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L.J. van der Werff
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Comparison of anterior cruciate ligament loading during two functional tasks in rehabilitated athletes
An evaluation of the ACL load using musculoskeletal modeling
The anterior cruciate ligament (ACL) is a critical stabilizer of the knee joint, and ACL injuries are common in pivoting sports such as soccer, basketball, and handball. Following ACL reconstruction (ACLR), determining when an athlete can safely return to sport (RTS) remains a clinical challenge, with reinjury rates reported as high as 20% or more. Current RTS assessment tools, such as the single leg hop for distance (SLH) and unilateral countermovement jump (uCMJ), primarily quantify whole-limb performance symmetry between limbs rather than directly capturing the mechanical load on the reconstructed ligament.
This study aimed to quantify and compare ACL loading during the SLH and uCMJ in 19 ACLR-rehabilitated athletes using musculoskeletal modeling and a vector-projection ACL force estimation approach in OpenSim. Two comparisons were made: impaired versus unimpaired limb, and athletes who had versus had not regained their pre-injury sport activity level, and to the healthy controls. A lower-limb musculoskeletal model was scaled to each participant, followed by inverse kinematics, inverse dynamics, and static optimization to estimate muscle forces and knee joint reaction forces (JRFs). ACL load was estimated by projecting the anterior-posterior and medial-lateral JRF components onto the instantaneous ACL orientation vector, with an additional indirect contribution from tibial slope accounted for.
No significant differences in ACL peak load, JRFs, or knee kinematics were found between the impaired and unimpaired limbs, or between the ACLR group and healthy controls. Peak ACL loads were approximately 3.2bw during the SLH and 2.8bw during the uCMJ across all three groups. This absence of asymmetry is likely attributable to the long average follow-up period of approximately 3.8 years post-injury, which exceeds the typical RTS clearance timeframe, suggesting that compensatory movement strategies had largely resolved by the time of testing.
When comparing athletes who had and had not regained their pre-injury activity level, the not regained group showed significantly lower peak ACL loads during the SLH (2.6bw) compared to the control group (3.2bw) and the regained group (3.5bw), as confirmed by post-hoc analysis. A similar pattern was observed during the uCMJ, where the not regained group again showed the lowest peak load (2.3bw), compared to 2.8bw in controls and 3.1bw in the regained group, though this difference did not reach statistical significance in post-hoc analysis. This reduced loading may reflect a deliberate unloading strategy, a more cautious rate of loading, and the biomechanical effect of a more extended knee posture, which reduces both quadriceps demand and the scalar projection of joint forces onto the ACL vector.
Across both tasks, the SLH produced higher peak ACL loads than the uCMJ and demonstrated greater sensitivity in detecting between-group differences, supporting its value as a clinically relevant assessment tool even when performed under submaximal conditions. Key limitations include the small and imbalanced sample in the not regained group, the use of a 1 degree of freedom (DoF) knee model, the absence of EMG data, and the use of generic rather than subject-specific ACL attachment coordinates. Future work should incorporate a higher DoF knee model, EMG-informed optimization, and testing of athletes at earlier rehabilitation stages to better capture ACL loading deficits near the time of RTS clearance. ...
This study aimed to quantify and compare ACL loading during the SLH and uCMJ in 19 ACLR-rehabilitated athletes using musculoskeletal modeling and a vector-projection ACL force estimation approach in OpenSim. Two comparisons were made: impaired versus unimpaired limb, and athletes who had versus had not regained their pre-injury sport activity level, and to the healthy controls. A lower-limb musculoskeletal model was scaled to each participant, followed by inverse kinematics, inverse dynamics, and static optimization to estimate muscle forces and knee joint reaction forces (JRFs). ACL load was estimated by projecting the anterior-posterior and medial-lateral JRF components onto the instantaneous ACL orientation vector, with an additional indirect contribution from tibial slope accounted for.
No significant differences in ACL peak load, JRFs, or knee kinematics were found between the impaired and unimpaired limbs, or between the ACLR group and healthy controls. Peak ACL loads were approximately 3.2bw during the SLH and 2.8bw during the uCMJ across all three groups. This absence of asymmetry is likely attributable to the long average follow-up period of approximately 3.8 years post-injury, which exceeds the typical RTS clearance timeframe, suggesting that compensatory movement strategies had largely resolved by the time of testing.
When comparing athletes who had and had not regained their pre-injury activity level, the not regained group showed significantly lower peak ACL loads during the SLH (2.6bw) compared to the control group (3.2bw) and the regained group (3.5bw), as confirmed by post-hoc analysis. A similar pattern was observed during the uCMJ, where the not regained group again showed the lowest peak load (2.3bw), compared to 2.8bw in controls and 3.1bw in the regained group, though this difference did not reach statistical significance in post-hoc analysis. This reduced loading may reflect a deliberate unloading strategy, a more cautious rate of loading, and the biomechanical effect of a more extended knee posture, which reduces both quadriceps demand and the scalar projection of joint forces onto the ACL vector.
Across both tasks, the SLH produced higher peak ACL loads than the uCMJ and demonstrated greater sensitivity in detecting between-group differences, supporting its value as a clinically relevant assessment tool even when performed under submaximal conditions. Key limitations include the small and imbalanced sample in the not regained group, the use of a 1 degree of freedom (DoF) knee model, the absence of EMG data, and the use of generic rather than subject-specific ACL attachment coordinates. Future work should incorporate a higher DoF knee model, EMG-informed optimization, and testing of athletes at earlier rehabilitation stages to better capture ACL loading deficits near the time of RTS clearance. ...
The anterior cruciate ligament (ACL) is a critical stabilizer of the knee joint, and ACL injuries are common in pivoting sports such as soccer, basketball, and handball. Following ACL reconstruction (ACLR), determining when an athlete can safely return to sport (RTS) remains a clinical challenge, with reinjury rates reported as high as 20% or more. Current RTS assessment tools, such as the single leg hop for distance (SLH) and unilateral countermovement jump (uCMJ), primarily quantify whole-limb performance symmetry between limbs rather than directly capturing the mechanical load on the reconstructed ligament.
This study aimed to quantify and compare ACL loading during the SLH and uCMJ in 19 ACLR-rehabilitated athletes using musculoskeletal modeling and a vector-projection ACL force estimation approach in OpenSim. Two comparisons were made: impaired versus unimpaired limb, and athletes who had versus had not regained their pre-injury sport activity level, and to the healthy controls. A lower-limb musculoskeletal model was scaled to each participant, followed by inverse kinematics, inverse dynamics, and static optimization to estimate muscle forces and knee joint reaction forces (JRFs). ACL load was estimated by projecting the anterior-posterior and medial-lateral JRF components onto the instantaneous ACL orientation vector, with an additional indirect contribution from tibial slope accounted for.
No significant differences in ACL peak load, JRFs, or knee kinematics were found between the impaired and unimpaired limbs, or between the ACLR group and healthy controls. Peak ACL loads were approximately 3.2bw during the SLH and 2.8bw during the uCMJ across all three groups. This absence of asymmetry is likely attributable to the long average follow-up period of approximately 3.8 years post-injury, which exceeds the typical RTS clearance timeframe, suggesting that compensatory movement strategies had largely resolved by the time of testing.
When comparing athletes who had and had not regained their pre-injury activity level, the not regained group showed significantly lower peak ACL loads during the SLH (2.6bw) compared to the control group (3.2bw) and the regained group (3.5bw), as confirmed by post-hoc analysis. A similar pattern was observed during the uCMJ, where the not regained group again showed the lowest peak load (2.3bw), compared to 2.8bw in controls and 3.1bw in the regained group, though this difference did not reach statistical significance in post-hoc analysis. This reduced loading may reflect a deliberate unloading strategy, a more cautious rate of loading, and the biomechanical effect of a more extended knee posture, which reduces both quadriceps demand and the scalar projection of joint forces onto the ACL vector.
Across both tasks, the SLH produced higher peak ACL loads than the uCMJ and demonstrated greater sensitivity in detecting between-group differences, supporting its value as a clinically relevant assessment tool even when performed under submaximal conditions. Key limitations include the small and imbalanced sample in the not regained group, the use of a 1 degree of freedom (DoF) knee model, the absence of EMG data, and the use of generic rather than subject-specific ACL attachment coordinates. Future work should incorporate a higher DoF knee model, EMG-informed optimization, and testing of athletes at earlier rehabilitation stages to better capture ACL loading deficits near the time of RTS clearance.
This study aimed to quantify and compare ACL loading during the SLH and uCMJ in 19 ACLR-rehabilitated athletes using musculoskeletal modeling and a vector-projection ACL force estimation approach in OpenSim. Two comparisons were made: impaired versus unimpaired limb, and athletes who had versus had not regained their pre-injury sport activity level, and to the healthy controls. A lower-limb musculoskeletal model was scaled to each participant, followed by inverse kinematics, inverse dynamics, and static optimization to estimate muscle forces and knee joint reaction forces (JRFs). ACL load was estimated by projecting the anterior-posterior and medial-lateral JRF components onto the instantaneous ACL orientation vector, with an additional indirect contribution from tibial slope accounted for.
No significant differences in ACL peak load, JRFs, or knee kinematics were found between the impaired and unimpaired limbs, or between the ACLR group and healthy controls. Peak ACL loads were approximately 3.2bw during the SLH and 2.8bw during the uCMJ across all three groups. This absence of asymmetry is likely attributable to the long average follow-up period of approximately 3.8 years post-injury, which exceeds the typical RTS clearance timeframe, suggesting that compensatory movement strategies had largely resolved by the time of testing.
When comparing athletes who had and had not regained their pre-injury activity level, the not regained group showed significantly lower peak ACL loads during the SLH (2.6bw) compared to the control group (3.2bw) and the regained group (3.5bw), as confirmed by post-hoc analysis. A similar pattern was observed during the uCMJ, where the not regained group again showed the lowest peak load (2.3bw), compared to 2.8bw in controls and 3.1bw in the regained group, though this difference did not reach statistical significance in post-hoc analysis. This reduced loading may reflect a deliberate unloading strategy, a more cautious rate of loading, and the biomechanical effect of a more extended knee posture, which reduces both quadriceps demand and the scalar projection of joint forces onto the ACL vector.
Across both tasks, the SLH produced higher peak ACL loads than the uCMJ and demonstrated greater sensitivity in detecting between-group differences, supporting its value as a clinically relevant assessment tool even when performed under submaximal conditions. Key limitations include the small and imbalanced sample in the not regained group, the use of a 1 degree of freedom (DoF) knee model, the absence of EMG data, and the use of generic rather than subject-specific ACL attachment coordinates. Future work should incorporate a higher DoF knee model, EMG-informed optimization, and testing of athletes at earlier rehabilitation stages to better capture ACL loading deficits near the time of RTS clearance.