Abstract
Phenylketonuria (PKU) is a congenital metabolic disorder characterized by defective phenylalanine metabolism, leading to neurotoxicity when untreated. Early detection through newborn screening and timely dietary intervention can ensure normal intellectual development. YouTube is a widely used source for health-related content, but the quality of information remains variable. The objective of this study is to evaluate the quality and reliability of YouTube videos related to PKU using validated scoring tools. This cross-sectional study was conducted on December 30, 2024. A YouTube search using the keyword “phenylketonuria” yielded 150 videos, of which 104 met the inclusion criteria. Video parameters including number of views, likes, duration, and content type were collected. Reliability was assessed using the Journal of the American Medical Association Benchmark Criteria and the modified DISCERN questionnaire. Quality and accuracy were evaluated using the Global Quality Score. Statistical analyses were performed to determine the relationships between video characteristics and evaluation scores. Of the analyzed videos, 45% were animated and 68% were uploaded by healthcare professionals. The median number of views was 1064 (range: 12–806,000) and the median view ratio was 0.68 (range: 0–245.36). Significant associations were found between the year of upload and view ratio (P = .012), and between continent and both view ratio (P = .045) and daily views (P = .003). Likes-per-view differed significantly by country (P < .001). According to the Global Quality Score, 56% of videos were of medium quality, with the highest scores observed in videos from professional organizations and academic sources. Journal of the American Medical Association assessment showed that 58.3% of videos contained sufficient information. Modified DISCERN questionnaire revealed that 46.6% of videos had poor reliability. The majority of PKU-related YouTube videos analyzed were of low to moderate quality and reliability. Given the impact of such content on patient decision-making, healthcare authorities should take an active role in producing and promoting high-quality digital health content.
Keywords: DISCERN, Global Quality Score, JAMA Benchmark Criteria, phenylketonuria, YouTube video analysis
1. Introduction
Phenylketonuria (PKU; OMIM #261600) is a congenital metabolic disorder caused by mutations in phenylalanine hydroxylase gene (PAH).[1] Phenylalanine hydroxylase plays a role in the conversion of phenylalanine to tyrosine with the help of tetrahydrobiopterin (BH4), which acts as a cofactor. In the absence of phenylalanine hydroxylase, phenylalanine levels increase in the blood and the brain. Globally, the prevalence of PKU is 1:10,000 newborns, and varies by country and ethnic group. Turkey ranks among the countries with the highest rates of the disease, partly because of the high incidence of consanguineous marriages. In Turkey, the prevalence of PKU is 1 in 4200 live births.[2]
PKU was first described in 1934 by Dr Asbjørn Følling as a relatively common, identifiable cause of intellectual disability.[3] In the 1950s, Dr Bickel et al demonstrated that phenylalanine restriction improved intellect and outcome in affected individuals treated with diets limited to phenylalanine.[4] Phenylalanine (Phe) is an essential amino acid found in almost all existing proteins such as meat, milk, fish, eggs, seeds, and flour. In PKU, dietary treatment is the cornerstone of therapy and is based on natural protein restriction. Therefore, Phe-free amino acid supplements (protein substitutes) and low-protein foods should be consumed. Untreated patients with PKU exhibit progressive intellectual disability, behavioral problems, seizures, motor delays, eczematous rash, light skin, and hair pigmentation. PKU, which is classified as a toxic metabolic disorder, initially appears normal. However, with advancing age, signs such as neuromotor delays, behavioral issues, and autism spectrum disorder symptoms, become more pronounced.[5–7] Irreversible brain damage is inevitable in the absence of early diagnosis and treatment. It has been reported that untreated cases can experience an average IQ drop of up to 50 points (severe intellectual disability) by the age of 2 years.[8] Initiating a low-phenylalanine diet early in life and more recently, BH4 treatment for suitable patients has provided individuals the chance to achieve normal intelligence and lead a typical social life.[9]
PKU is the first metabolic disorder with an elucidated etiology, the first congenital metabolic disorder to be effectively treated, and the first disease included in newborn screening programs worldwide.[10] It serves as a reference point for all metabolic disorders. A history of newborn screening programs began with PKU screening. Understanding of PKU, guidelines, and the treatment landscape has evolved dramatically over the decades since the implementation of newborn screening. Compared to the first guideline on PKU published in 1993,[11] guidelines now advice to keep Phe concentrations below 360 μmol/L.[12] Updated recommendations to improve early diagnosis, timely treatment, and comprehensive care strategies continue to evolve.[13,14]
Parents are the primary caregivers of children with PKU. They are required to learn how to implement and maintain a phenylalanine-restricted diet as well as cope with the medical, psychological, and social consequences of this disease.[15] In addition to the traditional methods of parental education, social media has emerged as a significant tool for knowledge dissemination and education. These platforms are built on highly interactive digital frameworks that enable individuals and communities to share, create, modify, and discuss user-generated content.[16,17] It is widely recognized that patients frequently seek medical information on the Internet to better understand their diagnoses, explore treatment options, and actively participate in their healthcare.[18] Among the online platforms, Google ranks as the most visited website, followed closely by YouTube, a popular video-sharing platform.[19] As of February 2025, there are approximately 5.78 billion internet users and 5.24 billion social media users worldwide. YouTube (https://www.youtube.com), with over 2.5 billion active users, has emerged as the world’s second-largest social media platform and the leading video-sharing platform.[20] More than 500 hours of video content are uploaded to YouTube every minute. Approximately 80% of internet users obtain health information from YouTube, one of the most popular sources. The platform hosts approximately 1 billion hours of video content watched by nearly 1 billion users daily.[21] Despite its popularity, concerns have persisted regarding the quality and reliability of health-related content on YouTube. Because videos can be uploaded by anyone without formal verification and many are created for commercial purposes, the accuracy, completeness, and trustworthiness of the information presented remain questionable.[22] A review of 18 studies evaluating YouTube videos on various health topics revealed that although YouTube provides high-quality health-related information, it also contains conflicting and misleading content.[23]
Although there are more than 15,000 publications related to PKU, which can be found in PubMed, only a few are related to PKU and social media, and there are no publications investigating the popularity, reliability, and quality of YouTube videos related to PKU. Therefore, the primary aim of this study was to evaluate the quality, reliability and popularity of YouTube videos on PKU by analyzing factors such as view counts, content accuracy, and video sources. In addition, this study identifies the characteristics of the most popular, high-quality, and reliable videos, focusing on which sources upload such content and the features that contribute to their effectiveness and trustworthiness. This study aims to assess the current state of PKU-related information on YouTube and provide insights into improving online educational content to enhance public awareness and encourage a more accurate understanding of PKU.
2. Materials and methods
2.1. Ethics
The YouTube applications used in this study were publicly accessible and no unlisted or private videos, human or animal participants were considered due to accessibility limitations involved in the data collection process. As no personal or sensitive data were collected and no interaction with human or animal subjects occurred, ethical approval and informed consent were not required in accordance with institutional and international research ethics guidelines. The study protocol was designed in compliance with the Declaration of Helsinki 1964. Additionally, because the videos analyzed were publicly available, no special permission was required from YouTube.
2.2. Study design
This observational cross-sectional study was conducted on December 30, 2024, using the YouTube search engine (https://www.youtube.com) with the search term “phenylketonuria.” Two authors systematically compiled a list of videos and identified those that contained medical content. Google Incognito mode was employed during the search to minimize bias from search history and cookies. Only English-language videos directly related to PKU were included, whereas videos in other languages, those without audio or visual elements, and duplicates were excluded, in accordance with prior studies.[24,25] Following established methodologies in previous research, the videos were organized by view count, and the first 150 videos were initially assessed. After applying the exclusion criteria, 46 videos were excluded, leaving 104 videos for final analysis (Fig. 1). The reliability, quality, and content of each remaining video were evaluated thoroughly.
Figure 1.
Flowchart of the videos included in the study.
2.3. Data collection
2.3.1. Content analysis
Initially, each video was systematically assessed based on PKU-related topics including definition, pathophysiology, incidence, historical context, diagnostic criteria, diagnostic methods, treatment strategies, diet, outcomes, complications, morbidity, mortality, and prevention. This evaluation enabled a comprehensive analysis of the depth and breadth of information provided by each video.
Subsequently, the characteristics of each video were documented, including whether the content was presented in animated or live-action format. The qualifications of the uploaders were classified as academic, physician, society/professional organization, health-related website, patient, or news to assess the source’s reliability and authority. Additionally, the countries of origin and upload dates for each video were recorded, facilitating an analysis of the geographic distribution and timeliness of educational content.
