Quality and reliability of rectal prolapse short-video content on tiktok and bilibili: A cross-sectional content analysis

Abstract

Objective

To evaluate engagement metrics, content coverage, information quality, and reliability of rectal prolapse–related short videos on TikTok and Bilibili, and to assess the influence of uploader type.

Methods

We conducted a cross-sectional content analysis. Using visitor mode and the default comprehensive ranking algorithm of each platform, two researchers searched for “rectal prolapse” from October 14 to 22, 2025. The first 150 videos per platform were screened, yielding 256 unique eligible videos. Extracted variables included platform, upload date, duration, and engagement metrics (likes, comments, favorites, shares). Video quality and reliability were independently assessed by two colorectal surgeons using the Global Quality Scale (GQS), modified DISCERN (mDISCERN), and the JAMA benchmark; discrepancies were adjudicated by a senior expert.

Results

TikTok outperformed Bilibili across engagement metrics (all p<0.001); video duration did not differ significantly (p=0.068). Median GQS and JAMA scores were higher on TikTok (both p≤0.001); mDISCERN differences were smaller yet significant (p=0.008). Videos from specialists scored higher on GQS, mDISCERN, and JAMA (all p<0.001). Content emphasized treatment and symptoms, whereas prevention and differential diagnosis were under-covered. Engagement metrics were highly inter-correlated but showed very weak correlations with quality scores.

Conclusions

Video volume is increasing, whereas overall quality remains moderate. TikTok showed higher engagement and quality than Bilibili. Specialists play a key role in improving content. Platforms should incorporate reliability-weighted ranking signals and promote an essential-elements checklist for patient education.

Keywords

rectal prolapse short-video platforms health information quality tiktok bilibili

Introduction

Rectal prolapse is characterized by downward displacement and external protrusion of the rectal mucosa, the anal canal, full-thickness rectum, or, in some cases, a segment of the sigmoid colon.¹ Its main manifestation is rectal protrusion through the anus, often accompanied by incomplete evacuation, mucous discharge, pain, and bleeding.² Although rectal prolapse is relatively uncommon, with an estimated prevalence below 0.5% in the general population, it can occur across all ages and sexes, with a higher burden among women and older adults.^3–5 Although usually benign and rarely life-threatening, the condition is prone to recurrence and can be difficult to manage, substantially impairing quality of life and imposing psychological and economic stress.^6,7 Timely symptoms recognition, appropriate referral, and adherence to standardized care pathways are essential for reducing avoidable morbidity and supporting perioperative decision-making. Enhancing public awareness of red-flag symptoms and evidence-based treatment options may reduce anxiety, improve adherence, and lessen the burden on patients, caregivers, and health systems.

Short-form, highly interactive digital media have proliferated rapidly in recent years. Leveraging large user bases, algorithm-driven personalization, and strong engagement mechanisms, short-video platforms enable swift and extensive dissemination of medical information.⁸ These features can broaden reach, improve timeliness, and support tailored communication for diverse audiences. However, the same dynamics introduce vulnerabilities. In the absence of peer review and rigorous oversight, the quality and reliability of health information remain variable, increasing the risk of misinformation and misinterpretation.⁹ Studies across multiple clinical topics, including irritable bowel syndrome, knee osteoarthritis, anal fissure, and cervical cancer, have shown that short-video content often receives high engagement but has low-to-moderate educational quality and reliability, with uploader type and platform characteristics playing important roles.^10–13 These findings highlight the need for disease-specific evaluation of short-video content and for platform-level measures that improve source transparency and indicate whether information is up to date.

