JMIR Publications

ABSTRACT

Background:

Digital health interventions are increasingly being designed to support health behaviors. Although digital health interventions informed by behavioral science theories, models, and frameworks are more likely to be effective than those designed without, design teams often struggle to use these evidence-informed tools. Until now, little work has been done to clarify the ways in which behavioral science theories, models, and frameworks can add value to digital health design.

Objective:

The aim of this paper is to better understand how digital health design leaders select and use theories, models, and frameworks in design practice. The following questions are addressed: (1) How do design leaders perceive the value of theories, models, and frameworks in digital health design? (2) What considerations do design leaders make when selecting and applying theories, models, and frameworks (3) What do design leaders think is needed in the future to advance the utility of theories, models, and frameworks in digital health design?

Methods:

This paper uses a qualitative description design to understand the experiences and perspectives of digital health design leaders. Participants were identified through purposive and snowball sampling. Semi-structured interviews were conducted using Zoom software. Interviews were audio recorded and transcribed using Otter.ai software. Three researchers coded a sample of the interview transcripts and confirmed the coding strategy. One researcher completed the qualitative analysis following an inductive thematic analysis approach.

Results:

Design leaders had mixed opinions on the value of behavioral science theories, models, and frameworks in digital health design. Leaders suggested that theories, models, and frameworks added the most value when seen as a starting point rather than the final destination for evidence-informed design. Specifically, these tools added value when they acted as a “gateway drug” to behavioral science, supported health behavior conceptualization, were balanced with expert knowledge, were balanced with user-centered design, were complementary to existing design methods, and when they supported both individual- and systems-level thinking. Design leaders also felt that there was considerable nuance in selecting the most value-adding theories, models, and frameworks. Considerations should be made about their source, appropriateness, complexity, accessibility, adaptability, evidence base, purpose, influence, audience, fit with team expertise, fit with team culture, and fit with external pressures. Design leaders suggested multiple opportunities to advance the use of theories, models, and frameworks. These included improving their reporting, design, and accessibility, as well as improving design team capacity to use them appropriately in practice.

Conclusions:

When designing a digital health behavior change intervention, utilizing theories, models, and frameworks can help design teams systematically integrate behavioral insights. The future of digital health behavior change design demands an easier way for designers to integrate these tools in practice. Solutions are likely multifaceted, including improvements in tool design, accessibility, and training. Clinical Trial: n/a