2.3.2. Popularity calculating
For each video, several variables were systematically recorded, including view count, number of likes and dislikes, video duration, upload date, comment count, video source/uploader type, and uploader’s country. To evaluate video popularity, additional metrics were calculated and documented, such as the view ratio, dislike ratio, Video Power Index (VPI), VPI-like ratio, likes-per-view, daily views, daily likes, and like-to-view ratio.
The like ratio was used to assess the overall positive reception of a video by viewers and was calculated as (likes × 100)/(likes + dislikes).[26] The VPI, a metric for evaluating a video’s popularity, was calculated as [(like ratio × view ratio)/100].[27–29] The VPI is a well-established metric in the literature, serving as a tool to evaluate the effectiveness of video content on online platforms by measuring its visibility, viewer engagement, and overall performance.[29] This metric offers valuable insights into a video’s ability to capture attention, foster engagement, and achieve high performance among its audience.
2.3.3. Assessment of reliability and quality
The videos included in this study were assessed for their reliability and quality. To conduct this analysis, the Global Quality Scale (GQS),[24,25,29] mDISCERN (modified Quality Criteria for Consumer Health Information on Treatment Choices),[24,25,30] and the Journal of the American Medical Association (JAMA) Benchmark Criteria[24,25,30] were utilized (Table 1). The GQS was employed to evaluate video quality, whereas the mDISCERN and JAMA Benchmark Criteria were used to assess reliability.[24,25,31] The mDISCERN scale is a 5-item Likert tool developed to assess the reliability of online health information. Initially created in 1999 as a 16-item scale to evaluate written health materials, the DISCERN scale was adapted by Singh et al in 2021 into a 5-item version specifically for YouTube videos.[24,32] Each item is rated on a scale from 1 to 5, with higher scores indicating greater reliability. Scores below 3 suggest low reliability, 3 indicates moderate reliability, and scores above 3 indicate high reliability. This concise scale serves as a useful instrument for evaluating the reliability of digital media and online health videos, thereby ensuring the provision of accurate information (Table 1).[25,30,32] Additionally, the JAMA Benchmark Criteria were applied to the evaluation of videos that were developed by Silberg in 1997. This tool assesses the “Authorship, Attribution, Disclosure, and Currency” of health-related content. Scores ranging from 0 to 1 indicate insufficient information, 2 to 3 suggest partially sufficient information, and 4 signifies fully sufficient information, with higher scores reflecting greater reliability (Table 1).[25,29,31] Lastly, the GQS, a 5-question Likert scale rated from 1 to 5, was used to evaluate video quality. Higher scores correlated with higher video quality, with scores of 1 to 2 considered low quality, 3 as intermediate quality, and scores of 4 to 5 as high quality (Table 1).[24,25,29]
Table 1.
Components of GQS, modified DISCERN, and JAMA Benchmark Evaluation Criteria.
| JAMA Benchmark Criteria | Total score (0–4 points)[24,25,30] |
|---|---|
| Authorship | 1 point (authors and contributors, their affiliations, and relevant credentials should be provided) |
| Attribution | 1 point (references and sources for all content should be listed) |
| Disclosure | 1 point (conflicts of interest, funding, sponsorship, advertising, support, and video ownership should be fully disclosed) |
| Currency | 1 point (dates that on which the content was posted and updated should be indicated; JAMA is used to evaluate the accuracy and reliability of information) |
| Modified DISCERN criteria[24,25,30] | Total score (0–5 points) |
|---|---|
| 1. Are the aims clear and achieved? | 0–1 point |
| 2. Are reliable sources of information used? | 0–1 point |
| 3. Is the information presented balanced and unbiased? | 0–1 point |
| 4. Are additional sources of information listed for patient reference? | 0–1 point |
| 5. Are areas of uncertainty mentioned? | 0–1 point |
| GQS[24,25,29] | Score |
|---|---|
| Poor quality, poor flow of the site, most information missing, not at all useful for patients | 1 |
| Generally poor quality and poor flow, some information listed but many important topics missing, of very limited use to patients | 2 |
| Moderate quality, suboptimal flow, some important information is adequately discussed but others poorly discussed, somewhat useful for patients | 3 |
| Good quality and generally good flow, most of the relevant information is listed, but some topics not covered, useful for patients | 4 |
| Excellent quality and excellent flow, very useful for patients | 5 |
GQS = Global Quality Score, JAMA = Journal of American Medical Association, modified DISCERN = modified Quality Criteria for Consumer Health Information on Treatment Choices.
2.4. Statistical analysis
The acquired data were analyzed using the Statistical Package for Social Sciences (SPSS, Chicago) 24.0 software. Frequencies are presented as numbers (n) and percentages (%), while continuous variables are expressed as medians (minimum–maximum). Kolmogorov–Smirnov and Shapiro–Wilk tests were used to determine whether continuous variables followed a normal distribution pattern. Depending on the number of groups, the Mann–Whitney U test and Kruskal–Wallis test were used to analyze continuous variables. Pearson Chi-square test was used to analyze categorical data. Pearson correlation tests were used to compare the groups for correlation analyses. A P-value of <.05 was considered indicative of a statistically significant difference.
3. Results
A total of 150 videos were initially retrieved from YouTube (https://www.youtube.com) using the search term “phenylketonuria.” Of these, 46 videos were excluded based on the following criteria: 20 short videos, 22 non-English, 3 duplicates, and 1 faulty link. Consequently, 104 videos were included in the final analysis (Fig. 1).
The analyzed videos uploaded between 2009 and 2024 were viewed 2,734,784 times, averaging 26,296 ± 97,654 views per video. Total duration of the analyzed videos was 14 hours 41 minutes 8 seconds (52,868 seconds) with median video length 311.50 seconds, ranging from 1 to 4688 seconds. Videos created by academicians had the longest median duration, with an average of 633 seconds (range: 145–4688 seconds). Statistically significant differences were observed between video upload years and content topics concerning pathophysiology, genetics, diagnosis methods, morbidity, mortality, and prevention (P = .034, P = .002, P = .031, P = .003, P = .015, and P < .001 respectively; Table 2). Furthermore, statistically significant differences were observed between uploaded years and engagement metrics, including views, likes, dislikes, comments, VPI-like ratio, view ratio, VPI, dislike ratio, likes-per-view, and likes ratio/views per day, which were identified in relation to video upload years, with P-values ranging from <.001 to <.050, respectively (Tables 3 and 4).
Table 2.
Comparison of the video content, reliability, and quality of videos over the years and upload source.
