The Utility of Functional Data Analyses to Reveal Between-Limbs Asymmetries in Those With a History of Anterior Cruciate Ligament Reconstruction

Abstract

Context Researchers have traditionally used motion capture to quantify discrete biomechanical data points (peak values) during hop testing. However, these analyses restrict the evaluation to a single time point (ie, certain percentage of stance) and provide only a narrow view of movement. Applying more comprehensive analyses may help investigators identify important characteristics that are masked by the discrete analyses often used to screen patients for activity. Objective To examine the utility of functional data analyses to reveal asymmetries that are undetectable using discrete (ie, single time point) evaluations in participants with a history of anterior cruciate ligament reconstruction (ACLR) who achieved clinical hop symmetry. Design Cross-sectional study. Setting Laboratory. Patients or Other Participants Fifteen participants with unilateral ACLR (age = 21 ± 3 years, time from surgery = 4 ± 3 years) and 15 control participants without ACLR (age = 23 ± 2 years). Intervention(s) Lower extremity biomechanics during the triple–hop-for-distance task for the ACLR and contralateral limbs of patients and a representative limb of control participants were measured. Main Outcome Measure(s) Peak sagittal-plane joint power, joint work, and power profiles were determined. Results Using discrete analyses, we identified lower peak knee power and work in the ACLR limb compared with the contralateral and control limbs (P < .05) but were unable to demonstrate differences at the ankle or hip. Using functional data analyses, we observed asymmetries at the ankle, knee, and hip between the ACLR and contralateral or control limbs throughout stance (P < .05) and revealed that these asymmetries stemmed from knee power deficits that were prominent during early loading. Conclusions Despite achieving hop-distance symmetry, the ACLR limbs absorbed less power. Although this information was revealed using the discrete analyses, underlying asymmetries at the ankle and hip were masked. Using functional data analyses, we found interlimb asymmetries at the ankle, knee, and hip throughout ground contact and more fully elucidated the extent and source of asymmetries that can be used by clinicians and researchers alike to guide clinical decision making.

Combining Inertial Sensors and Machine Learning to Predict vGRF and Knee Biomechanics during a Double Limb Jump Landing Task

“…For KPA only, we were unable to identify prior work based on our defined criteria. Instead, we referenced White et al [ 40 ], who reported a single limb landing task. We scaled the reported joint powers due to differing normalization so that the reported mean in the healthy controls (17.21) was equivalent to our mean of 2.01.…”