Web-Based Video Platforms as Sources of Information on Body Image Dissatisfaction in Adolescents: Content and Quality Analysis of a Cross-Sectional Study
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| Wydane w: | JMIR Formative Research vol. 9 (2025), p. e71652-e71664 |
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| Dostęp online: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3246990381 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2561-326X | ||
| 024 | 7 | |a 10.2196/71652 |2 doi | |
| 035 | |a 3246990381 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Liu, Li | |
| 245 | 1 | |a Web-Based Video Platforms as Sources of Information on Body Image Dissatisfaction in Adolescents: Content and Quality Analysis of a Cross-Sectional Study | |
| 260 | |b JMIR Publications |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Background:Body image dissatisfaction among children and adolescents is a significant public health concern and is associated with numerous physical and mental problems. Social media platforms, including TikTok, BiliBili, and YouTube, have become popular sources of health information. However, the quality and reliability of content related to body image dissatisfaction have not been comprehensively evaluated.Objective:The primary goal of this study was to examine the quality and reliability of videos related to body image dissatisfaction on TikTok, BiliBili, and YouTube.Methods:The keywords “body image dissatisfaction” were searched on YouTube, TikTok, and BiliBili in November 2024. Videos were collected based on platform-specific sort filters, including the filter of “Most liked” on TikTok and the filter of “Most viewed” on BiliBili and YouTube. The top 100 videos on each platform were reviewed and screened in the study. After excluding videos that were (1) not in English or Chinese, (2) duplicates, (3) irrelevant, (4) no audio or visual, (5) contained advertisements, and (6) with a Global Quality Scale (GQS) score of 1, the final sample consisted of 64 videos, which formed the basis of our research and subsequent findings. Two reviewers (LL and JNY) screened, selected, extracted data, and evaluated all videos using the GQS, the Modified DISCERN (mDISCERN) scores, and the Modified Journal of the American Medical Association (mJAMA) benchmark criteria. Statistical analysis was performed using SPSS (version 28.0; IBM Corp).Results:In total, 64 videos were analyzed in the study, including 20 from TikTok, 13 from BiliBili, and 31 from YouTube. The median duration of the involved videos was 3.01 (IQR 1.00-5.94) minutes on TikTok, 3.52 (IQR 2.36-5.63) minutes on BiliBili, and 4.86 (IQR 3.10-6.93) minutes on YouTube. Compared with the other 2 platforms, BiliBili videos received higher likes and more comments. The majority of the videos (n=40, 62%) were uploaded by self-media. The quality of the videos on YouTube shows the highest overall scores. Videos uploaded by professional authors had significantly higher GQS, mDISCERN, and mJAMA scores compared to those uploaded by nonprofessionals. There was no significant correlation between video quality and the number of views or likes. However, the number of views and likes were significantly positively correlated. Furthermore, a significant correlation was found between the mJAMA, mDISCERN, and GQS scores.Conclusions:Web-based video platforms have become an important source for adolescents to access health information. However, the lack of a significant correlation between video quality and the number of likes and comments poses a challenge for users seeking reliable health information. It is suggested that the quality of the videos on health information would be taken into consideration in the recommendation algorithm on web-based video platforms. | |
| 610 | 4 | |a TikTok Inc | |
| 653 | |a Agreements | ||
| 653 | |a Self image | ||
| 653 | |a Multimedia | ||
| 653 | |a Video recordings | ||
| 653 | |a Cross-sectional studies | ||
| 653 | |a Teenagers | ||
| 653 | |a Body image | ||
| 653 | |a Laboratory animals | ||
| 653 | |a Educational films | ||
| 653 | |a Statistical analysis | ||
| 700 | 1 | |a Yang, Jianning | |
| 700 | 1 | |a Tan, Fengmei | |
| 700 | 1 | |a Luo, Huan | |
| 700 | 1 | |a Chen, Yanhua | |
| 700 | 1 | |a Zhao, Xiaolei | |
| 773 | 0 | |t JMIR Formative Research |g vol. 9 (2025), p. e71652-e71664 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3246990381/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3246990381/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3246990381/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |