SellTC_2007_JOR_Predictors of Proximal Tibia Anterior

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Predictors of Proximal Tibia Anterior Shear Forceduring a Vertical Stop-JumpTimothy C. Sell, Cheryl M. Ferris, John P. Abt, Yung-Shen Tsai, Joseph B. Myers, Freddie H. Fu, Scott M. Lephart

Neuromuscular Research Laboratory, Department of Sports Medicine and Nutrition, School of Health andRehabilitation Sciences University of Pittsburgh, 3200 S. Water Street, Pittsburgh, Pennsylvania 15203

Received 4 August 2006; accepted 1 May 2007

Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/jor.20459

ABSTRACT: Anterior cruciate ligament (ACL) continues to be a signicant medical issue forathletesparticipatingin sportsandrecreationalactivities. Biomechanicalanalyseshave determinedthat anterior shear force is themostdirect loading mechanismof theACL anda probablecomponentof noncontact ACLinjury. The purpose of this study was to examine thebiomechanical predictors of proximal tibiaanterior shearforce duringa stop-jumptask. A biomechanical andelectromyographic(EMG) analysis of the knee was conducted while subjects performed a vertical stop-jump task. Thetask was chosento simulate an athletic maneuver that included a landing with a sharp decelerationand a changein direction. Thenal regression model indicatedthat posterior ground reaction force,

external knee exion moment, knee exion angle, integrated EMG activity of the vastus lateralis,and sex (female) would signicantly predict proximal tibia anterior shear force ( p < 0.0001, R2 0.8609). Knee exionmoment hadthe greatestinuenceon proximal tibia anterior shear force.The mathematical relationships elucidated in the current study support previous clinical and basicscience research examining noncontact ACL injuries. This data provides important evidence forclinicians who are examining the risk factors for these injuries and developing/validating training programs to reduce the incidence of injury. 2007 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop ResKeywords: ACL; knee; shear force; biomechanics; injury

INTRODUCTION

Anterior cruciate ligament (ACL) injuries continueto be a signicant health concern for young individuals attempting to lead an active, healthylifestyle. 1 4 Each year, 1 in 1000 individualsbetween the ages of 15 and 25 will suffer an ACL injury 5 with over 50,000 reconstructivesurgeries performed annually. 6 The majority of these injuries occur during participation insports and recreational activities, 710 and are theresult of a noncontact mechanism of injury. 7,8,10

Noncontact ACL injury prevention is particularly

important to female athletes, as epidemiologicalresearch has demonstrated that females areat a signicantly higher risk for suffering this injury. 1114 Injury prevention training pro-grams have been designed to modify thepotential risk factors and reduce noncontact ACLinjuries 1520 by attempting to induce neuromus-

cular and biomechanical adaptations that may

decrease knee joint loading and ACL strain.One of the joint forces that can increase ACL

strain and lead to ligament rupture is proximaltibia anterior shear force. Although the loading pattern of theknee duringnoncontact ACL injuriesis most likely multidirectional and multi-planar, 8,10,21 proximal tibia anterior shear force isa probable component given that it represents themost direct loading mechanism of the ACL. 2225

Currently, the in vivo biomechanical character-istics that predict an increased proximal tibiaanterior shear force are unclear. Measurable

in vivo biomechanical characteristics that maypredict proximal tibia anterior shear force includeground reaction forces, knee joint kinematics, jointresultant moments estimated through inversedynamics procedures, and myoelectrical activityof theknee musculature measured through surfaceelectromyography(EMG).One studyhas examinedtherelationship among knee joint kinematics, knee joint kinetics, and ground reaction forces, 26 anddemonstrated that greater ground reaction forcesand knee extension moments correlate withgreater proximal tibia anterior shear force. The

JOURNAL OF ORTHOPAEDIC RESEARCH 2007 1

Correspondence to : Timothy C. Sell (Telephone: 412-432-3800; Fax: 412-432-3801; E-mail: [email protected])

2007 Orthopaedic Research Society. Published by Wiley Periodicals,Inc.


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current study proposes to examine similar varia-bles with the addition of EMG.

