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YouTube is the second-most visited webpage in the world and boasts over 2 billion users and 500 h of videos uploaded every hour. Despite this popularity, relatively few articles have discussed the practical use of searching and YouTube as a research tool and source of data. The purpose of our paper is to propose a step-by-step schematic for utilizing the YouTube platform. Our discussions include (a) when/whether to use YouTube for research; (b) selecting an appropriate research design; (c) how to search for YouTube data; (d) what data can be pulled from YouTube; and (e) the contextual limitations for interpreting YouTube data. Further, we provide practical strategies and considerations when searching, collecting, or interpreting YouTube data. These discussions are informed by our own work using the YouTube platform. Effective methods used to search for YouTube data are likely to extend beyond simply searching the platform itself; the search strategy and search results themselves should also be documented. While not exhaustive, we feel these considerations and strategies present themselves as a conceptual foothold for future research using the YouTube platform.

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Background The UN's High Commissioner's request for a moratorium on the use and adoption of specific Artificial Intelligence (AI) systems that pose serious risk to human rights, this commentary explores the current environment and future implications of using third-party wearable technologies in research for participants’ data privacy and data security. While wearables have been identified as tools for improving users’ physical and mental health and wellbeing by providing users with more personalized data and tailored interventions, the use of this technology does not come without concern. Objective Primarily, as researchers, we are concerned with enmeshment of corporate and research interests and what this can mean for participant data. Methods By drawing on specific sections of the UN Report ‘The right to privacy in the digital age’, we discuss the conflicts between corporate and research agendas and point out the current and future implications of the involvement of third-party companies for participant data privacy, data security and data usage. Finally, we offer suggestions for researchers and third-party wearable developers for conducting ethical and transparent research with wearable tech. Conclusion We propose that this commentary be used as a foothold for further discussions about the ethical implications of using third-party wearable tech in research.

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The purpose of our study was to describe characteristics of behavior change techniques (BCTs) employed by popular YouTube fitness channels and examine relationships between BCTs used and engagement metrics (e.g. views, likes, comments). Seventy-five videos were coded according to BCT Taxonomy v1. Multi-level modeling was conducted between BCTs and engagement metrics. Fifty-four unique BCTs were used, with “Demonstration of behavior” and “Instruction on how to perform the behavior” used the most. The number of BCTs employed was 12.5 ± 6.65 and BCTs were all unrelated to engagement metrics ( ps > 0.05). Application of BCTs within YouTube varies from traditional exercise interventions.