The Impact of robotic-Assisted gait training on functionality and gain velocity in subacute and chronic stroke patients: A systemic REVIEW and Meta-analysis and Randomized controlled trials (RCTs)

Background: The purpose of the study is to provide evidence for gait velocity improvements through the meta-analysis of Randomized Controlled Trials (RCTs) and to demonstrate the efficacy of robotic-assisted gait training (RAGT) for functional gait recovery in poststroke survivors (RCTs).

Methods: For this systematic review, relevant RCTs were found by searching PubMed, Web of Science, Wiley Online Library, Science Direct, Science Robotics, Scopus, UpToDate, MEDLINE, Google Scholar, CINAHL (The Cumulative Index of Nursing and Allied Health Literature), EMBASE (Excerpta Medica Database), and EBSCO.
Results: An independent reviewer thoroughly assessed all the articles that were included in the study. The included RCTs have a PEDro score ranging from 6 to 8. 1,371 studies were found in the initial database examination. After additional screening, 9 studies were ultimately chosen for a systematic review and meta-analysis. Five of the nine studies focused on subacute stroke, while four looked at chronic stroke. The effect size for gait speed in the meta-analysis ranged from -0.91 to 0.64, with a total effect size of -0.12 across all investigations. The subacute stroke total effect size was determined to be -0.48 during subgroup analysis, and the chronic stroke total impact size was determined to be 0.04.

Conclusion: No discernible differences between Robotics-assisted gait training (RAGT) and traditional gait training (CPT), according to a meta-analysis. According to the systematic study, the Robotics-assisted gait training (RAGT) application showed a superior or comparable effect to traditional gait training (CPT) in a poststroke group. There were no appreciable differences between Robotics-assisted gait training (RAGT) and traditional physical therapy (CPT), according to a meta-analysis of gait speed combining all the research. included here. The sub-analysis of chronic stroke survivors, however, revealed a marginally favorable effect of RAGT on gait speed.

Introduction:

Stroke is the third most common cause of mortality worldwide [1]. Depending on the part of the brain that is affected, stroke has been linked to a variety of deficits, leading to the establishment of temporary or permanent activity restrictions in the majority of stroke survivors [2]. It is thought that females are more likely to get a stroke before the age of 30, whereas males often experience a stroke after the age of 30 due to changing lifestyle and stress factors [2]. The paradigm for stroke has clearly changed as a result of these results. By 2030, it is predicted that there could be up to 70 million stroke survivors worldwide if the current scenario is allowed to continue [3].
Interventions in rehabilitation are required to deal with the effects of stroke and lessen the number of deficits among stroke survivors. One cutting-edge technology that the affected people can use to increase their social interaction and physical exercise is robotics [3]. Growing evidence suggests that the use of robotics among stroke patients results in an early return to walking [3-5]-Nearly 60% of people who have survived a stroke experience sensory-motor dysfunction [6]. Particularly, reduced stance time and aberrant gait parameters may be caused by sensory-motor deficits that mostly impact the lower limbs [6]. Up to 65% of stroke survivors are thought to experience lower limb issues after their stroke [7]. The most widely acknowledged therapy in the contemporary clinical setting is conservative physical therapy, which is the typical approach for early intervention. The therapist can help improve the movement of paretic or paralyzed lower limbs by using robots in this way [8].Currently, a variety of robotic devices are offered commercially. These choices have been divided into groups based on the motions to which they relate. For instance, while “end-effector robots” move the feet, which are frequently supported by a footplate, “exoskeletons” move joints like the hips, knees, and ankles in conjunction with phases of gait [3]. Robotics-assisted gait training (RAGT) is used either alone or in conjunction with conventional physiotherapy. Additionally, studies have shown that combining Robotics-assisted gait training (RAGT)with traditional physical therapy (CPT) has benefits in terms of greater training volume and more repeatable, symmetrical gait patterns that may be performed with less strain on the therapist [1]. However, there is growing agreement that RAGT should not completely replace traditional physical therapy [3] since the therapists themselves ultimately give distinct benefits to the treatment procedures in the stroke population. The equipment used is simply those in the hands of a physical therapist.

Regaining the ability to walk is essential for post-stroke patients to recover; one objective, sensitive, and reliable gait metric is walking speed. A good walking pace is a key component of social engagement [9-11].
As a result, there still seems to be a lack of research on the benefits of Robotics-assisted gait training (RAGT) combined with traditional physical therapy (CPT) for functional gait recovery in stroke survivors. By conducting a comprehensive review and meta-analysis of randomized controlled trials, the study’s current goal is to produce evidence for the effectiveness of RAGT on functional recovery and gait speed (RCTs).

Methodology:

Search Methods
Following a search approach based on the defined inclusion criteria, an independent reviewer assessed related studies. The reviewer retrieved the entire texts of all pertinent RCT’s and then used the Physiotherapy Evidence Database (PEDro) scale to rank the methodological quality of each study. For the review, all research that received 4 or more points on the PEDro scale was considered.
Search Strategy
We used the following databases for this study: PubMed, Web of Science, Wiley Online Library, ScienceDirect, Science Robotics, Scopus, UpToDate, MEDLINE, Google Scholar, CINAHI, EMBASE, and EBSCO. In this study, studies that were released between 2000 and 2020 were analyzed. “RAGT,” “stroke,” “subacute,” “chronic,” “cerebral accidents,” “hemiparesis,” “hemiplegia,” “functional recovery,” and “gait parameters were used as search terms to retrieve papers. From the reference lists of the chosen research publications, reviewers also found other studies. Studies could only be included if they were written in English.

Types of Studies

Only RCTs that looked at the effects of RAGT in subacute and chronic stroke survivors were included in this review, Criteria for Selection Randomized controlled trials (RCT) that assessed the use of Robotics-assisted gait training (RAGT) alone, Robotics-assisted gait training (RAGT) in combination with traditional gait training (CPT), or Robotics-assisted gait training (RAGT) against traditional gait training (CPT) in a control group on subacute/chronic stroke survivors older than 18 years of age were included. Additionally, the studies that included gait characteristics and static exoskeleton device training as one of the outcome measures were also included in the review procedure.
The review procedure rejected research involving acute stroke survivors, nonrandomized trials, experimental groups using end-effector robotics or dynamic exoskeleton devices, and robotic investigations on upper limb function.

Types of Outcome Measures

Gait velocity/walking speed served as the main outcome indicator for the current review. Secondary outcome measurements were those for muscular tone, lower limb muscle power, gait, functional balance, confidence in one’s ability to balance during an activity, and quality of life.
Duration of the Intervention
According to the American Heart Association’s recommendations for physical exercise following a stroke, the length of the interventions for the included studies was taken into consideration in this evaluation [12].
Methodological Quality
An impartial reviewer assessed the studies, and in the event of a tie, the views of two additional reviewers were taken into account.

During the review process, these reviewers weren’t made blind to the authors, organizations, or publications. Based on the Physiotherapy Evidence Database (PEDro) rating system, the quality of the RCTs was rated as exceptional (9-10 points), good (6-8 points), fair (4-5 points), or bad (4 points). Studies classified as Level 1 evidence and regarded as high-quality evidence have a PEDro score of 6 points. Additionally, these studies were categorized into Snider et al. [13]’s ta (high) and th (moderate) levels of evidence.
Assessment Criteria for Performing a Meta-Analysis

As a gauge of effect size, we employed the correlation coefficient (r-value). The before-and-after mean and the standard deviation. (SD) values of the experimental and control groups were used to calculate the effect magnitude. In order to establish stabilized mean differences, the differences between the before and after mean and SD values for the groups above were averaged. The Standard Error (SE) was determined using the stabilized mean differences and the total number of participants in a particular study. This SE was used to generate the stabilized mean difference value, 95% confidence interval, and r value. The pooled effect sizes were divided into four categories: modest (0.2-0.5), moderate (0.5-0.8), and high (>0.8).

