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. 2025 Mar 31:13:e57599.
doi: 10.2196/57599.

Accelerometry-Assessed Physical Activity and Circadian Rhythm to Detect Clinical Disability Status in Multiple Sclerosis: Cross-Sectional Study

Nicole Bou Rjeily  1 , Muraleetharan Sanjayan  1 , Pratim Guha Niyogi  2 , Blake E Dewey  1 , Alexandra Zambriczki Lee  1 , Christy Hulett  1 , Gabriella Dagher  1 , Chen Hu  1 , Rafal D Mazur  1 , Elena M Kenney  1 , Erin Brennan  1 , Anna DuVal  1 , Peter A Calabresi  1 , Vadim Zipunnikov  2 , Kathryn C Fitzgerald  1 , Ellen M Mowry  1
Affiliations

Accelerometry-Assessed Physical Activity and Circadian Rhythm to Detect Clinical Disability Status in Multiple Sclerosis: Cross-Sectional Study

Nicole Bou Rjeily et al. JMIR Mhealth Uhealth. .

Abstract

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.

Keywords: ActiGraph; accelerometer; accelerometry; circadian rhythm; disability; multiple sclerosis; physical activity; progressive.

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Conflict of interest statement

Conflicts of Interest: EMM discloses research funding from Biogen and Roche/Genentech, consulting for BeCareLink, LLC, and royalties for editorial duties for UpToDate. PAC is/has been Principal Investigator on grants to Johns Hopkins University from Genentech and Principia. He has served as a paid consultant for Lilly, Avidea Technologies, Idorsia, Nervgen, and Biogen. All other authors have no disclosures.

Figures

Figure 1.
Figure 1.. Conceptual representation of associations between multiple sclerosis (MS) subtype and the physical activity (PA) and circadian rhythm (CR) domains performed in standard regression modeling (left) versus the associations between MS subtype and 3 sets of independent (uncorrelated) latent variables representing joint-PA-CR, individual-PA, and individual-CR information after JIVE (Joint and Individual Variation Explained) decomposition (right). Regression analysis with JIVE components as predictors can reveal and distinguish joint and individual associations between the PA and CR domains and MS subtype.
Figure 2.
Figure 2.. First 5 functional principal components (fPCs). fPC 2, 3, and 4 values are multiplied by −1.
Figure 3.
Figure 3.. Total activity counts (TAC) for each 2-hour interval over the course of 24 hours for PMS versus RRMS. The 2-sample t test P values show the 2-hour intervals when TAC was significantly different between the 2 groups. As an additional reference, the logistic regression P values are shown with the TAC 2-hour intervals used as predictors, after adjusting for age, sex, and BMI. MS: multiple sclerosis; PMS: progressive multiple sclerosis; RRMS: relapsing-remitting multiple sclerosis.
Figure 4.
Figure 4.. Joint and Individual Variation Explained (JIVE) by the 2 JIVE accelerometry-derived domains of circadian rhythm (CR) and physical activity (PA).
See this image and copyright information in PMC

References

    1. Inojosa H, Schriefer D, Ziemssen T. Clinical outcome measures in multiple sclerosis: a review. Autoimmun Rev. 2020 May;19(5):102512. doi: 10.1016/j.autrev.2020.102512. doi. Medline. - DOI - PubMed
    1. Albrecht H, Wötzel C, Erasmus LP, Kleinpeter M, König N, Pöllmann W. Day-to-day variability of maximum walking distance in MS patients can mislead to relevant changes in the Expanded Disability Status Scale (EDSS): average walking speed is a more constant parameter. Mult Scler. 2001 Apr;7(2):105–109. doi: 10.1177/135245850100700206. doi. Medline. - DOI - PubMed
    1. Christogianni A, Bibb R, Davis SL, et al. Temperature sensitivity in multiple sclerosis: an overview of its impact on sensory and cognitive symptoms. Temperature (Austin) 2018 Jul 3;5(3):208–223. doi: 10.1080/23328940.2018.1475831. doi. - DOI - PMC - PubMed
    1. Katz Sand I, Krieger S, Farrell C, Miller AE. Diagnostic uncertainty during the transition to secondary progressive multiple sclerosis. Mult Scler. 2014 Oct;20(12):1654–1657. doi: 10.1177/1352458514521517. doi. - DOI - PubMed
    1. Inojosa H, Proschmann U, Akgün K, Ziemssen T. A focus on secondary progressive multiple sclerosis (SPMS): challenges in diagnosis and definition. J Neurol. 2021 Apr;268(4):1210–1221. doi: 10.1007/s00415-019-09489-5. doi. Medline. - DOI - PubMed

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