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Automatic Assessment of the 2-Minute Walk Distance for Remote Monitoring of People with Multiple Sclerosis

Kontaxis, Spyridon; Laporta, Estela; Garcia, Esther; Martinis, Matteo; Leocani, Letizia; Roselli, Lucia; Buron, Mathias Due; ... RADAR, CNS Consortium; + view all (2023) Automatic Assessment of the 2-Minute Walk Distance for Remote Monitoring of People with Multiple Sclerosis. Sensors , 23 (13) , Article 6017. 10.3390/s23136017. Green open access

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Abstract

The aim of this study was to investigate the feasibility of automatically assessing the 2-Minute Walk Distance (2MWD) for monitoring people with multiple sclerosis (pwMS). For 154 pwMS, MS-related clinical outcomes as well as the 2MWDs as evaluated by clinicians and derived from accelerometer data were collected from a total of 323 periodic clinical visits. Accelerometer data from a wearable device during 100 home-based 2MWD assessments were also acquired. The error in estimating the 2MWD was validated for walk tests performed at hospital, and then the correlation (r) between clinical outcomes and home-based 2MWD assessments was evaluated. Robust performance in estimating the 2MWD from the wearable device was obtained, yielding an error of less than 10% in about two-thirds of clinical visits. Correlation analysis showed that there is a strong association between the actual and the estimated 2MWD obtained either at hospital (r = 0.71) or at home (r = 0.58). Furthermore, the estimated 2MWD exhibits moderate-to-strong correlation with various MS-related clinical outcomes, including disability and fatigue severity scores. Automatic assessment of the 2MWD in pwMS is feasible with the usage of a consumer-friendly wearable device in clinical and non-clinical settings. Wearable devices can also enhance the assessment of MS-related clinical outcomes.

Type: Article
Title: Automatic Assessment of the 2-Minute Walk Distance for Remote Monitoring of People with Multiple Sclerosis
Location: Switzerland
Open access status: An open access version is available from UCL Discovery
DOI: 10.3390/s23136017
Publisher version: https://doi.org/10.3390/s23136017
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Keywords: Science & Technology, Physical Sciences, Technology, Chemistry, Analytical, Engineering, Electrical & Electronic, Instruments & Instrumentation, Chemistry, Engineering, wearable device, accelerometer sensor, walk tests, disability level, fatigue severity, DISABILITY STATUS SCALE, ABILITY
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology
URI: https://discovery.ucl.ac.uk/id/eprint/10175868
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