Effects of robotic gait training after stroke: A meta-analysis.


Journal

Annals of physical and rehabilitation medicine
ISSN: 1877-0665
Titre abrégé: Ann Phys Rehabil Med
Pays: Netherlands
ID NLM: 101502773

Informations de publication

Date de publication:
Nov 2020
Historique:
received: 17 01 2019
revised: 27 02 2020
accepted: 28 02 2020
pubmed: 2 4 2020
medline: 15 7 2021
entrez: 2 4 2020
Statut: ppublish

Résumé

Robotic devices are often used in rehabilitation and might be efficient to improve walking capacity after stroke. First to investigate the effects of robot-assisted gait training after stroke and second to explain the observed heterogeneity of results in previous meta-analyses. All randomized controlled trials investigating exoskeletons or end-effector devices in adult patients with stroke were searched in databases (MEDLINE, EMBASE, CENTRAL, CINAHL, OPENGREY, OPENSIGLE, PEDRO, WEB OF SCIENCE, CLINICAL TRIALS, conference proceedings) from inception to November 2019, as were bibliographies of previous meta-analyses, independently by 2 reviewers. The following variables collected before and after the rehabilitation program were gait speed, gait endurance, Berg Balance Scale (BBS), Functional Ambulation Classification (FAC) and Timed Up and Go scores. We also extracted data on randomization method, blinding of outcome assessors, drop-outs, intention (or not) to treat, country, number of participants, disease duration, mean age, features of interventions, and date of outcomes assessment. We included 33 studies involving 1466 participants. On analysis by subgroups of intervention, as compared with physiotherapy alone, physiotherapy combined with body-weight support training and robot-assisted gait training conferred greater improvement in gait speed (+0.09m/s, 95% confidence interval [CI] 0.03 to 0.15; p=0.002), FAC scores (+0.51, 95% CI 0.07 to 0.95; p=0.022) and BBS scores (+4.16, 95% CI 2.60 to 5.71; p=0.000). A meta-regression analysis suggested that these results were underestimated by the attrition bias of studies. Robot-assisted gait training combined with physiotherapy and body-weight support training seems an efficient intervention for gait recovery after stroke.

Sections du résumé

BACKGROUND BACKGROUND
Robotic devices are often used in rehabilitation and might be efficient to improve walking capacity after stroke.
OBJECTIVE OBJECTIVE
First to investigate the effects of robot-assisted gait training after stroke and second to explain the observed heterogeneity of results in previous meta-analyses.
METHODS METHODS
All randomized controlled trials investigating exoskeletons or end-effector devices in adult patients with stroke were searched in databases (MEDLINE, EMBASE, CENTRAL, CINAHL, OPENGREY, OPENSIGLE, PEDRO, WEB OF SCIENCE, CLINICAL TRIALS, conference proceedings) from inception to November 2019, as were bibliographies of previous meta-analyses, independently by 2 reviewers. The following variables collected before and after the rehabilitation program were gait speed, gait endurance, Berg Balance Scale (BBS), Functional Ambulation Classification (FAC) and Timed Up and Go scores. We also extracted data on randomization method, blinding of outcome assessors, drop-outs, intention (or not) to treat, country, number of participants, disease duration, mean age, features of interventions, and date of outcomes assessment.
RESULTS RESULTS
We included 33 studies involving 1466 participants. On analysis by subgroups of intervention, as compared with physiotherapy alone, physiotherapy combined with body-weight support training and robot-assisted gait training conferred greater improvement in gait speed (+0.09m/s, 95% confidence interval [CI] 0.03 to 0.15; p=0.002), FAC scores (+0.51, 95% CI 0.07 to 0.95; p=0.022) and BBS scores (+4.16, 95% CI 2.60 to 5.71; p=0.000). A meta-regression analysis suggested that these results were underestimated by the attrition bias of studies.
CONCLUSIONS CONCLUSIONS
Robot-assisted gait training combined with physiotherapy and body-weight support training seems an efficient intervention for gait recovery after stroke.

Identifiants

pubmed: 32229177
pii: S1877-0657(20)30065-8
doi: 10.1016/j.rehab.2020.02.008
pii:
doi:

Types de publication

Journal Article Meta-Analysis Systematic Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

518-534

Informations de copyright

Copyright © 2020 Elsevier Masson SAS. All rights reserved.

Auteurs

Geoffroy Moucheboeuf (G)

Service de Médecine Physique et Réadaptation, Pôle de neurosciences cliniques, Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France; HACS team-U1219 INSERM Bordeaux Population Health & University of Bordeaux, Bordeaux, France.

Romain Griffier (R)

Department of Public Health, Faculty of medicine, University of Bordeaux, Bordeaux, France.

David Gasq (D)

Toulouse NeuroImaging Center (ToNIC), Université de Toulouse & Inserm, Toulouse, France; Department of Functional Physiological Explorations, University Hospital of Toulouse, Toulouse, France.

Bertrand Glize (B)

Service de Médecine Physique et Réadaptation, Pôle de neurosciences cliniques, Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France; HACS team-U1219 INSERM Bordeaux Population Health & University of Bordeaux, Bordeaux, France.

Laurent Bouyer (L)

Department of Rehabilitation, Faculty of Medicine, Université Laval, Québec, Canada.

Patrick Dehail (P)

Service de Médecine Physique et Réadaptation, Pôle de neurosciences cliniques, Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France; HACS team-U1219 INSERM Bordeaux Population Health & University of Bordeaux, Bordeaux, France.

Helene Cassoudesalle (H)

Service de Médecine Physique et Réadaptation, Pôle de neurosciences cliniques, Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France; HACS team-U1219 INSERM Bordeaux Population Health & University of Bordeaux, Bordeaux, France. Electronic address: helene.cassoudesalle@chu-bordeaux.fr.

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