Date of Award

May 2022

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Occupational Therapy

First Advisor

Brooke Slavens

Committee Members

Donald Neumann, Donald Basel, Jun Zhang, Victoria Moerchen, Stephen Cobb

Keywords

Activities of daily living, dynamics, Ehlers-Danlos Syndrome, Gait, machine learning, Shoulder

Abstract

Hypermobile Ehlers-Danlos syndrome (hEDS) is an inherited connective tissue disorder, often under-diagnosed, and presenting with frequent chronic pain and severe musculoskeletal symptoms that can drastically reduce the quality of life during one’s life span. While there are limited quantitative approaches in the literature on adult movements, the biomechanics of movements during activities of daily living (ADLs) in children have not been investigated comprehensively. Therefore, the primary purpose of this dissertation was to characterize the biomechanics of the musculoskeletal system and investigate the biomechanics of hEDS by quantifying joint dynamics and muscle activations during ADLs and gait in the pediatric population. A major clinical concern is joint instability, with the highest incidences occurring in the shoulder (Chapter 2), hip, knee, and ankle (Chapters 3-5). This instability can lead to the development of pain and pathologies in children with hEDS. As a result, these joints are the focus of this dissertation with an aim to characterize the biomechanics of hEDS in children.Chapter 2 investigates three-dimensional (3-D) shoulder complex kinematics during ADLs and shoulder active range of motion. The results indicated significant differences in shoulder joint ranges of motion compared to typically developing children (TD) during daily activities, such as reaching across the body and reaching to a back pocket; and active range of motion during shoulder flexion, abduction, scaption, and extension. Finally, a potential clinical assessment was proposed to calculate reachable workspace, which may ultimately help clinicians plan more accurate individualized treatments and rehabilitation strategies. The primary focus of Chapter 3 was characterizing the lower extremity joints during gait in children with hEDS compared to TD children. Ankle joint results revealed significant differences in all three planes of motion during gait in children with hEDS. Moreover, the knee power results indicated less absorption during late stance and early swing phases. Chapter 4 investigated gait patterns during speed changes in hEDS and TD. The results demonstrated alterations in pelvis, hip, knee, and ankle joint kinematics differing from TD children. These changes may be related to previously reported proprioception deficits, reduced muscle strength, and balance impairments. In chapter 5, as another potential tool to elucidate the findings of differences in gait, seven machine learning models were applied to classify hEDS gait dynamics. This was done by inspecting the time-series joint kinematics and the important features of the classifiers. Radial basis function support vector machine and fully connected neural networks with a three-layer depth exhibited promising results in distinguishing between hEDS and TD gait kinematics. Analyzing movement patterns in children with hEDS to improve diagnosis, mobility, and musculoskeletal pain management may lead to more effective rehabilitation therapies and pain management strategies leading to an enhanced quality of life in the patient.

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