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Abstract

Objective: This study aimed to assess the quality of the information in WeChat and TikTok videos related to hypertension and diabetes treatment. Methods: A sample of 120 Chinese videos was collected based on specific inclusion and exclusion criteria. The quality was evaluated using DISCERN, JAMA and the latest edition of Chinese guidelines for hypertension and diabetes prevention and treatment, and two observers independently scored each video using the three assessment tools. Results: Among all 120 videos, only 10 scored above 38 points in DISCERN, with 45 videos rated as “very poor”. None of the video met all JAMA criteria simultaneously, and there were gaps in accuracy and completeness compared to the two guidelines. Furthermore, there was no significant correlation between information quality and the number of likes and comments. Conclusion: The current quality of information on the treatment of hypertension and diabetes on WeChat and TikTok was unsatisfactory. Consequently, the government should strengthen oversight of information quality, and social media platforms should actively review health-related content to prevent inaccurate information dissemination. Individuals should enhance their digital and health literacy.

Keywords

Chinese videos hypertension and diabetes quality evaluation treatment-related information WeChat and TikTok

Introduction

Hypertension and diabetes are two of the most common and serious chronic diseases worldwide.¹ Hypertension is one of the major risk factors for ischemic heart disease, stroke, and other cardiovascular diseases, as well as chronic kidney disease and dementia.² The World Health Organization (WHO) estimates that hypertension is directly or indirectly responsible for at least nine million deaths globally every year.³ Diabetes, a disease of the endocrine system characterized by abnormally high blood glucose levels, is one of the fastest-growing diseases worldwide.⁴ Uncontrolled diabetes can lead to multiorgan and systemic damage, affecting the heart, kidneys, nerves, and blood vessels, which impairs quality of life and increases the mortality rate.⁵ Global diabetes-related health expenditures were estimated at 966 billion USD in 2021 and are projected to increase to 1054 billion USD by 2045.⁶ The high prevalence and burden of these two chronic diseases have raised great concern in China. The Chinese National Health Commission (NHC) has identified the prevention and treatment of hypertension and diabetes as key priorities.⁷ The emphasis on utilizing the Internet to improve the health management of individuals with hypertension and diabetes has now become a central focus.

The prevalence of hypertension and diabetes increased from 2013 to 2018, and the visiting rate of these two chronic diseases increased by 24.7% and 8.3% respectively.⁸ However, the adequate treatment rate did not significantly improve.⁹ Therefore, it is inadequate to solely rely on hospital-centric, fragmented, and treatment-based healthcare delivery system to manage chronic disease.¹⁰ Moreover, emphasizing patient responsibility and self-management represents a promising strategy for chronic condition intervention.¹¹ Previous studies have shown that improving individual’s health literacy helps prevent and control chronic.^12,13 Meanwhile, it has been recognized that internet-based health information, including articles, videos, and other resources, can improve patients’ health literacy, assisting them in managing chronic diseases.^14,15 As a result, being attentive to health information, correctly acquiring, understanding, distinguishing, and applying health information has been identified as a key health literacy skill in the “Chinese Citizen Health Literacy - Basic Knowledge and Skills (2024 Edition)”.¹⁶

Recently, social media platforms that support the sharing of short videos are emerging as an important channel for disseminating health information.^17–19 A ranking indicated that WeChat and TikTok are the leading social media platforms in China,²⁰ 98.35% of the participants reported seeking health information via WeChat.²¹ TikTok in China has also emerged as a potentially powerful medium for disseminating health information.²² Various studies have highlighted the capacity of these platforms to facilitate patients with hypertension and diabetes in self-management.^23,24 Nevertheless, research assessing the quality of hypertension and diabetes treatment information available on WeChat and TikTok has been limited, and the findings regarding the quality of such content have been inconsistent. Therefore, this study aimed to evaluate the quality of treatment information on hypertension and diabetes on WeChat and TikTok by the Quality Criteria for Consumer Health Information (DISCERN), Journal of American Medical Association (JAMA) benchmarks, as well as the Chinese Guidelines for Hypertension Prevention and Control (2022) and the National Guidelines for Grassroots Diabetes Prevention and Management (2022) (referred to as “the two Guidelines” hereafter).

Materials and methods

Search strategy and data extraction

This is an observational study. In this study, we entered the keywords “糖尿病的治疗 (diabetes treatment)” and “高血压的治疗 (hypertension treatment)” in the WeChat and TikTok search fields to identify relevant videos. The “diabetes” refers to “type 2 diabetes”. We adopted specific inclusion and exclusion criteria to ensure that the included videos met the research objectives. The inclusion criteria were as follows: (1) videos related to hypertension or diabetes treatment information, (2) videos with a playback duration of more than 1 min, and (3) videos with an upload time within the last 6 months. The exclusion criteria were as follows: (1) Unrelated videos such as advertisements, (2) Videos with insufficient duration, (3) Videos with 0 likes, and (4) Content repetition in videos (it refers to the exact duplication of information, redundant explanations, or duplicate videos with similar content). Take collecting diabetes treatment information video on WeChat as an example, we entered “diabetes treatment” in the search field, and filtered the included conditions on the interface, secondly, browsed and collected videos in order of likes. Based on inclusion and exclusion criteria, we excluded videos with repetition content, while ensuring compliance with the minimum sample size standard for statistical analysis (n ≥ 30). After the aforementioned search process, we included a total of 120 video samples for research – 60 from WeChat (30 on hypertension and 30 on diabetes) and 60 from TikTok (30 on hypertension and 30 on diabetes).