| Video content | n (%) | Date/years* | Source* | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022–2024 n (%) |
2019–2021 n (%) |
<2018 n (%) |
P | Academic | Physician | Society/professional organization | Health-related website | Patient | News | Commercial | P | ||
| Animation | + | 12 (11.5%) | 18 (17.3%) | 17 (16.3%) | .128 | 12 (11.5%) | 10 (9.6%) | 15 (14.4%) | 6 (5.8%) | 0 (0%) | 0 (0%) | 4 (3.8%) | .074 |
| − | 22 (21.2%) | 12 (11.5%) | 23 (22.1%) | 11 (10.6%) | 7 (6.7%) | 16 (15.4%) | 9 (8.7%) | 8 (7.7%) | 3 (2.9%) | 3 (2.9%) | |||
| Definition | + | 29 (27.9%) | 30 (28.8%) | 35 (33.7%) | .101 | 23 (22.1%) | 17 (16.3%) | 27 (26.0%) | 14 (13.5%) | 5 (4.8%) | 3 (2.9%) | 5 (4.8%) | .019 |
| − | 5 (4.8%) | 0 (0.0%) | 5 (4.8%) | 0 (0%) | 0 (0%) | 4 (3.8%) | 1 (1.0%) | 3 (2.9%) | 0 (0%) | 2 (1.9%) | |||
| Pathophysiology | + | 25 (24.0%) | 27 (26.0%) | 25 (24.0%) | .034 | 21 (20.2%) | 17 (16.3%) | 26 (25.0%) | 11 (10.6%) | 0 (0%) | 1 (1.0%) | 1 (1.0%) | <.001 |
| − | 9 (8.7%) | 3 (2.9%) | 15 (14.4%) | 2 (1.9%) | 0 (0%) | 5 (4.8%) | 4 (3.8%) | 8 (7.7%) | 2 (1.9%) | 6 (5.8%) | |||
| Incidence | + | 14 (13.5%) | 10 (9.6%) | 7 (6.7%) | .075 | 8 (7.7%) | 9 (8.7%) | 5 (4.8%) | 6 (5.8%) | 1 (1.0%) | 1 (1.0%) | 1 (1.0%) | .120 |
| − | 20 (19.2%) | 20 (19.2%) | 33 (31.7%) | 15 (14.4%) | 8 (7.7%) | 26 (25.0%) | 9 (8.7%) | 7 (6.7%) | 2 (1.9%) | 6 (5.8%) | |||
| History | + | 6 (5.8%) | 3 (2.9%) | 2 (1.9%) | .210 | 5 (4.8%) | 1 (1.0%) | 4 (3.8%) | 0 (0%) | 1 (1.0%) | 0 (0%) | 0 (0%) | .360 |
| − | 28 (26.9%) | 27 (26.0%) | 38 (36.5%) | 18 (17.3%) | 16 (15.4%) | 27 (26.0%) | 15 (14.4%) | 7 (6.7%) | 3 (2.9%) | 7 (6.7%) | |||
| Genetic | + | 23 (22.1%) | 23 (22.1%) | 15 (14.4%) | .002 | 18 (17.3%) | 12 (11.5%) | 19 (18.3%) | 10 (9.6%) | 0 (0%) | 1 (1%) | 1 (1%) | .001 |
| − | 11 (10.6%) | 7 (6.7%) | 25 (24.0%) | 5 (4.8%) | 5 (4.8%) | 12 (11.5%) | 5 (4.8%) | 8 (7.7%) | 2 (1.9%) | 6 (5.8%) | |||
| Diagnosis | + | 25 (24.0%) | 26 (25.0%) | 28 (26.9%) | .250 | 17 (16.3%) | 15 (14.4%) | 25 (24.0%) | 12 (11.5%) | 5 (4.8%) | 2 (1.9%) | 3 (2.9%) | .312 |
| − | 9 (8.7%) | 4 (3.8%) | 12 (11.5%) | 6 (5.8%) | 2 (1.9%) | 6 (5.8%) | 3 (2.9%) | 3 (2.9%) | 1 (1.0%) | 4 (3.8%) | |||
| Diagnostic methods | + | 15 (14.6%) | 20 (19.4%) | 14 (13.6%) | .031 | 14 (13.6%) | 9 (8.7%) | 16 (15.5%) | 3 (2.9%) | 3 (2.9%) | 1 (1.0%) | 3 (2.9%) | .275 |
| − | 18 (17.5%) | 10 (9.7%) | 26 (25.2%) | 8 (8.7%) | 7 (6.7%) | 15 (14.6%) | 12 (11.7%) | 5 (4.9%) | 2 (1.9%) | 4 (3.9%) | |||
| Treatment | + | 25 (24.0%) | 26 (25.0%) | 27 (26.0%) | .181 | 17 (16.3%) | 10 (9.8%) | 27 (26.0%) | 9 (8.7%) | 7 (6.7%) | 3 (2.9%) | 5 (4.8%) | .216 |
| − | 9 (8.7%) | 4 (3.8%) | 13 (12.5%) | 6 (5.8%) | 7 (6.7%) | 4 (3.8%) | 6 (5.8%) | 1 (1%) | 0 (0%) | 2 (1.9%) | |||
| Diet | + | 24 (23.1%) | 26 (25.0%) | 29 (27.9%) | .261 | 15 (14.4) | 10 (9.6%) | 26 (25.0%) | 11 (10.6%) | 8 (7.7%) | 3 (2.9%) | 6 (5.8%) | .165 |
| − | 10 (9.6%) | 4 (3.8%) | 11 (10.6%) | 8 (7.7%) | 7 (6.7%) | 5 (4.8%) | 4 (3.8%) | 0 (0%) | 0 (0%) | 1 (1%) | |||
| Outcome | + | 23 (22.1%) | 23 (22.1%) | 27 (26.0%) | .655 | 12 (11.5) | 9 (8.7%) | 26 (25.0%) | 12 (11.5%) | 8 (7.7%) | 3 (2.9%) | 3 (3.9%) | .011 |
| − | 11 (10.6%) | 7 (6.7%) | 13 (12.5%) | 11 (10.6) | 8 (7.7%) | 5 (4.8%) | 3 (2.9%) | 0 (0%) | 0 (0%) | 4 (3.8%) | |||
| Complications | + | 19 (18.4%) | 15 (14.6%) | 17 (16.5%) | .439 | 9 (8.7%) | 7 (6.8%) | 19 (18.4%) | 8 (7.8%) | 5 (4.9%) | 1 (1.0%) | 2 (1.9%) | .542 |
| − | 14 (13.6%) | 15 (14.6%) | 23 (22.3%) | 13 (12.6) | 11 (10.6) | 12 (11.7%) | 7 (6.8%) | 3 (2.9%) | 2 (1.9%) | 5 (4.9%) | |||
| Morbidity | + | 19 (18.4%) | 5 (4.9%) | 13 (12.6%) | .003 | 7 (6.8%) | 3 (2.9%) | 13 (12.6%) | 5 (4.9%) | 6 (5.8%) | 1 (1.0%) | 2 (1.9%) | .195 |
| − | 14 (13.6%) | 25 (24.3%) | 27 (26.2%) | 15 (14.6) | 14 (13.6) | 18 (17.5%) | 10 (9.7%) | 2 (1.9%) | 2 (1.9%) | 5 (4.9%) | |||
| Mortality | + | 8 (7.8%) | 0 (0.0%) | 5 (4.9%) | .015 | 4 (3.9%) | 0 (0%) | 4 (3.9%) | 1 (1.0%) | 3 (2.9%) | 1 (1%) | 0 (0%) | .116 |
| − | 25 (24.3%) | 30 (29.1%) | 35 (34.0%) | 18 (17.5%) | 17 (16.5) | 27 (26.2%) | 14 (13.6%) | 5 (4.9%) | 2 (1.9%) | 7 (6.8%) | |||
| Prevention | + | 13 (12.6%) | 0 (0.0%) | 7 (6.8%) | <.001 | 5 (4.9%) | 3 (2.9%) | 6 (5.8%) | 3 (2.9%) | 2 (1.9%) | 1 (1.0%) | 0 (0%) | .879 |
| − | 20 (19.4%) | 30 (29.1%) | 33 (32.0%) | 17 (16.5%) | 14 (13.6) | 25 (24.3%) | 12 (11.7%) | 6 (5.8%) | 2 (1.9%) | 7 (6.8%) | |||
| JAMA | Insufficient data (1 point) | 14 (13.6%) | 8 (7.8%) | 14 (13.6%) | .008 | 7 (6.8%) | 12 (11.7%) | 6 (5.8%) | 5 (4.9%) | 2 (1.9%) | 2 (1.9%) | 2 (1.9%) | .106 |
| Partially sufficient data (2 or 3 points) | 13 (12.6%) | 22 (21.4%) | 25 (24.3%) | 13 (12.6%) | 5 (4.9%) | 21 (20.4%) | 10 (9.7%) | 5 (4.9%) | 1 (1.0%) | 5 (4.9%) | |||
| Completely sufficient data (4 points) | 6 (5.8%) | 0 (0.0%) | 1 (1.0%) | 2 (1.9%) | 0 (0%) | 4 (3.9%) | 0 (0%) | 1 (1.0%) | 0 (0%) | 0 (0%) | |||
| GQS | Low quality (1 or 2 points) | 10 (9.7%) | 8 (7.8%) | 10 (9.7%) | .706 | 9 (8.7%) | 9 (8.7%) | 7 (6.8%) | 2 (1.9%) | 0 (0%) | 0 (0%) | 1 (1.0%) | .040 |
| Intermediate quality (3 points) | 16 (15.5%) | 19 (18.4%) | 24 (23.3%) | 11 (10.7%) | 8 (7.8%) | 19 (18.4%) | 10 (9.7%) | 4 (3.9%) | 2 (1.9%) | 5 (4.9%) | |||
| High quality (4–5 points) | 7 (6.8%) | 3 (2.9%) | 6 (5.8%) | 2 (1.9%) | 0 (0%) | 5 (4.9%) | 3 (2.9%) | 4 (3.9%) | 1 (1.0%) | 1 (1.0%) | |||
| Modified DISCERN | 1 point (very poor) | 5 (4.9%) | 4 (3.9%) | 3 (2.9%) | .112 | 4 (3.9%) | 6 (5.8%) | 1 (1.0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (1.0%) | .131 |
| 2 points (poor) | 14 (13.6%) | 15 (14.6%) | 19 (18.4%) | 10 (9.7%) | 8 (7.8%) | 14 (13.6%) | 9 (8.7%) | 2 (1.9%) | 1 (1.0%) | 4 (3.9%) | |||
| 3 points (fair) | 8 (7.8%) | 11 (10.7%) | 17 (16.5%) | 5 (4.9%) | 3 (2.9%) | 12 (11.7%) | 6 (5.8%) | 6 (5.8%) | 2 (1.9%) | 2 (1.9%) | |||
| 4 points (good) | 3 (2.9%) | 0 (0.0%) | 0 (0.0%) | 2 (1.9%) | 0 (0%) | 1 (1.0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | |||
| 5 points (excellent) | 3 (2.9%) | 0 (0%) | 1 (1.0%) | 1 (1.0%) | 0 (0%) | 3 (2.9%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | |||
P values in bold are statistically significant (P < .05).
