Evaluation of the Information Quality of Bone Mineral Density–Related Videos on TikTok

Evaluation of the Information Quality of Bone Mineral Density–Related Videos on TikTok | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Evaluation of the Information Quality of Bone Mineral Density–Related Videos on TikTok Yue Chen, Lizhen Gan, Qibiao Wu, Yunchuan Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8217421/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background TikTok has emerged as a prominent source of health information for the general public. However, the quality of bone mineral density (BMD)-related content on the platform remains largely unevaluated using validated tools. Objective This study aimed to assess the information quality of BMD-related videos on TikTok via the Global Quality Score (GQS), the DISCERN instrument, and the Journal of the American Medical Association (JAMA) benchmark criteria and to examine the relationship between video quality and viewer engagement metrics. Methods The top 100 videos based on the default relevance ranking for the keyword "bone mineral density" were retrieved on September 10, 2025. Video characteristics, uploader type, and engagement metrics (likes, comments, shares, view count) were recorded. Two independent raters assessed information quality via the GQS, DISCERN, and JAMA instruments. Interrater reliability was high (class correlation coefficient = 0.89). Statistical analyses included descriptive statistics, Kruskal‒Wallis tests with post hoc Dunn's tests (Bonferroni-corrected), and Spearman correlation analysis. Results The largest proportion of videos were produced by science communicators (51%, n = 51), defined as individuals or organizations without verifiable medical credentials, followed by medical professionals (40%, n = 40) and news media outlets (9%, n = 9). The overall quality of videos was suboptimal across all assessment tools. The mean total DISCERN score was 42.5 (SD = 16.8), with only 11% of the videos rated as "excellent" and 46% rated as "poor" or "very poor". Consistent with this finding, the mean GQS and JAMA scores were also low at 2.73 (SD = 1.32) and 2.47 (SD = 1.09), respectively. Compared with those uploaded by science communicators, videos uploaded by medical professionals scored significantly higher across all the quality instruments (e.g., DISCERN total score: mean = 51.75, SD = 16.81) (mean = 34.88, SD = 11.84; p < 0.001). A weak but significant negative correlation was found between the view count and DISCERN score (ρ = -0.20, p = 0.042), indicating that greater popularity was associated with lower information quality. Conclusion The overall quality of BMD-related content on TikTok is suboptimal, with popular videos often lacking accuracy and depth. There is a critical need for improved quality control, collaboration between health professionals and content creators, and platform-led initiatives to increase the reliability of health information disseminated via social media. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research TikTok bone mineral density social media health information quality assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Bone mineral density (BMD) is a critical clinical indicator reflecting the concentration of minerals, primarily calcium and phosphorus, within bone tissue [ 1 ] . It serves as a key parameter for diagnosing osteoporosis and assessing fracture risk. Osteoporosis, characterized by low BMD, leads to weakened bones and increased susceptibility to fractures, which are significant contributors to morbidity and mortality, particularly among older adults. [ 2 , 3 ] . The gold standard for BMD measurement is dual-energy X-ray absorptiometry (DXA) [ 4 ] , with results expressed as T scores for diagnostic classification [ 5 , 6 ] . BMD is influenced by a multitude of factors, including age, lifestyle (e.g., diet, physical activity), medications (e.g., corticosteroids), and specific medical conditions (e.g., hyperthyroidism, rheumatoid arthritis) [ 7 ] . Notably, postmenopausal women experience accelerated bone loss due to decreased estrogen levels [ 8 ] . Globally, osteoporosis affects approximately 200 million women, with prevalence rates increasing with age. For example, approximately 10% of women aged 60, 20% aged 70, 40% aged 80, and 67% aged 90 are affected by osteoporosis [ 9 , 10 ] . In men, the prevalence is lower but still notable, with 1 in 5 men over the age of 50 expected to experience an osteoporotic fracture in their lifetime [ 11 – 13 ] . These statistics underscore the growing public health challenge posed by osteoporosis, especially in aging populations. The burden of osteoporosis-related fractures is substantial. In 2019, the five countries with the highest disease burdens due to low BMD-related fractures were India, China, the United States, Japan, and Germany, accounting for more than 50% of the global burden [ 14 ] . This highlights the widespread impact of osteoporosis and the need for effective prevention and management strategies worldwide. The proliferation of digital health platforms offers novel avenues for patient education on bone health management. While social media platforms such as TikTok can significantly increase public awareness, they also pose a substantial risk of disseminating misleading or inaccurate information, which can adversely affect patient decision-making and health outcomes [ 15 ] . As a highly popular platform, especially among younger demographics, TikTok's short-form video format has become a prominent source of health information. However, the platform's algorithmic preference for engaging content may prioritize virality over accuracy. To our knowledge, no study has systematically evaluated the quality of BMD-related information on TikTok via validated instruments such as the GQS, DISCERN, and JAMA benchmarks, creating a critical evidence gap. Therefore, this cross-sectional study aimed to evaluate the information quality of BMD-related videos on TikTok and explore the relationship between quality and audience engagement. Specifically, we sought to answer the following research questions: What is the overall quality of BMD-related videos on TikTok as measured by the GQS, DISCERN and JAMA instruments? Does information quality differ significantly based on the type of video uploader (e.g., medical professional, science communicator, news media)? Is there a correlation between video engagement metrics (e.g., views, likes) and quality scores (GQS, DISCERN, JAMA)? Methods Retrieval Strategy and Data Extraction On September 10, 2025, a systematic search was conducted on TikTok via the keyword "bone mineral density." The search was performed in a clean browser environment (cache cleared) to ensure standardized, replicable results. The platform's default sorting algorithm, which ranks videos by an undisclosed combination of relevance and popularity, was used. The top 100 videos from this search were selected for analysis, a conventional sample size in social media content analysis that effectively captures the most visible content [ 16 – 18 ] . For each video, the following data were extracted: release date, uploader name, uploader type, video duration (seconds), and engagement metrics (number of likes, comments, shares, and total view count). All the data were compiled into a structured database for analysis. Video Classification Videos were classified by uploader source and content theme. Uploader Source : Medical professionals: Individuals with verifiable healthcare