Algorithmic Short-Video Platforms and Health Decision-Making in Rhinitis: A Multi-Platform Digital Health Analysis

Algorithmic Short-Video Platforms and Health Decision-Making in Rhinitis: A Multi-Platform Digital Health Analysis | 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 Research Article Algorithmic Short-Video Platforms and Health Decision-Making in Rhinitis: A Multi-Platform Digital Health Analysis Xianzhen Chen, Rexidanmu Hudabai, Shuwen Tang, Huiting Chen, Yufeng Cao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8997024/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Short-form video platforms have become major sources of health information, yet concerns persist regarding misinformation quality and its influence on patient decision-making, particularly within algorithm-driven digital ecosystems where popularity and informational quality may diverge. Methods A multi-platform cross-sectional analysis was conducted on 179 short videos from TikTok and Bilibili using five validated assessment instruments: Global Quality Scale (GQS), modified DISCERN (mDISCERN), Patient Education Materials Assessment Tool (PEMAT), Video Information Quality Index (VIQI), and JAMA benchmark criteria. In parallel, a questionnaire survey of 106 patients with rhinitis assessed social media exposure and its impact on healthcare decisions. Multivariable regression analyses identified predictors of video quality and behavioral influence. This study integrates digital content analytics with patient-reported behavioral outcomes. Results Videos produced by professional creators demonstrated significantly higher quality, transparency, and reliability than those by non-professionals (all p < .001), while engagement metrics showed weak correlations with quality indicators. Patient experience–sharing and symptom-focused videos exerted the strongest influence on healthcare decisions despite lower informational quality. Higher educational attainment was independently associated with increased susceptibility to video-influenced decisions. Engagement metrics were weakly associated with informational quality, indicating a decoupling between algorithmic popularity and evidence-based credibility. Conclusion Short-form video platforms function as algorithmically mediated health environments in which popularity poorly reflects informational rigor, and experiential narratives disproportionately shape patient behavior. These findings underscore the need for platform governance reforms, clinician participation in digital ecosystems, and evidence-based digital health communication strategies to mitigate misinformation-driven behavioral risks. Rhinitis Social media Patient education Public health TikTok BiliBili Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Rhinitis is a highly prevalent chronic inflammatory airway condition that imposes a substantial burden on quality of life and healthcare systems worldwide.[ 1 ] Despite the availability of well-established, evidence-based management strategies—including those outlined in international clinical guidelines,[ 2 ] treatment adherence and appropriate healthcare-seeking behavior among patients with rhinitis remain suboptimal in real-world settings.[ 3 , 4 ] Increasingly, patients rely on digital information sources rather than traditional clinical encounters to guide symptom interpretation, treatment choices, and self-management decisions.[ 5 – 7 ] Short-form video based social media platforms, such as TikTok, YouTube Shorts, and Instagram Reels, have rapidly become dominant components of the contemporary digital health information ecosystem.[ 8 ] These platforms operate as algorithmically curated health information environments rather than neutral repositories of content. These platforms employ algorithm-driven content delivery and immersive audiovisual formats that efficiently shape users’ health perceptions and behavioral intentions. While short videos offer scalable opportunities for public health education, they also facilitate the rapid dissemination of low-quality, misleading, or commercially biased medical content, raising growing concerns regarding health misinformation in digital environments.[ 9 – 11 ] Algorithm-mediated exposure not only increases content reach but also reshapes risk perception, trust formation, and treatment heuristics, thereby exerting downstream effects on health decision-making.[ 12 ] Algorithm-mediated exposure may further amplify this influence by reinforcing users’ pre-existing beliefs and fostering echo chambers that normalize misinformation and distort risk perception.[ 13 , 14 ] These mechanisms are particularly salient for chronic conditions requiring sustained self-management and long-term patient engagement. For rhinitis, a condition often perceived as mild or self-limiting, patients may be especially susceptible to short-video content that promotes non–evidence-based interventions or discourages professional medical care. Repeated exposure to such content through short-video platforms may undermine guideline-concordant management and contribute to inappropriate health decisions. However, despite the expanding role of short-video platforms in digital health communication, rhinitis remains underrepresented in the misinformation and social media health literature.[ 15 , 16 ] No prior study has simultaneously integrated multi-platform content quality assessment with patient-reported behavioral outcomes in rhinitis within an algorithm-driven short-video environment.[ 15 , 17 , 18 ] Therefore, this study conducted a multi-platform cross-sectional analysis of short-form video content related to rhinitis, examining content characteristics, informational quality, and associations with patient healthcare decision-making. In addition, telephone-based follow-up among outpatient rhinitis patients was performed to assess how exposure to these videos influenced real-world treatment behaviors. This work situates rhinitis within the broader digital health misinformation ecosystem and informs platform governance and clinical digital engagement strategies. Materials and methods Search Strategy and Data Collection This cross-sectional study integrated content analysis of short-form social media videos with a questionnaire-based survey of patients with rhinitis to investigate the influence of digital video content on healthcare decision-making ( Fig. 1 ). Short videos were collected from two major Chinese platforms: TikTok (Douyin, Chinese version) and BiliBili. Using the Chinese keyword “鼻炎” (rhinitis), we retrieved the top 100 videos ranked by each platform’s default “comprehensive sorting” algorithm during a predefined sampling window. After applying eligibility criteria, 179 videos were retained for analysis. As algorithm-ranked content was sampled, findings primarily reflect high-visibility information environments rather than the full universe of rhinitis-related videos. In parallel, an online questionnaire survey was conducted using convenience sampling to recruit patients diagnosed with rhinitis. The survey captured participants’ social media usage patterns, exposure to rhinitis-related short videos, and subsequent healthcare decisions. A total of 106 valid responses were obtained. Videos were included if they met the following criteria: (1) primary focus on rhinitis (symptoms, etiology, treatment, or self-care); (2) publication at least 7 days prior to data collection to ensure stabilization of engagement metrics; and (3) sufficient audiovisual quality to permit reliable content evaluation. Exclusion criteria were: (1) irrelevant or minimally relevant content; (2) duplicated or near-duplicate uploads; (3) purely promotional or commercial advertisements; and (4) videos that were deleted or made private post-publication. Extracted Data Elements During screening, the following information was extracted from each eligible video: (1) Basic characteristics: platform type, video Uniform Resource Locator (URL), publication date, duration (seconds), and engagement metrics (likes, comments, saves, shares, and views); (2) Uploader characteristics: professional background (e.g., certified physician, healthcare institution, health influencer, non-medical organization, or general user), platform verification status, and follower count; (3) Content characteristics: thematic category (disease awareness, treatment options, daily management, patient experience sharing), presentation format (monologue, case narrative, animated explainer), and clinical decision-making context (e.g., symptom self-assessment, treatment selection, myth clarification) ( Supplementary File 1 ). Video Quality Assessment Two trained researchers conducted independent and objective evaluations of video quality using multidimensional standardized tools, including: 1) Global Quality Score (GQS)[ 19 ]: A 1–5 scale evaluating overall video quality across four dimensions—information accuracy, structural clarity, practicality, and objectivity—with 1 indicating poor and 5 indicating excellent; 2) Modified Information Dissemination Score (mDISCERN)[ 20 ]: Comprising five core questions focusing on source credibility, evidence support, and potential bias, scored 1 point for "yes" and 0 points for "no," with higher total scores indicating greater reliability; 3) Patient Education Material Assessment (PEMAT)[ 21 ]: Divided into PEMAT-U (comprehensibility) and PEMAT-A (action orientation), with percentage scores calculated; 4) Video Information Quality Index (VIQI)[ 22 ]: Rated across five dimensions—information completeness, accuracy, timeliness, and others. 5) JAMA Score[ 23 ]: A standardized tool for evaluating online health information quality, focusing on accuracy, completeness, timeliness, objectivity, and readability. Prior to formal scoring, both raters independently evaluated 20 pilot videos and achieved an intraclass correlation coefficient (ICC) ≥ 0.80, indicating excellent inter-rater reliability. All evaluations were based on complete audiovisual and textual content, without reference to platform-generated recommendation tags or user comments ( Supplementary Files 2–3 ). Questionnaire Survey A structured questionnaire was developed to assess patients’ demographic characteristics, rhinitis-related clinical features, social media usage behaviors, and the perceived influence of short videos on healthcare decision-making ( Supplementary File 4 ). The survey was administered between January and February 2026 via online dissemination and patient community recruitment. Logical branching was applied such that respondents who reported no exposure to rhinitis-related videos were redirected to the end of the survey. Quality control measures included IP address restrictions, duplicate-response screening, and manual review. Questionnaires with patterned responses or > 10% missing data were excluded. Statistical Analysis Data were analyzed using IBM SPSS Statistics version 27.0. Continuous variables, which mostly followed non-normal distributions, were summarized as medians with interquartile ranges [M (IQR)] and compared using the Mann–Whitney U test (for two groups) or Kruskal–Wallis H test (for ≥ 3 groups), with post hoc pairwise comparisons adjusted via Dunn’s test and Bonferroni correction. Categorical variables were presented as frequencies and percentages [n (%)], with group differences assessed by chi-square (χ²) tests. Spearman’s rank correlation was used to examine associations between video engagement metrics and quality scores. Multivariable logistic regression identified factors associated with changes in patients’ healthcare decisions attributable to short video exposure. All tests were two-tailed, with statistical significance defined as P < 0.05 after adjustment for multiple comparisons using the Benjamini–Hochberg procedure to control the false discovery rate (FDR). Results Basic Characteristics of Videos A total of 179 videos were ultimately included in the analysis, sourced from BiliBili (n = 91) and TikTok (n = 88). Videos from the two platforms exhibited significant differences across multiple characteristics (Table 1 ). The median duration of BiliBili videos (188.5 seconds) was significantly longer than that of TikTok videos (78.5 seconds; p < 0.001). Similarly, the median time since upload was substantially greater for BiliBili videos (460.0 days) compared with TikTok videos (66.0 days; p < 0.001), suggesting a longer content lifecycle on BiliBili, whereas TikTok demonstrates a faster content turnover rate. In terms of user engagement metrics, TikTok videos showed significantly higher median counts for likes, comments, shares, favorites, and views than Bilibili videos ( p < 0.001 for all), indicating that TikTok is more effective at fostering user interaction and amplifying content reach. Table 1 Comparison of characteristics between different short-video platforms. Variables Bilibili (N = 91, median (IQR)) Tiktok (N = 88, median (IQR)) W P value Comments 1.0 (0.0–27.0) 860.5 (133.8–3521.5) 854.5 < 0.001 Days Since Release 460.0 (132.0–1083.5) 66.0 (19.8–127.2) 6519.0 < 0.001 Favorites 108.0 (7.5–1154.5) 5968.0 (315.2–28145.2) 1693.0 < 0.001 Video duration 188.5 (83.8–470.8) 78.5 (50.0–116.8) 5948.5 < 0.001 Likes 102.0 (10.0–903.0) 14868.0 (938.8–60943.8) 1284.5 < 0.001 Shares 41.0 (3.0–250.5) 5751.5 (183.5–36261.5) 1397.5 < 0.001 Coins 15.0 (0.0–107.5) - - - Views 4559.0 (871.0–56000.0) - - - Uploader Characteristics Uploaders were categorized as professional (n = 100) or non-professional (n = 79) based on occupational background and platform verification status (Table 2 ) . Among professional uploaders, 87.0% were physicians and 95.0% were officially verified. In contrast, non-professional uploaders were predominantly independent content creators (97.5%), with only 12.7% holding platform verification. Professional uploaders demonstrated substantially greater influence, with a median follower count of 82,000 compared with 6,021 among non-professionals ( p < 0.001). Similarly, professional accounts had significantly higher cumulative likes (median 278,500; p = 0.004). Table 2 Comparison of characteristics between different uploaders. Variables Professional uploader (N = 100) Unprofessional uploader (N = 79) W/ χ 2 P value Fans 82000.0 (3963.0–534250.0) 6021.0 (1667.5–56500.0) 2642.5 < 0.001 Total likes 278500.0 (26750.0–7357250.0) 45000.0 (6125.0–337500.0) 2952.0 0.004 Uploader 164.2 < 0.001 Self-media 2 (2.0%) 77 (97.5%) Official Media 11 (11.0%) 2 (2.5%) Doctor 87 (87.0%) 0 (0) Verification 120.0 < 0.001 Yes 95 (95.0%) 10 (12.7%) No 5 (5.0%) 69 (87.3%) Video Content Categorization Videos were classified into three thematic categories: disease awareness, treatment options, and patient experience sharing (Fig. 2 ). Across both platforms, disease awareness and treatment-related videos together accounted for over 70% of content. Treatment-focused videos constituted a larger proportion on TikTok (51.0%) than on BiliBili (37.4%) (Figs. 2 A and 2 B). Regarding clinical decision-making contexts, treatment selection was the most prevalent scenario on both platforms (47.3% on BiliBili and 50.0% on TikTok). TikTok featured a substantially higher proportion of myth-busting content (19.3%) compared with BiliBili (5.5%), whereas BiliBili included more content addressing special populations (13.2% vs. 6.8%) (Figs. 2 C and 2 D). From the uploader perspective, professional creators primarily focused on disease awareness and treatment options, whereas non-professional creators more frequently shared personal patient experiences (Figs. 2 E and 2 F). Although treatment selection was the dominant decision-making context for both groups (49.0% among professionals vs. 48.1% among non-professionals), non-professional creators rarely produced myth-busting content and instead emphasized experience-based themes such as daily care (Figs. 2 G and 2 H). Video Quality Assessment Patient Education Materials Assessment Tool (PEMAT) At the platform level, BiliBili videos exhibited a narrower and overall higher distribution of PEMAT-U scores, indicating greater consistency in information clarity and organization, whereas TikTok videos demonstrated wider score dispersion, reflecting greater variability in understandability (Fig. 3 A). Median PEMAT-A scores did not differ significantly between platforms, although BiliBili again showed a more concentrated distribution and TikTok greater heterogeneity (Fig. 3 B). When stratified by content type, disease awareness videos achieved the highest PEMAT-U scores, while treatment option videos scored highest on PEMAT-A. In contrast, patient experience videos consistently demonstrated the lowest scores on both dimensions, likely reflecting their subjective narrative structure and limited instructional focus (Figs. 3 C and 3 D). Across decision-making contexts, all video types demonstrated high understandability; however, notable differences emerged in actionability. Myth-busting and special population content achieved the highest PEMAT-A scores, indicating superior capacity to provide concrete behavioral guidance. Symptom self-assessment videos showed the lowest actionability, primarily serving awareness rather than decision-support functions (Figs. 3 E and 3 F). Video Information Quality Index (VIQI) Significant differences were observed between platforms across overall VIQI scores and all subdomains ( p < 0.001). On BiliBili, disease awareness videos achieved the highest median VIQI total score (16.0; IQR 13.0–18.0), followed by treatment option and patient experience videos. A similar pattern was observed on TikTok, with disease awareness videos achieving the highest median VIQI score (15.5; IQR 11.5–17.0). Across all subscales, disease awareness videos consistently outperformed other categories, particularly in content accuracy (VIQI-2) and educational value (VIQI-4). Patient experience videos scored lowest across all dimensions, likely due to their personalized narratives, limited structure, and frequent inclusion of anecdotal or non-evidence-based claims (Table 3 ). Table 3 Comparison of VIQI scores for videos with different content types (grouped by platform) Variables Disease Awareness Treatment Plan Patient Experience χ 2 p BiliBili VIQI 16.0 (13.0–18.0) 12.0 (10.2–14.8) 9.5 (8.0–12.5) 22.53 < 0.001 VIQI-1 4.0 (3.0–5.0) 3.0 (3.0–3.0) 3.0 (2.0–3.2) 21.95 < 0.001 VIQI-2 5.0 (4.0–5.0) 4.0 (3.0–5.0) 3.0 (2.0–3.0) 33.30 < 0.001 VIQI-3 3.0 (2.0–5.0) 2.0 (2.0–3.0) 2.0 (1.0–3.0) 14.01 < 0.001 VIQI-4 4.0 (3.0–4.0) 3.0 (3.0–4.0) 3.0 (2.0–3.0) 14.26 < 0.001 TikTok VIQI 15.5 (11.5–17.0) 13.0 (12.0–16.0) 8.0 (7.0–10.0) 33.69 < 0.001 VIQI-1 4.0 (3.0–5.0) 4.0 (3.0–4.0) 3.0 (2.0–3.0) 27.41 < 0.001 VIQI-2 4.0 (4.0–5.0) 4.0 (3.0–5.0) 2.0 (2.0–2.5) 38.90 < 0.001 VIQI-3 3.0 (2.0–3.0) 3.0 (2.0–3.5) 2.0 (1.0–2.0) 22.97 < 0.001 VIQI-4 4.0 (3.0–4.0) 3.0 (3.0–4.0) 2.0 (2.0–3.0) 26.34 < 0.001 Overall Video Quality and Reliability (GQS and mDISCERN) Stratified analyses revealed that videos uploaded by professional creators consistently achieved significantly higher GQS and mDISCERN scores than those uploaded by non-professionals across both platforms (all p < 0.001; Figs. 4 A and 4 B), confirming uploader expertise as a major determinant of informational quality. Across content categories, disease awareness videos demonstrated the highest quality and reliability, whereas patient experience videos performed poorest. Although median mDISCERN scores were numerically identical across categories, distributional differences were statistically significant ( p < 0.05; Fig. 4 C). Similar patterns were observed on TikTok (Fig. 4 D). Across clinical decision-making contexts, videos addressing myth-busting and symptom self-assessment achieved the highest quality scores, whereas daily care content consistently scored lowest (BiliBili: p = 0.003 and p = 0.014; TikTok: p = 0.010 and p = 0.020; Figs. 4 E and 4 F), indicating considerable scope for improving standardized, evidence-based self-management guidance in digital health media. Video Information Transparency (JAMA Benchmark Criteria) The JAMA benchmark criteria assess the transparency of health-related educational videos and serve as an important indicator of their credibility. Videos from uploaders with different levels of professional expertise showed statistically significant differences in JAMA scores ( p < 0.001), with videos by professional uploaders scoring significantly higher than those by non-professional uploaders (Fig. 5 ). Correlation and Regression Analyses This study employed statistical analyses to identify key data-driven factors influencing video quality. As shown in Fig. 6 , the Global Quality Scale (GQS) and modified DISCERN (mDISCERN) scores were significantly positively correlated ( p < 0.001), confirming consistency between overall video quality and information reliability. The total Video Information Quality Index (VIQI) score also demonstrated strong positive correlations with both GQS and mDISCERN. Among VIQI subscales, content accuracy (VIQI-1) and logical coherence (VIQI-3) showed the strongest associations. The JAMA benchmark score exhibited moderate to strong positive correlations with both GQS and mDISCERN ( p < 0.001). In contrast, user engagement metrics showed limited relevance: the number of likes was only weakly correlated with GQS ( p 0.05). TikTok videos had a high median like count (14,868.0), yet this popularity did not translate into higher quality ratings. Furthermore, neither the number of saves/favorites nor time since upload was significantly correlated with either quality metric ( p > 0.05). Subsequently, stepwise multiple linear regression analyses were conducted with GQS and mDISCERN as dependent variables to quantitatively identify their core predictors.For GQS, the optimal regression model included only two predictors: information transparency (JAMA score) and production quality (total VIQI score). This model achieved an adjusted R ² of 0.754, indicating that these two variables jointly explain 75.4% of the variance in GQS scores ( Supplementary file 5 , Table 1 * ). Specifically, both JAMA score (β = 0.457, p < 0.001) and total VIQI score (β = 0.131, p < 0.001) were significant positive predictors. Variance inflation factors (VIFs) for both predictors were below 10, confirming the absence of severe multicollinearity ( Supplementary file 5 , Table 2 * ).For mDISCERN, the final model included three predictors: JAMA score (β = 0.373, p < 0.001), understandability (PEMAT-U) (β = 0.016, p = 0.019), and total VIQI score (β = 0.106, p = 0.037), with an adjusted R ² of 0.777. The model was statistically significant overall ( F = 78.948, p < 0.001) ( Supplementary file 5 , Tables 3 * and 4* ). Collectively, these regression results consistently confirm that information transparency and production quality are the core determinants of both overall video quality and information reliability. Notably, metrics of public engagement—such as likes and view counts—were not retained in either final model, further underscoring the lack of an inherent association between content popularity and informational quality. Survey Findings on Patient Decision-Making Participants were categorized into “influenced” and “non-influenced” groups based on whether short-form videos affected their healthcare decisions. No significant between-group differences were observed for age, sex, occupation, disease duration, rhinitis subtype, or treatment modality (all p > 0.05). However, educational attainment differed significantly: compared with participants with high school education or below, those with vocational college education (OR = 11.50, 95% CI: 2.32–87.87, p = 0.006) and those with bachelor’s degrees or higher (OR = 6.67, 95% CI: 1.52–46.67, p = 0.023) were significantly more likely to report video-influenced healthcare decisions (Table 4 ). Table 4 Basic Characteristics of Participants in the Questionnaire Survey Variables Changing Not changing P value n 65 41 Sex (%) Male 15 (23.1) 13 (31.7) 0.370 Female 50 (76.9) 28 (68.3) Age (%) 18–25 14 (21.5) 13 (31.7) 0.084 26–35 32 (49.2) 11 (26.8) 36–45 10 (15.4) 11 (26.8) 46–55 5 (7.7) 1 (2.4) > 55 4 (6.2) 5 (12.2) Education (%) Middle school 0 (0.0) 1 (2.4) 0.008 High school 2 (3.1) 8 (19.5) Vocational Undergraduate 23 (35.4) 8 (19.5) Undergraduate or above 40 (61.5) 24 (58.5) Occupation (%) Public Institution staff/Civil Servant 22 (33.8) 6 (14.6) 0.041 Company staff 15 (23.1) 8 (19.5) Freelancer 17 (26.2) 9 (22.0) Retirement 3 (4.6) 3 (7.3) Student 7 (10.8) 12 (29.3) Others 1 (1.5) 3 (7.3) Duration of rhinitis (%) 10Y 6 (9.2) 6 (14.6) Characteristics (%) Perennial 26 (40.0) 24 (58.5) 0.074 Seasonal 39 (60.0) 17 (41.5) Rhinitis allgery (%) 42 (64.6) 19 (46.3) 0.073 chronic (%) 17 (26.2) 15 (36.6) 0.283 sinusitis (%) 10 (15.4) 10 (24.4) 0.310 other (%) 1 (1.5) 1 (2.4) 1.000 Treatment medicine (%) 33 (50.8) 18 (43.9) 0.552 physical (%) 11 (16.9) 7 (17.1) 1.000 TCM (%) 6 (9.2) 2 (4.9) 0.481 operation (%) 6 (9.2) 7 (17.1) 0.242 none (%) 21 (32.3) 10 (24.4) 0.511 other (%) 0 (0.0) 3 (7.3) 0.055 Platform Tiktok (%) 50 (76.9) 35 (85.4) 0.328 Bilibili (%) 14 (21.5) 9 (22.0) 1.000 Wechat (%) 24 (36.9) 17 (41.5) 0.685 Rednote (%) 43 (66.2) 28 (68.3) 1.000 Youtube (%) 16 (24.6) 7 (17.1) 0.470 Usage time (%) 5h 3 (4.6) 4 (9.8) Frequency (%) < 1/Mo 13 (20.0) 9 (22.0) 0.201 1–2/Mo 12 (18.5) 12 (29.3) 1–2/w 30 (46.2) 11 (26.8) 3–5/w 10 (15.4) 8 (19.5) Everyday 0 (0.0) 1 (2.4) Content Etiology (%) 37 (56.9) 15 (36.6) 0.048 Treatment (%) 32 (49.2) 25 (61.0) 0.317 Care (%) 37 (56.9) 16 (39.0) 0.110 Symptom (%) 31 (47.7) 10 (24.4) 0.024 Experence (%) 25 (38.5) 5 (12.2) 0.004 Other (%) 2 (3.1) 4 (9.8) 0.203 Reliability (%) Totally unbelievable 0 (0.0) 1 (2.4) 0.304 Unbelievable 11 (16.9) 8 (19.5) Average 31 (47.7) 24 (58.5) Reliable 21 (32.3) 8 (19.5) Total reliable 2 (3.1) 0 (0.0) Comprehension (%) Totally unaware 0 (0.0) 1 (2.4) 0.567 Unaware 17 (26.2) 15 (36.6) Average 19 (29.2) 10 (24.4) Aware 27 (41.5) 14 (34.1) Total aware 2 (3.1) 1 (2.4) Utility (%) Totally useless 0 (0.0) 1 (2.4) 0.280 Useless 15 (23.1) 8 (19.5) Average 22 (33.8) 20 (48.8) Useful 26 (40.0) 12 (29.3) Totally useful 2 (3.1) 0 (0.0) Spearman correlation analysis confirmed a positive association between educational level and degree of decision influence, whereas age, disease duration, time spent viewing videos, and perceived content reliability scores showed no significant correlations ( |r| 0.05) (Fig. 7 ). Univariate logistic regression identified male sex and specific content types as potential predictors of decision influence; however, only educational level and content type achieved statistical significance (Table 5 ). Videos focusing on etiology (OR = 2.29, 95% CI: 1.04–5.20, p = 0.043), symptoms (OR = 2.83, 95% CI: 1.22–6.94, p = 0.018), and patient experience sharing (OR = 4.50, 95% CI: 1.67–14.44, p = 0.005) were significantly associated with behavioral impact. Table 5 Univariate logistic regression. Variables OR (95%CI) P value Score 0.87 (0.72–1.04) 0.121 Sex Female 1 [Reference] Male 0.65 (0.27–1.56) 0.328 Age > 55 1 [Reference] 18–25 1.35 (0.29–6.51) 0.701 26–35 3.64 (0.82–17.16) 0.088 36–45 1.14 (0.23–5.76) 0.873 46–55 6.25 (0.64–148.43) 0.154 Education Undergraduate or above 1 [Reference] Middle school 0.00 (NA - Inf) 0.992 High school 0.15 (0.02–0.66) 0.023 Vocational Undergraduate 1.72 (0.68–4.67) 0.261 Occupation Company staff 1 [Reference] Freelancer 1.01 (0.31–3.30) 0.990 Others 0.18 (0.01–1.64) 0.162 Public Institution staff/Civil Servant 1.96 (0.57–7.07) 0.291 Retirement 0.53 (0.08–3.47) 0.497 Student 0.31 (0.08–1.08) 0.071 Duration of rhinitis 10Y 0.45 (0.09–2.12) 0.318 Characteristics Perennial 1 [Reference] Seasonal 2.12 (0.96–4.75) 0.064 Rhinitis Allgery 2.11 (0.96–4.74) 0.066 Chronic 0.61 (0.26–1.43) 0.256 Sinusitis 0.56 (0.21–1.52) 0.252 Other 0.63 (0.02–16.10) 0.742 Treatment Medicine 1.32 (0.60–2.91) 0.491 Physical 0.99 (0.35–2.92) 0.984 TCM 1.98 (0.43–14.01) 0.416 Operation 0.49 (0.15–1.60) 0.237 None 1.48 (0.62–3.68) 0.384 Other 0.00 (NA - Inf) 0.990 Platform Tiktok 0.57 (0.19–1.56) 0.292 Bilibili 0.98 (0.38–2.59) 0.960 Wechat 0.83 (0.37–1.85) 0.640 Rednote 0.91 (0.39–2.08) 0.820 Youtube 1.59 (0.61–4.51) 0.361 Usage time 5h 0.30 (0.03–2.58) 0.288 Frequency < 1/Mo 1 [Reference] 1–2/Mo 0.69 (0.21–2.22) 0.537 1–2/w 1.89 (0.63–5.71) 0.255 3–5/w 0.87 (0.24–3.08) 0.822 Everyday 0.00 (NA - Inf) 0.991 Content Etiology 2.29 (1.04–5.20) 0.043 Treatment 0.62 (0.28–1.36) 0.239 Care 2.06 (0.94–4.65) 0.075 Symptom 2.83 (1.22–6.94) 0.018 Experence 4.50 (1.67–14.44) 0.005 Other 0.29 (0.04–1.58) 0.169 Reliability Average 1 [Reference] Totally unbelievable Inf (0.00 - NA) 0.994 Unbelievable 1.06 (0.37–3.14) 0.908 Reliable 2.03 (0.79–5.61) 0.153 Totally reliable Inf (0.00 - NA) 0.992 Comprehension Average 1 [Reference] Totally unaware Inf (0.00 - NA) 0.991 Unaware 0.60 (0.21–1.66) 0.327 Aware 1.02 (0.37–2.76) 0.977 Total aware 1.05 (0.09–24.29) 0.968 Utility Average 1 [Reference] Totally useless 0.00 (NA - Inf) 0.994 Useless 1.70 (0.61–5.04) 0.320 Useful 1.97 (0.80–5.01) 0.146 Totally useful Inf (0.00 - NA) 0.992 In multivariable analysis adjusting for potential confounders (Table 6 ), educational level, symptom-focused content, and patient experience sharing remained independent predictors of video-influenced healthcare decisions, with patient experience content demonstrating the strongest effect size. Some subgroup estimates were unstable due to small sample sizes. Collectively, these findings indicate that algorithmic visibility and user engagement metrics are weak proxies for informational rigor, and that experiential narratives disproportionately influence patient decision-making despite lower content quality. Table 6 Multivariate Logistic Regression Variables OR (95%CI) P value Education Undergraduate or above 1 [Reference] Middle school 0.00 (NA - Inf) 0.991 High school 0.06 (0.00–0.37) 0.008 Vocational Undergraduate 1.63 (0.58–4.78) 0.360 Content Symptom 2.80 (1.08–7.76) 0.039 Experence 8.89 (2.34–58.61) 0.005 Discussion This study systematically evaluated within algorithmically curated short-video health environments, information transparency, and real-world impact on patients’ medical decision-making of rhinitis-related short videos across multiple platforms, yielding three key findings. Although content produced by professional creators demonstrates higher quality, the algorithm-driven platform environment can still amplify low-quality or misleading health information. User engagement metrics—such as likes and view counts—do not reliably reflect the scientific accuracy of the information. Exposure to short videos—particularly those featuring patient experience sharing and symptom-focused content—is independently associated with changes in patients’ medical decisions. Algorithmic environments and health misinformation prevalence Short-form video platforms have become major conduits for public health information, yet low barriers to content creation and recommendation systems that optimize for engagement produce environments conducive to misinformation dissemination.[ 24 , 25 ] Established frameworks of health misinformation dissemination highlight the interplay among information quality, user characteristics, and external (algorithmic) environments as determinants of users’ ability to identify credible health content. These mechanisms suggest platforms do not merely reflect user interests but actively shape exposure and perceived relevance, thereby influencing health knowledge and behaviors. Recent systematic evidence points to the problem’s scope and urgency: health misinformation prevalence on social media remains high across platforms and topic areas, with emotionally resonant and narrative-driven content particularly likely to spread widely despite poor quality.[ 26 , 27 ] For example, quality evaluations of short health education videos on YouTube and TikTok showed overall low adherence to informational standards, with minimal correlation between engagement metrics and content reliability. These findings align with our current results that engagement metrics are poor proxies for informational accuracy, underscoring structural shortcomings in current platform recommendation systems.[ 28 ] Narrative and visual authority in short videos The design of short videos themselves may exacerbate the risk of misinformation uptake. Users are exposed to multimodal content where visual authority cues and personal narratives can convey credibility independently of factual accuracy. The persuasive combination of confident presenters, certificates, narrative framing, and emotive cues is particularly problematic because it may override analytic processing, leading individuals to accept incorrect health claims as legitimate.[ 29 ] This is consistent with cognitive models showing that users rely variably on central (analytical) and peripheral (heuristic) cues when discerning misinformation in short videos, influenced by content structure and narrative complexity.[ 9 , 30 ] This helps explain why lower-quality patient experience videos in our dataset exerted stronger behavioral influence. Health literacy, perception, and decision-making Public perceptions of the social media information environment reflect concerns about misinformation prevalence and discernment difficulty. Survey data indicate that a large proportion of social media users perceive health misinformation as widespread and find it challenging to distinguish true from false information, with these perceptions linked to both health communication behaviors and health decision-making.[ 5 , 31 ] In our study, the observed association between video exposure and self-reported healthcare decisions echoes this pattern, reinforcing the notion that perceived credibility and ease of discernment critically influence health behavior. Furthermore, experimental evidence suggests that targeted educational interventions—such as educational video modules—can significantly improve individuals’ abilities to identify misinformation in messaging environments.[ 25 , 32 ] While short-form video platforms differ in format, the principle that tailored, media-specific literacy interventions can enhance critical appraisal skills remains relevant for public health efforts[ 33 ]. Our finding that higher educational attainment was associated with greater susceptibility to video-influenced decisions may reflect increased exposure rather than superior discernment. Implications for governance and intervention Given the dynamic nature of misinformation dissemination on short-form platforms, multilevel strategies are essential, including algorithmic re-ranking of evidence-based content, platform-embedded credibility labeling, and clinician-led content production strategies. Systematic reviews of health misinformation countermeasures emphasize the importance of platform accountability, policy support, and individual-level literacy enhancement strategies to combat misinformation effectively.[ 34 , 35 ] In particular, governance approaches that integrate structural credibility signals, platform-level moderation, and algorithmic weighting of evidence-based sources may help align content visibility with informational quality. At the clinical level, providers should recognize the influence of short-form video exposure on patient expectations and decisions, incorporating discussions about digital health information sources into routine care. Educational and policy interventions should also address narrative-focused persuasion, preparing patients to evaluate both content accuracy and the persuasive elements that drive misinformation spread. Strengths and limitations This study’s key strengths include its multi-platform design, the use of multiple validated quality assessment measures, and the linkage of content evaluation with behavioral outcomes. However, limitations include its cross-sectional design, which restricts causal inference; reliance on top-ranked video samples, potentially limiting representativeness; and the focus on Chinese-language platforms, which may affect direct generalizability to Western social media ecosystems. Nonetheless, the underlying mechanisms of algorithmic influence and narrative-driven misinformation dissemination are broadly consistent with global evidence. Conclusion Short-form video platforms operate as algorithmically shaped health information ecosystems in which popularity is a poor indicator of quality and personal narratives can exert disproportionate influence on health behavior. Addressing misinformation requires coordinated strategies encompassing platform governance reform, clinician engagement, and evidence-based health literacy interventions. Enhanced understanding of the structural and cognitive mechanisms underlying misinformation dissemination will be critical to promoting safer, more informed digital health environments. Declarations Acknowledgements The authors would like to express their gratitude to the video uploaders for their contributions to public health. Authors’ contributions Haiyu Hong and Wenxuan Zhou conceived and designed the study; Shuwen Tang and Huiting Chen collected the data; Yufeng Chao and Baoyi Chen reviewed and scored the videos; Rexidanmu Hudabai analyzed the data; Xianzhen Chen wrote the original draft; Xianzhen Chen and Rexidanmu Hudabai reviewed and edited the manuscript. Generative AI statement The authors declare that no Generative AI tools were used in the development or editing of this article. Funding This study was supported by the Natural Science Foundation of Guangdong Province, China (grant No. 2025A1515012592) Availability of data and materials The data sets generated during and/or analyzed during this study are available from the corresponding author on reasonable request. Ethics approval and consent to participate This study did not involve human biological specimens, animal subjects, or identifiable clinical datasets. All analyzed data were publicly available at the time of collection from TikTok and Bilibili, and did not involve private user information or require active user interaction. The study adhered to the data usage policies of the respective platforms. Specifically: For TikTok , we followed ByteDance’s Community Guidelines and Content Policy, ensuring that only publicly shared videos were accessed via public browsing. No API-based scraping or automated data collection methods were used. For Bilibili , we complied with the platform's User Agreement and Public Content Access Rules, collecting data solely through manual viewing and recording of publicly available content. No special permissions or institutional approvals from the platforms were required, as the data collection method involved only passive observation and analysis of openly accessible content, consistent with standard practices for academic research in digital content analysis.The study was reviewed by the Ethics Committee of the Fifth Affiliated Hospital of Sun Yat-sen University, and the ethical approval number (Approval No. 2026-K34-1 ) pertains specifically to the survey section of this research. For the survey component of this study, all participants provided written informed consent prior to participation. The study was reviewed by the Ethics Committee of the Fifth Affiliated Hospital of Sun Yat-sen University, which confirmed that ethical approval was not required due to the use of publicly available data and absence of sensitive or personal information ( Supplementary File 6 ). The research adhered to the principles of the Declaration of Helsinki and all applicable national regulations regarding research ethics. Consent for publication Not applicable. Competing interests The authors declare no competing interests Author details 1 Department of Otolaryngology, Head and Neck Surgery, the Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China； 2 Department of Infectious Diseases, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China； 3 Department of anesthesiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, Guangdong Province, China； 4 Allergy Center, the Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China； 5 Sleep Center, the Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China； 6 Health Promotion Center of Zhuhai , Zhuhai, Guangdong Province, China. # These authors contributed equally to this work. References J.A. Bernstein, J.S. Bernstein, R. Makol, S. Ward, Allergic Rhinitis: A Review, JAMA 331 (2024) 866–877. https://doi.org/10.1001/jama.2024.0530. B. Sousa-Pinto, J. Bousquet, R.J. Vieira, etc, Allergic Rhinitis and Its Impact on Asthma (ARIA)-EAACI Guidelines-2024-2025 Revision: Part I-Guidelines on Intranasal Treatments, Allergy (2025). https://doi.org/10.1111/all.70131. I. Baiardini, S. Novakova, S. Mihaicuta, I.K. Oguzulgen, G.W. Canonica, Adherence to treatment in allergic respiratory diseases, Expert Rev. Respir. Med. 13 (2019) 53–62. https://doi.org/10.1080/17476348.2019.1554438. G.C. Steven, Shared decision making in allergic rhinitis: An approach to the patient, Ann. Allergy Asthma Immunol. Off. Publ. Am. Coll. Allergy Asthma Immunol. 125 (2020) 268–272. https://doi.org/10.1016/j.anai.2020.06.032. C.L. Ventola, Social media and health care professionals: benefits, risks, and best practices, P T Peer-Rev. J. Formul. Manag. 39 (2014) 491–520. S.S.-L. Tan, N. Goonawardene, Internet Health Information Seeking and the Patient-Physician Relationship: A Systematic Review, J. Med. Internet Res. 19 (2017) e9. https://doi.org/10.2196/jmir.5729. W. Osman, F. Mohamed, M. Elhassan, A. Shoufan, Is YouTube a reliable source of health-related information? A systematic review, BMC Med. Educ. 22 (2022) 382. https://doi.org/10.1186/s12909-022-03446-z. L. Nguyen, J. Walters, S. Paul, S. Monreal Ijurco, G.E. Rainey, N. Parekh, G. Blair, M. Darrah, Feeds, feelings, and focus: A systematic review and meta-analysis examining the cognitive and mental health correlates of short-form video use, Psychol. Bull. 151 (2025) 1125–1146. https://doi.org/10.1037/bul0000498. W.-Y.S. Chou, A. Oh, W.M.P. Klein, Addressing Health-Related Misinformation on Social Media, JAMA 320 (2018) 2417–2418. https://doi.org/10.1001/jama.2018.16865. I.J. Borges do Nascimento, A.B. Pizarro, J.M. Almeida, N. Azzopardi-Muscat, M.A. Gonçalves, M. Björklund, D. Novillo-Ortiz, Infodemics and health misinformation: a systematic review of reviews, Bull. World Health Organ. 100 (2022) 544–561. https://doi.org/10.2471/BLT.21.287654. V. Suarez-Lledo, J. Alvarez-Galvez, Prevalence of Health Misinformation on Social Media: Systematic Review, J. Med. Internet Res. 23 (2021) e17187. https://doi.org/10.2196/17187. R.J. Limaye, T.A. Holroyd, M. Blunt, A.F. Jamison, M. Sauer, R. Weeks, B. Wahl, K. Christenson, C. Smith, J. Minchin, B. Gellin, Social media strategies to affect vaccine acceptance: a systematic literature review, Expert Rev. Vaccines 20 (2021) 959–973. https://doi.org/10.1080/14760584.2021.1949292. M. Cinelli, G. De Francisci Morales, A. Galeazzi, W. Quattrociocchi, M. Starnini, The echo chamber effect on social media, Proc. Natl. Acad. Sci. U. S. A. 118 (2021) e2023301118. https://doi.org/10.1073/pnas.2023301118. D. Kbaier, A. Kane, M. McJury, I. Kenny, Prevalence of Health Misinformation on Social Media-Challenges and Mitigation Before, During, and Beyond the COVID-19 Pandemic: Scoping Literature Review, J. Med. Internet Res. 26 (2024) e38786. https://doi.org/10.2196/38786. C.L.-N. Remvig, C.S. Diers, H. Meteran, S.F. Thomsen, T. Sigsgaard, S. Høj, H. Meteran, YouTube as a source of (mis)information on allergic rhinitis, Ann. Allergy Asthma Immunol. Off. Publ. Am. Coll. Allergy Asthma Immunol. 129 (2022) 612–617. https://doi.org/10.1016/j.anai.2022.06.031. S. Kumar, A. Dutta, S. Vallur, R. Singh, A Content Analysis of YouTube Videos on Allergic Rhinitis Management, Indian J. Otolaryngol. Head Neck Surg. Off. Publ. Assoc. Otolaryngol. India 77 (2025) 1760–1767. https://doi.org/10.1007/s12070-025-05396-6. L. Wang, Y. Chen, D. Zhao, T. Xu, F. Hua, Quality and Dissemination of Uterine Fibroid Health Information on TikTok and Bilibili: Cross-Sectional Study, JMIR Form. Res. 9 (2025) e75120. https://doi.org/10.2196/75120. A. Lahooti, A. Hassan, B. Critelli, D. Westerveld, C. Newberry, S. Kumar, R.Z. Sharaiha, Quality and Popularity Trends of Weight Loss Procedure Videos on TikTok, Obes. Surg. 33 (2023) 714–719. https://doi.org/10.1007/s11695-022-06409-x. A. Bernard, M. Langille, S. Hughes, C. Rose, D. Leddin, S. Veldhuyzen van Zanten, A systematic review of patient inflammatory bowel disease information resources on the World Wide Web, Am. J. Gastroenterol. 102 (2007) 2070–2077. https://doi.org/10.1111/j.1572-0241.2007.01325.x. D. Charnock, S. Shepperd, G. Needham, R. Gann, DISCERN: an instrument for judging the quality of written consumer health information on treatment choices, J. Epidemiol. Community Health 53 (1999) 105–111. https://doi.org/10.1136/jech.53.2.105. S.J. Shoemaker, M.S. Wolf, C. Brach, Development of the Patient Education Materials Assessment Tool (PEMAT): a new measure of understandability and actionability for print and audiovisual patient information, Patient Educ. Couns. 96 (2014) 395–403. https://doi.org/10.1016/j.pec.2014.05.027. Z. Liu, Y. Chen, Y. Lin, M. Ai, D. Lian, Y. Zhang, Y. Shen, F. Yu, YouTube/ Bilibili/ TikTok videos as sources of medical information on laryngeal carcinoma: cross-sectional content analysis study, BMC Public Health 24 (2024) 1594. https://doi.org/10.1186/s12889-024-19077-6. T. Ni, Y. Jiang, Y. Liu, J. Zhou, H. Xie, S. Yang, J. Ruan, D. Guo, Z. Lin, Z. Han, Quality evaluation of information about sudden sensorineural hearing loss on TikTok videos: Cross-sectional study, Digit. Health 11 (2025) 20552076251406648. https://doi.org/10.1177/20552076251406648. R. Wang, H. Yang, Y. Wang, X. Zhai, Understanding how users identify health misinformation in short videos: an integrated analysis using PLS-SEM and fsQCA, Front. Public Health 13 (2025) 1713794. https://doi.org/10.3389/fpubh.2025.1713794. E. Alsaad, S. AlDossary, Educational Video Intervention to Improve Health Misinformation Identification on WhatsApp Among Saudi Arabian Population: Pre-Post Intervention Study, JMIR Form. Res. 8 (2024) e50211. https://doi.org/10.2196/50211. A. Gaysynsky, N. Senft Everson, K. Heley, W.-Y.S. Chou, Perceptions of Health Misinformation on Social Media: Cross-Sectional Survey Study, JMIR Infodemiology 4 (2024) e51127. https://doi.org/10.2196/51127. Y. Hu, Y. Yang, N. Chen, Z. Pang, X. Zhong, J. Sun, How to select high-quality health educational short videos on social media? insights from youtube and douyin, BMC Med. Educ. 25 (2025) 1418. https://doi.org/10.1186/s12909-025-08018-5. R. Dimitroyannis, D. Fenton, S. Cho, R. Nordgren, J.M. Pinto, C.R. Roxbury, A Social Media Quality Review of Popular Sinusitis Videos on TikTok, Otolaryngol.--Head Neck Surg. Off. J. Am. Acad. Otolaryngol.-Head Neck Surg. 170 (2024) 1456–1466. https://doi.org/10.1002/ohn.688. S. N, Y. P, Y. S, Exploring the Relationship Between Social Media Use and Symptoms of Depression and Anxiety Among Children and Adolescents: A Systematic Narrative Review, Cyberpsychology Behav. Soc. Netw. 27 (2024). https://doi.org/10.1089/cyber.2023.0456. K. Bryanov, V. Vziatysheva, Determinants of individuals’ belief in fake news: A scoping review determinants of belief in fake news, PloS One 16 (2021) e0253717. https://doi.org/10.1371/journal.pone.0253717. C. J, W. Y, Social Media Use for Health Purposes: Systematic Review, J. Med. Internet Res. 23 (2021). https://doi.org/10.2196/17917. L. Jiang, W. Wu, X. Jiang, F. Jiang, Evaluating the quality of educational TikTok videos on diabetic retinopathy: a cross-sectional study, Front. Public Health 13 (2025) 1718587. https://doi.org/10.3389/fpubh.2025.1718587. A.L. Beam, I.S. Kohane, Big Data and Machine Learning in Health Care, JAMA 319 (2018) 1317–1318. https://doi.org/10.1001/jama.2017.18391. Y. Wang, M. McKee, A. Torbica, D. Stuckler, Systematic Literature Review on the Spread of Health-related Misinformation on Social Media, Soc. Sci. Med. 240 (2019) 112552. https://doi.org/10.1016/j.socscimed.2019.112552. L. Keikha, A. Shahraki-Mohammadi, A. Nabiolahi, Strategies and prerequisites for combating health misinformation on social media: a systematic review, BMC Public Health 26 (2025) 139. https://doi.org/10.1186/s12889-025-25858-4. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile1.docx supplementaryfile2.docx supplementaryfile3.xls Supplementaryfile4.docx supplemantaryfile5.docx supplemantaryfile6.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 02 Apr, 2026 Editor assigned by journal 31 Mar, 2026 Editor invited by journal 10 Mar, 2026 Submission checks completed at journal 09 Mar, 2026 First submitted to journal 09 Mar, 2026 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8997024","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617953974,"identity":"621bfcff-5442-48de-97ca-ebb0c8a5aa2f","order_by":0,"name":"Xianzhen Chen","email":"","orcid":"","institution":"The Fifth Affiliated Hospital of Sun Yat-Sen University","correspondingAuthor":false,"prefix":"","firstName":"Xianzhen","middleName":"","lastName":"Chen","suffix":""},{"id":617953975,"identity":"214187e7-b4d4-439d-961b-e949208f05c3","order_by":1,"name":"Rexidanmu Hudabai","email":"","orcid":"","institution":"The Fifth Affiliated Hospital 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17:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8997024/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8997024/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106532914,"identity":"1d0a44e7-7185-4045-93a0-6c00d062845c","added_by":"auto","created_at":"2026-04-09 14:56:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":256292,"visible":true,"origin":"","legend":"\u003cp\u003eVideo Data Collection, Screening, and Analysis Flowchart.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8997024/v1/0ad3d3a32594edc6428473f0.png"},{"id":106532901,"identity":"778031b1-b672-453b-9012-11b060c6fa1e","added_by":"auto","created_at":"2026-04-09 14:55:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":574567,"visible":true,"origin":"","legend":"\u003cp\u003eContent analysis of videos on Bilibili and TikTok.\u003c/p\u003e\n\u003cp\u003e(A) Distribution of video content types on Bilibili (disease awareness, treatment options, patient experience sharing).\u003cstrong\u003e(B)\u003c/strong\u003e Distribution of video content types on TikTok (disease awareness, treatment options, patient experience sharing).\u003cstrong\u003e(C)\u003c/strong\u003eDecision-making contexts addressed in Bilibili videos (symptom self-assessment, treatment selection, daily care, myth-busting, special populations).\u003cstrong\u003e(D)\u003c/strong\u003e Decision-making contexts addressed in TikTok videos (symptom self-assessment, treatment selection, daily care, myth-busting, special populations).\u003cstrong\u003e(E)\u003c/strong\u003eContent types in videos created by professional uploaders (disease awareness, treatment options, patient experience sharing).