Summary
The Digital Athlete model showed that during the stance phase of a drop vertical jump, the strain of the anteromedial bundle of the ACLR autograft and native contralateral ligament exhibit a U-shaped curve with increased maximum strain rate increased in both ipsilateral and contralateral ACLs of the graft re-injury group.
Abstract
Introduction
ACL reconstruction (ACLR) aims to restore dynamic knee stability and facilitate a safe return to sport. Characterizing autograft strain and strain rate during high-impact landing tasks, such as the drop vertical jump (DVJ), can uncover biomechanical patterns that may contribute to ipsilateral reinjury risk. Further, recent advancements in biomechanics and artificial intelligence have introduced the “Digital Athlete”, a data-driven simulation that can model movement patterns and injury risk scenarios. Our purpose was to prospectively evaluate ipsilateral graft and contralateral ACL loading characteristics in athletes following quadriceps tendon ACLR that experienced secondary injury.
Methods
Our injury group comprised seven athletes (6 females; 16.3±1.0 yrs; 1.7±0.1 m; 64.4±7.2 kg) from an ongoing prospective longitudinal ACL registry reinjured their ipsilateral autograft between 9 and 26 months following surgical reconstruction. Our control group comprised sex-, height-, weight-, and leg-dominant-matched participants from the registry who have not experienced a secondary injury. We estimated ACL strain by simulating lab-based motion capture data during the stance phase of a DVJ at six months post-ACLR. Our OpenSim musculoskeletal model included six ligament fibers to form the anteromedial ACL bundle in the knee. We extracted the maximum strain and maximum positive strain rate and compared between groups using bootstrap resampled paired t-tests, as well as paired-samples statistical parametric map (SPM) for entire time-series data with statistical significance set a priori at a<0.05.
Results
The Digital Athlete model showed that during the stance phase of a DVJ, the strain of the anteromedial bundle of both the ACLR autograft and native contralateral ligament exhibit a U-shaped curve with the highest strain values occurring at the beginning and end of the jump and the highest strain rate just before the peak strain at the end of stance. There were no between-group differences in maximum ipsilateral autograft strain (p=0.140) or contralateral ACL strain (p=0.344). However, the maximum strain rate was 13.3% greater (p=0.037) in the ipsilateral ACLs and 15.5% greater (p=0.022) in the contralateral ACLs of the re-injury group, compared to the control group.
Discussion
With our current small sample size, the maximum ipsilateral and contralateral ACL strain was not significantly different between the re-injury and control groups. However, the increased ACL strain rate observed in individuals who sustained a retear may indicate that neuromuscular control strategies associated with the rate at which the graft is dynamically loaded and unloaded may contribute to re-injury risk.
Conclusion
Our results indicate that future training programs may benefit from prioritization of strategies that reduce rapid ACL loading during landing tasks. The proposed musculoskeletal simulation approach, coupled with recently developed tools for generating these simulations using inexpensive mobile sensors (e.g., smartphones) highlights a transformative opportunity to simulate and refine optimal movement solutions that reduce ACL and autograft strain during dynamic tasks. By leveraging Digital Athlete modeling, rehabilitation programs can tailor return-to-sport training strategies to promote safer movement patterns, ultimately decreasing secondary ACL injury risk following ACLR. Providing athletes with data-driven insights can enhance knee stability, optimize performance, and enhance injury resilience.