GQS = Global Quality Score, JAMA = Journal of American Medical Association Benchmark Criteria, DISCERN = Quality Criteria for Consumer Health Information on Treatment Choices.
Pearson Chi-square test.
Table 3.
Video parameters according to animation, years, source, country, continent, reliability, and quality parameters [median (min–max)].
| Median (min–max) | View | Like | Dislike | Comment | Date | Duration | ||
|---|---|---|---|---|---|---|---|---|
| Animation | No | (n = 57) | 756.00(31.00–806,000.00) | 18.00(0.00–22,000.00) | 0.00(0.00–133.00) | 1.00(0.00–158.00) | 2020(2010–2024) | 301.00(66.00–4688.00) |
| Yes | (n = 47) | 1581.00(12.00–232,532.00) | 18,000.00(0.00–4900.00) | 0.00(0.00–220.00) | 1.00(0.00–113.00) | 2020(2009–2024) | 386.00(1.00–2660.00) | |
| P | .535 | .756 | .580 | .852 | .636 | .940 | ||
| Years | 2022–2024 | (n = 34) | 290.00 (31.00–48,604.00) | 8.00(1.00–1100.00) | 0.00(0.00–10.00) | 0.00(0.00–42.00) | 2024(2022–2023) | 347.00(66.00–4688.00) |
| 2019–2021 | (n = 30) | 3313.00(79.00–375,270.00) | 42.50(1.00–6500.00) | 0.00(0.00–119.00) | 2.00(0.00–135.00) | 2020(2019–2021) | 308.50(132.00–2660.00) | |
| <2018 | (n = 40) | 4073.00 (12.00–806,000.00) | 30.00(0.00–22,000.00) | 1.00(0.00–220.00) | 1.00(0.00–158.00) | 2016(2009–2018) | 304.00(1.00–1048.00) | |
| P | <.001 | .012 | <.001 | .029 | <.001 | .410 | ||
| Source | Academic | (n = 23) | 698.00(36.00–232,532.00) | 18.00(1.00–4900.00) | 0.00(0.00–220.00) | 1.00(0.00–59.00) | 2020(2014–2024) | 633.00(145.00–4688.00) |
| Physician | (n = 17) | 1009.00(110.00–806,000.00) | 18.00(1.00–12,000.00) | 0.00(0.00–125.00) | 2.00(0.00–158.00) | 2021(2014–2023) | 441.00(97.00–1601.00) | |
| Society/professional organization | (n = 31) | 2395.00(31.00–375,270.00) | 20.00(0.00–6500.00) | 0.00(0.00–119.00) | 0.00(0.00–135.00) | 2019(2009–2024) | 280.00(73.00–2315.00) | |
| Health-related website | (n = 15) | 965.00(12.00–151,904.00) | 16.00(0.00–22,000.00) | 0.50(0.00–133.00) | 2.00(0.00–108.00) | 2019(2013–2024) | 240.00(1.00–513.00) | |
| Patient | (n = 8) | 3892.50(86.00–48,414.00) | 35.50 (4.00–440.00) | 0.00(0–6.0) | 4.50(0.00–98.00) | 2016.50(2011–2023) | 243.00(66.00–1441.00) | |
| News | (n = 3) | 319.00(197.00–543.00) | 9.00(5.00–9.00) | 0.00(0.00–2.00) | 0.00(0.00–0.00) | 2023(2018–2024) | 290.00(195.00–1518.00) | |
| Commercial | (n = 7) | 1427.00(12.00–63,720.00) | 5.00(0.00–506.00) | 0.00(0.00–20.00) | 0.00(0.00–6.00) | 2018(2015–2023) | 245.00(57.00–426.00) | |
| P | .714 | .783 | .964 | .171 | .296 | .014 | ||
| Country | USA | (n = 47) | 798.00(12.00–806,000.00) | 10.00(0–6500.00) | 0.00(0.00–119.00) | 0.00(0.00–135.00) | 2019(2010–2024) | 243.00(1.00–4688.00) |
| UK | (n = 10) | 679.50(31.00–14,562.00) | 19.00(1.00–307.00) | 0.00(0.00–3.00) | 0.50(0.00–18.00) | 2021.5(2009–2024) | 281.50(80.00–885.00) | |
| India | (n = 14) | 807.50(99.00–48,604.00) | 17.50(4.00–1100.00) | 0.00(0.00–14.00) | 0.00(0.00–42.00) | 2019.50(2013–2024) | 426.50(97.00–2660.00) | |
| Other | (n = 33) | 1581.00(31.00–399,655.00) | 40.00(1.00–22,000.00) | 0.00(0.00–220.00) | 2.00(0.00–158.00) | 2020(2009–2024) | 374.00(80.00–2660.00) | |
| P | .421 | .059 | 0594 | .141 | .064 | .025 | ||
| Continent | America | (n = 50) | 979.00(12.00–806,000.00) | 16.00(0.00–6500.00) | 0.00(0.00–119.00) | 0.00(0.00–135.00) | 2018.50(2010–2024) | 244.00(1.00–4488.00) |
| Non-America | (n = 54) | 1064.00(31.00–399,655.00) | 29.00(1.00–22,000.00) | 0.00(0.00–220.00) | 1.50(0.00–158.00) | 2021(2009–2024) | 380.00(80.00–2660.00) | |
| P | .725 | .045 | .481 | .046 | .004 | .018 | ||
| GQS | Low quality | (n = 28) | 828.50(12.00–24,921.00) | 14.50(0.00–484.00) | 0.00(0.00–9.00) | 0.00(0.00–42.00) | 2020(2013–2024) | 443.50(1.00–2660.00) |
| Medium quality | (n = 59) | 1921.00(12.00–806,000.00) | 35.00(0.00–22,000.00) | 0.00(0.00–220.00) | 1.00(0.00–158.00) | 2020(2009–2024) | 280.00(57.00–1048.00) | |
| High quality | (n = 16) | 695.00(44.00–83,000.00) | 17.50(1.00–1300.00) | 00.00(0.00–28.00) | 0.50(0.00–56.00) | 2021(2010–2024) | 478.50(132.00–4688.00) | |
| P | .258 | .397 | .187 | .573 | .827 | .028 | ||
| JAMA | Insufficient data | (n = 36) | 887.50(12.00–806,000.00) | 10.00(0.00–220,000.00) | 0.00(0.00–133.00) | 0.50(0.00–108.00) | 2020(2009–2024) | 294.00(1.00–2660.00) |
| Partially sufficient data | (n = 60) | 2020.00(12.00–399,655.00) | 30.00(0.00–12,000.00) | 0.00(0.00–220.00) | 1.00(0.00–158.00) | 2019.50(–2024) | 311.50(57.00–1656.00) | |
| Completely sufficient data | (n = 7) | 99.00(32.00–11,031.00) | 7.00(1.00–78.00) | 0.00(0.00–2.00) | 0.00(0.00–6.00) | 2023(2010–2024) | 1441.00(444.00–4688.00) | |
| P | .059 | .140 | .158 | .320 | .097 | .001 | ||
| mDISCERN | Very poor | (n = 10) | 418.00(110.00–15,098.00) | 10.50(4.00–238.00) | 0.00(0.00–9.00) | 0.00(0.00–42.00) | 2020.50(2017–2023) | 532.50(97.00–1601.00) |
| Poor | (n = 49) | 1576.00(12.00–399,655.00) | 18.00(0.00–22,000.00) | 0.00(0.00–133.00) | 1.00(0.00–158.00) | 2020(2009–2024) | 280.00(1.00–2660.00) | |
| Fair | (n = 36) | 1510.00(12.00–806,000.00) | 26.00(0.00–6500.00) | 0.00(0.00–220.00) | 1.00(0.00–113.00) | 2019.50(2011–2024) | 276.50(57.00–1518.00) | |
| Good | (n = 4) | 395.50(31.00–375,270.00) | 7.00(2.00–7800.00) | 0.00(0.00–119.00) | 1.00(0.00–135.00) | 2022.50 (2019–2023) | 740.50(333.00–940.00) | |
| Excellent | (n = 4) | 398.50(44.00–11,031.00) | 18.00(0.00–22,000.00) | 0.00(0.00–2.00) | 0.00(0.00–6.00) | 2022.50(2010–2024) | 1919.50(444.00–4688.00) | |
| P | .378 | .815 | .833 | .633 | .328 | .006 | ||
Kruskal–Wallis test. “Bonferroni adjustment” was used in multigroup analyses. Bold font indicates statistical significance (P < .05).
