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Lookup NU author(s): Professor Lynn RochesterORCiD, Dr Lou Sutcliffe, Isabel Neatrour
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© The Author(s) 2026. Background: Multiple sclerosis (MS) is a common cause of disability in working age adults. Current clinical assessments are inadequate at disability assessment or predicting clinically relevant outcomes. Loss of mobility is an important functional disability to people with MS. Mobilise-D aims to develop, validate, and implement a digital mobility solution which measures unsupervised mobility performance across several chronic conditions, including MS, using a single wearable device. Methods: Six hundred two adults with MS, an Expanded Disability Status Scale (EDSS) score of 3.0–6.5, documented disability worsening over the previous 2 years and a 30-day freedom from relapses, were recruited across four European centres. Results: Of 1416 invited, 602 participants (42%) were recruited. Primary recruitment sources were clinicians (42%) and local registries (42%). Among 616 who declined screening, the main reasons were a lack of interest (44%), the time commitment (25%) or the travel involved (13%). Participants had a mean age of 52 years; 64% were female, with a median EDSS score of 5.0. Of those, 56% had relapsing-remitting MS, 33% secondary progressive MS and 10% primary progressive MS. Falls occurred in 58% of participants in the 12 months prior to recruitment. Of those recruited, 556 (93%) participants had valid mobility data recorded. Conclusions: The longitudinal collection of clinical and unsupervised mobility assessments will provide a comprehensive dataset, allowing for the determination of digital mobility assessments’ construct validity, predictive capacity, responsiveness, and clinical meaningfulness. Novel insights into real-world mobility that describe both walking activity and gait outcomes will be gained. Trial registration: The study was registered at the ISRCTN registry on 12/10/2020, titled “Clinical validation of a mobility monitor to measure and predict health outcomes” (ISRCTN Number: 12051706).
Author(s): Brittain G, Buckley E, Lanfranchi V, Long M, Tsaktanis T, Rothhammer V, Hansen C, Sturner KH, Maetzler W, Rochester L, Sutcliffe L, Neatrour I, Vereijken B, Buekers J, Garcia-Aymerich J, Koch S, Armengol C, Gassner H, Jansen C-P, Rooks D, Leocani L, Brichetto G, Costa GD, Becker C, Comi G, Sharrack B
Publication type: Article
Publication status: Published
Journal: Trials
Year: 2026
Volume: 27
Online publication date: 17/01/2026
Acceptance date: 19/12/2025
Date deposited: 09/02/2026
ISSN (electronic): 1745-6215
Publisher: BioMed Central Ltd
URL: https://doi.org/10.1186/s13063-025-09404-6
DOI: 10.1186/s13063-025-09404-6
Data Access Statement: The datasets generated during and/or analysed during the current study are not publicly available yet. The Mobilise-D consortium has collected a high volume of quantitative and qualitative data related to its primary objectives. In particular, comprehensive data have been collected within the cross-sectional technical validation study (TVS, 2020–2022) and the longitudinal clinical validation study (CVS, 2021–2024). The partners involved in the Mobilise-D project are dedicated to sharing the data, algorithms, and code generated during the project with the wider scientific community. In doing so, we are committed to considering and respecting the data and privacy rights of the study participants, adhering to the relevant laws, and ensuring that the needs of the Mobilise-D researchers who are writing and publishing research papers are met. Additionally, we will only release data that has been subject to a rigorous quality assurance process.
PubMed id: 41547821
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