Quality and Reliability Analysis of Short Videos Related to COPD Smoking Cessation on TikTok and Kwai: a Cross-sectional Study

Quality and Reliability Analysis of Short Videos Related to COPD Smoking Cessation on TikTok and Kwai: a Cross-sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Quality and Reliability Analysis of Short Videos Related to COPD Smoking Cessation on TikTok and Kwai: a Cross-sectional Study Xiaojuan Li, Yi Wang, Siqi Chen, Yuling Li, Hongxia Pu, Renli Deng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8338348/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Chronic obstructive pulmonary disease (COPD) is a major global health issue, and smoking cessation is the most effective intervention to slow its progression. While short-video platforms have become a popular source for health information, the quality of COPD smoking cessation content remains unclear. This study aimed to evaluate the quality and reliability of such videos on TikTok and Kwai, and to compare differences across release sources and platforms. A search using the term "COPD quit smoking" on both platforms yielded a final sample of 200 short videos. Video quality and reliability were assessed using completeness score, Global Quality Score (GQS), modified DISCERN (mDISCERN), and Medical Quality Video Evaluation Tool (MQ-VET) scores. The results showed that despite the overall poor video quality (mean GQS = 3; mDISCERN = 2), TikTok videos showed significantly higher mDISCERN and completeness scores than Kwai ( P < 0.01). Videos from respiratory specialists received significantly higher quality scores and user engagement than those from other sources ( P < 0.05). Video quality was positively correlated with video duration and completeness. These findings highlight the need for greater regulation of online medical content and encourage medical professionals in producing accurate, high-quality health information. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research COPD smoking cessation short video information quality TikTok Kwai Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Chronic obstructive pulmonary disease (COPD) is a common, preventable condition characterized by incompletely reversible bronchial obstruction 1 . Its pathogenesis involves chronic airflow limitation from mucus hypersecretion and persistent inflammation, presenting as chronic bronchitis or emphysema 2 . These manifestations worsen during acute exacerbations, potentially resulting in hospitalization or death in severe cases. According to WHO projects COPD will become the third leading cause of death by 2030 3 . Global annual mortality attributable to COPD expected to reach 4.4 million by 2040 4 . In addition, a meta-analysis published in the Lancet reported a global COPD prevalence of up to 10.3% 5 . The China Pulmonary Health (CPH) study indicates the prevalence of COPD in adults over 20 years old was 13.7%. The number of COPD patients in China is estimated at approximately 100 million 6 . With accelerating population aging, the COPD prevalence is expected to continue rising, underscoring it as a major global public health challenge. Risk factors for COPD include smoking, respiratory infection, air pollution, and chronic comorbidities 7 . Smoking is widely recognized as a major factor in the development and progression of COPD, with tobacco use demonstrably associated with increased prevalence and mortality rates 8 . Epidemiological data indicate COPD prevalence rates of 13.7% among smokers, 10.9% among former smokers, and 6.2% among non-smokers 6 , demonstrating a significantly elevated risk associated with active smoking. Mechanistically, smoke components like tar and nicotine impair mucociliary clearance and cause bronchoconstriction. At a molecular level, smoke triggers immune cell infiltration and a protease-antiprotease imbalance, culminating in parenchymal destruction and emphysema 2 . Duration and intensity of smoking are critical determinants of COPD risk. Consequently, smoking cessation remains the most effective and cost-efficient intervention to reduce the risk of COPD development and attenuate accelerated decline in lung function 9 . However, COPD patients encounter multiple challenges in disease awareness and management, including limited understanding of smoking hazards and the importance of smoking cessation, inadequate self-management capabilities for smoking cessation, and difficulties in accessing and evaluating credible smoking cessation information. These issues not only impede the process of smoking cessation but also directly affect the control and prognosis of COPD 10 . Currently, social short-video platforms such as TikTok, Kwai, and Bilibili function as potent and accessible channels for health information dissemination, offering potential advantages in COPD prevention and management. These include simplifying complex medical knowledge to enhance disease awareness and self-management capabilities and strengthening smoking cessation motivation through patient narratives to promote healthy behavior change. They also establish patient communities to share experiences and foster social support networks. Importantly, social media can reach patients in remote areas or with mobility problems to compensate for limitations in traditional offline health education 11–13 . Nevertheless, the substantial quantity of short videos raises concerns regarding variable content quality. The dissemination of inaccurate or misleading health information through non-professional videos may lead to delayed diagnoses, inappropriate treatment, and potentially severe health outcomes for patients with COPD. Although a large number of short videos related to smoking cessation for COPD exist on TikTok and Kwai, the quality of these videos remains inadequately evaluated. Therefore, this study aims to analyze the quality of COPD smoking cessation short videos on TikTok and Kwai. Method Data collection New accounts were registered and activated on each platform and used these new accounts to search for the Chinese keyword "慢阻肺戒烟 (COPD quit smoking)" on the Chinese versions of TikTok and Kwai on August 9, 2025. Videos from the default sorting algorithm of each platform were selected for initial screening. Duplicated, irrelevant, muted, non-Chinese and advertising videos were excluded, and 200 videos were finally included in analysis (Fig. 1 ). The primary reason for selecting default videos was that platforms generally recommend content based on algorithms considering metrics like click-through rates, popularity, and user preferences. This approach reflects the content most users actually encounter, enabling an assessment of the quality and accuracy of smoking cessation information that COPD patients are actually exposed to. Additionally, choosing default results instead of manual screening can avoid subjective bias and ensure data objectivity 14 . Video screening and data extraction were conducted by two respiratory specialist nurses. Two independent reviewers recorded and assessed basic video characteristics and content quality. Data were recorded in Excel spreadsheets, with a third reviewer appointed to conduct collaborative assessments for disputed issues. Collected video information included the video platform, source, publication date, duration, number of likes, comments, and saves, content descriptions, and quality ratings. Instruments Two investigators (both senior respiratory physicians from tertiary hospitals) evaluated the quality and reliability of the videos using the Global Quality Score (GQS), the modified DISCERN (mDISCERN) tool, and the Medical Quality Video Evaluation Tool (MQ-VET). The GQS is a commonly used instrument for assessing informational quality of videos, scored on a 5-point Likert scale ranging from 1 (very poor) to 5 (excellent), with higher scores representing superior quality 15,16 . The mDISCERN tool evaluates video reliability based on clarity, relevance, traceability, robustness, and fairness 17,18 , scored from 0 to 5, with higher scores indicating greater reliability 19 . The MQ-VET score is a validated and reliable instrument for standardizing online medical video assessments, demonstrating an overall Cronbach's α of 0.72 20 . The scale comprises four domains with a total of 15 items, each scored from 1 to 5. Higher total scores indicate greater video reliability. In addition, according to smoking cessation guidelines 21 , the evaluation content was developed to include tobacco dependence diagnosis, the relationship between COPD and smoking, harms of smoking, benefits of quitting, cessation interventions and withdrawal symptoms. Each aspect was categorized as not involved (0 points), partially explained (1 points), and fully explained (2 points). Video producers were classified into three categories: respiratory specialists, non-respiratory specialists, and individual users (without medical backgrounds). This study used Gooseeker software to collect user comments. Gooseeker is a free web data capture tool in China with ease of use. Unlike Python, Gooseeker includes built-in web scraping capabilities that require no manual coding and are easy to learn. All data were collated in Microsoft Excel. The analysis of comment attitudes was completed through the Weiciyun platform. This software is a professional and easy-to-operate web text analysis tool that performs exceptionally well in Chinese text processing. Compared with similar tools, Weiciyun can provide efficient and accurate automated analysis solutions when processing large amounts of comment data. The classification of each video is based on the main attitude expressed in the comments. The steps are as follows 22 : (1) importing user comments extracted from each video; (2) combining custom emotional words and adjusting emoji settings, including using terms such as "like," "heart," "clap," and "thank you" for positive attitudes and terms such as "sobbing," "cracking up," and "awkward laugh" for negative attitudes; (3) automatically analyze the proportion of positive, neutral and negative attitudes in each video comment; (4) determine the comment attitude category of the video according to the highest proportion identified. Statistical analysis Continuous variables were presented as mean ± standard deviation (SD) or median and interquartile range (IQR) based on whether they followed a normal distribution and analyzed using t -tests or Mann-Whitney U tests. Categorical variables were presented as frequencies and percentages and analyzed using chi-square tests or Fisher's exact probability test. Correlations among different scores and video characteristics were evaluated using Spearman's rank correlation coefficient. Cohen's kappa coefficient was used to evaluate the consistency of scores between two independent raters. Statistical analysis were conducted using SPSS version 29.0, and figures were