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Fragmentation, circadian amplitude, and fractal pattern of daily-living physical activity in people with multiple sclerosis: Is there relevant information beyond the total amount of physical activity?

  • Amit Salomon
    Affiliations
    Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
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  • Irina Galperin
    Affiliations
    Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
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  • David Buzaglo
    Affiliations
    Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
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  • Anat Mirelman
    Affiliations
    Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel

    Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel

    Department of Neurology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
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  • Keren Regev
    Affiliations
    Neuroimmunology and Multiple Sclerosis Unit of the Neurology Division, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
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  • Arnon Karni
    Affiliations
    Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel

    Department of Neurology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

    Neuroimmunology and Multiple Sclerosis Unit of the Neurology Division, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
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  • Tanja Schmitz-Hübsch
    Affiliations
    NeuroCure, Charité – Universitaetsmedizin Berlin, Berlin, Germany

    Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Medicine and Charité – Universitaetsmedizin Berlin, Berlin, Germany
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  • Friedemann Paul
    Affiliations
    NeuroCure, Charité – Universitaetsmedizin Berlin, Berlin, Germany

    Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Medicine and Charité – Universitaetsmedizin Berlin, Berlin, Germany

    Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
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  • Hannes Devos
    Affiliations
    Mobility Consortium, Department of Physical Therapy, Rehabilitation Science, and Athletic Training, School of Health Professions, University of Kansas Medical Center, Kansas City, KS, United States
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  • Jacob J Sosnoff
    Affiliations
    Mobility Consortium, Department of Physical Therapy, Rehabilitation Science, and Athletic Training, School of Health Professions, University of Kansas Medical Center, Kansas City, KS, United States

    Illinois Multiple Sclerosis Research Collaborative, Interdisciplinary Health Science Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA
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  • Eran Gazit
    Affiliations
    Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
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  • Raz Tamir
    Affiliations
    Owlytics Healthcare Ltd., Ramat-Gan, Israel
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  • Nathaniel Shimoni
    Affiliations
    Owlytics Healthcare Ltd., Ramat-Gan, Israel
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  • Jeffrey M Hausdorff
    Correspondence
    Corresponding author at: Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
    Affiliations
    Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel

    Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel

    Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

    Rush Alzheimer's Disease Center and Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
    Search for articles by this author
Published:August 17, 2022DOI:https://doi.org/10.1016/j.msard.2022.104108

      Abstract

      Background

      Physical activity is lower in people with multiple sclerosis (pwMS) compared to healthy controls. Previous work focused on studying activity levels or activity volume, but studies of daily-living rest-activity fragmentation patterns, circadian rhythms, and fractal regulation in pwMS are limited. Based on findings in other cohorts, one could suggest that these aspects of daily-living physical activity will provide additional information about the health and well-being of pwMS. Therefore, here, we aimed to (1) identify which fragmentation, fractal, and circadian amplitude measures differ between pwMS and healthy controls, (2) evaluate the relationship between fragmentation, fractal, and circadian amplitude measures and disease severity, and (3) begin to evaluate the added value of those measures, as compared to more conventional measures of physical activity (e.g., mean signal vector magnitude (SVM). A global measure of the overall volume of physical activity).

      Methods

      132 people with relapsing-remitting MS (47±11 yrs, 69.7% female, Expanded Disability Status Scale, EDSS, median (IQR): 3 (2–4)) and 90 healthy controls (46±11 yrs, 47.8% female) were asked to wear a 3D accelerometer on their lower back for 7 days. Rest-activity fragmentation, circadian amplitude, fractal regulation, and mean SVM metrics were extracted. PwMS and healthy controls were compared using independent samples t-tests and linear regression, including comparisons adjusted for mean SVM to control for the effect of physical activity volume. Spearman correlations between measures and logistic regressions were used to identify the clinical condition based on the measures that differed significantly after adjusting for SVM. All analyses included adjustments for demographic and clinical parameters (e.g., age, sex).

      Results

      Multiple measures of activity fragmentation significantly differed between pwMS and healthy controls, reflecting a more fragmented active behavior in pwMS. PwMS had a lower circadian rhythm amplitude, indicating a smaller amplitude in the circadian changes of daily activity, and weaker temporal correlations as based on the fractal analysis. When taking into account physical activity volume, one circadian amplitude measure and one fractal measure remained significantly different in pwMS and controls. Fragmentation measures and circadian amplitude measures were significantly associated with disability level as measured by the EDSS; the association with circadian amplitude remained significant, even after adjusting for the mean SVM.

      Conclusion

      The physical activity patterns of pwMS differ from those of healthy individuals in rest-activity fragmentation, the amplitude of the circadian rhythm, and fractal regulation. Measures describing these aspects of activity provide information that is not captured in the total volume of physical activity and could, perhaps, augment the monitoring of disease progression and evaluation of the response to interventions.

      Keywords

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