Published on in Vol 22, No 9 (2020): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21204, first published .
Factors Engaging Users of Diabetes Social Media Channels on Facebook, Twitter, and Instagram: Observational Study

Factors Engaging Users of Diabetes Social Media Channels on Facebook, Twitter, and Instagram: Observational Study

Factors Engaging Users of Diabetes Social Media Channels on Facebook, Twitter, and Instagram: Observational Study

Journals

  1. Ash G, Griggs S, Nally L, Stults-Kolehmainen M, Jeon S, Brandt C, Gulanski B, Spanakis E, Baker J, Whittemore R, Weinzimer S, Fucito L. 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 2021;6(3):e28309 View
  2. Grantham J, Verishagen C, Whiting S, Henry C, Lieffers J. Evaluation of a Social Media Campaign in Saskatchewan to Promote Healthy Eating During the COVID-19 Pandemic: Social Media Analysis and Qualitative Interview Study. Journal of Medical Internet Research 2021;23(7):e27448 View
  3. Kong W, Song S, Zhao Y, Zhu Q, Sha L. TikTok as a Health Information Source: Assessment of the Quality of Information in Diabetes-Related Videos. Journal of Medical Internet Research 2021;23(9):e30409 View
  4. Ittefaq M, Zain A, Bokhari H. Opioids in Satirical News Shows: Exploring Topics, Sentiments, and Engagement in Last Week Tonight on YouTube. Journal of Health Communication 2023;28(1):53 View
  5. Miller A, Heiland S. #ProtectNature—How Characteristics of Nature Conservation Posts Impact User Engagement on Facebook and Twitter. Sustainability 2021;13(22):12768 View
  6. Hamamoto R, Koyama T, Kouno N, Yasuda T, Yui S, Sudo K, Hirata M, Sunami K, Kubo T, Takasawa K, Takahashi S, Machino H, Kobayashi K, Asada K, Komatsu M, Kaneko S, Yatabe Y, Yamamoto N. Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information. Experimental Hematology & Oncology 2022;11(1) View
  7. Nguyen C, Pham C, Jackson A, Ellison N, Sinclair K. Online Food Security Discussion Before and During the COVID-19 Pandemic in Native Hawaiian and Pacific Islander Community Groups and Organizations: Content Analysis of Facebook Posts. Asian/Pacific Island Nursing Journal 2022;6(1):e40436 View
  8. Bian D, Shi Y, Tang W, Li D, Han K, Shi C, Li G, Zhu F. The Influencing Factors of Nutrition and Diet Health Knowledge Dissemination Using the WeChat Official Account in Health Promotion. Frontiers in Public Health 2021;9 View
  9. D'Souza R, Daraz L, Hooten W, Guyatt G, Murad M. Users' Guides to the Medical Literature series on social media (part 2): how to appraise studies using data from platforms. BMJ Evidence-Based Medicine 2022;27(1):15 View
  10. Zhang C, Zheng H, Wang Q. Driving Factors and Moderating Effects Behind Citizen Engagement With Mobile Short-Form Videos. IEEE Access 2022;10:40999 View
  11. Hayman M, Keppel M, Stanton R, Thwaite T, Alfrey K, Alley S, Harrison C, Keating S, Schoeppe S, Cannon S, Haakstad L, Gjestvang C, Williams S. A mixed-methods exploration of attitudes towards pregnant Facebook fitness influencers. BMC Public Health 2023;23(1) View
  12. Stemmer M, Parmet Y, Ravid G. What are IBD Patients Talking About on Twitter? Using Natural Language Understanding to Investigate Patients’ Tweets. SN Computer Science 2023;4(4) View
  13. Syn S. Gaze movement analysis examined how people view and interact with health information on Facebook pages. Health Information & Libraries Journal 2023 View
  14. Chen J, Lin Y, Tang X, Deng S. Fostering netizens to engage in rumour-refuting messages of government social media: a view of persuasion theory. Behaviour & Information Technology 2023:1 View
  15. Liang X, Yan M, Li H, Deng Z, Lu Y, Lu P, Cai S, Li W, Fang L, Xu Z. WeChat official accounts’ posts on medication use of 251 community healthcare centers in Shanghai, China: content analysis and quality assessment. Frontiers in Medicine 2023;10 View
  16. Wu N, Wang S, Brazeau A, Chan D, Mussa J, Nakhla M, Elkeraby M, Ell M, Prevost M, Lepine L, Panagiotopoulos C, Mukerji G, Butalia S, Henderson M, Da Costa D, Rahme E, Dasgupta K. Supporting and Incentivizing Peer Leaders for an Internet-Based Private Peer Community for Youths With Type 1 Diabetes: Social Network and Directed Content Analysis. Journal of Medical Internet Research 2023;25:e48267 View
  17. Tesch Z, Prónay S, Buzás N. Can the group effect dominate the influence of the child on the parent's decision to care for type 1 diabetes?. Journal of Pediatric Nursing 2024 View
  18. Scheer E, Werner N, Coller R, Nacht C, Petty L, Tang M, Ehlenbach M, Kelly M, Finesilver S, Warner G, Katz B, Keim-Malpass J, Lunsford C, Letzkus L, Desai S, Valdez R. Designing for caregiving networks: a case study of primary caregivers of children with medical complexity. Journal of the American Medical Informatics Association 2024 View

Books/Policy Documents

  1. Zörgő S, Jeney A, Csajbók-Veres K, Mkhitaryan S, Susánszky A. Advances in Quantitative Ethnography. View
  2. Anoop V. Advances in Computing and Data Sciences. View