Effects of integrating rhythmic arm swing into robot-assisted walking in patients with subacute stroke: a randomized controlled pilot study

Efficacy of Robots-Assisted Therapy in Patients With Stroke: A Meta-analysis Update

BACKGROUND

Robot-assisted therapy (RAT) could address an unmet need to relieve the strain on healthcare providers and intensify treatment in the context of an increasing stroke incidence. A comprehensive meta-analysis could provide firmer data about the topic by considering methodology limitations discovered in previous reviews and providing more rigorous evidence.

OBJECTIVE

This meta-analysis study identifies RAT's efficacy for patients with stroke.

METHODS

A systematic search of the 7 databases from January 10 to February 1, 2022, located relevant publications. We used the updated Cochrane risk-of-bias checklist for 52 trials to assess the methodologic quality of the included studies. The efficacy of RAT for patients with stroke was estimated using a pooled random-effects model in the Stata 16 software application.

RESULTS

The final analysis included 2774 patients with stroke from 52 trials. In those patients, RAT was proven to improve quality of movement (mean difference, 0.15; 95% confidence interval, 0.03-0.28) and to reduce balance disturbances (mean difference, -1.28; 95% confidence interval, -2.48 to -0.09) and pain (standardized mean difference, -0.34; 95% confidence interval, -0.58 to -0.09).

CONCLUSIONS

Robot-assisted therapy seems to improve the quality of mobility and reduce balance disturbances and pain for patients with stroke. These findings will help develop advanced rehabilitation robots and could improve health outcomes by facilitating health services for healthcare providers and patients with stroke.

Evidence-based improvement of gait in post-stroke patients following robot-assisted training: A systematic review

BACKGROUND

The recovery of walking after stroke is a priority goal for recovering autonomy. In the last years robotic systems employed for Robotic Assisted Gait Training (RAGT) were developed. However, literature and clinical practice did not offer standardized RAGT protocol or pattern of evaluation scales.

OBJECTIVE

This systematic review aimed to summarize the available evidence on the use of RAGT in post-stroke, following the CICERONE Consensus indications.

METHODS

The literature search was conducted on PubMed, Cochrane Library and PEDro, including studies with the following criteria: 1) adult post-stroke survivors with gait disability in acute/subacute/chronic phase; 2) RAGT as intervention; 3) any comparators; 4) outcome regarding impairment, activity, and participation; 5) both primary studies and reviews.

RESULTS

Sixty-one articles were selected. Data about characteristics of patients, level of disability, robotic devices used, RAGT protocols, outcome measures, and level of evidence were extracted.

CONCLUSION

It is possible to identify robotic devices that are more suitable for specific phase disease and level of disability, but we identified significant variability in dose and protocols. RAGT as an add-on treatment seemed to be prevalent. Further studies are needed to investigate the outcomes achieved as a function of RAGT doses delivered.

Mechanical Design and Control System Development of a Rehabilitation Robotic System for Walking With Arm Swing

Background: Interlimb neural coupling implies that arm swing should be included during gait training to improve rehabilitation outcomes. We previously developed several systems for production of walking with arm swing, but the reaction forces on the foot sole during usage of the systems were not satisfactory and there was potential to improve control system performance. This work aimed to design and technically evaluate a novel system for producing walking with synchronised arm and leg movement and with dynamic force loading on the foot soles.

Methods: The robotic system included a passive curved treadmill and a trunk frame, upon which the rigs for the upper and lower limbs were mounted. Ten actuators and servocontrollers with EtherCAT communication protocol controlled the bilateral shoulder, elbow, hip, knee and ankle joints. Impedance control algorithms were developed and ran in an industrial PC. Flexible pressure sensors recorded the plantar forces on the foot soles. The criteria of implementation and responsiveness were used to formally evaluate the technical feasibility of the system.

Results: Using impedance algorithms, the system produced synchronous walking with arm swing on the curved treadmill, with mean RMS angular tracking error <2° in the 10 joint profiles. The foot trajectories relative to the hip presented similar shapes to those during normal gait, with mean RMS displacement error <1.5 cm. A force pattern that started at the heel and finished at the forefoot was observed during walking using the system, which was similar to the pattern from overground walking.

Conclusion: The robotic system produced walking-like kinematics in the 10 joints and in the foot trajectories. Integrated with the curved treadmill, the system also produced walking-like force patterns on the foot soles. The system is considered feasible as far as implementation and responsiveness are concerned. Future work will focus on improvement of the mechanical system for future clinical application.

