JMIR Publications

ABSTRACT

Background:

Developers, designers, and researchers employ rapid prototyping methods to project the adoption and acceptability of their health intervention technology (HIT) before the technology becomes mature enough to be deployed. Although these methods are useful for gathering feedback that advances the HITs being developed, they rarely provide usable evidence that can contribute to our broader understanding of HITs.

Objective:

In this work, we develop and demonstrate a variation of vignette testing that supports developers and designers in evaluating early-stage HIT designs while generating usable evidence for the broader research community.

Methods:

We propose a method called Health Concept Surveying (HCS) for untangling the causal relationships that people develop around conceptual HITs. In HCS, researchers gather reactions to design concepts through a scenario-based survey instrument. As the researcher manipulates characteristics related to their HIT, the survey instrument also measures proximal cognitive factors according to a health behavior change model to project how HIT design decisions may affect the adoption and acceptability of a HIT. Responses to the survey instrument are analyzed using structural equation modeling to untangle the causal effects of these factors on outcome variables.

Results:

We demonstrate HCS in three case studies of sensor-based health-screening apps. Our first study (N = 54) shows that a wait-time incentive could influence more people to go see a dermatologist after a positive test for skin cancer. Our second study (N = 54), evaluating a similar application design, shows that while visual explanations of algorithmic decisions could increase participant trust in negative test results, that trust would not have been enough to affect people’s decision-making. Our third study (N = 263) shows that people may prioritize test specificity or sensitivity depending on the nature of the medical condition.

Conclusions:

Beyond the findings from our three case studies, our research uses the framing of the Health Belief Model to elicit and understand the intrinsic and extrinsic factors that may affect the adoption and acceptability of a HIT without having to build a working prototype. We make our survey instrument publicly available so that others can leverage it for their own investigations.