Kinematic observations of the mechanism of injury, kinematic analysis of individuals at riskfor noncontact ACL injury, and ACL strainstudies have shown that certain movement pat-terns and joint positions place an individual atgreater risk for injury. For example, femaleathletes participating in high-risk sports (for ACLinjury) who land with an increased dynamic kneevalgus are at greater risk for injury, 27 whichsupports previous research that many noncontact ACL injuries occur during landings with the kneein a valgus position. 10,21 Noncontact ACL injuriesalso typically occur when individuals land withdecreased knee exion. 8,10 This landing positionincreases ACL strain compared to larger exionangles. 2325,28,29 In addition, the majority of evi-dence indicates that females, who are at greaterrisk for ACL injury, perform dynamic sports taskswith both increased knee valgus angles 3034 anddecreased knee exion angles. 3032,3436

Knee joint resultant moments, as estimatedthrough inverse dynamics, can provide valuableinsight into the loading patterns of the knee,especially when combined with EMG data. In thesagittal plane, a net external knee exion momenttypically exists throughout the majority of thestance phase of a stop-jump task and represents anet internal quadriceps moment 34,37 or internalquadriceps requirement. In situ and in vivo ACL

strain increases38,39

under this condition (quad-riceps loading) with greater increases observed atlow exionangles. 38 In the frontalplane, knee jointmoment (valgus or varus) can increase ACLstrain when combined with a proximal anteriortibial force. 40,41 Valgus moment, as estimatedthrough inverse dynamics, has also been impli-cated as a predictor of ACL injury in femaleathletes. 27 Hewett et al. 27 demonstrated thatfemale athletes who perform jump-landing taskswith a greater knee valgus moment are more likelyto sufferan injury than those whoperform thesametask with less valgus moment.

The identication of neuromuscular and biome-chanical characteristics that can predict dangerousloading patterns may provide important evidencethat support the use of proximal tibia anteriorshear force and other biomechanical variables forfuture prospective studies and development of injury prevention programs. The purposes of thisstudy was to determine if a select group of neuro-muscular and biomechanical characteristics areable to signicantly predict proximal tibia anteriorshear force. Those characteristics included knee

exion angle, knee valgus angle, external kneeexion moment, external knee valgus moment,integrated EMG (IEMG) of the vastus lateralis andsemitendinosus, and sex. We hypothesized that anequation based on these variables would be able tosignicantly predict proximal tibia anterior shearforce.

MATERIALS AND METHODSSubjects

Thirty-six healthy high school basketball players(19 males, 17 females) participated. All subjects werecurrently participating in organized basketball at leastthree times per week at the time of testing. Subjectdemographics are presented in Table 1. Subjectswere excluded from the study if they had a history of serious musculoskeletal injury, any musculoskeletalinjury within the past 6 months, or suffer fromany disorder that interfered with sensory input, mus-culoskeletal function, or motor function. All subjectsprovided written informed consent in accordancewith the Universitys Institutional Review Board priorto participation.

Data Collection and Reduction

Anthropometric measurements were recorded for eachsubject. They included height and weight, segmentallengths, and circumferences of the thighs and shanks,diameters of the ankles and knees, feet length andwidth, lateral malleoli height, and pelvic width. Thestop-jump task was then demonstrated to each of thesubjects. The technique for the stop-jump task consistedof the following: (1) an initial starting point measured as40% of the subjects height from the edge of the forceplates, (2) a two-legged broad jump with a two-legged landing on the force plates (one foot on eachplate), and (3) immediate jump for maximumvertical height (Fig. 1). To promote natural performanceof the task, subjects were provided the following instructions: (1) begin each jump at the designatedstarting point, (2) land with one foot on each force plate,and (3) then immediately jump off the force plates formaximum height. All subjects were allowed to practice

Table 1. Descriptive Data (Means and StandardDeviations) for All of the Subjects, the Male Subjects,and the Female Subjects

Variable

Group

Total(n 36)

Males(n 19)

Females(n 17)

Age (years) 16.1 1.3 16.3 1.5 15.9 1.1Body mass (kg) 68.2 10.4 72.1 9.4 63.8 10.0Height (m) 1.75 0.09 1.80 0.08 1.70 0.07

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the jump until they were comfortable with the task(approximately three to ve trials).

