Jaya Shanker Tedla1, Snehil Dixit2, Kumar Gular2, Mohammed Abohashrh3. 1. Department of Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia, jtedla@kku.edu.sa. 2. Department of Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia. 3. Department of Basic Medical Science, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia.
Abstract
BACKGROUND: The review is intended to provide the effectiveness of robotic-assisted gait training (RAGT) for functional gait recovery in poststroke survivors through a systematic review and to provide evidence for gait speed improvements through the meta-analysis of randomized controlled trials (RCTs). SUMMARY: In this systematic review, PubMed, Web of Science, Wiley Online Library, Science Direct, Science Robotics, Scopus, UpToDate, MEDLINE, Google Scholar, -CINHAL, EMBASE, and EBSCO were reviewed to identify relevant RCTs. Articles included in the study were thoroughly examined by 2 independent reviewers. The included RCTs were having a PEDro score between 6 and 8 points. The initial database review yielded 1,371 studies and, following further screening; 9 studies finally were selected for systematic review and meta-analysis. Out of the 9 studies, 4 were on chronic stroke and 5 were on subacute stroke. The meta-analysis of gait speed showed an effect size value ranging between -0.91 and 0.64, with the total effect size of all the studies being -0.12. During subgroup analysis, the subacute stroke total effect size was identified as -0.48, and the chronic stroke total effect size was noted as 0.04. Meta-analysis revealed no significant differences between RAGT and conventional gait training (CGT). Key Messages: Our systematic review revealed that the RAGT application demonstrated a better or similar effect to that of CGT in a poststroke population. A meta-analysis of gait speed involving all the studies identified here indicated no significant differences between RAGT and CGT. However, the subanalysis of chronic stroke survivors showed a slight positive effect of RAGT on gait speed.
BACKGROUND: The review is intended to provide the effectiveness of robotic-assisted gait training (RAGT) for functional gait recovery in poststroke survivors through a systematic review and to provide evidence for gait speed improvements through the meta-analysis of randomized controlled trials (RCTs). SUMMARY: In this systematic review, PubMed, Web of Science, Wiley Online Library, Science Direct, Science Robotics, Scopus, UpToDate, MEDLINE, Google Scholar, -CINHAL, EMBASE, and EBSCO were reviewed to identify relevant RCTs. Articles included in the study were thoroughly examined by 2 independent reviewers. The included RCTs were having a PEDro score between 6 and 8 points. The initial database review yielded 1,371 studies and, following further screening; 9 studies finally were selected for systematic review and meta-analysis. Out of the 9 studies, 4 were on chronic stroke and 5 were on subacute stroke. The meta-analysis of gait speed showed an effect size value ranging between -0.91 and 0.64, with the total effect size of all the studies being -0.12. During subgroup analysis, the subacute stroke total effect size was identified as -0.48, and the chronic stroke total effect size was noted as 0.04. Meta-analysis revealed no significant differences between RAGT and conventional gait training (CGT). Key Messages: Our systematic review revealed that the RAGT application demonstrated a better or similar effect to that of CGT in a poststroke population. A meta-analysis of gait speed involving all the studies identified here indicated no significant differences between RAGT and CGT. However, the subanalysis of chronic stroke survivors showed a slight positive effect of RAGT on gait speed.
Authors: Susanne Palmcrantz; Anneli Wall; Katarina Skough Vreede; Påvel Lindberg; Anna Danielsson; Katharina S Sunnerhagen; Charlotte K Häger; Jörgen Borg Journal: Front Neurosci Date: 2021-04-22 Impact factor: 4.677
Authors: Jenna Tosto-Mancuso; Laura Tabacof; Joseph E Herrera; Erica Breyman; Sophie Dewil; Mar Cortes; Loreene Correa-Esnard; Christopher P Kellner; Neha Dangayach; David Putrino Journal: Curr Neurol Neurosci Rep Date: 2022-03-12 Impact factor: 6.030