Impaired local dynamic stability during treadmill walking predicts future falls in patients with multiple sclerosis: A prospective cohort study

Tajali, Shirin; Mehravar, Mohammad; Negahban, Hossein; Dieën, Jaap H. van; Yazdi, Mohammad Jafar Shaterzadeh; Mofateh, Razieh

doi:10.1016/j.clinbiomech.2019.05.013

Cited by 23 publications

(33 citation statements)

References 36 publications

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“…For continuous monitoring of the gait function and identification of early gait abnormalities, a variety of diagnostic tests and specific digital systems for gait analysis are necessary [22]. Digital deep phenotyping of gait can be achieved by motion capture systems based on optoelectronic stereophotogrammetry, accelerometers or force sensors which were found to be more sensitive than the T25FW for differentiating between patients and healthy controls [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis

et al. 2021

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In multiple sclerosis (MS), gait impairment is one of the most prominent symptoms. For a sensitive assessment of pathological gait patterns, a comprehensive analysis and processing of several gait analysis systems is necessary. The objective of this work was to determine the best diagnostic gait system (DIERS pedogait, GAITRite system, and Mobility Lab) using six machine learning algorithms for the differentiation between people with multiple sclerosis (pwMS) and healthy controls, between pwMS with and without fatigue and between pwMS with mild and moderate impairment. The data of the three gait systems were assessed on 54 pwMS and 38 healthy controls. Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbor, and Support Vector Machines (SVM) with linear, radial basis function (rbf) and polynomial kernel were applied for the detection of subtle walking changes. The best performance for a healthy-sick classification was achieved on the DIERS data with a SVM rbf kernel (k = 0.49 ± 0.11). For differentiating between pwMS with mild and moderate disability, the GAITRite data with the SVM linear kernel (k = 0.61 ± 0.06) showed the best performance. This study demonstrates that machine learning methods are suitable for identifying pathologic gait patterns in early MS.

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Section: Introductionmentioning

confidence: 99%

Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis

et al. 2021

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“…Interestingly, the present results revealed subtle impairments also in the EDSS: 0-1.5 subgroup. Since both the present results on straight-line walking and previously published results on instrumented TUG (51) did not reveal abnormalities in EDSS: 0-1.5 patients, it can be suggested that the instrumented assessment of WHHT may be a sensitive tool (than those mentioned above) to identify, already from the very early phases of the disease, incipient balance and locomotor anomalies that become clinically evident only in the most advanced stages of MS (EDSS≥4) (52)(53)(54)(55).…”

Section: Discussionmentioning

confidence: 67%

Walking With Horizontal Head Turns Is Impaired in Persons With Early-Stage Multiple Sclerosis Showing Normal Locomotion

