximal and distal joint centers (e.g. thigh segment
distance was equal to the distance between the hip joint center and
knee joint center). In addition, the static trial was used to calculate
standing anatomical alignment measures.
2.2.2. Dynamic strength
Isokinetic knee extension/flexion (concentric/concentric muscle
action) strength was measured with the subject seated on the
dynamometer Biodex System II (Shirley, New York) and the trunk
perpendicular to the floor, the hip flexed to 90° and the knee flexed to
90°. Prior to each data collection set, a warm-up set, which consisted
of five submaximalknee flexion/extensions for each legat 300°/s,was
performed. The test session consisted of ten knee flexion/extension
repetitions for each leg at 300°/s. Peak flexion and extension torques
were recorded (Myer et al., 2009). The ratio of quadriceps to
hamstrings (QuadHam) strength peak isokinetic torque was calcu-
lated. Inclusion of relative quadriceps to hamstring strength measure-
ments at 300/s has been demonstrated to be related to increased ACL
injury risk in female athletes (Myer et al., 2009).
2.2.3. Landing biomechanics
Three-dimensional hip, knee and ankle kinematic and kinetic data
were quantified for the contact phase of three drop vertical jump
(DVJ) tasks. Each subject was instrumented by a single investigator
with 37 retroreflective markers placed on the sacrum, left PSIS,
sternum and bilaterally on the shoulder, elbow, wrist, ASIS, greater
trochanter, mid thigh, medial and lateral knee, tibial tubercle, mid
shank, distal shank, medial and lateral ankle, heel, dorsal surface of
the midfoot, lateral foot (5th metatarsal) and toe (between 2nd and
3rd metatarsals). First, a static trial was conducted in which the
subject was instructed to stand still with foot placement standardized
to the laboratory coordinate system. This static measurement was
used as each subject's neutral (zero) alignment; subsequent kine-
matic measures were referenced in relation to this position (Ford et
al., 2007). The DVJ involved the subject standing on top of a box
(31 cm high) with their feet positioned 35 cm apart (Ford et al., 2007;
Hewett et al., 2005). They were instructed to drop directly down off
the box and immediately perform a maximum vertical jump, raising
both arms while jumping for a basketball rebound (Ford et al., 2003).
All trials were collected by a single investigator with EVaRT
(Version 4, Motion Analysis Corporation, Santa Rosa, CA) using a
motion analysis system consisting of ten digital cameras (Eagle
cameras, Motion Analysis Corporation, Santa Rosa, CA) positioned in
the laboratory and sampled at 240 Hz. Prior to data collection, the
motion analysis system was calibrated based on the manufacturer's
recommendation. Two force platforms (AMTI, Watertown, MA) were
sampled at 1200 Hz and time synchronized with the motion analysis
system. The force platforms were embedded into the floor and
positioned 8 cm apart so that each foot would contact a different
platform during the stance phase of the drop vertical jump (Ford et al.,
Following data collection, the motion and force data were further
analyzed in Visual3D (Version 4.0, C-Motion, Inc.). The procedures
within Visual3D first consisted of the development of a static model
customized for each subject (Ford et al., 2007). 3D marker trajectories
from each trial were filtered at a cutoff frequency of 12 Hz (Ford et al.,
2007). 3D knee joint angles were calculated according to the Cardan/
data were utilized to calculate knee joint moments using inverse
dynamics (Andriacchi et al., 1997; Winter, 1990, pp. 91–95). Net
external knee moments were reported in the current study and
represent the external load on the joint. Peak knee abduction angle
andmomentwere identifiedduringthedeceleration phaseof theinitial
G.D. Myer et al. / Clinical Biomechanics 25 (2010) 693–699
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stance phase of the DVJ. The deceleration phase was operationally
defined from initial contact (vertical ground reaction force first
exceeded 10 N) to the lowest vertical position of the body center of
mass. Knee valgus motion was calculated as the 2D displacement of the
knee in the coronal plane, from just prior to initial contact to the end of
the deceleration phase of the drop jump landing task. The left side data
were retained for statistical analysis.