To date, no systematic assessment has examined short-video content on rectal prolapse. Focusing on TikTok and Bilibili, this study applied established instruments, including GQS, mDISCERN, and the JAMA benchmarks, to quantify content coverage, quality, and engagement metrics. Outcomes were compared across uploader categories (Specialists, Non-specialists, and Individual users), and correlations between engagement metrics and quality indices were analyzed. By generating evidence specific to rectal prolapse from two major Chinese short-video platforms, this study aims to inform patient education strategies, such as essential content checklists and source transparency indicators, as well as platform governance measures, such as quality-weighted recommendation signals and clearer authorship and update metadata. The resulting evaluative framework is intended to guide clinicians, content creators, and platform operators, supporting safer dissemination of rectal-prolapse information and more reliable navigation of digital health content^14,15

Methods

1. Study design

This cross-sectional content analysis was conducted from October 14 to October 22, 2025. Video retrieval, screening, and extraction of basic video characteristics, including engagement metrics, were completed on October 14, 2025. Video duration, uploader type classification, and quality assessments using GQS, mDISCERN, and JAMA were completed over the following three days. The remaining time was used for data verification and resolution of scoring discrepancies. The objective was to systematically evaluate short videos related to “rectal prolapse” on TikTok (TikTok China; https://www.douyin.com) and Bilibili (https://www.bilibili.com), focusing on engagement performance, content characteristics, information quality, and reliability.

2. Ethical considerations

Only publicly available data were used. No personally identifiable information was collected or processed, and no human subjects were involved. In line with prevailing standards and the nature of the study, formal ethical approval was not required.

3. Search strategy and data extraction

To mitigate the influence of algorithmic personalization, two researchers performed searches in logged-out visitor mode on TikTok and Bilibili, with browser cache and search history cleared before searching. The Chinese keyword “直肠脱垂” (“rectal prolapse”) was used, and results were retrieved under each platform’s default comprehensive ranking. This strategy was based on methods used in previous studies, which suggested that videos beyond the common threshold of 100 have limited influence on the results.^16–18 Therefore, the top 150 videos per platform were initially included to ensure sample representativeness and to provide sufficient space for subsequent exclusion of ineligible videos. The videos were then filtered according to predefined criteria to form the final sample.

Exclusion criteria were: (1) advertising/merchandising/pure commercial promotion; (2) irrelevant topics (e.g., other anorectal diseases or hashtag-only relevance without substantive content); (3) duplicate or mirrored re-uploads; and (4) non-Chinese videos. Figure 1 depicts the detailed collection workflow.

Figure 1.

Study flowchart of video identification and inclusion.

During data extraction, we captured the following variables: platform (Bilibili or TikTok), upload date, video duration (seconds), counts of likes, comments, shares, and favorites. All data were obtained and verified via publicly available platform APIs to ensure consistency.

4. The source and content of videos

Video sources were classified into three categories based on the uploader’s identity: specialists, non-specialists and individual users. Specialists referred to colorectal/proctology physicians; non-specialists included other clinicians outside proctology and practitioners of traditional Chinese medicine; individual users encompassed patients, science communicators, and organizations. Video content focused on rectal-prolapse–related knowledge across six domains: definition, causes and risk factors, symptoms, treatment, prevention, and differential diagnosis. For each domain, report the count and proportion as n (%).

5. Video quality assessment

We evaluated video quality using three instruments: GQS (Table S1), mDISCERN for consumer health information (Table S2), and the JAMA benchmarks (Table S3). GQS^15,19 is a generic tool assessing overall quality of medical information/videos, including coherence and usefulness to patients. mDISCERN^20,21 adapts the original DISCERN to appraise consumer health information regarding source transparency, completeness, and potential bias. The JAMA benchmarks^19,22 evaluate information reliability across four criteria: authorship/attribution, references/citations, disclosure of conflicts of interest, and currency.

