Evidence map›Paper›PMID 42104298›Full record

ArticleBMC public health2026

The quality of video information related to smoking cessation on mainstream short-video platforms in China: a cross-sectional study.

Xingwang Qiu, Yuancen Shi, Xia Luo, Shixuan Yuan, Jin Zhang, Hua Guan

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Article in BMC public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Xingwang Qiu *School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Yuancen Shi *School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Xia Luo *School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Shixuan YuanSchool of Medicine, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Jin ZhangSchool of Medicine, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Hua GuanDepartment of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China. 2450985532@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTobacco use remains one of the greatest public health challenges worldwide. Social short-video platforms have become the primary channel through which the public obtains smoking-cessation information. Grounded in the Health Belief Model and the Theory of Planned Behavior, this study evaluates the information quality of smoking-cessation short videos on Chinese short-video platforms.

methodsWe analyzed 262 video samples from four major platforms-TikTok, Kwai, Bilibili, and BuzzVideo. Two researchers who received standardized training independently evaluated each video using the Medical Quality Video Evaluation Tool (MQ-VET), the Global Quality Scale (GQS), and the mDISCERN score. Finally, we performed multiple linear regression to identify factors influencing video quality and user interaction.

resultsOf the 262 videos included, only 17.6% were produced by medical experts. Overall information quality was low: median MQ-VET was 44 (41-47), median GQS was 2 (2-3), and median mDISCERN was 2 (1-2). In multivariable regression, videos produced by Medical experts (β = 0.586, p < 0.001) and by Public welfare organizations (β = 0.130, p = 0.001) had significantly higher quality than those produced by Individual users. For user engagement, measured by number of likes, information quality (MQ-VET) (β = 0.215, p = 0.009), TikTok as the platform (β = 0.358, p < 0.001), and Bilibili as the platform (β = 0.485, p < 0.001) were significant positive predictors. Quality scores correlated positively with user interaction (ρ = 0.14-0.35, p < 0.005), whereas video duration correlated negatively with interaction (ρ = -0.14 to -0.29, p < 0.01).

conclusionContent about smoking cessation on mainstream Chinese short-video platforms is predominantly user-generated, and it is often fragmented, scientifically weak, and lacking elements of behavior-change psychology. Despite these shortcomings, high-quality videos still attract substantial user engagement. To harness the broad reach of these platforms, we propose constructing a four-party cooperation framework among government, platforms, experts, and users grounded in the "Healthy China 2030" initiative, establishing a quality-certification system, and incentivizing medical experts to produce rigorous, high-quality content.

Indexed as

Smoking CessationVideo RecordingChinaCross-Sectional StudiesDigital MediaHumansBilibiliBuzzVideoCross-sectional studyInformation qualityKwaiShort videosSmoking cessationTikTok

Identifiers

PMID42104298
PMCPMC13321630

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LicenceCC BY-NC-ND
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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.