Evaluation of Clinical Gait Analysis parameters in patients affected by Multiple Sclerosis: Analysis of kinematics

Neuro-orthopaedic check-up and walking in people with multiple sclerosis: toward a more specific assessment to improve rehabilitation results

The rehabilitation process of people with multiple sclerosis (PwMS) is a challenge, and decision-making requires a thorough assessment to increase the chances of success in rehabilitation planning. The aim of this study was to investigate the importance of the neuroorthopaedic check-Up (NOChU) for gait prognosis. Participated in the study 105 PwMS with different levels of impairment. The NOChU includes measurements of passive range of motion (ROM), muscle strength, and spasticity. Additionally, was carried out the spatial-temporal analysis of the walking, Timed Up and Go test, and 6-min walk test. ROM remained relatively preserved to perform daily life activities except for ankle dorsiflexion. Muscle strength was also relatively preserved. Spasticity affected especially the ankle muscles, clearly the sural triceps. Among the NOChU measurements the catch seemed to have the most impact on walking on its different phases and on other activities. Accurate NOChU measurements play a crucial role in clinical settings, guiding informed decisions in rehabilitation planning. Future research endeavours could focus on exploring the correlations between NOChU deficiencies and the decline in walking capabilities among PwMS, with the goal of proposing personalized treatment strategies that address their specific requirements.

Kinematic Gait Analysis in People with Mild-Disability Multiple Sclerosis Using Statistical Parametric Mapping: A Cross-Sectional Study

Multiple sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system. Gait abnormalities, such as altered joint kinematics, are common in people with MS (pwMS). Traditional clinical gait assessments may not detect subtle kinematic alterations, but advances in motion capture technology and analysis methods, such as statistical parametric mapping (SPM), offer more detailed assessments. The aim of this study was to compare the lower-limb joint kinematics during gait between pwMS and healthy controls using SPM analysis. Methods: A cross-sectional study was conducted involving pwMS and healthy controls. A three-dimensional motion capture system was used to obtain the kinematic parameters of the more affected lower limb (MALL) and less affected lower limb (LALL), which were compared using the SPM analysis. Results: The study included 10 pwMS with mild disability (EDSS ≤ 3) and 10 healthy controls. The results showed no differences in spatiotemporal parameters. However, significant differences were observed in the kinematics of the lower-limb joints using SPM. In pwMS, compared to healthy controls, there was a higher anterior pelvis tilt (MALL, p = 0.047), reduced pelvis elevation (MALL, p = 0.024; LALL, p = 0.044), reduced pelvis descent (MALL, p = 0.033; LALL, p = 0.022), reduced hip extension during pre-swing (MALL, p = 0.049), increased hip flexion during terminal swing (MALL, p = 0.046), reduced knee flexion (MALL, p = 0.04; LALL, p < 0.001), and reduced range of motion in ankle plantarflexion (MALL, p = 0.048). Conclusions: pwMS with mild disability exhibit specific kinematic abnormalities during gait. SPM analysis can detect alterations in the kinematic parameters of gait in pwMS with mild disability.

Gait Analysis in Neurorehabilitation: From Research to Clinical Practice

When brain damage occurs, gait and balance are often impaired. Evaluation of the gait cycle, therefore, has a pivotal role during the rehabilitation path of subjects who suffer from neurological disorders. Gait analysis can be performed through laboratory systems, non-wearable sensors (NWS), and/or wearable sensors (WS). Using these tools, physiotherapists and neurologists have more objective measures of motion function and can plan tailored and specific gait and balance training early to achieve better outcomes and improve patients' quality of life. However, most of these innovative tools are used for research purposes (especially the laboratory systems and NWS), although they deserve more attention in the rehabilitation field, considering their potential in improving clinical practice. In this narrative review, we aimed to summarize the most used gait analysis systems in neurological patients, shedding some light on their clinical value and implications for neurorehabilitation practice.

