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

在这个项目中，我们将评估一种新的方法来裁剪行为的有效性 2型糖尿病患者自我管理对个体行为和血糖的干预 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 表明个体的生理和血糖功能存在显著差异，他们的 影响糖尿病自我管理的文化、社会和经济环境。这些 这些发现突显了个人量身定制医疗和行为的必要性 干预措施。然而，到目前为止提出的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 个性化的自我管理策略，例如改变饮食和体力活动。 此外，裁剪通常依赖于裁剪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。使用Self-Stop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，因此 这些数据突出了它们的“行为表型”，但这些数据很少用于裁剪。这个 这项研究的持续重点是开发糖尿病自我干预的信息学干预 管理，特别关注具有自我监控数据的个人发现和 解决问题以改善血糖控制。在拟议的研究中，我们将介绍 GLucoType，依赖于通过自我监控收集的数据的计算模式分析 识别与血糖控制不良相关的行为模式并制定 改变有问题的行为的个性化行为目标。在初步研究中，我们 已经确定1)计算表型方法可以准确地识别系统 个人活动与血糖水平变化之间的联系；2)这些模式可以是 在某种程度上自动翻译成用自然语言表达的行为目标 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，并参与GLucoType以实现个人自我 糖尿病的管理。在这项拟议的研究中，我们将评估GLucoType在 使用以实践为基础的研究网络(PBRN)进行的随机对照试验 纽约大都会地区获得联邦资格的社区卫生中心(FQHC)。

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.