Results Using the mentioned search approach, 1,371 studies were found in total, 133 of which were restricted to Robotic-assisted gait training (RAGT) in the stroke population. Only 32 Randomized Controlled Trials (RCTs) were chosen after these 133 studies underwent a careful screening process based on the selection criteria. The 32 studies included 10 that did not employ gait speed as an outcome metric, 4 that had Physiotherapy Evidence Database (PEDro) scores below 4, and 9 that had the experimental group use end-effector and dynamic skeleton robots. For the systematic review and meta-analysis, only 9 randomized control trials (14-22] were included. The flow of the studies that were eventually incorporated into the review process is detailed in Figure 1. Table 1 contains the Physiotherapy Evidence Database (PEDro) scores for each of the included studies. The PEDro ratings for the featured Randomized Controlled Trials (RCTs) range from 6 to 8.

Conducted as part of the 9 Randomized Controlled Trials (RCTs). In the trials mentioned above, therapist-assisted gait training [14, 21], manual body weight supported treadmill training [15], conventional gait training [16, 20], walking activities [18], and over-ground gait training [19], with or without CPT, were compared with RAGT in the experimental group.

Four of the nine studies that were considered also included a traditional gait training (CPT) intervention in addition to RAGT. In contrast, none of the remaining 5 studies that included a Robotics-assisted gait training (RAGT) group used any CPT. In seven tests, the Lokomat® (Hocoma AG, Volketswil, Switzerland) was the machine that was most frequently utilized. The RAGT group typically used the Lokomat® gait helper robot [19] and gait trainer robot [20] devices for gait training. Treatment sessions ranged in length from 30 minutes to an hour, were scheduled three to four times per week, lasted four to ten weeks, and produced a total of 12 to 25 sessions. All RCTs used gait speed as their main outcome indicator. The 36-item Short Form Health Survey, the 6-and 10-minute walking tests, the Functional Ambulatory Capacity Scale, the Functional Independence Measure, the Fugel Meyer Lower Extremity Assessment, the Rivermead Mobility Index, muscle torque, the Barthel Index, the Stroke Impact Scale, the Berg Balance Scale, and the Frenchay Activities Index were also used as additional outcome measures. Table 2 lists the specifics of all the primary and secondary outcome measures.

In 2 studies, out of the 9 included RCTs, which examined gait speed, the experimental group significantly outperformed the control group in terms of the outcome measures [20, 22]. There were no appreciable changes in the outcomes between the experimental group and control group setups in the 6 trials [14-16, 19-21]. In one study [17], the control group outperformed the experimental group in terms of outcomes.
For each of the nine RCTs, a meta-analysis of the gait speed was conducted. Additionally, separate subgroup analyses for the RCTs on subacute and chronic stroke were carried out. The effect magnitude ranged from -0.91 to 0.64 and was trivially little (-0.12) across all 9 investigations. The subacute stroke total effect size was tiny at -0.48, and the chronic stroke total effect size was equally insignificant at 0.04. The results of the subgroup analysis did not substantially differ from the total effect size values. Out of all these meta-analyses, only chronic stroke patients saw a marginally better difference between the robotic group and the control group. Figure 2 and Table 3 contain the meta-analyses’ specifics.