generated with GraphPad Prism version 10. P -value < 0.05 was considered statistically significant. Results Video characteristics Based on inclusion and exclusion criteria, a total of 100 videos were included from each of the TikTok and Kwai platforms for data extraction and analysis. Baseline characteristics of the study sample are summarized in Table 1 . The median number of likes and saves for the 200 videos was 136 (IQR: 34–625) and 23 (IQR: 5-143), respectively. The median comment count was 11 (IQR: 3–45), and the median video duration was 68 seconds (IQR: 49–125). The median content completeness score was 5 (IQR: 4–7). Regarding quality and reliability assessment tools, the median GQS score was 3 (IQR: 3–4), the median mDISCERN score was 2 (IQR: 2–3), and the median MQ-VET score was 50 (IQR: 46–55). These results suggest that the overall completeness, quality, and reliability of the video content were generally low to moderate. Among all included videos, 189 publishers (94.5%) were platform-certified respiratory specialists, 8 publishers (4%) were non-respiratory specialists (physicians from other departments), and 3 publishers (1.5%) were non-medical professionals individual users. Table 1 Characteristics of 200 COPD smoking cessation-related short videos on TikTok and Kwai. Characteristic N = 200 Likes , Median (IQR) 136 (34, 625) Comments , Median (IQR) 11 (3, 45) Saves , Median (IQR) 23 (5, 143) Duration , Median (IQR) 68 (49, 105) Days since published , Median (IQR) 263 (103, 689) Completeness scores , Median (IQR) 5 (4, 7) GQS scores , Median (IQR) 3 (3, 4) mDISCERN scores , Median (IQR) 2 (2, 3) MQ-VET scores , Median (IQR) 50 (46, 55) Respiratory specialists , n (%) 94.5% Non-respiratory specialists , n (%) 4% Individual user , n (%) 1.5% Video Completeness The analysis of the completeness of COPD smoking cessation videos found that 60.5% of the videos fully explained the causal relationship between smoking and COPD, while only 1% did not address this topic. However, 55.0% of the videos did not provide any explanation regarding the diagnosis of tobacco dependence. Fully explained on the health hazards of smoking was only presented in 11.5% of the videos. Additionally, 66.5% of the videos did not mention the withdrawal symptoms, and only 11.0% offered a complete description of the clinical manifestations of withdrawal reactions. Smoking cessation interventions were covered in 79.5% of the videos (Fig. 2 ). Video Content Comparison Significant differences in content coverage were observed between the platforms (Table 2 ). Specifically for tobacco dependence diagnosis, more videos on Kwai omitted the topic than on TikTok (66% vs. 44%, P = 0.006). Regarding the benefits of quitting, TikTok outperformed Kwai in full explanations (48% vs. 29%), while Kwai had a higher omission rate (47% vs. 20%, P < 0.001). Notably, both platforms performed well in explaining the smoking-COPD causal link (64% vs. 57%, P = 0.594). In contrast, withdrawal symptoms were frequently overlooked, with high non-mention rates (63% vs. 70%) and low complete explanation rates (9% vs. 13%, P = 0.151). Table 2 Comparison of the integrity of TikTok and Kwai video content. Video content Not involved Partial explanation Full explanation P value TikTok Kwai TikTok Kwai TikTok Kwai Diagnosis , n (%) 44 (44%) 66 (66%) 47 (47%) 30 (30%) 9 (9%) 4 (4%) 0.006 Link to COPD , n (%) 1 (1%) 1 (1%) 35 (35%) 42 (42%) 64 (64%) 57 (57%) 0.594 Harms , n (%) 34 (34%) 48 (48%) 51 (51%) 44 (44%) 15 (15%) 8 (8%) 0.081 Benefits , n (%) 20 (20%) 47 (47%) 32 (32%) 24 (24%) 48 (48%) 29 (29%) < 0.001 Treatment , n (%) 19 (19%) 22 (22%) 48 (48%) 33 (33%) 33 (33%) 45 (45%) 0.089 Symptoms , n (%) 63 (63%) 70 (70%) 28 (28%) 17 (17%) 9 (9%) 13 (13%) 0.151 Video quality and reliability of different platforms The consistency of completeness scores, GQS, mDISCERN, and MQ-VET scores between the two raters was excellent, with Cohen's kappa values of 0.901, 0.821, 0.743, and 0.767 and intraclass correlation coefficients (ICC) of 0.998, 0.995, 0.993, and 0.987, respectively. Comparative analysis revealed significant differences in user engagement metrics or video duration between the two platforms ( P > 0.05) (Table 3 ). Although the median duration exceeded one minute on both platforms, TikTok videos were slightly longer than Kwai. In addition, TikTok videos demonstrated significantly higher mDISCERN scores (median: 3 vs. 2, P < 0.001) and completeness scores (median: 6 vs. 5, P < 0.05) compared to Kwai. Conversely, Kwai videos were published more recently than TikTok videos (median: 171 vs. 416 days, P 0.05). These trends were consistently illustrated in the violin plots (Fig. 3 ). Table 3 Comparative analysis of video characteristics collected by TikTok and Kwai. Characteristic Platform P value TikTok ( N = 100) Kwai ( N = 100) Likes Median (IQR) 113 (35, 601) 165 (31, 642) 0.858 Comments Median (IQR) 12 (3, 51) 9 (2, 39) 0.154 Saves Median (IQR) 22 (6, 200) 23 (4, 138) 0.394 Duration Median (IQR) 73 (51, 120) 66 (48, 89) 0.063 Days since published Median (IQR) 171 (56, 451) 416 (143, 1155) < 0.001 GQS scores Median (IQR) 3 (2, 4) 3 (3, 3) 0.639 mDISCERN scores Median (IQR) 3 (2, 3) 2 (2, 2) < 0.001 MQ-VET scores Median (IQR) 50 (46, 55) 50 (47, 54) 0.918 Completeness scores Median (IQR) 6(4, 7) 5 (3, 6) 0.001 Respiratory specialists n (%) 90 (90%) 99 (99%) 0.349 Non-respiratory specialists n (%) 7 (7%) 1 (1%) Individual user n (%) 3 (3%) 0 (0%) Video quality and reliability of different sources We compared the distribution of completeness and quality scores among videos uploaded by respiratory specialists, non-respiratory specialists, and individual users. The results indicated that videos produced by respiratory specialists demonstrated significantly superior performance in terms of likes, saves, GQS scores, and MQ-VET scores compared to the other two groups ( P < 0.05) (Table 4 ). As shown in Fig. 4 (A-D), violin plots revealed significant differences in both distribution patterns and central tendencies across the four quality metrics (completeness, GQS, mDISCERN, and MQ-VET scores) among different video sources. In general, respiratory specialists demonstrated significantly higher quality scores than non-respiratory specialists. While individual users attained relatively high scores on certain metrics, they exhibited greater variability in quality, suggesting substantial differences in video quality stability among individual users. Table 4 Comparison of 200 COPD smoking cessation-related videos from different sources. Respiratory specialists ( N = 189) Non-respiratory specialists ( N = 8) Individual users ( N = 3) P Value Likes Median (IQR) 167 (36, 664) 27 (8, 111) 13 (0, 7) 0.004 Comments Median (IQR) 11 (3,48) 6 (1, 16) 3 (0, 2) 0.329 Saves Median (IQR) 25 (8, 189) 3 (2, 5) 4 (0, 3) 0.001 Completeness score Median (IQR) 5 (4, 7) 4 (3, 4) 3 (0, 2) 0.105 GQS score Median (IQR) 3 (3, 4) 2 (2, 3) 2 (0, 1) 0.018 mDISCERN score Median (IQR) 2 (2, 3) 2 (2, 2) 2 (0, 1) 0.225 MQ-VET score Median (IQR) 50 (47, 55) 44 (42, 46) 42 (0, 38) 0.007 Video Comments Attitude The distribution of emotional attitudes in comments on COPD smoking cessation videos across the two platforms is shown in Fig. 4 . Each video was categorized into positive, negative, or neutral attitudes based on the sentiment expressed in the comments. Results indicate that positive, negative, and neutral attitudes accounted for 63.0%, 11.0%, and 26.0% of TikTok comments, respectively. For Kwai videos, the proportions were 50.0%, 32.0%, and 18.0%. The difference in comment sentiment distribution between the two platforms was statistically significant (χ²=13.206, P < 0.001). Further between-group comparisons revealed that compared to TikTok, Kwai had a significantly higher proportion of negative comments (11% vs. 32%, P = 0.043), while the proportion of positive attitudes was noticeably lower and a lower proportion of positive comments (63% vs. 50%, P < 0.001). Correlation analysis Due to the non-normal distribution of all variables, Spearman's correlation coefficient was employed to analyze relationships between variables (Fig. 5 ). The results indicated that video comments ( r = 0.155, P < 0.05) and saves ( r = 0.140, P < 0.05) showed significant positive correlations with video duration, suggesting a synergistic relationship between video length and user engagement intensity. Furthermore, the video published days was positively correlated with engagement metrics, including likes ( r = 0.349, P < 0.001), comments ( r = 0.220, P < 0.05), and saves ( r = 0.194, P < 0.05), indicating that older videos may accumulated greater user interaction. Video duration also correlated with quality-related scores, including completeness score ( r = 0.458, P < 0.001), GQS ( r = 0.451, P < 0.001), mDISCERN ( r = 0.450, P < 0.001), and MQ-VET ( r = 0.538, P < 0.001). The correlation between content quality scores and interactivity indicators was predominantly positive. Notably, completeness scores demonstrated the strongest correlations with GQS, mDISCERN, and MQ-VET scores, indicating that more comprehensive content is associated with higher quality and reliability. Discussion This study systematically evaluated the quality and reliability of COPD smoking cessation short videos on TikTok and Kwai. The results indicated the overall information quality of the two platforms was not ideal, but TikTok videos significantly outperform Kwai in terms of content completeness and reliability (mDISCERN). Regarding video sources, videos published by respiratory specialists exhibited higher user engagement and quality scores than those produced by non-respiratory specialists and individual users, consistent with the conclusions of previous studies on short videos about liver cancer and acute pancreatitis 23,24 . In addition, video duration was significantly positively correlated with content integrity and various quality scores. This cross-sectional study used three validated assessment instruments (GQS, mDISCERN, and MQ-VET) to evaluate the quality and reliability of COPD smoking cessation videos on the social media platforms TikTok and Kwai. The results demonstrate that the overall quality and reliability of these videos were suboptimal, consistent with previous reports on the poor quality of disease-related information disseminated via social media 25–29 . TikTok videos received higher scores than Kwai in mDISCERN and completeness scores, this may reflect the differences between the two platforms in user groups, content ecology, etc. Although the median video duration on both platforms exceeds one minute, TikTok videos tend to be slightly longer, which may be one reason they can accommodate more complete information. This finding aligns with previous research on orthognathic surgery videos, which indicates that longer videos typically provide more in-depth and reliable