This study contained uploaded videos from 6 months before February 15, 2023. We extracted information about each video and its source, including the video release date, duration, title, likes, comments, upload sources (individuals, medical organizations, news or media organizations), and content types (drug therapy, lifestyle interventions, comprehensive treatment, traditional Chinese medicine treatment and scientific or frontier knowledge). We recorded the extracted data in Excel 2019 (Microsoft Corp).

Evaluated methods and dimensions

DISCERN and JAMA have been verified as effective tools for evaluating online websites or Internet information,^25,26 therefore, we used these tools to evaluate the reliability and specificity of video information in our study. Additionally, we employed supplemented the results by using the two Guidelines to assess the accuracy and completeness of medical video content.

The DISCERN tool was developed collectively by Oxford University and British Library employees, is intended for individuals who utilize or create information regarding treatment choices.^27,28 DISCERN empowers consumers and information providers to assess the quality of such information. This tool comprises 15 critical questions, each corresponding to a distinct quality criterion, and an overall quality rating. The first section (Questions 1-8) addresses reliability, while the second section (Questions 9-15) focuses on the quality of the information regarding treatment options. In contrast, the third section (Question 16) presents the overall data quality rating. Each question is rated on a five-point scale from “no” to “yes”.²⁸ DISCERN scores ranging from 63 to 75 points are considered “excellent”, scores from 51 to 62 points are classified as “good”, scores from 39 to 50 points are labeled as “average”, scores from 28 to 38 points are deemed “poor”, and scores below 28 points are categorized “very poor”.²⁹

The JAMA benchmark consists of four criteria: authorship, attribution, disclosure, and currency.³⁰ It provides consumers and professionals with a framework to assess the credibility, reasonableness, and usefulness of Internet health information. This evaluation helps individuals make informed decisions on how to apply this information in practical settings.³⁰ Each criterion that meets the requirements is awarded one point. The maximum JAMA score is four points, indicating the highest quality (Tables 1 and 2).

Table 1.

DISCERN scoring system.

	Question	Content	Score
Section1	1	Are the aims clear?	1-5
	2	Does it achieve its aims?	1-5
	3	Is it relevant?	1-5
	4	Is it clear what sources of information were used to compile the publication (other than the author or producer)?	1-5
	5	Is it clear when the information used or reported in the publication was produced?	1-5
	6	Is it balanced and unbiased?	1-5
	7	Does it provide details of additional sources of support and information?	1-5
	8	Does it refer to areas of uncertainty?	1-5
Section2	9	Does it describe how each treatment works?	1-5
	10	Does it describe the benefits of each treatment?	1-5
	11	Does it describe the risks of each treatment?	1-5
	12	Does it describe what would happen if no treatment is used?	1-5
	13	Does it describe how the treatment choices affect overall quality of life?	1-5
	14	Is it clear that there may be more than one possible treatment choice?	1-5
	15	Does it provide support for shared decision-making?	1-5
Section3	16	Based on the answers to all of the above questions, rate the overall quality of the publication as a source of information about treatment choices	1-5

Table 2.

JAMA scoring system.

Section	Criteria	Score
Authorship	Authors and contributors, their affiliations, and relevant credentials should be provided	0 or 1
Attribution	References and sources for all content should be listed clearly, and all relevant copyright information noted	0 or 1
Disclosure	Conflicts of interest, video ownership, funding, sponsorship, advertising, and support should be fully disclosed	0 or 1
Currency	Dates that content was posted and updated should be indicated	0 or 1

Moreover, we validated these findings by integrating the two Guidelines to evaluate the accuracy and completeness of specific medical content presented in the videos.^31,32 We quantified the elements specified in the guidelines, compared the textual content of the videos with the guidelines, and assigned respective scores to those that met the criteria. For detailed scoring, please refer to Table 3.

Table 3.

The scoring criteria for the accuracy and completeness of the two Guidelines.

Section	Criteria	Scores
Accuracy	(1) Completely accurate	5
	(2) Partially accurate	3-4
	(3) Not very accurate but containing no errors	2
	(4) Wildly inaccurate and containing misinformation	1
Completeness	(1) Treatment goals	1-5
	(2) Treatment strategy
	(3) Non-drug therapy
	(4) Drug therapy
	(5) Management of associated risk factors
	(6) Follow-up and record

In summary, we combined the results of the DISCERN and JAMA tools and the two Guidelines to determine the overall quality of the videos.