GQS = Global Quality Score, JAMA = Journal of American Medical Association Benchmark Criteria, mDISCERN = modified Quality Criteria for Consumer Health Information on Treatment Choices, n = number of videos.
Table 4.
Video popularity parameters according to animation years, source, country, reliability, and quality parameters [median (min–max)].
| Median (min–max) | VPI-like ratio | View ratio | VPI | Dislike ratio | Likes-per-view | Daily views like | Likes ratio/views per day | ||
|---|---|---|---|---|---|---|---|---|---|
| Animation | No | (n = 57) | 100.00(81.25–100.00) | 0.5981(0.03–245.36) | 0.5981(0.03–240.21) | 0.00(0.00–18.75) | 27.570.00–422.90 | 1.82(0.00–14.48) | 166(0.040–3842.11) |
| Yes | (n = 47) | 100.00(91.54–100.00) | 1.45(0.00–125.82) | 2.16(0.03–121.23) | 0.00(0.00–8.46) | 23.390.00–163.62 | 1.47(0.00–11.76) | 46.24(0.77–3220.59.00) | |
| P | .321 | .441 | .236 | .321 | .077 | .441 | .869 | ||
| Years | 2022–2024 | (n = 34) | 100.00(97.76–100.00) | 0.4288(.03–44.39) | 0.4288(0.03–43.99) | 0.00(0.00–2.24) | 20.8639(0.90–109.23) | 3.2427(0.51–11.76) | 233.3126(2.23–3220.59) |
| 2019–2021 | (n = 30) | 100.00(91.54–100.00) | 3.14(.05–171.36) | 3.12(0.05–168.28) | 0.00(0.00–8.46) | 30.04(0.48–163.62) | 1.7078(0.20–8.97) | 34.0482(0.57–1848.10) | |
| <2018 | (n = 40) | 99.23(81.25–100.00) | 1.33(0.00–245.36) | 2.04(0.03–240.21) | 0.7660(0.00–18.75) | 28.71(0.00–422.90) | 0.9802(0.00–14.48) | 53.9817(0.40–3842.11) | |
| P | <.001 | .050 | .019 | <.001 | <.001 | .512 | 0.015 | ||
| Source | Academic | (n = 23) | 100.00(92.98–100.00) | 0.6320(0.08–125.82) | 0.6320(0.08–121.23) | 0.0000(0.00–7.02) | 29.4949(0.90–163.62) | 2.0721(0.75–8.97) | 158.2370(0.77–1258.62) |
| Physician | (n = 17) | 100.00(97.90–100.00) | 2.1589(0.15–245.36) | 2.1589(0.15–240.21) | 0.0000(0.00–2.10) | 29.4139(0.48–109.23) | 2.5066(0.17–9.98) | 46.3198(0.40–663.64) | |
| Society/professional organization | (n = 31) | 100.00(83.33–100.00) | 0.8139(0.04–171.36) | 1.3243(0.04–168.28) | 0.0000(0.00–16.67) | 23.5484(0.00–158.85) | 1.4757(0.00–11.76) | 70.6419(0.57–2354.84) | |
| Health-related website | (n = 15) | 100.00(96.95–100.00) | 0.5019(0.00–52.02) | 0.5308(0.03–51.71) | 0.0000(0.00–3.05) | 35.5166(0.00–422.90) | 1.5663(0.00–14.48) | 188.9692(1.91–3220.59) | |
| Patient | (n = 8) | 100.00(81.25–100.00) | 2.0412(0.12–14.74) | 2.0412(0.12–14.64) | 0.0000(0.00–18.075) | 26.8947(4.13–48.83) | 0.9634(0.09–6.30) | 53.9817(6.74–848.84) | |
| News | (n = 3) | 100.00(81.82–100.00) | 0.4370(0.21–0.54) | 0.4370(0.17–0.54) | 0.00(0.00–18.18) | 16.6751(11.44–42.35) | 1.6575(1.57–4.57) | 228.8401(185.28–384.98) | |
| Commercial | (n = 7) | 100.00(96.20–100.00) | 0.4887(0.03–21.82) | 1.4035(0.03–20.99) | 0.00(0.00–3.80) | 14.3970(0.00–38.42) | 0.9861(0.00–1.63) | 123.8795(4.41–3842.11) | |
| P | .959 | .857 | .857 | .959 | .077 | .441 | .869 | ||
| Country | USA | (n = 43) | 100.0000(81.25–100.00) | 0.4493(0.00–245.36) | 0.5324(0.03–240.21) | 0.0000(0.00–18.75) | 17.8703(0.00–79.80) | 1.1829(0.00–11.76) | 187.8216(0.40–3842.11) |
| UK | (n = 10) | 100.0000(99.03–100.00) | 0.7229(0.04–8.64) | 0.7229(0.04–8.56) | 0.0000(0.00–0.97) | 22.8568(3.63–50.15) | 2.1422(0.11–6.87) | 140.5534(11.46–2354.84) | |
| India | (n = 14) | 100.0000(83.33–100.00) | 0.6325(0.11–44.39) | 0.5721(0.11–43.99) | 0.0000(0.00–16.67) | 32.5938(16.68–66.08) | 2.1237(0.63–7.07) | 156.3765(2.23–943.97) | |
| Other | (n = 37) | 100.0000(83.33–100.00) | 2.1589(0.03–156.42) | 2.1589(0.03–154.81) | 0.0000(0.00–16.67) | 36.5000(0.48–422.90) | 2.2632(0.24–14.48) | 46.3198(0.63–3220.59) | |
| P | .126 | .246 | .460 | .126 | <.001 | .002 | .466 | ||
| Continent | America | (n = 50) | 100.0000(81.25–100.00) | 0.5106(0.00–245.36) | 0.5820(0.03–240.21) | 0.0000(0.00–18.75) | 20.2943(0.00–79.80) | 1.2034(0.00–11.76) | 167.7897(0.40–3842.11) |
| Non-America | (n = 54) | 100.0000(94.52–100.00) | 1.1085(0.03–156.42) | 1.0728(0.03–154.81) | 0.0000(0.00–5.48) | 36.0963(0.48–422.90) | 2.4696(0.11–14.48) | 90.9839(0.63–3220.59) | |
| P | .960 | .231 | .476 | .960 | <.001 | .003 | .529 | ||
| GQS | Low quality | (n = 28) | 100.0000(83.33–100.00) | 0.6882(0.00–125.82) | 0.7178(0.03–121.23) | 0.0000(0.00–16.67) | 27.6085(0.00–163.62) | 1.7387(0.00–9.98) | 121.1810(0.77–3220.59) |
| Medium quality | (n = 59) | 100.0000(81.82–100.00) | 0.9137(0.04–245.36) | 1.2319(0.04–240.21) | 0.0000(0.00–18.18) | 24.7819(0.00–422.90) | 1.6575(0.00–14.48) | 72.5225(0.40–2354.84) | |
| High quality | (n = 16) | 100.0000(81.25–100.00) | 0.6119(0.03–122.57) | 0.6119(0.03–119.50) | 0.0000(0.00–18.75) | 28.9440(0.64–51.62) | 1.5264(0.09–7.07) | 163.6092(0.80–3842.11) | |
| P | .960 | .231 | .476 | .960 | <.001 | .003 | .529 | ||
| JAMA | Insufficient data | (n = 36) | 100.0000(81.82–100.00) | 0.6882(0.00–245.36) | 0.6511(0.03–240.21) | 0.0000(0.00–18.18) | 24.1381(0.00–422.90) | 1.5719(0.00–14.48) | 127.4738(0.40–3220.59) |
| Partially sufficient data | (n = 60) | 100.0000(81.25–100.00) | 1.3363(0.03–171.36) | 1.4205(0.03–168.28) | 0.0000(0.00–18.75) | 28.9251(0.00–158.85) | 1.6072(0.00–11.76) | 70.3954(0.57–3842.11) | |
| Completely sufficient data | (n = 7) | 100.0000(97.50–100.00) | 0.1356(0.04–122.57) | 0.1356(0.04–119.50) | 0.0000(0.00–2.50) | 23.5484(0.64–51.62) | 4.1908(0.71–7.07) | 737.3737(0.80–2354.84) | |
| P | .162 | .277 | .205 | .162 | .224 | .553 | .198 | ||
| mDISCERN | Very poor | (n = 10) | 100.0000(96.36–100.00) | 0.2682(0.07–125.82) | 0.2682(0.07–121.23) | 0.0000(0.00–3.64) | 46.7401(1.89–163.62) | 2.9373(0.51–9.98) | 430.2118(0.77–1529.94) |
| Poor | (n = 49) | 100.0000(81.82–100.00) | 1.6404(0.00–156.42) | 2.1589(0.03–154.81) | 0.0000(0.00–18.18) | 22.4297(0.00–422.90) | 1.3228(0.00–14.48) | 46.3198(0.63–3220.59) | |
| Fair | (n = 36) | 100.0000(81.25–100.00) | 0.6025(0.03–245.36) | 0.7634(0.03–240.21) | 0.0000(0.00–18.75) | 34.1273(0.00–79.80) | 1.5668(0.00–11.76) | 136.2687(0.40–3842.11) | |
| Good | (n = 4) | 100.0000(98.20–100.00) | 0.3838(0.04–171.36) | 0.3838(0.04–168.28) | 0.0000(0.00–1.80) | 33.7138(23.55–45.89) | 3.6331(1.73–4.19) | 447.8054(0.57–2354.84) | |
| Excellent | (n = 4) | 100.0000(97.50–100.00) | 0.3865(0.12–122.57) | 0.3865(0.12–119.50) | 0.0000(0.00–2.50) | 13.7861(0.64–51.62) | 2.7742(0.71–7.07) | 447.1253(0.80–829.55) | |
