The Quality and Dissemination of Health Information Pertaining to Iron-Deficiency Anemia on TikTok : A Cross-Sectional Study

The Quality and Dissemination of Health Information Pertaining to Iron-Deficiency Anemia on TikTok : A Cross-Sectional Study | 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 The Quality and Dissemination of Health Information Pertaining to Iron-Deficiency Anemia on TikTok : A Cross-Sectional Study Ruibin Jing, Xue Pang, Sibao Huang, He Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9005872/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 Iron deficiency anemia (IDA) remains a major global public health challenge, disproportionately affecting vulnerable groups such as women of reproductive age and children. While social media platforms like TikTok have become primary channels for health information dissemination, the quality and reach of IDA-related educational content on these platforms remain underexplored. This study systematically evaluated the quality, source characteristics, and user engagement of IDA-related short videos on the Chinese TikTok platform. Methods In this cross-sectional study, we screened the top 100 “iron deficiency anemia” videos on TikTok. After excluding advertisements, duplicates, non-Chinese content, and poor-quality videos, 72 were included. We recorded uploader type, educational background, professional credentials, content category, duration, and engagement metrics (likes, shares, collects, comments). Quality was assessed using GQS, mDISCERN, and JAMA criteria. Statistical analyses included descriptive statistics, Kruskal–Wallis H tests, Spearman correlation, and multiple linear regression. Results Of the 72 videos analyzed, 66.67% (n = 48) were uploaded by healthcare professionals, followed by organizations (18.06%) and general users (15.27%). Disease knowledge dissemination was the most common content category (56.94%). Median quality scores were: GQS 3 (IQR 2–4), mDISCERN 3 (IQR 3–3), and JAMA 2 (IQR 1–3), indicating moderate overall quality. Subgroup analysis showed that videos from uploaders with doctoral degrees and medical institutions scored significantly higher across all tools ( P < 0.01). User engagement metrics were strongly intercorrelated (R = 0.81–0.92, P < 0.001) but weakly correlated with quality scores. Multiple linear regression identified likes (β = 0.231, P < 0.001), shares (β = 0.145, P = 0.027), and mDISCERN score (β = 0.278, P < 0.001) as positive predictors of GQS, while JAMA score was a negative predictor (β = -0.135, P = 0.035). Conclusion Although medical professionals are the primary contributors, the overall quality of IDA-related TikTok content remains moderate, with limited depth, evidence, and actionability. The weak correlation between user engagement and objective quality scores underscores the complexity of health communication on social media. These findings highlight the need for enhanced content moderation, creator training, and improved public health literacy to foster critical evaluation of online health information. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Iron Deficiency Anemia Health Information Social Media TikTok Quality Assessment Health Communication Cross-sectional Study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Contributions to the Literature Text box 1. Contributions to the literature • This is the first study to systematically evaluate the quality and dissemination of IDA health information on a major short-video platform, addressing a critical gap in public health messaging to vulnerable groups via social media. • Our findings reveal that while medical professionals dominate content creation, video popularity does not align with informational quality, highlighting a fundamental tension between engagement metrics and educational value in digital health communication. • The results provide actionable evidence for platforms, health authorities, and clinicians to improve content governance, creator training, and public health literacy initiatives in the evolving landscape of social media-based health education. 1 Background Iron deficiency anemia (IDA) is a leading nutritional deficiency disorder worldwide and a persistent public health challenge. According to the World Health Organization, over 1.6 billion people globally are anemic, with IDA accounting for more than 60% of cases [ 1 ] . Prevalence is highest in low- and middle-income countries. High-risk groups include women of reproductive age, preschool children, and adolescents. Among women of reproductive age, prevalence reaches 31.2% [ 2 ] , and among adolescent girls—due to menstrual blood loss and growth demands—it ranges from 40% to 88%. In China, the overall IDA prevalence is 20.1%, affecting approximately 20.5% of children under five and over 38% of pregnant women [ 3 ] . IDA causes immediate symptoms such as fatigue, pallor, and cognitive impairment, and leads to long-term consequences including delayed development in adolescents, increased risks of preterm birth and low birth weight, and reduced productivity in adults, thereby impeding improvements in national health literacy [ 4 ] . In the digital era, social media has become a primary source of health information for the public. According to the China Health Communication Development Report (2023), 73.4% of Chinese residents seek health information online, with 55.2% citing social media as their main source [ 5 ] . TikTok is a key health communication platform due to its unique ecosystem. It relies on short, fragmented videos (typically 15–60 seconds) and algorithmic recommendations to rapidly disseminate health content to a broad audience, particularly adolescents and young to middle-aged adults. This makes TikTok an ideal platform for analyzing the dissemination of IDA-related health information [ 6 ] . The quality of health information directly influences public health decision-making and disease prevention effectiveness. The World Health Organization has emphasized that standardized health education is crucial for improving iron supplementation adherence and reducing IDA prevalence [ 7 ] . However, the low barrier to content creation on social media leads to significant variability in information quality and reliability [ 8 ] . Studies show that health content on short-video platforms often contains professional inaccuracies [ 9 ] . For example, 37% of IDA dietary management videos on TikTok have been reported to present one-sided or imprecise information. Similarly, content on other conditions, such as uterine fibroids and osteoporosis, posted by non-professionals is less accurate and evidence-based than that by healthcare professionals [ 10 ] . Whether IDA-related content on TikTok exhibits similar quality issues requires systematic investigation. Current research on IDA health communication has notable gaps, particularly regarding social media dissemination. Existing literature focuses largely on traditional approaches such as in-person education and supplementation interventions, while studies on IDA-related social media content are fragmented and lack a systematic analytical framework. 2 Materials and methods 2.1 Extract basic information A systematic search was conducted on TikTok using the keyword “iron deficiency anemia” to identify videos available before January 1, 2026. To minimize bias from search history and personalized recommendations, data were collected without user account authentication. Videos were sorted by TikTok’s default comprehensive ranking algorithm, and the top 100 videos were selected for initial analysis, regardless of publication date. A rigorous screening process was subsequently implemented to exclude the following types of content: (1) Advertising or promotional content, (2) duplicated or substantially similar content, (3) featuring animal, (4) non-Chinese or lack of sound quality and poor quality. Following this refinement, researchers systematically recorded and analyzed nine key parameters: publication platform, video title, engagement metrics (likes, comments, collects, and shares), video upload time, video duration, and source category. Video sources were classified into three distinct groups: medical professionals, institutional organizations, general users. 2.2 Quality Assessment Tools 2.2.1 Journal of the American Medical Association(JAMA) The JAMA benchmark criteria were utilized to assess video reliability, encompassing 4 distinct criteria: (1) authorship; (2) attribution, including copyright information, references, and sources of content; (3) currency, including the initial date and subsequent updates; and (4) disclosure of conflicts of interest, funding, sponsorship, advertising support, and video ownership. Each criterion scores 1 point, with higher scores indicating greater reliability [ 11 ] . 2.2.2 Modified DISCERN Tool(mDIS) The Modified DISCERN criteria were utilized to assess the quality, transparency, and clinical utility of decision-making information in iron deficiency anemia short videos, encompassing 5 distinct criteria: (1) reliability of evidence, including alignment with clinical guidelines and peer-reviewed citations; (2) presentation of treatment options, including coverage of evidence-based interventions and individual suitability; (3) balance of benefits and harms, including objective discussion of intervention efficacy and limitations; (4) author expertise and transparency, including disclosure of credentials and conflicts of interest; and (5) clarity of purpose, including audience tailoring and avoidance of ambiguous language. Each criterion is scored on a 1–5 score, with higher scores indicating greater quality and utility for informed health decisions [ 12 ] . 2.2.3 Global Quality Score(GQS) The Global Quality Score (GQS) criteria were utilized to assess the quality of health information in iron deficiency anemia short videos, encompassing 4 distinct criteria: (1) authorship & credibility, including the professional background and verifiable credentials of content creators; (2) attribution & evidence base, including citations to peer-reviewed studies, clinical practice guidelines, or authoritative medical resources to support claims; (3) currency & timeliness, including the publication date and alignment of recommendations with up-to-date (past 5 years) clinical evidence; and (4) disclosure of conflicts of interest, including sponsorship by supplement brands, affiliate links, or other financial or non-financial associations that may bias content. Each criterion scores 1 point, with higher scores indicating greater information quality and clinical relevance [ 13 ] . 2.3 Statistical analysis All analyses were conducted using IBM SPSS Statistics (Version 28.0). Normality was assessed using Shapiro–Wilk tests. Non-normally distributed continuous variables were compared with Kruskal–Wallis H or Mann–Whitney U tests, and normally distributed variables with one-way ANOVA or t-tests. Correlations were examined using Spearman’s rank-order correlation. Data are presented as median (IQR) for non-normal variables, mean ± SD for normal variables, and frequency (percentage) for categorical variables. Multiple linear regression was used to identify predictors of the Global Quality Score (GQS), including engagement metrics, duration, JAMA score, and mDISCERN score. Inter-rater reliability was assessed using Cronbach’s alpha. A two-tailed P < 0.05 was considered significant, and P < 0.01 highly significant. 2.4 Ethical Considerations This study used only publicly available TikTok videos and did not involve clinical data, human specimens, animals, or personal identifiers. As there was no interaction with users, the study was exempt from ethical review. 