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dc.rights.licenseopenen_US
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorMOUCHEBOEUF, Geoffroy
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorGRIFFIER, Romain
dc.contributor.authorGASQ, D.
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorGLIZE, Bertrand
ORCID: 0000-0001-9618-2088
IDREF: 178853267
dc.contributor.authorBOUYER, L.
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorDEHAIL, Patrick
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorCASSOUDESALLE, Helene
dc.date.accessioned2021-03-18T10:09:24Z
dc.date.available2021-03-18T10:09:24Z
dc.date.issued2020-11
dc.identifier.issn1877-0657en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/26726
dc.description.abstractEnBACKGROUND: Robotic devices are often used in rehabilitation and might be efficient to improve walking capacity after stroke. 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: 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: 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 (+0.09 m/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: Robot-assisted gait training combined with physiotherapy and body-weight support training seems an efficient intervention for gait recovery after stroke.
dc.language.isoENen_US
dc.title.enEffects of robotic gait training after stroke: A meta-analysis
dc.title.alternativeAnn Phys Rehabil Meden_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1016/j.rehab.2020.02.008en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed32229177en_US
bordeaux.journalAnnals of Physical and Rehabilitation Medicineen_US
bordeaux.page518-534en_US
bordeaux.volume63en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - U1219en_US
bordeaux.issue6en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.teamHACSen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-03173049
hal.version1
hal.date.transferred2021-09-03T08:05:57Z
hal.exporttrue
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