The Anterior cruciate ligament (ACL) is an important ligament in the knee. Non-contact ACL injuries are a common occurrence among athletes, leading to large financial burdens and long term physical concerns. The underlying biomechanics leading to these non-contact ACL injuries are unknown, in part due to limited experimental studies investigating the mechanics of dynamic activities. Understanding these mechanics is critical for injury prevention and risk analysis.
The primary objective of this study was to investigate the underlying sagittal plane mechanics leading to increasing ACL strain during jump landing. A hybrid in-vivo/computational/in-vitro approach was used to measure ACL strain in relation to these mechanics. Motion capture was performed on ten subjects performing a single leg jump landing and both whole-body kinematics and ground reaction forces were collected. Musculoskeletal models were driven using this data to estimate the lower limb muscle forces from the jump landing. Five cadaver knee specimens were instrumented to measure ACL strain and mounted on a dynamic knee simulator. Muscle forces and sagittal plane kinematics were then applied on the cadaver specimens, dynamically recreating the activity. Strain in the anterior cruciate ligament was measured for each simulation. Bivariate correlation and multivariate linear regression analyses were performed with both maximum ACL strain and time to maximum ACL strain with the sagittal plane mechanics measured during the motion capture.
Correlation analysis found increasing ACL strain was correlated with increasing ground reaction forces, increasing body weight, decreasing hip flexion angles, increasing hip extension moments, and increasing trunk extension moments, among others. Time to max ACL strain was correlated with increasing knee flexion angles and increasing knee angle velocities. The multivariate linear regression revealed anatomical factors account for most of the variance in maximum ACL strain, but suggests landing softly by increasing joint angles and absorbing impact, are important strategies for reducing ACL strain. Time to max ACL strain regression was influenced by anatomic factors and knee velocities.
An athlete may have little or no control over the anatomic factors contributing to ACL strain, but altering their landing strategy to reduce the chance of injury. The empirical relationship developed between increasing joint angles, energy absorption and ACL strain in this study could be used to estimate the relative strain between jumps and to develop training programs designed to reduce an athlete’s risk of injury
