Affiliations: [a] Department of Biomechanical Engineering, Vilnius Gediminas Technical University, Vilnius, Lithuania | [b] Department of Mechanics and Applied Computer Science, Bialystok University of Technology, Bialystok, Poland | [c] Department of Neurology and Neurosurgery, Centre for Neurology, Vilnius University, Vilnius, Lithuania
Correspondence:
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Corresponding author: Jurgita Ziziene, Department of Biomechanical Engineering, Vilnius Gediminas Technical University, J. Basanavicius str. 28, MR-II 2-109, 03224 Vilnius, Lithuania. Tel.: +370 52744748; E-mail: jurgita.ziziene@vgtu.lt.
Abstract: BACKGROUND: Upper extremity (UE) motor function deficits are commonly noted in multiple sclerosis (MS) patients and assessing it is challenging because of the lack of consensus regarding its definition. Instrumented biomechanical analysis of upper extremity movements can quantify coordination with different spatiotemporal measures and facilitate disability rating in MS patients. OBJECTIVE: To identify objective quantitative parameters for more accurate evaluation of UE disability and relate it to existing clinical scores. METHODS: Thirty-four MS patients and 24 healthy controls (CG) performed a finger-to-nose test as fast as possible and, in addition, clinical evaluation kinematic parameters of UE were measured by using inertial sensors. RESULTS: Generally, a higher disability score was associated with an increase of several temporal parameters, like slower task performance. The time taken to touch their nose was longer when the task was fulfilled with eyes closed. Time to peak angular velocity significantly changed in MS patients (EDSS > 5.0). The inter-joint coordination significantly decreases in MS patients (EDSS 3.0–5.5). Spatial parameters indicated that maximal ROM changes were in elbow flexion. CONCLUSIONS: Our findings have revealed that spatiotemporal parameters are related to the UE motor function and MS disability level. Moreover, they facilitate clinical rating by supporting clinical decisions with quantitative data.