After demonstration and practice of the vertical stop- jump task, subjects were prepped for EMG analysis.Surface EMG activity was collected bilaterally on thevastus lateralis (VL) and semitendinosus (ST). Surface

electrodes were placed over the appropriate muscle bellyin line with the direction of the bers with an interelec-trode distance of approximately 20 mm. Electrode siteswereshaved, abraded, andcleaned with isopropyl alcoholto reduce impedance. Electrode placement sites werebased on Delagi et al. 42 A single ground electrode wasplaced over the anterior aspect of the tibia just distal andmedial to the tibial tuberosity. All electrode sites werelocated via palpation of each subjects anatomy and wereconrmed following application of electrodes throughvisual inspection of signals on the oscilloscope during standardized manual muscle testing. 43 Surface EMGsignals were collected at 1200 Hz via an eight channeltelemetric system (Noraxon USA Inc., Scottsdale, AZ).Electromyographic signals were recorded using silver silver chloride, pregelled bipolar surface electrodes(Medicotest, Inc., Rolling Meadows, IL).

Electromyographic data during a 5-s maximumvoluntary isometric contraction (MVIC) were collectedfor the knee exors and extensors utilizing the BiodexSystem 3 Multi-Joint Testing and RehabilitationSystem(Biodex Medical Inc., Shirley, NY). This data wereprocessed and used for normalization of the correspond-ing muscles EMG activity during the dynamic task.Subjects were seated in thechair andsecured withstrapsaround the torso, pelvis, and thigh of the leg performing

the MVIC. The axis of the dynamometer was positionedso it was aligned with the axis of rotation of the kneebeing tested, which was positioned in 60 8 of exion. Theorder of MVIC data collection was the same for eachsubject (knee extensor data collected rst).

Subjects were prepped for the biomechanical analysis

of the stop-jump task. A total of 15 retroreectivemarkers were utilized for data collection of three dimen-sional (3D) coordinate data during the vertical stop-jumptask. The marker system used was based on Kadabaet al., 44 as developed at the HelenHayes Hospital in New York. Retroreective markers were placed bilaterallyover the second metatarsal head, posterior aspect of the heel, lateral malleolus, femoral epicondyle, anteriorsuperior iliac spine, and the L5S1 disc space. Themarkers were secured to the subject with double-sidedtape. Four other markers were attached to wandsand secured bilaterally with straps, prewrap, andathletic tape to the lateral aspect of the subjects thighand shank. Careful attention was paid to marker place-ment and attachment as to not interfere with the EMGelectrodes.

Three dimensional coordinate data were collectedandcalculated using a 3D optical capture system (Vicon,Centennial, CO). This motion analysis system includedsix high-speed (120 Hz) optical cameras (Pulnix Indus-trialProductDivision, Sunnyvale,CA) instrumented andsynchronized using Peak Motus software (version 7.2, Vicon). Ground reaction force data during the jump taskswere collected at 1200 Hz utilizing two force plates(Kistler Corporation, Worthington, OH) that were ushwith the surrounding surface of a custom-built ooring

Figure 1. Vertical stop-jump task: (A) start, (B) approach to force plates, (C) initial contact,(D) peak knee exion, (E) end contact, (F) apex of vertical jump.

