Remote monitoring in the home validates clinical gait measures for multiple sclerosis

Title: Remote monitoring in the home validates clinical gait measures for multiple sclerosis
Authors: Supratak, A
Datta, G
Gafson, AR
Nicholas, R
Guo, Y
Matthews, PM
Item Type: Journal Article
Abstract: Background: The timed 25-foot walk (T25FW) is widely used as a clinic performance measure, but has yet to be directly validated against gait speed in the home environment. Objectives: To develop an accurate method for remote assessment of walking speed and to test how predictive the clinic T25FW is for real-life walking. Methods: An AX3-Axivity tri-axial accelerometer was positioned on 32 MS patients (Expanded Disability Status Scale [EDSS] 0–6) in the clinic, who subsequently wore it at home for up to 7 days. Gait speed was calculated from these data using both a model developed with healthy volunteers and individually personalized models generated from a machine learning algorithm. Results: The healthy volunteer model predicted gait speed poorly for more disabled people with MS. However, the accuracy of individually personalized models was high regardless of disability (R-value = 0.98, p-value = 1.85 × 10−22). With the latter, we confirmed that the clinic T25FW is strongly predictive of the maximum sustained gait speed in the home environment (R-value = 0.89, p-value = 4.34 × 10−8). Conclusion: Remote gait monitoring with individually personalized models is accurate for patients with MS. Using these models, we have directly validated the clinical meaningfulness (i.e., predictiveness) of the clinic T25FW for the first time.
Issue Date: 13-Jul-2018
Date of Acceptance: 22-Jun-2018
URI: http://hdl.handle.net/10044/1/72500
DOI: https://doi.org/10.3389/fneur.2018.00561
ISSN: 1664-2295
Publisher: Frontiers Media
Start Page: 1
End Page: 9
Journal / Book Title: Frontiers in Neurology
Volume: 9
Copyright Statement: © 2018 Supratak, Datta, Gafson, Nicholas, Guo and Matthews. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY)(http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: Science & Technology
Life Sciences & Biomedicine
Clinical Neurology
Neurosciences
Neurosciences & Neurology
multiple sclerosis
real world data
biomarkers
gait
actigraphy
remote sensing technology
WALKING SPEED
ACCELEROMETRY
VARIABILITY
IMPAIRMENT
PARAMETERS
FATIGUE
IMPACT
MS
actigraphy
biomarkers
gait
multiple sclerosis
real world data
remote sensing technology
Science & Technology
Life Sciences & Biomedicine
Clinical Neurology
Neurosciences
Neurosciences & Neurology
multiple sclerosis
real world data
biomarkers
gait
actigraphy
remote sensing technology
WALKING SPEED
ACCELEROMETRY
VARIABILITY
IMPAIRMENT
PARAMETERS
FATIGUE
IMPACT
MS
1109 Neurosciences
1103 Clinical Sciences
1701 Psychology
Publication Status: Published
Article Number: ARTN 561
Online Publication Date: 2018-07-13
Appears in Collections:Faculty of Medicine



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