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Updated: Jun 24, 2026
The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool
Published on: June 30, 2014
Accelerometry-Assessed Physical Activity and Circadian Rhythm to Detect Clinical Disability Status in Multiple
Nicole Bou Rjeily1, Muraleetharan Sanjayan1, Pratim Guha Niyogi2
1Department of Neurology, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Pathology 627, Baltimore, MD, 21287, United States, 1 4437226779.
Background:
Tools for measuring clinical disability status in people with multiple sclerosis (MS) are limited. Accelerometry objectively assesses physical activity and circadian rhythmicity profiles in the real-world environment and may potentially distinguish levels of disability in MS.
Objective:
This study aims to determine if accelerometry can detect differences in physical activity and circadian rhythms between relapsing-remitting multiple sclerosis (RRMS) and progressive multiple sclerosis (PMS) and to assess the interplay within person between the 2 domains of physical activity (PA) and circadian rhythm (CR) in relation to MS type.
Methods:
This study represents an analysis of the baseline data from the prospective HEAL-MS (home-based evaluation of actigraphy to predict longitudinal function in multiple sclerosis) study. Participants were divided into 3 groups based on the Expanded Disability Status Scale (EDSS) criteria for sustained disability progression: RRMS-Stable, RRMS-Suspected progression, and PMS. Baseline visits occurred between January 2021 and March 2023. Clinical outcome measures were collected by masked examiners. Participants wore the GT9X Link ActiGraph on their nondominant wrists for 2 weeks. After adjusting for age, sex, and BMI, a logistic regression model was fitted to evaluate the association of each accelerometry metric with odds of PMS versus RRMS. We also evaluated the association of accelerometry metrics in differentiating the 2 RRMS subtypes. The Joint and Individual Variation Explained (JIVE) model was used to assess the codependencies between the PA and CR domains and their joint and individual association with MS subtype.
Results:
A total of 253 participants were included: 86 with RRMS-Stable, 82 with RRMS-Suspected progression, and 85 with PMS. Compared to RRMS, participants with PMS had lower total activity counts (β=-0.32, 95% CI -0.61 to -0.03), lower time spent in moderate to vigorous physical activity (β=-0.01, 95% CI -0.02 to -0.004), higher active-to-sedentary transition probability (β=5.68, 95% CI 1.86-9.5), lower amplitude (β=-0.0004, 95% CI -0.0008 to -0.0001), higher intradaily variability (β=4.64, 95% CI 1.45-7.84), and lower interdaily stability (β=-4.43, 95% CI -8.77 to -0.10). Using the JIVE model for PA and CR domains, PMS had higher first joint component (β=0.367, 95% CI 0.088-0.656), lower PA-1 component (β=-0.441, 95% CI -0.740 to -0.159), and lower PA-2 component (β=-0.415, 95% CI -0.717 to -0.126) compared to RRMS. No significant differences were detected between the 2 RRMS subtypes except for lower relative amplitude in those with suspected progression (β=-5.26, 95% CI -10.80 to -0.20).
Conclusions:
Accelerometry detected differences in physical activity patterns between RRMS and PMS. More advanced analytic techniques may help discern differences between the 2 RRMS subgroups. Longitudinal follow-up is underway to assess the potential for accelerometry to detect or predict disability progression.