information 30 . It should be noted that TikTok's relative advantage does not imply high overall quality, as its content still exhibits considerable room for improvement. COPD smoking cessation videos created by respiratory specialists demonstrated markedly superior quality compared to those from other sources, underscoring the pivotal role of professional expertise in disseminating health information. Specialist physicians are more likely to develop content based on evidence-based guidelines, such as the WHO guidelines for smoking cessation, thereby providing more comprehensive and reliable medical advice. Additionally, research suggests a positive correlation between content completeness and overall quality. However, shorter videos often result in overly simplified content, making it difficult to fully explain complex concepts such as the disease mechanisms, withdrawal symptoms, and intervention measures involved in COPD smoking cessation. While short-form videos have an advantage in attracting users, their fleeting nature may sacrifice the depth and completeness of information, resulting in lower overall quality. Correlation analysis revealed longer video duration was positively correlated with user engagement metrics (comments, saves, likes), a finding supported by previous studies 23,31 . This indicates that detailed COPD smoking cessation videos possesses sustainable dissemination value. However, no consistent significant positive correlation was found between user engagement and video quality ratings. Some studies even reported a slight negative correlation between the two 32,33 . The discrepancy in conclusions may be attributed to variations in users' ability to discern information quality across different disease contexts. It is worth noting that videos posted for longer tend to accumulate more interactions, which is probably a reflection of their cumulative effect rather than their intrinsic quality. This also suggests that users may struggle to discern the quality of information. Furthermore, recommendation algorithms that rely on popularity and engagement metrics may inadvertently amplify the reach of low-quality content, thereby posing a potential risk to patients' access to accurate health information. This study observed that while most comments expressed positive or neutral attitudes toward COPD smoking cessation videos and generally recognized the importance of quitting smoking, there was a significant discrepancy between users' subjective evaluations and the objective quality of the content. Many low-quality videos still garnered substantial numbers of likes and saves. Analysis of comment sections revealed that content user interactions consisted of emotional resonance (e.g, “ This really motivated me ”), personal experience sharing (e.g, “ I quit in a week using this method ”), and brief endorsements (e.g, “ I saved it , ” “ It worked ”). This suggests that user engagement is largely influenced by emotional appeal and superficial credibility rather than by rational judgments based on scientific rigor. Users tend to trust content that aligns with their expectations and resonates emotionally, rarely questioning sources or accuracy of the information. Therefore, platforms and medical creators should implement algorithmic optimization and enhance professional certification measures to guide users toward evidence-based content and promote health behavioral changes. The disease burden of COPD is heavy, and smoking cessation is the core of its prevention and treatment 34 . As an increasingly important health information source, short video platforms have the potential to address gaps in traditional health education 35 . However, the current study reveals concerning deficiencies in overall quality, including missing critical data such as withdrawal symptoms and tobacco dependence diagnoses. Low-quality short video information may mislead patients, delay treatment, and exacerbate disease burden. To improve the quality of short videos, platforms should prioritize promoting certified medical professionals, and implement clearly verification badges to assist users in identifying authoritative sources. Medical creators should employ narrative and visual techniques to produce content that is both scientifically accurate and engaging to raise patients awareness of smoking cessation. Meanwhile, the public should be educated to prioritize content published by certified medical professionals when accessing online health information, and to maintain a critical mindset towards information from non-professional sources. The advantage of this study lies in the first systematic comparison of COPD smoking cessation content on two major short-video platforms in China, TikTok and Kwai. Multiple verified tools such as GQS, mDISCERN, and MQ-VET were adopted for multi-dimensional assessment. The consistency among raters was high and the results were reliable. The research also has several limitations. Firstly, cross-sectional design can only reflect the content status at a certain point in time, while the short video platform content updates rapidly and requires longitudinal tracking. Secondly, we analyzed only the top 100 videos on each platform. While this method is widely used and reflects what regular users most frequently encounter, it may not represent all the content of the platform. Finally, this study only focused on Chinese videos, and the generalizability of the conclusions may be limited. Future research can be extended to other languages and cultural contexts to promote the dissemination of reliable online medical information worldwide. Conclusion The overall information quality and reliability of COPD smoking cessation short videos on both platforms are relatively low, though videos on TikTok demonstrated marginally superior quality than those on Kwai. Analyzing different video sources reveals that content published by respiratory specialists exhibits superior quality and higher reliability. Video duration and completeness are closely associated with information quality. Therefore, meticulously selecting high-quality, reliable videos on these platforms is imperative for effectively guiding smoking cessation among COPD patients. Declarations Funding This work was supported by the National Science and Technology Innovation 2030,Noncommunicable Chronic Diseases-National Science and Technology Major Project (GrantN0.2024ZD0524300. 2024ZD0524304). Authors' contributions X.L., Y.W., and R.D. designed the study, Y.L. and S.C. collected the data. X.L. and Y.W. drafted an early version of the manuscript. R.D. and X.L. completed the revision of the manuscript. S.C. and H.P. checked the manuscript for lexical and grammatical correctness. Y.W., performed the data analysis and image processing. R.D. and X.L. improved the image quality and counted the relevant clinical information of the data set used. R.D., supervised the study. All authors contributed to the writing of the manuscript and gave final approval for submission and publication. Data Availability Statement The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests. Conflict of interest The authors have no conflicts of interest to declare. Ethical approval Not applicable. Informed consent Not applicable. Patient releases Not applicable. References Nationale VersorgungsLeitlinie COPD , (2021). Upadhyay, P. et al. Animal models and mechanisms of tobacco smoke-induced chronic obstructive pulmonary disease (COPD). Journal of toxicology and environmental health. Part B, Critical reviews 26 , 275-305, (2023). Kahnert, K., Jörres, R. A., Behr, J. & Welte, T. The Diagnosis and Treatment of COPD and Its Comorbidities. Deutsches Arzteblatt international 120 , 434-444, (2023). Foreman, K. J. et al. Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016-40 for 195 countries and territories. Lancet (London, England) 392 , 2052-2090, (2018). Adeloye, D. et al. Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic review and modelling analysis. The Lancet. Respiratory medicine 10 , 447-458, (2022). Wang, C. et al. Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): a national cross-sectional study. Lancet (London, England) 391 , 1706-1717, (2018). Qian, Y., Cai, C., Sun, M., Lv, D. & Zhao, Y. Analyses of Factors Associated with Acute Exacerbations of Chronic Obstructive Pulmonary Disease: A Review. International journal of chronic obstructive pulmonary disease 18 , 2707-2723, (2023). Cook, S. F. et al. Cigarettes, ENDS Use, and Chronic Obstructive Pulmonary Disease Incidence: A Prospective Longitudinal Study. Am J Prev Med 65 , 173-181, (2023). van Eerd, E. A., van der Meer, R. M., van Schayck, O. C. & Kotz, D. Smoking cessation for people with chronic obstructive pulmonary disease. The Cochrane database of systematic reviews 2016 , Cd010744, (2016). Nakken, N. et al. Knowledge gaps in patients with COPD and their proxies. BMC pulmonary medicine 17 , 136, (2017). Welch, V., Petkovic, J., Pardo Pardo, J., Rader, T. & Tugwell, P. Interactive social media interventions to promote health equity: an overview of reviews. Health promotion and chronic disease prevention in Canada : research, policy and practice 36 , 63-75, (2016). Zhao, X., Yao, X., Sui, B. & Zhou, Y. Current status of short video as a source of information on lung cancer: a cross-sectional content analysis study. Frontiers in oncology 14 , 1420976, (2024). Xu, Q. R. et al. Online short videos promoting public breast cancer literacy: a pretest-posttest control group trial on efficiency, attitude, and influencing factors. Frontiers in public health 11 , 1198780, (2023). Song, S. et al. Short-Video Apps as a Health Information Source for Chronic Obstructive Pulmonary Disease: Information Quality Assessment of TikTok Videos. Journal of medical Internet research 23 , e28318, (2021). Balci, A. S., Cabuk, K., Topcu, H., Efe, A. C. & Ulas, M. G. Evaluation of the reliability, utility, and quality of the lid loading videos on YouTube. International ophthalmology 43 , 2065-2072, (2023). Mukewar, S., Mani, P., Wu, X., Lopez, R. & Shen, B. YouTube and inflammatory bowel disease. Journal of Crohn's & colitis 7 , 392-402, (2013). Aydin, M. F. & Aydin, M. A. Quality and reliability of information available on YouTube and Google pertaining gastroesophageal reflux disease. International journal of medical informatics 137 , 104107, (2020). Sun, F., Yang, F. & Zheng, S. Evaluation of the Liver Disease Information in Baidu Encyclopedia and Wikipedia: Longitudinal Study. Journal of medical Internet research 23 , e17680, (2021). Langille, M. et al. Systematic review of the quality of patient information on the internet regarding inflammatory bowel disease treatments. Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association 8 , 322-328, (2010). Guler, M. A. & Aydın, E. O. Development and validation of a tool for evaluating YouTube-based medical videos. Irish journal of medical science 191 , 1985-1990, (2022). Organization, W. H. in WHO clinical treatment guideline for tobacco cessation in adults (World Health Organization, 2024). Li, Z. et al. Hip fractures in Chinese TikTok (Douyin) short videos: an analysis of information quality, content and user comment attitudes. Frontiers in public health 13 , 1563188, (2025). Zheng, S. et al. Quality and Reliability of Liver Cancer-Related Short Chinese Videos on TikTok and Bilibili: Cross-Sectional Content Analysis Study. J Med Internet Res 25 , e47210, (2023). Mao, T. et al. Evaluation of TikTok videos on acute pancreatitis: content quality and reliability analysis. BMC Public Health 24 , 1216, (2024). Cui, N. et al. Quality Assessment of TikTok as a Source of Information About Mitral Valve Regurgitation in China: Cross-Sectional Study. J Med Internet Res 26 , e55403, (2024). Chen, Y. et al. The quality and reliability of short videos about thyroid nodules on BiliBili and TikTok: Cross-sectional study. Digit Health 10 , 20552076241288831, (2024). Liu, Z. et al. YouTube/ Bilibili/ TikTok videos as sources of medical information on laryngeal carcinoma: cross-sectional content analysis study. BMC Public Health 24 , 1594, (2024). Wang, M. et al. Bilibili, TikTok, and YouTube as sources of information on gastric cancer: assessment and analysis of the content and quality. BMC Public Health 24 , 57, (2024). He, F. et al. Quality and reliability of pediatric pneumonia related short videos on mainstream platforms: cross-sectional study. BMC Public Health 25 , 1896, (2025). Xia, L., Bao, J., Yao, K., Zhang, J. & Yu, W. Evaluation of the quality and reliability of Chinese content about orthognathic surgery on BiliBili and TikTok: a cross-sectional study. Sci Rep 15 , 28967, (2025). Zhaksylyk, A., Yessirkepov, M., Akyol, A. & Kocyigit, B. F. YouTube as a Source of Information on Public Health Ethics. J Korean Med Sci 39 , e61, (2024). Sun, F., Zheng, S. & Wu, J. Quality of Information in Gallstone Disease Videos on TikTok: Cross-sectional Study. J Med Internet Res 25 , e39162, (2023). Mueller, S. M. et al. Fiction, Falsehoods, and Few Facts: Cross-Sectional Study on the Content-Related Quality of Atopic Eczema-Related Videos on YouTube. J Med Internet Res 22 , e15599, (2020). Lu, W. et al. Tobacco and COPD: presenting the World Health Organization (WHO) Tobacco Knowledge Summary. Respir Res 25 , 338, (2024). Kong, W., Song, S., Zhao, Y. C., Zhu, Q. & Sha, L. TikTok as a Health Information Source: Assessment of the Quality of Information in Diabetes-Related Videos. J Med Internet Res 23 , e30409, (2021). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 14 Jan, 2026 Reviewers invited by journal 19 Dec, 2025 Editor invited by journal 15 Dec, 2025 Editor assigned by journal 12 Dec, 2025 Submission checks completed at journal 12 Dec, 2025 First submitted to journal 11 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8338348","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":562984606,"identity":"d0530bc2-bf4e-4ce5-89d9-a2ae5c7036be","order_by":0,"name":"Xiaojuan Li","email":"","orcid":"","institution":"Affiliated Hospital of Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojuan","middleName":"","lastName":"Li","suffix":""},{"id":562984607,"identity":"47f1a20b-e469-4519-9971-6ad1d95dd4db","order_by":1,"name":"Yi Wang","email":"","orcid":"","institution":"Affiliated Hospital of Zunyi Medical 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16:20:06","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98887,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8338348/v1/9ae7701eba295b53e83eb672.html"},{"id":99307201,"identity":"7c571625-fca2-4a7c-84ad-1af676b966fb","added_by":"auto","created_at":"2025-12-31 16:05:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47960,"visible":true,"origin":"","legend":"\u003cp\u003eSearch strategy and video screening procedure.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8338348/v1/5063cc46d99669aba362ff48.png"},{"id":98815852,"identity":"b25f177d-8942-4525-ad1c-cbb69de1ee2b","added_by":"auto","created_at":"2025-12-22 16:20:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47250,"visible":true,"origin":"","legend":"\u003cp\u003eThe completeness of 200 video contents related to COPD smoking cessation.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8338348/v1/c276b9c45f7e8df6f2cafb99.png"},{"id":98815844,"identity":"9b09d6b8-3900-43e9-971f-8045a8c5be91","added_by":"auto","created_at":"2025-12-22 16:20:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":94896,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of video information quality from different platforms. (\u003cstrong\u003eA\u003c/strong\u003e) Distribution of completeness scores for TikTok and Kwai. (\u003cstrong\u003eB\u003c/strong\u003e) Distribution of GQS scores for TikTok and Kwai. (\u003cstrong\u003eC\u003c/strong\u003e) Distribution of mDISCERN scores for TikTok and Kwai. (\u003cstrong\u003eD\u003c/strong\u003e) Distribution of MQ-VET scores for TikTok and Kwai.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8338348/v1/d78699e637a655bb3970a1d9.png"},{"id":98815851,"identity":"390c55be-830e-4411-8f84-3668b30ad899","added_by":"auto","created_at":"2025-12-22 16:20:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":18107,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of video comment attitudes on different platforms.\u003cstrong\u003e *\u003c/strong\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, \u003cstrong\u003e***\u003c/strong\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8338348/v1/bf6d51657d52aa5575bee19b.png"},{"id":98815850,"identity":"81967b76-1274-4d74-a683-008228935780","added_by":"auto","created_at":"2025-12-22 16:20:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":25390,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation among video likes, comments, saves, duration, days since published, completeness scores, GQS, mDISCERN, and MQ-VET\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8338348/v1/1c73a80324de4ee0a502c6d6.png"},{"id":99322283,"identity":"f9c75933-1adb-4bd8-af8f-3fedfc1abbf5","added_by":"auto","created_at":"2025-12-31 16:43:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1432139,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8338348/v1/2de34ddb-ed82-4c0d-8f81-762233411500.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quality and Reliability Analysis of Short Videos Related to COPD Smoking Cessation on TikTok and Kwai: a Cross-sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic obstructive pulmonary disease (COPD) is a common, preventable condition characterized by incompletely reversible bronchial obstruction \u003csup\u003e1\u003c/sup\u003e. Its pathogenesis involves chronic airflow limitation from mucus hypersecretion and persistent inflammation, presenting as chronic bronchitis or emphysema \u003csup\u003e2\u003c/sup\u003e. These manifestations worsen during acute exacerbations, potentially resulting in hospitalization or death in severe cases.\u003c/p\u003e \u003cp\u003eAccording to WHO projects COPD will become the third leading cause of death by 2030 \u003csup\u003e3\u003c/sup\u003e. Global annual mortality attributable to COPD expected to reach 4.4\u0026nbsp;million by 2040 \u003csup\u003e4\u003c/sup\u003e. In addition, a meta-analysis published in the Lancet reported a global COPD prevalence of up to 10.3% \u003csup\u003e5\u003c/sup\u003e. The China Pulmonary Health (CPH) study indicates the prevalence of COPD in adults over 20 years old was 13.7%. The number of COPD patients in China is estimated at approximately 100\u0026nbsp;million \u003csup\u003e6\u003c/sup\u003e. With accelerating population aging, the COPD prevalence is expected to continue rising, underscoring it as a major global public health challenge.\u003c/p\u003e \u003cp\u003eRisk factors for COPD include smoking, respiratory infection, air pollution, and chronic comorbidities \u003csup\u003e7\u003c/sup\u003e. Smoking is widely recognized as a major factor in the development and progression of COPD, with tobacco use demonstrably associated with increased prevalence and mortality rates \u003csup\u003e8\u003c/sup\u003e. Epidemiological data indicate COPD prevalence rates of 13.7% among smokers, 10.9% among former smokers, and 6.2% among non-smokers \u003csup\u003e6\u003c/sup\u003e, demonstrating a significantly elevated risk associated with active smoking. Mechanistically, smoke components like tar and nicotine impair mucociliary clearance and cause bronchoconstriction. At a molecular level, smoke triggers immune cell infiltration and a protease-antiprotease imbalance, culminating in parenchymal destruction and emphysema \u003csup\u003e2\u003c/sup\u003e. Duration and intensity of smoking are critical determinants of COPD risk. Consequently, smoking cessation remains the most effective and cost-efficient intervention to reduce the risk of COPD development and attenuate accelerated decline in lung function \u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, COPD patients encounter multiple challenges in disease awareness and management, including limited understanding of smoking hazards and the importance of smoking cessation, inadequate self-management capabilities for smoking cessation, and difficulties in accessing and evaluating credible smoking cessation information. These issues not only impede the process of smoking cessation but also directly affect the control and prognosis of COPD \u003csup\u003e10\u003c/sup\u003e. Currently, social short-video platforms such as TikTok, Kwai, and Bilibili function as potent and accessible channels for health information dissemination, offering potential advantages in COPD prevention and management. These include simplifying complex medical knowledge to enhance disease awareness and self-management capabilities and strengthening smoking cessation motivation through patient narratives to promote healthy behavior change. They also establish patient communities to share experiences and foster social support networks. Importantly, social media can reach patients in remote areas or with mobility problems to compensate for limitations in traditional offline health education \u003csup\u003e11\u0026ndash;13\u003c/sup\u003e. Nevertheless, the substantial quantity of short videos raises concerns regarding variable content quality. The dissemination of inaccurate or misleading health information through non-professional videos may lead to delayed diagnoses, inappropriate treatment, and potentially severe health outcomes for patients with COPD.\u003c/p\u003e \u003cp\u003eAlthough a large number of short videos related to smoking cessation for COPD exist on TikTok and Kwai, the quality of these videos remains inadequately evaluated. Therefore, this study aims to analyze the quality of COPD smoking cessation short videos on TikTok and Kwai.