Currently submitted to: Journal of Medical Internet Research
Date Submitted: May 12, 2026
Open Peer Review Period: May 14, 2026 - Jul 9, 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Warning: This is an author submission that is not peer-reviewed or edited.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.
The Co-Tech Taxonomy: A Health CASCADE Framework for Evaluating Digital Technologies in Participatory Health Research and Co-Creation
ABSTRACT
Background:
Co-creation is increasingly used in health research, public health, and participatory initiatives to support inclusive, collaborative, and evidence-informed problem-solving. However, the integration of digital technologies into co-creation processes remains fragmented and largely ad hoc, with limited frameworks available to guide technology selection, evaluation, and development.
Objective:
This study aimed to develop the Co-Tech Taxonomy, an empirically grounded evaluative framework for assessing digital technologies used in co-creation and participatory digital health ecosystems.
Methods:
Using the Nickerson–Varshney–Muntermann (NVM) taxonomy-building method, the taxonomy was developed through the analysis of six foundational conceptual and empirical frameworks related to co-creation, participatory processes, and digital technologies. The taxonomy was subsequently refined through iterative empirical classification of 84 technologies used in co-creation contexts.
Results:
The final taxonomy consists of seven functional dimensions: governance, inclusivity, methodology, collaboration, engagement, data management, and cognitive support. Each dimension is operationalised across three progressive levels of co-creation alignment. The empirical mapping revealed that current digital ecosystems remain insufficiently aligned with participatory collaboration requirements, particularly regarding governance, inclusivity, and AI-supported cognitive facilitation. While communication and data-management functionalities were comparatively mature, participatory governance, collaborative decision-making, and AI explainability remained underdeveloped across most evaluated technologies. The taxonomy also enabled the development of a three-tier indicative certification model to support technology assessment and implementation.
Conclusions:
The Co-Tech Taxonomy provides a structured evaluative framework for assessing existing technologies, identifying implementation and innovation gaps, and guiding the development of more inclusive, transparent, interoperable, and AI-ready participatory digital infrastructures. The framework offers a practical foundation for strengthening digitally supported co-creation and participatory collaboration within health-related contexts.
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Copyright
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