credentials (e.g., physicians, nurses, allied health professionals). Science communicators: Individuals or organizations disseminating health information without verifiable professional medical credentials. News media: Official accounts of news outlets (e.g., television networks, online newspapers). Content Theme : Videos were categorized on the basis of their primary focus into one of the following mutually exclusive themes: pathophysiology (explanation of disease processes), diagnosis (methods and criteria for diagnosis), risk factors, medical treatment (pharmacological and procedural interventions), lifestyle management (diet, exercise, and other nonpharmacological advice), and disease burden/Epidemiology. Quality Assessment 1. Global quality score (GQS) The overall quality of the videos was evaluated via a 5-point global quality score (GQS), with detailed criteria provided in Supplementary Table 1. Two independent raters assessed all video materials and reported excellent interrater agreement (ICC = 0.91). Scoring discrepancies were addressed through discussion or, when necessary, adjudication by a senior researcher. The DISCERN Instrument To evaluate the quality of the health information presented, we employed the DISCERN instrument, a validated tool consisting of 16 items grouped into three domains: Reliability (Items 1–8) : Pertains the trustworthiness of content. Treatment choices (Items 9–15) : Focus on information related to treatment options. Overall Rating (Item 16) : Provides a global assessment of quality. Each item is rated from 1 (lowest) to 5 (highest), yielding a total score between 16 and 80. These totals are categorized as follows: 16–26 (very poor), 27–38 (poor), 39–50 (fair), 51–62 (good), and 63–80 (excellent). Two trained raters independently applied the instrument, achieving excellent interrater reliability (ICC = 0.89). Disagreements were resolved through discussion or by a third senior rater. JAMA Benchmark Criteria Video quality was further analyzed via the Journal of the American Medical Association (JAMA) benchmark criterion, which comprises four key components: authorship, attribution, currency, and disclosure (see Supplementary Table 2). Each criterion was assigned 1 point if fully satisfied. Two trained evaluators independently conducted the assessment, which demonstrated high interrater consistency. Divergent scores were reconciled via consensus or by a third researcher. Statistical analysis Data are presented as medians with interquartile ranges (IQRs) for nonnormally distributed continuous variables and as the means with standard deviations (SDs) for scaled scores. Categorical data are presented as frequencies and percentages. The Shapiro‒Wilk test confirmed that most continuous variables (e.g., engagement metrics) deviated from normality; therefore, nonparametric tests were employed. The Kruskal‒Wallis test was used to compare quality scores across uploader sources and content themes. Post hoc pairwise comparisons were conducted via Dunn's test with Bonferroni correction to control for Type I error. Spearman's rank correlation coefficient (ρ) was used to assess relationships between continuous variables (e.g., video duration, engagement metrics, and quality scores). All analyses were performed via SPSS (version 27) and GraphPad Prism (version 10.1.2). A two-tailed p value of < 0.05 was considered statistically significant. Results Video characteristics The descriptive statistics of the 100 analyzed videos are summarized in Table 1 . Notably, science communicators produced the largest proportion of videos (51%), followed by medical professionals (40%) and news media (9%). Table 1 Characteristics of TikTok Videos on Bone Mineral Density by Uploader Source (n = 100) Video source Video Duration (s), Median (IQR) Likes, Median (IQR) Comments, Median (IQR) View Count, Median (IQR) Followers, Median (IQR) Days Active, Median (IQR) Doctor (n = 40) 189.50(433) 520.00(2160) 40.50(131) 33405.00(141725) 212000.00(825675) 853.50(1363) Science Comm. (n = 51) 42.00(94) 726.00(2870) 17.00(69) 31059.00(135318) 76700.00(494680) 540.00(695) News Media (n = 9) 82.00(275) 149.00(4203) 6.00(189) 13773.00(136844) 348000.00(3348030) 1094.00(1837) Notable differences were observed in video characteristics across uploader sources. Videos from medical professionals were significantly longer (median duration: 189.5 seconds) than those from science communicators (42.0 seconds) or news media (82.0 seconds). In terms of engagement, videos from medical professionals garnered the highest median number of comments (40.5), whereas videos from science communicators received the highest median number of likes (726.0). News media accounts had the largest median follower count (348,000) but the lowest median view count (13,773) and number of likes (149.0) among the groups. With respect to content themes, lifestyle management (68% of videos) and diagnosis (55%) were the most frequently covered topics, whereas pathophysiology (25%) was the least discussed (Fig. 1 ). The chart shows that lifestyle management and diagnosis were the most common themes, whereas pathophysiology was the least common. Quality Assessment The quality assessment revealed significant disparities based on the uploader source (Table 2 ). Compared with those from science communicators, videos from medical professionals achieved significantly higher mean scores across all three quality instruments (all p < 0.001). Table 2 Quality assessment scores by upload source Publisher Category DISCERN, Mean (SD) GQS, Mean (SD) JAMA, Mean (SD) p-valve Doctor (n = 40) 51.75(16.81) 3.58 (1.26) 3.15 (0.95) <0.001 Science Comm. (n = 51) 34.88(11.84) 2.04 (1.00) 1.92 (0.91) <0.001 News Media (n = 9) 48.11(8.18) 3.11 (0.78) 2.67 (0.71) <0.001 This pattern of superior quality for medical professionals was consistently visualized in the distribution of scores across the three instruments (Fig. 2 ). Boxplots for the DISCERN, GQS, and JAMA scores clearly illustrate that videos from medical professionals (doctors) had not only higher median scores but also a greater upper quartile range than did those from science communicators (Science Comm.), whose scores were concentrated at the lower end. News media (News Media) consistently occupied an intermediate position. Videos from medical professionals also scored significantly higher across all sections of the DISCERN instrument (Table 3 ). News media videos were scored intermediately and were not significantly different from medical professionals in post hoc analysis for most sections. Boxplots representing the distributions of (A) DISCERN, (B) global quality score (GQS), and (C) JAMA benchmark scores for videos from different uploader sources. The central line in each box indicates the median, the box represents the interquartile range (IQR), and the whiskers extend to 1.5IQR. Table 3 DISCERN Instrument Scores by Uploader Source and Section Video Source Publication Reliability, Mean (SD) Treatment Choices, Mean (SD) Overall Rating, Mean (SD) Total Score, Mean (SD) Doctor (n = 40) 27.43(7.59) 21.08(9.434) 3.25(1.03) 51.75(16.81) Science Comm. (n = 51) 19.75(8.90) 12.98(4.913) 2.16(0.73) 34.88(11.84) News Media (n = 9) 25.11(5.58) 19.78(3.114) 3.22(0.67) 48.11(8.17) p value < 0.001 < 0.001 < 0.001 < 0.001 Post hoc pairwise comparisons confirming these differences are detailed in Table 4 . Table 4 Post hoc Pairwise Comparisons (Dunn's Test with Bonferroni Correction) of DISCERN Scores between Uploader Sources Section Comparison Mean Difference Adjusted p value