| P | .704 | .372 | .677 | .704 | <.001 | .007 | .702 | ||
Kruskal–Wallis test. “Bonferroni adjustment” was used in multigroup analyses. Bold font indicates statistical significance (P < .05).
GQS = Global Quality Score, JAMA = Journal of American Medical Association Benchmark Criteria, mDISCERN = Modified Quality Criteria for Consumer Health Information on Treatment Choices, n = Number of videos, VPI = Video Power Index.
Of the analyzed videos, 45.2% featured animations (Table 2). No statistically significant difference was found between the upload year, video sources, or any engagement metrics with animation presence (P > .05, Tables 2–4). Analysis of the uploaders’ qualifications showed that the most common sources of videos were society/professional organizations, health-related websites, and physicians, comprising 29%, 22%, and 16% of the videos, respectively (Table 2). A total of 71 videos (68%) were uploaded by professionals, whereas 33 (32%) were uploaded by nonprofessionals. The general characteristics of all the videos are presented in Table 2. Statistically significant differences were observed between the sources regarding definition, pathophysiology, genetics, and outcome (P = .019, P < .001, P = .001, and P = .011, respectively; Table 2). Statistically significant differences were observed between the sources and durations of the videos (P = .014; Table 3) and between countries in terms of definition, pathophysiology, incidence, diet treatment, outcome, and morbidity of the disease (P = .015, P = .001, P = .027, P = .003, P = .009, and P = .032, respectively; Tables 2 and 3).
3.1. Video popularity
The number of views is a key metric for evaluating the video popularity and audience impact. The median view count across all videos was 1064.00 (range: 12–806,000; Table 5). Statistically significant differences in view counts were observed with respect to upload date (P < .001; Table 3). The number of likes ranged from 0 to 22,000.00 with a median of 18.00, whereas the median number of dislikes was 0.00 (range: 0–220.00). The median number of comments per video was 1.00 (range: 0–158.00; Table 5). Statistically significant differences in the number of likes were observed with the year of upload and continent (P = .012, and P = .045, respectively; Table 3). Statistically significant differences in dislikes and comments were observed with respect to upload year only (P < .001 and P = .029, respectively; Table 3). The video duration (in seconds) differed significantly by source, country of origin, and continent (P = .014, P = .025, and P = .018, respectively; Table 3).
Table 5.
Video characteristics of YouTube videos.
| Video parameters | Median | Minimum–maximum |
|---|---|---|
| Number of views | 1064.00 | 12.00–806,000.00 |
| Likes | 18.00 | 0–22,000.00 |
| Dislikes | 0.00 | 0–220.00 |
| Comments | 1.00 | 0–158.00 |
| Upload date | 2020.00 | 2009.00–2024.00 |
| Duration (s) | 311.50 | 1–4688 |
| VPI-like ratio | 100.00 | 81.25–100.00 |
| View ratio | 0.68 | 0–245.36 |
| VPI | 0.79 | 0.03–240.21 |
| Dislike ratio | 0.00 | 0.00–18.75 |
| Likes-per-view | 1.64 | 0.00–14.48 |
| Daily views like | 25.48 | 0.00–422.90 |
| Like ratio/view day | 118.87 | 0.40–3842.11 |
| JAMA | 2.00 | 1.00–12.00 |
| mDISCERN | 2.00 | 1.00–5.00 |
| GQS | 3.0 | 1.00–5.00 |
GQS = Global Quality Score, JAMA = Journal of American Medical Association Benchmark criteria, mDISCERN = modified Quality Criteria for Consumer Health Information on Treatment Choices, VPI = Video Power Index.
The median view ratio was 0.68 (range: 0–245.36; Table 5). Statistically significant differences in view ratio were observed with the year of upload (P = .050; Table 4). The median VPI score was 0.79 (range: 0.03–240.21) and the median VPI-like ratio was 100.00% (range: 81.25–100.00; Table 5).
The median likes-per-view was 1.64 (range: 0.00–14.48), while the median daily views per like were 25.48 (range: 0.00–422.90), and the median likes ratio/views per day was 118.87 (range: 0.40–3842.11; Table 5). Significant differences in likes-per-view and daily views were observed based on country and continent (P < .001, P = .002, P < .001, P = .003, respectively; Table 4). Additionally, significant differences in likes ratio/views per day were noted with the year of upload (P = .015; Table 4).
3.2. Assessment of reliability and quality
The median GQS for all videos analyzed was 3.0, with scores ranging from 1.00 to 5.00 (Table 5). Based on the GQS evaluation, 56% videos were classified as medium quality, whereas 15% were classified as high-quality. The highest mean GQS was observed for videos uploaded by society/professional organizations and academic sources, both with a mean score of 3.00 (range: 2.00–5.00 and 1.00–5.00, respectively). Statistically significant differences were observed in the GQS scores among the sources (P = .040; Table 2). Statistical analysis indicated significant correlations between the GQS and various video parameters. Specifically, the GQS scores were significantly different between the animation content, source, country, and continent (P < .001, P < .001, P < .001, and P < .001, respectively; Table 3).
The median JAMA score was 2.0 (range: 1.00–12.00) (Table 5). According to the JAMA evaluation, in terms of data sufficiency, 35% of the videos contained insufficient data, 58.3% partially sufficient data, and 6.7% completely sufficient data. The highest median JAMA was observed in videos uploaded by society/professional organizations, with a median score of 3.00 (range: 2.00–4.0). There were statistically significant differences in JAMA scores among the video upload years (P = .008; Table 2). Additionally, the JAMA scores were statistically significant between the video durations (P = .001; Table 3). The median mDISCERN score was 2.0 (range: 1.00–5.00). Based on the mDISCERN evaluation, 11.7% were very poor, poor 46.6%, fair 35%, good 2.9%, and excellent 3.9%. The highest median mDISCERN was observed in videos uploaded by society/professional organizations, patients, and news, with a median score of 3.00 (range: 1.00–5.00, 2.00–3.00, and 2.00–3.00, respectively; Table 3).
