Dataset Information

Do Patients with Multiple Sclerosis Derive More Benefit from Robot-Assisted Gait Training Compared with Conventional Walking Therapy on Motor Function? A Meta-analysis.

ABSTRACT:

Objective

To determine whether robot-assisted gait training (RAGT) is more effective in improving mobility, endurance, gait performance, and balance in patients with multiple sclerosis (MS) compared with conventional walking rehabilitation treatment (CWT).

Data sources

Sources included the Cochrane Library, PubMed, Embase, and Science Direct databases.

Review method

All possible articles were retrieved by two independent investigators and relevant articles were gathered. Studies on adult patients (older than 19?years old) suffering from MS were included, regardless the subtype of MS diagnosis. Finally, we identified seven studies that comprised 205 patients with MS.

Results

We identified seven studies comprising 205 patients with MS in our meta-analysis. The pooled mean difference (MD) for the six-minute walk test (6MWT) was 14.25 [95% confidence interval (CI) 3.19 to 25.32, Z?=?2.53, P?=?0.01, I²?=?54%], which indicates that RAGT is superior to CWT on improving endurance. No significant improvement on using RAGT was found regarding the Berg Balance Scale (MD?=?-0.59, 95% CI: -2.7 to 1.52, Z?=?0.55, P?=?0.58, I²?=?51%), 10-meter walk test [standard mean difference (SMD)?=?0.03, 95% CI: -0.26 to 0.31, Z?=?0.18, P?=?0.86, I²?=?48%] timed up and go (TUG) test (MD?=?-1.04, 95% CI: -8.68 to 6.60, Z?=?0.27, P?=?0.79), or stride length (SMD?=?0.36, 95% CI: -0.13 to 0.85, Z?=?0.73, P?=?0.15).

Conclusion

We can conclude that RAGT can bring more benefits on improving 6MWT among MS patients, but it is not enough to make a clinically significance conclusion. Considering the limitation of our study, it takes reservations about recommending all MS patients to take RAGT as primary rehabilitation intervention. Unless patients with progressive MS can take conventional rehabilitation in early time, RAGT would be a suitable substitute.

SUBMITTER: Xie X

PROVIDER: S-EPMC5468403 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Do Patients with Multiple Sclerosis Derive More Benefit from Robot-Assisted Gait Training Compared with Conventional Walking Therapy on Motor Function? A Meta-analysis.

Xie Xiao X Sun Hao H Zeng Qing Q Lu Pengcheng P Zhao Yijin Y Fan Tao T Huang Guozhi G

Frontiers in neurology 20170613

<h4>Objective</h4>To determine whether robot-assisted gait training (RAGT) is more effective in improving mobility, endurance, gait performance, and balance in patients with multiple sclerosis (MS) compared with conventional walking rehabilitation treatment (CWT).<h4>Data sources</h4>Sources included the Cochrane Library, PubMed, Embase, and Science Direct databases.<h4>Review method</h4>All possible articles were retrieved by two independent investigators and relevant articles were gathered. St ...[more]

PMID: 28659856

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Project description:BACKGROUND:Gait disorders are major symptoms of neurological diseases affecting the quality of life. Interventions that restore walking and allow patients to maintain safe and independent mobility are essential. Robot-assisted gait training (RAGT) proved to be a promising treatment for restoring and improving the ability to walk. Due to heterogenuous study designs and fragmentary knowlegde about the neural correlates associated with RAGT and the relation to motor recovery, guidelines for an individually optimized therapy can hardly be derived. To optimize robotic rehabilitation, it is crucial to understand how robotic assistance affect locomotor control and its underlying brain activity. Thus, this study aimed to investigate the effects of robotic assistance (RA) during treadmill walking (TW) on cortical activity and the relationship between RA-related changes of cortical activity and biomechanical gait characteristics. METHODS:Twelve healthy, right-handed volunteers (9 females; M?=?25?±?4?years) performed unassisted walking (UAW) and robot-assisted walking (RAW) trials on a treadmill, at 2.8?km/h, in a randomized, within-subject design. Ground reaction forces (GRFs) provided information regarding the individual gait patterns, while brain activity was examined by measuring cerebral hemodynamic changes in brain regions associated with the cortical locomotor network, including the sensorimotor cortex (SMC), premotor cortex (PMC) and supplementary motor area (SMA), using functional near-infrared spectroscopy (fNIRS). RESULTS:A statistically significant increase in brain activity was observed in the SMC compared with the PMC and SMA (p?<?0.05), and a classical double bump in the vertical GRF was observed during both UAW and RAW throughout the stance phase. However, intraindividual gait variability increased significantly with RA and was correlated with increased brain activity in the SMC (p?=?0.05; r?=?0.57). CONCLUSIONS:On the one hand, robotic guidance could generate sensory feedback that promotes active participation, leading to increased gait variability and somatosensory brain activity. On the other hand, changes in brain activity and biomechanical gait characteristics may also be due to the sensory feedback of the robot, which disrupts the cortical network of automated walking in healthy individuals. More comprehensive neurophysiological studies both in laboratory and in clinical settings are necessary to investigate the entire brain network associated with RAW.

