ArticleDigital health
TikTok, Bilibili, and Xiaohongshu as sources of information on frailty: Cross-sectional content analysis study.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- TikTok, Xiaohongshu, and Bilibili as sources of medical information on lung cancer: a multi-platform content evaluation.Journal of thoracic disease · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The frailty syndrome is among the most prevalent geriatric syndromes, while social media has become a pivotal place for retrieving health information. Objective: The objectives of this study are to investigate the quality of frailty-related videos on major Chinese social media platforms and examine the correlation of the quality with user engagement. Methods: Collect the videos about frailty from TikTok, Bilibili, and Xiaohongshu. Document the general characteristics, uploader information, and content features of each video. Evaluate the quality of each video with the Journal of the American Medical Association (JAMA) benchmark criteria, the Global Quality Score (GQS), modified DISCERN (mDISCERN), and the Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT-A/V). Results: We examined 126 videos in current study. Overall, quality was not promising with a mean JAMA score of 1.1 (SD=0.8), GQS of 2.8 (SD=1.0), mDISCERN of 3.0 (SD=0.8), PEMAT-understandability of 76.5% (SD=15.6%), and PEMAT-actionability of 49.7% (SD=40.2%). Among the platforms, Bilibili had the highest quality videos, and Xiaohongshu had the lowest videos quality. Videos produced by organizations, non-profit groups, medical-related personnel, certified authors, expert monologue, and question & answer is better. The correlation between video quality and user engagement metrics was negligible. Conclusions: The video quality on social media platforms remains inadequate, offering limited utility to users. Frequently, the viewers cannot precisely determine if content from videos is valid or not. First, uploaders need to optimize video quality and second the oversight of platforms should be strengthened to improve public health literacy and raise awareness.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.