背景:在中国,二尖瓣反流(MR)是最常见的心血管瓣膜疾病。然而,在中国,这种情况的发生率很高,再加上健康知识水平低和手术治疗率相对较低。TikTok拥有大量与疾病和健康知识相关的内容,为观众提供相关信息的访问。然而,尚未对专门针对MR的视频质量进行调查或评估。
目的:本研究旨在评估中国TikTokMR视频的质量。
方法:对2023年9月9日的中文版TikTok进行了横断面研究。纳入了MR的前100个视频,并使用定量评分工具进行了评估,例如改良的DISCERN(mDISCERN),美国医学会杂志(JAMA)基准标准,全球质量评分(GQS),和视听内容的患者教育材料评估工具(PEMAT-A/V)。进行了相关和逐步回归分析,以检查视频质量与各种特征之间的关系。
结果:我们获得了88个有效的视频文件,其中大部分(n=81,92%)是由认证医生上传的,主要是心脏外科医生,和心脏病学家。与个人相比,新闻机构/组织和医生的GQS得分更高(新闻机构/组织与个人,P=.001;医生与个人之间,P=.03)。此外,新闻机构/组织的PEMAT可理解性得分高于个人(P=0.01)。关注疾病知识的视频在GQS中得分更高(P<.001),PEMAT可理解性(P<.001),与涵盖手术病例的视频相比,PEMAT可操作性(P<.001)。与手术病例相比,门诊病例的PEMAT可操作性评分更高(P<0.001)。此外,聚焦于外科技术的视频的PEMAT可操作性评分低于关于疾病知识的视频(P=.04).观察到的最强相关性是竖起大拇指和评论(r=0.92,P<.001),竖起大拇指和收藏夹(r=0.89,P<.001),竖起大拇指并分享(r=0.87,P<.001),评论和收藏夹(r=0.81,P<.001),评论和分享(r=0.87,P<.001),以及收藏夹和股票(r=0.83,P<.001)。逐步回归分析确定长度(P<.001),\"\"内容(P<.001),“和”医生(P=0.004)“是GQS的重要预测因子。最终模型(模型3)解释了GQSs中50.1%的方差。GQS的预测方程如下:GQS=3.230-0.294×含量-0.274×医师0.005×长度。该模型具有统计学意义(P=.004),并且没有显示出多重共线性或自相关的问题。
结论:我们的研究表明,尽管TikTok上大多数与MR相关的视频都是由认证医师上传的,确保专业和科学的内容,总体质量评分不理想.尽管这些视频具有教育价值,提供的指导往往是不够的。从我们的分析中得出的GQS预测方程提供了有价值的见解,但应在研究范围之外谨慎应用。它建议创作者应专注于改善视频的内容和呈现方式,以提高在社交媒体上共享的健康信息的质量。
BACKGROUND: In 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.
OBJECTIVE: This study aims to assess the quality of videos about MR on TikTok in China.
METHODS: A 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.
RESULTS: We 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.
CONCLUSIONS: Our 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.