\u003cstrong\u003e(F)\u003c/strong\u003e Content types in videos created by non-professional uploaders (disease awareness, treatment options, patient experience sharing).\u003cstrong\u003e(G)\u003c/strong\u003e Decision-making contexts in videos by professional uploaders (symptom self-assessment, treatment selection, daily care, myth-busting, special populations).\u003cstrong\u003e(H)\u003c/strong\u003eDecision-making contexts in videos by non-professional uploaders (symptom self-assessment, treatment selection, daily care, myth-busting, special populations).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8997024/v1/f3762573db9258c4bada7ef6.png"},{"id":106532929,"identity":"68f9c44e-738a-416c-b022-442ed1f68f3e","added_by":"auto","created_at":"2026-04-09 14:56:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":288905,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of understandability (PEMAT-U) and actionability (PEMAT-A) of rhinitis-related videos.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Patient Education Materials Assessment Tool (PEMAT)—understandability scores for videos on TikTok and Bilibili.\u003cstrong\u003e(B)\u003c/strong\u003e PEMAT—actionability scores for videos on TikTok and Bilibili.\u003cstrong\u003e(C)\u003c/strong\u003ePEMAT—understandability scores by video content type.\u003cstrong\u003e(D)\u003c/strong\u003e PEMAT—actionability scores by video content type.\u003cstrong\u003e(E)\u003c/strong\u003e PEMAT—understandability scores by decision-making context.\u003cstrong\u003e(F)\u003c/strong\u003ePEMAT—actionability scores by decision-making context. PEMAT-U: Patient Education Materials Assessment Tool – UnderstandabilityPEMAT-A: Patient Education Materials Assessment Tool – Actionability.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8997024/v1/d134f76959eb6d37c5128d38.png"},{"id":106532915,"identity":"cea5976b-00e2-4e20-8eed-b82559673d9d","added_by":"auto","created_at":"2026-04-09 14:56:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":210660,"visible":true,"origin":"","legend":"\u003cp\u003eGQS and mDISCERN scores of rhinitis-related videos on BiliBili and TikTok.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e GQS and mDISCERN scores by uploader type (professional vs. non-professional) on BiliBili.\u003cstrong\u003e(B)\u003c/strong\u003e GQS and mDISCERN scores by uploader type (professional vs. non-professional) on TikTok.\u003cstrong\u003e(C)\u003c/strong\u003e GQS and mDISCERN scores by content category (disease awareness, treatment options, patient experience) on BiliBili.\u003cstrong\u003e(D)\u003c/strong\u003e GQS and mDISCERN scores by content category (disease awareness, treatment options, patient experience) on TikTok.\u003cstrong\u003e(E)\u003c/strong\u003e GQS and mDISCERN scores by decision-making context (symptom self-assessment, treatment selection, daily care, myth-busting, special populations) on BiliBili.\u003cstrong\u003e(F)\u003c/strong\u003e GQS and mDISCERN scores by decision-making context (symptom self-assessment, treatment selection, daily care, myth-busting, special populations) on TikTok.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8997024/v1/fb432d0e3365fd09b3bb659c.png"},{"id":106532890,"identity":"0015678d-0190-4d8d-947b-6d07bc046476","added_by":"auto","created_at":"2026-04-09 14:55:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":99716,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of JAMA scores across different platforms and uploaders.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8997024/v1/90da83b1f68b1acb0d1e3816.png"},{"id":106532912,"identity":"e14d9b14-71a1-4dbb-b6df-8ebeed792b14","added_by":"auto","created_at":"2026-04-09 14:56:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":540419,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman correlation analysis between various video characteristics and GQS and mDISCERN scores.\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003e indicates p \u0026lt; 0.05; **\u003c/em\u003e indicates \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01; *** indicates \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; **** indicates \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8997024/v1/ee79c70c3edd754c495c76e7.png"},{"id":106533322,"identity":"0c808dc7-dbf9-4dc2-96cd-39d14883ddd7","added_by":"auto","created_at":"2026-04-09 14:57:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":88660,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Analysis Between Social Media Usage Habits and Medical Decision-Making\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8997024/v1/7d3aacbcbeca69f54a4d17c0.png"},{"id":106959701,"identity":"cdf7d248-4beb-47bd-a82b-82be9577429c","added_by":"auto","created_at":"2026-04-15 09:13:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3879148,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8997024/v1/d518f5d1-d752-4a1a-8fde-7342a3e64f7d.pdf"},{"id":106533081,"identity":"9dbc3e9e-1b5b-4578-a8ae-affb9f3c9c13","added_by":"auto","created_at":"2026-04-09 14:56:23","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15509,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8997024/v1/b73ccdb7760007e625fdd60a.docx"},{"id":106724809,"identity":"5bf6ae5a-1664-452e-8add-5db2dbc20451","added_by":"auto","created_at":"2026-04-12 18:29:53","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21849,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8997024/v1/4ef4c2fef6c9631e596b5c6a.docx"},{"id":106533315,"identity":"22ba95d8-0b90-4eb5-b57b-07ea9eb6417b","added_by":"auto","created_at":"2026-04-09 14:57:03","extension":"xls","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":209920,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfile3.xls","url":"https://assets-eu.researchsquare.com/files/rs-8997024/v1/699733e61827209db3a09e3b.xls"},{"id":106532911,"identity":"d7a288a9-569d-4574-8638-3ffba7b9c3fe","added_by":"auto","created_at":"2026-04-09 14:56:00","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":20074,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile4.docx","url":"https://assets-eu.researchsquare.com/files/rs-8997024/v1/d87dd482ce98401445640a9c.docx"},{"id":106533149,"identity":"0799b041-8b37-4ce2-a09f-3ca2bd398814","added_by":"auto","created_at":"2026-04-09 14:56:30","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":29998,"visible":true,"origin":"","legend":"","description":"","filename":"supplemantaryfile5.docx","url":"https://assets-eu.researchsquare.com/files/rs-8997024/v1/970c7778621a514ad9adec8b.docx"},{"id":106533075,"identity":"547a3a68-13c0-4f88-afb8-83ab7054b97c","added_by":"auto","created_at":"2026-04-09 14:56:22","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":468187,"visible":true,"origin":"","legend":"","description":"","filename":"supplemantaryfile6.docx","url":"https://assets-eu.researchsquare.com/files/rs-8997024/v1/62bf7d8e63912c387e6f2357.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Algorithmic Short-Video Platforms and Health Decision-Making in Rhinitis: A Multi-Platform Digital Health Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRhinitis is a highly prevalent chronic inflammatory airway condition that imposes a substantial burden on quality of life and healthcare systems worldwide.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] Despite the availability of well-established, evidence-based management strategies\u0026mdash;including those outlined in international clinical guidelines,[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] treatment adherence and appropriate healthcare-seeking behavior among patients with rhinitis remain suboptimal in real-world settings.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Increasingly, patients rely on digital information sources rather than traditional clinical encounters to guide symptom interpretation, treatment choices, and self-management decisions.[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eShort-form video based social media platforms, such as TikTok, YouTube Shorts, and Instagram Reels, have rapidly become dominant components of the contemporary digital health information ecosystem.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] These platforms operate as algorithmically curated health information environments rather than neutral repositories of content. These platforms employ algorithm-driven content delivery and immersive audiovisual formats that efficiently shape users\u0026rsquo; health perceptions and behavioral intentions. While short videos offer scalable opportunities for public health education, they also facilitate the rapid dissemination of low-quality, misleading, or commercially biased medical content, raising growing concerns regarding health misinformation in digital environments.[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAlgorithm-mediated exposure not only increases content reach but also reshapes risk perception, trust formation, and treatment heuristics, thereby exerting downstream effects on health decision-making.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] Algorithm-mediated exposure may further amplify this influence by reinforcing users\u0026rsquo; pre-existing beliefs and fostering echo chambers that normalize misinformation and distort risk perception.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] These mechanisms are particularly salient for chronic conditions requiring sustained self-management and long-term patient engagement.\u003c/p\u003e \u003cp\u003eFor rhinitis, a condition often perceived as mild or self-limiting, patients may be especially susceptible to short-video content that promotes non\u0026ndash;evidence-based interventions or discourages professional medical care. Repeated exposure to such content through short-video platforms may undermine guideline-concordant management and contribute to inappropriate health decisions. However, despite the expanding role of short-video platforms in digital health communication, rhinitis remains underrepresented in the misinformation and social media health literature.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eNo prior study has simultaneously integrated multi-platform content quality assessment with patient-reported behavioral outcomes in rhinitis within an algorithm-driven short-video environment.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Therefore, this study conducted a multi-platform cross-sectional analysis of short-form video content related to rhinitis, examining content characteristics, informational quality, and associations with patient healthcare decision-making. In addition, telephone-based follow-up among outpatient rhinitis patients was performed to assess how exposure to these videos influenced real-world treatment behaviors. This work situates rhinitis within the broader digital health misinformation ecosystem and informs platform governance and clinical digital engagement strategies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSearch Strategy and Data Collection\u003c/h2\u003e \u003cp\u003eThis cross-sectional study integrated content analysis of short-form social media videos with a questionnaire-based survey of patients with rhinitis to investigate the influence of digital video content on healthcare decision-making \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eShort videos were collected from two major Chinese platforms: TikTok (Douyin, Chinese version) and BiliBili. Using the Chinese keyword \u0026ldquo;鼻炎\u0026rdquo; (rhinitis), we retrieved the top 100 videos ranked by each platform\u0026rsquo;s default \u0026ldquo;comprehensive sorting\u0026rdquo; algorithm during a predefined sampling window. After applying eligibility criteria, 179 videos were retained for analysis. As algorithm-ranked content was sampled, findings primarily reflect high-visibility information environments rather than the full universe of rhinitis-related videos.\u003c/p\u003e \u003cp\u003eIn parallel, an online questionnaire survey was conducted using convenience sampling to recruit patients diagnosed with rhinitis. The survey captured participants\u0026rsquo; social media usage patterns, exposure to rhinitis-related short videos, and subsequent healthcare decisions. A total of 106 valid responses were obtained.\u003c/p\u003e \u003cp\u003eVideos were included if they met the following criteria: (1) primary focus on rhinitis (symptoms, etiology, treatment, or self-care); (2) publication at least 7 days prior to data collection to ensure stabilization of engagement metrics; and (3) sufficient audiovisual quality to permit reliable content evaluation. Exclusion criteria were: (1) irrelevant or minimally relevant content; (2) duplicated or near-duplicate uploads; (3) purely promotional or commercial advertisements; and (4) videos that were deleted or made private post-publication.\u003c/p\u003e \u003cp\u003eExtracted Data Elements\u003c/p\u003e \u003cp\u003eDuring screening, the following information was extracted from each eligible video:\u003c/p\u003e \u003cp\u003e(1) Basic characteristics: platform type, video Uniform Resource Locator (URL), publication date, duration (seconds), and engagement metrics (likes, comments, saves, shares, and views);\u003c/p\u003e \u003cp\u003e(2) Uploader characteristics: professional background (e.g., certified physician, healthcare institution, health influencer, non-medical organization, or general user), platform verification status, and follower count;\u003c/p\u003e \u003cp\u003e(3) Content characteristics: thematic category (disease awareness, treatment options, daily management, patient experience sharing), presentation format (monologue, case narrative, animated explainer), and clinical decision-making context (e.g., symptom self-assessment, treatment selection, myth clarification) (\u003cb\u003eSupplementary File 1\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVideo Quality Assessment\u003c/h3\u003e\n\u003cp\u003eTwo trained researchers conducted independent and objective evaluations of video quality using multidimensional standardized tools, including: 1) Global Quality Score (GQS)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]: A 1\u0026ndash;5 scale evaluating overall video quality across four dimensions\u0026mdash;information accuracy, structural clarity, practicality, and objectivity\u0026mdash;with 1 indicating poor and 5 indicating excellent; 2) Modified Information Dissemination Score (mDISCERN)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]: Comprising five core questions focusing on source credibility, evidence support, and potential bias, scored 1 point for \"yes\" and 0 points for \"no,\" with higher total scores indicating greater reliability; 3) Patient Education Material Assessment (PEMAT)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]: Divided into PEMAT-U (comprehensibility) and PEMAT-A (action orientation), with percentage scores calculated; 4) Video Information Quality Index (VIQI)[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]: Rated across five dimensions\u0026mdash;information completeness, accuracy, timeliness, and others. 5) JAMA Score[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]: A standardized tool for evaluating online health information quality, focusing on accuracy, completeness, timeliness, objectivity, and readability. Prior to formal scoring, both raters independently evaluated 20 pilot videos and achieved an intraclass correlation coefficient (ICC)\u0026thinsp;\u0026ge;\u0026thinsp;0.80, indicating excellent inter-rater reliability. All evaluations were based on complete audiovisual and textual content, without reference to platform-generated recommendation tags or user comments (\u003cb\u003eSupplementary Files 2\u0026ndash;3\u003c/b\u003e).\u003c/p\u003e\n\u003ch3\u003eQuestionnaire Survey\u003c/h3\u003e\n\u003cp\u003eA structured questionnaire was developed to assess patients\u0026rsquo; demographic characteristics, rhinitis-related clinical features, social media usage behaviors, and the perceived influence of short videos on healthcare decision-making (\u003cb\u003eSupplementary File 4\u003c/b\u003e). The survey was administered between January and February 2026 via online dissemination and patient community recruitment. Logical branching was applied such that respondents who reported no exposure to rhinitis-related videos were redirected to the end of the survey. Quality control measures included IP address restrictions, duplicate-response screening, and manual review. Questionnaires with patterned responses or \u0026gt;\u0026thinsp;10% missing data were excluded.