Two associate chief physicians in proctology (Reviewer A: NML; Reviewer B: GZY) independently assessed all included videos after standardized training on GQS, mDISCERN, and JAMA to ensure consistency. Before assessment, platform-specific information, including interfaces, watermarks, layouts, and video formats, was removed or blurred as much as possible. The two reviewers completed their ratings independently and were unaware of each other’s scores. If discrepancies existed between the two reviewers, the final decision was made by a senior expert with more than 40 years of proctology practice (XZW). To ensure the evaluation aligned with established clinical standards, the assessment strictly followed the 2017 AAO-HNSF guidelines and the Bárány Society recommendations. Inter-rater reliability was assessed using Cohen’s kappa coefficients and interpreted according to the Landis and Koch classification. The results showed high inter-rater reliability, with Cohen’s kappa coefficients of 0.828 for GQS, 0.846 for mDISCERN, and 0.876 for JAMA (all P < .001), indicating substantial to almost perfect agreement between the two reviewers.

6. Statistical analysis

Normally distributed data were summarized as mean ± standard deviation (SD), and non-normally distributed data as median (interquartile range, IQR). Categorical variables were reported as counts and percentages. For two-group comparisons, we used the independent-samples t test for normal data and the Mann–Whitney U test for non-normal data. For three or more groups, we applied the Kruskal–Wallis H test, followed by Dunn’s post-hoc pairwise tests with Bonferroni correction when the overall result was significant. Bonferroni-adjusted P values were reported for post-hoc pairwise comparisons. Spearman’s rank correlation (ρ) was used to examine associations between basic metrics (likes, favorites, shares, comments, video duration) and quality/reliability scores. Statistical significance was defined as two-sided P < 0.05. All analyses were conducted in R (version 4.3.2).

Results

1. Trends in video quantity over time

According to the predefined inclusion and exclusion criteria, we screened the top 150 videos per platform under the default comprehensive ranking and obtained a final sample of n = 256. Upload dates ranged from November 2, 2019 to October 14, 2025. As illustrated in Figure 2, the number of videos shows an overall upward trajectory with clustering in 2024 and 2025. On TikTok, more than half of the included videos were posted in 2025, indicating increasingly active production and dissemination of related content on this platform in recent years.

Figure 2.

Trend in the number of videos by year.

2. Video characteristics

We analyzed 256 videos from Bilibili and TikTok, with 116 (45.31%) assigned to the Bilibili group and 140 (54.69%) to the TikTok group. Overall, the median numbers of likes, favorites, comments, and shares per video were 70.00, 24.00, 6.00, and 17.50, respectively, and the median video duration was 73.00 seconds. Regarding video quality, the median scores were 2.00 for GQS, 2.00 for mDISCERN, and 2.00 for JAMA (details in Table 1). In between-platform comparisons, the Bilibili group showed lower medians for likes, favorites, comments, and shares than the TikTok group, indicating significant differences between the two groups. No statistically significant difference was observed for the median video duration (details in Table 2).

Table 1.

General characteristics, quality, and reliability of the videos.

Variables	Total
Platforms [n (%)]	256(100%)
General information
Video length(s),M (Q1,Q3)	73.00 (48.00, 123.25)
Likes,M (Q1,Q3)	70.00 (14.75, 265.00)
Collections,M (Q1,Q3)	24.00 (4.00, 126.50)
Comments,M (Q1,Q3)	6.00 (1.00, 30.50)
Shares,M (Q1,Q3)	17.50 (2.75, 90.25)
Uploader characteristics
Specialists	172(67.19%)
Non-specialists	32(12.50%)
Individual users	52(20.31%)
Video quality
GQS score,M (Q1,Q3)	2.00 (2.00, 2.00)
mDISCERN score,M (Q1,Q3)	2.00 (2.00, 3.00)
JAMA score,M (Q1,Q3)	2.00 (2.00, 2.00)

Z: Mann-Whitney test.

M: Median, Q₁: 1st Quartile, Q₃: 3st Quartile.

Table 2.

Comparison of characteristics between different short-video platforms.