2.3. Statistical analyses
Data were exported to SPSS (SPSS for Windows version 16.0
Chicago, IL, USA) and SAS®, version 9.1 (SAS Institute, Cary, NC, USA)
for statistical analyses. Imputation of missing data observed for the
Statistical solutions, Ltd, Cork, Ireland). The imputation method
employed was based on ordinary least-squares regression, using age
and BMI as covariates and was necessary for less than 2% of the
observations of any independent variable. Pearson's correlation
coefficients were calculated to assist in the initial screening of
surrogate clinic-based predictor variables (Myer et al., in press-a) by
selecting significant correlates (Pb0.01) of the independent labora-
tory-based variables (peak knee abduction angle, peak knee extensor
moment, knee flexion RoM, BMI Z-score and tibia length) previously
shown to predict both KAM and the dichotomized high KAM (KAM
N25.25 Nm) status with high R2(0.78), sensitivity (85%) and
specificity (93%) (Myer et al., in press-a). The cut-point used to
classify the dependent variable status was N25.25 Nm of KAM, which
was based on published prediction modeling of ACL injury risk. This
cut-point yielded the maximal sensitivity and specificity for predic-
tion of ACL injury risk during a DVJ (Hewett et al., 2005; Myer et al.,
2007). Using this classification, subjects were categorized into a
dichotomous (high KAM; “yes” or “no”) as the dependent variable.
For the final step in model development, multivariable logistic
regression with a backward elimination strategy was employed. The
logistic regression model was estimated using a logit link. An alpha
level of 0.05 was used to judge statistical significance in all models. A
nomogram was produced from the secondary classification model to
software. R is available as Free Software under the terms of the Free
(Lucent Technologies, Murray Hill, NJ; http://www.r-project.org).
Mean and 95% CI for independent variables used in the model
development are presented in Table 1, together with the correlation
coefficients of the clinic-based surrogate predictor to its laboratory-
based principal (Myer et al., in press-a) independent variable. The
initial prediction of high KAM, was performed using logistic
regression analysis in the training dataset (N=598). The final logistic
regression model, which included the independent predictors: knee
valgus motion, knee flexion RoM, mass, tibia length and QuadHam
ratio, predicted high KAM status with 55% sensitivity and 86%
specificity (Pb0.001). This model was predictive of high KAM status
with a C statistic of 0.83. This logistic regression model was validated
by solving the prediction equation for each of the 100 subjects in the
validation group and determining into which group (high KAM vs.
low KAM) they were classified. The resultant prediction equation
yielded a sensitivity of 58% and a specificity of 87% to predict
N25.25 Nm of KAM during the drop vertical jump.
Based on the relatively low risk of harm due to treatment (neuro-
groups to predict the outcome with increased sensitivity by examination
of the ROC (receiver operating characteristic) curve. Accordingly, the
prediction of secondary classification of high KAM (N21.74 Nm of KAM)
was repeated using logistic regression analysis techniques in the training
dataset(N=598). The resultantlogistic regressionmodel,predictedhigh
KAM status with 73% sensitivity and 70% specificity (Pb0.001). Table 2
shows the odds ratio (OR) and 95% confidence limits for the predictors of
high KAM (N21.74 Nm of KAM). This model was predictive of high KAM
status with a C statistic of 0.81. The ROC curve for this model is presented
in Fig. 1. This logistic model was also validated by solving the prediction
equation for each subject in the validation group, yielded a sensitivity of
72% and a specificityof 72%topredict N21.74 Nm of KAM during the DVJ.
Fig. 2 presents a predictive, clinician friendly nomogram devel-
oped from the analysis described above that can be used to predict
high KAM (N21.74 Nm KAM) based on tibia length, knee valgus
motion, knee flexion RoM, body mass and quadriceps to hamstrings
ratio. The nomogram code generates an equally distributed, segment-
ed line representing standardized measurable units for each predictor
variable. The range of values for each predictor is determined based
on the data used to create the regression model, and therefore
creation of the nomogram, requires a sufficiently powered regression
model. The magnitude of each intra-segment distance is in direct
proportion to that individual variable's strength of association (Beta
coefficient) to the predicted outcome as determined in the combined
multivariate regression equation.
The purpose of the current study was to develop a “clinician
friendly” landing assessment tool derived from the highly predictive
laboratory-based measurements that would be easy to use and would
facilitate the potential for widespread use in clinical and field settings.
