A Data-Driven Behavior Modeling and Analysis Framework for Diabetic Patients on Insulin Pumps

About 30%-40% of Type 1 Diabetes (T1D) patients in the United States use insulin pumps. Current insulin infusion systems require users to manually input meal carb count and approve or modify the system-suggested meal insulin dose. Users can give correction insulin boluses at any time. Since meal carbohydrates and insulin are the two main driving forces of the glucose physiology, the user-specific eating and pump-using behavior has a great impact on the quality of glycemic control. In this paper, we propose an “Eat, Trust, and Correct” (ETC) framework to model the T1D insulin pump users’ behavior. We use machine learning techniques to analyze the user behavior from a clinical dataset that we collected on 55 T1D patients who use insulin pumps. We demonstrate the usefulness of the ETC behavior modeling framework by performing in silico experiments. To this end, we integrate the user behavior model with an individually parameterized glucose physiological model, and perform probabilistic model checking on the user-in-the-loop system. The experimental results show that switching behavior types can significantly improve a patient’s glycemic control outcomes. These analysis results can boost the effectiveness of T1D patient education and peer support

Effectiveness of Continuous Subcutaneous Insulin Infusion Therapy Education in a Clinic Setting

Diabetes affects an estimated 29.1 million Americans, with approximately another 1/3 of Americans not yet diagnosed. Complications associated with diabetes include heart disease, stroke, hypertension, blindness, kidney disease, neuropathy and death. All of these complications can be prevented with optimal control of blood glucose levels. Advances in technology provide people living with diabetes (PLWD) a multitude of treatment options such as continuous subcutaneous insulin infusion (CSII) therapy. Unfortunately, sustained improvement in glycated hemoglobin A1c (HgA1c) is not always achieved even with this advanced therapy. The purpose of this doctoral project was to educate nurses on CSII therapy and promote improved patient compliance, knowledge and ultimately improve HgA1c control. This doctoral project is an evaluation of an Evidence-Based Quality Improvement Project (EB-QIP) that evaluated nurse-led educational sessions for PLWD using CSII therapy. The integrated theory of health behavior change was used to guide the project. The CDC process evaluation model was used to evaluate the outcomes of the education sessions. Results showed that patients who were instructed by the nurses who took part in the EB-QIP had a reduction in the overall HgA1c by an average of 1.1 points 3-months post-education. The project promotes positive social change through establishing the effectiveness of an EB-QIP that focused on the use of education on CSII therapy in improving outcomes for patients living with diabetes