Effect of robotic-assisted gait training on objective biomechanical measures of gait in persons post-stroke: a systematic review and meta-analysis

Abstract

Background Robotic-Assisted Gait Training (RAGT) may enable high-intensive and task-specific gait training post-stroke. The effect of RAGT on gait movement patterns has however not been comprehensively reviewed. The purpose of this review was to summarize the evidence for potentially superior effects of RAGT on biomechanical measures of gait post-stroke when compared with non-robotic gait training alone. Methods Nine databases were searched using database-specific search terms from their inception until January 2021. We included randomized controlled trials investigating the effects of RAGT (e.g., using exoskeletons or end-effectors) on spatiotemporal, kinematic and kinetic parameters among adults suffering from any stage of stroke. Screening, data extraction and judgement of risk of bias (using the Cochrane Risk of bias 2 tool) were performed by 2–3 independent reviewers. The Grading of Recommendations Assessment Development and Evaluation (GRADE) criteria were used to evaluate the certainty of evidence for the biomechanical gait measures of interest. Results Thirteen studies including a total of 412 individuals (mean age: 52–69 years; 264 males) met eligibility criteria and were included. RAGT was employed either as monotherapy or in combination with other therapies in a subacute or chronic phase post-stroke. The included studies showed a high risk of bias (n = 6), some concerns (n = 6) or a low risk of bias (n = 1). Meta-analyses using a random-effects model for gait speed, cadence, step length (non-affected side) and spatial asymmetry revealed no significant differences between the RAGT and comparator groups, while stride length (mean difference [MD] 2.86 cm), step length (affected side; MD 2.67 cm) and temporal asymmetry calculated in ratio-values (MD 0.09) improved slightly more in the RAGT groups. There were serious weaknesses with almost all GRADE domains (risk of bias, consistency, directness, or precision of the findings) for the included outcome measures (spatiotemporal and kinematic gait parameters). Kinetic parameters were not reported at all. Conclusion There were few relevant studies and the review synthesis revealed a very low certainty in current evidence for employing RAGT to improve gait biomechanics post-stroke. Further high-quality, robust clinical trials on RAGT that complement clinical data with biomechanical data are thus warranted to disentangle the potential effects of such interventions on gait biomechanics post-stroke.

Hardware Development and Safety Control Strategy Design for a Mobile Rehabilitation Robot

“…Therefore, the development of rehabilitation exoskeletons is gradually shifting towards soft exoskeletons [5,6]. Patients trained with robotic assistance combined with physical therapy have a better chance of walking independently than those who did not use mechanical aid [7]. Relative motion-based treadmill gait training systems, such as GaitTrainer [1], LOPES [8], ALEX [9], and Lokomat [10] are frequently integrated with the bodyweight support system (BSS) and lower limb exoskeletons to realize CPM.…”

Core Sets of Kinematic Variables to Consider for Evaluation of Gait Post-stroke

“…However, the resulting raw data is vast and may yield almost endless biomechanical descriptors. Therefore, the need for a consensus regarding which variables to focus on in evaluation and rehabilitation as the most relevant and informative descriptors of gait post-stroke has thus been pointed out repeatedly ( Krasovsky and Levin, 2010 ; Wikström et al, 2014 ; Wonsetler and Bowden, 2017 ; Sharififar et al, 2019 ; Nedergård et al, 2021 ).…”

“…There is however a lack of consensus concerning which standard variables to incorporate in whole-body analyses for evaluation of gait post-stroke. This lack of agreement aggravates comparisons between research studies and may detriment the process of evaluating gait rehabilitation post-stroke ( Sharififar et al, 2019 ; Nedergård et al, 2021 ). The aim of this study was hence to contribute toward such a consensus by identifying a core set of a few kinematic variables to discriminate post-stroke gait from the gait of non-disabled controls.…”

“…Jonas Selling, Andrew Strong, and Gunilla E Frykberg are gratefully acknowledged for their assistive role and support of the work. Content of the manuscript in a preliminary version has been included in a doctoral thesis by Nedergård (2021) , Umeå university: “Taking the next step: Whole-body biomechanical gait analysis, and user-perspectives on robotic-assisted gait training post-stroke.”…”

Robot-assisted gait training: more randomized controlled trials are needed! Or maybe not?

“…It might well be that the course of the chosen parameter settings during a robot-assisted gait training intervention combined with a quantification of the therapy goals might contain much more relevant information than two clinical assessments at single time points before and after the intervention. It could also provide a deeper insight into the ongoing discussion on responders vs. non-responders [ 34 – 37 ].…”