Publication Reliability Doctor vs. Science Comm. 7.68 < 0.001 Doctor vs. News Media 2.31 0.720 Science Comm. vs. News Media -5.37 0.064 Treatment Choices Doctor vs. Science Comm. 8.10 < 0.001 Doctor vs. News Media 1.30 1.000 Science Comm. vs. News Media -6.80 < 0.001 Overall Rating Doctor vs. Science Comm. 1.09 < 0.001 Doctor vs. News Media 0.03 1.000 Science Comm. vs. News Media -1.07 0.003 Total Score Doctor vs. Science Comm. 16.87 < 0.001 Doctor vs. News Media 3.64 0.860 Science Comm. vs. News Media -13.23 0.003 The distribution of total DISCERN scores across quality categories is presented in Table 5 . The majority of videos (46%) were rated as "poor" or "very poor," whereas only 11% achieved an "excellent" rating. Table 5 Distribution of DISCERN total scores (n = 100) DISCERN Quality Category Score Range Number of Videos, n (%) Very Poor 16–26 19(19%) Poor 27–38 27(27%) Fair 39–50 21(21%) Good 51–62 22(22%) Excellent 63–80 11(11%) The quality of the BMD-related videos also varied significantly depending on the specific content theme (Fig. 3 ). Post hoc analysis revealed that videos focusing on medical treatment and diagnosis achieved the highest mean DISCERN scores. In contrast, content providing practical lifestyle management advice consistently received the lowest scores across all DISCERN dimensions (all P < 0.01 compared with medical treatment videos). *Bar graphs represent the mean scores (± standard deviation) for (A) publication reliability, (B) treatment choices, (C) overall rating, and (D) total score across different video content themes. *P < 0.05, **P < 0.01 indicate significant differences between groups on the basis of post hoc Dunn's test following a significant Kruskal‒Wallis test. * A visual summary of the quality disparities by uploader source is presented in the heatmap (Fig. 4 ). The gradient clearly illustrates the superior performance of medical professionals, who exhibit darker shades (indicating higher scores) across all quality dimensions, particularly in 'publication reliability' and 'treatment choices'. This finding is consistent with the score distributions shown in Fig. 2 . The heatmap visualizes the mean scores for publication reliability, treatment choices, overall rating, and total score across the three uploader sources. Lighter shades of yellow indicate higher mean DISCERN scores, reflecting better quality. Correlation analysis Spearman correlation analysis revealed a complex relationship between video characteristics (Fig. 5 ). As expected, user engagement metrics (likes, comments, view count) were strongly positively correlated with each other (all ρ > 0.70, p < 0.01). Video duration showed a significant weak negative correlation with view count (ρ = -0.25, p < 0.05). Most importantly, a significant weak negative correlation was observed between the view count and the total DISCERN score (ρ = -0.20, p = 0.042), suggesting that more widely viewed videos tended to be of lower quality. *Variables include video duration, days active, comments, likes, view count, and total DISCERN score. Correlation coefficients (ρ) are color-coded (red: positive; blue: negative). *P < 0.05, *P < 0.01. Discussion A central finding of this study is the significant disparity in quality on the basis of uploader source, a trend that was robustly demonstrated across all three assessment instruments (DISCERN, GQS, and JAMA) and clearly visualized in the score distributions (Fig. 2 ). Compared with those produced by science communicators, videos created by medical professionals demonstrated superior reliability and provided more comprehensive information on treatment choices. This aligns with expectations, as healthcare professionals are trained in evidence-based medicine and are likely to adhere to clinical guidelines. Conversely, while science communicators often possess skills in making content engaging and accessible, their lack of formal medical training may lead to oversimplification or the omission of crucial nuances, resulting in lower quality scores. These findings underscore a critical trade-off between content accessibility and clinical accuracy in public health messaging on social media platforms. Our analysis reveals that a substantial proportion (46%) of BMD-related videos on TikTok are of "poor" or "very poor" quality according to the DISCERN instrument. This finding is consistent with a growing body of literature documenting the variable quality of health information on social media platforms, including YouTube and Instagram, for conditions ranging from diabetes to cancer [ 19 – 21 ] . The predominance of low-quality content underscores a significant public health risk, as users may base critical health decisions on inaccurate or incomplete information. The observed weak negative correlation between view count and DISCERN score (ρ = -0.20, p = 0.042) is particularly noteworthy. This suggests that the algorithmic mechanisms driving content virality on TikTok may not be prioritized and might even disadvantage high-quality, nuanced information. This phenomenon, where entertaining or sensationalist content garners more attention than evidence-based material does, has been noted on other platforms [ 22 ] . The platform's emphasis on brevity might further exacerbate this issue for complex topics such as BMD, where simplified messages can lack the necessary context or caveats. Limitations This study has several limitations. First, the subjective nature of quality assessment via tools such as DISCERN, despite high interrater reliability, introduces the potential for bias. Second, the sample was limited to the top 100 videos for a single English-language keyword, which may not be fully representative of all BMD-related content on TikTok, potentially introducing selection bias. The dynamic nature of social media feed algorithms also means that results may vary over time. Future research should include larger, longitudinal samples and explore content in other languages. Conclusion In conclusion, this study reveals a significant quality gap in bone mineral density-related information on TikTok. While the platform holds great potential for health education, the current content landscape is characterized by generally poor information quality, with highly viewed videos often being less reliable. Videos from medical professionals are a notably more trustworthy source. These findings underscore the urgent need for multistakeholder initiatives involving healthcare providers, content creators, and platform regulators to increase the quality and reliability of health information on social media. Future efforts should focus on promoting high-quality content creation by healthcare professionals, developing platform algorithms that prioritize information accuracy, and potentially implementing certification systems for credible health communicators. Declarations Human Ethics and Consent to Participate declarations: not applicable. Funding This study was funded by the General Administration of Sport of China (Grant No. 2025TK028) and the Science and Technology Development Fund, Macau SAR (Grant Nos. 0048/2023/AFJ and 0164/2023/RIA3). Author Contribution Yue Chen and Lizhen Gan are co-first authors who contributed equally to this work. Yue Chen collected the data, scored the video quality, and wrote the main manuscript text. Lizhen Gan scored the video quality, conducted the statistical analysis, and reviewed the manuscript. Yunchuan Wu and Qibiao Wu are both corresponding authors, with Yunchuan Wu serving as the primary corresponding author. All authors reviewed the manuscript. 