Improving Glycemic Management in Patients with Type 1 Diabetes Using a Context-aware Automated Insulin Delivery System

使用情境感知自动胰岛素输送系统改善 1 型糖尿病患者的血糖管理

• 批准号: 10402778
• 负责人: Peter G Jacobs
• 金额: $ 55.62万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-15 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10402778
• 关键词: Accelerometer; Adult; Aerobic Exercise; Algorithms; Awareness; Beds; Behavior; Blindness; Calibration; Cellular Phone; Clinic; Clinical Research; Collaborations; Custom; Data; Data Analyses; Data Collection; Data Set; Diabetes Mellitus; Dose; Eating; Engineering; Event; Exercise; Fatty acid glycerol esters; Food; Future; Glucose; Glycosylated hemoglobin A; Grant; Heart Rate; Home; Hyperglycemia; Hypoglycemia; Insulin; Insulin Infusion Systems; Insulin-Dependent Diabetes Mellitus; Kidney Failure; Knowledge; Lead; Learning; Life; Maps; Measures; Movement; Outcome; Outcome Measure; Outpatients; Participant; Patients; Pattern; Persons; Physical activity; Physiological; Pilot Projects; Pump; Randomized; Randomized Clinical Trials; Restaurants; Risk; Running; Safety; Schedule; Stress; System; Tactile; Testing; Time; Wrist; arm; base; blood glucose regulation; cohort; design; effectiveness evaluation; expectation; fitbit; fitness; glucose monitor; glucose sensor; glycemic control; high risk; improved; industry partner; machine learning algorithm; novel; prediction algorithm; primary outcome; recruit; research clinical testing; response; sensor; smart watch; two-arm study; usability; wearable sensor technology

Project Summary Automated insulin delivery (AID) systems offer substantial opportunities for helping people with type 1 diabetes (T1D) to improve glucose control and lower HbA1c. However, the AID has only shown a benefit during the nighttime when meals, exercise, and stress do not significantly challenge the AID. Furthermore, hypoglycemia (<70 mg/dL) remains a common occurrence in people with type 1 diabetes and continues to occur even in the setting of AID, particularly with exercise. Integrating context awareness into an AID has the potential to improve glycemic time in range (70-180 mg/dL) during the daytime and reduce and possibly eliminate hypoglycemia. Contextual information can include inferred food intake, insulin dosing, inferred exercise type and duration, as well as movement patterns. An AID can be designed to recognize contextual patterns that relate to poor glycemic responses to meals and hypoglycemia and then adjust insulin dosing in response to these patterns in advance and help mitigate these problems. In this grant, we will explore how contextual information may be used within an AID to help (1) avoid hypoglycemia and (2) reduce postprandial dysglycemia. We will first conduct a data gathering study whereby we will collect a rich data set from people with T1D who will use sensor augmented pump therapy to manage their glucose. Data will be collected from these 30 patients over 28 days; data will include multivariable contextual information including continuous glucose monitoring (CGM) data, insulin data, food data, physical activity data (heart rate and accelerometry), as well as indoor/outdoor contextual movement patterns gathered using a novel beacon-based context-aware sensing system called MotioWear developed by our group in collaboration with our industry partner MotioSens. Next, we will utilize this contextual data set to construct a Bayesian glucose prediction algorithm. This will include a clustering algorithm that will group contextual sequences that are similar with each other and which lead to similar glycemic outcomes. This context-aware glucose prediction algorithm will be integrated into an adaptive, personalized, smartwatch-based context-aware AID (CA-AID) system. Contextual patterns that have a high likelihood of leading to hypoglycemia or postprandial dysglycemia will inform an insulin dosing aggressiveness factor to be adjusted for similar contextual sequences observed in the future (i.e. the CA-AID will reduce insulin for contextual sequences with high likelihood of hypoglycemia such as aerobic exercise). We expect that integrating context awareness into an AID will lead to significant improvements in time in target range during the day and will help reduce time in hypoglycemia. The CA-AID will be evaluated for safety in a small pilot study. We will then evaluate the CA-AID within a 6 week clinical study in 40 adults with type 1 diabetes on insulin pump therapy. Twenty will receive the CA-AID while the other 20 will receive a standard (non-context-aware) AID. The primary outcome measures of this study is the percent time in range. We hypothesize that the CA-AID will increase time in range by 10% as compared with a non-context aware AID.

项目概要 自动胰岛素输送 (AID) 系统为帮助 1 型糖尿病患者提供了大量机会 (T1D) 改善血糖控制并降低 HbA1c。然而，AID 仅在 夜间，此时进餐、运动和压力不会对 AID 产生显着影响。此外，低血糖 (<70 mg/dL) 在 1 型糖尿病患者中仍然很常见，甚至在 AID 的设置，尤其是运动。将情境感知集成到 AID 中可能会 白天改善血糖时间范围 (70-180 mg/dL)，并减少并可能消除 低血糖。上下文信息可以包括推断的食物摄入量、胰岛素剂量、推断的运动类型 和持续时间，以及运动模式。 AID 可以被设计为识别上下文模式 与对膳食和低血糖的不良血糖反应有关，然后根据情况调整胰岛素剂量 提前这些模式有助于缓解这些问题。在这笔赠款中，我们将探讨上下文如何 信息可在 AID 中使用，以帮助 (1) 避免低血糖和 (2) 减少餐后血糖 血糖异常。我们将首先进行数据收集研究，从人们那里收集丰富的数据集 患有 T1D 的患者将使用传感器增强泵疗法来控制血糖。数据将收集自 这 30 名患者在 28 天内；数据将包括多变量上下文信息，包括连续的 血糖监测 (CGM) 数据、胰岛素数据、食物数据、体力活动数据（心率和加速度测量）、 以及使用新颖的基于信标的情境感知收集的室内/室外情境运动模式 我们的团队与行业合作伙伴 MotioSens 合作开发了名为 MotioWear 的传感系统。 接下来，我们将利用该上下文数据集构建贝叶斯葡萄糖预测算法。这将 包括一个聚类算法，该算法将对彼此相似的上下文序列进行分组，并且 导致类似的血糖结果。这种上下文感知的葡萄糖预测算法将被集成到 自适应、个性化、基于智能手表的情境感知 AID (CA-AID) 系统。上下文模式具有 导致低血糖或餐后血糖异常的可能性很高，将告知胰岛素剂量 针对未来观察到的类似上下文序列调整攻击性因子（即 CA-AID 会减少低血糖可能性的背景序列（例如有氧运动）的胰岛素。我们 期望将情境感知集成到 AID 中将导致目标时间的显着改善 白天范围内，有助于减少低血糖时间。 CA-AID 将在以下环境中进行安全评估： 小型试点研究。然后，我们将在为期 6 周的临床研究中对 40 名 1 型成人患者进行 CA-AID 评估 糖尿病接受胰岛素泵治疗。 20 人将获得 CA-AID，另外 20 人将获得标准 （非上下文感知）援助。本研究的主要结果指标是范围内的时间百分比。我们 假设与非上下文感知 AID 相比，CA-AID 将在范围内的时间增加 10%。