A nomogram was developed from the logistic regression analyses that
Means, 95% confidence intervals (CIs) and correlation coefficients for independent
predictor variables used in model development (N=598).
Mean 95% confidence
interval for mean
Peak knee abduction angle (°)
Knee valgus motion (cm)b
Knee flexion RoM(deg)c
BMI Z-score (No. of SD)
Tibia length (cm)c
Peak knee extensor moment (Nm)
aCorrelation is significant at the 0.01 level (2-tailed).
bSurrogate clinic-based predictors employed in the logistic regression model.
cPredictor variables were included in both laboratory-based and clinic-based
Odds Ratio (OR) estimates and 95% CI for independent variables used in model
OR 95% confidence interval
Knee valgus motion (cm)
Knee flexion RoM (°)
Body mass (kg)
Tibia length (cm)
G.D. Myer et al. / Clinical Biomechanics 25 (2010) 693–699
Author's personal copy
can be used to predict high KAM(N21.74 Nm KAM) based onclinically
measured tibia length, knee valgus motion, knee flexion RoM, body
mass and quadriceps to hamstrings ratio.
The outcome tool (Fig. 2) developed from the regression analysis
mechanics based on the described clinically obtainable measures tibia
length, knee valgus motion, knee flexion range of motion, body mass
and quadriceps to hamstrings ratio. Clinic-based tibia length can be
measured using a standard measuring tape to quantify the distance
between the lateral knee joint line to the lateral malleous. Body mass
can be measured on a calibrated physician scale. Two dimensional
(clinic-based) frontal and sagittal plane knee kinematic data can be
captured with standard video cameras. QuadHam ratio is traditionally
captured on isokinetic dynamometers in a clinical setting. If an
isokinetic testing device is not readily available, then a surrogate
measure ofthe QuadHamratiocanbedeveloped basedonthe athlete's
body mass. The surrogate QuadHam ratio measure is obtained when a
female athlete'smass (kg)is multipliedby0.01andtheresultant value
be input into the nomogram to represent QuadHam ratio for the
Landing sequence images used for knee valgus motion (Fig. 3A–B)
and knee flexion RoM (Fig. 3C–D) clinic-based measurement can be
captured via the “print screen” feature available on most personal
computers or they can be captured with freeware software such as
VirtualDub software (copyright 1998–2009 Avery Lee). Recom-
mended software for kinematic coordinate data capture and calcula-
tion are suggested to be performed with ImageJ (Rasband, W.S.,
ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA,
http://rsb.info.nih.gov/ij/, 1997–2009) software that is also available
without surcharge (Fig. 3) (Myer et al., in press-b).
Fig. 4A provides a completed algorithm for the presented subject
using the followingclinically feasible measurements quantified onher
left leg: Tibia length: 36 cm; Knee valgus motion: 3.3 cm; Knee flexion
RoM: 63.4°; mass: 48.2 kg; QuadHam: 1.78. Based on her demon-
strated measurements, the prediction nomogram would indicate that
this subject would have a 45% chance to demonstrate high KAM
during her measured drop vertical jump. Fig. 4B presents the
completed algorithm for the same subject with the following clinic-
based measurement of knee valgus motion on her right leg. The red
solid lines indicate the shift in her measured knee valgus motion from
3.3 cm onherleft legto 6.0 cmor herright.Accordingly,thischangein
knee valgus motion shifts her probability of high knee load on her
right leg to be 66% during this trial of the drop vertical jump.
Recent studies demonstrate that neuromuscular training reduces
the high KAM risk factor for ACL injury, increases performance and
decreases knee and ACL injury incidence in female athletes (Hewett et
al., 1996, 2004, 2006a; Myer et al., 2004, 2005, 2006a,b, 2007).
However, re-evaluation of ACL injury rates in female athletes indicate
that this important health issue has yet to be resolved, as increased
knowledge and application of injury prevention techniques have not
led to measureable reductions in ACL injury incidence in female
athletes (Agel et al., 2005). A recent investigation by Grindstaff et al.
(2006) indicated that standard, non-targeted neuromuscular training
programs may require application to 89 female athletes to prevent a
single ACL injury. It is possible that the identification of female
athletes whodemonstrate risk factors for ACL injury such as high KAM
Fig. 1. ROC curve for prediction of high KAM (N21.74 Nm of KAM). Cross bar indicates
maximum sensitivity and specificity of the prediction model.