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University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yunchuan","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-11-27 03:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8217421/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8217421/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101257200,"identity":"51ea1c50-640d-4cda-9df7-c869af38bd1e","added_by":"auto","created_at":"2026-01-27 19:18:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":188535,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of videos addressing different bone mineral density-related content themes (n=100).\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8217421/v1/d4a708ff830f4caee4f29a02.jpg"},{"id":101257199,"identity":"63fb8b2d-1fb1-437f-be9b-c148bdced6e6","added_by":"auto","created_at":"2026-01-27 19:18:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112176,"visible":true,"origin":"","legend":"\u003cp\u003eDistributions of quality scores by Uploader source.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8217421/v1/24b1a36244477c1a85f0ed12.jpg"},{"id":101257205,"identity":"5173472b-5bd9-4ec7-aa0d-4f4e6ba0250c","added_by":"auto","created_at":"2026-01-27 19:18:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":185242,"visible":true,"origin":"","legend":"\u003cp\u003eDISCERN Scores for TikTok Videos by Content Theme.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8217421/v1/fffba31e448305c9a8c28aa7.jpg"},{"id":101297278,"identity":"e4c8417e-2713-47d0-8854-9a5a24c7264f","added_by":"auto","created_at":"2026-01-28 09:26:19","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":113628,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of the mean DISCERN scores by upload source.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8217421/v1/e4e56fe54459270aa20430f3.jpg"},{"id":101297542,"identity":"eef02779-c669-4044-aba5-ab5104d6856f","added_by":"auto","created_at":"2026-01-28 09:27:50","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":42705,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman correlation heatmap of video characteristics and DISCERN scores.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8217421/v1/b1159882f2bf4ae73fab9aff.jpg"},{"id":109044878,"identity":"0af928b1-3e47-484c-8956-3f6df7722d40","added_by":"auto","created_at":"2026-05-12 05:13:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":950019,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8217421/v1/014794a8-1a78-4c25-b092-7e02fccf48d6.pdf"},{"id":101257202,"identity":"c00a474f-65c3-44a2-bb19-2aea69e19290","added_by":"auto","created_at":"2026-01-27 19:18:22","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":18050,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8217421/v1/63adc6c883f705ffff21e495.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of the Information Quality of Bone Mineral Density–Related Videos on TikTok","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBone mineral density (BMD) is a critical clinical indicator reflecting the concentration of minerals, primarily calcium and phosphorus, within bone tissue \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. It serves as a key parameter for diagnosing osteoporosis and assessing fracture risk. Osteoporosis, characterized by low BMD, leads to weakened bones and increased susceptibility to fractures, which are significant contributors to morbidity and mortality, particularly among older adults. \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The gold standard for BMD measurement is dual-energy X-ray absorptiometry (DXA)\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, with results expressed as T scores for diagnostic classification\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBMD is influenced by a multitude of factors, including age, lifestyle (e.g., diet, physical activity), medications (e.g., corticosteroids), and specific medical conditions (e.g., hyperthyroidism, rheumatoid arthritis)\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Notably, postmenopausal women experience accelerated bone loss due to decreased estrogen levels\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGlobally, osteoporosis affects approximately 200\u0026nbsp;million women, with prevalence rates increasing with age. For example, approximately 10% of women aged 60, 20% aged 70, 40% aged 80, and 67% aged 90 are affected by osteoporosis\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. In men, the prevalence is lower but still notable, with 1 in 5 men over the age of 50 expected to experience an osteoporotic fracture in their lifetime\u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. These statistics underscore the growing public health challenge posed by osteoporosis, especially in aging populations.\u003c/p\u003e \u003cp\u003eThe burden of osteoporosis-related fractures is substantial. In 2019, the five countries with the highest disease burdens due to low BMD-related fractures were India, China, the United States, Japan, and Germany, accounting for more than 50% of the global burden\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. This highlights the widespread impact of osteoporosis and the need for effective prevention and management strategies worldwide.\u003c/p\u003e \u003cp\u003eThe proliferation of digital health platforms offers novel avenues for patient education on bone health management. While social media platforms such as TikTok can significantly increase public awareness, they also pose a substantial risk of disseminating misleading or inaccurate information, which can adversely affect patient decision-making and health outcomes\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. As a highly popular platform, especially among younger demographics, TikTok's short-form video format has become a prominent source of health information. However, the platform's algorithmic preference for engaging content may prioritize virality over accuracy. To our knowledge, no study has systematically evaluated the quality of BMD-related information on TikTok via validated instruments such as the GQS, DISCERN, and JAMA benchmarks, creating a critical evidence gap.\u003c/p\u003e \u003cp\u003eTherefore, this cross-sectional study aimed to evaluate the information quality of BMD-related videos on TikTok and explore the relationship between quality and audience engagement. Specifically, we sought to answer the following research questions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat is the overall quality of BMD-related videos on TikTok as measured by the GQS, DISCERN and JAMA instruments?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDoes information quality differ significantly based on the type of video uploader (e.g., medical professional, science communicator, news media)?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIs there a correlation between video engagement metrics (e.g., views, likes) and quality scores (GQS, DISCERN, JAMA)?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eRetrieval Strategy and Data Extraction\u003c/h2\u003e \u003cp\u003eOn September 10, 2025, a systematic search was conducted on TikTok via the keyword \"bone mineral density.