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eNew accounts were registered and activated on each platform and used these new accounts to search for the Chinese keyword \"慢阻肺戒烟 (COPD quit smoking)\" on the Chinese versions of TikTok and Kwai on August 9, 2025. Videos from the default sorting algorithm of each platform were selected for initial screening. Duplicated, irrelevant, muted, non-Chinese and advertising videos were excluded, and 200 videos were finally included in analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The primary reason for selecting default videos was that platforms generally recommend content based on algorithms considering metrics like click-through rates, popularity, and user preferences. This approach reflects the content most users actually encounter, enabling an assessment of the quality and accuracy of smoking cessation information that COPD patients are actually exposed to. Additionally, choosing default results instead of manual screening can avoid subjective bias and ensure data objectivity \u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eVideo screening and data extraction were conducted by two respiratory specialist nurses. Two independent reviewers recorded and assessed basic video characteristics and content quality. Data were recorded in Excel spreadsheets, with a third reviewer appointed to conduct collaborative assessments for disputed issues. Collected video information included the video platform, source, publication date, duration, number of likes, comments, and saves, content descriptions, and quality ratings.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInstruments\u003c/h3\u003e\n\u003cp\u003eTwo investigators (both senior respiratory physicians from tertiary hospitals) evaluated the quality and reliability of the videos using the Global Quality Score (GQS), the modified DISCERN (mDISCERN) tool, and the Medical Quality Video Evaluation Tool (MQ-VET). The GQS is a commonly used instrument for assessing informational quality of videos, scored on a 5-point Likert scale ranging from 1 (very poor) to 5 (excellent), with higher scores representing superior quality \u003csup\u003e15,16\u003c/sup\u003e. The mDISCERN tool evaluates video reliability based on clarity, relevance, traceability, robustness, and fairness \u003csup\u003e17,18\u003c/sup\u003e, scored from 0 to 5, with higher scores indicating greater reliability \u003csup\u003e19\u003c/sup\u003e. The MQ-VET score is a validated and reliable instrument for standardizing online medical video assessments, demonstrating an overall Cronbach's \u003cem\u003eα\u003c/em\u003e of 0.72 \u003csup\u003e20\u003c/sup\u003e. The scale comprises four domains with a total of 15 items, each scored from 1 to 5. Higher total scores indicate greater video reliability. In addition, according to smoking cessation guidelines \u003csup\u003e21\u003c/sup\u003e, the evaluation content was developed to include tobacco dependence diagnosis, the relationship between COPD and smoking, harms of smoking, benefits of quitting, cessation interventions and withdrawal symptoms. Each aspect was categorized as not involved (0 points), partially explained (1 points), and fully explained (2 points). Video producers were classified into three categories: respiratory specialists, non-respiratory specialists, and individual users (without medical backgrounds).\u003c/p\u003e \u003cp\u003eThis study used Gooseeker software to collect user comments. Gooseeker is a free web data capture tool in China with ease of use. Unlike Python, Gooseeker includes built-in web scraping capabilities that require no manual coding and are easy to learn. All data were collated in Microsoft Excel. The analysis of comment attitudes was completed through the Weiciyun platform. This software is a professional and easy-to-operate web text analysis tool that performs exceptionally well in Chinese text processing. Compared with similar tools, Weiciyun can provide efficient and accurate automated analysis solutions when processing large amounts of comment data. The classification of each video is based on the main attitude expressed in the comments. The steps are as follows\u003csup\u003e22\u003c/sup\u003e: (1) importing user comments extracted from each video; (2) combining custom emotional words and adjusting emoji settings, including using terms such as \"like,\" \"heart,\" \"clap,\" and \"thank you\" for positive attitudes and terms such as \"sobbing,\" \"cracking up,\" and \"awkward laugh\" for negative attitudes; (3) automatically analyze the proportion of positive, neutral and negative attitudes in each video comment; (4) determine the comment attitude category of the video according to the highest proportion identified.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median and interquartile range (IQR) based on whether they followed a normal distribution and analyzed using \u003cem\u003et\u003c/em\u003e-tests or Mann-Whitney U tests. Categorical variables were presented as frequencies and percentages and analyzed using chi-square tests or Fisher's exact probability test. Correlations among different scores and video characteristics were evaluated using Spearman's rank correlation coefficient. Cohen's kappa coefficient was used to evaluate the consistency of scores between two independent raters. Statistical analysis were conducted using SPSS version 29.0, and figures were generated with GraphPad Prism version 10. \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eVideo characteristics\u003c/h2\u003e \u003cp\u003eBased on inclusion and exclusion criteria, a total of 100 videos were included from each of the TikTok and Kwai platforms for data extraction and analysis. Baseline characteristics of the study sample are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median number of likes and saves for the 200 videos was 136 (IQR: 34\u0026ndash;625) and 23 (IQR: 5-143), respectively. The median comment count was 11 (IQR: 3\u0026ndash;45), and the median video duration was 68 seconds (IQR: 49\u0026ndash;125). The median content completeness score was 5 (IQR: 4\u0026ndash;7). Regarding quality and reliability assessment tools, the median GQS score was 3 (IQR: 3\u0026ndash;4), the median mDISCERN score was 2 (IQR: 2\u0026ndash;3), and the median MQ-VET score was 50 (IQR: 46\u0026ndash;55). These results suggest that the overall completeness, quality, and reliability of the video content were generally low to moderate. Among all included videos, 189 publishers (94.5%) were platform-certified respiratory specialists, 8 publishers (4%) were non-respiratory specialists (physicians from other departments), and 3 publishers (1.5%) were non-medical professionals individual users.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of 200 COPD smoking cessation-related short videos on TikTok and Kwai.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;200\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLikes\u003c/b\u003e, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136 (34, 625)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComments\u003c/b\u003e, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (3, 45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSaves\u003c/b\u003e, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (5, 143)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDuration\u003c/b\u003e, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (49, 105)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDays since published\u003c/b\u003e, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e263 (103, 689)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCompleteness scores\u003c/b\u003e, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (4, 7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGQS scores\u003c/b\u003e, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3, 4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emDISCERN scores\u003c/b\u003e, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2, 3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-VET scores\u003c/b\u003e, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (46, 55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRespiratory specialists\u003c/b\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-respiratory specialists\u003c/b\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndividual user\u003c/b\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eVideo Completeness\u003c/h2\u003e \u003cp\u003eThe analysis of the completeness of COPD smoking cessation videos found that 60.5% of the videos fully explained the causal relationship between smoking and COPD, while only 1% did not address this topic. However, 55.0% of the videos did not provide any explanation regarding the diagnosis of tobacco dependence. Fully explained on the health hazards of smoking was only presented in 11.5% of the videos. Additionally, 66.5% of the videos did not mention the withdrawal symptoms, and only 11.0% offered a complete description of the clinical manifestations of withdrawal reactions. Smoking cessation interventions were covered in 79.5% of the videos (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVideo Content Comparison\u003c/h3\u003e\n\u003cp\u003eSignificant differences in content coverage were observed between the platforms (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically for tobacco dependence diagnosis, more videos on Kwai omitted the topic than on TikTok (66% vs. 44%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). Regarding the benefits of quitting, TikTok outperformed Kwai in full explanations (48% vs. 29%), while Kwai had a higher omission rate (47% vs. 20%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, both platforms performed well in explaining the smoking-COPD causal link (64% vs. 57%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.594). In contrast, withdrawal symptoms were frequently overlooked, with high non-mention rates (63% vs. 70%) and low complete explanation rates (9% vs. 13%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.151).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the integrity of TikTok and Kwai video content.