Epilepsy is one of the most prevalent chronic neurological disorders worldwide, affecting approximately 70 million people globally and imposing substantial burdens on patients, families, and healthcare systems. Its multifaceted treatment landscape spanning antiepileptic drug (AED) therapy, epilepsy surgery, ketogenic dietary therapy, and neuromodulation makes accurate health information critical for patient decision-making and treatment adherence. Short-video platforms such as TikTok (Douyin) and Bilibili have emerged as primary channels through which the public accesses health-related content, yet the quality and reliability of epilepsy-related content on these platforms remain largely unexamined. A cross-sectional content analysis was conducted. We systematically retrieved videos via keyword search on TikTok (Douyin) and Bilibili, using the terms "dianxian" (epilepsy) and "jingfeng" (seizure/convulsion). For each platform, we collected the top 100 unique videos ranked by the platform's default relevance algorithm, with duplicate results from the two search terms removed. After applying pre-specified inclusion and exclusion criteria, 182 videos were included in the final analysis. Two physicians independently assessed the videos using a multi-instrument framework with clear applicable boundaries: Global Quality Score (GQS, for overall educational quality across all content types), modified DISCERN (mDISCERN, exclusively for treatment information reliability), JAMA benchmark criteria (for source transparency, not direct clinical accuracy), and a novel Treatment Misinformation Risk Scale (TMRS, specifically for epilepsy treatment-related content). Inter-rater reliability was assessed using the intraclass correlation coefficient (ICC). Engagement metrics and uploader characteristics were also recorded, with sensitivity analyses performed to control for confounding from uneven content theme distribution between platforms. A total of 182 videos were analyzed (96 from TikTok, 86 from Bilibili). The overall educational quality was suboptimal (mean GQS: 2.65 ± 0.93; mDISCERN: 2.12 ± 0.89 for treatment-containing videos). Bilibili videos demonstrated significantly higher performance across all instruments: overall educational quality (GQS: 3.11 ± 0.87 vs. 2.24 ± 0.84, P < 0.001), treatment information reliability (mDISCERN: 2.56 ± 0.81 vs. 1.74 ± 0.76, P < 0.001), and source transparency (JAMA: 2.18 ± 0.72 vs. 1.42 ± 0.68, P < 0.001). The mean normalized TMRS score was 1.15 ± 0.62, with TikTok showing significantly higher treatment misinformation risk (1.41 ± 0.54) than Bilibili (0.86 ± 0.53, P < 0.001). TMRS scores were positively correlated with likes (rho = 0.46, P < 0.001), shares (rho = 0.43, P < 0.001), and comments (rho = 0.39, P < 0.001), while quality scores showed no significant correlation with engagement. Sensitivity analyses confirmed that the observed platform differences were not confounded by differences in content theme distribution. Epilepsy-related content on China's major short-video platforms is of concerningly poor quality, with treatment misinformation receiving disproportionately higher user engagement. These findings highlight the urgent need for collaborative efforts among neurologists, platform operators, and health authorities to improve the quality of epilepsy health information in the digital environment.
Zhang et al. (Fri,) studied this question.