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system. Following the retroreective marker setup,subjects were allowed to practice the stop-jumpsa second time (approximately three to ve trials).Subjects performed a total of ve jumps with at least30 s of rest between each jump. The rst three successful jumps were utilized for data processing. A successful jump was dened as a jump that began at the proper

starting point with a two-legged landing with one foot oneach force plate followed by a vertical jump.Raw analog data from the force plates were used to

calculate the ground reaction force data for each jumptrial and were ltered using a fourth-order Butterworthlter (zero phase shift) at a cutoff frequency of 100 Hz.The raw coordinate data were also ltered with a fourth-order Butterworth lter (zero phase shift) with aoptimized cutoff frequency (typically 5 Hz). 45 Raw analog data from the force plates were used to calculate theground reaction force data for each jump trial. All jointkinematicandkineticcalculations were performedin theKinecalc module of the Peak Motus software package(Vicon, Centennial, Englewood, CO). Joint kinematiccalculations were based on Vaughan et al. 46 An inversedynamics procedure was used to calculate the jointresultant moments and forces and is brieydescribed here.

Resultant joint forces and moments were calculatedbased on body segment parameters (measured andestimated), 4648 linear kinematics, centers of gravity,angular kinematics, and ground reaction forces based onGreenwood. 49 Joint resultant forces were calculatedbased on the acceleration and mass of each segment thatis determined by rst calculating the change in linearmomentum. Joint resultant moments were calculated ina similar manner. They were calculated by rst deter-

mining the rate of change in angular momentum, whichwas based on the moments of inertia, segmental angularvelocities, and segmental angular accelerations. Thesecalculations were rst performed distally then throughan inverse dynamics procedure that was calculatedproximally through the kineticchain. The joint resultantmoment and forces calculated using this procedure werethe estimated external moments and forces and werebased on the ground reaction forces and segment inertialforces. 50 In addition, the proximal tibia anterior shearforce includes all the soft tissue forces and joint contactforces at the knee, and does represent the shear forcetransmitted to the ACL or the shear force applied by thepatellar tendon. Joint resultant forces were normalizedto body weight and joint resultant moments werenormalized to body weight*height. 50,51

Joint kinematic data, joint kinetic data, and groundreaction force data were exported to Matlab (Release 12,The MathWorks, Natick, MA) for identication of thevariables of interest.Theground reaction force data wereused to calculatethe maximum posteriorground reactionforce (maximum deceleration force) during the initialstance phase of the stop-jump tasks. This point was thenidentied in the joint kinetic and kinematic data todetermine proximal tibia anterior shear force, kneeexion/extension moment, knee exion/extension angle,

andthe knee valgus/varusangleat thepointof maximumdeceleration. Data were averaged across three trials.

Raw analog data from the MVIC, synchronized rawanalog data from the stop-jump trials, and the groundreaction force data from the stop-jump trials wereimported into Matlab for data processing. The meanvalue of each MVIC was used for normalization of the

EMG during the stop-jump trials.52

Both the MVIC andtrial EMG data were processed with a linear envelopeprior to normalizationusing a Butterworth lter (fourth-order, zero-phase shift, cutoff frequency of 12 Hz). Thepoint of peak posterior ground reaction force (maximumdeceleration of thebody) was identied in each jump trialusing thegroundreaction force data. From this referencepoint, the IEMG was calculated for each muscle for the150 ms prior to maximum deceleration of the body. Datafor each EMG variable was averaged across the samestop-jump trials used in the kinematic and kineticanalysis.

Data Analysis A stepwise multiple regression model were t using Stata (Stata 8; Stata Corporation, College Station, TX)to determine which neuromuscular and biomechanicalvariables signicantly predict proximal tibia anteriorshear force at the time of maximum deceleration (peakposterior ground reaction force). The predictor variablesincluded knee exion angle, knee valgus angle, kneeexion moment, knee valgus moment, IEMG of thevastus lateralis and semitendinosus, and sex. Theresponse variable was proximal tibia anterior shearforce at the time of maximum deceleration. Pairwisecorrelations were also performed to further examine therelationships between the biomechanical predictor var-iables and the response variable. Finally, the normal-ized beta coefcients for the predictor variables wereestimated to assess the relative predictive power of eachof the predictor variables. An alpha level of 0.05 wasselected to determine if predictor variables would beincluded in the nal equation, for determining thesignicance of the model in predicting the responsevariable, and for determining if the pairwise correla-tions were signicant.

