Recruitment of the multiple sclerosis cohort within the European Mobilise-D clinical validation study—lessons learnt, baseline demographics and clinical characteristics

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).