Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/23303
Title: The impact of robot-mediated adaptive I-TRAVLE training on impaired upper limb function in chronic stroke and multiple sclerosis
Authors: MARIS, Anneleen 
CONINX, Karin 
Seelen, Henk
TRUYENS, Veronik 
DE WEYER, Tom 
Geers, Richard
Lemmens, Mieke
Coolen, Jolijn
Stupar, Sandra
LAMERS, Ilse 
FEYS, Peter 
Issue Date: 2018
Source: Disability and rehabilitation. Assistive technology (Print), 13(1), p.1-9
Abstract: Purpose: The current study aimed to investigate proof-of-concept efficacy of an individualized, robotmediated training regime for people with MS (pwMS) and stroke patients. Method: Thirteen pwMS and 14 chronic stroke patients performed 36 (stroke) or 40 (pwMS) training sessions with the I-TRAVLE system. Evaluation of upper limb function was performed at baseline, after training and at 3 months follow-up. Clinical outcome measures consisted of active range of motion (ROM), Motricity Index, Jamar handgrip strength, perceived fatigue and strength, Wolf Motor Function Test (WFMT) and ABILHAND. Robot-generated outcome measures consisted of movement velocity, ROM and actual covered distance compared to straight-line distance. Results: In pwMS, significant improvements were found after training in active shoulder ROM, handgrip strength, perceived strength and WMFT activities. No significant change in clinical outcome was found in stroke patients, except for perceived strength. Significant improvement in speed and movement duration was found after training in both groups. At follow-up, clinical outcome deteriorated in pwMS and was maintained in stroke patients. Conclusions: Robot-mediated training resulted in improved movement coordination in both groups, as well as clinical improvement in pwMS. Absence of functional improvements in stroke patients may relate to severe upper limb dysfunction at baseline.
Keywords: haptic feedback; virtual reality; multiple sclerosis; stroke; upper limb; rehabilitation
Document URI: http://hdl.handle.net/1942/23303
ISSN: 1748-3107
e-ISSN: 1748-3115
DOI: 10.1080/17483107.2016.1278467
ISI #: 000428746700001
Rights: © 2017 Informa UK Limited, trading as Taylor & Francis Group
Category: A1
Type: Journal Contribution
Validations: vabb 2020
Appears in Collections:Research publications

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