Adapting Diabetes Treatment Expert Systems to Patient's Expectations and Psychobehavioral Characteristics in Type 1 Diabetes. Glucose variability (GV) in type 1 diabetes (T1DM) is commonly viewed as a primary marker of glycemic control, potentially responsible, along with chronic hyperglycemia, for diabetes complications. This proposed project continues 20 years of research, which identified physiological and behavioral correlates of GV and successfully tested feedback control policies to reduce GV via simultaneous protection against hypoglycemia and systematic hyperglycemia in T1DM. Our primary hypothesis is that: Reducing glucose variability in T1DM can be optimally achieved by technology that is informed of, and adapted to, the individual psychobehavioral and metabolic profiles of patients/users. This can be achieved through personalization and automated adaptation of treatment policies, and through treatment intervention that corresponds to each patient's level of technology acceptance and is designed to maximize successful system use by tracking and reinforcing trust in the intervention. Therefore, in this project we plan to (i) confirm the efficacy of two previously designed technological interventions - Informative Decision Support System (iDSS) and Prescriptive Decision Support System (pDSS) - in reducing GV in T1DM patients during a 6-month long randomized cross-over clinical trial; (ii) show that subjects participating in this study will have technology intervention preferences (e.g. iDSS vs pDSS) that can be predicted by key parameters of their psychobehavioral profile and are prognostic of the level of GV control achievable by the intervention; and finally, we propose to define and validate a novel, measureable, index of technology acceptance and trust, by automatically observing user/system interactions. In summary, this project will demonstrate that CGM-based decision support systems can significantly reduce GV in T1DM, and that performance is predicted by psychobehavioral characteristics and expectations. We further introduce a novel index tracking technology acceptance and trust, predictive of system performance. Such index would ultimately enable future optimal self-adaptation of automated treatment strategies.