Variables	Bilibili	TikTok	P
Platforms [n (%)]	116(45.31%)	140(54.69%)
General information
Video length(s),M (Q1,Q3)	80.50 (44.00, 166.50)	68.50 (50.00, 96.75)	0.068
Likes,M (Q1,Q3)	12.50 (3.00, 60.00)	151.00 (67.25, 442.75)	<.001
Collections,M (Q1,Q3)	5.00 (1.00, 55.00)	49.50 (15.00, 142.50)	<.001
Comments,M (Q1,Q3)	2.00 (0.00, 9.00)	11.00 (4.00, 53.50)	<.001
Shares,M (Q1,Q3)	4.00 (1.00, 24.50)	40.00 (10.00, 165.50)	<.001
Uploader characteristics
Specialists	59(50.86%)	113(80.71%)
Non-specialists	18(15.52%)	14(10.00%)
Individual users	39(33.62%)	13(9.29%)
Video quality
GQS score,M (Q1,Q3)	2.00 (1.00, 2.00)	2.00 (2.00, 3.00)	<.001
mDISCERN score,M (Q1,Q3)	2.00 (2.00, 3.00)	2.00 (2.00, 3.00)	0.008
JAMA score,M (Q1,Q3)	2.00 (1.00, 2.00)	2.00 (2.00, 2.00)	<.001

3. Uploader characteristics

We further compared video characteristics by uploader type. Among the 256 videos, Specialists accounted for 172 (67.19%), Non-specialists for 32 (12.50%), and Individual users for 52 (20.31%), indicating that Specialists play a dominant role in producing and disseminating rectal-prolapse content.

On TikTok, 80.71% of rectal-prolapse videos were uploaded by Specialists, 10.00% by Non-specialists, and 9.29% by Individual users, indicating a concentrated source profile and underscoring Specialists as the primary content providers on TikTok. In contrast, Bilibili showed a more diverse distribution of uploaders (details in Table 2 and Figure 3).

Figure 3.

Uploader distribution overall and by platform. (a) Overall composition (N = 256). (b) Distribution by platform (TikTok vs Bilibili).

Regarding engagement metrics, no significant differences were observed among videos uploaded by Specialists, Non-specialists, and Individual users in terms of likes, comments, favorites, or shares (see Table 3). In contrast, there were significant differences in video duration and quality. Videos uploaded by Specialists performed better in quality, with median scores of 2.00 for GQS, 3.00 for mDISCERN, and 2.00 for JAMA.

Table 3.

Characteristics, quality, and reliability of the videos by different uploaders.

Variables	Specialists (n = 172)	Non-specialists (n = 32)	Individual users (n = 52)	H statistic	Overall P value	Pairwise comparisons, adjusted P value
General information
Likes,M (Q1,Q3)	79.00 (18.50,267.75)	171.00 (8.25,737.00)	53.50 (11.25,112.25)	5.47	0.065
Collections,M (Q1,Q3)	16.50 (4.00,98.25)	69.50 (9.50,301.00)	32.50 (6.75,149.00)	3.55	0.170
Comments,M (Q1,Q3)	7.00 (2.00,38.50)	15.00 (1.00,81.50)	4.00 (0.00,14.00)	4.24	0.120
Shares,M (Q1,Q3)	15.00 (3.00,84.00)	58.00 (2.75,244.00)	15.50 (2.00,81.50)	2.08	0.354
Video quality
GQS score,M (Q1,Q3)	2.00 (2.00,3.00)	2.00 (1.00,2.25)	2.00 (1.00,2.00)	25.60	<.001	1 vs 3: <.001
						1 vs 2: 0.130
						2 vs 3: 0.241
mDISCERN score,M (Q1,Q3)	3.00 (2.00,3.00)	2.00 (2.00,2.00)	2.00 (1.00,2.00)	40.95	<.001	1 vs 3: <.001
						1 vs 2: 0.001
						2 vs 3: 1.000
JAMA score,M (Q1,Q3)	2.00 (2.00,2.00)	2.00 (1.00,2.00)	1.00 (1.00,2.00)	79.24	<.001	1 vs 3: <.001
						1 vs 2: <.001
						2 vs 3: 0.196

Notes. 1 = Specialists; 2 = Non-specialists; 3 = Individual users.

M: Median, Q₁: 1st Quartile, Q₃: 3st Quartile.