3 Results 3.1 Characteristics of Iron Deficiency Anemia Videos A systematic search was conducted on TikTok using the keyword “iron deficiency anemia” to identify relevant content. The top 100 videos based on the platform’s integrated ranking were initially selected. Following a rigorous screening process, a total of 28 videos were excluded for the following reasons: 14 were advertisements or promotional material, 7 were duplicate or substantially similar content, 3 were animal-focused videos, and 4 were in a non-Chinese language or had missing or poor-quality audio. Consequently, 72 videos met the inclusion criteria and were retained for final analysis (Fig. 1 ). The 72 videos constituted the final sample for analysis (Table 1 ). In terms of engagement, videos received an average of 768.74 ± 2,151.39 collects (median: 121.50). Average duration was 248.71 ± 521.58 seconds (median: 91 seconds). For user interaction, videos garnered averages of 920.93 ± 2,179.00 likes (median: 75.50), 471.35 ± 1,068.40 shares (median: 37), and 730.08 ± 1,882.90 comments (median: 38). The earliest eligible IDA-related video was published in September 2025. Uploaders were categorized into three groups. Medical professionals accounted for the majority (66.67%, n = 48), followed by institutional organizations (18.06%, n = 13) and general users (15.27%, n = 11) (Fig. 2 A). Among medical professional uploaders, 13 held a bachelor’s degree or lower, 18 held a master’s degree, and 17 held a doctoral degree (Fig. 2 B). Of the 48 videos uploaded by doctors, 31 were posted by chief physicians, 10 by associate chief physicians, and 7 by attending or resident physicians (Fig. 2 C). Based on primary content theme, videos were most frequently classified as disease knowledge (n = 41), followed by outpatient cases (n = 16), and news or advertising (n = 15) (Fig. 2 D). Descriptive analysis of the three assessment tools indicated a consistent pattern in content quality. JAMA scores were concentrated at 1 point (41.6%, 30/72), whereas GQS scores were most frequently 3 points (33.33%, 24/72). The mDISCERN scores showed the highest concentration at 3 points (79.17%, 57/72), reflecting a relatively balanced distribution across quality dimensions (Fig. 3 ). Overall video characteristics are summarized in Table 1 . The median Global Quality Score (GQS) for the TikTok videos was 3 (IQR 2–4). The mDISCERN score also had a median of 3 (IQR 3–3), while the median JAMA benchmark score was 2 (IQR 1–3) (Table 2 ). Inter-rater reliability was assessed using Cronbach’s alpha. Based on the Landis and Koch criteria [ 14 ] , α > 0.80 indicates excellent agreement, 0.60–0.80 substantial agreement, 0.40–0.60 moderate agreement, and < 0.40 poor agreement. The obtained values for the JAMA, mDISCERN, and GQS scores were 0.621, 0.613, and 0.607, respectively, indicating substantial agreement across all three tools. Table 1 General characteristics of the videos on Tiktok. Characteristics Mean ± SD Median (range) Collect 768.736 ± 2151.393 121.500(12-10544) Duration 248.708521.580 91(17-2945) Like 920.931 ± 2179.003 75.500(17-10647) Share 471.347 ± 1068.395 37(25-4638) Comment 730.083 ± 1882.899 38(11-8420) JAMA 1.917(0.868) 2(1–3) mDISCERN 2.931 ± 0.877 3(1–4) GQS 3.181 ± 0.422 3(2–4) JAMA: Journal of the American Medical Association. mDISCERN: modified DISCERN. GQS: Global Quality Score. Table 2 General characteristics of the videos on Tiktok. Characteristics median (IQR) JAMA 2(1,3) mDISCERN 3(3,3) GQS 3(2,4) JAMA: Journal of the American Medical Association. mDISCERN: modified DISCERN. GQS: Global Quality Score. Table 3 Detailed characteristics of videos based on uploaders and content. Characteristics n Collect Duration Like Share Comment Uploaders Medical professionals 48 125.500 (106.5,147.5) 91.000 (44.3,177.0) 76.500 (68.0,618.3) 38.000 (32.0,561.8) 40.000 (35.0,275.3) Institutional organizations 13 111.000 (89.0,133.0) 200.000 (44.0,233.0) 72.000 (66.0,81.0) 33.000 (29.5,38.0) 36.000 (30.5,40.0) General users 11 104.000 (21.0,135.0) 61.000 (33.0,800.0) 77.000 (70.0,325.0) 38.000 (33.0,97.0) 35.000 (26.0,42.0) P 0.029* 0.773 0.609 0.198 0.043* Educational background Bachelor's and below 13 135.000(112.5,595.5) 56.000(35.5,133.0) 409.000(71.5,772.0) 121.000(34.0,663.5) 78.000(39.0,401.0) Master 18 118.000(100.5,131.5) 118.000(46.5,303.0) 71.000(64.0,78.5) 34.000(29.0,39.5) 36.000(31.0,42.5) Doctoral 17 130.500(112.5,2071.3) 107.000(44.5,165.8) 80.500 (70.5,3856.5) 40.500(33.8,1415.8) 40.500(35.8,3317.5) P 0.093 0.263 0.051 0.069 0.040* Professional titles Chief physician 31 123.000 (105.0,142.0) 60.000(41.0,178.0) 74.000(65.0,501.0) 37.000 (30.0,552.0) 40.000 (34.0,87.0) Associate chief physician 10 118.500 (92.8,135.5) 120.000 (27.3,228.8) 73.000(66.5,81.5) 35.500 (31.0,40.5) 37.000 (34.8,40.5) Attending/ registered physician 7 1601.000 (141.0,6301.0) 105.000 (61.0,159.0) 3321.000 (331.0,8350.0) 1033.000 (205.0,2911.0) 3302.000 (331.0,4624.0) P 0.021* 0.682 0.006** 0.012* 0.024* Content Disease knowledge 41 125.000(107.0,595.5) 92.000(42.0,183.0) 80.000(68.5,1071.0) 38.000(32.0,744.0) 42.000(36.5,630.0) Outpatient cases 16 119.000(91.0,139.8) 139.000(63.3,241.0) 71.500(62.8,79.5) 35.000(28.5,40.5) 35.500(30.3,39.8) News/ advertising 15 107.000(21.0,130.0) 61.000(35.0,202.0) 74.000(67.0,106.0) 37.000(31.0,84.0) 35.000(28.0,42.0) P 0.034* 0.622 0.063 0.139 0.003** Kruskal-Wallis H Test, * P < 0.05, ** P < 0.01. 3.2 Comparison of General Data A total of 72 videos were included for analysis. General characteristics are shown in Tables 1 and 2 . Engagement metrics (collects, likes, shares, comments) showed high variability, with standard deviations much larger than medians. When comparing videos by uploader category (Table 3 ), statistically significant differences were observed in the number of collects ( P = 0.029) and comments ( P = 0.043) among videos uploaded by medical professionals, institutional organizations, and general users. Videos from medical professionals showed relatively higher median values for both collects and comments. No significant differences were found in parameters such as video duration across these groups. Regarding the educational background of uploaders, significant differences were noted only in the number of comments ( P = 0.040) among videos posted by individuals with a bachelor’s degree or lower, a master’s degree, or a doctoral degree. In terms of professional titles, videos uploaded by chief physicians, associate chief physicians, and attending/resident physicians showed significant differences in collects ( P = 0.021), likes ( P = 0.006), shares ( P = 0.012), and comments ( P = 0.024). The attending/resident physician group demonstrated notably higher median values across multiple engagement indicators. When comparing videos by content category—disease knowledge, outpatient cases, and news/advertisements—significant differences were observed in collects ( P = 0.034) and comments ( P = 0.003). Videos focusing on disease knowledge generally achieved higher median engagement metrics. 3.3 Video Quality and Reliability Assessments Quality assessment results are summarized in Table 4 . The median JAMA score was 2 (IQR 1–3), with 41.67% of videos scoring 1. The median mDISCERN score was 3 (IQR 3–3; mean 2.93 ± 0.88), with 79.17% of videos attaining this score. The median Global Quality Score (GQS) was 3 (IQR 2–4; mean 3.18 ± 0.42). Overall, video quality was moderate. Although evidence citation and completeness were acceptable, informational depth and actionable guidance remained limited. In summary, while the content is generally accessible, it lacks strong actionability to effectively promote health-related behaviors. Table 4 Scores of the JAMA, mDISCERN, and Global Quality Score for the videos.. Scale, score Values (N = 72), n (%) JAMA 0 0(0) 1 30(41.67) 2 18(25.00) 3 24(33.33) 4 0(0) mDISCERN 1 0(0) 2 1(1.39) 3 57(79.17) 4 14(19.44) 5 0(0) Global Quality Score 1 2(2.78) 2 24(33.33) 3 23(31.94) 4 23(31.94) 5 0(0) 3.4 Subgroup Analysis Subgroup comparisons of video quality scores were performed based on uploader type, educational background, professional title, and video content (Table 5 , Figs. 3 – 6 ). JAMA scores differed significantly by uploader category ( P = 0.008): general users had a lower median score (1.000) than medical professionals and institutional organizations (both 2.000). No significant differences were found for mDISCERN or GQS across uploader groups. When grouped by educational background, all three quality scores showed highly significant differences (JAMA P = 0.0017, mDISCERN P = 0.0017, GQS P = 0.006). Uploaders with doctoral degrees achieved the highest median scores across all tools (JAMA: 3.000; mDISCERN: 3.000; GQS: 4.000), outperforming those with bachelor’s or master’s degrees (Fig. 4 ). For professional titles, significant differences were observed in JAMA ( P = 0.004) and mDISCERN scores ( P = 0.008) among physician ranks. Associate chief physicians attained the highest median mDISCERN score (3.500). GQS scores did not differ significantly across title groups ( P = 0.267). Content-based analysis revealed significant differences in JAMA ( P = 0.042) and mDISCERN scores ( P = 0.025) among disease-knowledge, outpatient-case, and news/advertising videos. Disease-knowledge videos had higher JAMA scores (median: 2.000); GQS scores did not vary by content type. In summary, uploader educational background exerted the most consistent and significant influence on overall quality scores. Video content type mainly affected metrics of informational standardization and reliability, while professional titles were more closely tied to perceived information quality and credibility. Table 5 Quality assessment of videos based on uploaders and content. Characteristics n JAMA mDISCERN Global Quality Score Uploaders Medical professionals 48 2.000(1.0,3.0) 3.000(3.0,3.0) 3.000(2.0,4.0) Institutional organizations 13 2.000(1.0,3.0) 3.000(3.0,3.5) 3.000(2.0,4.0) General users 11 1.000(1.0,1.0) 3.000(3.0,3.0) 3.000(2.0,4.0) P 0.008** 0.763 0.826 Educational background Bachelor's and below 13 1.000(1.0,2.0) 3.000(3.0,3.0) 2.000(2.0,3.0) Master 18 2.000(1.0,3.0) 3.000(3.0,3.0) 3.000(2.0,3.0) Doctoral 17 3.000(2.0,3.0) 3.000(3.0,4.0) 4.000(2.8,4.0) P 0.0017* 0.0017* 0.006** Professional titles Chief physician 31 2.000(1.0,3.0) 3.000(3.0,3.0) 3.000(2.0,3.0) Associate chief physician 10 3.000(2.0,3.0) 3.500(3.0,4.0) 3.500(2.0,4.0) Attending/ registered physician 7 3.000(2.0,3.0) 3.000(3.0,4.0) 3.000(2.0,4.0) P 0.004** 0.008** 0.267 Content Disease knowledge 41 2.000(1.0,3.0) 3.000(3.0,3.0) 3.000(2.0,3.5) Outpatient cases 16 1.000(1.0,2.0) 3.000(3.0,3.0) 3.000(2.0,4.0) News/ advertising 15 2.000(1.0,3.0) 3.000(3.0,4.0) 3.000(2.0,4.0) P 0.042* 0.025* 0.610 Kruskal-Wallis H Test, * P < 0.05, ** P < 0.01. 3.5 Correlation and Multiple Linear Regression Analysis Spearman correlation analysis was performed to examine relationships among video parameters (Fig. 7 ). Results showed strong positive correlations between user engagement metrics—likes, comments, collects, and shares (R = 0.81–0.92, all P < 0.001)—indicating high consistency across these participatory behaviors. In contrast, correlations between engagement indicators and quality scores (GQS and mDISCERN) were generally weak (most R < 0.5). Only likes vs. GQS (R = 0.243, P = 0.042) and mDISCERN vs. GQS (R = 0.341, P = 0.003) showed statistically significant positive correlations (Table 7 ), suggesting that popularity is not strongly linked to perceived quality. Multiple linear regression was conducted to identify factors influencing overall user engagement (Table 6 ). The final model (R² = 0.386, adjusted R² = 0.335, F(7,65) = 7.52, P < 0.001) showed that likes (β = 0.231, P < 0.001), shares (β = 0.145, P = 0.027), and mDISCERN score (β = 0.278, P < 0.001) were significant positive predictors of engagement, whereas JAMA score had a negative predictive effect (β = − 0.135, P = 0.035). These results indicate that content reliability (mDISCERN) and active engagement behaviors are key predictors of user interaction, while traditional academic benchmarks such as the JAMA score relate differently to public engagement metrics. Table 6 Multiple Linear Regression Results for Factors Influencing Short Video Engagement. Independent variable B Std. Error t P β VIF Constant 2.143 0.215 9.97 < 0.001** Duration 0.00012 0.00008 1.50 0.138 0.067 1.23 Like 0.00004 0.00001 3.64 < 0.001** 0.231 1.85 Share 0.00007 0.00003 2.27 0.027* 0.145 1.92 Collect 0.00002 0.00001 1.85 0.069 0.0989 2.05 Comment -0.00001 0.00002 -0.53 0.599 -0.023 1.78 JAMA -0.118 0.055 -2.15 0.035* -0.135 1.34 mDISCERN 0.321 0.076 4.22 < 0.001** 0.278 1.41 Model fit indicators:R 2 = 0.386,adjusted R 2 = 0.335,F(7, 65) = 7.52, P < 0.001, n = 72 B: Unstandardized coefficient β: Standardized coefficient (beta) * P < 0.05, ** P < 0.01 Table 7 Correlations Between User Engagement Metrics, Health Information Quality, and GQS. Variable M ± SD Corr. (GQS) Sig. GQS 2.76 ± 0.89 1.000 Duration 282.4 ± 498.7 0.043 0.736 Like 1568.3 ± 2523.4 0.243 0.042* Share 887.6 ± 1356.2 0.198 0.089 Collect 1300.2 ± 2375.6 0.215 0.063 Comment 1380.5 ± 2187.3 0.157 0.184 JAMA 1.89 ± 0.85 -0.172 0.120 mDISCERN 3.16 ± 0.37 0.341 0.003** JAMA: Journal of the American Medical Association. mDISCERN: modified DISCERN. GQS: Global Quality Score. 4 Discussion 4.1 Summary and interpretation of key findings This cross-sectional study of 72 IDA-related short videos on TikTok reveals several key findings. First, healthcare professionals are the primary content creators (66.67%), and higher professional titles are associated with greater user engagement. Second, overall information quality is moderate (median mDISCERN = 3, GQS = 3), with JAMA scores indicating a need for better standardization. Subgroup analysis shows that uploaders with doctoral degrees consistently achieve higher quality scores. Third, user engagement metrics are strongly intercorrelated but only weakly linked to perceived quality (GQS). Content reliability (mDISCERN score) is a positive predictor of engagement, whereas traditional academic rigor (JAMA score) shows a negative relationship. In summary, while IDA content on TikTok is accessible and broadly credible, it lacks depth, standardization, and actionability [ 15 ] . Creator credentials and evidence-based quality are crucial for enhancing communication effectiveness [ 16 ] . 