Data analysis

The data were summarized using descriptive statistics. We employed the Mann-Whitney U test to identify statistically significant differences between two groups or among more than two groups of variables. Spearman correlation analysis was utilized to assess the correlation between the quality evaluation score and the number of likes and comments. p < .05 was considered statistically significant. To mitigate the assessment bias, two cardiovascular physicians were invited to review the results. The intraclass correlation coefficient (ICC) was utilized to evaluate the repeatability or consistency of various measurement methods or evaluators for the same set of measurement results.³³ The ICC value ranges from 0 to l, where ICC >0.75 indicates strong consistency, and ICC <0.40 indicates poor consistency.³⁴ SPSS software (version 28.0; IBM Corp) was employed for analysis.

Patient information statement

This study did not focus on patients, and the video content analyzed did not contain any patient information, preserving patient confidentiality. Institutional review board approval was not necessary as all data were publicly available online. The term “videos” encompassed any openly accessible published video material.

Results

Characteristics of the videos

Table 4 presents the distribution of information types, video uploader sources, likes and comments on WeChat and TikTok. It is evident that lifestyle intervention information constitutes the largest proportion among the types of information. Most of the information was uploaded by “individuals”, particularly on TikTok. Moreover, the number of likes and comments on TikTok significantly exceeded those on WeChat.

Table 4.

Characteristics of the videos.

Variable	WeChat (N, %)		TikTok (N, %)
Variable	Hypertension	Diabetes	Hypertension	Diabetes
Information category
Drug therapy	7 (23.3%)	3 (10%)	8 (26.7%)	2 (6.7%)
Lifestyle intervention	10 (33.3%)	14 (46.7%)	7 (23.3%)	13 (43.3%)
Comprehensive treatment	3 (10%)	6 (20%)	6 (20%)	6 (20%)
Traditional Chinese medicine treatment	4 (13.3%)	2 (6.7%)	5 (16.7%)	6 (20%)
Scientific or frontier knowledge	6 (20%)	5 (16.7%)	4 (13.3%)	3 (10%)
Upload source
Individuals	10 (33.3%)	18 (60%)	30 (100%)	29 (96.7%)
Medical organizations	14 (46.7%)	10 (33.3%)	0	0
News or media organizations	6 (20%)	2 (6.7%)	0	1 (3.3%)
Number of likes	130 (9-3235)	91.5 (9-1213)	1369.5 (200-62000)	4049.5 (213-118000)
Number of comments	2 (0-202)	2 (0-34)	119.5 (6-4661)	63.5 (4-6764)

Evaluation results

Evaluation of hypertension and diabetes treatment information in WeChat and TikTok videos

Based on the DISCERN and JAMA scales, the overall quality of the treatment information for hypertension and diabetes was deemed poor in terms of the credibility and concreteness. The evaluation results from the DISCERN scale showed that the average scores for the 30 videos on diabetes treatment information and 30 videos on hypertension treatment information on WeChat were 29.77 and 29.83, respectively. Similarly, the average scores for the 30 videos on diabetes treatment information and 30 videos on hypertension treatment information on TikTok were 29.42 and 29.58, respectively. Among the 120 videos evaluated, only 10 received scores above 38 points, 65 were rated as “poor”, and 45 were rated as “very poor”. Notably, no video achieved an “excellent” rating. The detailed table with the average scores derived from the DISCERN assessment can be found in Table 5.

Table 5.

Evaluation results using the DISCERN instrument.

Questions	Scores, mean (SD)
Questions	WeChat		TikTok
	Hypertension	Diabetes	Hypertension	Diabetes
Question 1	2.38 (0.61)	2.48 (0.71)	2.58 (0.64)	2.55 (0.61)
Question 2	2.52 (0.98)	2.85 (0.87)	2.58 (0.74)	2.53 (0.73)
Question 3	2.40 (0.56)	2.48 (0.69)	2.45 (0.50)	2.23 (0.52)
Question 4	1.93 (1.28)	1.22 (0.67)	1.53 (0.89)	1.72 (1.13)
Question 5	2.30 (0.74)	2.07 (0.25)	2.05 (0.20)	2.07 (0.25)
Question 6	2.32 (0.74)	2.08 (0.59)	2.22 (0.50)	2.08 (0.46)
Question 7	1.12 (0.31)	1.23 (0.43)	1.38 (0.58)	1.37 (0.61)
Question 8	1.50 (0.59)	1.48 (0.76)	1.55 (0.69)	1.33 (0.53)
Question 9	2.10 (0.61)	2.22 (0.72)	1.98 (0.71)	2.10 (0.65)
Question 10	2.13 (0.64)	2.53 (0.66)	2.42 (0.53)	2.42 (0.67)
Question 11	1.40 (0.55)	1.45 (0.76)	1.53 (0.78)	1.30 (0.48)
Question 12	1.30 (0.50)	1.12 (0.28)	1.07 (0.17)	1.12 (0.36)
Question 13	1.42 (0.40)	1.23 (0.34)	1.25 (0.25)	1.25 (0.34)
Question 14	2.23 (0.95)	2.00 (0.80)	2.18 (0.84)	2.40 (0.59)
Question 15	1.62 (0.55)	1.98 (0.58)	1.47 (0.49)	1.55 (0.50)
Question 16	1.43 (0.47)	1.42 (0.49)	1.37 (0.37)	1.38 (0.39)

Specifically, the question “Does it describe what would happen if no treatment were received?” (Question 12) received the lowest average score, indicating a lack of discussion on the consequences of not receiving treatment in the video. Moreover, for the question “Is it clear what sources of information were used to compile the information (other than the author or producer)?” (Question 4), the lower score suggested inadequate detail on additional information sources provided in the videos, failing to fully depict the impact of the diseases on quality of life. The ICC value of 0.87 was obtained.

