2025 American Academy of Neurology Abstract Website

Basil Sharrack¹, Gavin Brittain¹, Mike Long², Ellen Buckley², Giancarlo Comi³, Letizia Leocani⁴, Gloria Dallas Costa⁴, Veit Rothhammer⁵, Klarissa Stürner⁶, Clint Hansen⁶, Giampaolo Brichetto⁷, Clemens Becker⁸, Brian Caulfield⁹, Alice Nieuwboer¹⁰, Christian Schlenstedt¹¹, Anat Mirelman¹², Jeff Hausdorff¹³, Heiko Gassner¹⁴, Pieter Ginis¹⁵, Lisa Alcock¹⁶, Dan Rooks¹⁷, Lynn Rochester¹⁶, Walter Maetzler¹⁸, Alison Yarnall¹⁶, Judith Garcia¹⁹
¹Sheffield Teaching Hospitals NHS Foundation Trust and The University of Sheffield, ²University of Sheffield, ³Department of Neurorehabilitation, ⁴University Vita-Salute San Raffaele, INSPE, ⁵Universität Erlangen-Nuernberg, ⁶Department of Neurology, UKSH Kiel, Kiel University, ⁷AISM Rehabilitation Service, Italian MS Society, ⁸Unit of Digital Geriatric Medicine, University Hospital Heidelberg, ⁹School of Public Health Physiotherapy and Sports Science, University College Dublin, ¹⁰KU Leuven, Department of Rehabilitation Sciences, ¹¹MSH Medical School Hamburg, The University of Kiel, ¹²Sackler School of Medicine and Sagol School of Neuroscience, ¹³Tel Aviv Medical Center, Tel Aviv University, ¹⁴University Hospital Erlangen, ¹⁵KU Leuven, ¹⁶University of Newcastle upon Tyne, ¹⁷Novartis, ¹⁸Kiel University, ¹⁹ISGlobal

Objective:

Examine the construct validity and acceptance of digital mobility outcomes (DMOs) in the largest study of unsupervised walking in people with neurological disease, Mobilise-D.

Background:

There is a rapidly growing number of digital health outcome measures. The unsupervised measurement of real-world mobility produces DMOs that represent a patient's gait and walking behaviour, which are not limited to typically used measures, such as step count.

Design/Methods:

600 people with Parkinson’s disease (PD) and 602 with multiple sclerosis (MS) were recruited across Europe. Clinical assessments were followed by 7 days of unsupervised mobility monitoring using a body-worn sensor. Valid data was considered as ≥3 days with ≥12 hours wear time per day. A variety of DMOs were extracted from walking bouts >10s duration, and segmented by walking bout duration. Construct validity was assessed by testing correlations based on an a priori hypothesis.

Results:

531 PD (mean age 66 years, 64% male, mean Movement Disorder Society-Unified Parkinson’s Disease Rating Scale total score of 48) and 556 MS (mean age 52 years, 65% female, median expanded disability status scale score of 5) participants had a valid assessment. Construct validity was met for 12 and 21 of 24 DMOs in PD and MS, respectively. Irrespective of disease, DMOs worsen as disease severity states increases. The most consistent results were seen in DMOs reflecting the amount and pace of walking.

Acceptance to being monitored was similar in both groups with ≥92% accepting of being remotely monitored during the study and ≥79% willing to wear the device as part of clinical care.

Conclusions:

Real-world mobility monitoring is a comprehensive method of gait assessment and a valid construct in people with MS and PD. Patients are ready and accepting of its potential use. Further analysis will help to establish the place of DMOs in future clinical care and trials.