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Published on: June 7, 2024
Robot-assisted gait training in multiple sclerosis patients: a randomized trial.
Isabella Schwartz1, Anna Sajin, Elior Moreh
1Department of Physical Medicine and Rehabilitation, Haddasah-Hebrew University Hospital, Jerusalem, Israel.
Summary
Robot-assisted gait training (RAGT) is safe for multiple sclerosis (MS) patients. While RAGT and conventional walking treatment (CWT) showed similar gait improvements, RAGT offers a feasible therapeutic option for severe MS walking disabilities.
Area of Science:
- Neurology
- Rehabilitation Medicine
- Biomedical Engineering
Background:
- Locomotor activity is crucial for multiple sclerosis (MS) patients.
- Robotic-assisted body weight-supported treadmill training shows promise for neurological gait impairments.
- The efficacy of robotic-assisted gait training (RAGT) in MS patients remains under investigation.
Purpose of the Study:
- To compare the effectiveness of RAGT versus conventional walking treatment (CWT) for gait and generalized functions in stable MS patients.
- To evaluate RAGT's impact on gait parameters, functional status, and quality of life in MS.
- To assess the long-term effects of RAGT and CWT at 3 and 6 months post-treatment.
Main Methods:
- Prospective randomized controlled trial involving 12 sessions of RAGT or CWT.
- Participants included MS patients with Expanded Disability Status Scale (EDSS) scores of 5-7.
- Primary outcomes: gait parameters; Secondary outcomes: functional and quality of life measures (FIM, EDSS).
Main Results:
- No significant difference in gait or functional improvements between RAGT and CWT groups.
- Both RAGT and CWT led to significant short-term improvements in gait, FIM, and EDSS scores.
- Most gait and functional benefits diminished by the 6-month follow-up.
Conclusions:
- Robot-assisted gait training (RAGT) is a feasible and safe intervention for MS patients.
- RAGT may serve as an effective supplementary therapy for MS patients with significant walking disabilities.
- Further research is needed to optimize RAGT protocols for sustained functional gains in MS.