In Table 6, the average JAMA rating for these videos was 2.08 points. Out of 120 total samples, 95 videos met the “Authorship”, and 84 videos met the “Currency” criteria. However, only a small number of videos met the “Attribution” and “Disclosure” criterion. Notably, no video satisfied all four criteria simultaneously. The ICC was calculated at 0.96.

Table 6.

Evaluation results using the JAMA benchmark.

Section	Score, mean (SD)
	WeChat		TikTok
	Hypertension	Diabetes	Hypertension	Diabetes
Authorship	0.87 (0.35)	0.77 (0.43)	0.78 (0.43)	0.77 (0.43)
Attribution	0.30 (0.47)	0.17 (0.38)	0.27 (0.45)	0.37 (0.49)
Disclosure	0.23 (0.43)	0.52 (0.51)	0.23 (0.43)	0.27 (0.45)
Currency	0.77 (0.48)	0.67 (0.58)	0.77 (0.43)	0.70 (0.47)

Furthermore, the scoring outcomes from the two Guidelines revealed inadequacies in both completeness and accuracy scores, with a particular emphasis on lower accuracy scores. The ICC was 0.97, indicating an acceptable level of consistency. Within the 120 videos assessed, none were deemed completely accurate, with some videos even containing incorrect information. For example, one video stated, “Diabetes patients exercise 2-3 times a week, 30 min each time”, whereas the guideline specifies that “adults with type 2 diabetes should engage in physical activity at least 150 min per week (e.g. exercising for 30 min each session, 5 days a week)”. (Table 7)

Table 7.

Evaluation outcomes using the two Guidelines.

Section	Score, mean (SD)
	WeChat		TikTok
	Hypertension	Diabetes	Hypertension	Diabetes
Accuracy	2.80 (0.55)	2.83 (0.87)	3.00 (0.79)	2.53 (0.68)
Completeness	2.40 (0.67)	3.13 (1.01)	2.63 (0.67)	3.17 (0.65)

Comparison of video information quality on WeChat and TikTok

In Table 8, we compared the mean scores of the videos providing hypertension and diabetes treatment information on WeChat and TikTok, respectively. The results showed that the total video quality score for hypertension treatment information on WeChat videos (39.40) exceeded that of TikTok videos (36.96). In contrast, the quality evaluation scores of diabetes treatment videos on TikTok (37.55) were higher than those on WeChat (37.11). When considering the final total score, the quality scores of WeChat videos were slightly higher than those of TikTok videos, with no significant difference observed between WeChat and TikTok (p = .42). It is evident that the video quality of hypertension and diabetes treatment information was unsatisfactory on both WeChat and TikTok platforms.

Table 8.

Comparison of video information quality from different platforms.

Platforms	Key words	Scores, mean (SD)				p Value
		DISCERN	JAMA	Guidelines	Total
WeChat	Hypertension	32.13 (4.63)	2.07 (0.52)	5.20 (1.00)	39.40	0.42
WeChat	Diabetes	29.08 (3.95)	2.10 (0.80)	5.93 (1.55)	37.11
TikTok	Hypertension	29.30 (4.83)	2.03 (0.61)	5.63 (1.22)	36.96
TikTok	Diabetes	30.15 (3.90)	2.10 (0.55)	5.63 (0.93)	37.55

Notes: ^***p < .01, ^**p < .05, ^*p < .1.

In Table 9, the sources of these videos were categorized into individuals, medical organizations, and news or media organizations. Regarding WeChat, videos related to hypertension or diabetes treatment information, videos from medical organizations received the highest score, followed by those from news or media organizations and individuals. However, there was no significant difference in the quality of treatment information among individuals, medical organizations, and news or media organizations (p > .05). As for TikTok, all but one video were uploaded by individuals, with only one video originating from a news or media organization. Consequently, we combined videos from medical organizations with those from news or media organizations, categorizing them into individual and organizational uploaders. Likewise, there was no significant difference between videos uploaded by individuals and organizations on both WeChat and TikTok (p > .05).

Table 9.

Comparison of the information quality of videos from different upload sources.

