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。

• 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
• 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
• 来源: https://reporter.nih.gov/project-details/10380910
• 关键词: Address; Area; Behavior; Behavior Therapy; Behavioral; Blood Glucose; Blood Pressure; Body mass index; Cardiometabolic Disease; Characteristics; Clinical; Cognitive; Communities; Computer Analysis; Control Groups; Data; Data Analyses; Development; Diabetes Mellitus; Diet; Emotional; Equilibrium; Equipment and supply inventories; Federally Qualified Health Center; Glycosylated hemoglobin A; Goals; HDL-triglyceride; Health; Health Personnel; High Density Lipoprotein Cholesterol; High Prevalence; Individual; Informatics; Intervention; LDL Cholesterol Lipoproteins; Language; Learning; Learning Module; Life Style; Low income; Medical; Methods; Monitor; Motivation; Multimedia; Neighborhood Health Center; New York; Non-Insulin-Dependent Diabetes Mellitus; Participant; Pattern; Phenotype; Physical activity; Physiology; Pilot Projects; Population; Practice based research; Primary Health Care; Problem Solving; Public Health; Randomized; Randomized Controlled Trials; Research; Science; Self Care; Self Efficacy; Self Management; Surveys; Techniques; Technology; Translating; United States Agency for Healthcare Research and Quality; Work; arm; behavior change; behavioral phenotyping; cardiovascular risk factor; clinical phenotype; clinical research site; design; diabetes educator; diabetes self-management; efficacy evaluation; genetic makeup; glycemic control; group intervention; improved; intervention delivery; mHealth; medically underserved; metropolitan; natural language; novel strategies; nutrition; personalized approach; practice-based research network; precision medicine; psychosocial; ranpirnase; sex; social; treatment as usual; underserved community; web site

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，我们将评估一种新的方法来定制行为的有效性， 2型糖尿病自我管理对个体行为和血糖的影响 使用计算学习和自我监测数据发现的配置文件。越来越多的证据 表明个体的生理和血糖功能存在显著差异， 影响糖尿病自我管理的文化、社会和经济环境。这些 这些发现强调了个性化医疗和行为治疗的必要性。 干预措施。然而，迄今为止提出的量身定制的行为干预措施通常侧重于 行为改变的动机和个人的心理社会特征，而不是 个性化的自我管理策略，如改变饮食和体育活动。 此外，裁剪通常依赖于专家对裁剪变量和决策的识别 规则，以及评估这些变量的标准调查。数据收集自 监测可以更准确地反映个人的行为和血糖模式， 强调他们的“行为表型”，但这些数据很少用于剪裁。的 这项研究的持续重点是发展糖尿病自我信息干预， 管理，特别注重通过自我监控数据进行个人发现， 解决问题以改善血糖控制。在本研究中，我们将介绍 GlucoType依赖于对通过自我监测收集的数据进行计算模式分析 用于识别与血糖控制不良相关的行为模式的技术， 个性化的行为目标，以改变问题行为。在初步研究中， 已经建立了1）计算表型分析方法可以准确地识别系统性 个人活动与血糖水平变化之间的关联; 2）这些模式可能是 自动转化为用自然语言表述的行为目标， 与糖尿病专家制定的目标一致，3）T2 DM患者可以 了解并遵循这些行为目标，并与GlucoType合作， 糖尿病的管理。在拟议的研究中，我们将评估GlucoType在以下方面的疗效： 一项随机对照试验，由一个基于实践的研究网络（PBRN）进行， 联邦合格的社区卫生中心（CIMCs）在大都会纽约地区。