Literature DB >> 24710969

Does robot-assisted gait rehabilitation improve balance in stroke patients? A systematic review.

Eva Swinnen1, David Beckwée2, Romain Meeusen3, Jean-Pierre Baeyens4, Eric Kerckhofs1.   

Abstract

The aim of this systematic review was to summarize the improvements in balance after robot-assisted gait training (RAGT) in stroke patients. Two databases were searched: PubMed and Web of Knowledge. The most important key words are "stroke," "RAGT," "balance," "Lokomat," and "gait trainer." Studies were included if stroke patients were involved in RAGT protocols, and balance was determined as an outcome measurement. The articles were checked for methodological quality by 2 reviewers (Cohen's κ = 0.72). Nine studies were included (7 true experimental and 2 pre-experimental studies; methodological quality score, 56%-81%). In total, 229 subacute or chronic stroke patients (70.5% male) were involved in RAGT (3 to 5 times per week, 3 to 10 weeks, 12 to 25 sessions). In 5 studies, the gait trainer was used; in 2, the Lokomat was used; in 1 study, a single-joint wearable knee orthosis was used; and in 1 study, the AutoAmbulator was used. Eight studies compared RAGT with other gait rehabilitation methods. Significant improvements (no to large effect sizes, Cohen's d = 0.01 to 3.01) in balance scores measured with the Berg Balance Scale, the Tinetti test, postural sway tests, and the Timed Up and Go test were found after RAGT. No significant differences in balance between the intervention and control groups were reported. RAGT can lead to improvements in balance in stroke patients; however, it is not clear whether the improvements are greater compared with those associated with other gait rehabilitation methods. Because a limited number of studies are available, more specific research (eg, randomized controlled trials with larger, specific populations) is necessary to draw stronger conclusions.

Entities:  

Keywords:  Berg Balance Scale; balance; gait; robot assistance; stroke

Mesh:

Year:  2014        PMID: 24710969     DOI: 10.1310/tsr2102-87

Source DB:  PubMed          Journal:  Top Stroke Rehabil        ISSN: 1074-9357            Impact factor:   2.119


  28 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.  Home-based technologies for stroke rehabilitation: A systematic review.

Authors:  Yu Chen; Kingsley Travis Abel; John T Janecek; Yunan Chen; Kai Zheng; Steven C Cramer
Journal:  Int J Med Inform       Date:  2018-12-11       Impact factor: 4.046

3.  A qualitative study on user acceptance of a home-based stroke telerehabilitation system.

Authors:  Yu Chen; Yunan Chen; Kai Zheng; Lucy Dodakian; Jill See; Robert Zhou; Nina Chiu; Renee Augsburger; Alison McKenzie; Steven C Cramer
Journal:  Top Stroke Rehabil       Date:  2019-11-04       Impact factor: 2.119

4.  Comparison of high-intensive and low-intensive electromechanical-assisted gait training by Exowalk® in patients over 3-month post-stroke.

Authors:  Chang Seon Yu; Yeon-Gyo Nam; Bum Sun Kwon
Journal:  BMC Sports Sci Med Rehabil       Date:  2022-07-10

5.  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

6.  The effects of body weight-supported treadmill training on static and dynamic balance in stroke patients: A pilot, single-blind, randomized trial.

Authors:  Rüstem Mustafaoğlu; Belgin Erhan; İpek Yeldan; Burcu Ersöz Hüseyinsinoğlu; Berrin Gündüz; Arzu Razak Özdinçler
Journal:  Turk J Phys Med Rehabil       Date:  2018-08-15

7.  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

8.  Effects of body weight support and guidance force settings on muscle synergy during Lokomat walking.

Authors:  Yosra Cherni; Maryam Hajizadeh; Fabien Dal Maso; Nicolas A Turpin
Journal:  Eur J Appl Physiol       Date:  2021-07-04       Impact factor: 3.078

9.  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

10.  Effects of robot-assisted gait training on the balance and gait of chronic stroke patients: focus on dependent ambulators.

Authors:  Duk Youn Cho; Si-Woon Park; Min Jin Lee; Dae Sung Park; Eun Joo Kim
Journal:  J Phys Ther Sci       Date:  2015-10-30
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