"Quantifying digital health misinformation in knee osteoarthritis: A cross-sectional analysis of YouTube content."

“量化膝骨关节炎领域的数字健康错误信息:对 YouTube 内容的横断面分析。”

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Abstract

BACKGROUND: Patients with knee osteoarthritis (OA) increasingly seek management advice on YouTube, where highly viewed content may contain health misinformation that diverges from guideline-concordant care. METHODS: A cross-sectional analysis was conducted on an audited dataset of 100 YouTube videos addressing knee pain and OA, selected via the YouTube API and strictly ordered by highest view count. Searches were performed from India in a cleared-history, logged-out environment using English and Hindi keywords, with default regional settings. Videos were classified into five intervention archetypes and benchmarked against AAOS 2023 and NICE NG226 guidelines. A newly proposed Digital Harm Score (DHS; 0-10) was developed de novo to quantify harm potential, based on (1) high-risk archetype, (2) explicit "quick-fix" claims (<7 days), and (3) omission or inclusion of core lifestyle pillars. Two orthopaedic reviewers independently applied the DHS, with inter-rater reliability assessed by intraclass correlation coefficient (ICC) and Cohen's κ. Associations were evaluated with Chi-square tests and effect sizes; median view counts were compared using the Mann-Whitney U test. RESULTS: The 100 videos accrued 392.8 million cumulative views; 15% were created by verifiable medical professionals, while >80% originated from laypersons. Inter-rater agreement for DHS was strong (ICC = 0.86), and for archetype classification (κ = 0.82). "Topical Kitchen Oils/Pastes" represented 15% of videos yet contributed disproportionately to maximum DHS values (χ(2) = 8.6, p = 0.0033; φ = 0.29). Videos explicitly promising a "quick fix" in less than 7 days had higher median view counts (3,200,982 vs. 2,781,539), but this did not reach statistical significance (p = 0.0708). Only 1% of videos explicitly mentioned weight loss. CONCLUSION: Highly viewed YouTube videos on knee OA frequently omit key lifestyle recommendations and promote unproven interventions. These digital patterns may contribute to delayed adoption of guideline-concordant management and suboptimal preoperative optimization in OA care.

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