TY - JOUR
T1 - The utility of wearable devices in assessing ambulatory impairments of people with multiple sclerosis in free-living conditions
AU - Sun, Shaoxiong
AU - Folarin, Amos A
AU - Zhang, Yuezhou
AU - Cummins, Nicholas
AU - Liu, Shuo
AU - Stewart, Callum
AU - Ranjan, Yatharth
AU - Rashid, Zulqarnain
AU - Conde, Pauline
AU - Laiou, Petroula
AU - Sankesara, Heet
AU - Dalla Costa, Gloria
AU - Leocani, Letizia
AU - Sørensen, Per Soelberg
AU - Magyari, Melinda
AU - Guerrero, Ana Isabel
AU - Zabalza, Ana
AU - Vairavan, Srinivasan
AU - Bailon, Raquel
AU - Simblett, Sara
AU - Myin-Germeys, Inez
AU - Rintala, Aki
AU - Wykes, Til
AU - Narayan, Vaibhav A
AU - Hotopf, Matthew
AU - Comi, Giancarlo
AU - Dobson, Richard Jb
AU - RADAR-CNS consortium
N1 - Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85141531810&partnerID=8YFLogxK
U2 - 10.1016/j.cmpb.2022.107204
DO - 10.1016/j.cmpb.2022.107204
M3 - Journal article
C2 - 36371974
SN - 0169-2607
VL - 227
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
M1 - 107204
ER -