Environmental Online Communication

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Taken together, these findings point to core human behaviors that are shared among like-minded social contacts, with eating and physical activity as major driving forces for these effects. Social contagion is a type of social influence in which behavior in one or many network members affects the behavior of the index participant.

Detection of this effect requires longitudinal data and network modeling, such as stochastic actor-oriented or instrumental variable approaches, to understand the spread of behaviors through social ties. For example, one study shows the spread of physical activity in 1 million users of a smartphone running application Antecedent exposures influencing both parties may be another contributor.

For example, rural environments with poor access to medical services may influence the habits of all members of the network with regard to seeking medical care. This may be explained by the inclusion of sex as a component of GERS 13 and prior observation that women tend to have larger social networks Another explanation is that larger network size reflects broader exposure to infectious agents that are associated with MS susceptibility, such as history of infectious mononucleosis Indeed, we observed a positive association between mononucleosis and network size among asymptomatic participants.

Finally, the role of genetic factors in network size is provocative, but the effect is modest and needs further investigation. Our study has limitations.

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First, we were unable to establish causality and directionality of the associations or the mechanisms of homophily in this cross-sectional study. Within the GEMS platform, we are gathering longitudinal social network data. Second, the primary outcome measure of neurological disability MSRS-R was skewed toward low scores due to the larger proportion of self-reported asymptomatic participants in the GEMS cohort who have low scores in this instrument. This could reduce the precision of our analyses due to a floor effect. Further, the study may be underpowered to compare asymptomatic and MS subgroups, given the modest number of the MS cases i.


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Larger studies of individuals with sporadic MS will better answer whether social network variables influence disease worsening in MS. Third, unmeasured confounders that influence report of social networks and functional disability could have affected our findings. We attempted to address this limitation by adjusting for major factors reported in the literature, including age, sex, and marital status.

While this approach may introduce unknown biases, prior work reassuringly had shown self-reported personal networks of intimate contacts to be accurate Finally, this study of the GEMS participants, who were recruited through advocacy groups, social media, and electronic communications, may not have broad generalizability because these participants are more socially engaged and better educated than the general population.

Future studies of more diverse populations and other chronic neurological disorders will be critical. The social environment is ubiquitous and important for understanding human disease etiologies and outcomes. Social network features, in general, represent an emerging group of metrics that inform aspects of health and disease, but are not currently well captured by many biomedical research studies.

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We outline an approach of quantitative social network analysis that is readily adaptable in clinical investigations. The questionnaire that we have developed for quantifying social networks is available through the open-source REDCap platform. These results suggest that interventions aimed at modulating network composition through education or treatment of members in a social network holds the promise of a novel complementary approach to managing MS onset and disease course.

The questionnaire was live for 6 weeks, with reminders sent to non-responders. At the time, the GEMS cohort included first-degree family members from across the United States recruited using patient advocacy groups, social media, and word-of-mouth The inclusion criteria were: being 18 to 50 years of age at enrollment and having at least one first-degree relative with a diagnosis of MS e.

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While asymptomatic family members who are at risk for MS represent the main focus of the GEMS project, we also recruited family members who already have a MS diagnosis for comparison in this cross-sectional study. MS cases were confirmed by review of medical records. The institutional review boards of all participating sites Partners HealthCare, National Institutes of Health, and University of Pittsburgh approved the study. All participants provided written informed consent.

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To compare the demographic characteristics between asymptomatic participants and confirmed MS cases, we performed a t test for age, chi-squared tests for dichotomous variables of sex, marital status, and living alone, as well as non-parametric Wilcoxon rank-sum tests for years of education and MSRS-R. Similarly, we performed non-parametric Wilcoxon rank-sum tests to compare network metrics between asymptomatic participants and participants with MS diagnosis. To assess the association with MSRS-R score, we performed a linear regression for each network variable, adjusting for age, sex, and marital status.

In this analysis, MSRS-R was modeled as the dependent variable and each network characteristic as the independent variable. Within each network metrics category structure and composition , we calculated the false discovery rate to adjust for multiple testing. To examine any potential bias due to non-normal distributions, we performed a sensitivity analysis applying non-parametric spearman correlation tests.

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To examine the hypothesis that as a category, social network variables were associated with the MSRS-R score, we performed an empirical omnibus test. In the first stage of this analysis, we calculated the P -values of association between each network variable and MSRS-R score using linear regression as described above.

We then compared this chi-squared statistic to an empirical distribution of chi-squared statistics as generated by 10, random permutations. The empirical omnibus P -value was then calculated as the number of times that the chi-squared statistic from the 10, permutations was greater than the true chi-squared statistic divided by the total number of permutations. The 90th and 95th empirical confidence intervals were determined using empirical P -values as generated by the 10, permutations.

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We performed the omnibus test in all participants as well as in the subset of asymptomatic participants and the subset of participants with MS diagnosis. Here, we performed linear regressions adjusting for age, modeling network size as the dependent variable, and the GERS and its components: history of infectious mononucleosis, sex, smoking status environmental risk score, and genetic risk score as the independent variables. All analyses were performed in R version 3.

All statistical tests were two-sided. Given the exploratory nature of the analysis and data, power calculations were not performed prior to analysis.

Permutations and nonparametric tests were used to avoid bias due to any non-normal data or unequal variances between groups, as necessary. R code used specifically for this project can be made available upon request. Holt-Lunstad, J. Social relationships and mortality risk: a meta-analytic review.

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follow Dhand, A. Social networks and neurological illness. Smith, K. Social networks and health. Berkman, L. Effects of treating depression and low perceived social support on clinical events after myocardial infarction: the enhancing recovery in coronary heart disease patients ENRICHD randomized trial.

JAMA , — Forster, A. A structured training programme for caregivers of inpatients after stroke TRACS : a cluster randomised controlled trial and cost-effectiveness analysis. Burt, R. Network items and the general social survey. Xia, Z.

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