Welcome to Psychiatryai.com: Latest Evidence - RAISR4D

Co-Develop-IT! Unifying Methodological Guideline for the Co-Design, Development, and Evaluation of Individually Tailored Technology-Enhanced Training and Rehabilitation Concepts: Consensus Development Study and Tutorial

AI Summary
  • Co-Develop-IT provides a consensus-based, operational guideline of eight iterative phases covering the full lifecycle of individually tailored DHT-enhanced training and rehabilitation.
  • Five preparatory contextual research phases precede generative codevelopment, strengthening contextualisation and tailoring to improve implementation prospects and address unmet clinical needs.
  • Guideline emphasises multidisciplinary teams, systematic patient and public involvement, and balancing scientific frameworks with real-world needs to promote practical application and impact.
Summarise with AI (MRCPsych/FRANZCP)

J Med Internet Res. 2026 May 22;28:e84163. doi: 10.2196/84163.

ABSTRACT

BACKGROUND: Applying digital health technologies (DHTs) for health promotion and disease prevention is recommended by official bodies such as the World Health Organization. User-centered co-design with systematic patient and public involvement is considered best practice for developing such complex interventions. Although well-established methodological guides and frameworks are available, an important gap is that they are either holistic but generic, offering minimal operational guidance, or context-specific and operational, but focusing only on subphases of establishing DHT-enhanced interventions.

OBJECTIVE: This paper presents a unifying consensus-based methodological guideline directed toward multidisciplinary expert teams coordinating projects on individually tailored DHTs. It delineates best practices with operational guidance for each step along the full lifecycle of DHT-enhanced training and rehabilitation concepts-from contextualization, through codevelopment, and evaluation to implementation.

METHODS: The Co-Develop-IT guideline was cocreated through a structured expert consensus process that integrated, refined, and expanded on well-established existing guides and frameworks to delineate holistic and context-specific, yet flexible enough, best practices. The process consisted of biweekly 90-minute hybrid meetings between August 2024 and February 2025, in combination with written elaboration, feedback, and revisions between meetings to gradually develop a consensus on best practice recommendations.

RESULTS: The Co-Develop-IT guideline consists of 8 iterative phases. It is applicable to any type of end users, exercise types, intended contexts of use (eg, primary health care, community health services, and telemedicine), and overarching goals (eg, health promotion and primary through tertiary disease prevention, including rehabilitation). The Co-Develop-IT guideline introduces 5 distinct preparatory contextual research phases preceding generative codevelopment. These phases are dedicated to the structured establishment of a more robust foundation to better tailor and steer codevelopment efforts toward successful implementation. In 2 application examples, we provide proof of concept that the resulting guideline fulfills its primary purpose of providing comprehensive, context-specific, and operational, yet flexible enough best practice recommendations.

CONCLUSIONS: The unifying Co-Develop-IT guideline provides comprehensive best practices with actionable operational guidance for establishing an appropriate balance between scientific theories and frameworks and the real-world needs of interest-holders in the establishment of individually tailored DHT-enhanced training and rehabilitation concepts. Applying Co-Develop-IT contributes to overcoming the lingering evidence-to-practice gap by consistently establishing a shared mission with relevant interest-holders and ensuring that all codevelopment steps are directed toward addressing an unmet need in (clinical) practice-ultimately promoting the practical application and impact of purpose-developed DHTs.

PMID:42172569 | DOI:10.2196/84163

Document this CPD

AI Search

Share Evidence Blueprint

QR Code

Search Google Scholar

Save as PDF

close chatgpt icon
ChatGPT

Enter your request.

Psychiatry AI: Real-Time AI Scoping Review