Abstract

BACKGROUND: Chronic ankle instability (CAI) patients have varying levels of mechanical and sensorimotor impairments that may lead to disparate functional movement patterns. Current literature on landing biomechanics in a CAI population, however, considers all patients as a homogeneous group. In our prior work, we identified 6 subgroups of movement patterns using lower extremity kinematics during a landing/cutting task and that showed promise in furthering understanding of movement patterns in a laboratory-based environment. To increase the utility of this methodology in clinical settings, there is a need to find easily administered clinical tests that can help identify multiple subgroups of movement patterns in a CAI population. The purpose of the present study was to identify clinical tests that would help identify frontal and sagittal kinematic movement pattern subgroups during a landing/cutting task. We hypothesized that clinical tests would help predict group assignment; which CAI patient is assigned to frontal and sagittal kinematic movement pattern subgroups, respectively. METHODS: We recruited 100 CAI patients from a university population. We used three-dimensional instrumented motion analysis to capture ankle, knee and hip kinematics as subjects performed a single-leg maximal jump landing/cutting task. We used sagittal and frontal joint angle waveforms to group CAI patients. We then used 12 demographic and clinical measures to predict these subgroups of CAI. These consisted of gender, Star Excursion Balance Test-Anterior (SEBT-ANT), Biodex static balance, figure 8 hop, triple crossover hop, dorsiflexion range of motion (DFROM), number of failed trials, body mass index, a score of Foot and Ankle Ability Measure-Activities of Daily Living (FAAM-ADL), a score of FAAM-Sports, number of "yes" responses on Modified Ankle Instability Index, and number of previous ankle sprains. First, we used functional principal component analysis to create representative curves for each CAI patient and plane from the 3 lower extremity joint angles. We then used these curves as inputs to a predictor-dependent product partition model to cluster each CAI patient to unique subgroups. Finally, we used a multinomial prediction model to examine the accuracy of predicting group membership from demographic and clinical metrics. RESULTS: The predictor-dependent product partition model identified 4 frontal and 5 sagittal movement pattern subgroups. Six predictors (e.g., gender, SEBT-ANT, figure 8 hop, triple crossover hop, DFROM, and FAAM-ADL) predicted group membership with 55.7% accuracy for frontal subgroups. Ten predictors (minus Biodex static balance and number of previous ankle sprains) predicted group membership with 59% accuracy for sagittal subgroups. CONCLUSION: Novel statistical analyses allowed us to predict group membership for multiple frontal and sagittal kinematic movement patterns during landing/cutting using a series of clinical predictors. However, due to relatively lower accuracy (56–59% accuracy), the clinical utility of the current prediction model may be limited. Future work should consider including other clinical predictors to maximize prediction accuracy for identifying multiple kinematic movement patterns during a landing/cutting task.

Degree

PhD

College and Department

Life Sciences; Exercise Sciences

Rights

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