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using IBM SPSS Statistics version 27.0. Continuous variables, which mostly followed non-normal distributions, were summarized as medians with interquartile ranges [M (IQR)] and compared using the Mann\u0026ndash;Whitney U test (for two groups) or Kruskal\u0026ndash;Wallis H test (for \u0026ge;\u0026thinsp;3 groups), with post hoc pairwise comparisons adjusted via Dunn\u0026rsquo;s test and Bonferroni correction. Categorical variables were presented as frequencies and percentages [n (%)], with group differences assessed by chi-square (χ\u0026sup2;) tests. Spearman\u0026rsquo;s rank correlation was used to examine associations between video engagement metrics and quality scores. Multivariable logistic regression identified factors associated with changes in patients\u0026rsquo; healthcare decisions attributable to short video exposure. All tests were two-tailed, with statistical significance defined as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 after adjustment for multiple comparisons using the Benjamini\u0026ndash;Hochberg procedure to control the false discovery rate (FDR).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eBasic Characteristics of Videos\u003c/h2\u003e\n \u003cp\u003eA total of 179 videos were ultimately included in the analysis, sourced from BiliBili (n\u0026thinsp;=\u0026thinsp;91) and TikTok (n\u0026thinsp;=\u0026thinsp;88). Videos from the two platforms exhibited significant differences across multiple characteristics (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The median duration of BiliBili videos (188.5 seconds) was significantly longer than that of TikTok videos (78.5 seconds; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, the median time since upload was substantially greater for BiliBili videos (460.0 days) compared with TikTok videos (66.0 days; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting a longer content lifecycle on BiliBili, whereas TikTok demonstrates a faster content turnover rate. In terms of user engagement metrics, TikTok videos showed significantly higher median counts for likes, comments, shares, favorites, and views than Bilibili videos (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all), indicating that TikTok is more effective at fostering user interaction and amplifying content reach.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of characteristics between different short-video platforms.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eBilibili (N\u0026thinsp;=\u0026thinsp;91, median (IQR))\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eTiktok (N\u0026thinsp;=\u0026thinsp;88, median (IQR))\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eComments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e1.0 (0.0\u0026ndash;27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e860.5 (133.8\u0026ndash;3521.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e854.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDays Since Release\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e460.0 (132.0\u0026ndash;1083.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e66.0 (19.8\u0026ndash;127.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6519.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFavorites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e108.0 (7.5\u0026ndash;1154.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5968.0 (315.2\u0026ndash;28145.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1693.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVideo duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e188.5 (83.8\u0026ndash;470.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e78.5 (50.0\u0026ndash;116.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e5948.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLikes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e102.0 (10.0\u0026ndash;903.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e14868.0 (938.8\u0026ndash;60943.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1284.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eShares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e41.0 (3.0\u0026ndash;250.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5751.5 (183.5\u0026ndash;36261.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1397.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCoins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e15.0 (0.0\u0026ndash;107.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eViews\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e4559.0 (871.0\u0026ndash;56000.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eUploader Characteristics\u003c/h3\u003e\n\u003cp\u003eUploaders were categorized as professional (n\u0026thinsp;=\u0026thinsp;100) or non-professional (n\u0026thinsp;=\u0026thinsp;79) based on occupational background and platform verification status (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e. Among professional uploaders, 87.0% were physicians and 95.0% were officially verified. In contrast, non-professional uploaders were predominantly independent content creators (97.5%), with only 12.7% holding platform verification. Professional uploaders demonstrated substantially greater influence, with a median follower count of 82,000 compared with 6,021 among non-professionals (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, professional accounts had significantly higher cumulative likes (median 278,500; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of characteristics between different uploaders.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eProfessional uploader (N\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eUnprofessional uploader (N\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eW/\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e82000.0 (3963.0\u0026ndash;534250.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6021.0 (1667.5\u0026ndash;56500.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2642.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotal likes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e278500.0 (26750.0\u0026ndash;7357250.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e45000.0 (6125.0\u0026ndash;337500.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2952.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eUploader\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e164.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSelf-media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e77 (97.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOfficial Media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e11 (11.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDoctor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e87 (87.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eVerification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e120.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e95 (95.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e10 (12.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e5 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e69 (87.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eVideo Content Categorization\u003c/h3\u003e\n\u003cp\u003eVideos were classified into three thematic categories: disease awareness, treatment options, and patient experience sharing (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Across both platforms, disease awareness and treatment-related videos together accounted for over 70% of content. Treatment-focused videos constituted a larger proportion on TikTok (51.0%) than on BiliBili (37.4%) (Figs. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\n\u003cp\u003eRegarding clinical decision-making contexts, treatment selection was the most prevalent scenario on both platforms (47.3% on BiliBili and 50.0% on TikTok). TikTok featured a substantially higher proportion of myth-busting content (19.3%) compared with BiliBili (5.5%), whereas BiliBili included more content addressing special populations (13.2% vs. 6.8%) (Figs. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\n\u003cp\u003eFrom the uploader perspective, professional creators primarily focused on disease awareness and treatment options, whereas non-professional creators more frequently shared personal patient experiences (Figs. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Although treatment selection was the dominant decision-making context for both groups (49.0% among professionals vs. 48.1% among non-professionals), non-professional creators rarely produced myth-busting content and instead emphasized experience-based themes such as daily care (Figs. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eVideo Quality Assessment\u003c/h2\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003ePatient Education Materials Assessment Tool (PEMAT)\u003c/h2\u003e\n \u003cp\u003eAt the platform level, BiliBili videos exhibited a narrower and overall higher distribution of PEMAT-U scores, indicating greater consistency in information clarity and organization, whereas TikTok videos demonstrated wider score dispersion, reflecting greater variability in understandability (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e\n \u003cp\u003eMedian PEMAT-A scores did not differ significantly between platforms, although BiliBili again showed a more concentrated distribution and TikTok greater heterogeneity (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). When stratified by content type, disease awareness videos achieved the highest PEMAT-U scores, while treatment option videos scored highest on PEMAT-A. In contrast, patient experience videos consistently demonstrated the lowest scores on both dimensions, likely reflecting their subjective narrative structure and limited instructional focus (Figs. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e\n \u003cp\u003eAcross decision-making contexts, all video types demonstrated high understandability; however, notable differences emerged in actionability. Myth-busting and special population content achieved the highest PEMAT-A scores, indicating superior capacity to provide concrete behavioral guidance. Symptom self-assessment videos showed the lowest actionability, primarily serving awareness rather than decision-support functions (Figs. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eVideo Information Quality Index (VIQI)\u003c/h2\u003e\n \u003cp\u003eSignificant differences were observed between platforms across overall VIQI scores and all subdomains (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). On BiliBili, disease awareness videos achieved the highest median VIQI total score (16.0; IQR 13.0\u0026ndash;18.0), followed by treatment option and patient experience videos. A similar pattern was observed on TikTok, with disease awareness videos achieving the highest median VIQI score (15.5; IQR 11.5\u0026ndash;17.0). Across all subscales, disease awareness videos consistently outperformed other categories, particularly in content accuracy (VIQI-2) and educational value (VIQI-4). Patient experience videos scored lowest across all dimensions, likely due to their personalized narratives, limited structure, and frequent inclusion of anecdotal or non-evidence-based claims (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of VIQI scores for videos with different content types (grouped by platform)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDisease Awareness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eTreatment Plan\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePatient Experience\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBiliBili\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVIQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e16.0 (13.0\u0026ndash;18.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e12.0 (10.2\u0026ndash;14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e9.5 (8.0\u0026ndash;12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e22.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVIQI-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4.0 (3.0\u0026ndash;5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3.0 (3.0\u0026ndash;3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3.0 (2.0\u0026ndash;3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e21.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVIQI-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e5.0 (4.0\u0026ndash;5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.0 (3.0\u0026ndash;5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3.0 (2.0\u0026ndash;3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e33.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVIQI-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3.0 (2.0\u0026ndash;5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.0 (2.0\u0026ndash;3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2.0 (1.0\u0026ndash;3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e14.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVIQI-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4.0 (3.0\u0026ndash;4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3.0 (3.0\u0026ndash;4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3.0 (2.0\u0026ndash;3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e14.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTikTok\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVIQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e15.5 (11.5\u0026ndash;17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e13.0 (12.0\u0026ndash;16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e8.0 (7.0\u0026ndash;10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e33.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVIQI-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4.0 (3.0\u0026ndash;5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.0 (3.0\u0026ndash;4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3.0 (2.0\u0026ndash;3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e27.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVIQI-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4.0 (4.0\u0026ndash;5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.0 (3.0\u0026ndash;5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2.0 (2.0\u0026ndash;2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e38.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVIQI-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3.0 (2.0\u0026ndash;3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3.0 (2.0\u0026ndash;3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2.0 (1.0\u0026ndash;2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e22.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVIQI-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4.0 (3.0\u0026ndash;4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3.0 (3.0\u0026ndash;4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2.0 (2.0\u0026ndash;3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e26.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eOverall Video Quality and Reliability (GQS and mDISCERN)\u003c/h2\u003e\n \u003cp\u003eStratified analyses revealed that videos uploaded by professional creators consistently achieved significantly higher GQS and mDISCERN scores than those uploaded by non-professionals across both platforms (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Figs. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), confirming uploader expertise as a major determinant of informational quality. Across content categories, disease awareness videos demonstrated the highest quality and reliability, whereas patient experience videos performed poorest. Although median mDISCERN scores were numerically identical across categories, distributional differences were statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Similar patterns were observed on TikTok (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e\n \u003cp\u003eAcross clinical decision-making contexts, videos addressing myth-busting and symptom self-assessment achieved the highest quality scores, whereas daily care content consistently scored lowest (BiliBili: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014; TikTok: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020; Figs. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF), indicating considerable scope for improving standardized, evidence-based self-management guidance in digital health media.