ArticleJMIR medical informatics2020
Diabetes Self-Management in the Age of Social Media: Large-Scale Analysis of Peer Interactions Using Semiautomated Methods.
Article in JMIR medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Experiences of living with long COVID and of accessing healthcare services: a qualitative systematic review.BMJ open · 2022Pooled it
- Content matters, context matters: unraveling behavior dynamics in an online health community for tobacco cessation.JAMIA open · 2026Article
- Toward Personalized Digital Experiences to Promote Diabetes Self-Management: Mixed Methods Social Computing Approach.JMIR diabetes · 2025Article
- Reporting of Ethical Considerations in Qualitative Research Utilizing Social Media Data on Public Health Care: Scoping Review.Journal of medical Internet research · 2024Article
- Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review.Journal of medical Internet research · 2023Article
- Experiences and disease self-management in individuals living with chronic kidney disease: qualitative analysis of the National Kidney Foundation's online community.BMC nephrology · 2022Article
- Pragmatics to Reveal Intent in Social Media Peer Interactions: Mixed Methods Study.Journal of medical Internet research · 2021Article
- Identifying Self-Management Support Needs for Pregnant Women With Opioid Misuse in Online Health Communities: Mixed Methods Analysis of Web Posts.Journal of medical Internet research · 2021Article
- Exchanges in a Virtual Environment for Diabetes Self-Management Education and Support: Social Network Analysis.JMIR diabetes · 2021Article
- Seeking Health Information and Social Support in the Diabetes Online Community.Frontiers in clinical diabetes and healthcare · 2021Article
- Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review.JMIR public health and surveillance · 2020Review
- Revealing Intention In Health-related Peer Interactions: Implications For Optimizing Patient Engagement In Self-health Management.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
backgroundOnline communities have been gaining popularity as support venues for chronic disease management. User engagement, information exposure, and social influence mechanisms can play a significant role in the utility of these platforms.
objectiveIn this paper, we characterize peer interactions in an online community for chronic disease management. Our objective is to identify key communications and study their prevalence in online social interactions.
methodsThe American Diabetes Association Online community is an online social network for diabetes self-management. We analyzed 80,481 randomly selected deidentified peer-to-peer messages from 1212 members, posted between June 1, 2012, and May 30, 2019. Our mixed methods approach comprised qualitative coding and automated text analysis to identify, visualize, and analyze content-specific communication patterns underlying diabetes self-management.
resultsQualitative analysis revealed that "social support" was the most prevalent theme (84.9%), followed by "readiness to change" (18.8%), "teachable moments" (14.7%), "pharmacotherapy" (13.7%), and "progress" (13.3%). The support vector machine classifier resulted in reasonable accuracy with a recall of 0.76 and precision 0.78 and allowed us to extend our thematic codes to the entire data set.
conclusionsModeling health-related communication through high throughput methods can enable the identification of specific content related to sustainable chronic disease management, which facilitates targeted health promotion.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.