126888 20241125101156.0 doi 10.3390/s23136017 sideral 134246 ART-2023-134246 eng Kontaxis, Spyridon Universidad de Zaragoza (orcid)0000-0002-1297-0691 Automatic assessment of the 2-minute walk distance for remote monitoring of people with multiple sclerosis 2023 Access copy available to the general public Unrestricted 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. info:eu-repo/grantAgreement/ES/DGA/T39-23R info:eu-repo/grantAgreement/EC/H2020/115902/EU/Remote Assessment of Disease and Relapse in Central Nervous System Disorders/RADAR-CNS This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 115902-RADAR-CNS info:eu-repo/grantAgreement/EUR/MICINN/TED2021-131106B-I00 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 3.4 2023 CHEMISTRY, ANALYTICAL 34 / 106 = 0.321 2023 Q2 T1 INSTRUMENTS & INSTRUMENTATION 24 / 76 = 0.316 2023 Q2 T1 ENGINEERING, ELECTRICAL & ELECTRONIC 122 / 353 = 0.346 2023 Q2 T2 0.786 2023 Instrumentation 2023 Q1 Analytical Chemistry 2023 Q1 Atomic and Molecular Physics, and Optics 2023 Q1 Information Systems 2023 Q2 Medicine (miscellaneous) 2023 Q2 Biochemistry 2023 Q2 Electrical and Electronic Engineering 2023 Q2 7.3 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Laporta, Estela Garcia, Esther Martinis, Matteo Leocani, Letizia Roselli, Lucia Buron, Mathias Due Guerrero, Ana Isabel Zabala, Ana Cummins, Nicholas Vairavan, Srinivasan Hotopf, Matthew Dobson, Richard J. B. Narayan, Vaibhav A. La Porta, Maria Libera Costa, Gloria Dalla Magyari, Melinda Sørensen, Per Soelberg Nos, Carlos Bailon, Raquel Universidad de Zaragoza (orcid)0000-0003-1272-0550 Comi, Giancarlo 5008 800 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Teoría Señal y Comunicac. 23, 13 (2023), 6017 [11 pp.] Sensors Sensors 1424-8220 750017 http://zaguan.unizar.es/record/126888/files/texto_completo.pdf Versión publicada 2623785 http://zaguan.unizar.es/record/126888/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:126888 articulos driver 2024-11-22-12:09:32 ARTICLE