Complementary use of statistical parametric mapping and gait profile score to describe walking alterations in multiple sclerosis: a cross-sectional study

Gait analysis is often used to study locomotor alterations in people with multiple sclerosis (PwMS), but the large number of extracted variables challenges the interpretability. In this paper, we analysed gait alterations by combining the Gait Profile Score (GPS), which summarizes kinematic locomotor deviations, and Statistical Parametric Mapping (SPM), which compares kinematics and kinetics over the whole gait cycle. Eleven PwMS and 11 speed-matched Healthy Controls (HC) underwent overground gait analysis. GPS were compared through independent-samples t-tests; sagittal-plane kinematics and power at hip, knee, and ankle were compared through SPM Hotelling's-T2 and SPM t-tests. Spearman's correlation coefficients (r) between GPS and clinical outcomes were also calculated. PwMS had higher GPS than HC (PwMS = 8.74 ± 2.13°; HC = 5.01 ± 1.41°;p < 0.001). Multivariate SPM found statistically significant differences at 0-49%, 70-80%, and 93-99% of stride (p < 0.05) and univariate analysis showed reduced ankle dorsiflexion, and lower knee flexion during pre-swing and swing. GPS correlated with Expanded Disability Status Scale (r = 0.65; 95%C.I.[0.04,0.91]; p = 0.04) and 2-Minute Walking Test (r = -0.65; 95%C.I.[-0.91,-0.04]; p = 0.04). GPS in conjunction with SPM revealed multi-joint kinematic alterations on sagittal plane involving distal joint angles, ankle and knee, during the stance phase with no changes at the proximal level. Gait deviations were more pronounced in PwMS with higher disability and walking limitations.

Muscle Coactivation Index during Walking in People with Multiple Sclerosis with Mild Disability, a Cross-Sectional Study

BACKGROUND

Multiple sclerosis (MS) is a progressive neurodegenerative disease characterized by axonal degeneration and demyelination. Changes in gait, related to joint kinematics and kinetics, especially at the ankle and knee, have been observed in people with MS (pwMS). Muscle coactivation plays an important role in joint stabilization; however, excessive coactivation may interfere with gait. The aim of this study was to analyze the differences in muscle activation during gait in pwMS compared to healthy individuals.

METHODS

A cross-sectional study was conducted involving pwMS and healthy controls. Surface electromyography was used to record muscle activity during gait. The main outcome measures were the coactivation index (CI) and the area under the curve (AUC), which were calculated for several pairs of lower extremity muscles.

RESULTS

Nine pwMS and nine healthy controls were included. When comparing the MS group to the control group, the AUC was significantly higher in the lateral gastrocnemius (p = 0.023) and the CI for the lateral gastrocnemius-anterior tibialis (p = 0.022) and gluteus maximus-lateral gastrocnemius (p = 0.047).

CONCLUSION

Mildly affected pwMS have altered muscle coactivation patterns during gait, especially in the most affected limb. The results highlight the importance of muscle coactivation in pwMS and its possible role in the early detection of gait abnormalities.

The association between cognition and gait disturbance in central nervous system demyelinating disorder with mild disability

INTRODUCTION

Gait disturbance in central nervous system (CNS) demyelinating disorders, including multiple sclerosis (MS) and neuromyelitis optica (NMO) is one of the most troublesome problems that has a direct impact on the quality of life. However, the associations between gait disturbance and other clinical variables of these two diseases have not been fully elucidated.

OBJECTIVE

This study aimed to evaluate gait disturbance using a computerized gait analysis system and its association with various clinical variables in patients with MS and NMO.

METHODS

A total of 33 patients (14 with MS and 19 with NMO) with minor disabilities, who were able to walk independently and had passed their acute phase, were enrolled in the study. Gait analysis were performed using a computer-based instrumented walkway system. (Walk-way MG-1000, Anima, Japan) Clinical variables, such as disease duration, medication, body mass index (BMI), hand grip power, and muscle mass were recorded. The Montreal Cognitive Assessment (MOCA), Beck Depression Inventory score-II (BDI), and fatigue scale were measured using the Functional Assessment of Chronic Illness Therapy-fatigue scale (FACIT-fatigue) scale. A trained neurologist scored the Expanded Disability Status Scale (EDSS).

RESULTS

Gait speed was the single parameter that showed a significant positive correlation with MOCA (p < 0.001). The stance phase time was the single parameter that showed a significant negative correlation with EDSS (p < 0.001). Hand grip strength showed a significant positive correlation with skeletal muscle mass as assessed by bioimpedance analysis (p < 0.05). The FACIT-fatigue scale score showed a significant negative correlation with the BDI (p < 0.001).

