Gamified Optimized Diabetes-management with AI powered Rural Telehealth (GODART)

项目概要/摘要 以血糖控制为目标的2型糖尿病（T2 DM）管理循证指南 （血红蛋白A1 c降低）包括饮食、体力活动（PA）、葡萄糖监测和 药物粘连然而，大多数T2 DM患者无法遵循这些指南 这是由于缺乏在初级保健环境中提供的一致的健康行为咨询。这个问题 作为回应，该项目旨在创建一个优化的 基于远程医疗的干预-游戏化优化糖尿病管理与人工智能驱动 农村远程保健（GODART）。GODART以社会认知理论为基础， 自动行为监控和远程辅导平台。GODART的核心是自动化的 对话式行为监控系统，使用自然语言理解技术。在这 项目，我们建议试点和可行性测试的各个组成部分，利用多阶段 优化策略（MOST）。社会变革管理计划是一项高效、严格的资源管理和持续的 制定优化干预措施的改进框架。我们的建议侧重于MOST 准备阶段，并将使用一个完整的析因实验。我们会先进行试验，并评估 评估两个不同的干预组成部分，每组两个级别， 实验条件这些小组将测试（i）固定与自适应（游戏化）奖励计划的效果 以及（ii）自动化与人工交付的每周健康指导。我们将以离职面谈结束这个项目 与一部分参与者一起进行。研究结果将有助我们了解推行这项计划的可行性， 干预及其在降低HbA 1c方面的初步有效性，导致充分把握度的确证性 有效性研究。

PROJECT SUMMARY/ABSTRACT Evidence-based guidelines for type 2 diabetes mellitus (T2DM) management aimed at glycemic control (reduced hemoglobin A1c) include a combination of diet, physical activity (PA), glucose monitoring, and medication adherences. However, the majority of individuals with T2DM are unable to follow these guidelines due to a lack of consistent health behavior counseling offered in the primary care setting. This problem is amplified in remote rural communities within the U.S. In response, this project aims to create an optimized telehealth-based intervention – Gamified Optimized Diabetes management with Artificial Intelligence–powered Rural Telehealth (GODART). GODART is grounded in the social cognitive theory and will serve as an automated behavior-monitoring and telecoaching platform. At the core, GODART is an automated conversational style behavior-monitoring system using natural language–understanding technologies. In this project, we propose to pilot and feasibility test the various components of GODART by leveraging multiphase optimization strategy (MOST). MOST is an efficient and rigorous resource-management and continuous- improvement framework for developing optimized interventions. Our proposal focuses on the MOST preparatory phase and will use a full factorial experimentation. We will pilot and assess the feasibility of and evaluate two different intervention components, with two levels in each of the groups, yielding four experimental conditions. These groups will test the effect of (i) a fixed vs. adaptive (gamified) rewards program and (ii) automated vs. human-delivered weekly health coaching. We will end the project with exit interviews conducted with a subset of participants. Study findings will help us learn the feasibility of delivering such an intervention and its preliminary effectiveness in reducing HbA1c, leading to adequately powered confirmatory effectiveness studies.