RESULTS

The means and standard deviations for each of thevariables are listed in Table 2. The multiple linearregression model is presented in Table 3. Based onthis model ve of the predictor variables weremaintained in the nal equation. Those variableswere peak posterior ground reaction force, kneeexion/extension moment, knee exion angle,IEMG activity of the VL, and sex. This modelaccounts for 86.1% of the variance in the proximaltibia anterior shear force during the vertical stop- jump task ( p < 0.001). For the individual predictorvariables, the coefcients reveal that the greater

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the peak posterior ground reaction force, kneeexion/extension moment, knee exion angle,IEMG activity of the VL, and being female wouldpredict higher proximal tibia anterior shear forces.The pairwise correlations between the responsevariable and the biomechanical predictor variablesare listed in Table 4. Proximal tibia anteriorshear force was signicantly correlated withpeak posterior ground reaction force, knee exionmoment, knee exion angle, knee valgusangle, and the IEMG activity of the VL. Of thosevariables, only knee exion moment had a strong correlation. 53 The normalized beta coefcients for

the regression model are listed in Table 5. Basedon the beta coefcients, knee exion/extensionmoment would have the most dramatic effect onproximal tibia anterior shear force. A one standard

deviation increase in knee exion/extension momentwould cause a predicted increase of 0.77 standarddeviations in the proximal tibia anterior shearforce.

DISCUSSION

The purpose of this study was to conduct abiomechanical and neuromuscular analysis of males and females performing a stop-jump taskand determine what characteristics are able topredict proximal tibia anterior shear force. We hypothesized that an equation based on knee

exion angle, knee valgus angle, knee exionmoment, knee valgus moment, IEMG of the vastuslateralis and semitendinosus, and sex would beable to signicantly predict proximal tibia anterior

Table 2. Biomechanical and Neuromuscular Data (Means Standard Deviations) for the Entire Group, MaleSubjects, and Female Subjects

Variable

Group

Total ( n 36) Males ( n 19) Females ( n 17)

Peak posterior ground reaction force (body weight) 0.77 0.25 0.83 0.28 0.71 0.19Proximal anterior tibia shear force (body weight) at

PPGRF0.29 0.22 0.23 0.18 0.36 0.25

Knee exion moment (body weight * height) at PPGRF 0.043 0.052 0.030 0.055 0.056 0.044Knee exion angle (degrees) at PPGRF 29.0 8.5 29.1 7.7 28.8 9.5Knee valgus angle (degrees) at PPGRF 0.8 5.7 1.9 5.6 0.3 5.8Knee valgus moment (body weight * height) at PPGRF 0.084 0.062 0.068 0.044 0.101 0.074IEMG activity of the VL (%MVIC*s) Prior to PPGRF

(150 ms)0.084 0.062 0.068 0.044 0.101 0.062

IEMG activity of the MH (%MVIC*s) Prior to PPGRF(150 ms)

0.127 0.225 0.115 0.271 0.140 0.161

PPGRF, peak posterior ground reaction force; IEMG, integrated electromyographic; VL, vastus lateralis; MH, semitendinosus.

Table 3. Multiple Linear Regression Model Predicting Proximal Tibia Anterior Shear Force

Multiple Linear Regression Model

Source SS df MS Observations 72Model 3.6828 5 0.7366 F(5,66) 86.68Residual 0.5952 66 0.0090 Prob > F p < 0.0001

Total 4.2780 71 0.0603 R2 0.8609 Adjusted R 2 0.8503

Predictor Variables Coefcient t p-value

Peak posterior ground reaction force 0.2760 3.94 0.000Knee exion/extension moment 3.9683 13.15 0.000Knee exion angle 0.0034 2.00 0.050IEMG activity of the VL 0.5179 2.62 0.011Sex 0.0593 2.40 0.019Constant 0.2421 3.05 0.003

This model with the predictorvariablespeakposteriorground reaction force, kneeexion/extension moment, knee exion angele,IEMG activity of theVL, andsex accountedfor 86.1%of thevarianceof theresponsevariable, proximal tibia anterior shear force.Theassociated p-value for this model is p < 0.0001. SS, sum of the squares; df, degrees of freedom; MS, mean squares; IEMG, integratedelectromyographic; VL, vastus lateralis.