CONCLUSION

In our patients with MS/NMO with mild disability, cognitive impairment was significantly correlated with gait speed, and the degree of disability was significantly correlated with stance phase time. Our findings may imply that early detection of a decrease in gait speed and an increase in stance phase time can predict the progression of cognitive impairment in patients with MS/NMO with mild disability.

Semi-supervised clustering of quaternion time series: Application to gait analysis in multiple sclerosis using motion sensor data

Recent approaches in gait analysis involve the use of wearable motion sensors to extract spatio-temporal parameters that characterize multiple aspects of an individual's gait. In particular, the medical community could largely benefit from this type of devices as they could provide the clinicians with a valuable tool for assessing gait impairment. Motion sensor data are however complex and there is an urgent unmet need to develop sound statistical methods for analyzing such data and extracting clinically relevant information. In this article, we measure gait by following the hip rotation over time and the resulting statistical unit is a time series of unit quaternions. We explore the possibility to form groups of patients with similar walking impairment by taking into account their walking data and their global decease severity with semi-supervised clustering. We generalize a compromise-based method named hclustcompro to unit quaternion time series by combining it with the proper dissimilarity quaternion dynamic time warping. We apply this method on patients diagnosed with multiple sclerosis to form groups of patients with similar walking deficiencies while accounting for the clinical assessment of their overall disability. We also compare the compromise-based clustering approach with the method mergeTrees that falls into a sub-class of ensemble clustering named collaborative clustering. The results provide a first proof of both the interest of using wearable motion sensors for assessing gait impairment and the use of prior knowledge to guide the clustering process. It also demonstrates that compromise-based clustering is a more appropriate approach in this context.

Machine learning corroborates subjective ratings of walking and balance difficulty in multiple sclerosis

Machine learning can discern meaningful information from large datasets. Applying machine learning techniques to raw sensor data from instrumented walkways could automatically detect subtle changes in walking and balance. Multiple sclerosis (MS) is a neurological disorder in which patients report varying degrees of walking and balance disruption. This study aimed to determine whether machine learning applied to walkway sensor data could classify severity of self-reported symptoms in MS patients. Ambulatory people with MS (n = 107) were asked to rate the severity of their walking and balance difficulties, from 1-No problems to 5-Extreme problems, using the MS-Impact Scale-29. Those who scored less than 3 (moderately) were assigned to the “mild” group (n = 35), and those scoring higher were in the “moderate” group (n = 72). Three machine learning algorithms were applied to classify the “mild” group from the “moderate” group. The classification achieved 78% accuracy, a precision of 85%, a recall of 90%, and an F1 score of 87% for distinguishing those people reporting mild from moderate walking and balance difficulty. This study demonstrates that machine learning models can reliably be applied to instrumented walkway data and distinguish severity of self-reported impairment in people with MS.

Angle-Angle Diagrams in the Assessment of Locomotion in Persons with Multiple Sclerosis: A Preliminary Study

Gait analysis is clinically relevant in persons with multiple sclerosis (PwMS) and consists of several joint angular displacement–time relationships and spatiotemporal parameters. However, it lacks representation by means of diagrams in which knee-angle/hip-angle and knee-angle/ankle-angle variations are plotted against each other at the same points of time. Three-dimensional kinematic analysis was performed on 20 subjects (10 PwMS/10 healthy controls, HCs), and the knee-angle/hip-angle and knee-angle/ankle-angle diagrams of both lower limbs were determined in the sagittal plane while walking on a motorized treadmill. The area (a quantifier of range of motion) and the perimeter (a quantifier of coordination) of angle-angle diagram loops were calculated. PwMS showed reduced knee-angle/ankle-angle loops compared to HCs (p < 0.05), whereas the hip-angle/ankle-angle loops between the PwMS and HCs was not significant (p > 0.05). Similarly, the activation of leg muscles showed significant differences between PwMS and HCs (p ranged from 0.05 to 0.001). The results indicate that the proposed knee-angle/hip-angle diagram is feasible and could be applied as a reliable tool in future studies aimed at assessing the acute and long-term effects of specific exercise programmes and/or pharmacological treatment in PwMS.

