Designing for Diabetes Decision Support Systems with Fluid Contextual Reasoning

Dmitri Katz, Blaine Price, Simon Holland, Nick Dalton

Research output: Contribution to conferencePaperpeer-review

3 Citations (Scopus)
9 Downloads (Pure)

Abstract

Type 1 diabetes is a potentially life-threatening chronic condition that requires frequent interactions with diverse data to inform treatment decisions. While mobile technologies such as blood glucose meters have long been an essential part of this process, designing interfaces that explicitly support decision-making remains challenging. Dual-process models are a common approach to understanding such cognitive tasks. However, evidence from the first of two studies 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.
Original languageEnglish
DOIs
Publication statusPublished - 20 Apr 2018
Event2018 ACM Conference on Human Factors in Computing Systems - Palais des Congrès de Montréal, Montréal, Canada
Duration: 21 Apr 201826 Apr 2018
https://chi2018.acm.org/

Conference

Conference2018 ACM Conference on Human Factors in Computing Systems
Abbreviated titleCHI 2018
CountryCanada
CityMontréal
Period21/04/1826/04/18
Internet address

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