You don't even mention how many got their gait 100% recovered using these therapies. You don't even have the correct outcome measures. I blame your mentors and senior researchers for such incompetency.
Oops, I'm not playing by the polite rules of Dale Carnegie, 'How to Win Friends and Influence People'.
Politeness will never solve anything in stroke. Yes, I'm a bomb thrower and proud of it. Someday a stroke 'leader' will ream me out for making them look bad by being truthful , I look forward to that day.
Effect of robotic-assisted gait training on objective biomechanical measures of gait in persons post-stroke: a systematic review and meta-analysis
Journal of NeuroEngineering and Rehabilitation volume 18, Article number: 64 (2021)
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(NOT GOOD ENOUGH - 100% RECOVERY IS THE GOAL. WHY THE FUCK ARE YOU ALLOWING YOUR TYRANNY OF LOW EXPECTATIONS TO GUIDE YOUR RESEARCH?) 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(NOT RECOVER!) 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.
Background
Technology-assisted interventions to enhance gait rehabilitation post-stroke are highly interesting from a clinical perspective. Robotic-assisted gait training (RAGT) employs electromechanical devices that assist stepping cycles by supporting body weight while automatizing the gait process through support and facilitation of movement in one or several lower limb joints. RAGT is suggested to be less energy-consuming and cardiorespiratory demanding when compared with walking without a robot [1]. Implementing RAGT may thus enable higher intensities and longer, task-specific training sessions when compared with non-robotic gait training.
Various forms of robotic devices are commercially available and they are commonly categorized according to the support they apply [2]. Treadmill-based RAGT (t-RAGT) is most commonly used in combination with body weight support [3]. This is either performed with end-effector robots that drive two footplates, simulating the phases of the gait, or with exoskeleton orthoses that move the lower body extremity joints in coordination with the phases of gait. Overground RAGT (o-RAGT) is provided by wearable powered exoskeletons that allow a person to walk overground on hard and flat surfaces [4], supposedly enabling the user to experience increased proprioceptive input when compared with the stationary treadmill training [5].
Earlier reviews revealed that RAGT, together with conventional physiotherapy, might have a slightly better or similar positive effect on gait speed and ambulation when compared with conventional gait training alone [6,7,8,9,10,11,12,13,14,15,16]. However, the need for a broadened perspective in the evaluation of gait ability after RAGT post-stroke has been highlighted [13, 15, 17, 18]. The International Classification of Functioning, Disability and Health (ICF), advocated by the World Health Organization, is a classification system widely used in clinical practice [19]. It is a foundation for understanding the patient’s personal and environmental resources and limitations, hence also used when evaluating rehabilitation effects from different perspectives. The classification system identifies three domains of a health condition: (1) body function (physiological and psychological) and structure (related to organs, limbs, etc.), (2) activity (related to the execution of a task, and (3) participation (related to involvement in a real-life situation). Although the domains are interrelated, measurements of all domains and contextual factors are necessary to describe a person’s condition from a holistic point of view. In a 2013 review, Geroin and colleagues [20] emphasize that a comprehensive post-intervention evaluation of RAGT, such as that of any other intervention, should use outcome measures that include all domains of the ICF. In general, tests that evaluate walking ability post-stroke address activity limitations alone (6 min Walk Test, Timed Up and Go, Functional Ambulation Category). These tests might fail to identify restrictions related to the domain of body function and structure since they do not investigate specific gait characteristics, such as coordination, muscle power, joint mobility or extremity positions during gait. In persons post-stroke, gains in walking ability following rehabilitation may be considered a result of the restitution of underlying impairments. However, improvements in activity measures could also partly be explained by an adaptation of non-optimal movement strategies that compensate for existing deficits [21, 22]. A paradigm shift has occurred in the research area of gait rehabilitation post-stroke [23], claiming that rehabilitation methods that stimulate the nervous system's ability to recover a normalized movement pattern should be preferred before those encouraging compensation for impaired mobility, motor control, and balance. In line with this, the quantitative evaluation of gait quality and movement pattern may allow for differentiation of recovery mechanisms and foster a deeper understanding of the effects of different gait rehabilitation interventions post-stroke [18, 23, 24]. To manage this, various biomechanical variables of temporal (related to time) or spatial (related to distance) information have been applied. These are derived from kinematic (parameters of registered position, motion and/or marker trajectories of interest to describe the locomotion pattern) or kinetic (registered forces that act on the body during movement) measures of gait [24]. A gait-assisting robot aims to replicate a movement pattern that is as close to normal as possible with regards to temporal and spatial parameters. It is also believed to generate more repetitions with regards to the number of steps during one training session as compared with non-robotic gait training. RAGT could thus be assumed to improve gait quality to a greater extent than training without a robot by normalizing the movement pattern and increasing training volume with a carryover effect to when the person is walking without the assisting robot. This review aims to summarize the level of evidence for any potential superior effects of RAGT (with or without a combination of non-robotic training) compared with non-robotic training alone on post-stroke gait movement pattern quantified with objective biomechanical measures.
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