Literature DB >> 29208476

What does best evidence tell us about robotic gait rehabilitation in stroke patients: A systematic review and meta-analysis.

Maria Federica Bruni1, Corrado Melegari1, Maria Cristina De Cola2, Alessia Bramanti2, Placido Bramanti2, Rocco Salvatore Calabrò3.   

Abstract

BACKGROUND: Studies about electromechanical-assisted devices proved the validity and effectiveness of these tools in gait rehabilitation, especially if used in association with conventional physiotherapy in stroke patients.
OBJECTIVE: The aim of this study was to compare the effects of different robotic devices in improving post-stroke gait abnormalities.
METHODS: A computerized literature research of articles was conducted in the databases MEDLINE, PEDro, COCHRANE, besides a search for the same items in the Library System of the University of Parma (Italy). We selected 13 randomized controlled trials, and the results were divided into sub-acute stroke patients and chronic stroke patients. We selected studies including at least one of the following test: 10-Meter Walking Test, 6-Minute Walk Test, Timed-Up-and-Go, 5-Meter Walk Test, and Functional Ambulation Categories.
RESULTS: Stroke patients who received physiotherapy treatment in combination with robotic devices, such as Lokomat or Gait Trainer, were more likely to reach better results, compared to patients who receive conventional gait training alone. Moreover, electromechanical-assisted gait training in association with Functional Electrical Stimulations produced more benefits than the only robotic treatment (-0.80 [-1.14; -0.46], p > .05).
CONCLUSIONS: The evaluation of the results confirm that the use of robotics can positively affect the outcome of a gait rehabilitation in patients with stroke. The effects of different devices seems to be similar on the most commonly outcome evaluated by this review.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  End-effector; Exoskeleton; Gait rehabilitation; Lokomat; Robotics; Stroke

Mesh:

Year:  2017        PMID: 29208476     DOI: 10.1016/j.jocn.2017.10.048

Source DB:  PubMed          Journal:  J Clin Neurosci        ISSN: 0967-5868            Impact factor:   1.961


  37 in total

1.  Influences of the biofeedback content on robotic post-stroke gait rehabilitation: electromyographic vs joint torque biofeedback.

Authors:  Federica Tamburella; Juan C Moreno; Diana Sofía Herrera Valenzuela; Iolanda Pisotta; Marco Iosa; Febo Cincotti; Donatella Mattia; José L Pons; Marco Molinari
Journal:  J Neuroeng Rehabil       Date:  2019-07-23       Impact factor: 4.262

2.  A Case Report on Robot-Aided Gait Training in Primary Lateral Sclerosis Rehabilitation: Rationale, Feasibility and Potential Effectiveness of a Novel Rehabilitation Approach.

Authors:  Simona Portaro; Laura Ciatto; Loredana Raciti; Enrico Aliberti; Riccardo Aliberti; Antonino Naro; Rocco Salvatore Calabrò
Journal:  Innov Clin Neurosci       Date:  2021 Apr-Jun

3.  Adjustable Parameters and the Effectiveness of Adjunct Robot-Assisted Gait Training in Individuals with Chronic Stroke.

Authors:  Shih-Ching Chen; Jiunn-Horng Kang; Chih-Wei Peng; Chih-Chao Hsu; Yen-Nung Lin; Chien-Hung Lai
Journal:  Int J Environ Res Public Health       Date:  2022-07-04       Impact factor: 4.614

4.  Impact of Intensive Gait Training With and Without Electromechanical Assistance in the Chronic Phase After Stroke-A Multi-Arm Randomized Controlled Trial With a 6 and 12 Months Follow Up.

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

5.  Comparisons between Locomat and Walkbot robotic gait training regarding balance and lower extremity function among non-ambulatory chronic acquired brain injury survivors.

Authors:  Hoo Young Lee; Jung Hyun Park; Tae-Woo Kim
Journal:  Medicine (Baltimore)       Date:  2021-05-07       Impact factor: 1.889

6.  Gait Event Detection for Stroke Patients during Robot-Assisted Gait Training.

Authors:  Andreas Schicketmueller; Juliane Lamprecht; Marc Hofmann; Michael Sailer; Georg Rose
Journal:  Sensors (Basel)       Date:  2020-06-16       Impact factor: 3.576

7.  A Systematic Review Establishing the Current State-of-the-Art, the Limitations, and the DESIRED Checklist in Studies of Direct Neural Interfacing With Robotic Gait Devices in Stroke Rehabilitation.

Authors:  Olive Lennon; Michele Tonellato; Alessandra Del Felice; Roberto Di Marco; Caitriona Fingleton; Attila Korik; Eleonora Guanziroli; Franco Molteni; Christoph Guger; Rupert Otner; Damien Coyle
Journal:  Front Neurosci       Date:  2020-06-30       Impact factor: 4.677

8.  The Effect of Robotic Assisted Gait Training With Lokomat® on Balance Control After Stroke: Systematic Review and Meta-Analysis.

Authors:  Federica Baronchelli; Chiara Zucchella; Mariano Serrao; Domenico Intiso; Michelangelo Bartolo
Journal:  Front Neurol       Date:  2021-07-06       Impact factor: 4.003

9.  Predictors of Functional Outcome in a Cohort of Hispanic Patients Using Exoskeleton Rehabilitation for Cerebrovascular Accidents and Traumatic Brain Injury.

Authors:  Lisa R Treviño; Peter Roberge; Michael E Auer; Angela Morales; Annelyn Torres-Reveron
Journal:  Front Neurorobot       Date:  2021-06-10       Impact factor: 2.650

10.  Shaping neuroplasticity by using powered exoskeletons in patients with stroke: a randomized clinical trial.

Authors:  Rocco Salvatore Calabrò; Antonino Naro; Margherita Russo; Placido Bramanti; Luigi Carioti; Tina Balletta; Antonio Buda; Alfredo Manuli; Serena Filoni; Alessia Bramanti
Journal:  J Neuroeng Rehabil       Date:  2018-04-25       Impact factor: 4.262

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.