Literature DB >> 22140197

Robotic-assisted step training (lokomat) not superior to equal intensity of over-ground rehabilitation in patients with multiple sclerosis.

Claude Vaney1, Brigitte Gattlen, Véronique Lugon-Moulin, André Meichtry, Rita Hausammann, Denise Foinant, Anne-Marie Anchisi-Bellwald, Cécilia Palaci, Roger Hilfiker.   

Abstract

BACKGROUND: Robot-assisted gait training (RAGT) has been suggested as an intervention to improve walking capacity in patients with multiple sclerosis (MS).
OBJECTIVE: This study aimed to evaluate whether RAGT (Lokomat) is superior to over-ground walking training in terms of quality of life, activity level, and gait.
METHODS: A total of 67 patients with MS with the Expanded Disability Status Scale (EDSS) 3.0 to 6.5 were randomized to walking or RAGT, in addition to multimodal rehabilitation. Primary outcomes were walking speed, activity level (estimated metabolic equivalent, metabolic equivalents [METs], using an accelerometer), and quality of life (Well-Being Visual Analogue Scale (VAS) and EQ-5D European VAS.
RESULTS: In all, 49 patients finished the interventions. Mean age was 56 years (range 36-74 years), mean EDSS was 5.8 (3.0-6.5), and the preferred walking speed at baseline was 0.56 m/s (0.06-1.43 m/s). Before rehabilitation, participants spent on average 68 min/d at an MET ≥ 3. The walking group improved gait speed nonsignificantly more than the RAGT; the upper bound of the confidence interval (CI) did not exclude a clinically relevant benefit (defined as a difference of 0.05 m/s) in favor of the walking group; the lower bound of the CI did exclude a clinically important benefit in favor of the Lokomat. Quality of life improved in both groups, with a nonsignificant between-group difference in favor of the walking group. Both groups had reduced their activity by 8 weeks after the rehabilitation.
CONCLUSION: It is unlikely that RAGT is better than over-ground walking training in patients with an EDSS between 3.0 and 6.5.

Entities:  

Mesh:

Year:  2011        PMID: 22140197     DOI: 10.1177/1545968311425923

Source DB:  PubMed          Journal:  Neurorehabil Neural Repair        ISSN: 1545-9683            Impact factor:   3.919


  23 in total

Review 1.  Robotic gait rehabilitation and substitution devices in neurological disorders: where are we now?

Authors:  Rocco Salvatore Calabrò; Alberto Cacciola; Francesco Bertè; Alfredo Manuli; Antonino Leo; Alessia Bramanti; Antonino Naro; Demetrio Milardi; Placido Bramanti
Journal:  Neurol Sci       Date:  2016-01-18       Impact factor: 3.307

2.  Should body weight-supported treadmill training and robotic-assistive steppers for locomotor training trot back to the starting gate?

Authors:  Bruce H Dobkin; Pamela W Duncan
Journal:  Neurorehabil Neural Repair       Date:  2012-03-12       Impact factor: 3.919

3.  Effect of Comorbidities on Outcomes of Neurorehabilitation Interventions in Multiple Sclerosis: A Scoping Review.

Authors:  Afolasade Fakolade; Etienne J Bisson; Julie Pétrin; Julie Lamarre; Marcia Finlayson
Journal:  Int J MS Care       Date:  2016 Nov-Dec

4.  Short-term Performance-based Error-augmentation versus Error-reduction Robotic Gait Training for Individuals with Chronic Stroke: A Pilot Study.

Authors:  P C Kao; S Srivastava; J S Higginson; S K Agrawal; J P Scholz
Journal:  Phys Med Rehabil Int       Date:  2015-11-12

5.  Critical Appraisal of Evidence for Improving Gait Speed in People with Multiple Sclerosis: Dalfampridine Versus Gait Training.

Authors:  Prudence Plummer
Journal:  Int J MS Care       Date:  2016 May-Jun

Review 6.  Wearable motion sensors to continuously measure real-world physical activities.

Authors:  Bruce H Dobkin
Journal:  Curr Opin Neurol       Date:  2013-12       Impact factor: 5.710

7.  Brain stimulation paired with novel locomotor training with robotic gait orthosis in chronic stroke: a feasibility study.

Authors:  Megan M Danzl; Kenneth C Chelette; Kara Lee; Dana Lykins; Lumy Sawaki
Journal:  NeuroRehabilitation       Date:  2013       Impact factor: 2.138

8.  Local dynamic stability as a responsive index for the evaluation of rehabilitation effect on fall risk in patients with multiple sclerosis: a longitudinal study.

Authors:  Roger Hilfiker; Claude Vaney; Brigitte Gattlen; André Meichtry; Olivier Deriaz; Véronique Lugon-Moulin; Anne-Marie Anchisi-Bellwald; Cécilia Palaci; Denise Foinant; Philippe Terrier
Journal:  BMC Res Notes       Date:  2013-07-09

9.  Treadmill training in multiple sclerosis: can body weight support or robot assistance provide added value? A systematic review.

Authors:  Eva Swinnen; David Beckwée; Droesja Pinte; Romain Meeusen; Jean-Pierre Baeyens; Eric Kerckhofs
Journal:  Mult Scler Int       Date:  2012-05-30

10.  Prefrontal, posterior parietal and sensorimotor network activity underlying speed control during walking.

Authors:  Thomas C Bulea; Jonghyun Kim; Diane L Damiano; Christopher J Stanley; Hyung-Soon Park
Journal:  Front Hum Neurosci       Date:  2015-05-12       Impact factor: 3.169

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