An Active Model of Research Translation for the General Public: Content Analysis of a YouTube-Based Health Podcast

• Published in: JMIR formative research Vol. 7; p. e46611
• Main Authors: Tsulukidze, Maka, Grande, Stuart W, Naslund, John A
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 05.12.2023
• Subjects: Codes; Content analysis; Data analysis; Data collection; Internet; Neurosciences; Original Paper; Patient education; Personal health; Social networks; Trends; United States > US; consumer education; health education; online health information; disseminating science; evidence translation; user engagement
• ISSN: 2561-326X; 2561-326X
• DOI: 10.2196/46611

Online health information seeking is changing the way people engage with health care and the health system. Recent changes in practices related to seeking, accessing, and disseminating scientific research, and in particular health information, have enabled a high level of user engagement. This study aims to examine an innovative model of research translation, The Huberman Lab Podcast (HLP), developed by Andrew Huberman, Professor of Neurobiology and Ophthalmology at the Stanford School of Medicine. The HLP leverages social media to deliver health information translated into specific, actionable practices and health strategies directly to the general public. This research characterizes the HLP as an Active Model of Research Translation and assesses its potential as a framework for replicability and wider adoption. We applied conventional content analysis of the YouTube transcript data and directed content analysis of viewers' YouTube comments to 23 HLP episodes released from January to October 2021, reflecting the time of data analysis. We selected 7 episodes and a welcome video, to describe and identify key characteristics of the HLP model. We analyzed viewer comments for 18 episodes to determine whether viewers found the HLP content valuable, accessible, and easy to implement. The key HLP features are direct-to-the-consumer, zero-cost, bilingual, and actionable content. We identified 3 main organizing categories and 10 subcategories as the key elements of the HLP: (1) Why: Educate and Empower and Bring Zero Cost to Consumer Information to the General Public; (2) What: Tools and Protocols; Underlying Mechanisms; and Grounded in Science; (3) How: Linear and Iterative Knowledge Building Process; Lecture-Style Sessions; Interactive and Consumer Informed; Easily Accessible; and Building the Community. Analysis of viewers' comments found strong consumer support for the key HLP model elements. This Active Model of Research Translation offers a way to synthesize scientific evidence and deliver it directly to end users in the form of actionable tools and education. Timely evidence translation using effective consumer engagement and education techniques appears to improve access and confidence related to health information use and reduces challenges to understanding and applying health information received from health providers. Framing complex content in an approachable manner, engaging the target audience, encouraging participation, and ensuring open access to the content meet current recommendations on innovative practices for leveraging social media or other digital platforms for disseminating science and research findings to the general public, and are likely key contributors to HLP impact and potential for success. The model offers a replicable framework for translating and disseminating scientific evidence. Similar active models of research translation can have implications for accessing health information and implementing health strategies for improved outcomes. Areas for further investigation are specific and measurable impacts on health, usability, and relevance of the model for reaching marginalized and high-risk populations.