Platforms	Key words	Upload source	Scores, mean (SD)				p Value
			DISCERN	JAMA	Guidelines	Total
WeChat	Hypertension	Individuals (n = 10)	27.00 (4.74)	1.80 (0.42)	4.70 (0.82)	33.50	0.92	0.19
		Medical organizations (n = 14)	29.14 (5.67)	2.21 (0.58)	5.43 (1.09)	36.78
		News or media organizations (n = 6)	28.17 (3.54)	2.17 (0.41)	5.50 (0.84)	35.84
	Diabetes	Individuals (n = 18)	28.11 (3.46)	1.83 (0.79)	5.56 (1.29)	35.50	0.91
		Medical organizations (n = 10)	29.80 (4.32)	2.50 (0.71)	6.50 (1.84)	38.80
		News or media organizations (n = 2)	29.00 (4.24)	2.50 (0.71)	6.50 (2.12)	38.00
TikTok	Hypertension	Individuals (n = 30)	29.40 (3.92)	2.03 (0.61)	5.63 (1.22)	37.06	-	0.32
		Medical organizations (n = 0)
		News or media organizations (n = 0)
	Diabetes	Individuals (n = 29)	29.28 (4.17)	2.07 (0.53)	5.66 (0.94)	37.01	-
		Medical organizations (n = 0)
		News or media organizations (n = 1)	25.00	2.00	5.00	32.00

Notes: 0.92 and 0.91 were the p-values for comparing the quality of information from three sources for hypertension and diabetes, respectively; 0.19 and 0.32 were the p-values for comparing the quality of information from individuals and organizations on WeChat and TikTok, respectively. ^***p < .01, ^**p < .05, ^*p < .1.

Information quality correlation with the numbers of likes and comments

The general audience typically does not utilize professional quality evaluation tools like DISCERN or JAMA on a daily basis to evaluate video information. However, the perceived quality of information may be influenced by the number of likes and comments on videos viewed. In this study, we considered the number of likes and comments on these videos as a subjective indicator of the perceived quality of video information by the general audience. There was no significant correlation among the DISCERN score, the number of likes (ρ = 0.10; p > .05), and the number of comments (ρ = 0.13; p > .05). The results for the Guideline score were similar (ρ = 0.02; p > .05 and ρ = 0.02; p > .05). However, a weak but significant correlation was found between the JAMA score and the number of likes (ρ = 0.23; p < .05) and comments (ρ = 0.21; p < .05) as shown in Table 10. Therefore, a high-quality video may not necessarily receive a large number of likes or comments conversely, a low number of likes and comments on a video does not automatically signify poor quality. Furthermore, we observed a significant positive correlation among the DISCERN score, JAMA score, and Guideline score. The correlation coefficient between the DISCERN score and the JAMA score was 0.49, between the DISCERN score and the Guideline score was 0.67, and between the JAMA score and the Guideline score was 0.55. This indicates that these three tools mutually validated each other, enhancing the reliability of the research results.

Table 10.

The correlation between quality evaluation and the number of likes and comments.

	DISCERN score	JAMA score	Guideline score	Likes	Comments
DISCERN score	1.00	—	—	—	—
JAMA score	0.49^**	1.00	—	—	—
Guideline score	0.67^*	0.55^*	1.00	—	—
Likes	0.10	0.23^*	0.02	1.00	—
Comments	0.13	0.21^*	0.02	0.90^***	1.00

Note: ^***p < .01, ^**p < .05, ^*p < .1.

Discussion

Principal findings

We employed the DISCERN tool, the JAMA benchmark, and two guidelines to assess the quality of WeChat and TikTok videos offering information on hypertension and diabetes treatment. Our analysis revealed inadequate quality of treatment information regarding credibility, specificity, accuracy, and completeness. Importantly, there was no discernible disparity in the quality of video information across various platforms and uploader sources. It is imperative to underscore that information quality should not be evaluated solely based on the quantity of likes and comments.

Comparison with prior work

Previous studies have investigated the quality of online information across various diseases and platforms. For instance, the study by Basch et al.³⁵ evaluated the quality of information on COVID-19 prevention and treatment in TikTok videos, while Liu et al.³⁶ assessed therapeutic health information on rare neurological diseases in WeChat videos. While limited studies have examined online information on hypertension and diabetes, a few notable ones are present. Yang et al.³⁷ analyzed WeChat articles on hypertension treatment, revealing low-quality hypertension treatment-related information. In contrast, Kong et al.²⁴ assessed information in diabetes-related TikTok videos and found it acceptable. Our study specifically focused on hypertension and diabetes, revealing a generally poor quality of treatment information related to these conditions in WeChat and TikTok videos. Furthermore, we enriched our assessment by incorporating guidelines specific to hypertension and diabetes.