4.2 Iron-Deficiency Anemia Video Characteristics and audience Interaction Analysis The 72 short videos on iron deficiency anemia (IDA) included in this study showed high variability in audience engagement metrics. Mean collects, likes, shares, and comments were 768.74 ± 2151.39, 920.93 ± 2179.00, 471.35 ± 1068.40, and 730.08 ± 1882.90, respectively; corresponding median values were 121.50, 75.50, 37, and 38 (Table 1 ). Engagement differed significantly by uploader category. Videos posted by medical professionals had higher median collects (125.50 vs. 104.00, P = 0.029) and comments (40.00 vs. 35.00, P = 0.043) than those from general users (Table 3 ). Among medical professionals, videos by attending/resident physicians achieved significantly higher median collects (1601.00), likes (3321.00), shares (1033.00), and comments (3302.00) compared to those by chief or associate chief physicians (all P < 0.05). Regarding content type, disease-knowledge videos performed best in median collects (125.00, P = 0.034) and comments (42.00, P = 0.003) relative to outpatient-case or news/advertising videos. In summary, audience engagement with IDA-related content on TikTok is highly heterogeneous. Content from certified medical professionals—particularly frontline clinicians—appears more effective in stimulating user interaction, while disease-knowledge videos hold an advantage in eliciting participatory feedback [ 17 – 19 ] . 4.3 Video Ratings and Quality Evaluation Multiple standardized tools were used to assess video quality. Overall, the videos demonstrated moderate informational reliability and general quality. The median mDISCERN score was 3 (IQR 3–3; mean 2.93 ± 0.88), with 79.17% (57/72) of videos scoring 3, indicating basic credibility. However, shortcomings were noted in evidence citation, treatment-option presentation, and conflict-of-interest disclosure, particularly regarding additional references for viewers [ 20 – 22 ] . The median Global Quality Score (GQS) was 3 (IQR 2–4; mean 3.18 ± 0.42), with scores distributed relatively evenly between 2 and 4, near the moderate-quality threshold—a finding consistent with prior reports. Subgroup analysis revealed a strong association between quality and creator background. Uploaders with doctoral degrees obtained significantly higher JAMA, mDISCERN, and GQS scores than other educational groups ( P < 0.01). Videos from medical professionals and institutions scored higher on the JAMA benchmark than those from general users ( P = 0.008). Furthermore, disease-knowledge videos consistently received higher ratings across all assessment tools compared to other content types. Nevertheless, JAMA scores indicated room for improvement in informational standardization, with a median of 2 (IQR 1–3) and 41.67% of videos scoring only 1 point. In summary, while current IDA-related content on the platform shows acceptable basic credibility and comprehensibility, it exhibits notable limitations in depth, evidence transparency, and actionability. Disease-specific educational content produced by creators with medical backgrounds represents a relatively more reliable information source [ 23 , 24 ] . 4.4 Correlation and Multiple Linear Regression Analysis Between Video Quality and Video Characteristic We conducted an in-depth correlation analysis and multiple linear regression to examine the relationship between quality metrics and video characteristics for IDA-related content on TikTok. Correlation analysis showed strong positive intercorrelations among user engagement metrics—likes, comments, collects, and shares (R = 0.81–0.92, all P < 0.001)—indicating high covariation in these participatory behaviors. This aligns with the network-driven dissemination typical of social media, where higher engagement promotes broader visibility, consistent with prior findings on social media network effects [ 25 – 27 ] . However, correlations between these engagement indicators and the overall quality score (GQS) were generally weak (most R < 0.3), suggesting that audience interaction does not directly reflect intrinsic informational quality. This observation resonates with evidence that emotional appeal and novelty often drive engagement more than educational value. Multiple linear regression identified key predictors of perceived video quality. The final model (R² = 0.386, adjusted R² = 0.335) showed that likes (β = 0.231, P < 0.001), shares (β = 0.145, P = 0.027), and mDISCERN score (β = 0.278, P < 0.001) were significant positive predictors of GQS, whereas JAMA score was a negative predictor (β = − 0.135, P = 0.035). These results indicate that content reliability (mDISCERN) and active engagement behaviors are central to perceived quality, while traditional academic benchmarks (JAMA) align differently with the participatory nature of public platforms. Our analysis clarifies the relationship between engagement and quality, offering an empirical perspective on health-science communication via social media. In the short-video context, both scientific reliability and the capacity to stimulate interaction jointly shape perceived quality; popularity alone, however, is not a reliable indicator of informational value [ 28 – 30 ] . 4.5 Practical Significance The practical implications of this study are fourfold. First, it confirms that short-video platforms such as TikTok have become a major channel for public health information. Medical professionals—who uploaded the majority of content (66.67%)—represent a key source of reliable information. This finding offers empirical support for government and health institutions to use social media for targeted health education and literacy promotion [ 31 , 32 ] . Second, the professional quality of video content (e.g., mDISCERN score) correlates positively with user engagement, while uploaders’ educational background and professional titles significantly affect informational reliability. This suggests that platforms and creators should emphasize scientific rigor, proper evidence citation, and source transparency to enhance credibility and clinical utility [ 31 – 33 ] . Third, the study identifies limitations in the depth and actionability of current content (e.g., median GQS = 3). Combined with algorithmic recommendations and time constraints, these may worsen information fragmentation [ 34 ] . Platforms should therefore optimize verification systems, strengthen content review, and develop formats that support in-depth science communication—such as video series or thematic compilations—to build a more authoritative health-knowledge ecosystem [ 35 ] . Finally, from a public-health perspective, this study provides evidence-based insights for balancing “popularity-driven” and “quality-driven” content dissemination. It underscores the need to promote high-quality content while guiding general users and improving public health literacy, thereby empowering individuals to make informed health decisions online [ 36 – 38 ] . 4.6 Study limitations and future directions Although this study systematically evaluated the quality and dissemination of IDA-related health education videos on TikTok, several limitations warrant acknowledgment. First, the sample was constrained to a single health topic (“iron deficiency anemia”), limiting generalizability to other chronic diseases or health domains. Videos were drawn from the top 100 in TikTok’s integrated ranking; despite rigorous screening, the final sample (n = 72) remains relatively small. The platform’s recommendation algorithm may also influence visibility and ranking, introducing potential selection bias and reducing representativeness of the dynamic content pool. Second, the cross-sectional design can identify correlations but cannot establish causality—for example, whether higher quality drives engagement or whether popular videos are perceived as higher quality due to greater exposure. Third, while multiple validated tools (GQS, mDISCERN, JAMA) were used to assess reliability and standardization, dimensions such as comprehensibility and actionability—whether users truly understand information and subsequently take health actions—received less attention. Furthermore, the study did not quantitatively examine platform-specific features (e.g., algorithmic recommendations, visual filters, background music) that may affect attention and perception, nor did it track viewers’ actual health behavior changes after watching videos, a key gap in assessing real-world impact. Future research should advance in several directions: Expand scope and depth—include a wider range of health topics (e.g., cardiovascular diseases, diabetes, sports nutrition), increase sample sizes, and conduct comparative studies across multiple platforms (e.g., Bilibili, WeChat Channels) to improve generalizability. Refine study design—adopt longitudinal or experimental interventions to clarify causal effects of health videos on knowledge, attitudes, and behaviors (e.g., iron-supplement adherence, health decision-making). Enrich evaluation frameworks—incorporate measures of comprehensibility, emotional resonance, and behavioral motivation, and develop assessment tools better suited to short-video media. Investigate underlying mechanisms—use data-mining techniques to analyze relationships between platform algorithms and health-information dissemination, and examine how uploader characteristics (e.g., credentials, communication style) interact with user demographics (e.g., age, health literacy) to affect information reception and impact. Promote translational application—collaborate with platform operators, healthcare institutions, and educators to co-design and pilot evidence-based, high-quality templates and creation guidelines for health science short videos, translating academic insights into practical drivers of public health literacy. 5 Conclusions IDA‑related content on TikTok is predominantly created by medical professionals (66.67%), whose advanced degrees and clinical titles are associated with higher content reliability and user engagement. A comprehensive quality evaluation reveals moderate overall quality (median scores ~ 3), with acceptable baseline credibility but notable limitations in depth, evidence standardization, and actionability. User engagement metrics are strongly intercorrelated yet only weakly linked to perceived quality. Content reliability (mDISCERN) positively predicts engagement, whereas traditional academic benchmarks (JAMA) show an inverse relationship, highlighting the tension between scientific rigor and communicative appeal in the short‑video format. The study indicates that while IDA‑related health information on TikTok is accessible and credible, it falls short of facilitating behavioral change. The findings support platform‑level efforts to enhance content governance through creator verification, improved review systems, and refined recommendation algorithms. They also guide professionals in developing more in‑depth and actionable content, while assisting the public in critically evaluating online health information. These insights offer valuable implications for advancing targeted health education via social media and improving national health literacy. Abbreviations Iron deficiency anemia IDA Declarations Ethics approval and consent to participate Not applicable. This study used only publicly available TikTok videos and did not involve human participants, clinical data, human specimens, or animals. No interaction with video uploaders or viewers occurred, and no personal identifiable information was collected. According to the Measures for the Ethical Review of Biomedical Research Involving Humans (National Health and Family Planning Commission of China, 2016, Article 39), research using publicly available data that does not involve human subjects or personal privacy is exempt from formal ethics review. Based on these national guidelines, the Ethics Committee of Shandong University of Aeronautics confirmed that no formal ethics approval was required for this study. As no human participants were involved, the requirement for informed consent was also waived by the same committee. Consent for publication Not applicable. This study did not involve human participants, and no individual person's data, images, or videos are presented in this manuscript. Therefore, no consent for publication was required. Availability of data and materials The datasets generated and analysed during the current study are available from the corresponding author on reasonable request. The original videos analysed in this study are publicly accessible on the TikTok platform; however, due to platform policies and the dynamic nature of online content, the specific video URLs and extracted data are not publicly archived but can be provided by the authors upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding No external funding. Authors' contributions RBJ and HC conceived and designed the study. RJ, XP, and SBH performed the data collection and video quality assessments. XP conducted the statistical analysis. RJ drafted the initial manuscript. HC supervised the study, contributed to data interpretation, and critically revised the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Chavan, R. S. et al. World Health Organization. Encyclopedia Food Health , : pp. 585–591. (2016). Ruel-Bergeron, J. C. et al. Global Update and Trends of Hidden Hunger, 1995–2011: The Hidden Hunger Index. Plos One . 