Bipedal hopping as a new measure to detect subtle sensorimotor impairment in people with multiple sclerosis

Kirkland, Megan C.; Wadden, Katie P.; Ploughman, Michelle

doi:10.1080/09638288.2020.1820585

Cited by 8 publications

(10 citation statements)

References 169 publications

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“…Our study findings are consistent with Kirkland et al in showing that bipedal hopping can detect and monitor sensorimotor control in pwMS who do not currently experience clinical deficits [ 4 ]. Compared to Kirkland et al, we used the CMJ instead of horizontal jumps as it is already a reliable and valid measure of muscle strength and neuromuscular control in sports medicine [ 14 , 36 ].…”

Section: Discussionsupporting

confidence: 92%

“…These findings have clinical relevance in the care of pwMS. Identifying early sensorimotor deficits is important to initiate early rehabilitative intervention [ 4 , 37 ]. The CMJ in MS can be useful not only for early detection but also for determining the exact movement impairment.…”

Section: Discussionmentioning

confidence: 99%

“…In the early stage of disease in MS, when pwMS have no or minimal disability, there are currently no sensitive objective assessments to identify subtle motor deficits in the lower extremities as well as to identify and quantify early deficits in complex movements. However, the ability to identify, specify and monitor subtle motor impairments is critical to the management of optimal disease-modifying and symptomatic treatment [ 4 ]. In the early stages of MS, neurological reserve can functionally compensate for neuronal damage caused by the demyelinating lesions through various mechanisms involving, for example, cerebral reorganization [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, in contrast to our study, Kirkland et al assessed the use of horizontal jumps on an instrumental walkway system with small sample size. The combination of multiple domains in one test enables the identification of deficits and facilitates adequate rehabilitation [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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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Robotic Rigor: Validity of the Kinarm End-Point Robot Visually Guided Reaching Test in Multiple Sclerosis

Bray,

Raza,

Avila

et al. 2024

Archives of Rehabilitation Research and Clinical Translation

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Bipedal hopping as a new measure to detect subtle sensorimotor impairment in people with multiple sclerosis

Cited by 8 publications

References 169 publications

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

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

Robotic Rigor: Validity of the Kinarm End-Point Robot Visually Guided Reaching Test in Multiple Sclerosis

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