\" The search was performed in a clean browser environment (cache cleared) to ensure standardized, replicable results. The platform's default sorting algorithm, which ranks videos by an undisclosed combination of relevance and popularity, was used. The top 100 videos from this search were selected for analysis, a conventional sample size in social media content analysis that effectively captures the most visible content\u003csup\u003e[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor each video, the following data were extracted: release date, uploader name, uploader type, video duration (seconds), and engagement metrics (number of likes, comments, shares, and total view count). All the data were compiled into a structured database for analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVideo Classification\u003c/h3\u003e\n\u003cp\u003eVideos were classified by uploader source and content theme.\u003c/p\u003e \u003cp\u003e \u003cb\u003eUploader Source\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMedical professionals: Individuals with verifiable healthcare credentials (e.g., physicians, nurses, allied health professionals).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eScience communicators: Individuals or organizations disseminating health information without verifiable professional medical credentials.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNews media: Official accounts of news outlets (e.g., television networks, online newspapers).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eContent Theme\u003c/b\u003e: Videos were categorized on the basis of their primary focus into one of the following mutually exclusive themes: pathophysiology (explanation of disease processes), diagnosis (methods and criteria for diagnosis), risk factors, medical treatment (pharmacological and procedural interventions), lifestyle management (diet, exercise, and other nonpharmacological advice), and disease burden/Epidemiology.\u003c/p\u003e \u003cp\u003e \u003cb\u003eQuality Assessment\u003c/b\u003e \u003c/p\u003e\n\u003ch3\u003e1. Global quality score (GQS)\u003c/h3\u003e\n\u003cp\u003eThe overall quality of the videos was evaluated via a 5-point global quality score (GQS), with detailed criteria provided in Supplementary Table\u0026nbsp;1. Two independent raters assessed all video materials and reported excellent interrater agreement (ICC\u0026thinsp;=\u0026thinsp;0.91). Scoring discrepancies were addressed through discussion or, when necessary, adjudication by a senior researcher.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eThe DISCERN Instrument\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eTo evaluate the quality of the health information presented, we employed the DISCERN instrument, a validated tool consisting of 16 items grouped into three domains:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eReliability (Items 1\u0026ndash;8)\u003c/b\u003e: Pertains the trustworthiness of content.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTreatment choices (Items 9\u0026ndash;15)\u003c/b\u003e: Focus on information related to treatment options.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eOverall Rating (Item 16)\u003c/b\u003e: Provides a global assessment of quality.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eEach item is rated from 1 (lowest) to 5 (highest), yielding a total score between 16 and 80. These totals are categorized as follows: 16\u0026ndash;26 (very poor), 27\u0026ndash;38 (poor), 39\u0026ndash;50 (fair), 51\u0026ndash;62 (good), and 63\u0026ndash;80 (excellent). Two trained raters independently applied the instrument, achieving excellent interrater reliability (ICC\u0026thinsp;=\u0026thinsp;0.89). Disagreements were resolved through discussion or by a third senior rater.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eJAMA Benchmark Criteria\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eVideo quality was further analyzed via the Journal of the American Medical Association (JAMA) benchmark criterion, which comprises four key components: authorship, attribution, currency, and disclosure (see Supplementary Table\u0026nbsp;2). Each criterion was assigned 1 point if fully satisfied. Two trained evaluators independently conducted the assessment, which demonstrated high interrater consistency. Divergent scores were reconciled via consensus or by a third researcher.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData are presented as medians with interquartile ranges (IQRs) for nonnormally distributed continuous variables and as the means with standard deviations (SDs) for scaled scores. Categorical data are presented as frequencies and percentages. The Shapiro‒Wilk test confirmed that most continuous variables (e.g., engagement metrics) deviated from normality; therefore, nonparametric tests were employed. The Kruskal‒Wallis test was used to compare quality scores across uploader sources and content themes. Post hoc pairwise comparisons were conducted via Dunn's test with Bonferroni correction to control for Type I error. Spearman's rank correlation coefficient (ρ) was used to assess relationships between continuous variables (e.g., video duration, engagement metrics, and quality scores). All analyses were performed via SPSS (version 27) and GraphPad Prism (version 10.1.2). A two-tailed p value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eVideo characteristics\u003c/h2\u003e \u003cp\u003eThe descriptive statistics of the 100 analyzed videos are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Notably, science communicators produced the largest proportion of videos (51%), followed by medical professionals (40%) and news media (9%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of TikTok Videos on Bone Mineral Density by Uploader Source (n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVideo source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVideo Duration (s), Median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLikes, Median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComments, Median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eView Count, Median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFollowers, Median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDays Active, Median (IQR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctor (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e189.50(433)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e520.00(2160)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.50(131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33405.00(141725)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e212000.00(825675)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e853.50(1363)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScience Comm. (n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.00(94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e726.00(2870)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.00(69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31059.00(135318)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e76700.00(494680)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e540.00(695)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNews Media (n\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.00(275)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e149.00(4203)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.00(189)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13773.00(136844)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e348000.00(3348030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1094.00(1837)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNotable differences were observed in video characteristics across uploader sources. Videos from medical professionals were significantly longer (median duration: 189.5 seconds) than those from science communicators (42.0 seconds) or news media (82.0 seconds). In terms of engagement, videos from medical professionals garnered the highest median number of comments (40.5), whereas videos from science communicators received the highest median number of likes (726.0). News media accounts had the largest median follower count (348,000) but the lowest median view count (13,773) and number of likes (149.0) among the groups.