When Algorithms Prescribe: A Cross-Sectional Study of Quality, Misinformation, and Engagement in Statin-Related Content on TikTok
Gharibyan, I.; Ahner, E.; Shao, R.; Sharma, D.; Navarsartian Tazehkand, T.; Diep, J.; Assoumou, B.
Show abstract
Background: Statins are key to preventing atherosclerotic cardiovascular disease and lowering low-density lipoprotein cholesterol and cardiovascular events. However, skepticism regarding their safety and value persists and is increasingly influenced by social media. TikTok has emerged as a major source of health information, but its content varies in quality and accuracy. This study evaluated the quality, attitudes, misinformation, and engagement of statin-related content on TikTok. Methods: Public TikTok videos were collected using predefined search terms and coded by creator type, thematic content, and overall attitude. Video quality was assessed using the DISCERN instrument, the Patient Education Materials Assessment Tool for Audiovisual Materials, and the Global Quality Score. False or misleading claims were independently reviewed by two cardiology fellows. Associations between engagement and quality were also examined. Results: Of 1,349 screened videos, 258 met inclusion criteria. Most were educational (91.0%), with non-physician healthcare providers (34.5%) as the largest creator group. Risks or negative effects were discussed more often than benefits (63.2% vs 42.2%), and 39.5% contained at least one false or misleading claim, most often from complementary and alternative medicine providers and wellness promoters. Quality differed by creator type across all instruments, with physician-created content scoring highest. Video popularity showed minimal association with informational quality. Conclusion: Statin-related TikTok content frequently emphasizes harms, often contains misinformation, and varies substantially in quality by creator type. Greater involvement of healthcare professionals on social media may help improve digital health literacy and counter misleading information about statin therapy.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Defining Destigmatizing Design Guidelines for Use in Sexual Health-Related Digital Technologies: A Delphi Study 93%
- Benefits and Challenges of Using Virtual Primary Care During the COVID-19 Pandemic: From Key Lessons to a Framework for Implementation 93%
- The NASSS (Non-Adoption, Abandonment, Scale-Up, Spread and Sustainability) framework use over time: A scoping review 92%
Similar papers in this journal
- Improving Patient Engagement in Phase 2 Clinical Trials with a Trial-specific Patient Decision Aid (tPDA): A Development and Usability Study 94%
- Has the pandemic enhanced and sustained digital health-seeking behaviour? A big data interrupted time-series analysis of Google Trends 93%
- Tracking private WhatsApp discourse about COVID-19: A longitudinal infodemiology study in Singapore 93%
Similar papers in this journal
- (Mis) Communicating The Gut Microbiome: A Cross-Sectional Assessment of Social Media Video Content 96%
- Applications and barriers to use of an mHealth iPhone application for self-management of chronic recurrent medical conditions: A Pilot Study 94%
- The Validity of the Parsley Symptom Index: an e-PROM designed for Telehealth 93%
Similar papers in this journal
- Can co-designed educational interventions help consumers think critically about asking ChatGPT health questions? Results from a randomised-controlled trial 94%
- Digital Health Tools for the Passive Monitoring of Depression: A Systematic Review of Methods 93%
- A typology of physician input approaches to using AI chatbots for clinical decision-making: a mixed methods study 92%
Similar papers in this journal
- Weight-normative messaging predominates on TikTok – a qualitative content analysis 93%
- Introducing the EMPIRE Index: A novel, value-based metric framework to measure the impact of medical publications 93%
- Investigating the Nature of Open Science Practices Across Complementary, Alternative, and Integrative Medicine Journals: An Audit 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.