Overall P values were calculated using the Kruskal-Wallis test. Pairwise comparisons were performed using Dunn’s post-hoc test with Bonferroni correction.

After Bonferroni correction, Dunn’s post-hoc tests showed that Specialists had significantly higher mDISCERN and JAMA scores than both Non-specialists and Individual users. For GQS, Specialists scored significantly higher than Individual users, whereas the difference between Specialists and Non-specialists was not statistically significant (details in Table 3).

4. Video content analysis

In the content analysis, treatment and symptoms were the most frequently discussed domains, appearing in 56.64% and 53.23% of videos, respectively, whereas differential diagnosis and prevention were mentioned only infrequently. This pattern was consistent across Bilibili and TikTok, with higher proportions on TikTok, suggesting comparatively greater content diversity on Bilibili (details in Table 4 and Figure 4).

Table 4.

Video content mentions.

Video content	Bilibili (n=116)	TikTok (n=140)	Total(n=256)
Definition and overview	26(22.41%)	31(22.14%)	57(22.27%)
Causes and risk factors	32(27.59%)	36(25.71%)	68(26.56%)
Symptoms	57(49.14%)	79(56.43%)	136(53.23%)
Treatment	58(50.00%)	87(62.14%)	145(56.64%)
Prevention	16(13.79%)	27(19.29%)	43(16.80%)
Differential diagnosis	21(18.10%)	10(7.14%)	31(12.11%)

Figure 4.

Coverage of video content domains. (a) Proportion addressing each domain. (b) Domain counts by platform.

5. Video quality analysis

As shown in Table 2, the median GQS scores were 2.00 (1.00, 2.00) for Bilibili and 2.00 (2.00, 3.00) for TikTok; the median JAMA scores were 2.00 (1.00, 2.00) for Bilibili and 2.00 (2.00, 2.00) for TikTok. For mDISCERN, both platforms had the same median of 2.00 (2.00, 3.00). Figure 5 depicts significant differences between platforms across GQS, mDISCERN, and JAMA, indicating that videos on TikTok generally exhibit higher quality and stronger reliability. Table 3 shows that when comparing video quality and reliability across different uploader identity types, there are significant differences in GQS, mDISCERN, and JAMA scores (details in Figure 6).

Figure 5.

Platform comparison of quality scores (GQS, mDISCERN, JAMA). (a) GQS by platform (violin plot). (b) mDISCERN by platform (violin plot). (c) JAMA by platform (violin plot).

Figure 6.

Quality scores by uploader type. (a)GQS by uploader type (density). (b) mDISCERN by uploader type (density). (c) JAMA by uploader type (density).

6. Correlation analysis

As shown in Figure 7, engagement metrics exhibited strong positive correlations: likes vs comments, likes vs shares, and likes vs favorites had correlation coefficients of 0.89, 0.91, and 0.90, respectively. In contrast, video duration (seconds) showed weak correlations with other variables. Correlations between engagement metrics and the quality/reliability scores (GQS, mDISCERN, and JAMA) were minimal and practically negligible. Because these engagement metrics were not simultaneously included in a multivariable regression model, the observed inter-correlations were interpreted descriptively and did not introduce multicollinearity into model estimation.

Figure 7.

Correlation matrix of engagement, video length, and quality.

7. Sensitivity analysis

To evaluate whether TCM-related videos influenced the main findings, we performed a sensitivity analysis after excluding 10 TCM-related videos. The analysis of the remaining 246 videos was generally consistent with the primary analysis. Significant differences in mDISCERN, JAMA, and GQS scores remained at both the uploader category and platform levels, indicating the robustness of the main conclusions (details in Table 5).

Table 5.

Comparison of GQS, mDISCERN, and JAMA scores across platforms and uploader categories (excluding TCM-related videos).