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eVideo Information Transparency (JAMA Benchmark Criteria)\u003c/h2\u003e\n \u003cp\u003eThe JAMA benchmark criteria assess the transparency of health-related educational videos and serve as an important indicator of their credibility. Videos from uploaders with different levels of professional expertise showed statistically significant differences in JAMA scores (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with videos by professional uploaders scoring significantly higher than those by non-professional uploaders (Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eCorrelation and Regression Analyses\u003c/h2\u003e\n \u003cp\u003eThis study employed statistical analyses to identify key data-driven factors influencing video quality. As shown in Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the Global Quality Scale (GQS) and modified DISCERN (mDISCERN) scores were significantly positively correlated (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming consistency between overall video quality and information reliability. The total Video Information Quality Index (VIQI) score also demonstrated strong positive correlations with both GQS and mDISCERN. Among VIQI subscales, content accuracy (VIQI-1) and logical coherence (VIQI-3) showed the strongest associations. The JAMA benchmark score exhibited moderate to strong positive correlations with both GQS and mDISCERN (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, user engagement metrics showed limited relevance: the number of likes was only weakly correlated with GQS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and showed no significant association with mDISCERN (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). TikTok videos had a high median like count (14,868.0), yet this popularity did not translate into higher quality ratings. Furthermore, neither the number of saves/favorites nor time since upload was significantly correlated with either quality metric (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003eSubsequently, stepwise multiple linear regression analyses were conducted with GQS and mDISCERN as dependent variables to quantitatively identify their core predictors.For GQS, the optimal regression model included only two predictors: information transparency (JAMA score) and production quality (total VIQI score). This model achieved an adjusted \u003cem\u003eR\u003c/em\u003e\u0026sup2; of 0.754, indicating that these two variables jointly explain 75.4% of the variance in GQS scores (\u003cstrong\u003eSupplementary file 5\u003c/strong\u003e, Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cstrong\u003e*\u003c/strong\u003e). Specifically, both JAMA score (\u0026beta;\u0026thinsp;=\u0026thinsp;0.457, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and total VIQI score (\u0026beta;\u0026thinsp;=\u0026thinsp;0.131, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significant positive predictors. Variance inflation factors (VIFs) for both predictors were below 10, confirming the absence of severe multicollinearity (\u003cstrong\u003eSupplementary file 5\u003c/strong\u003e, Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cstrong\u003e*\u003c/strong\u003e).For mDISCERN, the final model included three predictors: JAMA score (\u0026beta;\u0026thinsp;=\u0026thinsp;0.373, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), understandability (PEMAT-U) (\u0026beta;\u0026thinsp;=\u0026thinsp;0.016, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019), and total VIQI score (\u0026beta;\u0026thinsp;=\u0026thinsp;0.106, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037), with an adjusted \u003cem\u003eR\u003c/em\u003e\u0026sup2; of 0.777. The model was statistically significant overall (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;78.948, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cstrong\u003eSupplementary file 5\u003c/strong\u003e, Tables \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cstrong\u003e* and 4*\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003eCollectively, these regression results consistently confirm that information transparency and production quality are the core determinants of both overall video quality and information reliability. Notably, metrics of public engagement\u0026mdash;such as likes and view counts\u0026mdash;were not retained in either final model, further underscoring the lack of an inherent association between content popularity and informational quality.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eSurvey Findings on Patient Decision-Making\u003c/h2\u003e\n \u003cp\u003eParticipants were categorized into \u0026ldquo;influenced\u0026rdquo; and \u0026ldquo;non-influenced\u0026rdquo; groups based on whether short-form videos affected their healthcare decisions. No significant between-group differences were observed for age, sex, occupation, disease duration, rhinitis subtype, or treatment modality (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, educational attainment differed significantly: compared with participants with high school education or below, those with vocational college education (OR\u0026thinsp;=\u0026thinsp;11.50, 95% CI: 2.32\u0026ndash;87.87, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) and those with bachelor\u0026rsquo;s degrees or higher (OR\u0026thinsp;=\u0026thinsp;6.67, 95% CI: 1.52\u0026ndash;46.67, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023) were significantly more likely to report video-influenced healthcare decisions (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBasic Characteristics of Participants in the Questionnaire Survey\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eChanging\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNot changing\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e15 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e13 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e50 (76.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e28 (68.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e18\u0026ndash;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e14 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e13 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e26\u0026ndash;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e32 (49.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e11 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e36\u0026ndash;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e10 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e11 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e46\u0026ndash;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e5 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMiddle school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVocational Undergraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e23 (35.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUndergraduate or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e40 (61.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e24 (58.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePublic Institution staff/Civil Servant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e22 (33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCompany staff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e15 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFreelancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e17 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e9 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRetirement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e7 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e12 (29.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration of rhinitis (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e11 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.713\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1-3Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e20 (30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e10 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e4-6Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e21 (32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e13 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e7-10Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e7 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;10Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePerennial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e26 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e24 (58.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSeasonal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e39 (60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e17 (41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eRhinitis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eallgery (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e42 (64.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e19 (46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003echronic (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e17 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e15 (36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003esinusitis (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e10 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e10 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.310\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eother (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003emedicine (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e33 (50.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e18 (43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ephysical (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e11 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTCM (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eoperation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003enone (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e21 (32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e10 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eother (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatform\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTiktok (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e50 (76.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e35 (85.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBilibili (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e14 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e9 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWechat (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e24 (36.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e17 (41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRednote (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e43 (66.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e28 (68.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYoutube (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e16 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eUsage time (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e5 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1-3h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e27 (41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e20 (48.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3-5h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e30 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e15 (36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;5h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1/Mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e13 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e9 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1\u0026ndash;2/Mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e12 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e12 (29.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1\u0026ndash;2/w\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e30 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e11 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3\u0026ndash;5/w\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e10 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEveryday\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eContent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEtiology (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e37 (56.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e15 (36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTreatment (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e32 (49.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e25 (61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCare (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e37 (56.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e16 (39.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSymptom (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e31 (47.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e10 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eExperence (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e25 (38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOther (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eReliability (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotally unbelievable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUnbelievable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e11 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e31 (47.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e24 (58.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eReliable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e21 (32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotal reliable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eComprehension (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotally unaware\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUnaware\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e17 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e15 (36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e19 (29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e10 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAware\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e27 (41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e14 (34.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotal aware\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eUtility (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotally useless\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUseless\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e15 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e22 (33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e20 (48.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUseful\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e26 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e12 (29.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotally useful\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eSpearman correlation analysis confirmed a positive association between educational level and degree of decision influence, whereas age, disease duration, time spent viewing videos, and perceived content reliability scores showed no significant correlations (\u003cem\u003e|r|\u003c/em\u003e \u0026lt; 0.20, all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig. \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Univariate logistic regression identified male sex and specific content types as potential predictors of decision influence; however, only educational level and content type achieved statistical significance (Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Videos focusing on etiology (OR\u0026thinsp;=\u0026thinsp;2.29, 95% CI: 1.04\u0026ndash;5.20, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043), symptoms (OR\u0026thinsp;=\u0026thinsp;2.83, 95% CI: 1.22\u0026ndash;6.94, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018), and patient experience sharing (OR\u0026thinsp;=\u0026thinsp;4.50, 95% CI: 1.67\u0026ndash;14.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) were significantly associated with behavioral impact.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariate logistic regression.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eScore\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.87 (0.72\u0026ndash;1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 [Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.65 (0.27\u0026ndash;1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 [Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e18\u0026ndash;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.35 (0.29\u0026ndash;6.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e26\u0026ndash;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3.64 (0.82\u0026ndash;17.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e36\u0026ndash;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.14 (0.23\u0026ndash;5.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.873\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e46\u0026ndash;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6.25 (0.64\u0026ndash;148.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUndergraduate or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 [Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMiddle school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.00 (NA - Inf)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.15 (0.02\u0026ndash;0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVocational Undergraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.72 (0.68\u0026ndash;4.