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<p>With the growing availability of short video platforms such as TikTok and Bilibili, patients with diabetic kidney disease (DKD) are increasingly seeking health information through these channels. However, the quality and user engagement of DKD-related content on these platforms have not been systematically evaluated. This exploratory cross-sectional study assessed the quality and reliability of DKD-related short videos and examined predictors of user engagement. On April 4, 2025, the top 100 DKD-related videos were collected from each platform. Content quality and reliability were assessed using the Global Quality Score (GQS), the modified DISCERN (mDISCERN), and the Medical Quality Video Evaluation Tool (MQ-VET). An eXtreme Gradient Boosting (XGBoost) model was employed to predict the number of likes and identify associated predictors. Despite being shorter in length, TikTok videos received significantly more likes, saves, shares, and comments than those on Bilibili (all <i>p</i> &lt; 0.001), and scored higher on GQS and MQ-VET, with no significant difference in mDISCERN scores. Videos uploaded by professionals generally showed higher quality. Follower count, video length, and days since upload were the strongest predictors of engagement. Overall, TikTok videos exhibited higher quality and engagement than those on Bilibili; however, given the algorithm-driven sampling, uploader-level clustering, and tool adaptation, these findings should be interpreted as descriptive and exploratory rather than causal.</p>

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Information quality assessment and user engagement prediction of short videos about diabetic kidney disease on TikTok and bilibili

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月11日

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  • Shuo Lin,
  • Jianjie Ju,
  • Zhouhua Wang

摘要

With the growing availability of short video platforms such as TikTok and Bilibili, patients with diabetic kidney disease (DKD) are increasingly seeking health information through these channels. However, the quality and user engagement of DKD-related content on these platforms have not been systematically evaluated. This exploratory cross-sectional study assessed the quality and reliability of DKD-related short videos and examined predictors of user engagement. On April 4, 2025, the top 100 DKD-related videos were collected from each platform. Content quality and reliability were assessed using the Global Quality Score (GQS), the modified DISCERN (mDISCERN), and the Medical Quality Video Evaluation Tool (MQ-VET). An eXtreme Gradient Boosting (XGBoost) model was employed to predict the number of likes and identify associated predictors. Despite being shorter in length, TikTok videos received significantly more likes, saves, shares, and comments than those on Bilibili (all p < 0.001), and scored higher on GQS and MQ-VET, with no significant difference in mDISCERN scores. Videos uploaded by professionals generally showed higher quality. Follower count, video length, and days since upload were the strongest predictors of engagement. Overall, TikTok videos exhibited higher quality and engagement than those on Bilibili; however, given the algorithm-driven sampling, uploader-level clustering, and tool adaptation, these findings should be interpreted as descriptive and exploratory rather than causal.