WeChat and TikTok are important sources of health information

Currently, with WeChat and TikTok playing an increasingly important role in health information dissemination, more participants acknowledge these platforms as essential channels for obtaining health information.³⁸ At the same time, consistent with previous research findings, we found that most health information is contributed by individual users.²² This trend is particularly prominent on TikTok. This is because in China, while WeChat is also commonly used by institutions and official organizations, TikTok is predominantly used by individuals. However, WeChat and TikTok, known for their social and entertainment aspects characterized by low barriers to entry and high creative freedom, which may increase the likelihood of invalid health information to some extent.³⁹ A previous study indicates that reliable online health information can enhance patients’ health literacy and adherence to medical advice, potentially reducing the burden on individuals and the healthcare system.⁴⁰ On the contrary, inaccurate treatment information can negative impact on patients’ health.⁴¹ Moreover, our results indicated inadequate quality of hypertension and diabetes treatment information videos on WeChat and TikTok. Therefore, improving the quality of video information on platforms like WeChat and TikTok is imperative.

Strengthens supervision of health information uploaders

While previous studies have consistently revealed that health information is considered more credible when sourced from official websites rather than social media platforms,^24,42 our study found no statistically significant difference in the quality of video information uploaded by institutions and individuals on WeChat and TikTok. Probably, hypertension and diabetes are common chronic diseases, and their clinical treatment pathways are relatively clear.⁴³ As a result, treatment information has no big difference between different uploaders. Furthermore, professional medical organizations may tend to convey disease information using less easily understandable technical terms, while disease information shared by individuals, although easily accessible and understandable, may sacrifice information quality to attract traffic. This can result in a lack of significant quality differences between the two uploaders. Consequently, it is recommended that the government should strengthen oversight of information resources, define admission standards and information quality responsibilities of medical information uploaders, prohibit individuals without a professional medical background from sharing disease treatment information, and mandate that information providers assume relevant information quality responsibilities. Additionally, transparent disclosure helps to safeguard the rights of information recipients and facilitates the dissemination and effective utilization of health information.⁴⁴ Given the specialized nature of medical knowledge and varying levels of understanding among viewers, information providers should establish communication channels to allow viewers to seek clarification when needed. In sum, the infodemic requires collective responsibility to address. Information providers should not only disseminate clear, simple, and honest messages, but information users must also enhance their digital literacy and health literacy.⁴⁵

Correlation between likes, comments, and information quality

Internet information has a profound influence on shaping patients’ beliefs and behaviors.⁴⁶ The dynamics of social media are distinct as acting such as liking, sharing, or commenting can influence how individuals interpret health information.⁴⁷ A great number of “likes” are often perceived as more credible, fostering positive attitudes toward the brand.⁴⁸ For example, previous studies found an increasing focus on the use of “likes” in digital marketing.^49–52 Nevertheless, our study found no significant positive relationship between the evaluation of information quality and the number of likes and comments. One potential explanation was that users might not necessarily “like” or “comments” based on information quality but rather other personal preferences.⁵³ Therefore, this finding highlights the importance of individuals enhancing their information discernment skills and suggests that information users should be cautious when sharing comments and disseminating health information on platforms like WeChat, TikTok, and other social media platforms.

Limitations and future directions

This study has a few limitations. Firstly, the sample size of 120 meets the minimum statistical requirements but may be considered relatively small. Secondly, the continuous updating of WeChat and TikTok videos may have constrained our ability to conduct real-time searches. Nevertheless, this study addressed a gap in the existing literature and provided valuable insights into emerging social media platforms. Future research endeavors should consider increasing the sample size and diversifying the research content beyond disease treatment information. Investigating the utilization of social media platforms such as WeChat and TikTok to promote public health initiatives is necessary.

Conclusion

Accurate health information plays a crucial role in promoting chronic disease prevention and managing patient care. However, the current quality of information on the treatment of hypertension and diabetes on platforms like WeChat and TikTok is unsatisfactory. Therefore, it is essential for the government to enhance the evaluation and oversight of medical content within social media videos on platforms like WeChat and TikTok, particularly regarding the monitoring of health information uploaded by individuals. Simultaneously, social media platforms themselves should proactively review health-related content to combat the spread of inaccurate information. Additionally, individuals must prioritize improving their digital literacy and health literacy. This collective effort is essential for combating the infodemic and effectively utilizing health-related information.

Footnotes

Author contributions

All authors have accepted responsibility for the entire content of this manuscript and have approved its submission. Minxia Wu: Methodology, Writing review, and editing; Yongmei Yang: Conceptualization, Methodology, Writing review, and editing. Yanxing Chen: Data collection and cleaning.

Declaration of conflicting interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the The National Social Science Fund of China (22BGL250).

Ethical statement

ORCID iDs

Minxia Wu

Yongmei Yang

References

Zhou

Perel

Mensah

, et al. Global epidemiology, health burden and effective interventions for elevated blood pressure and hypertension. Nat Rev Cardiol 2021; 18(11): 785–802.

Kitt

Fox

Tucker

, et al. New approaches in hypertension management: a review of current and developing technologies and their potential impact on hypertension care. Curr Hypertens Rep 2019; 21: 44.

Heidenreich

Trogdon

Khavjou

, et al. Forecasting the future of cardiovascular disease in the United States: a policy statement from the American Heart Association. Circulation 2011; 123(8): 933.

Liu

Wang

, et al. Prevalence of chronic complications of type 2 diabetes mellitus in outpatients-a cross-sectional hospital-based survey in urban China. Health Qual Life Outcomes 2010; 8(1): 62.

