The utility of wearable devices in assessing ambulatory impairments of people with multiple sclerosis in free-living conditions

Sun, Shaoxiong ; Folarin, Amos A ; Zhang, Yuezhou ; Cummins, Nicholas ; Liu, Shuo ; Stewart, Callum ; Ranjan, Yatharth ; Rashid, Zulqarnain ; Conde, Pauline ; Laiou, Petroula ; Sankesara, Heet ; Dalla Costa, Gloria ; Leocani, Letizia ; Sørensen, Per Soelberg ; Magyari, Melinda ; Guerrero, Ana Isabel ; Zabalza, Ana ; Vairavan, Srinivasan ; Bailon, Raquel (Universidad de Zaragoza) ; Simblett, Sara ; Myin-Germeys, Inez ; Rintala, Aki ; Wykes, Til ; Narayan, Vaibhav A ; Hotopf, Matthew ; Comi, Giancarlo ; Dobson, Richard JB ; RADAR-CNS consortium
The utility of wearable devices in assessing ambulatory impairments of people with multiple sclerosis in free-living conditions
Resumen: Background and objectives Multiple sclerosis (MS) is a progressive inflammatory and neurodegenerative disease of the central nervous system affecting over 2.5 million people globally. In-clinic six-minute walk test (6MWT) is a widely used objective measure to evaluate the progression of MS. Yet, it has limitations such as the need for a clinical visit and a proper walkway. The widespread use of wearable devices capable of depicting patients’ activity profiles has the potential to assess the level of MS-induced disability in free-living conditions.
Methods In this work, we extracted 96 features in different temporal granularities (from minute-level to day-level) from wearable data and explored their utility in estimating 6MWT scores in a European (Italy, Spain, and Denmark) MS cohort of 337 participants over an average of 10 months’ duration. We combined these features with participants’ demographics using three regression models including elastic net, gradient boosted trees and random forest. In addition, we quantified the individual feature's contribution using feature importance in these regression models, linear mixed-effects models, generalized estimating equations, and correlation-based feature selection (CFS).
Results The results showed promising estimation performance with R2 of 0.30, which was derived using random forest after CFS. This model was able to distinguish the participants with low disability from those with high disability. Furthermore, we observed that the minute-level (≤ 8 minutes) step count, particularly those capturing the upper end of the step count distribution, had a stronger association with 6MWT. The use of a walking aid was indicative of ambulatory function measured through 6MWT.
Conclusions This study demonstrates the utility of wearables devices in assessing ambulatory impairments in people with MS in free-living conditions and provides a basis for future investigation into the clinical relevance.

Idioma: Inglés
DOI: 10.1016/j.cmpb.2022.107204
Año: 2022
Publicado en: Computer Methods and Programs in Biomedicine 227 (2022), 107204 [14 pp.]
ISSN: 0169-2607

Factor impacto JCR: 6.1 (2022)
Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 25 / 110 = 0.227 (2022) - Q1 - T1
Categ. JCR: MEDICAL INFORMATICS rank: 7 / 31 = 0.226 (2022) - Q1 - T1
Categ. JCR: ENGINEERING, BIOMEDICAL rank: 22 / 96 = 0.229 (2022) - Q1 - T1
Categ. JCR: COMPUTER SCIENCE, THEORY & METHODS rank: 15 / 111 = 0.135 (2022) - Q1 - T1

Factor impacto CITESCORE: 10.1 - Medicine (Q1) - Computer Science (Q1)

Factor impacto SCIMAGO: 1.118 - Computer Science Applications (Q1) - Software (Q1) - Health Informatics (Q1)

Tipo y forma: Article (Published version)
Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)

Creative Commons You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.


Exportado de SIDERAL (2024-03-18-15:15:39)


Visitas y descargas

Este artículo se encuentra en las siguientes colecciones:
Articles



 Record created 2022-12-02, last modified 2024-03-19


Versión publicada:
 PDF
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)