Consumer engagement ensures that health research reflects lived experiences and generates outcomes relevant to those most affected. However, frameworks guiding engagement in research about chronic conditions remain limited and often lack theoretical grounding.

To develop an integrated, evidence-based framework to support consumer engagement in research about chronic conditions.

We integrated findings from (1) a scoping review synthesising evidence-based resources supporting consumer engagement in research about chronic conditions (Resource Framework) and (2) a co-designed framework for recognising consumers' contributions to research within the Australian context (Recognition Framework). Our integration deployed the relational, structural, and symbolic domains of Honneth's recognition theory as an analytical lens and used joint displays to develop a comprehensive framework.

The framework demonstrates how relational, structural, and symbolic dimensions of recognition collectively support ethical and sustainable consumer engagement. Relational recognition (e.g., mutual learning, ongoing communication) strengthens interpersonal trust and shared decision-making; structural recognition (e.g., governance policies, remuneration, reimbursement) embeds engagement within institutional systems; and symbolic recognition (e.g., authorship, formal acknowledgement) legitimises consumers' expertise within research cultures. Together, these elements provide a comprehensive foundation for supporting meaningful engagement across research practices.

This integrated recognition theory-informed framework offers an evidence-based tool to inform the design and implementation of consumer engagement in research about chronic conditions. By positioning recognition for consumers' contribution as an ethical, structural, and symbolic principle, it offers a transferable framework to strengthen participatory practice and advance equity in research. While developed for chronic conditions research, the framework is likely transferable with contextual tailoring to other settings.