3.3. Correlation analysis of the quality and reliability of videos with their features
Table 6 presents the correlation analysis of the video, quality, and security parameters. The “r” in the table represents the correlation coefficient.
Table 6.
Correlation analysis: Spearman correlation test.
| Number of views | Number of likes | Number of dislikes | Number of comments | Date (year) | Video duration (s) | JAMA | mDISCERN | GQS | VPI-like ratio | View ratio | VPI | Dislike ratio | Likes-per-view | Daily views like | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| r s | P | r s | P | r s | P | r s | P | r s | P | r s | P | r s | P | r s | P | r s | P | r s | P | r s | P | r s | P | r s | P | r s | P | r s | P | |||
| Number of views | ||||||||||||||||||||||||||||||||
| Number of likes | 0.890 | <.001 | ||||||||||||||||||||||||||||||
| Number of dislikes | 0.751 | <.001 | 0.773 | <.001 | ||||||||||||||||||||||||||||
| Number of comments | 0.709 | <.001 | 0.707 | <.001 | 0.652 | <.001 | ||||||||||||||||||||||||||
| Date (year) | −0.463 | <.001 | −0.235 | .016 | −0.371 | <.001 | −0.239 | .015 | ||||||||||||||||||||||||
| Video duration (s) | 0.088 | .376 | 0.241 | .014 | 0.150 | .129 | 0.109 | .270 | 0.093 | .347 | ||||||||||||||||||||||
| JAMA | −0.128 | .196 | −0.105 | .292 | −0.062 | .535 | −0.093 | .349 | 0.051 | .609 | 0.204 | .039 | ||||||||||||||||||||
| m DISCERN | −0.005 | .962 | 0.018 | .858 | 0.067 | .504 | 0.060 | .549 | −0.029 | .769 | 0.064 | .522 | 0.481 | <.001 | ||||||||||||||||||
| GQS | 0.020 | .844 | −0.036 | .722 | −0.005 | .962 | −0.086 | .388 | −0.050 | .615 | −0.031 | .758 | 0.480 | <.001 | 0.578 | <.001 | ||||||||||||||||
| VPI-like ratio | −0.689 | <.001 | −0.701 | <.001 | −0.954 | <.001 | −0.567 | <.001 | 0.458 | <.001 | −0.108 | .286 | 0.038 | .706 | −0.071 | .483 | 0.006 | .956 | ||||||||||||||
| View ratio | 0.923 | <.001 | 0.886 | <.001 | 0.708 | <.001 | 0.709 | <.001 | −0.209 | .033 | 0.135 | .173 | −0.089 | .372 | −0.002 | .982 | 0.018 | .854 | −0.638 | <.001 | ||||||||||||
| VPI | 0.925 | <.001 | 0.879 | <.001 | 0.705 | <.001 | 0.703 | <.001 | −0.288 | .004 | 0.104 | .304 | −0.099 | .330 | 0.003 | .980 | 0.030 | .769 | −0.629 | <.001 | 0.999 | <.001 | ||||||||||
| Dislike ratio | 0.689 | <.001 | 0.701 | <.001 | 0.954 | <.001 | 0.567 | <.001 | −0.458 | <.001 | 0.108 | .286 | −0.038 | .706 | 0.071 | .483 | −0.006 | .956 | −0.999 | <.001 | 0.638 | <.001 | 0.629 | <.001 | ||||||||
| Daily views like | 0.071 | .472 | 0.367 | <.001 | 0.270 | <.001 | 0.133 | .179 | −0.074 | .457 | 0.326 | <.001 | 0.015 | .877 | 0.021 | .833 | −0.150 | .130 | −0.195 | .052 | −0.042 | .670 | −0.145 | .150 | 0.195 | .052 | ||||||
| Likes-per-view | −0.269 | .006 | 0.132 | .183 | 0.006 | .953 | −0.019 | 850 | −0.560 | <.001 | 0.429 | <.001 | 0.069 | .491 | 0.011 | .913 | −0.143 | .150 | 0.114 | .260 | −0.148 | .134 | −258 | .010 | −0.114 | .260 | 0.678 | <.001 | ||||
| Likes ratio/views per day | −0.929 | <.001 | −0.879 | <.001 | −0.716 | <.001 | −0.706 | <.001 | 0.299 | .003 | −0.098 | .334 | 0.099 | .329 | −0.004 | .969 | −0.036 | .720 | 0.645 | <.001 | −0.999 | <.001 | −0.999 | <.001 | −0.645 | <.001 | 0.144 | .152 | 0.263 | .008 | ||
Bold font indicates statistical significance (P < .05).
GQS = Global Quality Score, JAMA = Journal of American Medical Association benchmark criteria, mDISCERN = modified Quality Criteria for Consumer Health Information on Treatment Choices, rs = Spearman rho correlation coefficient, VPI = Video Power Index.
A strong correlation was observed between the number of views and both the number of likes and dislikes (r = 0.890, P < .001; r = 0.751, P < .001, respectively). Furthermore, there was a very strong correlation between the number of views and likes with the view ratio and VPI (r = 0.923, P < .001; r = 0.925, P < .001; r = 0.886, P < .001; r = 879, P < .001). There was a strong correlation between the number of dislikes, dislike ratio, and likes-per-view (r = 0.954, P < .001; r = 0.953, P < .001). Additionally, a moderate correlation was observed between the number of comments and view ratio, VPI, VPI-like ratio, and dislike ratio (r = 0.709, P < .001; r = 0.703, P < .001; r = 0.567, P < .001; and r = 0.567, P < .001, respectively) (Table 6). Similarly, the number of likes showed a moderate correlation with the dislike ratio (r = 0.701, P < .001) and a very strong correlation with the number of dislikes, view ratio, VPI, and like ratio per view (r = 0.904, P < .001; r = 0.873, P < .001; r = 0.906, P < .001; and r = 0.906, P < .001, respectively) (Table 6).
A strong negative correlation was found between the number of likes ratio/views per day and the number of views, likes, and dislikes (rs = −0.929, P < .001; rs = −0.879, P < .001; rs = −0.716, P < .001 respectively). Furthermore, a very strong negative correlation was observed between the VPI-like ratio and view ratio, VPI, and dislike ratio (rs = −0.638, P < .001; rs = −0.629, P < .001; rs = −0.999, P < .001, respectively Table 6). Finally, a very strong negative correlation was found between the VPI-like ratio and number of dislikes (rs = −0.954, P < .001; Table 6).
Spearman correlation analysis revealed a strong association between JAMA, GQS, and mDISCERN (P < .001) but a moderate correlation between GQS and mDISCERN (r = 0.578, P < .001). Additionally, the JAMA and mDISCERN scores were correlated (r = 0.481, P < .001).
4. Discussion
PKU is the most prevalent inherited metabolic disorder worldwide. Early detection and prompt treatment can prevent serious neurological complications. Newborn screening is essential for early diagnosis and a phenylalanine-restricted diet is the fundamental basis for PKU treatment.[33]
During treatment, educating the family about the disease is extremely important. Education may be provided through one-to-one or group teaching sessions, material handouts, or supportive applications. During the early years, parents receive continuous support from healthcare professionals, particularly specialist metabolic dietitians. Over the years, there has been uncertainty regarding the most effective ways dietitians can support their parents. Bernstein et al explored parents and patients’ perspectives on learning methods and concluded that one-on-one counseling was the most effective approach.[34] Another study indicated that group sessions were the most effective learning method.[35] Compared with traditional patient/parental education methods, social media has emerged as a vital tool for disseminating knowledge and facilitating education. Built on highly interactive digital platforms, social media enables individuals and communities to share, cocreate, edit, and engage in discussions on user-generated content. It offers instant access to large amounts of information. YouTube is one of the most popular social platforms in the world. However, no study has yet explored the impact of social media platforms on PKU care or the reliability of the information shared on such platforms. Moreover, with the evolving ways in which parents access medical information, dietitians and other professionals working in the field of inherited metabolic diseases may need to incorporate social media as a method of education.[16]
In the present study, YouTube videos on PKU were evaluated according to content categories, quality, reliability, and user interaction criteria. Because many of these videos are not professionally prepared or may contain commercial, promotional, or misleading information, this study aims to evaluate their content. This is the first comprehensive investigation of this topic in the literature. Animated content was present in 45.2% (n = 47) of the videos included in the study. According to the literature, the prevalence of animated content on medical-related YouTube videos ranges from 12%[24] to 29%.[32] The proportion of animated videos observed in this study was notably higher than previously reported. This may be because PKU is a congenital disorder that affects both children and adults, making it easier to explain in a simpler and more understandable way. However, no significant differences were observed between animated and non-animated (real) videos in terms of views, likes, view-like ratio, VPI, likes ratio/views per day, year of upload, source, content titles, or video metrics (Tables 2 and 3) As the topic is health-related, and viewers may prefer to watch more realistic, credible, and professional videos.
