作者
Shaoxiong Sun, Amos A Folarin, Yuezhou Zhang, Nicholas Cummins, Shuo Liu, Callum Stewart, Yatharth Ranjan, Zulqarnain Rashid, Pauline Conde, Petroula Laiou, Heet Sankesara, Gloria Dalla Costa, Letizia Leocani, Per Soelberg Sørensen, Melinda Magyari, Ana Isabel Guerrero, Ana Zabalza, Srinivasan Vairavan, Raquel Bailon, Sara Simblett, Inez Myin-Germeys, Aki Rintala, Til Wykes, Vaibhav A Narayan, Matthew Hotopf, Giancarlo Comi, Richard JB Dobson
发表日期
2022/12/1
期刊
Computer methods and programs in biomedicine
卷号
227
页码范围
107204
出版商
Elsevier
简介
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 …
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S Sun, AA Folarin, Y Zhang, N Cummins, S Liu… - Computer methods and programs in biomedicine, 2022