Peak knee joint moments accurately predict medial and lateral knee contact forces in patients with valgus malalignment

Holder, Jana; Drongelen, Stefan van; Uhlrich, Scott D.; Herrmann, Eva; Meurer, Andrea; Stief, Felix

doi:10.1038/s41598-023-30058-4

Cited by 6 publications

(1 citation statement)

References 67 publications

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“…For the reference simulation, muscle forces and JCF were calculated using static optimization with an equal weighting ( ) for all muscles, which is a commonly used approach to estimate muscle forces in people without neurological disorders 28 , 43 , 44 . Notable, JCF in OpenSim are calculated in a post-processing procedure based on the obtained joint kinematics and muscle forces 45 , which is a widely used approach 10 , 28 , 44 , 46 and has been shown to provide physiological plausible results with an acceptable accuracy 14 . …”

Section: Methodsmentioning

confidence: 99%

A framework based on subject-specific musculoskeletal models and Monte Carlo simulations to personalize muscle coordination retraining

Kainz,

Koller,

Wallnöfer

et al. 2024

Sci Rep

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Excessive loads at lower limb joints can lead to pain and degenerative diseases. Altering joint loads with muscle coordination retraining might help to treat or prevent clinical symptoms in a non-invasive way. Knowing how much muscle coordination retraining can reduce joint loads and which muscles have the biggest impact on joint loads is crucial for personalized gait retraining. We introduced a simulation framework to quantify the potential of muscle coordination retraining to reduce joint loads for an individuum. Furthermore, the proposed framework enables to pinpoint muscles, which alterations have the highest likelihood to reduce joint loads. Simulations were performed based on three-dimensional motion capture data of five healthy adolescents (femoral torsion 10°–29°, tibial torsion 19°–38°) and five patients with idiopathic torsional deformities at the femur and/or tibia (femoral torsion 18°–52°, tibial torsion 3°–50°). For each participant, a musculoskeletal model was modified to match the femoral and tibial geometry obtained from magnetic resonance images. Each participant’s model and the corresponding motion capture data were used as input for a Monte Carlo analysis to investigate how different muscle coordination strategies influence joint loads. OpenSim was used to run 10,000 simulations for each participant. Root-mean-square of muscle forces and peak joint contact forces were compared between simulations. Depending on the participant, altering muscle coordination led to a maximum reduction in hip, knee, patellofemoral and ankle joint loads between 5 and 18%, 4% and 45%, 16% and 36%, and 2% and 6%, respectively. In some but not all participants reducing joint loads at one joint increased joint loads at other joints. The required alteration in muscle forces to achieve a reduction in joint loads showed a large variability between participants. The potential of muscle coordination retraining to reduce joint loads depends on the person’s musculoskeletal geometry and gait pattern and therefore showed a large variability between participants, which highlights the usefulness and importance of the proposed framework to personalize gait retraining.

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