A pilot randomized controlled trial of robotic exoskeleton-assisted exercise rehabilitation in multiple sclerosis

Advanced neuroimaging techniques to explore the effects of motor and cognitive rehabilitation in multiple sclerosis

INTRODUCTION

Progress in magnetic resonance imaging (MRI) technology and analyses is improving our comprehension of multiple sclerosis (MS) pathophysiology. These advancements, which enable the evaluation of atrophy, microstructural tissue abnormalities, and functional plasticity, are broadening our insights into the effectiveness and working mechanisms of motor and cognitive rehabilitative treatments.

AREAS COVERED

This narrative review with selected studies discusses findings derived from the application of advanced MRI techniques to evaluate structural and functional neuroplasticity modifications underlying the effects of motor and cognitive rehabilitative treatments in people with MS (PwMS). Current applications as outcome measure in longitudinal trials and observational studies, their interpretation and possible pitfalls and limitations in their use are covered. Finally, we examine how the use of these techniques could evolve in the future to improve monitoring of motor and cognitive rehabilitative treatments.

EXPERT COMMENTARY

Despite substantial variability in study design and participant characteristics in rehabilitative studies for PwMS, improvements in motor and cognitive functions accompanied by structural and functional brain modifications induced by rehabilitation can be observed. However, significant enhancements to refine rehabilitation strategies are needed. Future studies in this field should strive to implement standardized methodologies regarding MRI acquisition and processing, possibly integrating multimodal measures. This will help identifying relevant markers of treatment response in PwMS, thus improving the use of rehabilitative interventions at individual level. The combination of motor and cognitive strategies, longer periods of treatment, as well as adequate follow-up assessments will contribute to enhance the quality of evidence in support of their routine use.

Focusing on neural mechanisms of exercise training benefits in multiple sclerosis

Exercise training (ET) should be a mainstay of comprehensive care in multiple sclerosis (MS), yet there is reluctance regarding its implementation among healthcare providers. This reluctance has its roots in the lack of evidence from randomized controlled trials (RCTs) that provide a neurobiological mechanism for beneficial outcomes. We argue that ET training is efficacious for improving mobility and cognitive dysfunction as hallmark consequences of MS, and propose an experimental medicine framework for guiding research focusing on CNS mechanisms-of-action for ET benefits. The framework establishes three hierarchical pathways as preconditions for applying a mediation framework in a fourth pathway for fully testing mechanistic research questions using a RCT. This paper describes the conceptual basis of the pathways, and then reviews the existing evidence within the pathways of the framework for ET effects on the CNS, mobility, and cognition in MS. Lastly, this paper discusses future directions and challenges for testing the pathways of the framework for advancing the evidence regarding CNS mechanisms of ET benefits in this population. Our objective is guiding the field toward evidence that facilitates the prescription and promotion of ET as a neurorestorative approach within MS clinical practice.

Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System

Smart algorithms for gait kinematic motion prediction in wearable assistive devices including prostheses, bionics, and exoskeletons can ensure safer and more effective device functionality. Although embedded systems can support the use of smart algorithms, there are important limitations associated with computational load. This poses a tangible barrier for models with increased complexity that demand substantial computational resources for superior performance. Forecasting through Recurrent Topology (FReT) represents a computationally lightweight time-series data forecasting algorithm with the ability to update and adapt to the input data structure that can predict complex dynamics. Here, we deployed FReT on an embedded system and evaluated its accuracy, computational time, and precision to forecast gait kinematics from lower-limb motion sensor data from fifteen subjects. FReT was compared to pretrained hyperparameter-optimized NNET and deep-NNET (D-NNET) model architectures, both with static model weight parameters and iteratively updated model weight parameters to enable adaptability to evolving data structures. We found that FReT was not only more accurate than all the network models, reducing the normalized root-mean-square error by almost half on average, but that it also provided the best balance between accuracy, computational time, and precision when considering the combination of these performance variables. The proposed FReT framework on an embedded system, with its improved performance, represents an important step towards the development of new sensor-aided technologies for assistive ambulatory devices.

Effects of exercise in people with multiple sclerosis: a systematic review and meta-analysis

Background

A growing body of studies have examined the effect of exercise in people with multiple sclerosis (MS), while findings of available studies were conflicting. This meta-analysis aimed to explore the effects of exercise on balance, walking ability, walking endurance, fatigue, and quality of life in people with MS.

