Quality, reliability and engagement of aortic dissection-related health information on TikTok: a cross-sectional study from China

Abstract

Background

TikTok (or named as Douyin in mainland China) has emerged as a major source of health information. Aortic dissection (AD) is a rapidly fatal emergency in which delayed recognition or misinformation can have catastrophic consequences, yet the quality of short-video content on this condition remains unclear. This study aimed to systematically assess the quality and reliability of AD-related videos on TikTok and to examine their association with user engagement and video features.

Methods

A systematic search was conducted on TikTok using the keyword “aortic dissection” to identify videos published before March 1, 2026. Videos were included if they addressed AD and excluded if they were duplicates, irrelevant to the topic, or involved medical insurance content; 151 videos were ultimately analyzed. Video features (uploader type: healthcare professionals, general users, or news media; duration; and engagement metrics) and quality/reliability were evaluated using the Global Quality Scale (GQS, 1–5) and modified DISCERN (mDISCERN, 0–5) by two independent specialists. Continuous variables are presented as median [interquartile range (IQR)], and analyses used the Mann-Whitney U test, Spearman correlation, and multivariable linear regression.

Results

Across the 151 videos, overall quality was moderate [median GQS: 3 (IQR, 2–4) and median mDISCERN: 2 (IQR, 1–2)]. Most videos (92.72%) were uploaded by health professionals (mainly physicians). Videos posted by health professionals had significantly higher GQS and mDISCERN scores than those posted by non-health professionals (GQS: P<0.001; mDISCERN: P=0.003). In the adjusted models, video length was positively associated with GQS (β=0.002, P=0.003) and mDISCERN scores (β=0.001, P=0.049), whereas higher numbers of likes and comments were associated with lower GQS (likes: β=−0.002, P=0.005; comments: β=−0.041, P=0.004) and mDISCERN scores (likes: β=−0.001, P=0.02; comments: β=−0.024, P=0.007), demonstrating a distinct “popularity paradox”.

Conclusions

Although Chinese-language TikTok videos on AD are predominantly created by health professionals, overall quality remains suboptimal. Longer videos tend to be higher quality, whereas high-engagement content exhibits lower reliability. Measures such as adopting structured storytelling formats and platform-certified labels could help transform TikTok into a reliable tool for public education on critical illnesses.

Highlight box.

Key findings

• This study shows that although 92.72% of aortic dissection-related TikTok videos were uploaded by health professionals, overall quality and reliability were only moderate, and a clear “popularity paradox” was observed—higher engagement was associated with lower quality.

What is known and what is new?

• It is known that social media is a major source of health information, but content quality is often inconsistent.

• This study is the first to systematically evaluate Chinese-language TikTok videos on aortic dissection and quantitatively demonstrates the inverse relationship between engagement and information quality.

What is the implication, and what should change now?

• The findings highlight an urgent need for platform regulation and professional content optimization to prevent misinformation in time-critical diseases.

• Actions should include certified labeling systems, clinician training in short-video communication, and algorithm adjustments favoring high-quality medical content.

Introduction

Aortic dissection (AD) is a catastrophic cardiovascular emergency characterised by sudden intimal tear with subsequent intramural haematoma formation, false lumen propagation, and risk of rupture or malperfusion. Untreated type A dissection carries approximately 50% mortality within 48 hours, with risk increasing 1% to 2% per hour during the first day (1-3). Despite advances in surgical and endovascular management, in-hospital mortality remains 18% to 25%, and long-term outcomes are limited by late complications (4-6). Early recognition of hallmark symptoms, typically sudden severe tearing chest or back pain radiating to the interscapular region, is essential for timely transfer and intervention. Population-based surveys, however, consistently reveal low public awareness, especially in China, where delayed presentation significantly contributes to excess mortality (7,8). Rapid dissemination of accurate educational content is therefore critical to improve early diagnosis and survival in this highly time-sensitive condition.