10 (12), e0143497 (2015). Yamamoto, K. et al. Changes in the proportion of anemia among young women after the Great East Japan Earthquake: the Fukushima health management survey. Sci. Rep. 12 (1), 10805 (2022). Gupta, S. et al. 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Wu, J. et al. The quality and reliability of short videos about hypertension on TikTok: a cross-sectional study. Sci. Rep. 15 (1), 25042 (2025). Gong, X. et al. The quality of short videos as a source of coronary heart disease information on TikTok: cross-sectional study. JMIR Formative Res. 8 , e51513 (2024). Zheng, D. X. et al. Acne and social media: a cross-sectional study of content quality on TikTok. Pediatr. Dermatol. 38 (1), 336–338 (2021). Guo, F. et al. Quality assessment of radiotherapy health information on short-form video platforms of TikTok and Bilibili: cross-sectional study. JMIR cancer . 11 (1), e73455 (2025). Zhang, H. et al. Quality of information in osteoporosis videos on TikTok: A cross-sectional study. Archives Osteoporos. 20 (1), 115 (2025). Zheng, S. et al. Quality and reliability of liver cancer–related short Chinese videos on TikTok and Bilibili: cross-sectional content analysis study. J. Med. Internet. Res. 25 , e47210 (2023). Zhang, Y. et al. Quality assessment of spinal cord injury-related health information on short-form video platforms: Cross-sectional content analysis of TikTok, Kwai, and BiliBili. Digit. Health . 11 , 20552076251374226 (2025). Jang, C. W. et al. Reliability, quality, and educational suitability of TikTok videos as a source of information about scoliosis exercises: a cross-sectional study. in Healthcare. : MDPI. (2022). Qian, S., Gu, J. & Zhao, H. The quality and reliability of short videos about migraine on Chinese social Media platforms (BiliBili and TikTok): A cross-sectional study. Digit. Health . 12 , 20552076261415929 (2026). Li, B. et al. Quality assessment of health science-related short videos on TikTok: A scoping review. Int. J. Med. Informatics . 186 , 105426 (2024). Huang, M. et al. Assessing the quality of educational short videos on dry eye care: a cross-sectional study. Front. Public. Health . 13 , 1542278 (2025). Yang, Y. et al. Assessing the Video Content Quality of TikTok and Bilibili as Health Information Sources for Systemic Lupus Erythematosus: A Cross-Sectional Study. Int. J. Rheum. Dis. 28 (6), e70341 (2025). Turnock, B. Public health (Jones & Bartlett, 2012). Additional Declarations No competing interests reported. Supplementary Files TableS1..docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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05:24:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9005872/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9005872/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105984592,"identity":"7b7b61b8-1ae0-4da4-92e4-60329565fa7a","added_by":"auto","created_at":"2026-04-02 07:15:20","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62493,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of video selection.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9005872/v1/2a9d42841a4f478f734040d1.jpeg"},{"id":105984588,"identity":"a3256a2a-e413-45cc-8d1d-ecfb9d4462dd","added_by":"auto","created_at":"2026-04-02 07:15:20","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":71989,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of videos on Iron-Deficiency Anemia from different sources and with different contents on TikTok.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9005872/v1/f90309cd0d567ac67fed8770.jpeg"},{"id":106094172,"identity":"11ee238a-2f5b-4346-a508-4c0d6696e874","added_by":"auto","created_at":"2026-04-03 11:41:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":176743,"visible":true,"origin":"","legend":"\u003cp\u003eComparisons of JAMA, mDISCERN, and Global Quality Scores by uploaders type.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9005872/v1/be43d9c3bbaa3e00bd39f752.png"},{"id":106093643,"identity":"af08c964-d8c8-4358-814b-554d288613b4","added_by":"auto","created_at":"2026-04-03 11:38:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":84236,"visible":true,"origin":"","legend":"\u003cp\u003eComparisons of JAMA, mDISCERN, and Global Quality Scores by educational background type.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9005872/v1/403c07b51c4cbfc7da1d19ce.png"},{"id":105984589,"identity":"4a427c42-fc93-467d-b671-62b5c7e092d2","added_by":"auto","created_at":"2026-04-02 07:15:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":238379,"visible":true,"origin":"","legend":"\u003cp\u003eComparisons of JAMA, mDISCERN, and Global Quality Scores by professional titles type.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9005872/v1/da55030c5f44702b816307f2.png"},{"id":105984593,"identity":"aa2e74c1-3947-44b4-956f-64ba43e50404","added_by":"auto","created_at":"2026-04-02 07:15:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":133786,"visible":true,"origin":"","legend":"\u003cp\u003eComparisons of JAMA, mDISCERN, and Global Quality Scores by content type.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-9005872/v1/c6ccd82f9912591995587401.png"},{"id":105984591,"identity":"4121f542-7a74-4691-8e52-455d4bab4e1a","added_by":"auto","created_at":"2026-04-02 07:15:20","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":603779,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis heat map.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-9005872/v1/71450e7df1d97bbb5d4077db.png"},{"id":106728201,"identity":"af8bc876-50a9-4f3d-a359-d07e5ae0d126","added_by":"auto","created_at":"2026-04-12 18:42:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2708100,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9005872/v1/ddd357ff-54a8-440c-a018-d9d436ffc5d0.pdf"},{"id":105984587,"identity":"27e6b7e8-8697-473e-99fb-7ca243b7897f","added_by":"auto","created_at":"2026-04-02 07:15:20","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":15115,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1..docx","url":"https://assets-eu.researchsquare.com/files/rs-9005872/v1/4357822462e58fa205c3a313.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe Quality and Dissemination of Health Information Pertaining to Iron-Deficiency Anemia on TikTok : A Cross-Sectional Study\u003c/p\u003e","fulltext":[{"header":"Contributions to the Literature","content":" \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eText box 1. Contributions to the literature\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026bull; This is the first study to systematically evaluate the quality and dissemination of IDA health information on a major short-video platform, addressing a critical gap in public health messaging to vulnerable groups via social media.\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026bull; Our findings reveal that while medical professionals dominate content creation, video popularity does not align with informational quality, highlighting a fundamental tension between engagement metrics and educational value in digital health communication.\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026bull; The results provide actionable evidence for platforms, health authorities, and clinicians to improve content governance, creator training, and public health literacy initiatives in the evolving landscape of social media-based health education.\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e"},{"header":"1 Background","content":"\u003cp\u003eIron deficiency anemia (IDA) is a leading nutritional deficiency disorder worldwide and a persistent public health challenge. According to the World Health Organization, over 1.6\u0026nbsp;billion people globally are anemic, with IDA accounting for more than 60% of cases\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Prevalence is highest in low- and middle-income countries. High-risk groups include women of reproductive age, preschool children, and adolescents. Among women of reproductive age, prevalence reaches 31.2%\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, and among adolescent girls\u0026mdash;due to menstrual blood loss and growth demands\u0026mdash;it ranges from 40% to 88%. In China, the overall IDA prevalence is 20.1%, affecting approximately 20.5% of children under five and over 38% of pregnant women\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. IDA causes immediate symptoms such as fatigue, pallor, and cognitive impairment, and leads to long-term consequences including delayed development in adolescents, increased risks of preterm birth and low birth weight, and reduced productivity in adults, thereby impeding improvements in national health literacy\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the digital era, social media has become a primary source of health information for the public. According to the China Health Communication Development Report (2023), 73.4% of Chinese residents seek health information online, with 55.2% citing social media as their main source\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. TikTok is a key health communication platform due to its unique ecosystem. It relies on short, fragmented videos (typically 15\u0026ndash;60 seconds) and algorithmic recommendations to rapidly disseminate health content to a broad audience, particularly adolescents and young to middle-aged adults. This makes TikTok an ideal platform for analyzing the dissemination of IDA-related health information\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe quality of health information directly influences public health decision-making and disease prevention effectiveness. The World Health Organization has emphasized that standardized health education is crucial for improving iron supplementation adherence and reducing IDA prevalence\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. However, the low barrier to content creation on social media leads to significant variability in information quality and reliability\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Studies show that health content on short-video platforms often contains professional inaccuracies\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. For example, 37% of IDA dietary management videos on TikTok have been reported to present one-sided or imprecise information. Similarly, content on other conditions, such as uterine fibroids and osteoporosis, posted by non-professionals is less accurate and evidence-based than that by healthcare professionals\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Whether IDA-related content on TikTok exhibits similar quality issues requires systematic investigation.\u003c/p\u003e \u003cp\u003eCurrent research on IDA health communication has notable gaps, particularly regarding social media dissemination. Existing literature focuses largely on traditional approaches such as in-person education and supplementation interventions, while studies on IDA-related social media content are fragmented and lack a systematic analytical framework.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Extract basic information\u003c/h2\u003e \u003cp\u003eA systematic search was conducted on TikTok using the keyword \u0026ldquo;iron deficiency anemia\u0026rdquo; to identify videos available before January 1, 2026. To minimize bias from search history and personalized recommendations, data were collected without user account authentication. Videos were sorted by TikTok\u0026rsquo;s default comprehensive ranking algorithm, and the top 100 videos were selected for initial analysis, regardless of publication date.