Methods

We searched PubMed, Web of Science, Scopus, and Cochrane databases, through March 1, 2024. Inclusion criteria were: (1) RCTs; (2) included an intervention and control group; (3) had people with MS as study subjects; (4) had balance, walking ability, walking endurance, fatigue, or quality of life as the outcome measures. Exclusion criteria were: (1) non-English publications; (2) animal model publications; (3) review articles; and (4) conference articles. A meta-analysis was conducted to calculate weighted mean difference (WMD) and 95% confidence interval (CI). Cochrane risk assessment tool and Physiotherapy Evidence Database (PEDro) scale were used to evaluate the methodological quality of the included studies.

Results

Forty studies with a total of 56 exercise groups (n = 1,300) and 40 control groups (n = 827) were eligible for meta-analysis. Exercise significantly improved BBS (WMD, 3.77; 95% CI, 3.01 to 4.53, P < 0.00001), TUG (WMD, -1.33; 95% CI, -1.57 to -1.08, P < 0.00001), MSWS-12 (WMD, -2.57; 95% CI, -3.99 to -1.15, P = 0.0004), 6MWT (WMD, 25.56; 95% CI, 16.34 to 34.79, P < 0.00001), fatigue (WMD, -4.34; 95% CI, -5.83 to -2.84, P < 0.00001), and MSQOL-54 in people with MS (WMD, 11.80; 95% CI, 5.70 to 17.90, P = 0.0002) in people with MS. Subgroup analyses showed that aerobic exercise, resistance exercise, and multicomponent training were all effective in improving fatigue in people with MS, with resistance exercise being the most effective intervention type. In addition, a younger age was associated with a larger improvement in fatigue. Furthermore, aerobic exercise and multicomponent training were all effective in improving quality of life in people with MS, with aerobic exercise being the most effective intervention type.

Conclusion

Exercise had beneficial effects in improving balance, walking ability, walking endurance, fatigue, and quality of life in people with MS. Resistance exercise and aerobic exercise are the most effective interventions for improving fatigue and quality of life in people with MS, respectively. The effect of exercise on improving fatigue was associated with the age of the participants, with the younger age of the participants, the greater the improvement in fatigue.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=371056, identifier: CRD42022371056.

Neuroimaging Technology in Exercise Neurorehabilitation Research in Persons with MS: A Scoping Review

There is increasing interest in the application of neuroimaging technology in exercise neurorehabilitation research among persons with multiple sclerosis (MS). The inclusion and focus on neuroimaging outcomes in MS exercise training research is critical for establishing a biological basis for improvements in functioning and elevating exercise within the neurologist's clinical armamentarium alongside disease modifying therapies as an approach for treating the disease and its consequences. Indeed, the inclusion of selective neuroimaging approaches and sensor-based technology among physical activity, mobility, and balance outcomes in such MS research might further allow for detecting specific links between the brain and real-world behavior. This paper provided a scoping review on the application of neuroimaging in exercise training research among persons with MS based on searches conducted in PubMed, Web of Science, and Scopus. We identified 60 studies on neuroimaging-technology-based (primarily MRI, which involved a variety of sequences and approaches) correlates of functions, based on multiple sensor-based measures, which are typically targets for exercise training trials in MS. We further identified 12 randomized controlled trials of exercise training effects on neuroimaging outcomes in MS. Overall, there was a large degree of heterogeneity whereby we could not identify definitive conclusions regarding a consistent neuroimaging biomarker of MS-related dysfunction or singular sensor-based measure, or consistent neural adaptation for exercise training in MS. Nevertheless, the present review provides a first step for better linking correlational and randomized controlled trial research for the development of high-quality exercise training studies on the brain in persons with MS, and this is timely given the substantial interest in exercise as a potential disease-modifying and/or neuroplasticity-inducing behavior in this population.

Comparison of the dynamics of exoskeletal-assisted and unassisted locomotion in an FDA-approved lower extremity device: Controlled experiments and development of a subject-specific virtual simulator

