Designing for Diabetes Decision Support Systems with Fluid Contextual Reasoning

Katz, Dmitri S.; Price, Blaine A.; Holland, Simon and Dalton, Nicholas Sheep (2018). Designing for Diabetes Decision Support Systems with Fluid Contextual Reasoning. In: CHI '18 Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, ACM, New York, NY, USA, article no. 625.

DOI: https://doi.org/10.1145/3173574.3174199

Abstract

Type 1 diabetes is a potentially life-threatening chronic condition that requires frequent interactions with diverse data to inform treatment decisions. While mobile technolo- gies such as blood glucose meters have long been an essen- tial part of this process, designing interfaces that explicitly support decision-making remains challenging. Dual-process models are a common approach to understanding such cog- nitive tasks. However, evidence from the first of two stud- ies we present suggests that in demanding and complex situations, some individuals approach disease management in distinctive ways that do not seem to fit well within existing models. This finding motivated, and helped frame our second study, a survey (n=192) to investigate these behaviors in more detail. On the basis of the resulting analysis, we posit Fluid Contextual Reasoning to explain how some people with diabetes respond to particular situations, and discuss how an extended framework might help inform the design of user interfaces for diabetes management.

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