Machine learning classification of multiple sclerosis patients based on raw data from an instrumented walkway

Background

Using embedded sensors, instrumented walkways provide clinicians with important information regarding gait disturbances. However, because raw data are summarized into standard gait variables, there may be some salient features and patterns that are ignored. Multiple sclerosis (MS) is an inflammatory neurodegenerative disease which predominantly impacts young to middle-aged adults. People with MS may experience varying degrees of gait impairments, making it a reasonable model to test contemporary machine leaning algorithms. In this study, we employ machine learning techniques applied to raw walkway data to discern MS patients from healthy controls. We achieve this goal by constructing a range of new features which supplement standard parameters to improve machine learning model performance.

Results

Eleven variables from the standard gait feature set achieved the highest accuracy of 81%, precision of 95%, recall of 81%, and F1-score of 87%, using support vector machine (SVM). The inclusion of the novel features (toe direction, hull area, base of support area, foot length, foot width and foot area) increased classification accuracy by 7%, recall by 9%, and F1-score by 6%.

Conclusions

The use of an instrumented walkway can generate rich data that is generally unseen by clinicians and researchers. Machine learning applied to standard gait variables can discern MS patients from healthy controls with excellent accuracy. Noteworthy, classifications are made stronger by including novel gait features (toe direction, hull area, base of support area, foot length and foot area).

The Timing of Kinematic and Kinetic Parameters during Gait Cycle as a Marker of Early Gait Deterioration in Multiple Sclerosis Subjects with Mild Disability

This study aimed to evaluate walking in multiple sclerosis (MS) patients with mild disability. A case control study with 8 mild disability MS patients and 10 controls was conducted. This study analyzed spatiotemporal, kinematic, and kinetic parameters. We also analyzed the timing of these parameters, as a percentage of the gait cycle. The MS patients and controls walked with a similar gait pattern. However, there were differences in the timing of the biomechanical parameters. The timing of toe-off was at 62–63% of gait cycle in MS subjects while in controls it was at 59.94% (p = 0.009 to 0.027 vs. to controls). The peak of knee flexion during swing was at 74–76% of gait cycle in MS subjects while in controls was at 72% (p = 0.027 to 0.034). While the peak of ankle dorsiflexion during stance occurred at 48–50% in MS subjects, while in controls it was at 46% (p = 0.001 to 0.009), and the peak of plantar flexion during pre-swing was at 66% in MS subjects vs. 64% in controls (p = 0.001). At the kinetic pattern, the first peak of the vertical ground reaction force occurred at 14% of gait cycle in controls while in MS patients it was at 17–20% (p = 0.012 to 0.021). MS subjects with mild disability walked with similar spatiotemporal parameters, joint angles and moments compared to controls. However, our results suggest that those changed the temporal occurrences, expressed as percentage of the gait cycle, of the kinematic and kinetic parameters.

A Low-Cost, Portable, and Wireless In-Shoe System Based on a Flexible Porous Graphene Pressure Sensor

Monitoring gait patterns in daily life will provide a lot of biological information related to human health. At present, common gait pressure analysis systems, such as pressure platforms and in-shoe systems, adopt rigid sensors and are wired and uncomfortable. In this paper, a biomimetic porous graphene–SBR (styrene-butadiene rubber) pressure sensor (PGSPS) with high flexibility, sensitivity (1.05 kPa⁻¹), and a wide measuring range (0–150 kPa) is designed and integrated into an insole system to collect, process, transmit, and display plantar pressure data for gait analysis in real-time via a smartphone. The system consists of 16 PGSPSs that were used to analyze different gait signals, including walking, running, and jumping, to verify its daily application range. After comparing the test results with a high-precision digital multimeter, the system is proven to be more portable and suitable for daily use, and the accuracy of the waveform meets the judgment requirements. The system can play an important role in monitoring the safety of the elderly, which is very helpful in today’s society with an increasingly aging population. Furthermore, an intelligent gait diagnosis algorithm can be added to realize a smart gait monitoring system.

Outcome measures used in trials on gait rehabilitation in multiple sclerosis: A systematic literature review

Background

Multiple Sclerosis (MS) is associated with impaired gait and a growing number of clinical trials have investigated efficacy of various interventions. Choice of outcome measures is crucial in determining efficiency of interventions. However, it remains unclear whether there is consensus on which outcome measures to use in gait intervention studies in MS.

