Refining Established Practices for Research Question Definition to Foster Interdisciplinary Research Skills in a...

Area of Science:

• Digital Health Research
• Data Science
• Interdisciplinary Collaboration

Background:

• Growing use of digital data in health research necessitates bridging deductive health research with iterative data science approaches.
• Lack of structured guidance hinders effective interdisciplinary collaboration and education in digital health.
• Methodological complexities require innovative solutions for merging diverse research paradigms.

Purpose of the Study:

• To provide tools and recommendations for interdisciplinary education and collaboration in digital health.
• To support educators, supervisors, and principal investigators in designing and guiding interdisciplinary projects.
• To facilitate the integration of data science methodologies within health research.

Main Methods:

• Developed a common terminology through a glossary for shared understanding.
• Identified established workflows for research question formulation.
• Examined adaptations of study workflows combining health research and data science methods.
• Conducted literature search and interdisciplinary expert workshops using consensus methodology.

Main Results:

• Created tools focusing on content, curriculum, methods, and teaching style for interdisciplinary settings.
• Established a shared glossary to define common terminologies and concepts.
• Proposed workflow adaptations for integrating data science, including acknowledging constraints, iterative approaches, and FAIR data principles.
• Enhanced research quality through reproducibility and findable, accessible, interoperable, and reusable (FAIR) data principles.

Conclusions:

• Research question formulation is crucial for digital data projects.
• Establishing shared terminology and using research tasks fosters interdisciplinary understanding.
• Tools and recommendations support training for interdisciplinary digital health projects.