Robotic exoskeletons have considerable, but largely untapped, potential to restore mobility in individuals with neurological disorders, and other conditions that result in partial or complete immobilization. The growing demand for these devices necessitates the development of technology to characterize the human-robot system during exoskeletal-assisted locomotion (EAL) and accelerate robot design refinements. The goal of this study was to combine controlled experiments with computational modeling to build a virtual simulator of EAL. The first objective was to acquire a minimum empirical dataset comprising human-robot kinematics, ground reaction forces, and electromyography during exoskeletal-assisted and unassisted locomotion from an able-bodied participant. The second objective was to quantify the dynamics of the human-robot system using a subject-specific virtual simulator reproducing EAL compared to the dynamics of normal gait. We trained an able-bodied participant to ambulate independently in a Food and Drug Administration-approved exoskeleton, the ReWalk P6.0 (ReWalk Robotics, Yoknaem, Israel). We analyzed the motion of the participant during exoskeletal-assisted and unassisted walking, sit-to-stand, and stand-to-sit maneuvers, with simultaneous measurements of (i) three-dimensional marker trajectories, (ii) ground reaction forces, (iii) electromyography, and (iv) exoskeleton encoder data. We created a virtual simulator in OpenSim, comprising a whole-body musculoskeletal model and a full-scale exoskeleton model, to determine the joint kinematics and moments during exoskeletal-assisted and unassisted maneuvers. Mean peak knee flexion angles of the human subject during exoskeletal-assisted walking were 50.1° ± 0.6° (left) and 52.6° ± 0.7° (right), compared to 68.6° ± 0.3° (left) and 70.7° ± 1.1° (right) during unassisted walking. Mean peak knee extension moments during exoskeletal-assisted walking were 0.10 ± 0.10 Nm/kg (left) and 0.22 ± 0.11 Nm/kg (right), compared to 0.64 ± 0.07 Nm/kg (left) and 0.73 ± 0.10 Nm/kg (right) during unassisted walking. This work provides a foundation for parametric studies to characterize the effects of human and robot design variables, and predictive modeling to optimize human-robot interaction during EAL.

Psychosocial Exoskeletons: Normal Development, Psychopathological and Sociopathological Phenomena, and Therapeutic Applications

In medical therapeutics, exoskeleton refers to external protections, supports, and appliances. By analogy, psychosocial exoskeletons can be envisioned as assistive psychological and social structures that brace and extend individuals' coping abilities. This article considers the utility of defining "psychosocial exoskeletons" as a framework to encompass psychological and social devices that enhance coping, and "therapeutic psychosocial exoskeletons" as devices assisting psychiatric treatment. Clinical observations were augmented via selective narrative review using PubMed and PsychInfo.A range of psychological and social devices were identified that constitute psychosocial exoskeletons. In extremes, psychosocial exoskeletons may prescribe comprehensive lifestyles including dress, rites, taboos, and acceptable relationships. These devices may enhance normal or psychopathological development.Therapeutic psychosocial exoskeletons consist of intentionally prescribed psychotherapeutic and sociotherapeutic interventions assembled to address patient-centered problems. Elements may include counseling and psychotherapies, peer-based recovery programs, institutional programs, and social interventions involving financial assistance, supported employment, pets, and other practical resources.

Participant characteristics of existing exercise studies in persons with multiple sclerosis - A systematic review identifying literature gaps

BACKGROUND

Exercise is a cornerstone in rehabilitation of persons with multiple sclerosis (pwMS), which is known to elicit beneficial effects on various symptoms and to have a potential disease-modifying effect. However, it remains to be elucidated if the existing MS exercise literature covers the full age and disability span of pwMS.

OBJECTIVE

To systematically review MS exercise studies and provide a detailed mapping of the demographic and clinical characteristics of the included pwMS.

METHODS

A systematic review of MS exercise studies were performed using MEDLINE and EMBASE. From the resulting MS exercise studies, mean sample characteristics were extracted.

RESULTS

4576 records were identified, from which 202 studies were included. Of these, 166 studies (82.2%) enrolled pwMS aged 35-54 years, 10.9% enrolled pwMS <35 years, and 6.9% enrolled pwMS ≥55 years (only 1.5% enrolled pwMS ≥60 years). A total of 118 studies (58.4%) reported Expanded Disability Status Scale (EDSS), with 88.1% of included pwMS having an EDSS between 2.0 and 6.5, while only one study enrolled pwMS with an EDSS ≥7.0. Finally, 80% of the studies included pwMS having a disease duration of 5-14.5 years.

CONCLUSION

Exercise studies in pwMS included primarily middle-aged (35-54 years) pwMS having an EDSS of 2.0-6.5 and a disease duration of 5-14.5 years. Few exercise studies were identified in young and older pwMS, in pwMS with mild disability and severe disability, and in pwMS having shorter or longer disease durations. These findings highlight the need for further investigation of exercise in these specific subgroups of pwMS as benefits of exercise might not generalize across subpopulations.

Neurorehabilitation in Multiple Sclerosis-A Review of Present Approaches and Future Considerations

Multiple sclerosis is an increasingly prevalent disease, representing the leading cause of non-traumatic neurological disease in Europe and North America. The most common symptoms include gait deficits, balance and coordination impairments, fatigue, spasticity, dysphagia and an overactive bladder. Neurorehabilitation therapeutic approaches aim to alleviate symptoms and improve the quality of life through promoting positive immunological transformations and neuroplasticity. The purpose of this study is to evaluate the current treatments for the most debilitating symptoms in multiple sclerosis, identify areas for future improvement, and provide a reference guide for practitioners in the field. It analyzes the most cited procedures currently in use for the management of a number of symptoms affecting the majority of patients with multiple sclerosis, from different training routines to cognitive rehabilitation and therapies using physical agents, such as electrostimulation, hydrotherapy, cryotherapy and electromagnetic fields. Furthermore, it investigates the quality of evidence for the aforementioned therapies and the different tests applied in practice to assess their utility. Lastly, the study looks at potential future candidates for the treatment and evaluation of patients with multiple sclerosis and the supposed benefits they could bring in clinical settings.