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCompany staff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 [Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFreelancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.01 (0.31\u0026ndash;3.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.18 (0.01\u0026ndash;1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePublic Institution staff/Civil Servant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.96 (0.57\u0026ndash;7.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRetirement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.53 (0.08\u0026ndash;3.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.31 (0.08\u0026ndash;1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration of rhinitis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 [Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1-3Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.91 (0.23\u0026ndash;3.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.886\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e4-6Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.73 (0.19\u0026ndash;2.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e7-10Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.45 (0.10\u0026ndash;1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;10Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.45 (0.09\u0026ndash;2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePerennial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 [Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSeasonal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.12 (0.96\u0026ndash;4.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eRhinitis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAllgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.11 (0.96\u0026ndash;4.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eChronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.61 (0.26\u0026ndash;1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSinusitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.56 (0.21\u0026ndash;1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.63 (0.02\u0026ndash;16.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMedicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.32 (0.60\u0026ndash;2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePhysical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.99 (0.35\u0026ndash;2.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTCM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.98 (0.43\u0026ndash;14.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOperation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.49 (0.15\u0026ndash;1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.48 (0.62\u0026ndash;3.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.00 (NA - Inf)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatform\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTiktok\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.57 (0.19\u0026ndash;1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBilibili\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.98 (0.38\u0026ndash;2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWechat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.83 (0.37\u0026ndash;1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRednote\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.91 (0.39\u0026ndash;2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYoutube\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.59 (0.61\u0026ndash;4.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eUsage time\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 [Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1-3h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.54 (0.07\u0026ndash;2.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3-5h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.80 (0.11\u0026ndash;4.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;5h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.30 (0.03\u0026ndash;2.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1/Mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 [Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1\u0026ndash;2/Mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.69 (0.21\u0026ndash;2.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1\u0026ndash;2/w\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.89 (0.63\u0026ndash;5.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3\u0026ndash;5/w\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.87 (0.24\u0026ndash;3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEveryday\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.00 (NA - Inf)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eContent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEtiology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.29 (1.04\u0026ndash;5.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.043\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.62 (0.28\u0026ndash;1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.06 (0.94\u0026ndash;4.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSymptom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.83 (1.22\u0026ndash;6.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eExperence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4.50 (1.67\u0026ndash;14.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.29 (0.04\u0026ndash;1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eReliability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 [Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotally unbelievable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eInf (0.00 - NA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUnbelievable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.06 (0.37\u0026ndash;3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eReliable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.03 (0.79\u0026ndash;5.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotally reliable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eInf (0.00 - NA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eComprehension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 [Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotally unaware\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eInf (0.00 - NA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUnaware\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.60 (0.21\u0026ndash;1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAware\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.02 (0.37\u0026ndash;2.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotal aware\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.05 (0.09\u0026ndash;24.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eUtility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 [Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotally useless\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.00 (NA - Inf)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUseless\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.70 (0.61\u0026ndash;5.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUseful\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.97 (0.80\u0026ndash;5.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotally useful\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eInf (0.00 - NA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn multivariable analysis adjusting for potential confounders (Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), educational level, symptom-focused content, and patient experience sharing remained independent predictors of video-influenced healthcare decisions, with patient experience content demonstrating the strongest effect size. Some subgroup estimates were unstable due to small sample sizes.\u003c/p\u003e\n \u003cp\u003eCollectively, these findings indicate that algorithmic visibility and user engagement metrics are weak proxies for informational rigor, and that experiential narratives disproportionately influence patient decision-making despite lower content quality.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultivariate Logistic Regression\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUndergraduate or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 [Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMiddle school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.00 (NA - Inf)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.06 (0.00\u0026ndash;0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVocational Undergraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.63 (0.58\u0026ndash;4.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eContent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSymptom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.80 (1.08\u0026ndash;7.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eExperence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e8.89 (2.34\u0026ndash;58.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study systematically evaluated within algorithmically curated short-video health environments, information transparency, and real-world impact on patients\u0026rsquo; medical decision-making of rhinitis-related short videos across multiple platforms, yielding three key findings. Although content produced by professional creators demonstrates higher quality, the algorithm-driven platform environment can still amplify low-quality or misleading health information. User engagement metrics\u0026mdash;such as likes and view counts\u0026mdash;do not reliably reflect the scientific accuracy of the information. Exposure to short videos\u0026mdash;particularly those featuring patient experience sharing and symptom-focused content\u0026mdash;is independently associated with changes in patients\u0026rsquo; medical decisions.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAlgorithmic environments and health misinformation prevalence\u003c/h2\u003e \u003cp\u003eShort-form video platforms have become major conduits for public health information, yet low barriers to content creation and recommendation systems that optimize for engagement produce environments conducive to misinformation dissemination.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] Established frameworks of health misinformation dissemination highlight the interplay among information quality, user characteristics, and external (algorithmic) environments as determinants of users\u0026rsquo; ability to identify credible health content. These mechanisms suggest platforms do not merely reflect user interests but actively shape exposure and perceived relevance, thereby influencing health knowledge and behaviors. Recent systematic evidence points to the problem\u0026rsquo;s scope and urgency: health misinformation prevalence on social media remains high across platforms and topic areas, with emotionally resonant and narrative-driven content particularly likely to spread widely despite poor quality.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] For example, quality evaluations of short health education videos on YouTube and TikTok showed overall low adherence to informational standards, with minimal correlation between engagement metrics and content reliability. These findings align with our current results that engagement metrics are poor proxies for informational accuracy, underscoring structural shortcomings in current platform recommendation systems.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eNarrative and visual authority in short videos\u003c/h2\u003e \u003cp\u003eThe design of short videos themselves may exacerbate the risk of misinformation uptake. Users are exposed to multimodal content where visual authority cues and personal narratives can convey credibility independently of factual accuracy. The persuasive combination of confident presenters, certificates, narrative framing, and emotive cues is particularly problematic because it may override analytic processing, leading individuals to accept incorrect health claims as legitimate.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] This is consistent with cognitive models showing that users rely variably on central (analytical) and peripheral (heuristic) cues when discerning misinformation in short videos, influenced by content structure and narrative complexity.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] This helps explain why lower-quality patient experience videos in our dataset exerted stronger behavioral influence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eHealth literacy, perception, and decision-making\u003c/h2\u003e \u003cp\u003ePublic perceptions of the social media information environment reflect concerns about misinformation prevalence and discernment difficulty. Survey data indicate that a large proportion of social media users perceive health misinformation as widespread and find it challenging to distinguish true from false information, with these perceptions linked to both health communication behaviors and health decision-making.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] In our study, the observed association between video exposure and self-reported healthcare decisions echoes this pattern, reinforcing the notion that perceived credibility and ease of discernment critically influence health behavior.\u003c/p\u003e \u003cp\u003eFurthermore, experimental evidence suggests that targeted educational interventions\u0026mdash;such as educational video modules\u0026mdash;can significantly improve individuals\u0026rsquo; abilities to identify misinformation in messaging environments.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] While short-form video platforms differ in format, the principle that tailored, media-specific literacy interventions can enhance critical appraisal skills remains relevant for public health efforts[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our finding that higher educational attainment was associated with greater susceptibility to video-influenced decisions may reflect increased exposure rather than superior discernment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eImplications for governance and intervention\u003c/h2\u003e \u003cp\u003eGiven the dynamic nature of misinformation dissemination on short-form platforms, multilevel strategies are essential, including algorithmic re-ranking of evidence-based content, platform-embedded credibility labeling, and clinician-led content production strategies. Systematic reviews of health misinformation countermeasures emphasize the importance of platform accountability, policy support, and individual-level literacy enhancement strategies to combat misinformation effectively.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] In particular, governance approaches that integrate structural credibility signals, platform-level moderation, and algorithmic weighting of evidence-based sources may help align content visibility with informational quality. At the clinical level, providers should recognize the influence of short-form video exposure on patient expectations and decisions, incorporating discussions about digital health information sources into routine care. Educational and policy interventions should also address narrative-focused persuasion, preparing patients to evaluate both content accuracy and the persuasive elements that drive misinformation spread.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis study\u0026rsquo;s key strengths include its multi-platform design, the use of multiple validated quality assessment measures, and the linkage of content evaluation with behavioral outcomes. However, limitations include its cross-sectional design, which restricts causal inference; reliance on top-ranked video samples, potentially limiting representativeness; and the focus on Chinese-language platforms, which may affect direct generalizability to Western social media ecosystems. Nonetheless, the underlying mechanisms of algorithmic influence and narrative-driven misinformation dissemination are broadly consistent with global evidence.