Session Information
Date: Tuesday, October 28, 2025
Title: (2547–2566) ARP Posters I
Session Type: Poster Session C
Session Time: 10:30AM-12:30PM
Background/Purpose: Axial spondyloarthritis (axSpA) is a chronic inflammatory disease of the axial skeleton that can cause significant pain and disability. Its variable and often non-specific presentation can contribute to diagnostic delays, patient frustration, and the spread of misinformation, especially on social media platforms where individuals increasingly seek medical advice. TikTok, a leading social media application, has become a popular source of health-related content, yet its medical accuracy remains largely unregulated. This study aimed to assess the quality and reliability of TikTok content related to axSpA to better understand its educational value and potential for misinformation.
Methods: In April 2025, we conducted a cross-sectional analysis of the top 105 English-language TikTok videos using search terms including “Axial Spondyloarthritis,” “AxSpA,” “nrAxSpA,” and “Ankylosing Spondylitis.” Videos were included if they were publicly accessible, in English, posted within the past four years, and had received at least 100 “likes.” Eligible videos featured educational content, patient stories, or clinician-generated material. Each video was assessed using a validated modified DISCERN tool covering five criteria: clarity of aims, use of reliable sources, balance and objectivity, provision of additional references, and acknowledgment of uncertainties. Each criterion was scored 0 (not met) or 1 (met), for a total score of 0–5. Videos were also rated for medical accuracy (accurate, partially accurate/misleading, inaccurate) based on adherence to evidence-based information. Descriptive statistics and Kruskal-Wallis tests compared DISCERN scores across uploader categories.
Results: Of the 105 videos reviewed, 66 met the inclusion criteria. The majority were uploaded by patients (77.3%), followed by physicians (9.1%), chiropractors (9.1%), influencers (3.0%), and physical therapists (1.5%). Content most frequently addressed symptoms (62.1%), management strategies (62.1%), personal experiences (62.1%), and disease descriptions (39.4%), with most videos falling into multiple categories. Overall, 18.2% of videos were rated accurate, 56.1% partially accurate or misleading, and 25.8% inaccurate. The mean DISCERN scores (± SD) by uploader type were: physicians 3.50 ± 1.05, chiropractors 2.00 ± 1.55, physical therapists 3.00 (single data point), influencers 0.50 ± 0.71, and patients 1.63 ± 0.85. A Kruskal-Wallis test demonstrated a significant difference in DISCERN scores across uploader groups (H = 18.05, p = 0.0012). Post-hoc pairwise comparisons revealed that videos uploaded by physicians had significantly higher DISCERN scores compared to those uploaded by patients (p = 0.0003), while other comparisons were not statistically significant.
Conclusion: TikTok offers opportunities for patient education and community-building but contains a high prevalence of partially accurate or inaccurate information. The diagnostic complexity of axSpA may amplify misinformation risks. These findings highlight the urgent need for greater healthcare professional engagement on social media to enhance content reliability and support informed patient decision-making.
Figure 1. Distribution of TikTok video uploaders. (n = 66)
Table 1. Accuracy rating of included videos (n = 66) using a 3-tiered system: accurate, partially accurate/misleading, or inaccurate
Table 2. DISCERN scores by uploader type
To cite this abstract in AMA style:
Rabie M, Harwell S. Quality and Accuracy of TikTok Videos on Axial Spondyloarthritis: A Modified DISCERN Analysis [abstract]. Arthritis Rheumatol. 2025; 77 (suppl 9). https://acrabstracts.org/abstract/quality-and-accuracy-of-tiktok-videos-on-axial-spondyloarthritis-a-modified-discern-analysis/. Accessed .« Back to ACR Convergence 2025
ACR Meeting Abstracts - https://acrabstracts.org/abstract/quality-and-accuracy-of-tiktok-videos-on-axial-spondyloarthritis-a-modified-discern-analysis/