Imperial College London
Alternate

Professor Paul M. Matthews

Faculty of Medicine, Department of Brain Sciences

Edmond and Lily Safra Chair, Head of Department
 
 
 
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Contact

 

+44 (0)20 7594 2855p.matthews

 
 
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Assistant

 

Ms Siobhan Dillon +44 (0)20 7594 2855

 
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Location

 

E502Burlington DanesHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Supratak:2018:10.3389/fneur.2018.00561,
author = {Supratak, A and Datta, G and Gafson, AR and Nicholas, R and Guo, Y and Matthews, PM},
doi = {10.3389/fneur.2018.00561},
journal = {Frontiers in Neurology},
pages = {1--9},
title = {Remote monitoring in the home validates clinical gait measures for multiple sclerosis},
url = {http://dx.doi.org/10.3389/fneur.2018.00561},
volume = {9},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Supratak,A
AU  - Datta,G
AU  - Gafson,AR
AU  - Nicholas,R
AU  - Guo,Y
AU  - Matthews,PM
DO  - 10.3389/fneur.2018.00561
EP  - 9
PY  - 2018///
SN  - 1664-2295
SP  - 1
TI  - Remote monitoring in the home validates clinical gait measures for multiple sclerosis
T2  - Frontiers in Neurology
UR  - http://dx.doi.org/10.3389/fneur.2018.00561
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000438533200001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.frontiersin.org/articles/10.3389/fneur.2018.00561/full
UR  - http://hdl.handle.net/10044/1/72500
VL  - 9
ER  -
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