OBJECTIVE: Social networks share medical content with no peer-review or fact-checking. In the present study we aimed to assess the quality, reliability, and level of misinformation in TikTok videos about polycystic ovary syndrome (PCOS). METHODS: We performed a cross-sectional analysis of TikTok videos retrieved using "PCOS" as the search term and analyzed using patient education materials assessment tool for audio-visual content (PEMAT A/V), modified DISCERN (mDISCERN), global quality scale (GQS), video information and quality index (VIQI) and misinformation assessment were employed. RESULTS: A total of 180 videos were included. Most videos were partially accurate (containing 25%-50% of false information) or uninformative (more than 50%) (56.7% and 6.6%, respectively) with a significantly higher proportion of inaccurate or uninformative videos from PCOS-patients than healthcare professionals (14.4% vs. 0%
P <
0.001) as well as for partially accurate videos (78.4% vs. 37.5%
P <
0.001). PEMAT A/V scores for understandability and actionability were 50% (interquartile range [IQR]: 33%-58%) and 25% (IQR: 25%-50%), respectively with significantly higher understandability for healthcare professionals (54% [IQR: 42%-71%] vs. 33% [IQR: 25%-50%], P <
0.001). Median mDISCERN was 2 (IQR: 1-3) (low degree of reliability), with videos by healthcare professionals scoring significantly higher than those by patients (2 [IQR: 2-3] vs. 1 [IQR: 0-2]
P = 0.001). Intermediate-low overall video quality was reported in VIQI with median score of 12 (IQR: 10-15) and significantly lower scores for patients (9 [IQR: 5-12] vs. 13 [IQR: 12-17]
P <
0.001). Similarly, median GQS score was overall intermediate for degree of usefulness (median 3 [IQR: 2-4]), but patient-created videos were of significantly lower quality (median 2 [IQR: 2-3] vs. 4 [IQR: 3-4]
P <
0.001). CONCLUSION: PCOS-related videos on TikTok were mostly misinformative and of low quality and reliability. Healthcare professionals' videos were more informative with had higher quality compared to patient-created content. Identifying and addressing low-quality content is crucial for guiding future public health initiatives and improving the dissemination of trustworthy medical information on social networks.