Running-Induced Fatigue Exacerbates Anteromedial ACL Bundle Stress in Females with Genu Valgum: A Biomechanical Comparison with Healthy Controls

Abstract

Genu valgum (GV) is a common lower limb deformity that may increase the risk of anterior cruciate ligament (ACL) injury. This study used OpenSim musculoskeletal modeling and kinematic analysis to investigate the mechanical responses of the ACL under fatigue in females with GV. Eight females with GV and eight healthy controls completed a running-induced fatigue protocol. Lower limb kinematic and kinetic data were collected and used to simulate stress and strain in the anteromedial ACL (A-ACL) and posterolateral ACL (P-ACL) bundles, as well as peak joint angles and knee joint stiffness. The results showed a significant interaction effect between group and fatigue condition on A-ACL stress. In the GV group, A-ACL stress was significantly higher than in the healthy group both before and after fatigue (p < 0.001) and further increased following fatigue (p < 0.001). In the pre-fatigued state, A-ACL strain was significantly higher during the late stance phase in the GV group (p = 0.036), while P-ACL strain significantly decreased post-fatigue (p = 0.005). Additionally, post-fatigue peak hip extension and knee flexion angles, as well as pre-fatigue knee abduction angles, showed significant differences between groups. Fatigue also led to substantial changes in knee flexion, adduction, abduction, and hip/knee external rotation angles within the GV group. Notably, knee joint stiffness in this group was significantly lower than in controls and decreased further post-fatigue. These findings suggest that the structural characteristics of GV, combined with exercise-induced fatigue, exacerbate A-ACL loading and compromise knee joint stability, indicating a higher risk of ACL injury in fatigued females with GV.

Keywords: anterior cruciate ligament; genu valgum; knee model; strain; stress.

Figure 1

Overview of the musculoskeletal model and the experimental procedure. (A) Fluoroscopic knee images were collected and processed using DFIS. (B) Locations of reflective markers on the musculoskeletal model. (C) Placement of EMG sensors on the lower limbs to validate the model’s accuracy. (D) Schematic of the running biomechanics test. (E) Diagram of the fatigue protocol. (F) Visualization of the ACL physiological model constructed in OpenSim 4.3 (Stanford University, Stanford, CA, USA).

Figure 2

EMG and activation patterns of selected muscles. Simulated activations were generated using the OpenSim-based musculoskeletal model, and experimental EMG data were collected using Delsys sensors. Muscle activations were normalized on a scale from 0 (no activation) to 1 (full activation). The squared correlation coefficient (R²) indicates the goodness of fit between simulated and measured data.

Figure 3

ACL stress in the GV and control groups before and after fatigue. (A) Interaction analysis of ACL stress between the GV and control groups across pre- and post-fatigue conditions; (B) Comparison of ACL stress before and after fatigue within each group (GV vs. control); (C) Comparison of ACL stress between groups (GV vs. control) under different fatigue conditions.

Figure 4

ACL strain in the GV and control groups before and after fatigue. (A) Interaction analysis of ACL strain between the GV and control groups across pre- and post-fatigue conditions; (B) Comparison of ACL strain before and after fatigue within each group (GV vs. control); (C) Comparison of ACL strain between groups (GV vs. control) under different fatigue conditions.