\u003c/p\u003e \u003cp\u003eA rigorous screening process was subsequently implemented to exclude the following types of content: (1) Advertising or promotional content, (2) duplicated or substantially similar content, (3) featuring animal, (4) non-Chinese or lack of sound quality and poor quality. Following this refinement, researchers systematically recorded and analyzed nine key parameters: publication platform, video title, engagement metrics (likes, comments, collects, and shares), video upload time, video duration, and source category. Video sources were classified into three distinct groups: medical professionals, institutional organizations, general users.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Quality Assessment Tools\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Journal of the American Medical Association(JAMA)\u003c/h2\u003e \u003cp\u003eThe JAMA benchmark criteria were utilized to assess video reliability, encompassing 4 distinct criteria: (1) authorship; (2) attribution, including copyright information, references, and sources of content; (3) currency, including the initial date and subsequent updates; and (4) disclosure of conflicts of interest, funding, sponsorship, advertising support, and video ownership. Each criterion scores 1 point, with higher scores indicating greater reliability\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Modified DISCERN Tool(mDIS)\u003c/h2\u003e \u003cp\u003e The Modified DISCERN criteria were utilized to assess the quality, transparency, and clinical utility of decision-making information in iron deficiency anemia short videos, encompassing 5 distinct criteria: (1) reliability of evidence, including alignment with clinical guidelines and peer-reviewed citations; (2) presentation of treatment options, including coverage of evidence-based interventions and individual suitability; (3) balance of benefits and harms, including objective discussion of intervention efficacy and limitations; (4) author expertise and transparency, including disclosure of credentials and conflicts of interest; and (5) clarity of purpose, including audience tailoring and avoidance of ambiguous language. Each criterion is scored on a 1\u0026ndash;5 score, with higher scores indicating greater quality and utility for informed health decisions\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Global Quality Score(GQS)\u003c/h2\u003e \u003cp\u003eThe Global Quality Score (GQS) criteria were utilized to assess the quality of health information in iron deficiency anemia short videos, encompassing 4 distinct criteria: (1) authorship \u0026amp; credibility, including the professional background and verifiable credentials of content creators; (2) attribution \u0026amp; evidence base, including citations to peer-reviewed studies, clinical practice guidelines, or authoritative medical resources to support claims; (3) currency \u0026amp; timeliness, including the publication date and alignment of recommendations with up-to-date (past 5 years) clinical evidence; and (4) disclosure of conflicts of interest, including sponsorship by supplement brands, affiliate links, or other financial or non-financial associations that may bias content. Each criterion scores 1 point, with higher scores indicating greater information quality and clinical relevance\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses were conducted using IBM SPSS Statistics (Version 28.0). Normality was assessed using Shapiro\u0026ndash;Wilk tests. Non-normally distributed continuous variables were compared with Kruskal\u0026ndash;Wallis H or Mann\u0026ndash;Whitney U tests, and normally distributed variables with one-way ANOVA or t-tests. Correlations were examined using Spearman\u0026rsquo;s rank-order correlation. Data are presented as median (IQR) for non-normal variables, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for normal variables, and frequency (percentage) for categorical variables. Multiple linear regression was used to identify predictors of the Global Quality Score (GQS), including engagement metrics, duration, JAMA score, and mDISCERN score. Inter-rater reliability was assessed using Cronbach\u0026rsquo;s alpha. A two-tailed \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 highly significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Ethical Considerations\u003c/h2\u003e \u003cp\u003eThis study used only publicly available TikTok videos and did not involve clinical data, human specimens, animals, or personal identifiers. As there was no interaction with users, the study was exempt from ethical review.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Characteristics of Iron Deficiency Anemia Videos\u003c/h2\u003e \u003cp\u003eA systematic search was conducted on TikTok using the keyword \u0026ldquo;iron deficiency anemia\u0026rdquo; to identify relevant content. The top 100 videos based on the platform\u0026rsquo;s integrated ranking were initially selected. Following a rigorous screening process, a total of 28 videos were excluded for the following reasons: 14 were advertisements or promotional material, 7 were duplicate or substantially similar content, 3 were animal-focused videos, and 4 were in a non-Chinese language or had missing or poor-quality audio. Consequently, 72 videos met the inclusion criteria and were retained for final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe 72 videos constituted the final sample for analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In terms of engagement, videos received an average of 768.74\u0026thinsp;\u0026plusmn;\u0026thinsp;2,151.39 collects (median: 121.50). Average duration was 248.71\u0026thinsp;\u0026plusmn;\u0026thinsp;521.58 seconds (median: 91 seconds). For user interaction, videos garnered averages of 920.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2,179.00 likes (median: 75.50), 471.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1,068.40 shares (median: 37), and 730.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1,882.90 comments (median: 38). The earliest eligible IDA-related video was published in September 2025.\u003c/p\u003e \u003cp\u003eUploaders were categorized into three groups. Medical professionals accounted for the majority (66.67%, n\u0026thinsp;=\u0026thinsp;48), followed by institutional organizations (18.06%, n\u0026thinsp;=\u0026thinsp;13) and general users (15.27%, n\u0026thinsp;=\u0026thinsp;11) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Among medical professional uploaders, 13 held a bachelor\u0026rsquo;s degree or lower, 18 held a master\u0026rsquo;s degree, and 17 held a doctoral degree (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Of the 48 videos uploaded by doctors, 31 were posted by chief physicians, 10 by associate chief physicians, and 7 by attending or resident physicians (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Based on primary content theme, videos were most frequently classified as disease knowledge (n\u0026thinsp;=\u0026thinsp;41), followed by outpatient cases (n\u0026thinsp;=\u0026thinsp;16), and news or advertising (n\u0026thinsp;=\u0026thinsp;15) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eDescriptive analysis of the three assessment tools indicated a consistent pattern in content quality. JAMA scores were concentrated at 1 point (41.6%, 30/72), whereas GQS scores were most frequently 3 points (33.33%, 24/72). The mDISCERN scores showed the highest concentration at 3 points (79.17%, 57/72), reflecting a relatively balanced distribution across quality dimensions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Overall video characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe median Global Quality Score (GQS) for the TikTok videos was 3 (IQR 2\u0026ndash;4). The mDISCERN score also had a median of 3 (IQR 3\u0026ndash;3), while the median JAMA benchmark score was 2 (IQR 1\u0026ndash;3) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInter-rater reliability was assessed using Cronbach\u0026rsquo;s alpha. Based on the Landis and Koch criteria\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, α\u0026thinsp;\u0026gt;\u0026thinsp;0.80 indicates excellent agreement, 0.60\u0026ndash;0.80 substantial agreement, 0.40\u0026ndash;0.60 moderate agreement, and \u0026lt;\u0026thinsp;0.40 poor agreement. The obtained values for the JAMA, mDISCERN, and GQS scores were 0.621, 0.613, and 0.607, respectively, indicating substantial agreement across all three tools.\u003c/p\u003e \u003cp\u003e \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\u003eGeneral characteristics of the videos on Tiktok.\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\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (range)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e768.736\u0026thinsp;\u0026plusmn;\u0026thinsp;2151.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121.500(12-10544)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248.708521.580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91(17-2945)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLike\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e920.931\u0026thinsp;\u0026plusmn;\u0026thinsp;2179.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.500(17-10647)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e471.347\u0026thinsp;\u0026plusmn;\u0026thinsp;1068.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(25-4638)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e730.083\u0026thinsp;\u0026plusmn;\u0026thinsp;1882.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38(11-8420)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJAMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.917(0.868)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(1\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emDISCERN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.931\u0026thinsp;\u0026plusmn;\u0026thinsp;0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(1\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGQS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.181\u0026thinsp;\u0026plusmn;\u0026thinsp;0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(2\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eJAMA: Journal of the American Medical Association.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003emDISCERN: modified DISCERN.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eGQS: Global Quality Score.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eGeneral characteristics of the videos on Tiktok.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJAMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(1,3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emDISCERN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(3,3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGQS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(2,4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eJAMA: Journal of the American Medical Association.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003emDISCERN: modified DISCERN.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eGQS: Global Quality Score.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\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\u003eDetailed characteristics of videos based on uploaders and content.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCollect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLike\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eShare\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eComment\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\u003eUploaders\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical professionals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125.500\u003c/p\u003e \u003cp\u003e(106.5,147.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.000\u003c/p\u003e \u003cp\u003e(44.3,177.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.500\u003c/p\u003e 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colname=\"c6\"\u003e \u003cp\u003e33.000\u003c/p\u003e \u003cp\u003e(29.5,38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.000\u003c/p\u003e \u003cp\u003e(30.5,40.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral\u003c/p\u003e \u003cp\u003eusers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104.000\u003c/p\u003e \u003cp\u003e(21.0,135.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.000\u003c/p\u003e \u003cp\u003e(33.0,800.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.000\u003c/p\u003e \u003cp\u003e(70.0,325.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.000\u003c/p\u003e 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background\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor's\u003c/p\u003e \u003cp\u003eand below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.000(112.5,595.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.000(35.5,133.