Physician-dominated yet suboptimal: Evaluating the quality of Meniere's disease information on TikTok in China

医生主导但信息质量欠佳:评估中国TikTok上梅尼埃病信息的质量

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Abstract

BACKGROUND: Despite being a prevalent peripheral vestibular disorder in China, Meniere's disease (MD) suffers from low awareness, frequent misdiagnosis, and unsatisfactory treatment rates. As TikTok has become a prominent source of health information, no study has systematically evaluated the quality of its MD-related content. We therefore assessed the accuracy and reliability of MD videos on Chinese TikTok. METHODS: Top 100 videos for "Meniere's disease/syndrome" (TikTok, 1 May 2025) were analyzed. Quality was assessed using Video Information and Quality Index (VIQI), Global Quality Score (GQS), modified DISCERN (mDISCERN), and Patient Education Materials Assessment Tool for Audio-Visual Content (PEMAT-A/V). Descriptive statistics, correlation analyses, and predictive modeling were applied to 83 valid videos. RESULTS: Among 83 videos, 91.6% (n = 76) were physician-uploaded (primarily otolaryngologists/neurologists). Monologue, Q&A, and medical scenario formats showed superior quality. Symptoms dominated content (47%). Neurologists generated significantly higher normalized engagement per second than otolaryngologists (all adj. p < 0.05, r > 0.35). Physicians outperformed news agencies in GQS scores (adj. p < 0.05, r = 0.291). Otolaryngologists scored higher than both neurologists and Traditional Chinese Medicine practitioners in PEMAT-A/V Understandability (all adj. p < 0.05, r > 0.37). Attending physicians exceeded chief physicians on all quality metrics (all adj. p < 0.05, r > 0.35), an advantage potentially linked to their younger age, greater digital literacy, and more frequent social media use. Engagement metrics (likes, comments, favorites, shares) correlated strongly (r > 0.8). Predictive models for PEMAT-U/A were significant (p < 0.001), lacking multicollinearity/autocorrelation. CONCLUSION: Physician-created MD content ensures credibility but requires quality improvement. PEMAT-U/A models guide enhancements, though broader application needs validation. Key health informatics priorities include certified creator engagement, algorithm optimization, and innovative content design.

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