Currently submitted to: JMIR Research Protocols
Date Submitted: May 22, 2026
Open Peer Review Period: May 26, 2026 - Jul 21, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Vibe Coding as a Participatory Approach to Co-Designing Behavioral Interventions With People With Lived and Living Experience: A Methodological Framework Proposal
ABSTRACT
Background:
Behavioral interventions are more effective when co-designed with people who have lived and living experience (PWLLE) of the target issue. However, traditional co-design processes position participants as idea generators whose concepts must be translated into functional tools by professional developers, creating a gap between participant vision and final product. The emergence of vibe coding—a practice in which users describe desired software functionality in natural language and artificial intelligence (AI) generates the corresponding code—presents a novel opportunity to close this gap by enabling participants to build their own intervention tools directly.
Objective:
This paper proposes a methodological framework for integrating vibe coding into the co-design of behavioral interventions, whereby PWLLE are guided by researchers to brainstorm, prototype, and iteratively build digital tools that address their self-identified needs.
Methods:
This paper proposes a methodological framework for integrating vibe coding into the co-design of behavioral interventions, whereby PWLLE are guided by researchers to brainstorm, prototype, and iteratively build digital tools that address their self-identified needs.
Results:
N/A
Conclusions:
Vibe coding offers a transformative extension to participatory co-design methodologies in behavioral science. By enabling PWLLE to directly build the tools they need, this approach strengthens participation beyond consultation, aligns with self-determination theory (SDT), and generates interventions that are more contextually responsive. Future research should include feasibility trials, effectiveness evaluations, and the development of ethical guidelines for AI-mediated participatory research. Clinical Trial: N/A
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Copyright
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