Kinematic Clustering of Patients with Self-Reported Ankle Instability During a Jump Task

Abstract

Self-report survey instruments have traditionally defined ankle instability (AI). However, movement neuromechanics and dynamic stability in this patient population are quite variable, making it difficult to identify specific deficits that perpetuate the chronic nature of AI. PURPOSE: to identify clusters of kinematic (i.e., joint angle) characteristics in a large sample of patients with self-reported AI. METHODS: 80 subjects (23 ± 2 yrs) with a history of ankle sprains (4.4 ± 3.2), scored below 90% (83 ± 9) on the Foot and Ankle Ability Measure (FAAM) ADL, below 75% (62 ± 13) on the FAAM Sport, reported at least 2 “yes” responses (4 ± 1) on the MAII, and had no sprain in the previous 6 months. High-speed video (250 Hz) was used to measure 3D lower-extremity kinematics during 5 trials of a max vertical jump, onto a force plate, that was immediately followed by a lateral jump. Initial and peak joint angle, joint angle at take-off, time to peak angle, and total time on the plate were used to cluster kinematic characteristics. Cross-classification matrices were used to measure cluster dependences across the kinematic characteristics. RESULTS: Figure 1 presents the clusters of kinematics for lower extremity sagittal and frontal plane movements during the stance portion of the movement. 5-8 distinct clusters were identified, depending on the joint and plane. Each line represents a mean curve of identified clusters. The maximum proportion of subjects sharing similar clusters from two different kinematic characteristics ranged from 0.09 to 0.27. CONCLUSIONS: Multiple distinct kinematic characteristics were identified in a “homogenous” group of patients with self-reported AI, suggesting that multiple distinct neuromechanical alterations can exist in an AI patient population. These data should be considered when using self-report instruments to identify AI.