\u003c/p\u003e \u003cp\u003eWith respect to content themes, lifestyle management (68% of videos) and diagnosis (55%) were the most frequently covered topics, whereas pathophysiology (25%) was the least discussed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe chart shows that lifestyle management and diagnosis were the most common themes, whereas pathophysiology was the least common.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQuality Assessment\u003c/h3\u003e\n\u003cp\u003eThe quality assessment revealed significant disparities based on the uploader source (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared with those from science communicators, videos from medical professionals achieved significantly higher mean scores across all three quality instruments (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQuality assessment scores by upload source\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublisher Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDISCERN, Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGQS, Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJAMA, Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep-valve\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDoctor\u003c/b\u003e\u0026nbsp;(n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.75(16.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.58 (1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.15 (0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eScience Comm.\u003c/b\u003e\u0026nbsp;(n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.88(11.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.04 (1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.92 (0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNews Media\u003c/b\u003e\u0026nbsp;(n\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48.11(8.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.11 (0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.67 (0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis pattern of superior quality for medical professionals was consistently visualized in the distribution of scores across the three instruments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Boxplots for the DISCERN, GQS, and JAMA scores clearly illustrate that videos from medical professionals (doctors) had not only higher median scores but also a greater upper quartile range than did those from science communicators (Science Comm.), whose scores were concentrated at the lower end. News media (News Media) consistently occupied an intermediate position. Videos from medical professionals also scored significantly higher across all sections of the DISCERN instrument (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). News media videos were scored intermediately and were not significantly different from medical professionals in post hoc analysis for most sections.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eBoxplots representing the distributions of (A) DISCERN, (B) global quality score (GQS), and (C) JAMA benchmark scores for videos from different uploader sources. The central line in each box indicates the median, the box represents the interquartile range (IQR), and the whiskers extend to 1.5IQR.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDISCERN Instrument Scores by Uploader Source and Section\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVideo Source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublication Reliability, Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTreatment Choices, Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOverall Rating, Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal Score, Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctor (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.43(7.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.08(9.434)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.25(1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51.75(16.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScience Comm. (n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.75(8.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.98(4.913)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.16(0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.88(11.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNews Media (n\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.11(5.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.78(3.114)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.22(0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48.11(8.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePost hoc pairwise comparisons confirming these differences are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePost hoc Pairwise Comparisons (Dunn's Test with Bonferroni Correction) of DISCERN Scores between Uploader Sources\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSection\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean Difference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted p value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePublication Reliability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctor vs. Science Comm.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctor vs. News Media\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.720\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScience Comm. vs. News Media\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTreatment Choices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctor vs. Science Comm.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctor vs. News Media\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScience Comm. vs. News Media\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOverall Rating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctor vs. Science Comm.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctor vs. News Media\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScience Comm. vs. News Media\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTotal Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctor vs. Science Comm.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctor vs. News Media\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScience Comm. vs. News Media\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-13.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe distribution of total DISCERN scores across quality categories is presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The majority of videos (46%) were rated as \"poor\" or \"very poor,\" whereas only 11% achieved an \"excellent\" rating.