Discussion:
The systematic study emphasized Robotics-assisted gait training (RAGT) outcomes in the stroke population. The effects of robotic-assisted gait training on gait metrics, function, and patient quality of life were taken into account in this special review. In a meta-analysis, we took into account gait speed as one of the gait parameters.
Only 2 of the 9 trials that were included in this analysis demonstrated that RAGT had better effects than the control group.
The following factors contributed to RAGT’s higher results: more steps were practiced every session [20, 22]. It is possible to encourage symmetrical gait [22], and paretic leg step length symmetry is preferable [15]. Additionally, the Robotics-assisted gait training (RAGT) group is able to train at greater speeds and for longer periods, which can benefit cardiovascular training and aerobic metabolism [16, 23, 24), enhance muscle mass and decrease fat mass, and lessen the side effects of immobilization. Additionally, it creates the best joint kinematics during walking [22] and is long-term cost-effective [16]. Due to changes in body weight, speed control, and the application of raising or lowering guide pressures on the affected side, RAGT makes these advances possible [22, 25-28].
Common deficits in the stroke population, such as shaky balance, weakness, misalignment, unstable joints, and aberrant tone, can reduce movement efficiency and serve as a barrier to functional rehabilitation [17, 22, 26]. By allowing stroke survivors to mimic conventional gait patterns, temporarily reduce their body weight to achieve normal alignment, help with foot clearance, and support an automated intense walking training program, Robotics-assisted gait training (RAGT) may be able to help them overcome these issues [27].
Despite the fact that RAGT is a fantastic method for facilitating gait training, some researchers did note a few drawbacks, including skin integrity issues brought on by the excessive abnormal forces applied by straps attached to the paretic limb and fatigue brought on by the excessive intensity and duration [21]. In addition to limiting participant trunk and pelvic motions, several earlier robot designs also had negative effects on pelvic mobility during gait training [17]. This could be one of the factors preventing the gait improvements seen in some studies.
The researchers of one study hypothesized that over-ground training promotes gait capacity better than Robotics-assisted gait training (RAGT) [14], which is noteworthy. The researchers proposed that these enhancements resulted from higher metabolic activity and muscle activity that was driven by its maximum voluntary drive, which may have a positive impact on adaption. On the other hand, numerous authors have emphasized the drawbacks of over-ground gait training, such as the therapist being put through too much stress [15, 20], the risk of falling, deep vein thrombosis [16], and hypotensive episodes [21].
Additionally, over-ground gait training had worse training speed, duration, distances, gait symmetry, body alignment, and joint kinematics as compared to RAGT.

The following factors can have an impact on the improvements seen in stroke survivors using RAGT: the type of stroke, stage of the stroke, the extent and severity of the lesion, the type of machine used, the choice of treatment parameters such as intensity, duration, frequency, and total number of sessions, the patient’s psychological state, and the depth of prior training.

Recent earlier systematic evaluations found that Robotic-assisted gait training (RAGT) is just as beneficial as traditional training for stroke survivors [4. 5. 29, 30). However, in terms of customizable body weight support, early-stage gait training, and the potential for extended training sessions, RAGT is superior to conventional training. We discovered comparable results in the current systematic study, namely that robotic-assisted gait training is either superior to or on par with traditional training. However, the meta-analysis found no statistically significant difference in the gait speed attained with RAGT and traditional gait training.
Future studies should concentrate on the following techniques to increase the efficacy of Robotics-assisted gait training (RAGT): decreasing body weight support in accordance with subject capability, lengthening and accelerating walking time, and decreasing guidance force on the injured leg [22, 25, 26, 31]. Other crucial factors to take into account include expanding the sample size, protecting the integrity of the skin, and creating the best dosing schedule for each patient.

There are also additional crucial factors. It’s significant that the current systematic evaluation contrasted RAGT with traditional training alone and only considered Robotics-assisted gait training (RAGT) employing fixed-exoskeleton robots. Gait speed was the sole variable selected for meta-analysis. Future systematic reviews should take into account research on all categories of robots used in RAGT, and the comparison group should include a variety of alternatives rather than just traditional training. Some authors have argued that the advantages of more modern robots can be outweighed by their superior capabilities, such as their ability to facilitate pelvic movements [17, 32]. Future systematic reviews must take these factors into account and attempt to conduct reviews using advanced robotics technology alone. To better support the use of robots in stroke survivors, the completion of meta-analyses using additional end variables, such as function and quality of life, is similarly advocated.

Conclusion:

The results of the current systematic review highlight the benefits of Robotic-assisted gait training (RAGT) for stroke survivors since RAGT has similar effects to conventional training in terms of enhancing stroke survivors’ functional capacity. According to a meta-analysis of 9 studies, there is no discernible difference in gait speed between the RAGT group and the traditional training group (CPT). However, a sub-analysis of gait speed in situations of chronic illness revealed a negligibly significant positive finding on effect size.

Declarations

Ethics approval: None
Acknowledgments: The authors of this manuscript acknowledge that this article could not have been finished without the help of the many people involved in the course of data generation and major revision.
Funding: The Training Program for Young and Middle-aged Academic and Technical Leaders of Yunnan Province (No. 2015HB042). First Class Curriculum Pharmacology) Construction Project of Yunnan Province (2019-2-123)
Conflicts of interest: The authors declare that they have no conflict of interest.

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