Objective

We aimed to identify the commonly selected outcome measures in randomized controlled trials (RCTs) on gait rehabilitation interventions in people with MS. Additional aims were to identify which of the domains of the International Classification of Functioning, Disability and Health (ICF) are the most studied and to characterize how outcome measures are combined and adapted to MS severity.

Methods

Pubmed, Cochrane Central, Embase and Scopus databases were searched for RCT studies on gait interventions in people living with MS according to PRISMA guidelines.

Results

In 46 RCTs, we identified 69 different outcome measures. The most used outcome measures were 6-minute walking test and the Timed Up and Go test, used in 37% of the analyzed studies. They were followed by gait spatiotemporal parameters (35%) most often used to inform on gait speed, cadence, and step length. Fatigue was measured in 39% of studies. Participation was assessed in 50% of studies, albeit with a wide variety of scales. Only 39% of studies included measures covering all ICF levels, and Participation measures were rarely combined with gait spatiotemporal parameters (only two studies).

Conclusions

Selection of outcome measures remains heterogenous in RCTs on gait rehabilitation interventions in MS. However, there is a growing consensus on the need for quantitative gait spatiotemporal parameter measures combined with clinical assessments of gait, balance, and mobility in RCTs on gait interventions in MS. Future RCTs should incorporate measures of fatigue and measures from Participation domain of ICF to provide comprehensive evaluation of trial efficacy across all levels of functioning.

Predicting Multiple Sclerosis From Gait Dynamics Using an Instrumented Treadmill: A Machine Learning Approach

OBJECTIVE

Multiple Sclerosis (MS) is a neurological condition which widely affects people 50-60 years of age. While clinical presentations of MS are highly heterogeneous, mobility limitations are one of the most frequent symptoms. This study examines a machine learning (ML) framework for identifying MS through spatiotemporal and kinetic gait features.

METHODS

In this study, gait data during self-paced walking on an instrumented treadmill from 20 persons with MS and 20 age, weight, height, and gender-matched healthy older adults (HOA) were obtained. We explored two strategies to normalize data and minimize dependence on subject demographics; size-normalization (standard body size-based normalization) and regress-normalization (regression-based normalization using scaling factors derived by regressing gait features on multiple subject demographics); and proposed an ML based methodology to classify individual strides of older persons with MS (PwMS) from healthy controls. We generalized both across different walking tasks and subjects.

RESULTS

We observed that regress-normalization improved the accuracy of identifying pathological gait using ML when compared to size-normalization. When generalizing from comfortable walking to walking while talking, gradient boosting machine achieved the optimal subject classification accuracy and AUC of 94.3 and 1.0, respectively and for subject generalization, a multilayer perceptron resulted in the best accuracy and AUC of 80% and 0.86, respectively, both with regression-normalized data.

CONCLUSION

The integration of gait data and ML may provide a viable patient-centric approach to aid clinicians in monitoring MS.

SIGNIFICANCE

The results of this study have future implications for the way regression normalized gait features may be clinically used to design ML-based disease prediction strategies and monitor disease progression in PwMS.

Strategies for maintaining dynamic balance in persons with neurological disorders during overground walking

Maintaining a stable gait requires a dynamic balance control, that can be altered in persons with Multiple Sclerosis (MS), Stroke (ST), and Parkinson's disease (PD). The understanding of the strategy for Center of Mass (CoM) positioning adopted by patients during walking is important to be able to program treatments aimed at improving gait control and preventing falls. Forty-four persons with a mild-to-moderate neurological disorder (20 with MS, 14 with ST, 10 with PD) underwent clinical examination and gait analysis. Ten Healthy Subjects (HS) walking at matched speed provided the normative data. Dynamic balance was assessed using the margin of stability (MoS). It was calculated as the distance between the extrapolated Center of Pressure and the extrapolated CoM at mid-stance. The MoS values for lower limbs were calculated in patients and compared with speed-matched values of HS. Persons with neurological disorder showed increased MoS in the medio-lateral direction with respect to HS. Within-group comparison analysis showed a symmetry between lower limbs in HS (Mean (95%CI) [mm], dominant vs non-dominant limb, 43.3 (31.9-54.6) vs 42.9 (28.8-56.9)) and PD (less affected vs more affected limb, 71.1 (59.8-82.5) vs 72.5 (58.5-86.6)), while a significant asymmetry was found in MS (54.4 (46.4-62.4) vs 81.1 (71.2-91.1)) and ST (52.1 (42.6-61.7) vs 74.7 (62.8-86.6)) participants. The history of falls was comparable among PD, MS, and ST groups, and the MoS in the frontal plane showed a strong correlation with these records. Objective assessment of MoS revealed pathology-specific strategies showing different impacts in MS, ST, and PD on the ability to control CoM information to manage the balance between limbs during gait. MoS evaluation will provide useful information to address a tailored rehabilitation program and to monitor disease progression.

