The Interaction of Trunk-Load and Trunk-Position Adaptations on Knee Anterior Shear and Hamstrings Muscle Forces During Landing

Female athletes participating in high-risk sports suffer anterior cruciate ligament injury at a 4- to 6-fold greater rate than do male athletes. Prescreened female athletes with subsequent anterior cruciate ligament injury will demonstrate decreased neuromuscular control and increased valgus joint loading, predicting anterior cruciate ligament injury risk. Cohort study; Level of evidence, 2. There were 205 female athletes in the high-risk sports of soccer, basketball, and volleyball prospectively measured for neuromuscular control using 3-dimensional kinematics (joint angles) and joint loads using kinetics (joint moments) during a jump-landing task. Analysis of variance as well as linear and logistic regression were used to isolate predictors of risk in athletes who subsequently ruptured the anterior cruciate ligament. Nine athletes had a confirmed anterior cruciate ligament rupture; these 9 had significantly different knee posture and loading compared to the 196 who did not have anterior cruciate ligament rupture. Knee abduction angle (P<.05) at landing was 8 degrees greater in anterior cruciate ligament-injured than in uninjured athletes. Anterior cruciate ligament-injured athletes had a 2.5 times greater knee abduction moment (P<.001) and 20% higher ground reaction force (P<.05), whereas stance time was 16% shorter; hence, increased motion, force, and moments occurred more quickly. Knee abduction moment predicted anterior cruciate ligament injury status with 73% specificity and 78% sensitivity; dynamic valgus measures showed a predictive r2 of 0.88. Knee motion and knee loading during a landing task are predictors of anterior cruciate ligament injury risk in female athletes. Female athletes with increased dynamic valgus and high abduction loads are at increased risk of anterior cruciate ligament injury. The methods developed may be used to monitor neuromuscular control of the knee joint and may help develop simpler measures of neuromuscular control that can be used to direct female athletes to more effective, targeted interventions.

The mechanisms of anterior cruciate ligament injury in basketball are not well defined. To describe the mechanisms of anterior cruciate ligament injury in basketball based on videos of injury situations. Case series; Level of evidence, 4. Six international experts performed visual inspection analyses of 39 videos (17 male and 22 female players) of anterior cruciate ligament injury situations from high school, college, and professional basketball games. Two predefined time points were analyzed: initial ground contact and 50 milliseconds later. The analysts were asked to assess the playing situation, player behavior, and joint kinematics. There was contact at the assumed time of injury in 11 of the 39 cases (5 male and 6 female players). Four of these cases were direct blows to the knee, all in men. Eleven of the 22 female cases were collisions, or the player was pushed by an opponent before the time of injury. The estimated time of injury, based on the group median, ranged from 17 to 50 milliseconds after initial ground contact. The mean knee flexion angle was higher in female than in male players, both at initial contact (15 degrees vs 9 degrees , P = .034) and at 50 milliseconds later (27 degrees vs 19 degrees , P = .042). Valgus knee collapse occurred more frequently in female players than in male players (relative risk, 5.3; P = .002). Female players landed with significantly more knee and hip flexion and had a 5.3 times higher relative risk of sustaining a valgus collapse than did male players. Movement patterns were frequently perturbed by opponents. Preventive programs to enhance knee control should focus on avoiding valgus motion and include distractions resembling those seen in match situations.

This paper examined if an electromyography (EMG) driven musculoskeletal model of the human knee could be used to predict knee moments, calculated using inverse dynamics, across a varied range of dynamic contractile conditions. Muscle-tendon lengths and moment arms of 13 muscles crossing the knee joint were determined from joint kinematics using a three-dimensional anatomical model of the lower limb. Muscle activation was determined using a second-order discrete non-linear model using rectified and low-pass filtered EMG as input. A modified Hill-type muscle model was used to calculate individual muscle forces using activation and muscle tendon lengths as inputs. The model was calibrated to six individuals by altering a set of physiologically based parameters using mathematical optimisation to match the net flexion/extension (FE) muscle moment with those measured by inverse dynamics. The model was calibrated for each subject using 5 different tasks, including passive and active FE in an isokinetic dynamometer, running, and cutting manoeuvres recorded using three-dimensional motion analysis. Once calibrated, the model was used to predict the FE moments, estimated via inverse dynamics, from over 200 isokinetic dynamometer, running and sidestepping tasks. The inverse dynamics joint moments were predicted with an average R(2) of 0.91 and mean residual error of approximately 12 Nm. A re-calibration of only the EMG-to-activation parameters revealed FE moments prediction across weeks of similar accuracy. Changing the muscle model to one that is more physiologically correct produced better predictions. The modelling method presented represents a good way to estimate in vivo muscle forces during movement tasks.