Dynamically Tailoring Interventions for Problem-Solving in Diabetes Self-Management Using Self-Monitoring Data - a Randomized Controlled Trial.

使用自我监测数据动态定制干预措施以解决糖尿病自我管理中的问题 - 一项随机对照试验。

基本信息

批准号：
10380910
负责人：
Olena Mamykina
金额：
$ 63.67万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-04-01 至 2024-03-31
项目状态：
已结题

项目摘要

In this project, we will evaluate the efficacy of a novel approach to tailoring behavioral interventions for self-management of type 2 diabetes to individuals' behavioral and glycemic profiles discovered using computational learning and self-monitoring data. Growing evidence suggests significant differences in individuals' physiology and glycemic function, and their cultural, social, and economical circumstances that impact diabetes self-management. These discoveries highlight the need for personally tailoring both medical treatment and behavioral interventions. Yet tailored behavioral interventions proposed thus far typically focus on motivation for behavior change and individuals' psycho-social characteristics, rather than personalizing self-management strategies, such as changes in diet and physical activity. Moreover, tailoring typically relies on expert identification of tailoring variables and decision rules, and on standard surveys for assessment these variables. Data collected with self- monitoring can more accurately reflect an individual's behaviors and glycemic patterns, thus highlighting their “behavioral phenotypes”, yet such data are rarely utilized in tailoring. The ongoing focus of this research is on developing informatics interventions for diabetes self- management, with a specific focus on personal discovery with self-monitoring data and on problem-solving for improving glycemic control. In the proposed research we will introduce GlucoType that relies on computational pattern analysis of data collected with self-monitoring technologies to identify behavioral patterns associated with poor glycemic control and formulate personalized behavioral goals for changing problematic behaviors. In our preliminary studies we have established that 1) computational phenotyping methods can accurately identify systematic associations between individuals' activities and changes in BG levels; 2) these patterns can be automatically translated into behavioral goals formulated in a natural language in a way consistent with goals formulated by diabetes experts, and 3) individuals with T2DM can understand and follow these behavioral goals and engage with GlucoType for personal self- management of diabetes. In the proposed research we will evaluate GlucoType's efficacy in a randomized controlled trial conducted with a practice-based research network (PBRN) of Federally Qualified Community Health Centers (FQHCs) in the metropolitan New York area.

在这个项目中，我们将评估一种新型方法的效率 2型糖尿病自我管理的干预措施对个人的行为和血糖的干预措施使用计算学习和自我监控数据发现的配置文件。越来越多的证据提示个体的生理学和血糖功能及其显着差异，及其仅影响糖尿病的文化，社会和经济状况。这些发现强调了个人调整医疗和行为的需求干预措施。到目前为止，量身定制的行为干预措施通常集中在行为改变的动机和个人的心理社会特征，而不是个性化自我管理策略，例如饮食和体育锻炼的变化。此外，裁缝通常依赖于裁缝变量和决策的专家识别规则，以及评估这些变量的标准调查。自我收集的数据监测可以更准确地反映个人的行为和血糖模式，从而可以反映强调了它们的“行为表型”，但很少使用这些数据来裁缝。这项研究的持续重点是开发糖尿病的信息干预措施管理，特别关注使用自我监控数据的个人发现以及解决血糖控制的问题解决。在拟议的研究中，我们将介绍依赖于自我监控收集的数据的计算模式分析的葡萄型识别与不良血糖控制相关的行为模式并制定的技术改变有问题行为的个性化行为目标。在我们的初步研究中已经确定1）计算表型方法可以准确识别系统性个人活动与BG水平变化之间的关联； 2）这些模式可以是自动以一种自然语言的方式自动转化为行为目标与糖尿病专家制定的目标一致，3）具有T2DM的个人可以了解并遵循这些行为目标，并与葡萄型进行个人自我糖尿病的管理。在拟议的研究中，我们将评估葡萄型的效率由基于实践的研究网络（PBRN）进行的随机对照试验纽约大都会地区的联邦合格社区卫生中心（FQHC）。