JMIR Publications

ABSTRACT

Online health information seeking is changing the way people engage the health system. We sought to examine an innovative model of research translation, The Huberman Lab Podcast (HLP), developed by Andrew Huberman, Professor of Neurobiology and Ophthalmology at Stanford School of Medicine. HLP leverages social media to deliver health information translated into specific, actionable practices and health strategies directly to consumers. This research aims to characterize the HLP as a model of research translation and assess its potential for wider adoption and application. We applied two distinct qualitative analytical approaches: conventional content analysis of the YouTube transcript data and directed content analysis of viewers’ YouTube comments to 23 HLP episodes released by the time of data analysis. Key features of the HLP model include direct to consumer, zero cost, bilingual, and actionable content. We identified three main organizing categories and ten subcategories as the key elements of the model: 1) Why: Educate and empower; Bring zero cost to consumer information to the general public; 2) What: Tools and Protocols; Underlying mechanisms; Grounded in Science; 3) How: Linear and Iterative Knowledge Building Process; Lecture-Style Sessions; Interactive and Consumer Informed; Easily Accessible; Building the Community. Analysis of viewers’ comments found strong consumer support of the key HLP model elements. The HLP model of research translation, designed as an interactive and user-informed online product, offers an innovative way to synthesize scientific evidence and deliver it directly to end-users as actionable tools and education. The model offers a replicable framework for translating and disseminating scientific evidence. Similar active models of research translation may have meaningful implications for improving access to health information and the implementation of health strategies for improved outcomes. Additional areas for further investigation should include specific, measurable impacts on health, usability and relevance of the model for disadvantaged and high-risk populations.