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      Peer Support for Chronic Pain in Online Health Communities: Quantitative Study on the Dynamics of Social Interactions in a Chronic Pain Forum

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          Abstract

          Background

          Peer support for chronic pain is increasingly taking place on social media via social networking communities. Several theories on the development and maintenance of chronic pain highlight how rumination, catastrophizing, and negative social interactions can contribute to poor health outcomes. However, little is known regarding the role web-based health discussions play in the development of negative versus positive health attitudes relevant to chronic pain.

          Objective

          This study aims to investigate how participation in online peer-to-peer support communities influenced pain expressions by examining how the sentiment of user language evolved in response to peer interactions.

          Methods

          We collected the comment histories of 199 randomly sampled Reddit (Reddit, Inc) users who were active in a popular peer-to-peer chronic pain support community over 10 years. A total of 2 separate natural language processing methods were compared to calculate the sentiment of user comments on the forum (N=73,876). We then modeled the trajectories of users’ language sentiment using mixed-effects growth curve modeling and measured the degree to which users affectively synchronized with their peers using bivariate wavelet analysis.

          Results

          In comparison to a shuffled baseline, we found evidence that users entrained their language sentiment to match the language of community members they interacted with ( t 198=4.02; P<.001; Cohen d=0.40). This synchrony was most apparent in low-frequency sentiment changes unfolding over hundreds of interactions as opposed to reactionary changes occurring from comment to comment ( F 2,198=17.70; P<.001). We also observed a significant trend in sentiment across all users (β=–.02; P=.003), with users increasingly using more negative language as they continued to interact with the community. Notably, there was a significant interaction between affective synchrony and community tenure (β=.02; P=.02), such that greater affective synchrony was associated with negative sentiment trajectories among short-term users and positive sentiment trajectories among long-term users.

          Conclusions

          Our results are consistent with the social communication model of pain, which describes how social interactions can influence the expression of pain symptoms. The difference in long-term versus short-term affective synchrony observed between community members suggests a process of emotional coregulation and social learning. Participating in health discussions on Reddit appears to be associated with both negative and positive changes in sentiment depending on how individual users interacted with their peers. Thus, in addition to characterizing the sentiment dynamics existing within online chronic pain communities, our work provides insight into the potential benefits and drawbacks of relying on support communities organized on social media platforms.

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          Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs

          Effect sizes are the most important outcome of empirical studies. Most articles on effect sizes highlight their importance to communicate the practical significance of results. For scientists themselves, effect sizes are most useful because they facilitate cumulative science. Effect sizes can be used to determine the sample size for follow-up studies, or examining effects across studies. This article aims to provide a practical primer on how to calculate and report effect sizes for t-tests and ANOVA's such that effect sizes can be used in a-priori power analyses and meta-analyses. Whereas many articles about effect sizes focus on between-subjects designs and address within-subjects designs only briefly, I provide a detailed overview of the similarities and differences between within- and between-subjects designs. I suggest that some research questions in experimental psychology examine inherently intra-individual effects, which makes effect sizes that incorporate the correlation between measures the best summary of the results. Finally, a supplementary spreadsheet is provided to make it as easy as possible for researchers to incorporate effect size calculations into their workflow.
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            Biostatistical Analysis

            Designed for one/two-semester, junior/graduate-level courses in Biostatistics, Biometry, Quantitative Biology, or Statistics, the latest edition of this best-selling biostatistics text is both comprehensive and easy to read. It provides a broad and practical overview of the statistical analysis methods used by researchers to collect, summarize, analyze, and draw conclusions from biological research data. The Fourth Edition can serve as either an introduction to the discipline for beginning students or a comprehensive procedural reference for today's practitioners.
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              A Practical Guide to Wavelet Analysis

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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J Med Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                2024
                5 September 2024
                : 26
                : e45858
                Affiliations
                [1 ] School of Modeling, Simulation, and Training University of Central Florida Orlando, FL United States
                [2 ] Department of Informatics Luddy School of Informatics, Computing, and Engineering Indiana University Bloomington Bloomington, IN United States
                Author notes
                Corresponding Author: Aaron Necaise aaron.necaise@ 123456ucf.edu
                Author information
                https://orcid.org/0000-0002-1111-2898
                https://orcid.org/0000-0003-0026-7568
                Article
                v26i1e45858
                10.2196/45858
                11413547
                39235845
                e5deabe0-7b0d-4f73-a8e1-00286528cc97
                ©Aaron Necaise, Mary Jean Amon. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.09.2024.

                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 the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 24 January 2023
                : 10 March 2024
                : 20 May 2024
                : 24 June 2024
                Categories
                Original Paper
                Original Paper

                Medicine
                social media,chronic pain,peer support,sentiment analysis,wavelet analysis,nonlinear dynamics,growth curve modeling,online health communities,affective synchrony

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