Gait Pattern in People with Multiple Sclerosis: A Systematic Review

The aim of the present systematic review was to describe the gait pattern in people with multiple sclerosis (MS) by compiling the main findings obtained from studies using three-dimensional capture systems of human movement. The search was carried out in PubMed, Web of Science, Physiotherapy Evidence Database (PEDro), and the Cumulative Index to Nursing and Allied Health (CINAHL) databases. Studies that used three-dimensional gait analysis systems and that analyzed spatiotemporal, kinematic, kinetic, or electromyographic parameters, were included. The quality of the studies was assessed using the Critical Review Form-Quantitative Studies scale. 12 articles were included with 523 (342 women and 181 men) people with a diagnosis of MS. The present work suggests that people with MS have a decrease in speed and stride length, as well as an increase in double-stance intervals during gait. Likewise, it is common to observe a decrease in hip extension during the stance period, a decrease in knee flexion in the swing period, a decrease in ankle dorsiflexion in the initial contact and a decrease in ankle plantar flexion during the pre-swing phase. The subjects with MS decrease the hip extensor moment and the ankle power during the stance period of walking.

Doppler Radar for the Extraction of Biomechanical Parameters in Gait Analysis

The applicability of Doppler radar for gait analysis is investigated by quantitatively comparing the measured biomechanical parameters to those obtained using motion capturing and ground reaction forces. Nineteen individuals walked on a treadmill at two different speeds, where a radar system was positioned in front of or behind the subject. The right knee angle was confined by an adjustable orthosis in five different degrees. Eleven gait parameters are extracted from radar micro-Doppler signatures. Here, new methods for obtaining the velocities of individual lower limb joints are proposed. Further, a new method to extract individual leg flight times from radar data is introduced. Based on radar data, five spatiotemporal parameters related to rhythm and pace could reliably be extracted. Further, for most of the considered conditions, three kinematic parameters could accurately be measured. The radar-based stance and flight time measurements rely on the correct detection of the time instant of maximal knee velocity during the gait cycle. This time instant is reliably detected when the radar has a back view, but is underestimated when the radar is positioned in front of the subject. The results validate the applicability of Doppler radar to accurately measure a variety of medically relevant gait parameters. Radar has the potential to unobtrusively diagnose changes in gait, e.g., to design training in prevention and rehabilitation. As contact-less and privacy-preserving sensor, radar presents a viable technology to supplement existing gait analysis tools for long-term in-home examinations.

An intensive task-oriented circuit training positively impacts gait biomechanics in MS patients

BACKGROUND

An intensive task-oriented circuit training (TOCT) provides a valid approach in improving motor function in Multiple Sclerosis (MS).

OBJECTIVE

We aimed at testing the efficacy of TOCT on gait kinematics in MS patients with mild-moderate disability.

METHODS

Nineteen MS patients able of independent walking performed 3-D Gait Analysis before (T0) and after (T1) a two-week TOCT program. Patients were clustered in two different subgroups, according to clinical neurological impairments assessed with specific functional system of Expanded Disability Status Scale (EDSS): pyramidal (Group 1) and cerebellar (Group 2) subjects. Spatio-temporal and kinematic data were compared before and after the TOCT intervention in the total sample of patients and in the two selected subgroups at two time intervals.

RESULTS

Data obtained revealed increased dynamic ROM at knee joint after training in the whole study sample. Of note, knee dynamic excursion improved significantly in Group 1 but not in Group 2 patients after TOCT. Moreover, sagittal plane kinematics revealed significant modifications on knee and ankle biomechanics in Group 1 after rehabilitation.

