doi:10.3390/s23136017engKontaxis, SpyridonLaporta, EstelaGarcia, EstherMartinis, MatteoLeocani, LetiziaRoselli, LuciaBuron, Mathias DueGuerrero, Ana IsabelZabala, AnaCummins, NicholasVairavan, SrinivasanHotopf, MatthewDobson, Richard J. B.Narayan, Vaibhav A.La Porta, Maria LiberaCosta, Gloria DallaMagyari, MelindaSørensen, Per SoelbergNos, CarlosBailon, RaquelComi, GiancarloAutomatic assessment of the 2-minute walk distance for remote monitoring of people with multiple sclerosisART-2023-134246The 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.2023http://zaguan.unizar.es/record/12688810.3390/s23136017http://zaguan.unizar.es/record/126888oai:zaguan.unizar.es:126888info:eu-repo/grantAgreement/ES/DGA/T39-23Rinfo:eu-repo/grantAgreement/EC/H2020/115902/EU/Remote Assessment of Disease and Relapse in Central Nervous System Disorders/RADAR-CNSThis project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 115902-RADAR-CNSinfo:eu-repo/grantAgreement/EUR/MICINN/TED2021-131106B-I00Sensors 23, 13 (2023), 6017 [11 pp.]byhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccess