Poster Session 1
Category: Labor
Poster Session 1
McKensie Wall, MD, MPH
Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego
San Diego, California, United States
Minhazur R. Sarker, MD
Fellow Physician
Division of Maternal Fetal Medicine, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego
San Diego, California, United States
Isabel Katlaps, MD
Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego
San Diego, California, United States
Emma Roberts, MD
Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego
San Diego, California, United States
Rachel L. Wiley, MD, MPH (she/her/hers)
Maternal Fetal Medicine Fellow
Division of Maternal Fetal Medicine, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego
San Diego, California, United States
Gladys A. Ramos, MD (she/her/hers)
Clinical Professor, Maternal Fetal Medicine
Division of Maternal Fetal Medicine, Department of Obstetrics, Gynecology and Reproductive Sciences
San Diego, California, United States
Chia-Ling Nhan-Chang, MD
Division of Maternal Fetal Medicine, Department of Obstetrics, Gynecology and Reproductive Sciences
San Diego, California, United States
Reproductive age individuals often turn to social media for health information. We aimed to evaluate the quality and accuracy of TikTok content using #epidural or #labor.
Study Design:
We conducted a cross-sectional analysis of TikTok content using #epidural and #labor. The most popular videos for each hashtag were queried using the Apify TikTok Data Extractor scraper tool. Our primary outcome was the number of relevant videos for each hashtag. The secondary outcomes included pre-specified themes. Each video was assigned a Global Quality Score (GQS) - a validated tool rating medical content from 1 (low, use discouraged) to 5 (high, medically useful). If the content made medical claims, the mDISCERN score for each video was calculated – a validated scale to assess the accuracy of medical information.
Results:
Among the 68 #epidural and 81 #labor videos, 40 (58.8%) and 43 (53.1%) videos were relevant, respectively. The majority of videos were created by women and by individuals without credentials listed. The majority of content across both hashtags was personal experience with some medical education. The median [interquartile range, IQR] GQS for both #epidural and #labor was 1 [1,1] suggesting that the majority of content was poor quality and not at all medically useful for patients. Of the 9 (22.5%) #epidural videos making medical claims, only 3 (33.3%) were accurate and 6 (66.7%) were inaccurate with a median [IQR] mDISCERN score of 1 [1,1]. No #pitocin videos made medical claims.
Conclusion:
Our findings suggest that only slightly over half the top videos using #epidural or #labor are relevant to pregnancy. Additionally, the top content for #epidural and #labor on TikTok is over generally low quality and low usefulness to patients.