Preseason Biomechanical Screening Does Not Identify Differences Between Athletes With And Without Future ACL Injuries

Preseason movement screening has the potential to identify athletes at risk for injury; however, it has not reliably predicted ACL injury risk, likely due to the multifactorial injury mechanism and low incidence rate. Analyzing biomechanical time-series data across tasks in athletes who later sustain an ACL injury may reveal characteristics linked to ACL injury risk. PURPOSE: Determine if preseason lower-extremity biomechanics during walking, running, and jumping differ between Division I collegiate athletes who go on to sustain an ACL injury and matched controls. METHODS: Whole-body kinematics and ground reaction forces were collected during walking (1.2 m/s), running (4.16 m/s ± 0.4 m/s), and jumping on an instrumented treadmill as part of preseason performance testing in healthy athletes. 22 athletes who later sustained an ACL injury (14 female, mass = 81.0 ± 17.6 kg, height = 1.79 ± 0.08 m) and 22 healthy controls (14 female, mass = 78.6 ± 17.2 kg, height = 1.77 ± 0.1 m, matched by sex, sport, position, and bodyweight) were identified. Statistical parametric mapping (SPM) was used to compare preinjury ACL group involved limb knee, hip, and ankle sagittal plane angles, moments, and powers to 1) healthy controls and 2) ACL group uninjured limb. RESULTS: No differences were detected between athletes who later sustained an ACL injury and controls for lower-body joint kinematics and kinetics during the three tasks (all SPM{t} values < critical threshold). Knee joint angles, moments, and powers across tasks and the associated SPM results are shown in Figure 1. Within the ACL group, no significant between-limb differences were detected across all tasks and joints. CONCLUSION: Preseason biomechanical screening of running, walking, and jumping mechanics with SPM analysis is unlikely to identify athletes at risk for an ACL injury. Assessing tasks that better mimic the on-field environment (e.g. reactive, external stimuli, cognitive demand) may better identify ACL risk factors.