Social media has become the primary health information source for younger generations, with short-video platforms surpassing traditional channels (9). TikTok (or named as Douyin in mainland China), with over 700 million domestic users, dominates medical content dissemination through algorithm-driven exposure (10-12). Its short-format videos enable rapid viral spread of life-threatening disease information (13). Therefore, TikTok can play a pivotal role in reducing pre-hospital delays by providing timely, accessible, and easy-to-understand health information (14). TikTok’s algorithm-driven content distribution ensures that critical health messages reach a wide audience, especially targeting younger generations who are more likely to engage with these platforms. By raising awareness of the warning signs of life-threatening conditions such as AD, these platforms can empower individuals to recognize symptoms early and seek immediate medical attention. Furthermore, TikTok’s ability to present information in engaging formats, such as short, attention-grabbing videos, ensures that critical messages are not only seen but also retained. In high-risk medical emergencies, where every minute counts, the rapid dissemination of information can help individuals act quickly, potentially saving lives. Additionally, TikTok’s interactive features, such as comment sections and user engagement, foster real-time discussion and clarification, allowing viewers to share their experiences and seek advice, which further enhances the platform’s role in public health education and immediate response (9-14).

Previous evaluations of AD-related health information have largely focused on YouTube and Google, reporting inconsistent quality and frequently incomplete content (15,16). However, these findings may not be directly generalizable to China’s distinct health communication ecosystem. In China, TikTok, together with “super-apps” such as WeChat and local search engines, constitutes a primary infrastructure through which the public accesses health information; the production and dissemination of such content are further shaped by language context, platform governance, commercial incentives, and a rapidly expanding creator economy (17,18). TikTok’s short-form format (typically 15–60 seconds) and engagement-oriented recommendation algorithms may preferentially amplify emotionally salient or attention-grabbing narratives, thereby facilitating the spread of low-quality or even misleading content (19,20). Across multiple specialties, a “popularity paradox” has been described, meaning that the most liked or most shared short videos, including those on cardiovascular diseases such as coronary heart disease, are not necessarily the most scientifically accurate or reliable (21-25). To date, no study has systematically assessed Chinese-language TikTok videos on AD. Therefore, a platform- and context-specific evaluation of Chinese-language short-video content on AD is urgently needed, because for this time-critical and often fatal emergency, misinformation and delayed recognition may carry immediate and severe consequences.

This study aimed to evaluate the quality and reliability of AD-related health information on TikTok using the validated Global Quality Scale (GQS) and modified DISCERN (mDISCERN) instruments, characterise uploader types (health professionals vs. non-health professionals) and the heterogeneity of their content, and examine associations between engagement metrics and content quality.

Methods

Data collection

This study aimed to evaluate the quality of TikTok videos related to AD. A systematic search was conducted on TikTok in mainland China using the keyword “aortic dissection” without logging into an account. The search was limited to videos published before March 1, 2026, and the first 160 videos were selected. Inclusion criteria required that the videos were directly related to AD, while exclusion criteria removed duplicate videos, videos unrelated to the topic, and those related to medical insurance. Following the application of the inclusion and exclusion criteria, 6 duplicate videos, 2 videos related to medical insurance, and 1 unrelated video were excluded, leaving a total of 151 videos for analysis (Figure 1).

Figure 1.

Flowchart of video inclusion process on TikTok.

Each video was analyzed for the following variables: uploader type (doctor users, general users, or news agencies), video duration (in seconds), number of likes, number of collections, number of comments, and number of shares. All data were extracted on the same day to ensure consistency in the analysis.

Video quality assessment

Two independent cardiovascular specialists with extensive clinical experience evaluated all 151 eligible videos using the GQS [1–5] and the mDISCERN [0–5] instruments (item definitions and scoring criteria are provided in Tables S1,S2). Briefly, the GQS (26) is a global 5-point measure of overall educational quality and usefulness, incorporating the coherence/flow and practicality of the health information, whereas mDISCERN (27) is a 5-item checklist that assesses the reliability of health information (e.g., clarity of aims, use of credible sources, balance, provision of additional information, and acknowledgement of uncertainty), with higher scores indicating higher reliability. Although originally developed for broader online health information formats, both tools evaluate content-based constructs that are not platform-specific and are therefore applicable to short-form videos. To enhance consistency in the Chinese short-video context, raters followed a prespecified scoring rubric and rated videos independently. Inter-rater reliability was quantified using the intraclass correlation coefficient [ICC (3,1), two-way random-effects model with absolute agreement]. Agreement was excellent for GQS [ICC =0.902; 95% confidence interval (CI): 0.868–0.928] and good for mDISCERN (ICC =0.852; 95% CI: 0.802–0.891). When discrepancies arose, a third senior cardiovascular specialist with more than 15 years of clinical experience adjudicated to reach a final consensus score for analysis.