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVideo content\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNot involved\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePartial explanation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eFull explanation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTikTok\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKwai\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTikTok\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKwai\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTikTok\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKwai\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiagnosis\u003c/b\u003e,\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e(9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLink to COPD\u003c/b\u003e,\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHarms\u003c/b\u003e,\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e(8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBenefits\u003c/b\u003e,\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment\u003c/b\u003e,\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSymptoms\u003c/b\u003e,\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e(9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eVideo quality and reliability of different platforms\u003c/h3\u003e\n\u003cp\u003eThe consistency of completeness scores, GQS, mDISCERN, and MQ-VET scores between the two raters was excellent, with Cohen's kappa values of 0.901, 0.821, 0.743, and 0.767 and intraclass correlation coefficients (ICC) of 0.998, 0.995, 0.993, and 0.987, respectively. Comparative analysis revealed significant differences in user engagement metrics or video duration between the two platforms (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Although the median duration exceeded one minute on both platforms, TikTok videos were slightly longer than Kwai. In addition, TikTok videos demonstrated significantly higher mDISCERN scores (median: 3 vs. 2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and completeness scores (median: 6 vs. 5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to Kwai. Conversely, Kwai videos were published more recently than TikTok videos (median: 171 vs. 416 days, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, No significant inter-platform differences were observed in engagement metrics (likes, comments, saves) or GQS and MQ-VET scores (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These trends were consistently illustrated in the violin plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative analysis of video characteristics collected by TikTok and Kwai.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTikTok (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKwai (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLikes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113 (35, 601)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165 (31, 642)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComments\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (3, 51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (2, 39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSaves\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (6, 200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (4, 138)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDuration\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (51, 120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (48, 89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDays since published\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171 (56, 451)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e416 (143, 1155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGQS scores\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2, 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emDISCERN scores\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2, 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-VET scores\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (46, 55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (47, 54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCompleteness scores\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(4, 7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3, 6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRespiratory specialists\u003c/b\u003e\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-respiratory specialists\u003c/b\u003e\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndividual user\u003c/b\u003e\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eVideo quality and reliability of different sources\u003c/h2\u003e \u003cp\u003eWe compared the distribution of completeness and quality scores among videos uploaded by respiratory specialists, non-respiratory specialists, and individual users. The results indicated that videos produced by respiratory specialists demonstrated significantly superior performance in terms of likes, saves, GQS scores, and MQ-VET scores compared to the other two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (A-D), violin plots revealed significant differences in both distribution patterns and central tendencies across the four quality metrics (completeness, GQS, mDISCERN, and MQ-VET scores) among different video sources. In general, respiratory specialists demonstrated significantly higher quality scores than non-respiratory specialists. While individual users attained relatively high scores on certain metrics, they exhibited greater variability in quality, suggesting substantial differences in video quality stability among individual users.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of 200 COPD smoking cessation-related videos from different sources.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRespiratory specialists\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;189)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-respiratory specialists\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndividual\u003c/p\u003e \u003cp\u003eusers\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLikes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167 (36, 664)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (8, 111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (0, 7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComments\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (3,48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (1, 16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (0, 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSaves\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (8, 189)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2, 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (0, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCompleteness score\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (4, 7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (3, 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (0, 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGQS score\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3, 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emDISCERN score\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2, 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-VET score\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (47, 55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (42, 46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (0, 38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eVideo Comments Attitude\u003c/h2\u003e \u003cp\u003eThe distribution of emotional attitudes in comments on COPD smoking cessation videos across the two platforms is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Each video was categorized into positive, negative, or neutral attitudes based on the sentiment expressed in the comments. Results indicate that positive, negative, and neutral attitudes accounted for 63.0%, 11.0%, and 26.0% of TikTok comments, respectively. For Kwai videos, the proportions were 50.0%, 32.0%, and 18.0%. The difference in comment sentiment distribution between the two platforms was statistically significant (χ\u0026sup2;=13.206, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Further between-group comparisons revealed that compared to TikTok, Kwai had a significantly higher proportion of negative comments (11% vs. 32%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043), while the proportion of positive attitudes was noticeably lower and a lower proportion of positive comments (63% vs. 50%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis\u003c/h2\u003e \u003cp\u003eDue to the non-normal distribution of all variables, Spearman's correlation coefficient was employed to analyze relationships between variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The results indicated that video comments (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.155, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and saves (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.140, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) showed significant positive correlations with video duration, suggesting a synergistic relationship between video length and user engagement intensity. Furthermore, the video published days was positively correlated with engagement metrics, including likes (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.349, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), comments (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.220, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and saves (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.194, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that older videos may accumulated greater user interaction. Video duration also correlated with quality-related scores, including completeness score (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.458, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), GQS (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.451, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), mDISCERN (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.450, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and MQ-VET (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.538, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The correlation between content quality scores and interactivity indicators was predominantly positive. Notably, completeness scores demonstrated the strongest correlations with GQS, mDISCERN, and MQ-VET scores, indicating that more comprehensive content is associated with higher quality and reliability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study systematically evaluated the quality and reliability of COPD smoking cessation short videos on TikTok and Kwai. The results indicated the overall information quality of the two platforms was not ideal, but TikTok videos significantly outperform Kwai in terms of content completeness and reliability (mDISCERN). Regarding video sources, videos published by respiratory specialists exhibited higher user engagement and quality scores than those produced by non-respiratory specialists and individual users, consistent with the conclusions of previous studies on short videos about liver cancer and acute pancreatitis \u003csup\u003e23,24\u003c/sup\u003e. In addition, video duration was significantly positively correlated with content integrity and various quality scores.