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

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  • Spyridon Kontaxis
  • Estela Laporta
  • Esther Garcia
  • Matteo Martinis
  • Letizia Leocani
  • Lucia Roselli
  • Mathias Due Buron
  • Ana Isabel Guerrero
  • Ana Zabala
  • Nicholas Cummins
  • Srinivasan Vairavan
  • Matthew Hotopf
  • Richard J.B. Dobson
  • Vaibhav A. Narayan
  • Maria Libera La Porta
  • Gloria Dalla Costa
  • Melinda Magyari
  • Sørensen, Per Soelberg
  • Carlos Nos
  • Raquel Bailon
  • Giancarlo Comi
  • on behalf of the RADAR-CNS consortium

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.

Original languageEnglish
Article number6017
JournalSensors
Volume23
Issue number13
Number of pages11
ISSN1424-8220
DOIs
Publication statusPublished - 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

    Research areas

  • accelerometer sensor, disability level, fatigue severity, walk tests, wearable device

ID: 396928718