Statistical analysis

Data analysis was performed using R version 4.5.2 and SPSS version 29. Descriptive statistics were used to summarize the characteristics of the videos, including video length, engagement metrics (likes, comments, shares, and collections), and quality scores (GQS and mDISCERN). Continuous variables were presented as medians with interquartile ranges (IQRs). To compare the characteristics of videos uploaded by healthcare professionals and non-healthcare professionals, the Mann-Whitney U test was used for continuous variables.

To examine associations between video features and quality scores, Spearman’s rank correlation was applied. The choice of non-parametric methods was justified by the distribution of the engagement variables: Shapiro-Wilk normality tests indicated that video length, engagement metrics (likes, comments, shares, and collections), and quality scores (GQS and mDISCERN) deviated from normality (all P<0.001). Linear regression models were additionally fitted to evaluate the relationships of video length and engagement metrics with GQS and mDISCERN scores. All statistical tests were two-sided, and P<0.05 was considered statistically significant.

Results

TikTok usage distribution

Figure 2 illustrates the distribution of TikTok usage across three groups: Doctor users, General users, and News agencies. As shown, the majority of TikTok users (92.72%) are Doctor users, represented by the red segment of the donut chart. General users account for 4.64%, while News agencies represent 2.65%. These results indicate that TikTok is predominantly used by medical professionals in the context of AD, with general users and media-related entities comprising smaller proportions of the platform’s user base.

Figure 2.

Distribution of TikTok video sources.

Characteristics of TikTok videos related to AD

Table 1 presents the detailed characteristics of 151 TikTok videos related to AD. The median video length was 108.00 seconds, with a first quartile of 62.50 seconds and a third quartile of 191.50 seconds. In terms of engagement, the median number of likes was 1,218.00, with a range from 303.50 to 20,556.00. The median number of collections was 309.00 (IQR, 48.50–1,212.00). The median number of comments was 166.00 (IQR, 34.00–1,061.00), and the median number of shares was 212.00 (IQR, 43.00–1,057.00). Regarding quality, the median GQS score was 3.00 (IQR, 2.00–4.00), and the median mDISCERN score was 2.00 (IQR, 1.00–2.00).

Table 1. Detailed characteristics of aortic dissection videos on TikTok.

Parameters	Total (n=151)
Length (s)	108.00 (62.50–191.50)
Likes	1,218.00 (303.50–20,556.00)
Collections	309.00 (48.50–1212.00)
Comments	166.00 (34.00–1061.00)
Shares	212.00 (43.00–1,057.00)
GQS score	3.00 (2.00–4.00)
mDISCERN score	2.00 (1.00–2.00)

Data are presented as median (interquartile range). GQS, Global Quality Scale; mDISCERN, modified DISCERN.

Comparison of health professionals vs. non-health professionals

Table 2 compares the characteristics of TikTok videos from health professionals (n=140) and non-health professionals (n=11). No significant differences were observed in video length, likes, collections, comments, and shares between the two groups (P>0.05). However, Health professional videos had significantly higher GQS scores (P<0.001) and mDISCERN scores (P=0.003) compared to non-health professional videos. Figure 3A,3B further illustrate these differences. Figure 3A shows that Health professional videos have significantly higher GQS scores (P<0.001), while Figure 3B demonstrates that mDISCERN scores are also higher for Health professionals (P=0.003).

Table 2. Comparison of video sources according to video features on TikTok.

Parameters	Health professionals (n=140)	Non-health professionals (n=11)	P
Length (s)	109.00 (63.00–189.25)	94.00 (43.50–210.00)	0.61
Likes	1,214.50 (284.50–20,146.00)	2,587.00 (862.00–12,988.00)	0.44
Collections	294.00 (44.50–1,196.50)	519.00 (114.50–1,336.00)	0.63
Comments	151.50 (28.75–1,018.50)	719.00 (116.00–1,451.00)	0.11
Shares	232.00 (41.50–969.00)	158.00 (80.00–2,676.50)	0.49
GQS score	3.00 (2.00–4.00)	1.00 (1.00–2.00)	<0.001
mDISCERN score	2.00 (2.00–2.00)	1.00 (1.00–1.50)	0.003

Data are presented as median (interquartile range). GQS, Global Quality Scale; mDISCERN, modified DISCERN.

