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Katz, Dmitri
(2017).
DOI: https://doi.org/10.1145/3027063.3027127
Abstract
Type 1 Diabetes is a serious condition that demands careful balancing of lifestyle and medication to avoid serious complications. Current mobile health approaches for diabetes management are usually either automated insulin delivery systems or logbooks that depend on manual data collection and reflection. Both have their shortcoming such as loss of engagement and autonomy in the former approach, or fatigue and cognitive stress in the latter. Based on my pilot research, my thesis considers the wider implications of an approach that: (1) reduces workload through minimizing manual logging; (2) automates knowledge extraction from collected data (3) communicates insight in the right way at the right time; (4) creates a feedback loop that encourages previously effective behaviors. This thesis contributes to the exploration and evaluation of new strategies for mobile personalized support through ubiquitous computing technologies, and the development of tools for improving the lives of those with chronic health conditions