Use of Robotic Devices for Gait Training in Patients Diagnosed with Multiple Sclerosis: Current State of the Art

Multiple sclerosis (MS) is a neurodegenerative disease that produces alterations in balance and gait in most patients. Robot-assisted gait training devices have been proposed as a complementary approach to conventional rehabilitation treatment as a means of improving these alterations. The aim of this study was to investigate the available scientific evidence on the benefits of the use of robotics in the physiotherapy treatment in people with MS. A systematic review of randomized controlled trials was performed. Studies from the last five years on walking in adults with MS were included. The PEDro scale was used to assess the methodological quality of the included studies, and the Jadad scale was used to assess the level of evidence and the degree of recommendation. Seventeen studies met the eligibility criteria. For the improvement of gait speed, robotic devices do not appear to be superior, compared to the rest of the interventions evaluated. The methodological quality of the studies was moderate–low. For this reason, robot-assisted gait training is considered just as effective as conventional rehabilitation training for improving gait in people with MS.

Comparative Assessment of Robotic versus Classical Physical Therapy Using Muscle Strength and Ranges of Motion Testing in Neurological Diseases

The use of robotic systems in physical rehabilitation protocols has become increasingly attractive and has been given more focus in the last decade as a result of the high prevalence of motor deficits in the population, which is linked to an overburdened healthcare system. In accordance with current trends, three robotic devices have been designed, called ParReEx Elbow, ParReEx Wrist, and ASPIRE, which were designed to improve upper-limb medical recovery (shoulder, elbow, forearm, and wrist). The three automated systems were tested in a hospital setting with 23 patients (12 men and 11 women) suffering from motor deficits caused by various neurological diseases such as stroke, Parkinson’s disease, and amyotrophic lateral sclerosis (ALS). The patients were divided into three groups based on their pathology (vascular, extrapyramidal, and neuromuscular). Objective clinical measures, such as the Medical Research Council (MRC) scale, goniometry, and dynamometry, were used to compare pre- and post-rehabilitation assessments for both robotic-aided and manual physical rehabilitation therapy. The results of these tests showed that, with the exception of a few minor differences in muscular strength recovery, the robotic-assisted rehabilitation methods performed equally as well as the manual techniques, though only minor improvements were validated during short-term rehabilitation. The greatest achievements were obtained in the goniometric analysis where some rehabilitation amplitudes increased by over 40% in the vascular group, but the same analysis returned regressions in the neuromuscular group. The MRC scale analysis returned no significant differences, with most regressions occurring in the neuromuscular group. The dynamometric analysis mostly returned improvements, but the highest value evolution was 19.07%, which also in the vascular group. While the results were encouraging, more research is needed with a larger sample size and a longer study period in order to provide more information regarding the efficacy of both rehabilitation methods in neurological illnesses.

Wearable Robotic Gait Training in Persons with Multiple Sclerosis: A Satisfaction Study

Wearable exoskeletons have showed improvements in levels of disability and quality of life in people with neurological disorders. However, it is important to understand users’ perspectives. The aim of this study was to explore the patients’ and physiotherapists’ satisfaction from gait training with the EKSO GT^® exoskeleton in people with multiple sclerosis (MS). A cross-sectional study with 54 participants was conducted. Clinical data and self-administered scales data were registered from all patients who performed sessions with EKSO GT^®. To evaluate patients’ satisfaction the Quebec User Evaluation with Assistive Technology and Client Satisfaction Questionnaire were used. A high level of satisfaction was reported for patients and for physiotherapists. A moderate correlation was found between the number of sessions and the patients’ satisfaction score (rho = 0.532; p < 0.001), and an excellent correlation between the physiotherapists’ time of experience in neurology rehabilitation and the satisfaction with the possibility of combining the device with other gait trainings approaches (rho = 0.723; p = 0.003). This study demonstrates a good degree of satisfaction for people with MS (31.3 ± 5.70 out of 40) and physiotherapists (38.50 ± 3.67 out of 45 points) with the EKSO GT^®. Effectiveness, safety and impact on the patients’ gait were the most highly rated characteristics of EKSO GT^®. Features such as comfort or weight of the device should be improved from the patients’ perspectives.