Sun

Saeedi

Karuranga

, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract 2022; 183: 109119.

Cutler

Sorlie

Wolz

, et al. Trends in hypertension prevalence, awareness, treatment, and control rates in United States adults between 1988–1994 and 1999–2004. Hypertension 2008; 52(5): 818–827.

Liu

, et al. Evaluation of the three-in-one team-based care model on hierarchical diagnosis and treatment patterns among patients with diabetes: a retrospective cohort study using Xiamen's regional electronic health records. BMC Health Serv Res 2017; 17(1): 779.

The National Health Commission of the People's Republic of China . 2022 China health statistics yearbook. Beijing: China Union Medical College Press, 2022, https://www.nhc.gov.cn/mohwsbwstjxxzx/tjtjnj/202305/6ef68aac6bd14c1eb9375e01a0faa1fb/files/b05b3d958fc546d98261d165cea4adba.pdf (accessed 2 May 2023).

Wang

Peng

Zhao

, et al. Prevalence and treatment of diabetes in China, 2013-2018. JAMA 2021; 326(24): 2498–2506.

10.

Yip

Chen

, et al. 10 years of health-care reform in China: progress and gaps in universal health coverage. Lancet 2019; 394(10204): 1192–1204.

11.

Grady

Gough

. Self-management: a comprehensive approach to management of chronic conditions. Am J Publ Health 2014; 104(8): e25–e31.

12.

Abudireyimu

, et al. Chronic disease prevention literacy and its influence on behavior and lifestyle: a cross-sectional study in Xinjiang, China. BMC Publ Health 2023; 23(1): 1980.

13.

Schillinger

Grumbach

Piette

, et al. Association of health literacy with diabetes outcomes. JAMA 2002; 288(4): 475–482.

14.

Grosberg

Grinvald

Reuveni

, et al. Frequent surfing on social health networks is associated with increased knowledge and patient health activation. J Med Internet Res 2016; 18(8): e212.

15.

Gray

Klein

Noyce

, et al. The Internet: a window on adolescent health literacy. J Adolesc Health 2005; 37(3): 243.

16.

National Health Commission of the People's Republic of China . Chinese citizen health literacy - basic knowledge and skills (2024 Edition). Beijing: Office of the National Health Commission, 2024. https://www.nhc.gov.cn/xcs/s7852/202405/91fc9d0866bb4d87842b6a264745a71e.shtml (accessed 2 June 2024).

17.

Southwick

Guntuku

Klinger

, et al. Characterizing COVID-19 content posted to TikTok: public sentiment and response during the first phase of the COVID-19 pandemic. J Adolesc Health 2021; 69(2): 234–241.

18.

Hassona

Taimeh

Marahleh

, et al. YouTube as a source of information on mouth (oral) cancer. Oral Dis 2016; 22(3): 202–208.

19.

Holmes

. Why people living with and beyond cancer use the internet. Integr Cancer Ther 2019; 18: 1534735419829830.

20.

Top 10 social software brands in 2023. Maigoo. URL: https://www.maigoo.com/maigoo/1211sjmt_index.html (accessed 15 February 2023).

21.

Zhang

Wen

Liang

, et al. How the public uses social media WeChat to obtain health information in China: a survey study. BMC Med Inf Decis Making 2017; 17(Suppl 2): 66.

22.

Song

Xue

Zhao

, et al. Short-video apps as a health information source for chronic obstructive pulmonary disease: information quality assessment of TikTok videos. J Med Internet Res 2021; 23(12): e28318.

23.

Chen

Zhou

, et al. The value of WeChat application in chronic diseases management in China. Comput Methods Progr Biomed 2020; 196: 105710.

24.

Kong

Song

Zhao

, et al. TikTok as a health information source: assessment of the quality of information in diabetes-related videos. J Med Internet Res 2021; 23(9): e30409.

25.

Olkun

Demirkaya

. Evaluation of internet information about lingual orthodontics using DISCERN and JAMA tools. Turkish J Orthod 2018; 31(2): 50–54.

26.

Abdulai

A-F

Howard

Parmar

, et al. The quality, suitability, and readability of web-based resources on endometriosis-associated dyspareunia: a systematic review. Health Inf J 2024; 30(1): 14604582241231151.

27.

Charnock

Shepperd

Needham

, et al. DISCERN: an instrument for judging the quality of written consumer health information on treatment choices. J Epidemiol Community Health 1999; 53(2): 105–111.

28.

Charnock

. The DISCERN handbook: quality criteria for consumer health information on treatment choices. London: Radcliffe: University of Oxford and The British Library, 1998, pp. 7–51.

29.

Eksi Ozsoy

. Evaluation of YouTube videos about smile design using the DISCERN tool and Journal of the American Medical Association benchmarks. J Prosthet Dent 2021; 125(1): 151–154.

30.

Silberg

Lundberg

Musacchio

. Assessing, controlling, and assuring the quality of medical information on the internet: Caveant lector et viewor — Let the reader and viewer beware. JAMA 1997; 277(15): 1244–1245.

