Plastic and reconstructive surgery. Global open · 2026
Health Communication Patterns in Breast Implant Illness Content on TikTok: A DISCERN-based Analysis by Creator Type.
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Kastury Madhulika R, Khan Fatima Z, Edson Amber L, Royfman Abigail M, Thompson Noelle T, Tadrousse Kirollos, Ayyala Haripriya S, Simman Richard
Аннотация
BACKGROUND: Breast implant illness (BII) describes a constellation of patient-reported symptoms attributed to implants. Despite lacking diagnostic consensus, BII remains prominent on TikTok, where health-related content often lacks expert oversight. This study evaluates the presentation, quality, and reliability of BII-related TikTok videos using the DISCERN framework, comparing healthcare professional (HCP) and non-HCP content.
METHODS: This observational study analyzed the top 30 videos from 5 BII-related hashtags. Videos were categorized by creator type and independently assessed by 4 raters using the DISCERN tool. Statistical methods included the Mann-Whitney U test, intraclass correlation coefficients, analysis of variance, and multivariable linear regression with log-transformed engagement metrics.
RESULTS: Of 150 videos screened, 36 met inclusion criteria. HCP videos (41.7%, n = 15) demonstrated significantly higher DISCERN reliability (20.9 versus 16.4, P < 0.001) and total scores (31.6 versus 27.8, P = 0.03). Treatment quality scores did not differ (P = 0.22). Among HCPs, follower count was positively associated with DISCERN score (P = 0.032; interaction P = 0.031); like count trended negatively without reaching significance (P = 0.055). The final model explained 73.5% of the variance in DISCERN scores (R 2 = 0.735; adjusted R 2 = 0.694; F (5, 30) = 17.71, P < 0.001).
CONCLUSIONS: The reliability and quality of BII content on TikTok varied by creator type. HCP videos scored higher across DISCERN domains, especially when paired with higher follower counts. These findings signify the need for platform-specific strategies to promote evidence-based voices in digital health communication.
METHODS: This observational study analyzed the top 30 videos from 5 BII-related hashtags. Videos were categorized by creator type and independently assessed by 4 raters using the DISCERN tool. Statistical methods included the Mann-Whitney U test, intraclass correlation coefficients, analysis of variance, and multivariable linear regression with log-transformed engagement metrics.
RESULTS: Of 150 videos screened, 36 met inclusion criteria. HCP videos (41.7%, n = 15) demonstrated significantly higher DISCERN reliability (20.9 versus 16.4, P < 0.001) and total scores (31.6 versus 27.8, P = 0.03). Treatment quality scores did not differ (P = 0.22). Among HCPs, follower count was positively associated with DISCERN score (P = 0.032; interaction P = 0.031); like count trended negatively without reaching significance (P = 0.055). The final model explained 73.5% of the variance in DISCERN scores (R 2 = 0.735; adjusted R 2 = 0.694; F (5, 30) = 17.71, P < 0.001).
CONCLUSIONS: The reliability and quality of BII content on TikTok varied by creator type. HCP videos scored higher across DISCERN domains, especially when paired with higher follower counts. These findings signify the need for platform-specific strategies to promote evidence-based voices in digital health communication.