Growing evidence highlights significant differences in individuals' physiology and glycemic function and their cultural, social, and economical circumstances that impact diabetes self-management. These discoveries paved the way for precision medicine-an approach to personalizing medical treatment to an individual's genetic makeup, clinical history, and lifestyle. Computational learning methods have been successfully used for identifying clinical phenotypes-observable manifestations of diseases. Studies showed the benefits of tailoring not only medical treatment, but also behavioral interventions; however, tailoring typically relies on expert identification of tailoring variables and decision rules, and on standard surveys. Data collected with self-monitoring can more accurately reflect an individual's behaviors and glycemic patterns, thus highlighting their "behavioral phenotypes", yet such data are rarely utilized in tailoring.
The ongoing focus of this research is on facilitating problem-solving in diabetes self-management. Well-developed problem-solving skills are essential to diabetes management result in better diabetes self-care behaviors lead to improvements in clinical outcomes and can be fostered with face-to-face interventions. Previous research suggested problem identification and generation of alternatives as critical steps in problem-solving in diabetes. In previous work, the investigators developed an informatics intervention that relied on expert-generated knowledge for assisting individuals on these steps of problem-solving. In this pilot feasibility study, the investigators study an alternative solution that relies on computational pattern analysis of data collected with self-monitoring technologies to tailor the problem-solving assistance to individuals' unique behavioral phenotypes. The intervention, GlucoType uses computational learning methods to identify systematic patterns in individuals' diet, physical activity, and sleep, captured with custom-built and commercial self-monitoring technologies, and correlates these patterns with fluctuations in individuals' blood glucose levels. GlucoType then uses this information to 1) identify behavioral patterns associated with high glycemic excursion, 2) formulate personalized goals to modify these behaviors, 3) provide in-the-moment decision support to help individuals be more consistent in meeting their goals.