\u003c/p\u003e \u003cp\u003eThis cross-sectional study used three validated assessment instruments (GQS, mDISCERN, and MQ-VET) to evaluate the quality and reliability of COPD smoking cessation videos on the social media platforms TikTok and Kwai. The results demonstrate that the overall quality and reliability of these videos were suboptimal, consistent with previous reports on the poor quality of disease-related information disseminated via social media \u003csup\u003e25\u0026ndash;29\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTikTok videos received higher scores than Kwai in mDISCERN and completeness scores, this may reflect the differences between the two platforms in user groups, content ecology, etc. Although the median video duration on both platforms exceeds one minute, TikTok videos tend to be slightly longer, which may be one reason they can accommodate more complete information. This finding aligns with previous research on orthognathic surgery videos, which indicates that longer videos typically provide more in-depth and reliable information\u003csup\u003e30\u003c/sup\u003e. It should be noted that TikTok's relative advantage does not imply high overall quality, as its content still exhibits considerable room for improvement.\u003c/p\u003e \u003cp\u003eCOPD smoking cessation videos created by respiratory specialists demonstrated markedly superior quality compared to those from other sources, underscoring the pivotal role of professional expertise in disseminating health information. Specialist physicians are more likely to develop content based on evidence-based guidelines, such as the WHO guidelines for smoking cessation, thereby providing more comprehensive and reliable medical advice.\u003c/p\u003e \u003cp\u003eAdditionally, research suggests a positive correlation between content completeness and overall quality. However, shorter videos often result in overly simplified content, making it difficult to fully explain complex concepts such as the disease mechanisms, withdrawal symptoms, and intervention measures involved in COPD smoking cessation. While short-form videos have an advantage in attracting users, their fleeting nature may sacrifice the depth and completeness of information, resulting in lower overall quality.\u003c/p\u003e \u003cp\u003eCorrelation analysis revealed longer video duration was positively correlated with user engagement metrics (comments, saves, likes), a finding supported by previous studies \u003csup\u003e23,31\u003c/sup\u003e. This indicates that detailed COPD smoking cessation videos possesses sustainable dissemination value. However, no consistent significant positive correlation was found between user engagement and video quality ratings. Some studies even reported a slight negative correlation between the two\u003csup\u003e32,33\u003c/sup\u003e. The discrepancy in conclusions may be attributed to variations in users' ability to discern information quality across different disease contexts. It is worth noting that videos posted for longer tend to accumulate more interactions, which is probably a reflection of their cumulative effect rather than their intrinsic quality. This also suggests that users may struggle to discern the quality of information. Furthermore, recommendation algorithms that rely on popularity and engagement metrics may inadvertently amplify the reach of low-quality content, thereby posing a potential risk to patients' access to accurate health information.\u003c/p\u003e \u003cp\u003eThis study observed that while most comments expressed positive or neutral attitudes toward COPD smoking cessation videos and generally recognized the importance of quitting smoking, there was a significant discrepancy between users' subjective evaluations and the objective quality of the content. Many low-quality videos still garnered substantial numbers of likes and saves. Analysis of comment sections revealed that content user interactions consisted of emotional resonance (e.g, \u0026ldquo; \u003cem\u003eThis really motivated me\u003c/em\u003e \u0026rdquo;), personal experience sharing (e.g, \u0026ldquo;\u003cem\u003eI quit in a week using this method\u003c/em\u003e \u0026rdquo;), and brief endorsements (e.g, \u0026ldquo; \u003cem\u003eI saved it\u003c/em\u003e, \u0026rdquo; \u0026ldquo;\u003cem\u003eIt worked\u003c/em\u003e \u0026rdquo;). This suggests that user engagement is largely influenced by emotional appeal and superficial credibility rather than by rational judgments based on scientific rigor. Users tend to trust content that aligns with their expectations and resonates emotionally, rarely questioning sources or accuracy of the information. Therefore, platforms and medical creators should implement algorithmic optimization and enhance professional certification measures to guide users toward evidence-based content and promote health behavioral changes.\u003c/p\u003e \u003cp\u003eThe disease burden of COPD is heavy, and smoking cessation is the core of its prevention and treatment\u003csup\u003e34\u003c/sup\u003e. As an increasingly important health information source, short video platforms have the potential to address gaps in traditional health education\u003csup\u003e35\u003c/sup\u003e. However, the current study reveals concerning deficiencies in overall quality, including missing critical data such as withdrawal symptoms and tobacco dependence diagnoses. Low-quality short video information may mislead patients, delay treatment, and exacerbate disease burden.\u003c/p\u003e \u003cp\u003eTo improve the quality of short videos, platforms should prioritize promoting certified medical professionals, and implement clearly verification badges to assist users in identifying authoritative sources. Medical creators should employ narrative and visual techniques to produce content that is both scientifically accurate and engaging to raise patients awareness of smoking cessation. Meanwhile, the public should be educated to prioritize content published by certified medical professionals when accessing online health information, and to maintain a critical mindset towards information from non-professional sources.\u003c/p\u003e \u003cp\u003eThe advantage of this study lies in the first systematic comparison of COPD smoking cessation content on two major short-video platforms in China, TikTok and Kwai. Multiple verified tools such as GQS, mDISCERN, and MQ-VET were adopted for multi-dimensional assessment. The consistency among raters was high and the results were reliable. The research also has several limitations. Firstly, cross-sectional design can only reflect the content status at a certain point in time, while the short video platform content updates rapidly and requires longitudinal tracking. Secondly, we analyzed only the top 100 videos on each platform. While this method is widely used and reflects what regular users most frequently encounter, it may not represent all the content of the platform. Finally, this study only focused on Chinese videos, and the generalizability of the conclusions may be limited. Future research can be extended to other languages and cultural contexts to promote the dissemination of reliable online medical information worldwide.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe overall information quality and reliability of COPD smoking cessation short videos on both platforms are relatively low, though videos on TikTok demonstrated marginally superior quality than those on Kwai. Analyzing different video sources reveals that content published by respiratory specialists exhibits superior quality and higher reliability. Video duration and completeness are closely associated with information quality. Therefore, meticulously selecting high-quality, reliable videos on these platforms is imperative for effectively guiding smoking cessation among COPD patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Science and Technology Innovation 2030,Noncommunicable Chronic Diseases-National Science and Technology Major Project (GrantN0.2024ZD0524300. 2024ZD0524304).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.L., Y.W., and R.D. designed the study, Y.L. and S.C. collected the data. X.L. and Y.W. drafted an early version of the manuscript. R.D. and X.L. completed the revision of the manuscript. S.C. and H.P. checked the manuscript for lexical and grammatical correctness. Y.W., performed the data analysis and image processing. R.D. and X.L. improved the image quality and counted the relevant clinical information of the data set used. R.D., supervised the study. All authors contributed to the writing of the manuscript and gave final approval for submission and publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient releases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cem\u003eNationale VersorgungsLeitlinie COPD\u003c/em\u003e, \u0026lt;https://goldcopd.org/2022-gold-reports/\u0026gt; (2021).\u003c/li\u003e\n\u003cli\u003eUpadhyay, P.\u003cem\u003e et al.\u003c/em\u003e Animal models and mechanisms of tobacco smoke-induced chronic obstructive pulmonary disease (COPD). \u003cem\u003eJournal of toxicology and environmental health. Part B, Critical reviews\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 275-305, (2023).\u003c/li\u003e\n\u003cli\u003eKahnert, K., J\u0026ouml;rres, R. A., Behr, J. \u0026amp; Welte, T. The Diagnosis and Treatment of COPD and Its Comorbidities. \u003cem\u003eDeutsches Arzteblatt international\u003c/em\u003e \u003cstrong\u003e120\u003c/strong\u003e, 434-444, (2023).\u003c/li\u003e\n\u003cli\u003eForeman, K. J.\u003cem\u003e et al.\u003c/em\u003e Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016-40 for 195 countries and territories. \u003cem\u003eLancet (London, England)\u003c/em\u003e \u003cstrong\u003e392\u003c/strong\u003e, 2052-2090, (2018).\u003c/li\u003e\n\u003cli\u003eAdeloye, D.\u003cem\u003e et al.\u003c/em\u003e Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic review and modelling analysis. \u003cem\u003eThe Lancet. Respiratory medicine\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 447-458, (2022).\u003c/li\u003e\n\u003cli\u003eWang, C.\u003cem\u003e et al.\u003c/em\u003e Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): a national cross-sectional study. \u003cem\u003eLancet (London, England)\u003c/em\u003e \u003cstrong\u003e391\u003c/strong\u003e, 1706-1717, (2018).\u003c/li\u003e\n\u003cli\u003eQian, Y., Cai, C., Sun, M., Lv, D. \u0026amp; Zhao, Y. Analyses of Factors Associated with Acute Exacerbations of Chronic Obstructive Pulmonary Disease: A Review. \u003cem\u003eInternational journal of chronic obstructive pulmonary disease\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 2707-2723, (2023).\u003c/li\u003e\n\u003cli\u003eCook, S. F.\u003cem\u003e et al.\u003c/em\u003e Cigarettes, ENDS Use, and Chronic Obstructive Pulmonary Disease Incidence: A Prospective Longitudinal Study. \u003cem\u003eAm J Prev Med\u003c/em\u003e \u003cstrong\u003e65\u003c/strong\u003e, 173-181, (2023).