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e 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align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChief physician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123.000 (105.0,142.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.000(41.0,178.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.000(65.0,501.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.000 (30.0,552.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.000 (34.0,87.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e 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colname=\"c5\"\u003e \u003cp\u003e0.006**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.024*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eContent\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125.000(107.0,595.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.000(42.0,183.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.000(68.5,1071.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.000(32.0,744.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.000(36.5,630.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutpatient\u003c/p\u003e \u003cp\u003ecases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.000(91.0,139.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139.000(63.3,241.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.500(62.8,79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.000(28.5,40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.500(30.3,39.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNews/\u003c/p\u003e \u003cp\u003eadvertising\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107.000(21.0,130.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.000(35.0,202.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.000(67.0,106.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.000(31.0,84.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.000(28.0,42.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eKruskal-Wallis H Test, *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Comparison of General Data\u003c/h2\u003e \u003cp\u003eA total of 72 videos were included for analysis. General characteristics are shown in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Engagement metrics (collects, likes, shares, comments) showed high variability, with standard deviations much larger than medians. When comparing videos by uploader category (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), statistically significant differences were observed in the number of collects (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029) and comments (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043) among videos uploaded by medical professionals, institutional organizations, and general users. Videos from medical professionals showed relatively higher median values for both collects and comments. No significant differences were found in parameters such as video duration across these groups. Regarding the educational background of uploaders, significant differences were noted only in the number of comments (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040) among videos posted by individuals with a bachelor\u0026rsquo;s degree or lower, a master\u0026rsquo;s degree, or a doctoral degree. In terms of professional titles, videos uploaded by chief physicians, associate chief physicians, and attending/resident physicians showed significant differences in collects (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021), likes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), shares (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012), and comments (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024). The attending/resident physician group demonstrated notably higher median values across multiple engagement indicators. When comparing videos by content category\u0026mdash;disease knowledge, outpatient cases, and news/advertisements\u0026mdash;significant differences were observed in collects (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034) and comments (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). Videos focusing on disease knowledge generally achieved higher median engagement metrics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Video Quality and Reliability Assessments\u003c/h2\u003e \u003cp\u003eQuality assessment results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The median JAMA score was 2 (IQR 1\u0026ndash;3), with 41.67% of videos scoring 1. The median mDISCERN score was 3 (IQR 3\u0026ndash;3; mean 2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88), with 79.17% of videos attaining this score. The median Global Quality Score (GQS) was 3 (IQR 2\u0026ndash;4; mean 3.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42). Overall, video quality was moderate. Although evidence citation and completeness were acceptable, informational depth and actionable guidance remained limited. In summary, while the content is generally accessible, it lacks strong actionability to effectively promote health-related behaviors.\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\u003eScores of the JAMA, mDISCERN, and Global Quality Score for the videos..\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScale, score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValues (N\u0026thinsp;=\u0026thinsp;72), n (%)\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\u003eJAMA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(41.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(25.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(33.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emDISCERN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57(79.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(19.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlobal Quality Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(2.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(33.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(31.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(31.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Subgroup Analysis\u003c/h2\u003e \u003cp\u003eSubgroup comparisons of video quality scores were performed based on uploader type, educational background, professional title, and video content (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). JAMA scores differed significantly by uploader category (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008): general users had a lower median score (1.000) than medical professionals and institutional organizations (both 2.000). No significant differences were found for mDISCERN or GQS across uploader groups. When grouped by educational background, all three quality scores showed highly significant differences (JAMA \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0017, mDISCERN \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0017, GQS \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). Uploaders with doctoral degrees achieved the highest median scores across all tools (JAMA: 3.000; mDISCERN: 3.000; GQS: 4.000), outperforming those with bachelor\u0026rsquo;s or master\u0026rsquo;s degrees (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For professional titles, significant differences were observed in JAMA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) and mDISCERN scores (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) among physician ranks. Associate chief physicians attained the highest median mDISCERN score (3.500). GQS scores did not differ significantly across title groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.267). Content-based analysis revealed significant differences in JAMA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042) and mDISCERN scores (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025) among disease-knowledge, outpatient-case, and news/advertising videos. Disease-knowledge videos had higher JAMA scores (median: 2.000); GQS scores did not vary by content type. In summary, uploader educational background exerted the most consistent and significant influence on overall quality scores. Video content type mainly affected metrics of informational standardization and reliability, while professional titles were more closely tied to perceived information quality and credibility.\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\u003eQuality assessment of videos based on uploaders and content.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJAMA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emDISCERN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGlobal Quality Score\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\u003eUploaders\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical professionals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.000(1.0,3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.000(3.0,3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.000(2.0,4.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitutional organizations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.000(1.0,3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.000(3.0,3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.000(2.0,4.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral\u003c/p\u003e \u003cp\u003eusers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(1.0,1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.000(3.0,3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.000(2.0,4.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational background\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor's\u003c/p\u003e \u003cp\u003eand below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(1.0,2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.000(3.0,3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.000(2.0,3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.000(1.0,3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.000(3.0,3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.000(2.0,3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctoral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.000(2.0,3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.000(3.0,4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.000(2.8,4.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0017*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0017*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProfessional titles\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChief physician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.000(1.0,3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.000(3.0,3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.000(2.0,3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssociate\u003c/p\u003e \u003cp\u003echief physician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.000(2.0,3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.500(3.0,4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.500(2.0,4.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttending/\u003c/p\u003e \u003cp\u003eregistered physician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.000(2.0,3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.000(3.0,4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.000(2.0,4.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eContent\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.000(1.0,3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.000(3.0,3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.000(2.0,3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutpatient\u003c/p\u003e \u003cp\u003ecases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(1.0,2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.000(3.0,3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.000(2.0,4.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNews/\u003c/p\u003e \u003cp\u003eadvertising\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.000(1.0,3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.000(3.0,4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.000(2.0,4.