Effectiveness of Intervention Based on End-effector Gait Trainer in Older Patients With Stroke: A Systematic Review

Objective: The objective of the article is to analyze the effects of the end-effector technology for gait rehabilitation on acute, subacute, and chronic stroke in order to verify the efficacy of the treatment in older people, based on evidence from randomized controlled trials, and thus increase the clinical knowledge for future applications in the hospital setting.

Design: A systematic review of the literature was conducted in October 2018. The data were collected from Cochrane, Embase, Scopus, and PubMed databases, analyzing manuscripts and articles of the last 10 years.

Setting: We included only randomized controlled trials written in English and aimed to study the effects of end-effector devices in improving walking in stroke patients. We selected 20 studies, and the results were divided into subacute stroke patients and chronic stroke patients.

Measures: Quality evaluation was performed using the PEDro scale. Of the 10 studies considered, 9 were randomized controlled trials. The PEDro scale score ranged from 7 to 10.

Results: Robotic-assisted gait trainer is more effective for subacute stroke patients with a lower function ambulation assessment, showing significant changes in independent walking ability. One possible explanation of the improvement of the gait speed and functional ambulation is the opportunity of receiving a more intensive and repetitive task-oriented training through end-effector robotic-based intervention.

Conclusions and implications: The use of robotic-assisted gait trainer, together with a conventional treatment, seems to improve the walking capability of patients. Future research trials should take into account the impact of the robotic end-effector gait training on the oldest population, as this target was only partially included in the studies examined. Availability of new evidence will support the design of innovative assistive models for the clinical rehabilitation setting, which will take into account the need of personalizing the intervention to support the growing oldest old population.