Variables	Total(n=246)	Bilibili (n=110)	TikTok (n=136)	P	Specialists (n=172)	Non-specialists(n=22)	Individual users (n=52)	P
GQS score,M (Q1,Q3)	2.00 (2.00, 2.00)	2.00 (1.00, 2.00)	2.00 (2.00, 3.00)	0.001	2.00 (2.00,3.00)	2.00 (1.00,2.00)	2.00 (1.00,2.00)	<.001
mDISCERN score,M (Q1,Q3)	2.00 (2.00, 3.00)	2.00 (2.00, 3.00)	2.00 (2.00, 3.00)	0.019	3.00 (2.00,3.00)	2.00 (2.00,2.00)	2.00 (1.00,2.00)	<.001
JAMA score,M (Q1,Q3)	2.00 (2.00, 2.00)	2.00 (1.00, 2.00)	2.00 (2.00, 2.00)	<.001	2.00 (2.00,2.00)	2.00 (1.00,2.00)	1.00 (1.00,2.00)	<.001

Discussion

This cross-sectional study systematically evaluated the dissemination patterns, content distribution, and information quality of rectal prolapse–related short videos on TikTok and Bilibili. The number of videos increased over time, yet median quality and reliability scores remained low to moderate, suggesting that growth in content volume was not accompanied by comparable improvement in informational rigor. Platform differences were evident: TikTok showed higher engagement and modest advantages in selected quality dimensions, particularly GQS and JAMA, whereas differences in mDISCERN scores were smaller. Uploader type was a key factor associated with quality. Specialists contributed a larger share of videos and achieved higher scores than Non-specialists and Individual users. Engagement metrics were strongly intercorrelated but only weakly associated with quality and reliability. These findings are consistent with studies across multiple clinical topics showing a persistent gaps between popularity and educational value in health-related short video.^10–13 Although these platforms meet a real demand for accessible health information, a structural tension persists between high engagement and limited quality. Strengthening platform governance and encouraging professional participation therefore remain essential.

Engagement metrics

In our sample, TikTok showed higher levels of likes, favorites, comments, and shares than Bilibili. This pattern may reflect algorithmic-driven amplification, stronger social feedback mechanisms, and more standardized production formats on TikTok. However, higher engagement does not necessarily indicate better educational value or stronger evidentiary support, which is consistent with findings from other clinical topics on short-video platforms.^14,23,24 Using engagement as a proxy for quality may increase the visibility of misleading or incomplete information and expand the audience exposed to misinformation.²⁵ Platform-level countermeasures should incorporate quality signals into ranking and recommendation systems, such as attribution of sources, citation of evidence, date of last update, and disclosure of conflicts of interest. Content creators should use structured presentation, clearly state indications and contraindications, and link to authoritative guidelines and peer-reviewed sources to support verification by both viewers and clinicians.²⁶ These measures may help preserve the reach advantages of short video while mitigating quality risks.

Content analysis

Topic coverage was mainly concentrated on treatment and symptoms, whereas prevention and differential diagnosis were underrepresented. This imbalance was observed on both platforms, although Bilibili contained a relatively higher share of differential-diagnosis content, suggesting somewhat greater thematic diversity. For rectal prolapse, diagnostic pitfalls and subtype recognition may influence referral timing and procedural choice. Insufficient coverage of these issues may delay treatment or prompt inappropriate self-management, potentially worsening bleeding, constipation, mucosal injury, and quality of life.²⁷ This gap is consistent with prior observations in short videos on hepatocellular carcinoma, cholelithiasis, colorectal polyps, and heart failure, where high-visibility topics predominate while decision-relevant information, such as red-flag signs, risk communication, and care pathways, remains undercovered.^14,16,24 A practical solution is to adopt essential content checklists for disease-focused videos, covering core diagnostic cues, symptom thresholds that warrant medical attention, complication risks, stepwise care pathways, and realistic expectations for outcomes and recovery. Platforms can promote these checklists through editorial tags, topic hubs, and creator prompts, thereby improving the baseline completeness of patient-facing content without constraining creative formats.

