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.
Adapting Diabetes Treatment Expert Systems to Patient's Expectations and Psychobehavioral Characteristics in Type 1 Diabetes. Type 1 diabetes mellitus (T1DM) is an autoimmune condition resulting in absolute insulin deficiency and a life- long need for insulin replacement, that, despite technological advances still frustrates patients' and clinicians' efforts to control glucose to near-normal levels, and results in excess mortality and complications. While average glycaemia has long been the key indicator for judging the quality of achieved glucose control, glucose variability (or the measure of glucose fluctuations in time) is typically at the root of clinicians' inability to safely achieve near-normal average glycaemia. Our project, in its 5th installment, has aimed at understanding the sources and mechanism of glucose variability (GV), and providing technological and behavioral solutions to control (reduce) it in T1DM. Following the success of several pilot trials of Decision Support Systems (DSS), we now propose to validate these approaches in a larger population and for significantly longer periods of time, and contrast the efficacy of providing information vs. advice to patients with T1DM; in so doing we will understand the impact of the patients' psycho-behavioral profile on the effectiveness of these interventions and design and validate a new index capable of tracking the trust and acceptance of a DSS user.
|Kovatchev, Boris P (2017) Metrics for glycaemic control - from HbA1c to continuous glucose monitoring. Nat Rev Endocrinol 13:425-436|
|Kovatchev, Boris; Cobelli, Claudio (2016) Glucose Variability: Timing, Risk Analysis, and Relationship to Hypoglycemia in Diabetes. Diabetes Care 39:502-10|
|Kovatchev, Boris P; Breton, Marc D (2015) Hemoglobin A1c and Self-Monitored Average Glucose: Validation of the Dynamical Tracking eA1c Algorithm in Type 1 Diabetes. J Diabetes Sci Technol 10:330-5|
|Kovatchev, Boris P (2015) Measures of Risk and Glucose Variability in Adults Versus Youths. Diabetes Technol Ther 17:766-9|
|Kovatchev, Boris P; Patek, Stephen D; Ortiz, Edward Andrew et al. (2015) Assessing sensor accuracy for non-adjunct use of continuous glucose monitoring. Diabetes Technol Ther 17:177-86|
|Fabris, Chiara; Patek, Stephen D; Breton, Marc D (2015) Are Risk Indices Derived From CGM Interchangeable With SMBG-Based Indices? J Diabetes Sci Technol 10:50-9|
|Lv, Dayu; Kulkarni, Sandip D; Chan, Alice et al. (2015) Pharmacokinetic Model of the Transport of Fast-Acting Insulin From the Subcutaneous and Intradermal Spaces to Blood. J Diabetes Sci Technol 9:831-40|
|Lv, Dayu; Breton, Marc D; Farhy, Leon S (2013) Pharmacokinetics modeling of exogenous glucagon in type 1 diabetes mellitus patients. Diabetes Technol Ther 15:935-41|
|Chan, A; Barrett, E J; Anderson, S M et al. (2012) Muscle microvascular recruitment predicts insulin sensitivity in middle-aged patients with type 1 diabetes mellitus. Diabetologia 55:729-36|
|Farhy, Leon S; Chan, Alice; Breton, Marc D et al. (2012) Association of Basal hyperglucagonemia with impaired glucagon counterregulation in type 1 diabetes. Front Physiol 3:40|
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