Videos on Bilibili, TikTok, and Xiaohongshu as Sources of Medical Information on Adenoid Hypertrophy: Cross-Sectional Content Analysis
Abstract
Abstract Background The clinical diagnosis rate of adenoid hypertrophy (AH) in children has increased in recent years, drawing growing attention from parents. Short-video platforms such as Bilibili, TikTok, and Xiaohongshu host a large volume of educational content on this condition. However, the quality and reliability of this information remain unclear. Objective This study aimed to evaluate the completeness, understandability, actionability, reliability, and overall quality of short videos on AH across Bilibili, TikTok, and Xiaohongshu and to explore factors associated with these quality metrics, including uploader characteristics and engagement indicators. Methods We collected 220 videos (Bilibili: n=90, 40.9%; TikTok: n=63, 28.6%; and Xiaohongshu: n=67, 30.5%) using newly registered accounts. Two independent reviewers evaluated video quality using a 6-item content completeness scale (score range 0-12), the Patient Education Materials Assessment Tool for Audiovisual Materials, the modified DISCERN instrument, and the Global Quality Scale (GQS). Interrater reliability was high (Cohen κ=0.77-0.993). Completeness assessed essential informational components of AH. As data were nonnormally distributed, results are presented as median (IQR). Cross-platform comparisons were conducted using the Kruskal-Wallis H test with post hoc Mann-Whitney U tests (with Bonferroni correction). Spearman correlation was used to explore associations between video characteristics (ie, duration and engagement metrics) and quality outcomes. Stepwise linear regression identified independent predictors of overall quality (GQS). Results Video duration differed significantly across platforms (Bilibili: median 113.5, IQR 66.5-271.5 seconds; TikTok: median 73, IQR 44-100 seconds; and Xiaohongshu: median 63, IQR 41-127.5 seconds; P
Fields of Study & Subjects
Delphi Technique in Research
Radiomics and Machine Learning in Medical Imaging
Cancer-related molecular mechanisms research
Publication details
- Journal / source
- JMIR Formative Research
- Publisher
- JMIR Publications Inc.
- Volume
- 10
- Pages
- e82923–e82923
- Publication date
- 14 May 2026
- Language
- EN