ABSTRACT Purpose Dynamic limb valgus, particularly high knee abduction moments, is a known risk factor for anterior cruciate ligament (ACL) injury and may result from poor static anatomic limb alignment, faulty biomechanics, or a combination of both. The purpose of this study was to assess the influence of static lower extremity anatomic alignment and dynamic kinematic/kinetic measures on knee abduction moments during sidestep cutting in adolescent athletes with recent ACL reconstruction. Methods This retrospective study included 50 adolescents with recent unilateral ACL reconstruction (18/50 female, mean age = 15.8 yr, 7.6 months postsurgery). Frontal plane hip-to-ankle imaging was used to measure mechanical axis deviation and tibial–femoral angle. Three-dimensional motion capture provided lower extremity kinematics and kinetics during quiet standing and during the loading phase (initial contact to peak knee flexion) of an anticipated 45° sidestep cut. Imaging, static motion capture, and dynamic motion capture measures were investigated as potential predictors of average dynamic knee abduction moment using correlation and backward stepwise linear regression. Results Dynamic knee abduction moment was best predicted by a combination of younger age and dynamic measures: trunk lean toward the planting limb, knee abduction and external rotation, and ankle inversion. Although static measures were correlated with dynamic knee abduction moment in univariate analysis, no static/anatomic variables entered the model once the dynamic measures were included. Conclusion Knee abduction moments during sidestep cutting were related to dynamic factors reflecting frontal and transverse plane motion. Static (anatomic) lower limb alignment did not influence knee abduction moments once these dynamic factors were considered. Knee abduction moments and ACL injury risk are therefore not dictated by anatomic alignment and can be altered through neuromuscular/biomechanical training.

BACKGROUND Dynamic limb valgus, combining hip adduction and internal rotation with knee abduction posture and moments, has been implicated in ACL injury. However, the contribution of static lower extremity alignment to dynamic limb valgus is unknown. This study assessed the relationships among lower extremity static alignment and dynamic kinematics and kinetics during side-step cutting in uninjured adolescent athletes. METHODS This prospective study included 88 limbs from 44 uninjured athletes aged 8-15 years (mean 12.3, SD 2.3; 19 (44%) female) who were evaluated during an anticipated 45° side-step cut. 3D lower extremity kinematics and kinetics from a custom 6 degree of freedom model were assessed while standing and during the loading phase of the cut from initial contact to peak knee flexion; 2-3 trials per limb were averaged for analysis. Femoral anteversion was measured for each limb with the participant lying prone. Relationships among static and dynamic measures were investigated using correlation and multiple linear regression. RESULTS In terms of static alignment, more static hip internal rotation and more static knee external rotation (tibia external relative to femur) were associated with more internal hip rotation and external knee rotation dynamically during cutting (r=0.34, p=0.001) (Table 1). Static hip adduction was also related to more external hip rotation and less hip flexion dynamically (p=0.24, p=0.02). More static knee abduction, external hip rotation and hip adduction were associated with higher average knee abduction angles during cutting (r=0.25, p=0.02). However, only static external knee rotation was associated with higher dynamic knee abduction moments (r=0.48, p<0.0001) (Figure 1). During cutting, positive associations were observed between hip flexion, knee flexion, and hip internal rotation (r=0.24, p=0.03). Knee adduction angles were related to more hip flexion, internal hip rotation, and knee external rotation (r=0.25, p=0.02). Additionally, lower peak knee flexion was associated with higher peak ground reaction force and more external knee rotation (r=0.24, p=0.02). Both simple correlation and multiple regression analysis indicated that higher knee abduction moments were related dynamically to higher knee abduction angles, greater knee external rotation, higher hip abduction angles, and greater hip internal rotation (R2=0.72, p<0.001). After considering dynamic metrics, no static measure remained significantly related to knee abduction moments. CONCLUSION/SIGNIFICANCE Static knee rotation was the only anatomic alignment measure associated with knee abduction moments during side-step cutting in uninjured adolescent athletes. Knee abduction moments were influenced more by dynamic posture than static alignment. As knee abduction moments have been implicated in ACL injury, this study supports the notion of dynamic limb valgus, specifically increased knee abduction and hip internal rotation, relating to ACL injury. Motion analysis can be used to identify these