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January 14, 2026Scientific Reports0 citationsOpen Access

A cross-sectional analysis of the quality and characteristics of sleep apnea hypopnea syndrome videos on YouTube, Bilibili, and TikTok

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XQXinyi QiuYZYa ZhouTYTing Yuan

Key Points

  • To evaluate the quality and characteristics of sleep apnea hypopnea syndrome videos across social media platforms.
  • Conducted a cross-sectional analysis of videos from YouTube, Bilibili, and TikTok.
  • Evaluated 903 videos, with 227 meeting inclusion criteria for quality analysis.
  • Compared content quality and engagement metrics across long-form and short-form platforms.
  • Higher-quality content was more prevalent on long-form platforms like YouTube and Bilibili.
  • Short-form platforms like TikTok demonstrated greater audience engagement but lower informational integrity.
  • Professional identity of content creators was a key predictor of video quality.

Abstract

Sleep Apnea Hypopnea Syndrome (SAHS) is a prevalent sleep disorder associated with substantial health risks, highlighting the need for improved public awareness. This cross-sectional analysis systematically evaluated the quality of SAHS-related videos on YouTube, Bilibili, and TikTok. Of 903 videos initially identified, 227 met the inclusion criteria for analysis. Cross-platform comparisons revealed that long-form platforms hosted higher-quality content, whereas short-form platforms generated greater engagement despite lower informational integrity. This study reveals a structural disconnect between informational quality and audience engagement, consistent with theories of algorithmic filtering. While professional identity remains a reliable predictor of quality, user engagement is largely driven by peripheral cues rather than medical accuracy. This study further contributes to the theoretical understanding of online health communication by situating platform-specific patterns within broader frameworks of algorithmic curation, heuristic processing, and trust formation. By integrating these theoretical perspectives with empirical quality assessments, the study offers a conceptually grounded explanation for why medically accurate content often remains less visible within algorithmic media environments. These findings underscore the need for platform-specific interventions that integrate credibility signals into recommendation algorithms to mitigate the spread of low-quality health information.

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Cite This Study

Qiu et al. (2026) studied this question.

synapsesocial.com/papers/6966f5183603a7c209c0e109https://doi.org/10.1038/s41598-025-34182-1
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