Evaluating the quality of TikTok videos on coronary artery disease using various scales to examine correlations with video characteristics and high-quality content

Background Coronary artery disease (CAD) is a major public health concern, yet reliable sources of relevant information are limited. TikTok, a popular social media platform in China, hosts diverse health-related videos, including those on CAD; however, their quality varies and is largely unassessed. Objective This study aimed to investigate the quality of CAD-related videos on TikTok and explore the correlation between video characteristics and high-quality videos. Methods A total of 122 CAD-related short videos on TikTok were analyzed on July 18, 2023. Basic video information and sources were extracted. Two evaluators independently scored each video using DISCERN (a health information quality scale), the Patient Education Materials Assessment Tool (PEMAT) and the Health on the Net (HONcode) scales. Videos were categorized into four groups based on their source, with the medical professional group further categorized by job titles. Simple linear analysis was used to examine the linear relationship across different scales and to explore the relationship between video characteristics (video length, time since posting, the number of "likes", comments and "favorites", and the number of followers of the video creator) and different scales. Results AQVideos were categorized into four groups based on their source: medical professionals (n = 98, 80.3%), user-generated content (n = 11, 9.0%), news programs (n = 4, 3.3%), and health agencies or organizations (n = 9, 7.4%). The score of DISCERN was 46.5 ± 7.6/80, the score rate of PEMAT was 79.2 ± 12.6%/100%, and the number of score items for HONcode was 1.4 ± 0.6/8. In Sect. 1 of DISCERN, user-generated content scored highest (29.1 ± 3.6), followed by medical professionals (28.6 ± 2.4), health agencies or organizations (28.0 ± 0.0) and news programs (28.0 ± 0.0)(P = 0.047). In HONcode, most videos met only one or two of the eight evaluation criteria. PEMAT scores varied slightly across categories without significant differences (P = 0.758). Medical professionals were further divided into senior (n = 69, 70.4%) and intermediate (n = 29, 29.6%) groups, with intermediate professionals scoring higher in DISCERN (P < 0.001). In simple linear analysis models, no linear correlation was found between DISCERN and PEMAT scores (P = 0.052). Time since posting on TikTok was negatively correlated with DISCERN (P = 0.021) and PEMAT scores (P = 0.037), and the number of "favorites" was positively correlated to DISCERN score (P = 0.007). Conclusion The quality of CAD-related videos on China's TikTok is inconsistent and varies across different evaluation scales. Videos posted by medical professionals with intermediate titles tended to offer higher quality, more up-to-date content, as reflected by higher "favorite" counts. HONcode may not be suitable for short video evaluation due to its low score rate, while DISCERN and PEMAT may be effective tools for short video evaluation. However, their lack of consistency in evaluation dimensions highlight the need for a tailored scoring system for short videos.