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of DISCERN total scores (n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDISCERN Quality Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScore Range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of Videos, n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u0026ndash;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(19%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u0026ndash;38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(27%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(21%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u0026ndash;62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(22%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63\u0026ndash;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(11%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe quality of the BMD-related videos also varied significantly depending on the specific content theme (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Post hoc analysis revealed that videos focusing on medical treatment and diagnosis achieved the highest mean DISCERN scores. In contrast, content providing practical lifestyle management advice consistently received the lowest scores across all DISCERN dimensions (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 compared with medical treatment videos).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e*Bar graphs represent the mean scores (\u0026plusmn;\u0026thinsp;standard deviation) for (A) publication reliability, (B) treatment choices, (C) overall rating, and (D) total score across different video content themes. *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 indicate significant differences between groups on the basis of post hoc Dunn's test following a significant Kruskal‒Wallis test. *\u003c/em\u003e \u003c/p\u003e \u003cp\u003eA visual summary of the quality disparities by uploader source is presented in the heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The gradient clearly illustrates the superior performance of medical professionals, who exhibit darker shades (indicating higher scores) across all quality dimensions, particularly in 'publication reliability' and 'treatment choices'. This finding is consistent with the score distributions shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThe heatmap visualizes the mean scores for publication reliability, treatment choices, overall rating, and total score across the three uploader sources. Lighter shades of yellow indicate higher mean DISCERN scores, reflecting better quality.\u003c/em\u003e \u003c/p\u003e\n\u003ch3\u003eCorrelation analysis\u003c/h3\u003e\n\u003cp\u003eSpearman correlation analysis revealed a complex relationship between video characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). As expected, user engagement metrics (likes, comments, view count) were strongly positively correlated with each other (all ρ\u0026thinsp;\u0026gt;\u0026thinsp;0.70, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Video duration showed a significant weak negative correlation with view count (ρ = -0.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Most importantly, a significant weak negative correlation was observed between the view count and the total DISCERN score (ρ = -0.20, p\u0026thinsp;=\u0026thinsp;0.042), suggesting that more widely viewed videos tended to be of lower quality.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e*Variables include video duration, days active, comments, likes, view count, and total DISCERN score. Correlation coefficients (ρ) are color-coded (red: positive; blue: negative). *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *P\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/em\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eA central finding of this study is the significant disparity in quality on the basis of uploader source, a trend that was robustly demonstrated across all three assessment instruments (DISCERN, GQS, and JAMA) and clearly visualized in the score distributions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared with those produced by science communicators, videos created by medical professionals demonstrated superior reliability and provided more comprehensive information on treatment choices. This aligns with expectations, as healthcare professionals are trained in evidence-based medicine and are likely to adhere to clinical guidelines. Conversely, while science communicators often possess skills in making content engaging and accessible, their lack of formal medical training may lead to oversimplification or the omission of crucial nuances, resulting in lower quality scores. These findings underscore a critical trade-off between content accessibility and clinical accuracy in public health messaging on social media platforms.\u003c/p\u003e \u003cp\u003eOur analysis reveals that a substantial proportion (46%) of BMD-related videos on TikTok are of \"poor\" or \"very poor\" quality according to the DISCERN instrument. This finding is consistent with a growing body of literature documenting the variable quality of health information on social media platforms, including YouTube and Instagram, for conditions ranging from diabetes to cancer\u003csup\u003e[\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. The predominance of low-quality content underscores a significant public health risk, as users may base critical health decisions on inaccurate or incomplete information.\u003c/p\u003e \u003cp\u003eThe observed weak negative correlation between view count and DISCERN score (ρ = -0.20, p\u0026thinsp;=\u0026thinsp;0.042) is particularly noteworthy. This suggests that the algorithmic mechanisms driving content virality on TikTok may not be prioritized and might even disadvantage high-quality, nuanced information. This phenomenon, where entertaining or sensationalist content garners more attention than evidence-based material does, has been noted on other platforms\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. The platform's emphasis on brevity might further exacerbate this issue for complex topics such as BMD, where simplified messages can lack the necessary context or caveats.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, the subjective nature of quality assessment via tools such as DISCERN, despite high interrater reliability, introduces the potential for bias. Second, the sample was limited to the top 100 videos for a single English-language keyword, which may not be fully representative of all BMD-related content on TikTok, potentially introducing selection bias. The dynamic nature of social media feed algorithms also means that results may vary over time. Future research should include larger, longitudinal samples and explore content in other languages.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study reveals a significant quality gap in bone mineral density-related information on TikTok. While the platform holds great potential for health education, the current content landscape is characterized by generally poor information quality, with highly viewed videos often being less reliable. Videos from medical professionals are a notably more trustworthy source. These findings underscore the urgent need for multistakeholder initiatives involving healthcare providers, content creators, and platform regulators to increase the quality and reliability of health information on social media. Future efforts should focus on promoting high-quality content creation by healthcare professionals, developing platform algorithms that prioritize information accuracy, and potentially implementing certification systems for credible health communicators.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003eHuman Ethics and Consent to Participate declarations: not applicable.