# Computational study of extrinsic factors affecting ACL strain during single-leg jump landing

## TL;DR

This study uses a computer model to understand how knee movements during a jump landing affect ACL strain and injury risk.

## Contribution

A validated finite element knee model and an empirical equation to predict ACL strain during dynamic activities.

## Key findings

- Maximum ACL strain during jump landing was 3.5 ± 2.2%, matching experimental results.
- Trunk, hip, and ankle flexion angles significantly reduce ACL strain (R2 = 90.04%).
- Soft landing techniques decrease ACL strain and can be modeled empirically.

## Abstract

Non-contact anterior cruciate ligament (ACL) injuries are a major concern in sport-related activities due to dynamic knee movements. There is a paucity of finite element (FE) studies that have accurately replicated the knee geometry, kinematics, and muscle forces during dynamic activities. The objective of this study was to develop and validate a knee FE model and use it to quantify the relationships between sagittal plane knee kinematics, kinetics and the resulting ACL strain.

3D images of a cadaver knee specimen were segmented (bones, cartilage, and meniscus) and meshed to develop the FE model. Knee ligament insertion sites were defined in the FE model via experimental digitization of the specimen’s ligaments. The response of the model was validated against multiple physiological knee movements using published experimental data. Single-leg jump landing motions were then simulated on the validated model with muscle forces and kinematic inputs derived from motion capture and rigid body modelling of ten participants.

The maximum ACL strain measured with the model during jump landing was 3.5 ± 2.2%, comparable to published experimental results. Bivariate analysis showed no significant correlation between body weight, ground reaction force and sagittal plane parameters (such as joint flexion angles, joint moments, muscle forces, and joint velocity) and ACL strain. Multivariate regression analysis showed increasing trunk, hip and ankle flexion angles decreases ACL strain (R2 = 90.04%, p < 0.05).

Soft landing decreases ACL strain and the relationship could be presented through an empirical equation. The model and the empirical relation developed in this study could be used to better predict ACL injury risk and prevention strategies during dynamic activities.

The online version contains supplementary material available at 10.1186/s12891-024-07372-7.