ArticleFrontiers in digital health2025
Knee osteoarthritis health information on China's TikTok: a cross-sectional analysis of content quality and public health relevance.
Article in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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: Knee osteoarthritis (KOA) is a chronic joint disorder that significantly affects the quality of life in the older adult. It is primarily characterized by notable knee pain following activity, which typically alleviates with rest. With the rapid growth of the internet, people increasingly rely on social media to obtain health-related information. Short-form videos, as an emerging format, play an important role in information dissemination. TikTok is currently the world's most downloaded application platform primarily dedicated to short-form video content. Against this backdrop, we observed a substantial number of KOA-related videos on TikTok, the quality and reliability of which have not yet been systematically evaluated. Objective: To assess the quality and reliability of KOA-related videos available on the domestic TikTok platform. Methods: A total of 100 KOA-related videos were retrieved and screened from TikTok. Basic metadata were extracted, and video content and format were categorized through coding. The source of each video was also documented. Two independent raters evaluated video quality using the DISCERN instrument, the Journal of the American Medical Association (JAMA) benchmark criteria, and the Global Quality Score (GQS). Results: Of 100 analyzed videos, 96 were posted by medical staff and 4 by science communicators. Eighty videos were audio-based (41% outpatient daily, 39% general science popularization), with others using graph-text formats. The video content is divided into 7 groups: disease prevention, diagnosis, symptoms, description, life-style and therapy, among which the video related to disease description is the most. The average DISCERN, JAMA and GQS scores of videos were 36.29, 1.24 and 2.45, respectively, and the overall quality was low. Further analysis shows that there are significant differences in video quality between science communicators and medical staff. The number of likes, comments, collections, and shares are strongly positively correlated with each other, and they are weakly positively correlated with the number of upload days and DISCERN scores. Conclusion: KOA-related content on TikTok demonstrates concerning quality limitations, with significant variation across source types. Given TikTok's expanding influence in health communication, urgent improvements and standardized quality control measures are needed.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.