Across-platform quality and reliability

Overall quality was low to moderate, with TikTok showing modest advantages in GQS and JAMA scores. These differences may partly reflect the higher proportion of Specialists on TikTok, more standardized presentation formats, and more consistent inclusion of basic reliability elements, such as authorship and date information. The small difference in mDISCERN suggests shared weaknesses across platforms in source transparency, referencing, and disclosure practices.^28,29 Both TikTok and Bilibili could address these deficits through upload requirements and interface cues. During upload, creators should be required or strongly encouraged to provide credential verification, cited sources, update dates, and conflict-of-interest disclosures. In the user interface, these elements should be displayed prominently and consistently, enabling viewers to make rapid credibility judgments. Platforms could also pilot quality-weighted recommendation systems, in which reliability signals provide a small cumulative ranking boost, provided that engagement is satisfactory and no policy violations are detected.

Across-uploader differences

Uploader identity was strongly associated with video quality. Videos from Specialists achieved higher GQS, mDISCERN, and JAMA scores than those from Non-specialists and Individual users, consistent with patterns reported for hepatocellular carcinoma, gallstone disease, and related topics.^14,24 On TikTok, the high proportion of Specialists, approaching 81%, suggests a concentration of professional sources and may partly explain the platform’s quality advantage. Bilibili showed a more diverse uploader mix and wider dispersion of quality scores. These findings suggest that professional creators are central to improving content quality, whereas content from Non-specialists and Individual users may be more prone to subjective narratives and lack of citations. Nevertheless, even videos from Specialists fell short of ideal standards for patient education, highlighting the need for additional scaffolding in evidence presentation and patient actionability. Platform policies could help by prioritizing credential verification, standardizing labeling of uploader type, and assigning ranking weights that favor compliance with reliability criteria. Creator education should emphasize concise statements of evidence level, scope of applicability, and links to clinical pathways, while avoiding jargon and using plain-language explanations.

Correlation analysis

We found strong positive correlations among engagement metrics, consistent with social amplification and feedback loops on short-video platforms. In contrast, correlations between engagement and quality or reliability were weak, and video duration was only weakly related to both engagement and quality, consistent with prior evidence across clinical topics.^29–31 These results suggest that simply extending video duration or optimizing for engagement is unlikely to improve information quality. More promising strategies include improving information structure, adding inline citations and update markers, and labeling key recommendations with evidence grades or guideline endorsements. For platform operators, visible credibility indicators at the point of decision, such as a compact badges for authorship verification and date of last update, may support user trust without disrupting viewing.

Contributions

To our knowledge, this study provides the first dual-platform assessment of rectal prolapse-related short videos in the Chinese context. Methodologically, we combined standardized sampling with three complementary instruments, namely GQS, mDISCERN, and JAMA, to generate a multi-dimensional profile of quality and reliability. Empirically, we quantified platform differences, characterized uploader contributions, and showed a clear separation between engagement and quality. Practically, we propose a governance framework that includes quality-signal weighting in recommendations, credential and disclosure fields during upload, and prominent display of authorship and update metadata. We also introduce a disease-specific essential content checklist for creators, which may be adapted by clinical departments and public health agencies to align patient-facing information with current guidance. Together, these contributions provide a basis for iterative quality improvement while preserving the reach and flexibility of short-video communication for patient education.

Limitations and future directions

The study has several limitations. First, the sample was derived from default search results and platforms rankings on Chinese short-video platforms at a specific time point, without inclusion of overseas platforms or full modeling personalized recommendations. This may have introduced platform and retrieval related selection bias and limited generalizability. Second, because view counts were not consistently and reliably available across TikTok and Bilibili, engagement indicators were analyzed as absolute counts only; therefore, view-normalized engagement rates, such as likes or comments per view, could not be calculated. Third, although we used three established instruments (GQS, mDISCERN, and JAMA), dual reviewers, and expert adjudication to improve consistency, these scales retain unavoidable subjectivity. Fourth, the cross-sectional design precluded longitudinal follow-up and causal inference, limiting our ability to capture temporal changes in content quality or infer effects on patient behaviors. Platform algorithms and temporal dynamics may also influence search results and reduce reproducibility. In addition, although sensitivity analysis excluding TCM-related videos showed stable results, the number of TCM-related videos was small, limiting the statistical power for separate comparisons between TCM and Western medicine videos.

