Quality Assessment of TikTok as a Source of Information About Mitral Valve Regurgitation in China: Cross-Sectional Study - Nannan Cui; Yuting Lu; Yelin Cao; Xiaofan Chen; Shuiqiao Fu; Qun Su (JMIR Publications, 2024)
BackgroundIn China, mitral valve regurgitation (MR) is the most common cardiovascular valve disease. However, patients in China typically experience a high incidence of this condition, coupled with a low level of health knowledge and a relatively low rate of surgical treatment. TikTok hosts a vast amount of content related to diseases and health knowledge, providing viewers with access to relevant information. However, there has been no investigation or evaluation of the quality of videos specifically addressing MR. ObjectiveThis study aims to assess the quality of videos about MR on TikTok in China. MethodsA cross-sectional study was conducted on the Chinese version of TikTok on September 9, 2023. The top 100 videos on MR were included and evaluated using quantitative scoring tools such as the modified DISCERN (mDISCERN), the Journal of the American Medical Association (JAMA) benchmark criteria, the Global Quality Score (GQS), and the Patient Education Materials Assessment Tool for Audio-Visual Content (PEMAT-A/V). Correlation and stepwise regression analyses were performed to examine the relationships between video quality and various characteristics. ResultsWe obtained 88 valid video files, of which most (n=81, 92%) were uploaded by certified physicians, primarily cardiac surgeons, and cardiologists. News agencies/organizations and physicians had higher GQS scores compared with individuals (news agencies/organizations vs individuals, P=.001; physicians vs individuals, P=.03). Additionally, news agencies/organizations had higher PEMAT understandability scores than individuals (P=.01). Videos focused on disease knowledge scored higher in GQS (P<.001), PEMAT understandability (P<.001), and PEMAT actionability (P<.001) compared with videos covering surgical cases. PEMAT actionability scores were higher for outpatient cases compared with surgical cases (P<.001). Additionally, videos focused on surgical techniques had lower PEMAT actionability scores than those about disease knowledge (P=.04). The strongest correlations observed were between thumbs up and comments (r=0.92, P<.001), thumbs up and favorites (r=0.89, P<.001), thumbs up and shares (r=0.87, P<.001), comments and favorites (r=0.81, P<.001), comments and shares (r=0.87, P<.001), and favorites and shares (r=0.83, P<.001). Stepwise regression analysis identified "length (P<.001)," "content (P<.001)," and "physicians (P=.004)" as significant predictors of GQS. The final model (model 3) explained 50.1% of the variance in GQSs. The predictive equation for GQS is as follows: GQS = 3.230 − 0.294 × content − 0.274 × physicians + 0.005 × length. This model was statistically significant (P=.004) and showed no issues with multicollinearity or autocorrelation. ConclusionsOur study reveals that while most MR-related videos on TikTok were uploaded by certified physicians, ensuring professional and scientific content, the overall quality scores were suboptimal. Despite the educational value of these videos, the guidance provided was often insufficient. The predictive equation for GQS developed from our analysis offers valuable insights but should be applied with caution beyond the study context. It suggests that creators should focus on improving both the content and presentation of their videos to enhance the quality of health information shared on social media.