The total viewing time for all videos was 14 hours, 41 minutes, and 8 seconds, with a mean video length of 311.50 seconds (Table 5). A statistically significant difference was observed between video duration and uploader type, country, and continent (Table 3). The videos uploaded by academics had the longest average duration of 633.00 seconds. Literature indicates variability in YouTube video duration based on research topics. For example, Toptan et al reported an average duration of 21.11 minutes for neonatal sepsis videos, whereas Gonen et al reported a mean of 7.27 minutes for hemophilia. In comparison, the videos analyzed in the present study averaged 311.50 seconds (approximately 5.19 minutes), highlighting that more in-depth and explanatory videos have been uploaded by academics. We found that video duration had a positive correlation with JAMA and DISCERN scores. Literature highlights that videos with higher reliability and quality scores tend to have longer durations.[36] Therefore, viewers seeking accurate and high-quality information should approach shorter videos cautiously.
In the present study, when videos were evaluated based on their sources, videos uploaded by society/professional organizations (29%), health-related websites (22%), and physicians (16%) comprised 71 videos (68%) (Table 2). Similarly, Adorisio et al[37] and Hartnett et al[38] examined YouTube videos on various medical topics and found that videos created by physicians were the most common. The proportion of videos uploaded by physicians across research topics ranges from 24%[28] to 57.3%.[39] The higher number of uploads by professionals in the present study, compared to other topics, may be explained by the need for the importance of early diagnosis of PKU as well as newborn screening and the necessity of genetic counseling for one of the most common metabolic and genetic disorders. These efforts aim to increase awareness and improve clinical outcomes through broad educational campaigns. Such initiatives are likely to encourage healthcare professionals to share more educational content on platforms such as YouTube, leading to a higher number of videos from reliable sources. In contrast, in the present study, analysis of quality and reliability in relation to the source of upload demonstrated a statistically significant association with the GQS, whereas no significant association was found between JAMA and mDISCERN scores (Table 2). This finding suggests that, in our study, while the source of the video influenced the GQS, it did not have a significant effect on the JAMA or mDISCERN scores. This implies that although professional sources may enhance the perceived quality of videos, they do not always impact the reliability or evidence-based nature assessed by the JAMA and mDISCERN criteria. This is similar to the literature.[32] To increase the availability of online health information, physicians need to express that patients should be selective when accessing medical information from the web.
In the present study, 41% of the evaluated videos were uploaded from the United States, 13% from India, and 10% from the United Kingdom. In terms of continent, 48% of the videos were uploaded from America and 52% were from non-American continents, in contrast with the results of a number of studies.[25,39,40] In this study, a considerable number of videos were uploaded from Asian countries where the prevalence of PKU is high. While the prevalence of PKU worldwide is approximately 1 in 10,000, in Turkey, it has been reported to be as high as 1:2600. Additional information regarding incidence rates in different countries is provided in Table 7,[41,42] but it can be concluded that the United States is the world leader in uploading videos on various medical topics on YouTube and videos with high reliability scores. This can be attributed to the fact that YouTube, a platform founded in the United States, is widely used by American users to disseminate information globally.
Table 7.
| Continents | Countries | Incidence of PKU |
|---|---|---|
| Asian populations | China | 1 in 17,000 |
| Korea | 1 in 41,000 | |
| Japan | 1 in 125,000 | |
| India | 1 in 6000 | |
| Turkey | 1 in 4200 | |
| European populations | Ireland | 1 in 4500 |
| Scotland | 1 in 5300 | |
| Czechoslovakia | 1 in 7000 | |
| Hungary | 1 in 11,000 | |
| Denmark | 1 in 12,000 | |
| France | 1 in 13,500 | |
| Norway | 1 in 14,500 | |
| United Kingdom | 1 in 14,300 | |
| Italy | 1 in 17,000 | |
| Finland | 1 in 200,000 | |
| North America | United States (Caucasians) | 1 in 10,000 |
| Canada | 1 in 22,000 | |
| Oceania | Australia | 1 in 10,000 |
| Latin American countries | Brazil | 1 in 25,294 |
| Argentina | 1 in 27,275 | |
| Chile | 1 in 19,510 |
PKU = phenylketonuria.
In terms of the year of upload, there was a significant correlation between video view, like, dislike, comments, and video upload years (Table 3). This situation may have been influenced by the increase in online work in recent years due to the coronavirus disease pandemic, leading people to spend more time on social media platforms, which could have affected the view rates over the years.[43,44]
Videos uploaded by professional organizations, societies, and academics provide more reliable and high-quality information. Given the increasing digitalization, YouTube is likely to become more prominent in the health field, and viewers should be advised to approach information from the platform with caution. Health authorities should be encouraged to develop social media practices for sharing information to safeguard public health. It is evident that future studies on various topics on social media platforms will contribute to raising patient awareness and supporting public health.
In conclusion, our study demonstrated that the general public utilizes YouTube videos to learn about PKU. An analysis of YouTube videos on PKU revealed that the majority of the videos were of low quality and reliability. The information presented in health-related videos can significantly influence patient decision making; therefore, health authorities should be encouraged to develop social media practices for sharing health information to safeguard public health. It is evident that YouTube videos have the potential to educate patients about diseases and treatment, challenge the stigma surrounding the condition, and encourage patients to seek intervention when necessary, as long as they are thoughtfully designed with contributions from healthcare professionals.
5. Limitations
Our study has several limitations. First, we only included videos in English, meaning that information and experience in other languages were not assessed, such as Latin American countries were not included in the study due to noncompliance with the eligibility criteria (e.g., language, video duration). Second, our analysis focused exclusively on YouTube videos and did not consider other patient education platforms. Although YouTube is the most widely used site for video content, there may be other websites or online sources that provide healthcare information of varying quality. Third, we categorized the videos based on the number of views, as of December 30, 2024. Because the number of views and viewer engagement on YouTube are continuously changing, our study represents only a snapshot of video popularity at that specific time. In addition, we focus on the top 150 videos. Although this allowed us to examine the most frequently accessed content, we may have missed other valuable information sources. Also, engagement metrics such as “likes” and “views” were used as indicators of popularity but these do not guarantee content reliability as it can be influenced by subjective viewer preferences or presentation style. Lastly, one of the limitations of the study is that statistical methods aimed at detecting potential data manipulation or artificial engagement, such as Benford law or machine learning-based anomaly detection, were not utilized. These approaches and more advanced statistical approaches such as multivariate analysis or time-based trend analysis could be applied in future studies with larger datasets and extended access to YouTube analytics.
Author contributions
Conceptualization: Bahar Kulu, Volkan Hanci.
Data curation: Bahar Kulu, Sibel Burçak Şahin Uyar, Batuhan Kulu.
Formal analysis: Bahar Kulu, Sibel Burçak Şahin Uyar, Batuhan Kulu, Volkan Hanci.
Investigation: Bahar Kulu.
Methodology: Volkan Hanci.
Project administration: Bahar Kulu.
Supervision: Volkan Hanci.
Validation: Bahar Kulu, Volkan Hanci.
Visualization: Bahar Kulu, Volkan Hanci.
Writing – original draft: Bahar Kulu.
Writing – review & editing: Bahar Kulu.
Abbreviations:
- GQS
- Global Quality Score
- JAMA
- Journal of the American Medical Association Benchmark Criteria
- mDISCERN
- modified Quality Criteria for Consumer Health Information on Treatment Choices
- Phe
- phenylalanine
- PKU
- phenylketonuria
- VPI
- Video Power Index
The authors have no funding and conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
How to cite this article: Kulu B, Uyar SBŞ, Kulu B, Hanci V. Evaluating the quality and reliability of YouTube videos about phenylketonuria. Medicine 2025;104:37(e44309).
Contributor Information
Sibel Burçak Şahin Uyar, Email: sibel.burcakuyar@gmail.com.
Batuhan Kulu, Email: doktorkulu@gmail.com.
Volkan Hanci, Email: vhanci@gmail.com.
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