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shear force. Our hypothesis was partially sup-ported as the multiple linear regression modelindicated that peak posterior ground reactionforce, knee exion/extension moment, knee exionangle, IEMG activity of the VL, and sex (female)signicantly predicted proximal tibia anteriorshear force. The results of our analysis haveimplications for future research related to theexamination of risk factors for noncontact ACLinjuries and the development of training programsto reduce the incidence of injury.

We chose to investigate proximal tibia anteriorshear force and its biomechanical predictorsbecause it is the most direct loading mechanism of the ACL, 2225 and it can be estimated throughinverse dynamics. It is important to note that

proximal tibia anterior shear force, as calculated inthis study, is a resultant force that includes all of the soft tissues joint contact forces acting at theknee and it does not represent a shear forcetransmitted to the ACL or the shear force appliedby the patellar tendon. Given these limitations, Yuet al. 54 described how proximal tibia anterior shearforce (estimated through inverse dynamics) may bean indicator of ACL loading. Their mathematicalanalysis and simulation indicated that an increasein proximal tibia anterior shear force will increasethe knee anterior drawer force, which should

positively correlate to ACL forces. Currently,only a few studies have estimated proximaltibia anterior shear force during a dynamictask. 26,34,37,5558 This force has been implicated asa potential risk factor for noncontact ACL injurydue to the demonstrated differences observedbetween males and females, with females perform-ing the dynamic sports tasks with signicantlygreater proximal tibia anterior shear force. 26,34,37

The inclusion of peak posterior ground reactionforce in the nal model supports previous analysesof the noncontact mechanism of injury, 8,10,21 whichrevealed that ACL injuries occur during differentsports maneuvers, but characteristic among them is a sharp deceleration of the body, which isrepresented by a posteriorly directed ground

reaction force. In our study, peak posterior groundreaction force occurred 0.028 0.19 s after initialfoot contact. This was prior to peak vertical groundreaction force (0.062 0.041 s after initial contact)and peak knee exion angle (0.172 0.040 s). Theregression equation indicated that proximal tibiaanterior shear force would increase as the posteriorground reaction force increased. Yu et al. 26 alsodemonstrated this relationship during a similarstop-jump task.The relationship between posteriorground reaction force and proximal tibia anteriorshear force is a debated topic. 54,59 We agree withtheassessment that posteriorground reaction forcecreates an external exion moment at the knee,which would need to be counteracted by an internalquadriceps force. 54 The quadriceps would have tocontract to control knee exion, which would resultin a anteriorly directed force at the proximal tibiadue to the effect of the patellar ligament. 54

Similar to posterior ground reaction forces, ouranalysis also indicated that an increase in externalknee exionmomentwould also predict an increasein proximal tibia anterior shear forces. The landing of the stop-jump task and subsequent spike in

Table 4. Pairwise Correlations between the Response Variable (Proximal Tibia Anterior Shear Force) and thePredictor Variables

Variable Correlation Coefcient p-value

Peak posterior ground reaction force (body weight) 0.2360 0.046Knee exion moment (body weight * height) at PPGRF 0.8986 p < 0.001Knee exion angle (degrees) at PPGRF 0.4318 p < 0.001Knee valgus angle (degrees) at PPGRF 0.2551 0.031Knee valgus moment (body weight * height) at PPGRF 0.1628 0.172IEMG activity of the VL (%MVIC*s) Prior to PPGRF (150 ms) 0.2531 0.032IEMG activity of the MH (%MVIC*s) Prior to PPGRF (150 ms) 0.0186 0.877Sex 0.3225 0.006

PPGRF, peak posterior ground reaction force; IEMG, integrated electromyographic; VL, vastus lateralis; MH, semitendinosus.