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was funded by the General Administration of Sport of China (Grant No. 2025TK028) and the Science and Technology Development Fund, Macau SAR (Grant Nos. 0048/2023/AFJ and 0164/2023/RIA3).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYue Chen and Lizhen Gan are co-first authors who contributed equally to this work. Yue Chen collected the data, scored the video quality, and wrote the main manuscript text. Lizhen Gan scored the video quality, conducted the statistical analysis, and reviewed the manuscript. Yunchuan Wu and Qibiao Wu are both corresponding authors, with Yunchuan Wu serving as the primary corresponding author. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data supporting the findings of this study are available within the paper and its Supplementary Information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNIEVES, J. W. Osteoporosis: the role of micronutrients2, 3 [J]. \u003cem\u003eAm. J. Clin. Nutr.\u003c/em\u003e \u003cb\u003e81\u003c/b\u003e (5), 1232S\u0026ndash;9S (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCHEN, Q. et al. High prevalence of low bone mineral density in middle-aged adults in Shanghai: a cross-sectional study [J]. \u003cem\u003eBMC Musculoskelet. 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TikTok as a Source of Health Information and Misinformation for Young Women in the United States: Survey Study [J]. \u003cem\u003eJMIR Infodemiology\u003c/em\u003e. \u003cb\u003e4\u003c/b\u003e, e54663 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePENG, G. et al. Evaluating the Reliability and Quality of Bariatric Surgery Educational Content on TikTok and Bilibili: a Cross-Sectional Content Analysis [J]. Obes Surg, (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWOLF, C. et al. #DepoProvera, An analysis of the most popular TikTok videos about Medroxyprogesterone Acetate [J]. Contraception, : 111256. (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWU, J. et al. Beyond virality: evaluating evidence-based content delivery in social media-based pediatric orthodontic education [J]. \u003cem\u003eBMC Oral Health\u003c/em\u003e. \u003cb\u003e25\u003c/b\u003e (1), 1603 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGARCIA L M, DARNALL B D, KRISHNAMURTHY, P. et al. Correction: Self-Administered Behavioral Skills-Based At-Home Virtual Reality Therapy for Chronic Low Back Pain: Protocol for a Randomized Controlled Trial [J]. \u003cem\u003eJMIR Res. Protoc.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (2), e27652 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNICKEL, B. et al. Social Media Posts About Medical Tests With Potential for Overdiagnosis [J]. \u003cem\u003eJAMA Netw. Open.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (2), e2461940 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZENONE, M. \u0026amp; OW, N. BARBIC S. TikTok and public health: a proposed research agenda [J]. \u003cem\u003eBMJ Glob Health\u003c/em\u003e, \u003cb\u003e6\u003c/b\u003e(11). (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLE\u0026oacute;N, D. A. V. I. S. L. S. \u0026amp; BOURK, B. Transformation of the media landscape: Infotainment versus expository narrations for communicating science in online videos [J]. \u003cem\u003ePublic. Underst. Sci.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e (7), 688\u0026ndash;701 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"TikTok, bone mineral density, social media, health information, quality assessment","lastPublishedDoi":"10.21203/rs.3.rs-8217421/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8217421/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTikTok has emerged as a prominent source of health information for the general public. However, the quality of bone mineral density (BMD)-related content on the platform remains largely unevaluated using validated tools.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to assess the information quality of BMD-related videos on TikTok via the Global Quality Score (GQS), the DISCERN instrument, and the Journal of the American Medical Association (JAMA) benchmark criteria and to examine the relationship between video quality and viewer engagement metrics.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe top 100 videos based on the default relevance ranking for the keyword \"bone mineral density\" were retrieved on September 10, 2025. Video characteristics, uploader type, and engagement metrics (likes, comments, shares, view count) were recorded. Two independent raters assessed information quality via the GQS, DISCERN, and JAMA instruments. Interrater reliability was high (class correlation coefficient\u0026thinsp;=\u0026thinsp;0.89). Statistical analyses included descriptive statistics, Kruskal‒Wallis tests with post hoc Dunn's tests (Bonferroni-corrected), and Spearman correlation analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe largest proportion of videos were produced by science communicators (51%, n\u0026thinsp;=\u0026thinsp;51), defined as individuals or organizations without verifiable medical credentials, followed by medical professionals (40%, n\u0026thinsp;=\u0026thinsp;40) and news media outlets (9%, n\u0026thinsp;=\u0026thinsp;9). The overall quality of videos was suboptimal across all assessment tools. The mean total DISCERN score was 42.5 (SD\u0026thinsp;=\u0026thinsp;16.8), with only 11% of the videos rated as \"excellent\" and 46% rated as \"poor\" or \"very poor\". Consistent with this finding, the mean GQS and JAMA scores were also low at 2.73 (SD\u0026thinsp;=\u0026thinsp;1.32) and 2.47 (SD\u0026thinsp;=\u0026thinsp;1.09), respectively. Compared with those uploaded by science communicators, videos uploaded by medical professionals scored significantly higher across all the quality instruments (e.g., DISCERN total score: mean\u0026thinsp;=\u0026thinsp;51.75, SD\u0026thinsp;=\u0026thinsp;16.81) (mean\u0026thinsp;=\u0026thinsp;34.88, SD\u0026thinsp;=\u0026thinsp;11.84; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A weak but significant negative correlation was found between the view count and DISCERN score (ρ = -0.20, p\u0026thinsp;=\u0026thinsp;0.042), indicating that greater popularity was associated with lower information quality.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe overall quality of BMD-related content on TikTok is suboptimal, with popular videos often lacking accuracy and depth. There is a critical need for improved quality control, collaboration between health professionals and content creators, and platform-led initiatives to increase the reliability of health information disseminated via social media.\u003c/p\u003e","manuscriptTitle":"Evaluation of the Information Quality of Bone Mineral Density–Related Videos on TikTok","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-27 19:18:17","doi":"10.21203/rs.3.rs-8217421/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"48797b6c-58ea-4f3b-8dc2-02572845e5b5","owner":[],"postedDate":"January 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61711061,"name":"Health sciences/Diseases"},{"id":61711062,"name":"Health sciences/Health care"},{"id":61711063,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-05-12T05:12:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-27 19:18:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8217421","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8217421","identity":"rs-8217421","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}