Evidence map›Paper›PMID 41938936›Full record

ArticleFrontiers in public health2026

Assessment of quality and reliability of Chinese-language myocardial infarction content on Bilibili and TikTok: a cross-sectional study.

Qingruo Mao, Li Zhong, Songzi Wang

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Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Qingruo MaoDepartment of Acupuncture, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Li ZhongChina Academy of Chinese Medical Sciences Data Center, Beijing, China.
Songzi WangDepartment of Acupuncture, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Myocardial infarction (MI) is a leading cause of cardiovascular mortality worldwide and requires timely treatment and accurate public awareness of risk factors, warning signs, and first aid. In China, short-video platforms such as TikTok (Douyin, Chinese mainland version) and Bilibili have become major health information sources, yet the quality and reliability of MI-related content remain inadequately evaluated. Objective: This cross-sectional study systematically assessed and compared the quality, reliability, and educational value of MI-related videos on TikTok and Bilibili. Methods: Using the keyword "" (myocardial infarction), we retrieved the top 100 videos from TikTok and Bilibili on September 1, 2025. After exclusions, 137 videos were included. Uploaders were classified as clinicians, patients, or traditional Chinese medicine practitioners. Quality was evaluated using GQS, mDISCERN, JAMA benchmarks, and PEMAT-U/A. Statistical analyses included Spearman correlation, Mann-Whitney Results: Bilibili videos were significantly longer but had much lower engagement than TikTok videos. Only mDISCERN scores differed significantly between platforms: Bilibili contained a higher proportion of high-reliability videos than TikTok, while JAMA, GQS, and PEMAT-U/A scores did not differ significantly. Uploader background significantly influenced quality outcomes. Clinicians and TCM practitioners achieved higher JAMA scores than patients, indicating greater formal credibility, whereas patients had a higher proportion of high mDISCERN scores, reflecting more detailed experiential content. Correlation analysis revealed a bidirectional effect of video length: longer duration was positively associated with mDISCERN and GQS scores but negatively associated with JAMA scores. Interaction metrics showed strong internal synergy but almost no correlation with professional quality scores, demonstrating a clear "quality popularity paradox."Content analysis showed an imbalanced pattern: particularly regarding emergency measures and medication safety information were severely lacking. Conclusion: MI-related content on Chinese short-video platforms is of moderate quality but characterized by a significant disconnect between popularity and educational value, as well as critical deficiencies in emergency response information. These findings underscore the urgent need for coordinated interventions, including platform-level quality control, collaborative content creation between professionals and platform, and enhanced public health literacy to ensure the safe and effective use of these platforms for health education.

Indexed as

LanguageMyocardial InfarctionVideo RecordingChinaCross-Sectional StudiesDigital MediaHumansReproducibility of ResultsBilibilihealth educationmyocardial infarctionsocial mediaTikTokvideo quality

Identifiers

PMID41938936
PMCPMC13043423

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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.