Table 5. Normalized Beta Coefcients for thePredictor Variables

Variable Beta Coefcient

Peak posterior ground reaction force 0.2099Knee exion/extension moment 0.7658Knee exion angle 0.1177IEMG activity of the VL 0.1310Sex 0.1213

IEMG, integrated electromyographic; VL, vastus lateralis.

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posterior ground reaction force creates an externalknee exion moment. The external knee exionmoment as measured through inverse dynamicsequates to a net internal quadriceps moment(quadriceps force). The quadriceps force canapply a proximal tibia anterior shear force viathe extensor mechanism (quadriceps tendon andpatellar ligament). 54 Without knowledge of the muscle forces, it is difcult to determine if theincreased internal quadriceps moment that pre-dicts greater proximal tibia anterior shear force isdue to an increased quadriceps force and/or adecreased hamstrings force. The results of theEMG analysis may provide some insight into thebasis of these differences. In the present study, anincrease in IEMG of the VL would predict greaterproximal tibia anterior shear force. The IEMG of the ST was not included in the nal regressionequation, and based on the results of this study,does not inuence proximal tibia anterior shearforce as estimated through inverse dynamics.Previous biomechanical analyses of cadavericknees have demonstrated that an increased quad-riceps force will increase the amount of anteriortibial translation and proximal tibia anterior shearforce. 2325,28,29 The results of the current in vivostudy support this previous in vitro research. In thenal regression model,both a greaterexternalkneeexion moment and IEMG of the VL would predicta greater proximal tibia anterior shear force.

The contrasting evidence between cadaveric

studies and the relationship between knee exionangle andproximal tibia anterior shear force in thisstudy may be due to the lack of an establishedrelationship between ACL strain, which increasesat knee angles close to full extension, and proximaltibia anterior shear force during a dynamictask. 2325,28,29 These individuals measured ACLstrain during static positioning of cadaveric knees.It is not clear what the in vivo strain is during dynamic jumping and landing activities. Only onepublished articled has measured in vivo strainduring a similar task (one-legged jump landing). 60

This was a case study, and did not includecalculation of proximal tibia anterior shear force.Further research is necessary to establish therelationship between knee exion angle and prox-imal tibia anterior shear force.

We acknowledge that the current study hascertain limitations. The accuracy of skin-basedmarker systems in estimating joint kinematics and joint kinetics has been questioned during gait. 6164

Although careful consideration and attention wasgiven to marker attachment, the errors due to skinmovement that have been reported duringgait may

be increased during the high-speed athletic tasksin this study. Although the regression analysisperformed in the current study is only a mathe-matical analysis of the relationship of biomechan-ical variables estimating knee joint kinematics,resultant knee joint forces and moments, EMGactivity of the knee musculature, and groundreaction forces, our model supports the reportedanatomicaland physiological implications for theserelationships.

CONCLUSION

The results of our analysis of biomechanicalpredictors of proximal tibia anterior shear forceindicate that an increasing posterior ground reac-tion force, knee exion moment, and IEMG of the VL would all predict an increase in proximal tibiaanterior shear force and potentially an increase in ACL forces. These mathematical relationshipssupport the previous clinical and basic scienceresearch examining the potential mechanism of noncontact ACL injury. These results provideclinicians important evidence to include thesepredictor variables as well as proximal tibiaanterior shear force as part of future prospectivestudies examining risk factors for noncontact ACLinjury and the validation of training studiesdesigned to reduce injury.

ACKNOWLEDGMENTSThe Jewish Healthcare Foundation provided nancialsupport for this project. I afrm that I have no nancialafliation (including research funding) or involvementwith any commercial organization that has a directnancial interest in any matter included in thismanuscript, except as disclosed in an attachment andcited in the manuscript. Any other conict of interest(i.e., personal associations or involvement as a director,ofcer, or expert witness) is also disclosed in an attach-ment.

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