Year: 2025
Type: article
Study design: Observational Study
Abstract: Introduction For people with multiple sclerosis (MS), mobility impairment is common and a significant contributor to reduced quality of life. With advancements in wearable technology, step count data has emerged as a promising method to track mobility and monitor functional decline. However, studies comparing the replicability of wearable mobility data using varying devices remain limited in MS populations. Methods This study investigates the reliability of step count data and its associations with clinical outcomes in MS patients using two independent cohorts with different wearable devices: California (CA) ( n = 97 Fitbit wrist sensor, 4-week wear) and Virginia (VA) ( n = 61; ActiGraph hip sensor, 7-day wear). We analyzed correlations between average daily step counts and common MS clinical measures [disability: Expanded Disability Status Scale (EDSS); walking speed: Timed 25-Foot Walk (T25FW)] as well as patient-reported outcomes (12-item MS walking scale, MSWS-12, Modified Fatigue Impact score, MFIS). Results Analysis of the VA cohort revealed similar average daily step counts to those seen in the CA cohort (6,010 vs. 5,478 steps/day). Step count variability (standard deviation) decreased with increasing EDSS in both cohorts. Step counts in the VA cohort were significantly correlated with EDSS ( r = −0.34), T25FW ( r = −0.58), MSWS-12 ( r = −0.57), and MFIS ( r = −0.45), similar to findings from the CA cohort. Additionally, within-subject reliability over 7 days was moderate (ICC = 0.599), with high correlations between 4-day and 7-day averages ( r ≥ 0.98). Discussion The step count analyses from two different wearable devices show replicable associations with clinical and patient-reported outcomes in MS, highlighting their promise as digital biomarkers for clinical monitoring and care, rehabilitation, and patient self-management.
Source: Frontiers in Neurology
Authors: Myla D. Goldman, Shanshan Chen, Bhavana Kunisetty, Jeffrey M. Gelfand, Bruce A. C. Cree, Valerie J. Block
Language: English
Subfield: Pathology and Forensic Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W4415684398