\u003c/li\u003e\n\u003cli\u003evan Eerd, E. A., van der Meer, R. M., van Schayck, O. C. \u0026amp; Kotz, D. Smoking cessation for people with chronic obstructive pulmonary disease. \u003cem\u003eThe Cochrane database of systematic reviews\u003c/em\u003e \u003cstrong\u003e2016\u003c/strong\u003e, Cd010744, (2016).\u003c/li\u003e\n\u003cli\u003eNakken, N.\u003cem\u003e et al.\u003c/em\u003e Knowledge gaps in patients with COPD and their proxies. \u003cem\u003eBMC pulmonary medicine\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 136, (2017).\u003c/li\u003e\n\u003cli\u003eWelch, V., Petkovic, J., Pardo Pardo, J., Rader, T. \u0026amp; Tugwell, P. Interactive social media interventions to promote health equity: an overview of reviews. \u003cem\u003eHealth promotion and chronic disease prevention in Canada : research, policy and practice\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 63-75, (2016).\u003c/li\u003e\n\u003cli\u003eZhao, X., Yao, X., Sui, B. \u0026amp; Zhou, Y. Current status of short video as a source of information on lung cancer: a cross-sectional content analysis study. \u003cem\u003eFrontiers in oncology\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 1420976, (2024).\u003c/li\u003e\n\u003cli\u003eXu, Q. R.\u003cem\u003e et al.\u003c/em\u003e Online short videos promoting public breast cancer literacy: a pretest-posttest control group trial on efficiency, attitude, and influencing factors. \u003cem\u003eFrontiers in public health\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1198780, (2023).\u003c/li\u003e\n\u003cli\u003eSong, S.\u003cem\u003e et al.\u003c/em\u003e Short-Video Apps as a Health Information Source for Chronic Obstructive Pulmonary Disease: Information Quality Assessment of TikTok Videos. \u003cem\u003eJournal of medical Internet research\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, e28318, (2021).\u003c/li\u003e\n\u003cli\u003eBalci, A. S., Cabuk, K., Topcu, H., Efe, A. C. \u0026amp; Ulas, M. G. Evaluation of the reliability, utility, and quality of the lid loading videos on YouTube. \u003cem\u003eInternational ophthalmology\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 2065-2072, (2023).\u003c/li\u003e\n\u003cli\u003eMukewar, S., Mani, P., Wu, X., Lopez, R. \u0026amp; Shen, B. YouTube and inflammatory bowel disease. \u003cem\u003eJournal of Crohn\u0026apos;s \u0026amp; colitis\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 392-402, (2013).\u003c/li\u003e\n\u003cli\u003eAydin, M. F. \u0026amp; Aydin, M. A. Quality and reliability of information available on YouTube and Google pertaining gastroesophageal reflux disease. \u003cem\u003eInternational journal of medical informatics\u003c/em\u003e \u003cstrong\u003e137\u003c/strong\u003e, 104107, (2020).\u003c/li\u003e\n\u003cli\u003eSun, F., Yang, F. \u0026amp; Zheng, S. Evaluation of the Liver Disease Information in Baidu Encyclopedia and Wikipedia: Longitudinal Study. \u003cem\u003eJournal of medical Internet research\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, e17680, (2021).\u003c/li\u003e\n\u003cli\u003eLangille, M.\u003cem\u003e et al.\u003c/em\u003e Systematic review of the quality of patient information on the internet regarding inflammatory bowel disease treatments. \u003cem\u003eClinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 322-328, (2010).\u003c/li\u003e\n\u003cli\u003eGuler, M. A. \u0026amp; Aydın, E. O. Development and validation of a tool for evaluating YouTube-based medical videos. \u003cem\u003eIrish journal of medical science\u003c/em\u003e \u003cstrong\u003e191\u003c/strong\u003e, 1985-1990, (2022).\u003c/li\u003e\n\u003cli\u003eOrganization, W. H. in \u003cem\u003eWHO clinical treatment guideline for tobacco cessation in adults\u003c/em\u003e (World Health Organization, 2024).\u003c/li\u003e\n\u003cli\u003eLi, Z.\u003cem\u003e et al.\u003c/em\u003e Hip fractures in Chinese TikTok (Douyin) short videos: an analysis of information quality, content and user comment attitudes. \u003cem\u003eFrontiers in public health\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 1563188, (2025).\u003c/li\u003e\n\u003cli\u003eZheng, S.\u003cem\u003e et al.\u003c/em\u003e Quality and Reliability of Liver Cancer-Related Short Chinese Videos on TikTok and Bilibili: Cross-Sectional Content Analysis Study. \u003cem\u003eJ Med Internet Res\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, e47210, (2023).\u003c/li\u003e\n\u003cli\u003eMao, T.\u003cem\u003e et al.\u003c/em\u003e Evaluation of TikTok videos on acute pancreatitis: content quality and reliability analysis. \u003cem\u003eBMC Public Health\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 1216, (2024).\u003c/li\u003e\n\u003cli\u003eCui, N.\u003cem\u003e et al.\u003c/em\u003e Quality Assessment of TikTok as a Source of Information About Mitral Valve Regurgitation in China: Cross-Sectional Study. \u003cem\u003eJ Med Internet Res\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, e55403, (2024).\u003c/li\u003e\n\u003cli\u003eChen, Y.\u003cem\u003e et al.\u003c/em\u003e The quality and reliability of short videos about thyroid nodules on BiliBili and TikTok: Cross-sectional study. \u003cem\u003eDigit Health\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 20552076241288831, (2024).\u003c/li\u003e\n\u003cli\u003eLiu, Z.\u003cem\u003e et al.\u003c/em\u003e YouTube/ Bilibili/ TikTok videos as sources of medical information on laryngeal carcinoma: cross-sectional content analysis study. \u003cem\u003eBMC Public Health\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 1594, (2024).\u003c/li\u003e\n\u003cli\u003eWang, M.\u003cem\u003e et al.\u003c/em\u003e Bilibili, TikTok, and YouTube as sources of information on gastric cancer: assessment and analysis of the content and quality. \u003cem\u003eBMC Public Health\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 57, (2024).\u003c/li\u003e\n\u003cli\u003eHe, F.\u003cem\u003e et al.\u003c/em\u003e Quality and reliability of pediatric pneumonia related short videos on mainstream platforms: cross-sectional study. \u003cem\u003eBMC Public Health\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 1896, (2025).\u003c/li\u003e\n\u003cli\u003eXia, L., Bao, J., Yao, K., Zhang, J. \u0026amp; Yu, W. Evaluation of the quality and reliability of Chinese content about orthognathic surgery on BiliBili and TikTok: a cross-sectional study. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 28967, (2025).\u003c/li\u003e\n\u003cli\u003eZhaksylyk, A., Yessirkepov, M., Akyol, A. \u0026amp; Kocyigit, B. F. YouTube as a Source of Information on Public Health Ethics. \u003cem\u003eJ Korean Med Sci\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, e61, (2024).\u003c/li\u003e\n\u003cli\u003eSun, F., Zheng, S. \u0026amp; Wu, J. Quality of Information in Gallstone Disease Videos on TikTok: Cross-sectional Study. \u003cem\u003eJ Med Internet Res\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, e39162, (2023).\u003c/li\u003e\n\u003cli\u003eMueller, S. M.\u003cem\u003e et al.\u003c/em\u003e Fiction, Falsehoods, and Few Facts: Cross-Sectional Study on the Content-Related Quality of Atopic Eczema-Related Videos on YouTube. \u003cem\u003eJ Med Internet Res\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, e15599, (2020).\u003c/li\u003e\n\u003cli\u003eLu, W.\u003cem\u003e et al.\u003c/em\u003e Tobacco and COPD: presenting the World Health Organization (WHO) Tobacco Knowledge Summary. \u003cem\u003eRespir Res\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 338, (2024).\u003c/li\u003e\n\u003cli\u003eKong, W., Song, S., Zhao, Y. C., Zhu, Q. \u0026amp; Sha, L. TikTok as a Health Information Source: Assessment of the Quality of Information in Diabetes-Related Videos. \u003cem\u003eJ Med Internet Res\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, e30409, (2021).\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COPD, smoking cessation, short video, information quality, TikTok, Kwai","lastPublishedDoi":"10.21203/rs.3.rs-8338348/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8338348/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChronic obstructive pulmonary disease (COPD) is a major global health issue, and smoking cessation is the most effective intervention to slow its progression. While short-video platforms have become a popular source for health information, the quality of COPD smoking cessation content remains unclear. This study aimed to evaluate the quality and reliability of such videos on TikTok and Kwai, and to compare differences across release sources and platforms. A search using the term \"COPD quit smoking\" on both platforms yielded a final sample of 200 short videos. Video quality and reliability were assessed using completeness score, Global Quality Score (GQS), modified DISCERN (mDISCERN), and Medical Quality Video Evaluation Tool (MQ-VET) scores. The results showed that despite the overall poor video quality (mean GQS\u0026thinsp;=\u0026thinsp;3; mDISCERN\u0026thinsp;=\u0026thinsp;2), TikTok videos showed significantly higher mDISCERN and completeness scores than Kwai (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Videos from respiratory specialists received significantly higher quality scores and user engagement than those from other sources (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Video quality was positively correlated with video duration and completeness. These findings highlight the need for greater regulation of online medical content and encourage medical professionals in producing accurate, high-quality health information.\u003c/p\u003e","manuscriptTitle":"Quality and Reliability Analysis of Short Videos Related to COPD Smoking Cessation on TikTok and Kwai: a Cross-sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 16:20:01","doi":"10.21203/rs.3.rs-8338348/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"114702324646036737625144817459397137676","date":"2026-01-14T07:22:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-19T09:44:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-15T11:50:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-12T06:17:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-12T06:16:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-11T15:26:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a98a5cab-eb12-4e51-ac59-aedb2e628928","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59955478,"name":"Health sciences/Diseases"},{"id":59955479,"name":"Health sciences/Health care"},{"id":59955480,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-12-22T16:20:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 16:20:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8338348","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8338348","identity":"rs-8338348","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}