A Proposed Approach for Conducting Studies That Use Data From Social Media Platforms

Assessing the Educational Value of YouTube Kids Videos Related to Anxiety, Depression, and ADHD

This Letter to the Editor reports the findings of a study we undertook to assess the quality and content of the mental health information that is provided to children via YouTube Kids. For videos on depression and anxiety, we found that all videos were useful or neutral (neither useful nor misleading). In contrast, most videos on attention-deficit/hyperactivity disorder (ADHD) were useful or neutral, but few were deemed misleading. Many of the videos on depression, anxiety, and ADHD promoted supportive statements and help-seeking behavior. Recommendations include calling on social media platforms to continue moderating content and involving more health care professionals and/or individuals with lived experiences to create accurate but engaging videos.

YouTube as a source of education in perioperative anesthesia for patients and trainees: a systematic review

BACKGROUND

Online video sharing platforms like YouTube (Google LLC, San Bruno, CA, USA) have become a substantial source of health information. We sought to conduct a systematic review of studies assessing the overall quality of perioperative anesthesia videos on YouTube.

METHODS

We searched Embase, MEDLINE, and Ovid for articles published from database inception to 1 May 2023. We included primary studies evaluating YouTube videos as a source of information regarding perioperative anesthesia. We excluded studies not published in English and studies assessing acute or chronic pain. Studies were screened and data were extracted in duplicate by two reviewers. We appraised the quality of studies according to the social media framework published in the literature. We used descriptive statistics to report the results using mean, standard deviation, range, and n/total N (%).

RESULTS

Among 8,908 citations, we identified 14 studies that examined 796 videos with 59.7 hr of content and 47.5 million views. Among the 14 studies that evaluated the video content quality, 17 different quality assessment tools were used, only three of which were externally validated (Global Quality Score, modified DISCERN score, and JAMA score). Per global assessment rating of video quality, 11/13 (85%) studies concluded the overall video quality as poor.

CONCLUSIONS

Overall, the educational content quality of YouTube videos evaluated in the literature accessible as an educational resource regarding perioperative anesthesia was poor. While these videos are in demand, their impact on patient and trainee education remains unclear. A standardized methodology for evaluating online videos is merited to improve future reporting. A peer-reviewed approach to online open-access videos is needed to support patient and trainee education in anesthesia.

STUDY REGISTRATION

Open Science Framework ( https://osf.io/ajse9 ); first posted, 1 May 2023.

Misinformation Persists in Complementary Health: Evaluating the Reliability and Quality of YouTube-Based Information on the Use of Acupuncture for Chronic Pain

Introduction

Acupuncture is commonly used to treat chronic pain. Patients often access public social media platforms for healthcare information when querying acupuncture. Our study aims to appraise the utility, accuracy, and quality of information available on YouTube, a popular social media platform, on acupuncture for chronic pain treatment.

Methods

Using search terms such as "acupuncture for chronic pain" and "acupuncture pain relief", the top 54 videos by view count were selected. Included videos were >1 minute duration, contained audio in English, had >7000 views, and was related to acupuncture. One primary outcome of interest was categorizing each video's usefulness as useful, misleading, or neither. Another primary outcome of interest was the quality and reliability of each video using validated instruments, including the modified DISCERN (mDISCERN) tool and the Global Quality Scale (GQS). The means were calculated for the video production characteristics, production sources, and mDISCERN and GQS scores. Continuous and categorical outcomes were compared using Student's t-test and chi-square test, respectively.

Results

Of the 54 videos, 57.4% were categorized as useful, 14.8% were misleading, and 27.8% were neither. Useful videos had a mean GQS and mDISCERN score of 3.77±0.67 and 3.48±0.63, respectively, while misleading videos had mean GQS and mDISCERN score of 2.50±0.53 and 2.38±0.52, respectively. 41.8% of the useful videos were produced by a healthcare institution while none of the misleading videos were produced by a healthcare institution. However, 87.5% of the misleading videos were produced by health media compared to only 25.8% of useful videos from health media.

Discussion

As patients increasingly depend on platforms like YouTube for trustworthy information on complementary health practices such as acupuncture, our study emphasizes the critical need for more higher-quality videos from unbiased healthcare institutions and physicians to ensure patients are receiving reliable information regarding this topic.

Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review

Digital data play an increasingly important role in advancing health research and care. However, most digital data in healthcare are in an unstructured and often not readily accessible format for research. Unstructured data are often found in a format that lacks standardization and needs significant preprocessing and feature extraction efforts. This poses challenges when combining such data with other data sources to enhance the existing knowledge base, which we refer to as digital unstructured data enrichment. Overcoming these methodological challenges requires significant resources and may limit the ability to fully leverage their potential for advancing health research and, ultimately, prevention, and patient care delivery. While prevalent challenges associated with unstructured data use in health research are widely reported across literature, a comprehensive interdisciplinary summary of such challenges and possible solutions to facilitate their use in combination with structured data sources is missing. In this study, we report findings from a systematic narrative review on the seven most prevalent challenge areas connected with the digital unstructured data enrichment in the fields of cardiology, neurology and mental health, along with possible solutions to address these challenges. Based on these findings, we developed a checklist that follows the standard data flow in health research studies. This checklist aims to provide initial systematic guidance to inform early planning and feasibility assessments for health research studies aiming combining unstructured data with existing data sources. Overall, the generality of reported unstructured data enrichment methods in the studies included in this review call for more systematic reporting of such methods to achieve greater reproducibility in future studies.

Multifaceted Role of Social Media in Healthcare: Opportunities, Challenges, and the Need for Quality Control

Social media, leveraging Web 2.0 technologies, plays a vital role in healthcare, medical education, and research by fostering collaboration and enabling research dissemination. Healthcare professionals use these platforms to improve public health literacy, but concerns about misinformation and content accuracy persist. In 2023, platforms like Facebook (Meta Platforms, Inc., Menlo Park, California, United States), YouTube (Google LLC, Mountain View, California, United States), Instagram (Meta Platforms, Inc.), TikTok (ByteDance Ltd, Beijing, China), and Twitter (X Corp., Carson City, Nevada, United States) have become essential in healthcare, offering patient communication, professional development, and knowledge-sharing opportunities. However, challenges such as breaches of patient confidentiality and unprofessional conduct remain. Social media has transformed medical education, providing unique networking and professional development opportunities. Further studies are needed to determine its educational value. Healthcare professionals must follow ethical and professional guidelines, particularly regarding patient privacy, confidentiality, disclosure rules, and copyright laws. Social media significantly impacts patient education and healthcare research. Platforms like WhatsApp (Meta Platforms, Inc.) effectively improve patient compliance and outcomes. Yet, the rapid dissemination of false news and misinformation on social media platforms presents risks. Researchers must consider potential biases and content quality when extracting data. Quality control and regulation are crucial in addressing potential dangers and misinformation in social media and healthcare. Stricter regulations and monitoring are needed due to cases of deaths resulting from social media trends and false news spread. Ethical frameworks, informed consent practices, risk assessments, and appropriate data management strategies are essential for responsible research using social media technologies. Healthcare professionals and researchers must judiciously use social media, considering its risks to maximize benefits and mitigate potential drawbacks. By striking the right balance, healthcare professionals can enhance patient outcomes, medical education, research, and the overall healthcare experience.

New Data Resources, Linkages, and Infrastructure for Cancer Health Economics Research: Main Topics From a Panel Discussion

Although a broad range of data resources have played a key role in the substantial achievements of cancer health economics research, there are now needs for more comprehensive data that represent a fuller picture of the cancer care experience. In particular, researchers need information that represents more diverse populations; includes more clinical details; and provides greater context on individual- and neighborhood-level factors that can affect cancer prevention, screening, treatment, and survivorship, including measures of financial health or toxicity, health-related social needs, and social determinants of health. This article highlights 3 critical topics for cancer health economics research: the future of the National Cancer Institute's Surveillance, Epidemiology, and End Results-Centers for Medicare & Medicaid Services-linked data resources; use of social media data for cancer outcomes research; and multi-site-linked electronic health record data networks. These 3 topics represent different approaches to enhance data resources, linkages, and infrastructures and are complementary strategies to provide more complete information on activities involved in and factors affecting the cancer control continuum. These and other data resources will assist researchers in examining the complex and nuanced questions now at the forefront of cancer health economics research.

Automatic Diagnosis of Mental Healthcare Information Actionability: Developing Binary Classifiers

We aimed to develop a quantitative instrument to assist with the automatic evaluation of the actionability of mental healthcare information. We collected and classified two large sets of mental health information from certified mental health websites: generic and patient-specific mental healthcare information. We compared the performance of the optimised classifier with popular readability tools and non-optimised classifiers in predicting mental health information of high actionability for people with mental disorders. sensitivity of the classifier using both semantic and structural features as variables achieved statistically higher than that of the binary classifier using either semantic (p < 0.001) or structural features (p = 0.0010). The specificity of the optimized classifier was statistically higher than that of the classifier using structural variables (p = 0.002) and the classifier using semantic variables (p = 0.001). Differences in specificity between the full-variable classifier and the optimised classifier were statistically insignificant (p = 0.687). These findings suggest the optimised classifier using as few as 19 semantic-structural variables was the best-performing classifier. By combining insights of linguistics and statistical analyses, we effectively increased the interpretability and the diagnostic utility of the binary classifiers to guide the development, evaluation of the actionability and usability of mental healthcare information.