Fig. 2. A clinician friendly nomogram that was developed from the regression analysis can be used to predict outcome based on tibia length, knee valgus motion, knee flexion RoM,
mass and quadriceps to hamstring ratio. To use the prediction nomogram one should place a straight edge vertically so that it touches the designated variable on the axis for each
predictor value, and record the value that each of the five predictors provides on the “points” axis at the top of the diagram. All of the recorded “points” measured using this method
are then summed and this value is located on the “total points” line with a straight edge. A vertical line drawn down from the “total points line” to the “probability line” identifies the
probability that the athlete will demonstrate high KAM (N21.74 Nm of knee abduction) during the drop vertical jump based on the utilized predictive variables.
G.D. Myer et al. / Clinical Biomechanics 25 (2010) 693–699
Author's personal copy
could improve the efficiency of neuromuscular training by targeting
gap between laboratory identification of injury risk factors (Hewett et
al., 2005; Padua et al., 2009) and clinical practice with this simplified
algorithm that can be utilized with clinic-based assessment tools. The
simplicity and lessened equipment and labor cost (relative to
laboratory-based assessments that may exceed $1000 per athlete)
associated withutilizationofthis toolmayfacilitatetheidentificationof
high ACL injury risk athletes on a more widespread basis in clinical and
field settings. Prior work has utilized laboratory-based measures to
determine the potential differing effects of neuromuscular training in
female athletes who demonstrate high KAM landing strategies relative
to those who do not (Myer et al., 2007). This prior study employed an
abridged version of a comprehensive training protocol shown to alter
biomechanical factors related to increased ACL injury risk in female
results of this study indicate that females who demonstrate increased
high KAM may be able to reduce this risk factor via targeted
neuromuscular training, while those athletes without this risk factor
may not reap similar benefits from the training program (Myer et al.,
on the high KAM risk factor, a linear regression analysis was performed
to examine the potential association between the pre-test measures of
high KAM and the change in this variable with neuromuscular training
potential to reduce KAM with training. In contrast, the control group
showed no similar causal relationship of pre-test KAM measures to
change in the post test measure. If this is the case, a logical extension of
these findings would be that it is more important to identify and target
athletes identifiedashigh KAM for injury prevention trainingprograms
than those with low KAM.
Myer and colleagues evaluated a comprehensive neuromuscular
training protocol that was developed to reduce ACL injury risk and
improve sports related performance measures. This comprehensive
protocol successfully reduced knee abduction torques by 21%, but the
potentially larger trainingeffects. This reported meanreductionin KAM
may be masked by subjects who already demonstrated a low value of a
measured risk factor that has been previously demonstrated (Knaus et
al., 1993), but is often ignored in clinical investigations (Harrell, 2001).
The current clinic-based tool can aid in the identification of female
athletes with high KAM landing mechanics who will most likely obtain
the greatest potential to significantly reduce dangerous knee loading
profiles with targeted neuromuscular training. In addition, neuromus-
cular training targeted directly to reduce KAM in those who demon-
strate high KAM landing mechanics may help athletes obtain landing
mechanics that will take them out of “high-risk” category. These goals
have not been previously achievable with non-targeted training
protocols (Myer et al., 2007). In addition, based on the low risk of the
treatment for high KAM (neuromuscular training), we chose to
influence thealgorithm forhighly sensitive prediction for this outcome.
