ArticleJournal of medical Internet research2025
Promoting Health Literacy With Human-in-the-Loop Video Understandability Classification of YouTube Videos: Development and Evaluation Study.
Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Production of a Health Awareness Video on Precautionary Measures Against Respiratory Infectious Diseases for Public Transportation Drivers: Protocol for a Scoping Review, a Delphi Study, and a Randomized Controlled Trial.JMIR research protocols · 2026Article
- Public health implications of allergic rhinitis information on YouTube and Bilibili: a cross-cultural analysis of content quality, engagement, and seasonal trends.BMC public health · 2026Article
- Article
- Agreement between artificial intelligence and expert evaluation in assessing the quality of asthma health education videos on YouTube and Bilibili: cross-sectional study.Frontiers in public health · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundAn estimated 93% of adults in the United States access the internet, with up to 80% looking for health information. However, only 12% of US adults are proficient enough in health literacy to interpret health information and make informed health care decisions meaningfully. With the vast amount of health information available in multimedia formats on social media platforms such as YouTube and Facebook, there is an urgent need and a unique opportunity to design an automated approach to curate online health information using multiple criteria to meet the health literacy needs of a diverse population.
objectiveThis study aimed to develop an automated approach to assessing the understandability of patient educational videos according to the Patient Education Materials Assessment Tool (PEMAT) guidelines and evaluating the impact of video understandability on viewer engagement. We also offer insights for content creators and health care organizations on how to improve engagement with these educational videos on user-generated content platforms.
methodsWe developed a human-in-the-loop, augmented intelligence approach that explicitly focused on the human-algorithm interaction, combining PEMAT-based patient education constructs mapped to features extracted from the videos, annotations of the videos by domain experts, and cotraining methods from machine learning to assess the understandability of videos on diabetes and classify them. We further examined the impact of understandability on several dimensions of viewer engagement with the videos.
resultsWe collected 9873 YouTube videos on diabetes using search keywords extracted from a patient-oriented forum and reviewed by a medical expert. Our machine learning methods achieved a weighted precision of 0.84, a weighted recall of 0.79, and an F
conclusionsWe developed a human-in-the-loop, scalable algorithm to assess the understandability of health information on YouTube. Our method optimally combines expert input with algorithmic support, enhancing engagement and aiding medical experts in recommending educational content. This solution also guides health care organizations in creating effective patient education materials for underserved health topics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.