et al. 2022

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BackgroundTurning the head while walking (an action often required during daily living) is particularly challenging to maintain balance. It can therefore potentially reveal subtle impairments in early-stage people with multiple sclerosis who still show normal locomotion (NW-PwMS). This would help in identifying those subjects who can benefit from early preventive exercise aimed at slowing the MS-related functional decline.ObjectivesTo analyze if the assessment of walking with horizontal head turns (WHHT) through inertial sensors can discriminate between healthy subjects (HS) and NW-PwMS and between NW-PwMS subgroups. To assess if the discriminant ability of the instrumented WHHT is higher compared to clinical scores. To assess the concurrent validity of the sensor-based metrics.MethodsIn this multicenter study, 40 HS and 59 NW-PwMS [Expanded Disability Status Scale (EDSS) ≤ 2.5, disease duration ≤ 5 years] were tested. Participants executed Item-6 of the Fullerton Advanced Balance scale-short (FAB-s) wearing three inertial sensors on the trunk and ankles. The item required to horizontally turn the head at a beat of the metronome (100 bpm) while walking. Signals of the sensors were processed to compute spatiotemporal, regularity, symmetry, dynamic stability, and trunk sway metrics descriptive of WHHT.ResultsMediolateral regularity, anteroposterior symmetry, and mediolateral stability were reduced in NW-PwMS vs. HS (p ≤ 0.001), and showed moderate discriminant ability (area under the receiver operator characteristic curve [AUC]: 0.71–0.73). AP symmetry and ML stability were reduced (p ≤ 0.026) in EDSS: 2–2.5 vs. EDSS: 0–1.5 subgroup (AUC: 0.69–0.70). The number of NW-PwMS showing at least one abnormal instrumented metric (68%) was larger (p ≤ 0.002) than the number of participants showing abnormal FAB-s-Item6 (32%) and FAB-s clinical scores (39%). EDSS: 2–2.5 subgroup included more individuals showing abnormal instrumented metrics (86%) compared to EDSS: 0–1.5 subgroup (57%). The instrumented metrics significantly correlated with FAB-s-Item6 and FAB-s scores (|Spearman's rs| ≥ 0.37, p < 0.001), thus demonstrating their concurrent validity.ConclusionThe instrumented assessment of WHHT provided valid objective metrics that discriminated, with higher sensitivity than clinical scores, between HS and NW-PwMS and between EDSS subgroups. The method is a promising tool to complement clinical evaluation, and reveal subclinical impairments in persons who can benefit from early preventive rehabilitative interventions.

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“…Moving treadmill training can solve the problem of the patient's lack of balance 3 Neural Plasticity function and the limitation of the training venue. In recent years, it has been more used for walking function training [13,14]. However, if patients have a foot drop, when simply using moving treadmill to walk, they will be prone to sprain their ankle due to the constant speed of the crawler.…”

Section: Discussionmentioning

confidence: 99%

Changes in Gait Characteristics of Stroke Patients with Foot Drop after the Combination Treatment of Foot Drop Stimulator and Moving Treadmill Training

Chen

Haiwang

et al. 2021

Neural Plasticity

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Objective. To study the changes in gait characteristics of stroke patients with foot drop after the combination treatment of foot drop stimulator and moving treadmill training and thus provide a basis for the improvement in a foot drop gait after stroke. Methods. Sixty patients with hemiplegia and foot drop caused by stroke were randomly divided into two groups of 30: the test group and the control group. Both groups received basic rehabilitation training. On this basis, the test group received the combination treatment of foot drop stimulator and moving treadmill training. The control group received foot drop stimulator training. Both groups received consecutive treatment for 3 weeks, five times a week, and every single time lasted for 30 minutes. Before and after the treatment, a gait watch three-dimensional gait analysis system was used to measure and record the maximum angles of flexion of the affected side’s hip, knee, and ankle; the pace; the step length asymmetry; the iEMG of the tibialis anterior muscle; the functional ambulation category; and Ashworth’s modified spasticity classification of the gastrocnemius. Results. After treatment, in the two groups, the maximum angles of flexion of the affected side’s hip, knee, and ankle improved, the pace increased, the step length asymmetry decreased, the iEMG of the tibialis anterior muscle increased, the functional ambulation category improved, and Ashworth’s modified spasticity classification of the gastrocnemius decreased, but the above changes in the test group were better than those in the control group. The difference is statistically significant ( p < 0.05 ). Conclusions. The combination treatment of the foot drop stimulator and moving treadmill can significantly improve stroke patients’ foot gait and promote the normalization of hip flexion, knee flexion, and ankle flexion. It can increase the pace, significantly reduce the step length asymmetry, reduce the muscle tone of the gastrocnemius, and improve walking function.

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Impaired local dynamic stability during treadmill walking predicts future falls in patients with multiple sclerosis: A prospective cohort study

Cited by 23 publications

References 36 publications

Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis

Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis

Walking With Horizontal Head Turns Is Impaired in Persons With Early-Stage Multiple Sclerosis Showing Normal Locomotion

Changes in Gait Characteristics of Stroke Patients with Foot Drop after the Combination Treatment of Foot Drop Stimulator and Moving Treadmill Training

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