CONCLUSIONS

These data point out the benefits of the task specific training on gait dynamics in mild impaired MS subjects, linking to treatment opportunity in patients with a prevalent pyramidal impairment.

Improved Gait of Persons With Multiple Sclerosis After Rehabilitation: Effects on Lower Limb Muscle Synergies, Push-Off, and Toe-Clearance

Introduction: Persons with MS (PwMS) have markedly reduced push-off and toe-clearance during gait compared to healthy subjects (HS). These deficits may result from alterations in neuromotor control at the ankle. To optimize rehabilitation interventions for PwMS, a crucial step is to evaluate if and how altered neuromotor control, as represented by muscle synergies, improves with rehabilitation. In this study we investigated changes in ankle motor control and associated biomechanical parameters during gait in PwMS, occurring with increase in speed after gait rehabilitation. Methods: 3D motion and EMG data were collected while 11 PwMS (age 50.3 + 11.1; EDSS 5.2 + 1.2) walked overground at self-selected speed before (T0) and after 20 sessions (T1) of intensive treadmill training. Muscle synergies were extracted using non-negative matrix factorization. Gait parameters were computed according to the LAMB protocol. Pearson's correlation coefficient was used to evaluate the similarity of motor modules between PwMS and HS. To assess differences in distal module activations representing neuromotor control at the ankle [Forward Propulsion (FPM) and Ground Clearance modules (GCM)], each module's activation timing was integrated over 100% of the gait cycle and the activation percentage index (API) was computed in six phases. Ten age matched HS provided two separate speed-matched normative datasets for T0 and T1. For speed independent comparison for the PwMs Z scores were calculated for all their gait variables. Results: In PwMS velocity increased significantly from T0 to T1 (0.74-0.90 m/s, p < 0.05). The activation profiles (API) of FPM and GCM of PwMS improved in pre-swing (p < 0.05): FPM (Mean [95% CI] [%]: T0: 12.5 [5.7-19.3] vs. T1: 9.0 [2.7-15.3]); GCM (T0: 26.7 [18.2-35.3] vs. T1: 24.5 [18.2-30.7]). This was associated with an increase in toe clearance (80.3 to 103.6 mm, p < 0.05) and a higher ankle power peak in pre-swing (1.53-1.93 W/kg, p < 0.05). Conclusion: Increased gait speed of PwMS after intensive gait training was consistent with improvements in spatio-temporal gait parameters. The most important finding of this study was the re-organization of distal leg modules related to neurophysiological changes induced by rehabilitation. This was associated with an improved ankle performance.

Clinical assessment, management, and rehabilitation of walking impairment in MS: an expert review

INTRODUCTION

One of the most common and life-altering consequences of Multiple Sclerosis (MS) is walking impairment. The distance, speed, and Gait pattern functions are components of the International Classification of Functioning, Disability, and Health (ICF) and are also predictors of dependency in terms of daily living activities in patients with MS (pwMS).

AREAS COVERED

This article provides an overview of walking impairment in pwMS, with focus on the assessment of gait and the rehabilitation approaches.

EXPERT OPINION

The authors recommend that pwMS undergo gait assessment integrating the ICF perspective using validated clinical outcome measures that cover spatiotemporal gait parameters. Moreover, assessment of walking speed with short walking capacity tests such as the timed 25-foot walk (T25FW) or the 10-m walk test (10 MWT) and tests for walking distance with middle distance tests such as the 2-min walk test (2MWT) and the 6-min walk test (6MWT). This review further highlights strategies that may restore walking function including pharmacological symptomatic treatment and non-pharmacological rehabilitation approaches such as exercise and task-specific training providing an appraisal of mobility targeted therapies to be considered when planning multidisciplinary comprehensive-care of pwMS. Finally, new and novel strategies such as motor imagery and rhythmic auditory stimulation have been developed to improve walking speed and distance in pwMS.

Trunk and pelvis movement compensation in people with multiple sclerosis: Relationships to muscle function and gait performance outcomes

BACKGROUND

Problems with gait are common in people with multiple sclerosis (MS), but little is known about pelvis and trunk kinematics, especially in the frontal plane.