Project description:In the last decade robotic devices have been applied in rehabilitation to overcome walking disability in neurologic diseases with promising results. Robot assisted gait training (RAGT) using the Lokomat seems not only to improve gait parameters but also the perception of well-being. Data on the psychosocial patient-robot impact are limited, in particular in the real-world of RAGT, in the rehabilitation setting. During rehabilitation training, the Lokomat can be considered an "assistive device for movement". This allowed the use of the Psychosocial Impact of Assistive Device Scale- PIADS to describe patient interaction with the Lokomat. The primary aim of this pilot study was to evaluate the psychosocial impact of the Lokomat in an in-patient rehabilitation setting using the PIADS; secondary aims were to assess whether the psychosocial impact of RAGT is different between pathological sub-groups and if the Lokomat influenced functional variables (Functional Independence Measure scale-FIM and parameters provided by the Lokomat itself). Thirty-nine consecutive patients (69% males, 54.0±18.0 years) eligible for Lokomat training, with etiologically heterogeneous walking disabilities (Parkinson's Disease, n = 10; Spinal Cord Injury, n = 21; Ictus Event, n = 8) were enrolled. Patients were assessed with the FIM before and after rehabilitation with Lokomat, and the PIADS was administered after the rehabilitative period with Lokomat. Overall the PIADS score was positive (35.8±21.6), as well as the three sub-scales, pertaining to "ability", "adaptability" and "self-esteem" (17.2±10.4, 8.9±5.5 and 10.1±6.6 respectively) with no between-group differences. All patients significantly improved in gait measure and motor FIM scale (difference after-before treatment values: 11.7±9.8 and 11.2±10.3 respectively), increased treadmill speed (0.4 ± 0.2m/s), reduced body weight support (-14.0±9.5%) and guidance force (-13.1 ± 10.7%). This pilot study indicates that Lokomat, in a real-world in-patient setting, may have a generalised approval, independent of disease, underlining the importance of the psycho-social framework for patients training with assistive robotic-devices.

Project description:BACKGROUND:A robotic exoskeleton device is an intelligent system designed to improve gait performance and quality of life for the wearer. Robotic technology has developed rapidly in recent years, and several robot-assisted gait devices were developed to enhance gait function and activities of daily living in elderly adults and patients with gait disorders. In this study, we investigated the effects of the Gait-enhancing Mechatronic System (GEMS), a new wearable robotic hip-assist device developed by Samsung Electronics Co, Ltd., Korea, on gait performance and foot pressure distribution in elderly adults. METHODS:Thirty elderly adults who had no neurological or musculoskeletal abnormalities affecting gait participated in this study. A three-dimensional (3D) motion capture system, surface electromyography and the F-Scan system were used to collect data on spatiotemporal gait parameters, muscle activity and foot pressure distribution under three conditions: free gait without robot assistance (FG), robot-assisted gait with zero torque (RAG-Z) and robot-assisted gait (RAG). RESULTS:We found increased gait speed, cadence, stride length and single support time in the RAG condition. Reduced rectus femoris and medial gastrocnemius muscle activity throughout the terminal stance phase and reduced effort of the medial gastrocnemius muscle throughout the pre-swing phase were also observed in the RAG condition. In addition, walking with the assistance of GEMS resulted in a significant increase in foot pressure distribution, specifically in maximum force and peak pressure of the total foot, medial masks, anterior masks and posterior masks. CONCLUSION:The results of the present study reveal that GEMS may present an alternative way of restoring age-related changes in gait such as gait instability with muscle weakness, reduced step force and lower foot pressure in elderly adults. In addition, GEMS improved gait performance by improving push-off power and walking speed and reducing muscle activity in the lower extremities. TRIAL REGISTRATION:NCT02843828 .

Project description:BackgroundMany patients with neurological movement disorders fear to fall while performing postural transitions without assistance, which prevents them from participating in daily life. To overcome this limitation, multi-directional Body Weight Support (BWS) systems have been developed allowing them to perform training in a safe environment. In addition to overground walking, these innovative/novel systems can assist patients to train many more gait-related tasks needed for daily life under very realistic conditions. The necessary assistance during the users' movements can be provided via task-dependent support designs. One remaining challenge is the manual switching between task-dependent supports. It is error-prone, cumbersome, distracts therapists and patients, and interrupts the training workflow. Hence, we propose a real-time motion onset recognition model that performs automatic support switching between standing-up and sitting-down transitions and other gait-related tasks (8 classes in total).MethodsTo predict the onsets of the gait-related tasks, three Inertial Measurement Units (IMUs) were attached to the sternum and middle of outer thighs of 19 controls without neurological movement disorders and two individuals with incomplete Spinal Cord Injury (iSCI). The data of IMUs obtained from different gait tasks was sent synchronously to a real-time data acquisition system through a custom-made Bluetooth-EtherCAT gateway. In the first step, data was applied offline for training five different classifiers. The best classifier was chosen based on F1-score results of a Leave-One-Participant-Out Cross-Validation (LOPOCV), which is an unbiased way of testing. In a final step, the chosen classifier was tested in real time with an additional control participant to demonstrate feasibility for real-time classification.ResultsTesting five different classifiers, the best performance was obtained in a single-layer neural network with 25 neurons. The F1-score of [Formula: see text] and [Formula: see text] are achieved on testing using LOPOCV and test data ([Formula: see text], participants = 20), respectively. Furthermore, the results from the implemented real-time classifier were compared with the offline classifier and revealed nearly identical performance (difference = [Formula: see text]).ConclusionsA neural network classifier was trained for identifying the onset of gait-related tasks in real time. Test data showed convincing performance for offline and real-time classification. This demonstrates the feasibility and potential for implementing real-time onset recognition in rehabilitation devices in future.

Dataset Information

Do Patients with Multiple Sclerosis Derive More Benefit from Robot-Assisted Gait Training Compared with Conventional Walking Therapy on Motor Function? A Meta-analysis.

Objective

Data sources

Review method

Results

Conclusion

Publications

Do Patients with Multiple Sclerosis Derive More Benefit from Robot-Assisted Gait Training Compared with Conventional Walking Therapy on Motor Function? A Meta-analysis.

Similar Datasets

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