Bipedal hopping timed to a metronome to detect impairments in anticipatory motor control in people with mild multiple sclerosis

Kirkland, Megan C.; Chen, Alice; Downer, Matthew B.; Holloway, Brett J.; Wallack, Elizabeth M.; Lockyer, Evan J.; Buckle, Natasha C.M.; Abbott, Courtney; Ploughman, Michelle

doi:10.1016/j.clinbiomech.2018.04.009

Cited by 5 publications

(3 citation statements)

References 187 publications

(352 reference statements)

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“…This difference may be relevant as a change of 15% could be significant [48] and the fact that the control group walked at a high-intensity level controlled by rating perceived exertion. Nonetheless, the intervention group introduced this intensity by metabolic stress with the BFR intervention, and controlled the rests with a metronome, which may have produced better results at the brain level, especially anticipatory motor control [49]. In self-selected walking velocity (SSWV), both groups improved between 0.46 and 0.49 m per second, which may indicate that subjects with knee arthroscopy obtain the same benefits from BFR intervention and mechanical stimulus of the loads [43].…”

Section: Speedmentioning

confidence: 99%

Effectiveness of Blood Flow Restriction on Functionality, Quality of Life and Pain in Patients with Neuromusculoskeletal Pathologies: A Systematic Review

Reina-Ruiz

Martínez-Cal

Molina-Torres

et al. 2023

IJERPH

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Background: Blood flow restriction is characterized as a method used during exercise at low loads of around 20–40% of a repetition maximum, or at a low-moderate intensity of aerobic exercise, in which cuffs that occlude the proximal part of the extremities can partially reduce arterial flow and fully restrict the venous flow of the musculature in order to achieve the same benefits as high-load exercise. Objective: The main objective of this systematic literature review was to analyze the effects of BFR intervention on pain, functionality, and quality of life in subjects with neuromusculoskeletal pathologies. Methods: The search to carry out was performed in PubMed, Cochrane, EMBASE, PEDro, CINHAL, SPORTDiscus, Trip Medical Database, and Scopus: “kaatsu” OR “ischemic training” OR “blood flow restriction” OR “occlusion resistance training” OR “vascular occlusion” OR “vascular restriction”. Results: After identifying 486 papers and eliminating 175 of them due to duplication and 261 after reading the title and abstract, 50 papers were selected. Of all the selected articles, 28 were excluded for not presenting a score equal to or higher than 6 points on the PEDro scale and 8 for not analyzing the target outcome variables. Finally, 14 papers were selected for this systematic review. Conclusions: The data collected indicate that the blood flow restriction tool is a therapeutic alternative due to its effectiveness under different exercise modalities. The benefits found include decreases in pain thresholds and improvement in the functionality and quality of life of the neuro-musculoskeletal patient during the first six weeks. However, the results provided by this tool are still not clear for medium- and long-term interventions.

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Section: Speedmentioning

confidence: 99%

Effectiveness of Blood Flow Restriction on Functionality, Quality of Life and Pain in Patients with Neuromusculoskeletal Pathologies: A Systematic Review

Reina-Ruiz

Martínez-Cal

Molina-Torres

et al. 2023

IJERPH

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“…To our knowledge, there are no studies that investigate CMJ performance in MS. Two pilot studies from Kirkland et al [ 21 , 22 ] provide the first evidence that jump tasks can detect differences between pwMS with mild disability and healthy controls and that they are a potentially useful measurement of lower limb function in pwMS. However, in contrast to our study, Kirkland et al assessed the use of horizontal jumps on an instrumental walkway system with small sample size.…”

Section: Introductionmentioning

confidence: 99%

Countermovement Jumps Detect Subtle Motor Deficits in People with Multiple Sclerosis below the Clinical Threshold

et al. 2023

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In the early stages of multiple sclerosis (MS), there are currently no sensitive assessments to evaluate complex motor functions. The countermovement jump (CMJ), a high-challenge task in form of a maximal vertical bipedal jump, has already been investigated as a reliable assessment in healthy subjects for lower extremity motor function. The aim was to investigate whether it is possible to use CMJ to identify subthreshold motor deficits in people with multiple sclerosis (pwMS). All participants (99 pwMS and 33 healthy controls) performed three maximal CMJs on a force plate. PwMS with full motor function and healthy controls (HC) did not differ significantly in age, disease duration, Body Mass Index and the Expanded Disability Scale Score. In comparison to HC, pwMS with full motor function demonstrated a significantly decreased CMJ performance in almost all observed kinetic, temporal and performance parameters (p < 0.05). With increasing disability in pwMS, it was also observed that jump performance decreased significantly. This study showed that the CMJ, as a high challenge task, could detect motor deficits in pwMS below the clinical threshold of careful neurological examination. Longitudinal studies are pending to evaluate whether the CMJ can be used as a standardized measure of disease progression.

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“…Patients with MS experience limb weakness, sensory loss, and foot drop which sometimes go undetected until clearly observed by others (Socie et al, 2013;Reich et al, 2018). Patients may sense subtle changes in their walking and balance before these symptoms can be detected by clinicians (Kirkland et al, 2018(Kirkland et al, , 2020. Machine learning methods have been successfully applied to estimate gait parameters from wearables in multiple sclerosis.…”

Section: Introductionmentioning

confidence: 99%

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

Combden

Jiang

et al. 2022

Front. Artif. Intell.

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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.

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Bipedal hopping timed to a metronome to detect impairments in anticipatory motor control in people with mild multiple sclerosis

Cited by 5 publications

References 187 publications

Effectiveness of Blood Flow Restriction on Functionality, Quality of Life and Pain in Patients with Neuromusculoskeletal Pathologies: A Systematic Review

Effectiveness of Blood Flow Restriction on Functionality, Quality of Life and Pain in Patients with Neuromusculoskeletal Pathologies: A Systematic Review

Countermovement Jumps Detect Subtle Motor Deficits in People with Multiple Sclerosis below the Clinical Threshold

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

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