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      Impact of the COVID-19 Pandemic on the Personal Networks and Neurological Outcomes of People With Multiple Sclerosis: Cross-Sectional and Longitudinal Case-Control Study

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          Abstract

          Background

          The coronavirus disease 2019 (COVID-19) pandemic has negatively affected the social fabric.

          Objective

          We evaluated the associations between personal social networks and neurological function in people with multiple sclerosis (pwMS) and controls in the prepandemic and pandemic periods.

          Methods

          During the early pandemic (March-December 2020), 8 cohorts of pwMS and controls completed a questionnaire quantifying the structure and composition of their personal social networks, including the health behaviors of network members. Participants from 3 of the 8 cohorts had additionally completed the questionnaire before the pandemic (2017-2019). We assessed neurological function using 3 interrelated patient-reported outcomes: Patient Determined Disease Steps (PDDS), Multiple Sclerosis Rating Scale-Revised (MSRS-R), and Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function. We identified the network features associated with neurological function using paired 2-tailed t tests and covariate-adjusted regressions.

          Results

          In the cross-sectional analysis of the pandemic data from 1130 pwMS and 1250 controls during the pandemic, having a higher percentage of network members with a perceived negative health influence was associated with worse disability in pwMS (MSRS-R: β=2.181, 95% CI 1.082-3.279; P<.001) and poor physical function in controls (PROMIS Physical Function: β=–5.707, 95% CI –7.405 to –4.010; P<.001). In the longitudinal analysis of 230 pwMS and 136 controls, the networks of all participants contracted, given an increase in constraint (pwMS-prepandemic: mean 52.24, SD 15.81; pwMS-pandemic: mean 56.77, SD 18.91; P=.006. Controls-prepandemic: mean 48.07, SD 13.36; controls-pandemic: mean 53.99, SD 16.31; P=.001) and a decrease in network size (pwMS-prepandemic: mean 8.02, SD 5.70; pwMS-pandemic: mean 6.63, SD 4.16; P=.003. Controls-prepandemic: mean 8.18, SD 4.05; controls-pandemic: mean 6.44, SD 3.92; P<.001), effective size (pwMS-prepandemic: mean 3.30, SD 1.59; pwMS-pandemic: mean 2.90, SD 1.50; P=.007. Controls-prepandemic: mean 3.85, SD 1.56; controls-pandemic: mean 3.40, SD 1.55; P=.01), and maximum degree (pwMS-prepandemic: mean 4.78, SD 1.86; pwMS-pandemic: mean 4.32, SD 1.92; P=.01. Controls-prepandemic: mean 5.38, SD 1.94; controls-pandemic: mean 4.55, SD 2.06; P<.001). These network changes were not associated with worsening function. The percentage of kin in the networks of pwMS increased (mean 46.06%, SD 29.34% to mean 54.36%, SD 30.16%; P=.003) during the pandemic, a change that was not seen in controls.

          Conclusions

          Our findings suggest that high perceived negative health influence in the network was associated with worse function in all participants during the pandemic. The networks of all participants became tighter knit, and the percentage of kin in the networks of pwMS increased during the pandemic. Despite these perturbations in social connections, network changes from the prepandemic to the pandemic period were not associated with worsening function in all participants, suggesting possible resilience.

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          Most cited references63

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          Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

          Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.
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            The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations.

            In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators.
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              lavaan: AnRPackage for Structural Equation Modeling

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                Author and article information

                Contributors
                Journal
                JMIR Public Health Surveill
                JMIR Public Health Surveill
                JPH
                JMIR Public Health and Surveillance
                JMIR Publications (Toronto, Canada )
                2369-2960
                2024
                6 February 2024
                : 10
                : e45429
                Affiliations
                [1 ] Columbia University Irving Medical Center New York, NY United States
                [2 ] University of Pittsburgh Pittsburgh, PA United States
                [3 ] Brigham and Women's Hospital Boston, MA United States
                [4 ] SUNY at Buffalo Buffalo, NY United States
                [5 ] Yale University New Haven, CT United States
                [6 ] University of Pennsylvania Philadelphia, PA United States
                Author notes
                Corresponding Author: Zongqi Xia zxia1@ 123456post.harvard.edu
                Author information
                https://orcid.org/0000-0002-2133-9607
                https://orcid.org/0000-0001-9113-0502
                https://orcid.org/0000-0001-6470-7548
                https://orcid.org/0000-0002-4146-8932
                https://orcid.org/0000-0001-5062-8275
                https://orcid.org/0000-0003-1941-3668
                https://orcid.org/0000-0002-8604-2951
                https://orcid.org/0000-0001-6732-151X
                https://orcid.org/0000-0002-1179-3937
                https://orcid.org/0000-0002-8057-2505
                https://orcid.org/0000-0003-1500-2589
                Article
                v10i1e45429
                10.2196/45429
                10879979
                38319703
                d880ecb4-c9ee-4677-ac98-92d8dec3a758
                ©Claire Riley, Shruthi Venkatesh, Amar Dhand, Nandini Doshi, Katelyn Kavak, Elle Levit, Christopher Perrone, Bianca Weinstock-Guttman, Erin Longbrake, Philip De Jager, Zongqi Xia. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 06.02.2024.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.

                History
                : 30 December 2022
                : 12 April 2023
                : 5 August 2023
                : 31 August 2023
                Categories
                Original Paper
                Original Paper

                neurology,neurodegenerative disease,multiple sclerosis,personal networks,covid-19

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