Evidence map›Paper›PMID 40479581›Full record

ArticleJournal of medical Internet research2025

The Most Popular Videos Promoting Breast Enhancement Products on TikTok: Cross-Sectional Content and User Engagement Analysis.

Jing Lin, Wanlin Li, Lian Zhu, Ning Li, Shi Chang

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jing LinDepartment of General Surgery, XiangYa Hospital, Central South University, No.87 XiangYa Road, Hunan, Changsha, 410008, China, 86 18119122648, 86 073189753999.ORCID http://orcid.org/0000-0003-2929-1350
Wanlin LiDepartment of General Surgery, XiangYa Hospital, Central South University, No.87 XiangYa Road, Hunan, Changsha, 410008, China, 86 18119122648, 86 073189753999.ORCID http://orcid.org/0009-0004-7979-8242
Lian ZhuDepartment of General Surgery, XiangYa Hospital, Central South University, No.87 XiangYa Road, Hunan, Changsha, 410008, China, 86 18119122648, 86 073189753999.ORCID http://orcid.org/0000-0003-3426-5712
Ning LiDepartment of General Surgery, XiangYa Hospital, Central South University, No.87 XiangYa Road, Hunan, Changsha, 410008, China, 86 18119122648, 86 073189753999.ORCID http://orcid.org/0000-0002-3477-2194
Shi ChangDepartment of General Surgery, XiangYa Hospital, Central South University, No.87 XiangYa Road, Hunan, Changsha, 410008, China, 86 18119122648, 86 073189753999.ORCID http://orcid.org/0000-0001-5291-0151

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The proliferation of health-related content on social media platforms has changed the way people access and interpret information about cosmetic medicine. TikTok (ByteDance) has become an important platform for sharing breast enhancement content, yet little is known about the quality, credibility, and impact of such information on user perceptions and decision-making. Objective: This paper aims to analyze the characteristics of breast enhancement videos, including uploader demographics, product details, promotional claims, and user engagements, to better understand the nature of the claims and products encountered by users. Methods: We conducted a cross-sectional content analysis of the top 150 most-liked breast enhancement videos via TikTok's web interface. The videos were coded according to the uploader's traits (gender expression and account type), product details (type and scientific evidence), and promotional strategies (testimonials and sponsorship disclosures). Engagement metrics (likes and shares) were recorded, and nonparametric tests (Mann-Whitney U test) were used to compare the engagement between licensed physicians and uncertified content creator uploaders. Descriptive statistics were calculated for all the variables. Results: Overall, 85 videos were included in the final analysis, with most uploaders presenting a feminine gender expression (59/85, 69.4%) and using uncertified content creator accounts (59/85, 69.4%). The most promoted product types were breast enhancement creams or oils (32/85, 37.6%) and breast implants (22/85, 25.9%). Most videos (71/85, 83.5%) depicted the products positively; however, most videos (78/85, 91.8%) provided no scientific evidence of the product's efficacy. Engagement metrics revealed that videos by licensed physicians received significantly higher thumbs up (median 9761, IQR 4975-19,492) than uncertified content creator uploaders (median 701, IQR 280-2604; P=.002). Only one video (1.2%) of the 85 videos included a "before and after" visual component, and most videos (75/85, 88.2%) omitted product purchasing details. Sponsorship disclosures were absent in most of posts (79/85, 92.9%). Conclusions: TikTok's short video format fosters widespread and rapid dissemination of breast enhancement information, representing a key strength in democratizing health communication. Its user-friendly interface and visual appeal also offer a valuable avenue for medical professionals to engage audiences more dynamically. However, the lack of rigorous content checks can amplify misleading or unverified claims. To address these weaknesses, implementing dual-mode content review could be essential for maximizing TikTok's capacity to support informed public health decision-making.

Indexed as

BreastSocial MediaVideo RecordingCross-Sectional StudiesFemaleHumansMalebody imagebreast enhancementcontent analysisengagement analysismisinformationsocial mediaTikTok

Identifiers

PMID40479581
PMCPMC12165446

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.