Figure 3.

Quality and reliability of aortic dissection–related TikTok videos by uploader type. (A) Comparison of the quality/reliability of aortic dissection-related TikTok videos based on the GQS between health professionals and non-health professionals. (B) Comparison of the quality/reliability of aortic dissection-related TikTok videos based on the mDISCERN scale between health professionals and non-health professionals. **, P<0.01; ***, P<0.001. GQS, Global Quality Scale; mDISCERN, modified DISCERN.

Correlation between video features

The Spearman correlation coefficients among video characteristics, user engagement metrics, and quality/reliability scores are presented in Figure 4. A significant positive correlation is observed between “likes” and other engagement metrics, including shares (r=0.94), comments (r=0.92), and collections (r=0.94), suggesting that videos with higher likes tend to have higher engagement. Video length has no significant correlation with mDISCERN but shows a positive correlation with GQS (r=0.3). GQS scores exhibit negative correlations with likes (r=−0.38) and comments (r=−0.42), with no correlation with collections or shares. mDISCERN scores are negatively correlated with likes (r=−0.3), collections (r=−0.18), comments (r=−0.32), and shares (r=−0.17), indicating that higher interaction videos tend to have lower content quality scores. A positive correlation is observed between GQS and mDISCERN (r=0.77).

Figure 4.

Correlation analysis heatmap between video engagement metrics (shares, comments, collections, likes) and content quality/reliability (GQS and mDISCERN). GQS, Global Quality Scale; mDISCERN, modified DISCERN. × indicates correlations that could not be computed due to zero variance or insufficient paired data.

Regression analysis for GQS and mDISCERN scores

Table 3 presents linear regression analyses examining the associations of video characteristics with GQS and mDISCERN scores. In the unadjusted model (Model 1), longer video length was positively associated with GQS (β=0.002, 95% CI: 0.000–0.003; P=0.01), whereas it was not significantly associated with mDISCERN (β=0.001, 95% CI: −0.000 to 0.001; P=0.09). Higher numbers of likes (per 1,000) and comments (per 1,000) were consistently associated with lower GQS (likes: β=−0.002, 95% CI: −0.003 to −0.001; P=0.004; comments: β=−0.016, 95% CI: −0.025 to −0.006; P=0.001) and lower mDISCERN scores (likes: β=−0.001, 95% CI: −0.002 to −0.000; P=0.01; comments: β=−0.026, 95% CI: −0.044 to −0.009; P=0.003). Collections (per 1,000) and shares (per 1,000) were not significantly associated with GQS (P=0.104 and 0.095, respectively), while both were inversely associated with mDISCERN (collections: β=−0.012, 95% CI: −0.022 to −0.002; P=0.02; shares: β=−0.005, 95% CI: −0.010 to −0.001; P=0.01).

Table 3. Linear regression analysis for GQS, and mDISCERN score.

Parameters	GQS score				mDISCERN score
	β	95% CI	P		β	95% CI	P
Model 1
Video length	0.002	0.000 to 0.003	0.01		0.001	−0.000 to 0.001	0.09
Likes (per 1,000)	−0.002	−0.003 to −0.001	0.004		−0.001	−0.002 to −0.000	0.01
Collections (per 1,000)	−0.007	−0.016 to 0.002	0.10		−0.012	−0.022 to −0.002	0.02
Comments (per 1,000)	−0.016	−0.025 to −0.006	0.001		−0.026	−0.044 to −0.009	0.003
Shares (per 1,000)	−0.020	−0.044 to 0.003	0.09		−0.005	−0.010 to −0.001	0.01
Model 2
Video length	0.002	0.001 to 0.003	0.003		0.001	0.000 to 0.002	0.049
Likes (per 1,000)	−0.002	−0.003 to −0.001	0.005		−0.001	−0.002 to −0.000	0.02
Collections (per 1,000)	−0.010	−0.026 to 0.006	0.23		−0.010	−0.021 to −0.000	0.045
Comments (per 1,000)	−0.041	−0.069 to −0.014	0.004		−0.024	−0.041 to −0.007	0.007
Shares (per 1,000)	−0.004	−0.010 to 0.003	0.26		−0.005	−0.009 to −0.001	0.03

Model 1 was unadjusted. Model 2 was adjusted for uploader type. CI, confidence interval; GQS, Global Quality Scale; mDISCERN, modified DISCERN.