Electromechanical-assisted training for walking after stroke

BACKGROUND

Electromechanical- and robot-assisted gait-training devices are used in rehabilitation and might help to improve walking after stroke. This is an update of a Cochrane Review first published in 2007 and previously updated in 2017.

OBJECTIVES

Primary • To determine whether electromechanical- and robot-assisted gait training versus normal care improves walking after stroke Secondary • To determine whether electromechanical- and robot-assisted gait training versus normal care after stroke improves walking velocity, walking capacity, acceptability, and death from all causes until the end of the intervention phase SEARCH METHODS: We searched the Cochrane Stroke Group Trials Register (last searched 6 January 2020); the Cochrane Central Register of Controlled Trials (CENTRAL; 2020 Issue 1), in the Cochrane Library; MEDLINE in Ovid (1950 to 6 January 2020); Embase (1980 to 6 January 2020); the Cumulative Index to Nursing and Allied Health Literature (CINAHL; 1982 to 20 November 2019); the Allied and Complementary Medicine Database (AMED; 1985 to 6 January 2020); Web of Science (1899 to 7 January 2020); SPORTDiscus (1949 to 6 January 2020); the Physiotherapy Evidence Database (PEDro; searched 7 January 2020); and the engineering databases COMPENDEX (1972 to 16 January 2020) and Inspec (1969 to 6 January 2020). We handsearched relevant conference proceedings, searched trials and research registers, checked reference lists, and contacted trial authors in an effort to identify further published, unpublished, and ongoing trials.

SELECTION CRITERIA

We included all randomised controlled trials and randomised controlled cross-over trials in people over the age of 18 years diagnosed with stroke of any severity, at any stage, in any setting, evaluating electromechanical- and robot-assisted gait training versus normal care.

DATA COLLECTION AND ANALYSIS

Two review authors independently selected trials for inclusion, assessed methodological quality and risk of bias, and extracted data. We assessed the quality of evidence using the GRADE approach. The primary outcome was the proportion of participants walking independently at follow-up.

MAIN RESULTS

We included in this review update 62 trials involving 2440 participants. Electromechanical-assisted gait training in combination with physiotherapy increased the odds of participants becoming independent in walking (odds ratio (random effects) 2.01, 95% confidence interval (CI) 1.51 to 2.69; 38 studies, 1567 participants; P < 0.00001; I² = 0%; high-quality evidence) and increased mean walking velocity (mean difference (MD) 0.06 m/s, 95% CI 0.02 to 0.10; 42 studies, 1600 participants; P = 0.004; I² = 60%; low-quality evidence) but did not improve mean walking capacity (MD 10.9 metres walked in 6 minutes, 95% CI -5.7 to 27.4; 24 studies, 983 participants; P = 0.2; I² = 42%; moderate-quality evidence). Electromechanical-assisted gait training did not increase the risk of loss to the study during intervention nor the risk of death from all causes. Results must be interpreted with caution because (1) some trials investigated people who were independent in walking at the start of the study, (2) we found variation between trials with respect to devices used and duration and frequency of treatment, and (3) some trials included devices with functional electrical stimulation. Post hoc analysis showed that people who are non-ambulatory at the start of the intervention may benefit but ambulatory people may not benefit from this type of training. Post hoc analysis showed no differences between the types of devices used in studies regarding ability to walk but revealed differences between devices in terms of walking velocity and capacity.

AUTHORS' CONCLUSIONS

People who receive electromechanical-assisted gait training in combination with physiotherapy after stroke are more likely to achieve independent walking than people who receive gait training without these devices. We concluded that eight patients need to be treated to prevent one dependency in walking. Specifically, people in the first three months after stroke and those who are not able to walk seem to benefit most from this type of intervention. The role of the type of device is still not clear. Further research should consist of large definitive pragmatic phase 3 trials undertaken to address specific questions about the most effective frequency and duration of electromechanical-assisted gait training, as well as how long any benefit may last. Future trials should consider time post stroke in their trial design.

Getting the Best Out of Advanced Rehabilitation Technology for the Lower Limbs: Minding Motor Learning Principles

Advanced technology, including gait-training devices, is increasingly being integrated into neurorehabilitation. However, to use gait-training devices to their optimal potential, it is important that they are applied in accordance with motor learning and locomotor training principles. In this article, we outline the most important principles and explain how advanced gait-training devices are best used to improve therapy outcome.