risky biomechanical patterns, and neuromuscular training can be used to correct them. Since knee abduction moments are primarily determined by dynamic posture, neuromuscular training can be used to reduce these moments and ACL injury risk. Figure 1: Association between average knee ad/abduction moment during cutting and static knee rotation. Table 1: Simple correlations among static and dynamic variables of interest; presented as correlation coefficient (p-value). Static Hip Rotation Anteversion Static Hip Adduction Static Knee Rotation Static Knee Adduction Dynamic Avg Hip Rotation Dynamic Peak Hip Adduction Dynamic Peak Hip Flexion Dynamic Avg Knee Rotation Dynamic Avg Knee Adduction Dynamic Peak Knee Flexion Dynamic Aye Knee Abduction Moment Peak GRF Static Hip Rotation --- Anteversion 0.18 (0.10) --- Static Hip Adduction -0.07 (0.51) -0.13 (0.22) --- Static Knee Rotation -0.28 (0.007) -0.16 (0.15) 0.07 (0.45) --- Static Knee Adduction 0.06 (0.57) 0.23 (0.03) -0.37 (0.0003) -0.30 (0.004) --- Dynamic Avg Hip Rotation 0.57 (<0.0001) 0.17 (0.11) -0.24 (0.02) -0.38 (0.0003) 0.25 (0.02) --- Dynamic Peak Hip Adduction 0.16 (0.13) 0.06 (0.57) 0.16 (0.15) 0.16 (0.15) -0.13 (0.22) 0.14 (0.18) --- Dynamic Peak Hip Flexion 0.03 (0.76) 0.02 (0.85) -0.34 (0.001 ) 0.03 (0.75) 0.10 (0.33) 0.24 (0.03) 0.13 (0.22) --- Dynamic Avg Knee Rotation -0.30 (0.004) -0.07 (0.53) -0.07 (0.50) 0.34 (0.001) 0.07 (0.52) -0.09 (0.38) -0.04 (0.74) 0.07 (0.51) --- Dynamic Avg Knee Adduction 0.40 (0.0001) 0.10 (0.35) -0.25 (0.02) -0.05 (0.62) 0.30 (0.005) 0.29 (0.005) 0.16 (0.13) 0.25 (0.02) -0.34 (0.001) --- Dynamic Peak Knee Flexion 0.14 (0.20) -0.09 (0.40) -0.19 (0.08) -0.04 (0.68) 0.21 (0.05) 0.25 (0.02) 0.02 (0.85) 0.39 (0.0002) 0.24 (0.02) 0.14 (0.20) --- Dynamic Avg Ext Knee Adduction Moment -0.11 (0.31) -0.06 (0.57) 0.09 (0.39) 0.48 (<0.0001 ) -0.10 (0.39) -0.24 (0.03) 0.41 (0.00 01) 0.04 (0.71) 0.46 (<0.0001 ) 0.26 (0.01 ) 0.09 (0.41) --- Peak GRF -0.06 (0.57) 0.21 (0.048 ) 0.02 (0.87) 0.05 (0.68) 0.05 (0.65) 0.02 (0.88) 0.04 (0.74) 0.02 (0.82) -0.05 (0.61) 0.04 (0.74) -0.46 (<0.0001) 0.14 (0.18) ---

Background: Dynamic limb valgus, particularly high knee abduction moments, are a known risk factor for anterior cruciate ligament (ACL) injury. High knee abduction moments may result from poor static anatomic limb alignment, faulty biomechanics, or a combination of both. The distinction is important because anatomic limb alignment is difficult to change, while dynamic factors can be addressed through neuromuscular or biomechanical training. Hypothesis/Purpose: This study assessed the influence of static (lower extremity anatomic alignment) and dynamic (kinematic and kinetic) factors on external knee abduction moments during side-step cutting in uninjured adolescent athletes. Methods: This retrospective study included 43 adolescents with recent unilateral ACL reconstruction (mean age 15.3 years, SD 2.0, range 10-21; 17/43 female; 3-12 months post-surgery, mean 6.5, SD 2.1). Frontal plane hip to ankle imaging (EOS) was used to measure mechanical axis deviation (perpendicular distance from the center of the femoral condyles to the mechanical axis line connecting the center of the femoral head to the center of the talar dome) and tibial-femoral angle. Femoral anteversion was measured during physical examination. 3D motion capture provided lower extremity kinematics and kinetics during quiet standing and during the loading phase (initial contact to peak knee flexion) of an anticipated 45° side-step cut, with 2-3 trials per limb averaged for analysis. Relationships among imaging, static motion capture, and dynamic motion capture measures were investigated using correlation, and backward stepwise linear regression was used to evaluate potential predictors of average dynamic knee abduction moment. Results: Dynamic knee abduction moment was best predicted by a combination of dynamic measures: knee and hip abduction, external knee rotation, lateral trunk lean towards the planting foot, and ankle inversion during cutting (Table 1.1). Although EOS frontal plane tibial-femoral angle was correlated with dynamic knee abduction moment (r=0.24, p=0.02), no static/anatomic variables entered the model once the dynamic measures were included. Conclusion: Knee abduction moments during side-step cutting were related to dynamic factors reflecting frontal plane trunk, hip, knee, and ankle motion, as well as external knee rotation. Static (anatomic) lower limb alignment did not influence knee abduction moments once these dynamic factors were considered. Knee abduction moments and ACL injury risk are therefore not dictated by anatomic alignment and can be altered through neuromuscular/biomechanical training. Table 1.1. Multivariable prediction of average knee abduction moment during cutting (R 2 =0.69) β P Dynamic ankle eversion -0.35 <0.001 Dynamic knee external rotation 0.69 <0.001 Dynamic hip abduction 0.18 0.004 Dynamic trunk contralateral lean 0.17 0.007 Dynamic knee abduction 0.39 <0.001