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.042*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.025*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eKruskal-Wallis H Test, *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Correlation and Multiple Linear Regression Analysis\u003c/h2\u003e \u003cp\u003eSpearman correlation analysis was performed to examine relationships among video parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Results showed strong positive correlations between user engagement metrics\u0026mdash;likes, comments, collects, and shares (R\u0026thinsp;=\u0026thinsp;0.81\u0026ndash;0.92, all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u0026mdash;indicating high consistency across these participatory behaviors. In contrast, correlations between engagement indicators and quality scores (GQS and mDISCERN) were generally weak (most R\u0026thinsp;\u0026lt;\u0026thinsp;0.5). Only likes vs. GQS (R\u0026thinsp;=\u0026thinsp;0.243, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042) and mDISCERN vs. GQS (R\u0026thinsp;=\u0026thinsp;0.341, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) showed statistically significant positive correlations (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), suggesting that popularity is not strongly linked to perceived quality.\u003c/p\u003e \u003cp\u003eMultiple linear regression was conducted to identify factors influencing overall user engagement (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The final model (R\u0026sup2; = 0.386, adjusted R\u0026sup2; = 0.335, F(7,65)\u0026thinsp;=\u0026thinsp;7.52, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showed that likes (β\u0026thinsp;=\u0026thinsp;0.231, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), shares (β\u0026thinsp;=\u0026thinsp;0.145, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027), and mDISCERN score (β\u0026thinsp;=\u0026thinsp;0.278, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significant positive predictors of engagement, whereas JAMA score had a negative predictive effect (β = \u0026minus;\u0026thinsp;0.135, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035). These results indicate that content reliability (mDISCERN) and active engagement behaviors are key predictors of user interaction, while traditional academic benchmarks such as the JAMA score relate differently to public engagement metrics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple Linear Regression Results for Factors Influencing Short Video Engagement.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLike\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.027*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJAMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.035*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emDISCERN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eModel fit indicators:R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.386,adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.335,F(7, 65)\u0026thinsp;=\u0026thinsp;7.52, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, n\u0026thinsp;=\u0026thinsp;72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eB: Unstandardized coefficient\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eβ: Standardized coefficient (beta)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations Between User Engagement Metrics, Health Information Quality, and GQS.\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCorr. (GQS)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGQS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e282.4\u0026thinsp;\u0026plusmn;\u0026thinsp;498.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLike\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1568.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2523.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.042*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e887.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1356.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1300.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2375.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1380.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2187.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJAMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emDISCERN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eJAMA: Journal of the American Medical Association.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003emDISCERN: modified DISCERN.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eGQS: Global Quality Score.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Summary and interpretation of key findings\u003c/h2\u003e \u003cp\u003eThis cross-sectional study of 72 IDA-related short videos on TikTok reveals several key findings. First, healthcare professionals are the primary content creators (66.67%), and higher professional titles are associated with greater user engagement. Second, overall information quality is moderate (median mDISCERN\u0026thinsp;=\u0026thinsp;3, GQS\u0026thinsp;=\u0026thinsp;3), with JAMA scores indicating a need for better standardization. Subgroup analysis shows that uploaders with doctoral degrees consistently achieve higher quality scores. Third, user engagement metrics are strongly intercorrelated but only weakly linked to perceived quality (GQS). Content reliability (mDISCERN score) is a positive predictor of engagement, whereas traditional academic rigor (JAMA score) shows a negative relationship. In summary, while IDA content on TikTok is accessible and broadly credible, it lacks depth, standardization, and actionability\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Creator credentials and evidence-based quality are crucial for enhancing communication effectiveness\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Iron-Deficiency Anemia Video Characteristics and audience Interaction Analysis\u003c/h2\u003e \u003cp\u003eThe 72 short videos on iron deficiency anemia (IDA) included in this study showed high variability in audience engagement metrics. Mean collects, likes, shares, and comments were 768.74\u0026thinsp;\u0026plusmn;\u0026thinsp;2151.39, 920.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2179.00, 471.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1068.40, and 730.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1882.90, respectively; corresponding median values were 121.50, 75.50, 37, and 38 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Engagement differed significantly by uploader category. Videos posted by medical professionals had higher median collects (125.50 vs. 104.00, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029) and comments (40.00 vs. 35.00, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043) than those from general users (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among medical professionals, videos by attending/resident physicians achieved significantly higher median collects (1601.00), likes (3321.00), shares (1033.00), and comments (3302.00) compared to those by chief or associate chief physicians (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Regarding content type, disease-knowledge videos performed best in median collects (125.00, P\u0026thinsp;=\u0026thinsp;0.034) and comments (42.00, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) relative to outpatient-case or news/advertising videos. In summary, audience engagement with IDA-related content on TikTok is highly heterogeneous. Content from certified medical professionals\u0026mdash;particularly frontline clinicians\u0026mdash;appears more effective in stimulating user interaction, while disease-knowledge videos hold an advantage in eliciting participatory feedback\u003csup\u003e[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Video Ratings and Quality Evaluation\u003c/h2\u003e \u003cp\u003eMultiple standardized tools were used to assess video quality. Overall, the videos demonstrated moderate informational reliability and general quality. The median mDISCERN score was 3 (IQR 3\u0026ndash;3; mean 2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88), with 79.17% (57/72) of videos scoring 3, indicating basic credibility. However, shortcomings were noted in evidence citation, treatment-option presentation, and conflict-of-interest disclosure, particularly regarding additional references for viewers\u003csup\u003e[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. The median Global Quality Score (GQS) was 3 (IQR 2\u0026ndash;4; mean 3.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42), with scores distributed relatively evenly between 2 and 4, near the moderate-quality threshold\u0026mdash;a finding consistent with prior reports. Subgroup analysis revealed a strong association between quality and creator background. Uploaders with doctoral degrees obtained significantly higher JAMA, mDISCERN, and GQS scores than other educational groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Videos from medical professionals and institutions scored higher on the JAMA benchmark than those from general users (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). Furthermore, disease-knowledge videos consistently received higher ratings across all assessment tools compared to other content types. Nevertheless, JAMA scores indicated room for improvement in informational standardization, with a median of 2 (IQR 1\u0026ndash;3) and 41.67% of videos scoring only 1 point. In summary, while current IDA-related content on the platform shows acceptable basic credibility and comprehensibility, it exhibits notable limitations in depth, evidence transparency, and actionability. Disease-specific educational content produced by creators with medical backgrounds represents a relatively more reliable information source\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Correlation and Multiple Linear Regression Analysis Between Video Quality and Video Characteristic\u003c/h2\u003e \u003cp\u003eWe conducted an in-depth correlation analysis and multiple linear regression to examine the relationship between quality metrics and video characteristics for IDA-related content on TikTok. Correlation analysis showed strong positive intercorrelations among user engagement metrics\u0026mdash;likes, comments, collects, and shares (R\u0026thinsp;=\u0026thinsp;0.81\u0026ndash;0.92, all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u0026mdash;indicating high covariation in these participatory behaviors. This aligns with the network-driven dissemination typical of social media, where higher engagement promotes broader visibility, consistent with prior findings on social media network effects\u003csup\u003e[\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, correlations between these engagement indicators and the overall quality score (GQS) were generally weak (most R\u0026thinsp;\u0026lt;\u0026thinsp;0.3), suggesting that audience interaction does not directly reflect intrinsic informational quality. This observation resonates with evidence that emotional appeal and novelty often drive engagement more than educational value. Multiple linear regression identified key predictors of perceived video quality. The final model (R\u0026sup2; = 0.386, adjusted R\u0026sup2; = 0.335) showed that likes (β\u0026thinsp;=\u0026thinsp;0.231, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), shares (β\u0026thinsp;=\u0026thinsp;0.145, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027), and mDISCERN score (β\u0026thinsp;=\u0026thinsp;0.278, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significant positive predictors of GQS, whereas JAMA score was a negative predictor (β = \u0026minus;\u0026thinsp;0.135, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035). These results indicate that content reliability (mDISCERN) and active engagement behaviors are central to perceived quality, while traditional academic benchmarks (JAMA) align differently with the participatory nature of public platforms. Our analysis clarifies the relationship between engagement and quality, offering an empirical perspective on health-science communication via social media. In the short-video context, both scientific reliability and the capacity to stimulate interaction jointly shape perceived quality; popularity alone, however, is not a reliable indicator of informational value\u003csup\u003e[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Practical Significance\u003c/h2\u003e \u003cp\u003eThe practical implications of this study are fourfold. First, it confirms that short-video platforms such as TikTok have become a major channel for public health information. Medical professionals\u0026mdash;who uploaded the majority of content (66.67%)\u0026mdash;represent a key source of reliable information. This finding offers empirical support for government and health institutions to use social media for targeted health education and literacy promotion\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Second, the professional quality of video content (e.g., mDISCERN score) correlates positively with user engagement, while uploaders\u0026rsquo; educational background and professional titles significantly affect informational reliability. This suggests that platforms and creators should emphasize scientific rigor, proper evidence citation, and