While an increased number of false positives may be predicted with
efforts to maximize sensitivity, female athletes predicted to demon-
strate both low and highKAM will likely gain improved performanceas
a serendipitous effect of neuromuscular training targeted to prevent
The risk for osteoarthritis in the female ACL injured population
ranges from 50 to 100%, (Myklebust and Bahr, 2005) with or without
surgical reconstruction of the ligament. This high risk of long term
osteoarthritis may be increased in those athletes who demonstrate
excessive and repetitive high KAM during participation in sports and
whoare at increasedriskof a kneevalgus injurymechanism(Meyeret
al., 2009; Hewett et al., 2005). Accordingly, the prevention of ACL
Fig. 3. A. The coordinate position of knee joint center is digitized in the frontal view measured at the frame prior to initial contact is used as the knee valgus position X1. B. The
coordinate position of knee joint center is digitized in the frontal view measured at the frame with maximum medial position and is utilized as the knee valgus position X2. The
calibrated displacement measure between the two digitized knee coordinates (X2–X1) is representative of knee valgus motion during the drop vertical jump. C. Knee flexion angle is
digitized at the frame prior to initial contact and recorded as the first measure of knee flexion RoM (Θ1). D. Knee flexion angle is digitized at the frame with maximum knee flexion
and recorded as the second measure of knee flexion RoM (Θ2).The displacement of knee flexion is calculated as the differences in knee flexion angles at the frame prior to initial
contact and maximum knee flexion (Θ1–Θ2) and is representative of knee flexion RoM.
G.D. Myer et al. / Clinical Biomechanics 25 (2010) 693–699
Author's personal copy
injury is currently the only effective intervention for these life-
altering injuries. The high predictive sensitivity and specificity of this
single factor points to the necessity to develop specific injury
prevention protocols targeted to athletes who demonstrate high
KAM, which increases their risk for ACL injury.
Theoretically, through identification of female athletes at greater
risk for ACL injury, prevention strategies to reduce an ACL injury could
be substantially improved. As mentioned previously, the current non-
targeted neuromuscular training programs require application to 89
female athletes to prevent one ACL injury (Grindstaff et al., 2006). The
current clinic-based assessment tool could increase the efficiency of
neuromuscular training if it were to be targeted to high KAM female
athletes. The results of the current investigation may aid in the
dissemination of assessment techniques required for the application
of targeted neuromuscular training intervention to high-risk popula-
tions. The authors acknowledge that the proposed algorithmic
approach may have limited utility to predict injury risk during
cutting, pivoting or maneuvers not associated with landing. Future
efforts should aim to validate the proposed algorithm using clinic-
based measurement techniques to determine the relationship to ACL
injury prediction from both cutting and landing injury mechanisms. In
addition, further research is warranted to delineate the most efficient
training methods to target females who demonstrate high KAM
landing mechanics to further improve the potential prophylactic
ACL injury leads to significant short-term disability and currently
there is no treatment that effectively prevents the long-term
debilitation associated with osteoarthritis that follows this injury.
Thus, prevention of ACL injuriesis crucial. The currentstudy addresses
the increased potential to reduce ACL injury and potentially the long
term osteoarthritis risk via identification of simple clinical measures
that can be used to asses high KAM landing mechanics. Specifically,
we have defined clinically obtainable correlates and developed a
prediction algorithm that employs these measures to identify female
athletes who demonstrate high KAM landing strategies that likely
place them at increased risk for ACL injury. The current investigation
provides the critical next step to merge the gap between research
findings and clinical practices with the presented clinic-based
assessment tool. The simplicity and lessened equipment and labor
cost associated with utilization of this tool relative to previous
techniques to measure high KAM may facilitate the identification of
high ACL injury risk athletes on a more widespread basis in clinical
and field settings. Implementation of the prediction tool developed in
the current study would likely increase both the efficacy and
efficiency of prevention strategies for non-contact ACL injury and its
widespread use may impact the endemic rise of this physically and
financially devastating injury in female athletes.
The authors would like to acknowledge funding support from the
National Institutes of Health/NIAMS Grants R01-AR049735, R01-
AR05563 and R01-AR056259. The authors would like to thank Boone
County Kentucky, School District, especially School Superintendent
Randy Poe, for participation in this study. We would also like to thank
Mike Blevins, Ed Massey, Dr. Brian Blavatt and the athletes of Boone
County public school district for their participation in this study. The
authors would also like to acknowledge the Sports Medicine
Biodynamics Team who worked together to make large data
collection sessions possible. Finally, the authors would like to
acknowledge Sam Wordeman for his assistance with the use of R-
project software and Dr. Mitch Rauh for his helpful advice throughout
this investigation. All authors are independent of any commercial
funder, had full access to all of the data in the study and take
responsibility for the integrity of the data and the accuracy of the data
Appendix A. Supplementary data
Supplementary data associated with this article can be found, in
the online version, at doi:10.1016/j.clinbiomech.2010.04.016.
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