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eShort-form video platforms operate as algorithmically shaped health information ecosystems in which popularity is a poor indicator of quality and personal narratives can exert disproportionate influence on health behavior. Addressing misinformation requires coordinated strategies encompassing platform governance reform, clinician engagement, and evidence-based health literacy interventions. Enhanced understanding of the structural and cognitive mechanisms underlying misinformation dissemination will be critical to promoting safer, more informed digital health environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their gratitude to the video uploaders for their contributions to public health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHaiyu Hong\u003c/strong\u003e and \u003cstrong\u003eWenxuan Zhou\u003c/strong\u003e conceived and designed the study; \u003cstrong\u003eShuwen Tang\u003c/strong\u003e and \u003cstrong\u003eHuiting Chen\u003c/strong\u003e collected the data; \u003cstrong\u003eYufeng Chao\u003c/strong\u003e and \u003cstrong\u003eBaoyi Chen\u003c/strong\u003e reviewed and scored the videos; \u003cstrong\u003eRexidanmu Hudabai\u0026nbsp;\u003c/strong\u003eanalyzed the data; \u003cstrong\u003eXianzhen Chen\u003c/strong\u003e wrote the original draft; \u003cstrong\u003eXianzhen Chen\u003c/strong\u003e and \u003cstrong\u003eRexidanmu Hudabai\u003c/strong\u003ereviewed and edited the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenerative AI statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no Generative AI tools were used in the development or editing of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Natural Science Foundation of Guangdong Province, China (grant No. 2025A1515012592)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data sets generated during and/or analyzed during this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve human biological specimens, animal subjects, or identifiable clinical datasets. All analyzed data were publicly available at the time of collection from TikTok and Bilibili, and did not involve private user information or require active user interaction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study adhered to the data usage policies of the respective platforms. Specifically:\u003c/p\u003e\n\u003cp\u003eFor \u003cstrong\u003eTikTok\u003c/strong\u003e, we followed ByteDance\u0026rsquo;s Community Guidelines and Content Policy, ensuring that only publicly shared videos were accessed via public browsing. No API-based scraping or automated data collection methods were used.\u003c/p\u003e\n\u003cp\u003eFor \u003cstrong\u003eBilibili\u003c/strong\u003e, we complied with the platform\u0026apos;s User Agreement and Public Content Access Rules, collecting data solely through manual viewing and recording of publicly available content.\u003c/p\u003e\n\u003cp\u003eNo special permissions or institutional approvals from the platforms were required, as the data collection method involved only passive observation and analysis of openly accessible content, consistent with standard practices for academic research in digital content analysis.The study was reviewed by the Ethics Committee of the Fifth Affiliated Hospital of Sun Yat-sen University, and the ethical approval number (Approval \u003cstrong\u003eNo. 2026-K34-1\u003c/strong\u003e) pertains specifically to the survey section of this research. For the survey component of this study, all participants provided written informed consent prior to participation. The study was reviewed by the Ethics Committee of the Fifth Affiliated Hospital of Sun Yat-sen University, which confirmed that ethical approval was not required due to the use of publicly available data and absence of sensitive or personal information ( \u003cstrong\u003eSupplementary File 6\u003c/strong\u003e). The research adhered to the principles of the Declaration of Helsinki and all applicable national regulations regarding research ethics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Department of Otolaryngology, Head and Neck Surgery, the Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China；\u003csup\u003e2\u003c/sup\u003e Department of Infectious Diseases, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China；\u003csup\u003e3\u003c/sup\u003e Department of anesthesiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, Guangdong Province, China；\u003csup\u003e4\u003c/sup\u003e Allergy Center, the Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China；\u003csup\u003e5\u003c/sup\u003e Sleep Center, the Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China；\u003csup\u003e6\u003c/sup\u003e Health Promotion Center of Zhuhai , Zhuhai, Guangdong Province, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e#\u003c/sup\u003eThese authors contributed equally to this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eJ.A. Bernstein, J.S. Bernstein, R. Makol, S. Ward, Allergic Rhinitis: A Review, JAMA 331 (2024) 866\u0026ndash;877. https://doi.org/10.1001/jama.2024.0530.\u003c/li\u003e\n \u003cli\u003eB. Sousa-Pinto, J. Bousquet, R.J. Vieira, etc, Allergic Rhinitis and Its Impact on Asthma (ARIA)-EAACI Guidelines-2024-2025 Revision: Part I-Guidelines on Intranasal Treatments, Allergy (2025). https://doi.org/10.1111/all.70131.\u003c/li\u003e\n \u003cli\u003eI. Baiardini, S. Novakova, S. Mihaicuta, I.K. Oguzulgen, G.W. Canonica, Adherence to treatment in allergic respiratory diseases, Expert Rev. Respir. Med. 13 (2019) 53\u0026ndash;62. https://doi.org/10.1080/17476348.2019.1554438.\u003c/li\u003e\n \u003cli\u003eG.C. Steven, Shared decision making in allergic rhinitis: An approach to the patient, Ann. Allergy Asthma Immunol. Off. Publ. Am. Coll. Allergy Asthma Immunol. 125 (2020) 268\u0026ndash;272. https://doi.org/10.1016/j.anai.2020.06.032.\u003c/li\u003e\n \u003cli\u003eC.L. Ventola, Social media and health care professionals: benefits, risks, and best practices, P T Peer-Rev. J. Formul. Manag. 39 (2014) 491\u0026ndash;520.\u003c/li\u003e\n \u003cli\u003eS.S.-L. Tan, N. Goonawardene, Internet Health Information Seeking and the Patient-Physician Relationship: A Systematic Review, J. Med. Internet Res. 19 (2017) e9. https://doi.org/10.2196/jmir.5729.\u003c/li\u003e\n \u003cli\u003eW. Osman, F. Mohamed, M. Elhassan, A. Shoufan, Is YouTube a reliable source of health-related information? A systematic review, BMC Med. Educ. 22 (2022) 382. https://doi.org/10.1186/s12909-022-03446-z.\u003c/li\u003e\n \u003cli\u003eL. Nguyen, J. Walters, S. Paul, S. Monreal Ijurco, G.E. Rainey, N. Parekh, G. Blair, M. Darrah, Feeds, feelings, and focus: A systematic review and meta-analysis examining the cognitive and mental health correlates of short-form video use, Psychol. Bull. 151 (2025) 1125\u0026ndash;1146. https://doi.org/10.1037/bul0000498.\u003c/li\u003e\n \u003cli\u003eW.-Y.S. Chou, A. Oh, W.M.P. Klein, Addressing Health-Related Misinformation on Social Media, JAMA 320 (2018) 2417\u0026ndash;2418. https://doi.org/10.1001/jama.2018.16865.\u003c/li\u003e\n \u003cli\u003eI.J. Borges do Nascimento, A.B. Pizarro, J.M. Almeida, N. Azzopardi-Muscat, M.A. Gon\u0026ccedil;alves, M. Bj\u0026ouml;rklund, D. Novillo-Ortiz, Infodemics and health misinformation: a systematic review of reviews, Bull. World Health Organ. 100 (2022) 544\u0026ndash;561. https://doi.org/10.2471/BLT.21.287654.\u003c/li\u003e\n \u003cli\u003eV. Suarez-Lledo, J. Alvarez-Galvez, Prevalence of Health Misinformation on Social Media: Systematic Review, J. Med. Internet Res. 23 (2021) e17187. https://doi.org/10.2196/17187.\u003c/li\u003e\n \u003cli\u003eR.J. Limaye, T.A. Holroyd, M. Blunt, A.F. Jamison, M. Sauer, R. Weeks, B. Wahl, K. Christenson, C. Smith, J. Minchin, B. Gellin, Social media strategies to affect vaccine acceptance: a systematic literature review, Expert Rev. Vaccines 20 (2021) 959\u0026ndash;973. https://doi.org/10.1080/14760584.2021.1949292.\u003c/li\u003e\n \u003cli\u003eM. Cinelli, G. De Francisci Morales, A. Galeazzi, W. Quattrociocchi, M. Starnini, The echo chamber effect on social media, Proc. Natl. Acad. Sci. U. S. A. 118 (2021) e2023301118. https://doi.org/10.1073/pnas.2023301118.\u003c/li\u003e\n \u003cli\u003eD. Kbaier, A. Kane, M. McJury, I. Kenny, Prevalence of Health Misinformation on Social Media-Challenges and Mitigation Before, During, and Beyond the COVID-19 Pandemic: Scoping Literature Review, J. Med. Internet Res. 26 (2024) e38786. https://doi.org/10.2196/38786.\u003c/li\u003e\n \u003cli\u003eC.L.-N. Remvig, C.S. Diers, H. Meteran, S.F. Thomsen, T. Sigsgaard, S. H\u0026oslash;j, H. Meteran, YouTube as a source of (mis)information on allergic rhinitis, Ann. Allergy Asthma Immunol. Off. Publ. Am. Coll. Allergy Asthma Immunol. 129 (2022) 612\u0026ndash;617. https://doi.org/10.1016/j.anai.2022.06.031.\u003c/li\u003e\n \u003cli\u003eS. Kumar, A. Dutta, S. Vallur, R. Singh, A Content Analysis of YouTube Videos on Allergic Rhinitis Management, Indian J. Otolaryngol. Head Neck Surg. Off. Publ. Assoc. Otolaryngol. India 77 (2025) 1760\u0026ndash;1767. https://doi.org/10.1007/s12070-025-05396-6.\u003c/li\u003e\n \u003cli\u003eL. Wang, Y. Chen, D. Zhao, T. Xu, F. Hua, Quality and Dissemination of Uterine Fibroid Health Information on TikTok and Bilibili: Cross-Sectional Study, JMIR Form. Res. 9 (2025) e75120. https://doi.org/10.2196/75120.\u003c/li\u003e\n \u003cli\u003eA. Lahooti, A. Hassan, B. Critelli, D. Westerveld, C. Newberry, S. Kumar, R.Z. Sharaiha, Quality and Popularity Trends of Weight Loss Procedure Videos on TikTok, Obes. Surg. 33 (2023) 714\u0026ndash;719. https://doi.org/10.1007/s11695-022-06409-x.\u003c/li\u003e\n \u003cli\u003eA. Bernard, M. Langille, S. Hughes, C. Rose, D. Leddin, S. Veldhuyzen van Zanten, A systematic review of patient inflammatory bowel disease information resources on the World Wide Web, Am. J. Gastroenterol. 102 (2007) 2070\u0026ndash;2077. https://doi.org/10.1111/j.1572-0241.2007.01325.x.\u003c/li\u003e\n \u003cli\u003eD. Charnock, S. Shepperd, G. Needham, R. Gann, DISCERN: an instrument for judging the quality of written consumer health information on treatment choices, J. Epidemiol. Community Health 53 (1999) 105\u0026ndash;111. https://doi.org/10.1136/jech.53.2.105.\u003c/li\u003e\n \u003cli\u003eS.J. Shoemaker, M.S. Wolf, C. Brach, Development of the Patient Education Materials Assessment Tool (PEMAT): a new measure of understandability and actionability for print and audiovisual patient information, Patient Educ. Couns. 96 (2014) 395\u0026ndash;403. https://doi.org/10.1016/j.pec.2014.05.027.\u003c/li\u003e\n \u003cli\u003eZ. Liu, Y. Chen, Y. Lin, M. Ai, D. Lian, Y. Zhang, Y. Shen, F. Yu, YouTube/ Bilibili/ TikTok videos as sources of medical information on laryngeal carcinoma: cross-sectional content analysis study, BMC Public Health 24 (2024) 1594. https://doi.org/10.1186/s12889-024-19077-6.\u003c/li\u003e\n \u003cli\u003eT. Ni, Y. Jiang, Y. Liu, J. Zhou, H. Xie, S. Yang, J. Ruan, D. Guo, Z. Lin, Z. Han, Quality evaluation of information about sudden sensorineural hearing loss on TikTok videos: Cross-sectional study, Digit. Health 11 (2025) 20552076251406648. https://doi.org/10.1177/20552076251406648.\u003c/li\u003e\n \u003cli\u003eR. Wang, H. Yang, Y. Wang, X. Zhai, Understanding how users identify health misinformation in short videos: an integrated analysis using PLS-SEM and fsQCA, Front. Public Health 13 (2025) 1713794. https://doi.org/10.3389/fpubh.2025.1713794.\u003c/li\u003e\n \u003cli\u003eE. Alsaad, S. AlDossary, Educational Video Intervention to Improve Health Misinformation Identification on WhatsApp Among Saudi Arabian Population: Pre-Post Intervention Study, JMIR Form. Res. 8 (2024) e50211. https://doi.org/10.2196/50211.\u003c/li\u003e\n \u003cli\u003eA. Gaysynsky, N. Senft Everson, K. Heley, W.-Y.S. Chou, Perceptions of Health Misinformation on Social Media: Cross-Sectional Survey Study, JMIR Infodemiology 4 (2024) e51127. https://doi.org/10.2196/51127.\u003c/li\u003e\n \u003cli\u003eY. Hu, Y. Yang, N. Chen, Z. Pang, X. Zhong, J. Sun, How to select high-quality health educational short videos on social media? insights from youtube and douyin, BMC Med. Educ. 25 (2025) 1418. https://doi.org/10.1186/s12909-025-08018-5.\u003c/li\u003e\n \u003cli\u003eR. Dimitroyannis, D. Fenton, S. Cho, R. Nordgren, J.M. Pinto, C.R. Roxbury, A Social Media Quality Review of Popular Sinusitis Videos on TikTok, Otolaryngol.--Head Neck Surg. Off. J. Am. Acad. Otolaryngol.-Head Neck Surg. 170 (2024) 1456\u0026ndash;1466. https://doi.org/10.1002/ohn.688.\u003c/li\u003e\n \u003cli\u003eS. N, Y. P, Y. S, Exploring the Relationship Between Social Media Use and Symptoms of Depression and Anxiety Among Children and Adolescents: A Systematic Narrative Review, Cyberpsychology Behav. Soc. Netw. 27 (2024). https://doi.org/10.1089/cyber.2023.0456.\u003c/li\u003e\n \u003cli\u003eK. Bryanov, V. Vziatysheva, Determinants of individuals\u0026rsquo; belief in fake news: A scoping review determinants of belief in fake news, PloS One 16 (2021) e0253717. https://doi.org/10.1371/journal.pone.0253717.\u003c/li\u003e\n \u003cli\u003eC. J, W. Y, Social Media Use for Health Purposes: Systematic Review, J. Med. Internet Res. 23 (2021). https://doi.org/10.2196/17917.\u003c/li\u003e\n \u003cli\u003eL. Jiang, W. Wu, X. Jiang, F. Jiang, Evaluating the quality of educational TikTok videos on diabetic retinopathy: a cross-sectional study, Front. Public Health 13 (2025) 1718587. https://doi.org/10.3389/fpubh.2025.1718587.\u003c/li\u003e\n \u003cli\u003eA.L. Beam, I.S. Kohane, Big Data and Machine Learning in Health Care, JAMA 319 (2018) 1317\u0026ndash;1318. https://doi.org/10.1001/jama.2017.18391.\u003c/li\u003e\n \u003cli\u003eY. Wang, M. McKee, A. Torbica, D. Stuckler, Systematic Literature Review on the Spread of Health-related Misinformation on Social Media, Soc. Sci. Med. 240 (2019) 112552. https://doi.org/10.1016/j.socscimed.2019.112552.\u003c/li\u003e\n \u003cli\u003eL. Keikha, A. Shahraki-Mohammadi, A. Nabiolahi, Strategies and prerequisites for combating health misinformation on social media: a systematic review, BMC Public Health 26 (2025) 139. https://doi.org/10.1186/s12889-025-25858-4.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Rhinitis, Social media, Patient education, Public health, TikTok, BiliBili","lastPublishedDoi":"10.21203/rs.3.rs-8997024/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8997024/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground\u003c/b\u003e Short-form video platforms have become major sources of health information, yet concerns persist regarding misinformation quality and its influence on patient decision-making, particularly within algorithm-driven digital ecosystems where popularity and informational quality may diverge.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e A multi-platform cross-sectional analysis was conducted on 179 short videos from TikTok and Bilibili using five validated assessment instruments: Global Quality Scale (GQS), modified DISCERN (mDISCERN), Patient Education Materials Assessment Tool (PEMAT), Video Information Quality Index (VIQI), and JAMA benchmark criteria. In parallel, a questionnaire survey of 106 patients with rhinitis assessed social media exposure and its impact on healthcare decisions. Multivariable regression analyses identified predictors of video quality and behavioral influence. This study integrates digital content analytics with patient-reported behavioral outcomes.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e Videos produced by professional creators demonstrated significantly higher quality, transparency, and reliability than those by non-professionals (all \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), while engagement metrics showed weak correlations with quality indicators. Patient experience\u0026ndash;sharing and symptom-focused videos exerted the strongest influence on healthcare decisions despite lower informational quality. Higher educational attainment was independently associated with increased susceptibility to video-influenced decisions. Engagement metrics were weakly associated with informational quality, indicating a decoupling between algorithmic popularity and evidence-based credibility.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusion\u003c/b\u003e Short-form video platforms function as algorithmically mediated health environments in which popularity poorly reflects informational rigor, and experiential narratives disproportionately shape patient behavior. These findings underscore the need for platform governance reforms, clinician participation in digital ecosystems, and evidence-based digital health communication strategies to mitigate misinformation-driven behavioral risks.\u003c/p\u003e","manuscriptTitle":"Algorithmic Short-Video Platforms and Health Decision-Making in Rhinitis: A Multi-Platform Digital Health Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 14:53:16","doi":"10.21203/rs.3.rs-8997024/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-02T09:36:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T11:40:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-10T04:49:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-09T23:17:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-03-09T18:48:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5b45fa6f-7f97-4717-9bdc-a2550f9ac454","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T14:53:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 14:53:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8997024","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8997024","identity":"rs-8997024","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}