ArticleJMIR diabetes2019
Glycated Hemoglobin Differences Among Blog-Reading Adults With Type 1 Diabetes Compared With Those Who Do Not Read Blogs: Cross-Sectional Study.
Article in JMIR diabetes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Patients' Use of Social Media for Diabetes Self-Care: Systematic Review.Journal of medical Internet research · 2020Pooled it
- Quality and dissemination of nasopharyngeal cancer health information on TikTok and Bilibili: Cross-sectional study.Medicine · 2026Article
- Determinants of the adoption of an online community for people with diabetes: a qualitative study.Scientific reports · 2025Article
- Quality and reliability evaluation of pancreatic cancer-related video content on social short video platforms: a cross-sectional study.BMC public health · 2025Article
- Knowledge, attitude, and practice toward Helicobacter pylori among residents in Northeast China.Scientific reports · 2025Article
- Use of Online Communities among People with Type 2 Diabetes: A Scoping Review.Current diabetes reports · 2024Article
- Current status of short video as a source of information on lung cancer: a cross-sectional content analysis study.Frontiers in oncology · 2024Article
- Quality of Spine Surgery Information on Social Media: A DISCERN Analysis of TikTok Videos.Neurospine · 2023Article
- A cross-sectional quality assessment of TikTok content on benign prostatic hyperplasia.World journal of urology · 2023Article
- The status quo of short videos as a health information source ofFrontiers in public health · 2023Article
- Navigating Diabetes Online Communities in Clinical Practice.Journal of diabetes science and technology · 2022Review
- Examining a Continuous Glucose Monitoring Plus Online Peer Support Community Intervention to Support Hispanic Adults With Type 2 Diabetes: Protocol for a Mixed Methods Feasibility Study.JMIR research protocols · 2022Article
- Mapping Individual Differences on the Internet: Case Study of the Type 1 Diabetes Community.JMIR diabetes · 2021Article
- TikTok as a Health Information Source: Assessment of the Quality of Information in Diabetes-Related Videos.Journal of medical Internet research · 2021Article
- Evaluation of Web-Based and In-Person Methods to Recruit Adults With Type 1 Diabetes for a Mobile Exercise Intervention: Prospective Observational Study.JMIR diabetes · 2021Article
- Participation of Patients With Type 2 Diabetes in Online Support Groups is Correlated to Lower Levels of Diabetes Self-Management.Journal of diabetes science and technology · 2021Article
- Qualitative Content Analysis of Type 1 Diabetes Caregiver Blogs and Correlations With Caregiver Challenges and Successes.Journal of patient experience · 2020Article
- A Facilitated Web-Based Self-Management Tool for People With Type 1 Diabetes Using an Insulin Pump: Intervention Development Using the Behavior Change Wheel and Theoretical Domains Framework.Journal of medical Internet research · 2020Article
- "Not Today, Diabetes": Using Blog Analysis to Understand Emotional Interactions and Support Among People With Type 1 Diabetes.Frontiers in clinical diabetes and healthcare · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundOf the estimated 23.1 million individuals diagnosed with diabetes, approximately 5% have type 1 diabetes (T1D). It has been proposed that this number will triple by 2050. With increases in technology use and resources available, many individuals are using insulin pumps and continuous glucose monitors (CGMs) to help manage their T1D. They are also using online resources such as social media to find more information and advice based on real-life experiences from peers. Blogs are a particular social media modality often used by people with T1D but have not been widely investigated.
objectiveThe purpose of this study was to assess glycated hemoglobin (HbA
methodsParticipants were recruited both by mail and by online T1D-themed blog postings. Respondents completed a secure online eligibility assessment and were asked questions related to their T1D, blog and internet use, and insulin pump and CGM use. Demographics were also collected. Differences between blog readers and blog nonusers were tested via chi-square and t tests. Mann-Whitney U tests, Fisher exact tests, and analyses of variance (ANOVA) were used to test for differences in self-reported HbA
resultsA total of 282 eligible participants completed the survey (214 blog readers, 68 blog nonusers). Average duration of diabetes was 21.2 years, 77.7% (219/282) were female, 81.2% (229/282) used an insulin pump, 66.3% (187/282) used a CGM, and 95.7% (270/282) were white. HbA
conclusionsThese results suggest that reading blogs is associated with lower HbA
Indexed as
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Registered trials
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.