source transparency to enhance credibility and clinical utility\u003csup\u003e[\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Third, the study identifies limitations in the depth and actionability of current content (e.g., median GQS\u0026thinsp;=\u0026thinsp;3). Combined with algorithmic recommendations and time constraints, these may worsen information fragmentation\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Platforms should therefore optimize verification systems, strengthen content review, and develop formats that support in-depth science communication\u0026mdash;such as video series or thematic compilations\u0026mdash;to build a more authoritative health-knowledge ecosystem\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Finally, from a public-health perspective, this study provides evidence-based insights for balancing \u0026ldquo;popularity-driven\u0026rdquo; and \u0026ldquo;quality-driven\u0026rdquo; content dissemination. It underscores the need to promote high-quality content while guiding general users and improving public health literacy, thereby empowering individuals to make informed health decisions online\u003csup\u003e[\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Study limitations and future directions\u003c/h2\u003e \u003cp\u003eAlthough this study systematically evaluated the quality and dissemination of IDA-related health education videos on TikTok, several limitations warrant acknowledgment. First, the sample was constrained to a single health topic (\u0026ldquo;iron deficiency anemia\u0026rdquo;), limiting generalizability to other chronic diseases or health domains. Videos were drawn from the top 100 in TikTok\u0026rsquo;s integrated ranking; despite rigorous screening, the final sample (n\u0026thinsp;=\u0026thinsp;72) remains relatively small. The platform\u0026rsquo;s recommendation algorithm may also influence visibility and ranking, introducing potential selection bias and reducing representativeness of the dynamic content pool. Second, the cross-sectional design can identify correlations but cannot establish causality\u0026mdash;for example, whether higher quality drives engagement or whether popular videos are perceived as higher quality due to greater exposure. Third, while multiple validated tools (GQS, mDISCERN, JAMA) were used to assess reliability and standardization, dimensions such as comprehensibility and actionability\u0026mdash;whether users truly understand information and subsequently take health actions\u0026mdash;received less attention. Furthermore, the study did not quantitatively examine platform-specific features (e.g., algorithmic recommendations, visual filters, background music) that may affect attention and perception, nor did it track viewers\u0026rsquo; actual health behavior changes after watching videos, a key gap in assessing real-world impact.\u003c/p\u003e \u003cp\u003eFuture research should advance in several directions:\u003c/p\u003e \u003cp\u003eExpand scope and depth\u0026mdash;include a wider range of health topics (e.g., cardiovascular diseases, diabetes, sports nutrition), increase sample sizes, and conduct comparative studies across multiple platforms (e.g., Bilibili, WeChat Channels) to improve generalizability.\u003c/p\u003e \u003cp\u003eRefine study design\u0026mdash;adopt longitudinal or experimental interventions to clarify causal effects of health videos on knowledge, attitudes, and behaviors (e.g., iron-supplement adherence, health decision-making).\u003c/p\u003e \u003cp\u003eEnrich evaluation frameworks\u0026mdash;incorporate measures of comprehensibility, emotional resonance, and behavioral motivation, and develop assessment tools better suited to short-video media.\u003c/p\u003e \u003cp\u003eInvestigate underlying mechanisms\u0026mdash;use data-mining techniques to analyze relationships between platform algorithms and health-information dissemination, and examine how uploader characteristics (e.g., credentials, communication style) interact with user demographics (e.g., age, health literacy) to affect information reception and impact.\u003c/p\u003e \u003cp\u003e Promote translational application\u0026mdash;collaborate with platform operators, healthcare institutions, and educators to co-design and pilot evidence-based, high-quality templates and creation guidelines for health science short videos, translating academic insights into practical drivers of public health literacy.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eIDA‑related content on TikTok is predominantly created by medical professionals (66.67%), whose advanced degrees and clinical titles are associated with higher content reliability and user engagement. A comprehensive quality evaluation reveals moderate overall quality (median scores\u0026thinsp;~\u0026thinsp;3), with acceptable baseline credibility but notable limitations in depth, evidence standardization, and actionability. User engagement metrics are strongly intercorrelated yet only weakly linked to perceived quality. Content reliability (mDISCERN) positively predicts engagement, whereas traditional academic benchmarks (JAMA) show an inverse relationship, highlighting the tension between scientific rigor and communicative appeal in the short‑video format. The study indicates that while IDA‑related health information on TikTok is accessible and credible, it falls short of facilitating behavioral change. The findings support platform‑level efforts to enhance content governance through creator verification, improved review systems, and refined recommendation algorithms. They also guide professionals in developing more in‑depth and actionable content, while assisting the public in critically evaluating online health information. These insights offer valuable implications for advancing targeted health education via social media and improving national health literacy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIron deficiency anemia\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIDA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eThis study used only publicly available TikTok videos and did not involve human participants, clinical data, human specimens, or animals. No interaction with video uploaders or viewers occurred, and no personal identifiable information was collected. According to the Measures for the Ethical Review of Biomedical Research Involving Humans (National Health and Family Planning Commission of China, 2016, Article 39), research using publicly available data that does not involve human subjects or personal privacy is exempt from formal ethics review. Based on these national guidelines, the Ethics Committee of Shandong University of Aeronautics confirmed that no formal ethics approval was required for this study. As no human participants were involved, the requirement for informed consent was also waived by the same committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study did not involve human participants, and no individual person\u0026apos;s data, images, or videos are presented in this manuscript. Therefore, no consent for publication was required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are available from the corresponding author on reasonable request. The original videos analysed in this study are publicly accessible on the TikTok platform; however, due to platform policies and the dynamic nature of online content, the specific video URLs and extracted data are not publicly archived but can be provided by the authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo external funding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRBJ and HC conceived and designed the study. RJ, XP, and SBH performed the data collection and video quality assessments. XP conducted the statistical analysis. RJ drafted the initial manuscript. HC supervised the study, contributed to data interpretation, and critically revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChavan, R. S. et al. World Health Organization. \u003cem\u003eEncyclopedia Food Health\u003c/em\u003e, : pp. 585\u0026ndash;591. (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuel-Bergeron, J. C. et al. Global Update and Trends of Hidden Hunger, 1995\u0026ndash;2011: The Hidden Hunger Index. \u003cem\u003ePlos One\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e (12), e0143497 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamamoto, K. et al. 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Dis.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (6), e70341 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurnock, B. \u003cem\u003ePublic health\u003c/em\u003e (Jones \u0026amp; Bartlett, 2012).\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":"Iron Deficiency Anemia, Health Information, Social Media, TikTok, Quality Assessment, Health Communication, Cross-sectional Study","lastPublishedDoi":"10.21203/rs.3.rs-9005872/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9005872/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIron deficiency anemia (IDA) remains a major global public health challenge, disproportionately affecting vulnerable groups such as women of reproductive age and children. While social media platforms like TikTok have become primary channels for health information dissemination, the quality and reach of IDA-related educational content on these platforms remain underexplored. This study systematically evaluated the quality, source characteristics, and user engagement of IDA-related short videos on the Chinese TikTok platform.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this cross-sectional study, we screened the top 100 \u0026ldquo;iron deficiency anemia\u0026rdquo; videos on TikTok. After excluding advertisements, duplicates, non-Chinese content, and poor-quality videos, 72 were included. We recorded uploader type, educational background, professional credentials, content category, duration, and engagement metrics (likes, shares, collects, comments). Quality was assessed using GQS, mDISCERN, and JAMA criteria. Statistical analyses included descriptive statistics, Kruskal\u0026ndash;Wallis H tests, Spearman correlation, and multiple linear regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf the 72 videos analyzed, 66.67% (n\u0026thinsp;=\u0026thinsp;48) were uploaded by healthcare professionals, followed by organizations (18.06%) and general users (15.27%). Disease knowledge dissemination was the most common content category (56.94%). Median quality scores were: GQS 3 (IQR 2\u0026ndash;4), mDISCERN 3 (IQR 3\u0026ndash;3), and JAMA 2 (IQR 1\u0026ndash;3), indicating moderate overall quality. Subgroup analysis showed that videos from uploaders with doctoral degrees and medical institutions scored significantly higher across all tools (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). User engagement metrics were strongly intercorrelated (R\u0026thinsp;=\u0026thinsp;0.81\u0026ndash;0.92, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but weakly correlated with quality scores. Multiple linear regression identified likes (β\u0026thinsp;=\u0026thinsp;0.231, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), shares (β\u0026thinsp;=\u0026thinsp;0.145, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027), and mDISCERN score (β\u0026thinsp;=\u0026thinsp;0.278, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as positive predictors of GQS, while JAMA score was a negative predictor (β = -0.135, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAlthough medical professionals are the primary contributors, the overall quality of IDA-related TikTok content remains moderate, with limited depth, evidence, and actionability. The weak correlation between user engagement and objective quality scores underscores the complexity of health communication on social media. These findings highlight the need for enhanced content moderation, creator training, and improved public health literacy to foster critical evaluation of online health information.\u003c/p\u003e","manuscriptTitle":"The Quality and Dissemination of Health Information Pertaining to Iron-Deficiency Anemia on TikTok : A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 07:15:15","doi":"10.21203/rs.3.rs-9005872/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":"44acaa30-8e1a-46b0-b0e6-a6865ddfdece","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65561858,"name":"Health sciences/Diseases"},{"id":65561859,"name":"Health sciences/Health care"},{"id":65561860,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-04-10T05:10:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 07:15:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9005872","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9005872","identity":"rs-9005872","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}