Adapting Diabetes Treatment Expert Systems to Patient's Expectations and Psychobehavioral Characteristics in Type 1 Diabetes

使糖尿病治疗专家系统适应患者的期望和心理行为 1型糖尿病的特点。 1型糖尿病(T1 DM)的血糖变异性(GV)通常被视为血糖控制的主要标志， 可能与慢性高血糖一起导致糖尿病并发症。这项拟议的项目 继续20年的研究，确定了GV的生理和行为相关性，并成功地 测试反馈控制策略，通过同时预防低血糖和系统性降低GV T1 DM合并高血糖。 我们的主要假设是：降低T1糖尿病患者的血糖变异性可以通过技术实现最佳 它被告知并适应患者/使用者的个体心理行为和代谢特征。这 可以通过个性化和自动调整治疗策略以及通过治疗实现 与每个患者的技术接受程度相对应的干预措施，旨在最大限度地 通过跟踪和加强干预中的信任来成功使用系统。 因此，在这个项目中，我们计划(I)确认先前设计的两种技术干预措施的有效性 -信息决策支持系统(IDSS)和规范性决策支持系统(PDSS)--减少 在为期6个月的随机交叉临床试验中，T1糖尿病患者的GV；(Ii)显示受试者 参与这项研究的人将拥有可预测的技术干预偏好(例如iDSS与PDSS 通过他们的心理行为特征的关键参数，并预测GV控制水平 最后，我们建议定义和验证一种新的、可测量的技术指数 接受和信任，通过自动观察用户/系统交互。 综上所述，该项目将证明基于CGM的决策支持系统可以显著减少 GV在T1 DM中，这种表现是通过心理行为特征和期望来预测的。我们 进一步介绍了一种新的指标跟踪技术：接受度和信任度，预测系统性能。 这样的索引最终将使自动化治疗策略的未来最佳自适应成为可能。

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.