After adjustment for uploader type (Model 2), the positive association between video length and both quality metrics became statistically significant (GQS: β=0.002, 95% CI: 0.001–0.003; P=0.003; mDISCERN: β=0.001, 95% CI: 0.000–0.002; P=0.049). The inverse associations of likes (per 1,000) and comments (per 1,000) with both GQS and mDISCERN remained statistically significant (all P<0.05). In contrast, collections (per 1,000) and shares (per 1,000) remained non-significant for GQS (P=0.23 and 0.26, respectively), but were still negatively associated with mDISCERN (collections: β=−0.010, 95% CI: −0.021 to −0.000; P=0.045; shares: β=−0.005, 95% CI: −0.009 to −0.001; P=0.03).

Discussion

This study provides the first systematic assessment of the quality and reliability of Chinese-language TikTok videos on AD, revealing three principal findings: (I) 92.72% of videos were uploaded by health professionals, markedly higher than the 30–60% typically reported for cardiovascular content on English-language TikTok; (II) although videos from health professionals achieved significantly higher scores, overall quality remained moderate [median GQS 3 (IQR, 2–4), mDISCERN 2 (IQR, 1–2)]; (III) video duration was positively correlated with GQS (r=0.30). After adjusting for uploader type as a covariate, longer video duration remained significantly associated with higher GQS (β=0.002) and was also significantly associated with higher mDISCERN (β=0.001). In contrast, higher engagement metrics (likes, comments, collections, and shares) were associated with lower quality and reliability scores (r=−0.17 to −0.42, β<0), demonstrating a clear “popularity paradox”.

The strikingly high proportion of health professional-generated content (92.72%) in our cohort represents a notable departure from the majority of published TikTok cardiovascular literature. English-language studies on YouTube consistently report physician or institutional authorship ranging from 22% to 68% across coronary artery disease, heart failure, atrial fibrillation, and endovascular procedures (28-31). Even within mainland China, health professionals contributed only 75.1% of coronary artery disease content and 85.6% of high-engagement hypertension videos (32,33). Our findings more closely resemble disease-specific patterns observed for acute stroke on TikTok, where neurologists accounted for 94.6% of videos, and for thyroid eye disease, where health professionals contributed 92% (34,35). These data suggest that clinical urgency and an extremely narrow therapeutic time window serve as the strongest drivers motivating specialist involvement. This inference is consistent with the highly lethal nature of AD in urban Chinese populations, which has an annual incidence of 2–3 per 100,000 and a 3-year mortality rate that remains as high as 16.3% (36).

Overall quality in the present study aligns closely with prior short-video cardiovascular research. Gong et al. reported median GQS 2 and mDISCERN 2 for heart failure videos on TikTok in China (37), while Wu et al. documented identical median GQS 3 and mDISCERN 2 for hypertension content on TikTok (32). Another study on TikTok reveals comparable results: median GQS 3 and mDISCERN 3 for mitral valve regurgitation (38), and mean mDISCERN-derived reliability scores of 2.1–3.3 for atherosclerosis content (33). Longer-form platforms exhibit similar limitations; Charl et al. found that only 53% of YouTube Aortic valve stenosis videos were of high educational value despite 74% originating from professional sources, with 47% rated as misleading or incomplete (39). Collectively, these findings underscore a platform-agnostic challenge: professional credentials alone are insufficient to ensure comprehensive, evidence-based education within the constraints of ultra-short formats.

The “popularity paradox” identified in the present study (likes: r=−0.38 with GQS, r=−0.30 with mDISCERN; comments: r=−0.42 with GQS, r=−0.32 with mDISCERN) is highly consistent with findings reported in global studies across multiple specialties. Ming et al. found that, on TikTok, videos concerning myopia uploaded by healthcare professionals exhibited significantly higher quality despite lower popularity metrics (P<0.018) (40). Similarly, another study on cardiovascular disease videos showed that higher-quality content was consistently associated with reduced user engagement (41). Our regression results (all β<0 for likes and comments on both outcomes) provide robust statistical corroboration that algorithmic preference for emotionally charged, sensational content systematically disadvantages scientifically rigorous material across specialties and linguistic ecosystems.

