Abstract

Patients with a history of lateral ankle sprain (LAS) may experience different levels of mechanical and/or sensorimotor deficits following their injuries. Although various factors, such as structural damage, sensorimotor adaptation, perceived instability, swelling and/or pain, can develop and perpetuate the condition of chronic ankle instability (CAI), most previous CAI research on biomechanics has considered all patients with CAI as a homogeneous group. Recent research has clustered patients with CAI into six distinct movement patterns during a maximal jump-landing/cutting task. This approach could motivate clinicians to develop appropriate rehabilitation programs for each patient with CAI depending on their movement patterns. However, evaluating patients with CAI for the quality of movement and sensorimotor deficits using a 3D motion capture system and a force plate is not easily accessible in clinical settings. PURPOSE: (i) to identify subgroups within the CAI population based on their movement patterns using inertial measurement unit (IMU) devices and (ii) to characterize each subgroup's functional movement patterns during maximal jump-landing/cutting relative to the uninjured controls. METHODS: A total of 100 patients with CAI (height = 1.76 ± 0.1 m, mass = 74.0 ± 14.9 kg) were assessed according to the Foot and Ankle Ability Measure (FAAM) (ADL: 84.3 ± 7.6%, Sport: 63.6 ± 8.6%) and the Ankle Instability Instrument (AII) (6.7 ± 1.2) and were fit into clusters based on their movement strategy during the maximal jump-landing/cutting task. A total of 21 uninjured controls (height = 1.74 ± 0.1 m, mass = 70.7 ± 13.4 kg) were compared with each cluster. Seven IMU sensors were placed on the base of the lumbar spine, lower and upper legs, and feet and participants performed 5 trials of the maximal jump-landing/cutting test. Joint kinematics in the lower extremity were collected during the task using IMU sensors. Data were reduced to functional curves; kinematic data from the sagittal and frontal planes were reduced to a single representative curve for each plane. Then, representative curves were clustered using a Bayesian clustering technique. Functional analyses of variance were used to identify between-group differences for outcome measures and describe specific movement characteristics of each subgroup. Pairwise comparison functions as well as 95% confidence interval (CI) bands were plotted to determine specific differences. If 95% CI bands did not cross the zero line, we considered the difference significant. RESULTS: Four distinct clusters were identified from the sagittal- and frontal-plane kinematic data. Specific movement patterns in each cluster compared to either uninjured controls or rest of patients with CAI were also identified. CONCLUSION: The IMUs were able to distinguish 4 clusters within the CAI population based on distinct movement patterns during a maximal jump-landing/cutting task. Thus, IMUs can be effective measuring devices to distinguish and characterize multiple distinct movement patterns without relying on a traditional 3D motion capture system. Clinicians should consider utilizing IMU devices to measure and evaluate specific movement patterns in the CAI population during multiplanar demanding tests before developing appropriate treatment interventions in clinical settings.

Degree

PhD

College and Department

Life Sciences; Exercise Sciences

Rights

https://lib.byu.edu/about/copyright/

Date Submitted

2022-12-07

Document Type

Dissertation

Handle

http://hdl.lib.byu.edu/1877/etd13051

Keywords

Chronic ankle instability, Bayesian clustering, functional data analysis

Language

english

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