Future work should extend this analysis to additional platforms, languages, and time points, and incorporate view-based engagement indicators when available. Subsequent studies could combine source-reliability tools with instruments such as PEMAT to assess understandability and actionability. Methodological work is also needed to validate automated approaches for evidence tracing and for identifying whether videos include key clinical information, such as indications, contraindications, and follow-up recommendations. Larger samples are warranted to compare TCM and Western medicine videos more robustly. Future studies should evaluate whether higher-quality videos improve patient understanding, decision-making, or healthcare utilization. Development of standardized content checklists and collaboration between clinicians and platforms may further improve reliability and patient safety.

Conclusion

We assessed the information quality of 256 rectal prolapse-related short videos from TikTok and Bilibili. Video volume increased over time, yet overall quality and reliability remained low to moderate. TikTok showed higher engagement and modest advantages in structural quality scores. Videos uploaded by Specialists had higher scores than those from Non-specialists and Individual users. Platforms should introduce reliability weighting into ranking systems and adopt an essential content checklist for creators, while making authorship, source disclosure, and evidence citation more visible at the point of viewing. These measures may improve understandability and actionability without reducing reach. Given the modest sample and the subjectivity inherent in GQS, mDISCERN, and JAMA ratings, these findings require validation across larger samples, additional platforms, and longer observation windows.

Supplemental material

Supplemental material - Quality and reliability of rectal prolapse short-video content on tiktok and bilibili: A cross-sectional content analysis

Supplemental material for Quality and reliability of rectal prolapse short-video content on tiktok and bilibili: A cross-sectional content analysis by Xiaowen Wang, Zuowu Xi, Mingliao Niu, and Zongyue Gao in DIGITAL HEALTH.

Footnotes

Acknowledgements

During manuscript preparation, AI-assisted tools were used only for language polishing and editorial refinement. No AI tools were used for data collection, video scoring, statistical analysis, figure generation, interpretation of results, or scientific decision-making. All authors reviewed, edited, and approved the final manuscript and take full responsibility for the accuracy and integrity of the work.

ORCID iD

Xiaowen Wang

Ethical considerations

This study did not involve clinical data, human specimens, or laboratory animals, or histological research. All information was obtained from publicly available videos on TikTok and Bilibili. Data collection fully complied with the terms of service of TikTok and Bilibili. No identifiable personal information was collected or analyzed, and no interaction with users was conducted; therefore, approval from an Institutional Review Board (IRB) or Independent Ethics Committee (IEC) was not required.

Author contributions

WXW was responsible for data curation, figure and table preparation, and manuscript writing. NML and GZY contributed to video selection, review, and scoring. XZW oversaw the overall study design and conducted the final video evaluation. All authors read and approved the final manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: National Administration of Traditional Chinese Medicine, project “Clinical Application and Mechanisms of Combined Zhang Zhongjing Classical Formulas” (Project No.: GZY-KJS-2022-041). Henan Province Key R&D and Promotion Special Project, Science and Technology Tackling Program (Project No.: 232102310280). Henan Province TCM Inheritance and Innovation Talent Program (Zhongjing Program), Leading Talents in TCM Disciplines (Document No.: Yu Wei TCM Letter [2021] No. 8). Henan Province Special Program for Scientific Research in Traditional Chinese Medicine (Project No.: 2023ZY2071). Henan Provincial Health Commission National TCM Clinical Research Base Project (Project No.: 2021JDZX2003).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The study data are available from the corresponding author upon reasonable request via email.*

Use of AI tool

Guarantor

All authors.

Supplemental material

Supplemental material for this article is available online.

Appendix

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Supplementary Material

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