The inverse relationship between engagement and quality is mechanistically attributable to TikTok’s cascaded recommendation algorithm, which prioritises early-stage interaction metrics (completion rate, likes, comments, and shares within the initial ~1,000 views) over factual accuracy (42). Content eliciting strong emotional arousal—fear, shock, or empathy—generates immediate engagement, thereby securing exponential exposure. The strongest negative correlations observed with likes (r=−0.38 for GQS, −0.30 for mDISCERN) and comments (r=−0.42 and −0.32, respectively) directly mirror this dynamic: creators often employ sensational hooks (“sudden tearing chest pain—patient collapses instantly”) while omitting critical elements such as differential diagnosis, risk stratification, Stanford classification, or urgent transfer protocols—core domains penalised by GQS and mDISCERN. In contrast, collections and shares, which reflect perceived long-term utility, exhibited weak or non-significant associations, consistent with findings in myopia-related TikTok content (40). Health professional videos scored significantly higher on both GQS (P<0.001) and mDISCERN (P=0.003) than non-professional videos (Figure 3A,3B), affirming the protective role of medical expertise. However, no inter-group differences were observed in any engagement metric, indicating that professional status alone cannot overcome algorithmic barriers. Achieving widespread dissemination of high-quality content requires clinicians to master short-form narrative techniques (e.g., compelling hooks within 15 seconds, visual metaphors for complex pathophysiology) while preserving scientific rigor.

The public health implications are considerable. With mortality rising 1–2% per hour in untreated acute AD (1-3) and TikTok boasting over 700 million daily active users in China (10-12), even videos with median engagement reach hundreds of thousands via algorithmic amplification. Low-quality or misleading content risks delaying presentation, promoting inappropriate self-diagnosis, or inducing undue anxiety. Evidence-based interventions are therefore urgently needed: (I) platform-level collaboration between TikTok and authoritative bodies (e.g., the Chinese College of Cardiovascular Physicians or the Chinese Medical Association) to implement a verified “high-quality cardiovascular content” certification with preferential recommendation weighting; (II) integration of medical short-video production training into residency curricula and continuing professional development, focusing on delivering complete, guideline-concordant messages within 15–60 seconds; (III) encouragement of partnerships between tertiary hospitals and professional multi-channel networks (MCNs) to produce standardised, serialised educational series; and (IV) inclusion of acute AD in national priority lists for health misinformation monitoring, aligned with recent NICE guidance in the UK (43).

Several limitations should be noted. First, the cross-sectional design captures content only up to March 1, 2026, preventing assessment of temporal changes. Second, social media retrieval is inherently susceptible to selection bias because results depend on platform search mechanics and ranking algorithms; although we searched without logging in to improve reproducibility, this approach may not fully reflect personalised feeds. Third, uploader’s professional status was self-reported and could not be universally verified. Fourth, restricting analyses to Chinese-language videos may limit generalisability to other linguistic settings. Finally, despite prior validation, GQS and mDISCERN involve subjective judgment.

Conclusions

This study found that 92.72% of Chinese-language TikTok videos on AD were uploaded by health professionals. Although videos from health professionals achieved significantly higher quality scores than those from non-professionals, overall quality remained only moderate (median GQS 3, mDISCERN 2). Longer video duration was associated with higher GQS and mDISCERN scores, whereas greater user engagement (particularly likes and comments) was consistently associated with lower quality and reliability, clearly demonstrating the “popularity paradox”.

Despite physician dominance, algorithmic incentives favour sensationalism over evidence-based content, potentially jeopardising public recognition of a condition in which mortality rises 1–2% per untreated hour. Urgent multifaceted interventions are required: clinician training in evidence-based short-video production, platform-certified high-quality labelling with preferential recommendation, standardised series by hospitals and societies, and enhanced regulatory oversight. Transforming TikTok into a reliable tool for life-saving education demands collaborative action from physicians, platforms, and policymakers.

Supplementary

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Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study did not involve any human participants, clinical data, laboratory animals, or histological research. All analyzed data were obtained from publicly available TikTok videos, and data collection was conducted in full accordance with TikTok’s terms of service. No private or personally identifiable information was collected or processed, and no interaction with users occurred. Therefore, ethical approval and informed consent were not required for this study.