RESEARCH QUESTION

Are pelvis and trunk kinematics in people with MS related to muscle function, spatiotemporal parameters, and gait performance?

METHODS

In this cross-sectional study, 20 people with MS (Expanded Disability Status Scale 1.5-5.5) and 10 people with comparable age and sex (CTL) underwent threedimensional gait analysis, muscle function assessments (hip and trunk strength and endurance), and gait performance measures (Timed 25-Foot Walk - T25FW, 2-Minute Walk Test - 2MWT). Frontal and sagittal plane pelvis and trunk excursion during the stance period of walking were compared between groups; and in the MS group, associations were determined between kinematic variables, muscle function, spatiotemporal parameters, and gait performance.

RESULTS

Compared to the CTL group, the MS group had significantly greater sagittal plane trunk and pelvis excursion for both the stronger (p = 0.031) and weaker (p = 0.042) sides; less frontal plane trunk and pelvis excursion for both the stronger (p = 0.008) and weaker (p = 0.024) sides; and more sagittal plane trunk excursion for the stronger side (p = 0.047) during stance phase. There were low-to-moderate correlations in the MS group for sagittal plane pelvis excursion with muscle function (p = 0.019 to 0.030), spatiotemporal parameters (p < 0.001 to 0.005), and gait performance (p = < 0.001 to 0.001). Using linear regression, frontal and sagittal plane pelvis excursion were significant predictors of both T25FW and 2MWT, explaining 34 % and 46 % of the variance of each gait performance measure, respectively.

SIGNIFICANCE

Rehabilitation interventions may consider addressing pelvis movement compensations in order to improve spatiotemporal parameters and gait performance in people with MS.

Ankle dysfunction in multiple sclerosis and the effects on walking

PURPOSE

Even in the early stage of the disease, for patients suffering from multiple sclerosis (MS), the most common and reported biomechanical alterations in the lower limb are located at the ankle joint. However, the effects of these impairments on gait deterioration should be discussed.

MATERIALS AND METHODS

This review was written according to the PRISMA guidelines. The search focussed on biomechanical changes (kinetic, kinematic, and electromyographic data) at the ankle during gait in MS patients. The search was performed in the databases: Pubmed, Web of Science, and Cochrane Library.

RESULTS

Eleven studies were included. The reduction in the ankle range of motion (RoM) induced by increased cocontractions of the tibialis anterior and triceps surae muscles could be a compensatory strategy to improve body-weight support and balance during the stance phase.

CONCLUSIONS

Future rehabilitation programmes should consider the control of weight support at the ankle during gait training.Implications for rehabilitationThe ankle supports and stabilises the body during the stance phase of gait.The reduced ankle range of motion in multiple sclerosis (MS), even at an early stage of the disease, is due to cocontractions of tibialis anterior and triceps surae and could be a compensatory strategy to be more stable.Rehabilitation programmes for MS patients should focus on the control of body segments motion during the weight transfer above the ankle.

Profiling walking dysfunction in multiple sclerosis: characterisation, classification and progression over time

Gait dysfunction is a common and relevant symptom in multiple sclerosis (MS). This study aimed to profile gait pathology in gait-impaired patients with MS using comprehensive 3D gait analysis and clinical walking tests. Thirty-seven patients with MS walked on the treadmill at their individual, sustainable speed while 20 healthy control subjects walked at all the different patient's paces, allowing for comparisons independent of walking velocity. Kinematic analysis revealed pronounced restrictions in knee and ankle joint excursion, increased gait variability and asymmetry along with impaired dynamic stability in patients. The most discriminative single gait parameter, differentiating patients from controls with an accuracy of 83.3% (χ2 test; p = 0.0001), was reduced knee range of motion. Based on hierarchical cluster and principal component analysis, three principal pathological gait patterns were identified: a spastic-paretic, an ataxia-like, and an unstable gait. Follow-up assessments after 1 year indicated deterioration of walking function, particularly in patients with spastic-paretic gait patterns. Our findings suggest that impaired knee/ankle control is common in patients with MS. Personalised gait profiles and clustering algorithms may be promising tools for stratifying patients and to inform patient-tailored exercise programs. Responsive, objective outcome measures are important for monitoring disease progression and treatment effects in MS trials.