31.

Diabetes Branch of Chinese Medical Association and National Primary diabetes Prevention and Management Office . National basic level diabetes prevention and management guidelines (2022). Chin J Intern Med 2022; 61(3): 249–262.

32.

The Disease Control Bureau of the Ministry of Health , the Hypertension Alliance (China), and the National Heart Deficiency Management Center. The Chinese Guidelines for Hypertension Prevention and Control. Beijing: Chinese Hypertension Prevention and Treatment Guidelines Revision Committee, 2022, https://www.docin.com/p-2570895142.html&key (accessed 20 February 2023).

33.

Azer

. Bibliometric analysis of the top-cited gastroenterology and hepatology articles. BMJ Open 2016; 6(2): e009889.

34.

Chong

Fraval

Chandrananth

, et al. Assessment of the quality of web-based information on bunions. Foot Ankle Int 2013; 34(8): 1134–1139.

35.

Basch

Hillyer

Jaime

. COVID-19 on TikTok: harnessing an emerging social media platform to convey important public health messages. Int J Adolesc Med Health 2022; 34(5): 367–369.

36.

Li-li

Xue-feng

Tan-xin

, et al. Quality assessment of the therapeutic health information of rare neurological diseases on WeChat official account. Chinese Journal of Disease Control 2020; 24(11): 1321–1326.

37.

Yang

Hou

Gong

, et al. Quality assessment of hypertension treatment–related information on WeChat: cross-sectional study. J Med Internet Res 2022; 24(10): e38567.

38.

Bujnowska-Fedak

Waligóra

Mastalerz-Migas

. The internet as a source of health information and services. Adv Exp Med Biol 2019; 12(11): 1–16.

39.

. Comparative analysis of video stories and user behaviors on WeChat and TikTok. Amsterdam: Atlantis Press, 2020, pp. 329–333.

40.

Daraz

Morrow

Ponce

, et al. Can patients trust online health information? A meta-narrative systematic review addressing the quality of health information on the internet. J Gen Intern Med 2019; 34: 1884–1891.

41.

Moorhead

Hazlett

Harrison

, et al. A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication. J Med Internet Res 2013; 15(4): e85.

42.

Walther

Wang

Loh

. The effect of top-level domains and advertisements on health web site credibility. J Med Internet Res 2004; 6(3): e24.

43.

De Boer

Bangalore

Benetos

, et al. Diabetes and hypertension: a Position statement by the american diabetes association. Diabetes Care 2017; 40(9): 1273–1284.

44.

Gajewski

. Can internet-based disclosure reduce information asymmetry? Adv Account 2015; 31(1): 115–124.

45.

The Lancet . The truth is out there, somewhere. Lancet 2020; 396(10247): 291.

46.

Jones

Sinclair

Courneya

. The effects of source credibility and message framing on exercise intentions, behaviors, and attitudes: an integration of the elaboration likelihood model and prospect theory. J Appl Soc Psychol 2003; 33(1): 179–196.

47.

Borah

Xiao

. The importance of ‘likes’: the interplay of message framing, source, and social endorsement on credibility perceptions of health information on Facebook. J Health Commun 2018; 23(4): 399–411.

48.

Seo

Kim

Choi

, et al. In “likes” we trust: likes, disclosures and firm-serving motives on social media. Eur J Market 2019; 53(10): 2173–2192.

49.

de Vries

Gensler

Leeflang

PSH

. Popularity of brand posts on brand fan pages: an investigation of the effects of social media marketing. J Interact Market 2012; 26(2): 83–91.

50.

Koroleva

Kane

. Relational affordances of information processing on Facebook. Inf Manag 2017; 54(5): 560–572.

51.

Naylor

Lamberton

West

. Beyond the “like” button: the impact of mere virtual presence on brand evaluations and purchase intentions in social media settings. J Mark 2012; 76(6): 105–120.

52.

Sedera

Lokuge

Atapattu

, et al. Likes—the key to my happiness: the moderating effect of social influence on travel experience. Inf Manag 2017; 54(6): 825–836.

53.

Chen

. Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Model User-adapt Interact 2012; 22: 317–355.

Evaluation of treatment information quality on hypertension and diabetes on WeChat and TikTok: A cross-sectional content analysis

Abstract

Keywords

Introduction

Materials and methods

Search strategy and data extraction

Evaluated methods and dimensions

Data analysis

Patient information statement

Results

Characteristics of the videos

Evaluation results

Evaluation of hypertension and diabetes treatment information in WeChat and TikTok videos

Comparison of video information quality on WeChat and TikTok

Information quality correlation with the numbers of likes and comments

Discussion

Principal findings

Comparison with prior work

WeChat and TikTok are important sources of health information

Strengthens supervision of health information uploaders

Correlation between likes, comments, and information quality

Limitations and future directions

Conclusion

Footnotes

Author contributions

Declaration of conflicting interests

Funding

Ethical statement

ORCID iDs

References