A new metric for characterizing limb loading dynamics in post anterior cruciate ligament reconstruction individuals

Abstract

BackgroundUnresolved neuromuscular deficits often persist in post-anterior cruciate ligament reconstruction (ACLR) individuals manifesting as altered impact and active peak force production during running that can contribute to detrimental limb loading. Elevated impact and active peaks are common in pathological populations indicating a stiffer limb loading strategy. Although impact and active peaks are sensitive to changes in limb loading, to our knowledge, there are no established, standardized measures or cutoff criteria to differentiate between healthy and pathological limb loading. However, prior studies have demonstrated that the ratio between traditional biomechanical measures can be used to successfully establish quantifiable and graphical ranges to delineate between healthy and pathological movement. Research questionTherefore, this study sought to exploit the impact-to-active peak ratio to generate a new, standardized metric to quantify and characterize limb loading dynamics in healthy controls and post-ACLR individuals during running. MethodsTwenty-eight post-ACLR individuals and 18 healthy controls performed a running protocol. Impact peak and active peak data were extracted from their strides as they ran at a self-selected speed. A linear regression model was fit to the healthy control data and the models 95 % prediction intervals were used to define a boundary region of healthy limb loading dynamics. ResultsThe post-ACLR individuals produced a higher impact-to-active peak ratio than the healthy controls indicating that they adopted a stiffer limb loading strategy. The boundary regions derived from the impact and active peak model successfully classified the healthy controls and post-ACLR individual’s limb loading dynamics with an accuracy, sensitivity, and specificity of 89 %, 100 %, and 75 %, respectively. SignificanceThe ability to effectively evaluate limb loading dynamics using impact and active peaks can provide clinicians with a new, non-invasive metric to quantify and characterize healthy and pathological movement in a clinical setting.