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

通过人工智能驱动的农村远程医疗 (GODART) 进行游戏化优化糖尿病管理

• 批准号: 10276845
• 负责人: Tapan S Mehta
• 金额: $ 29.7万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-06 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10276845
• 关键词: Address; American; Americas; Area; Artificial Intelligence; Behavior monitoring; Censuses; Centers for Disease Control and Prevention (U.S.); Cessation of life; Clinical Trials; Cognitive; Congresses; Counseling; County; Data; Deep South; Diabetes Mellitus; Diet; Educational Materials; Effectiveness; Future; Glycosylated hemoglobin A; Guidelines; Health; Health behavior; Human; Individual; Intervention; Interview; Knowledge; Learning; Life Style Modification; Motivation; National Institute of Diabetes and Digestive and Kidney Diseases; Natural Language Processing; Non-Insulin-Dependent Diabetes Mellitus; Outcome; Participant; Phase; Physical activity; Pilot Projects; Poverty; Prevalence; Recommendation; Resources; Rewards; Rural; Rural Community; Rural Population; Services; Structure; System; Technology; Telephone; Testing; Update; Urban Community; arm; base; cost; design; diabetes management; diabetes prevention program; effectiveness study; evidence based guidelines; experience; feasibility testing; glucose monitor; glycemic control; health disparity; health literacy; improved; innovation; marginalized population; mathematical ability; medication compliance; multiphase optimization strategy; patient oriented; primary care setting; primary outcome; programs; recruit; response; rural counties; social; social cognitive theory; telehealth; telephone coaching; usability

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.

项目摘要/摘要 旨在血糖控制的2型糖尿病（T2DM）管理的循证指南 （减少血红蛋白A1C）包括饮食，体育活动（PA），葡萄糖监测和 药物依从性。但是，大多数T2DM的人无法遵循这些准则 由于缺乏在初级保健环境中提供的健康行为咨询。这个问题是 该项目旨在在美国境内的偏远农村社区放大，旨在创建一个优化的 基于远程医疗的干预 - 具有人工智能的游戏优化的糖尿病管理 农村远程医疗（Godart）。 Godart以社会认知理论为基础，将作为一个 自动行为监控和远程手机平台。核心，戈达尔是一个自动化的 使用自然语言 - 理解技术的对话风格行为监测系统。在这个 项目，我们建议通过利用多相试验和可行性测试Godart的各个组成部分 优化策略（大多数）。大多数是高效且严格的资源管理和连续的 - 改进框架，用于开发优化的干预措施。我们的建议最重视 准备阶段，将使用完整的阶乘实验。我们将试行并评估和 评估两个不同的干预组件，每个组中有两个级别，产生了四个 实验条件。这些小组将测试（i）固定与自适应（加游戏）奖励计划的效果 （ii）自动化与人类服务的每周健康教练。我们将通过退出访谈结束项目 由参与者的子集进行。研究结果将帮助我们学习提供这样的可行性 干预及其在减少HBA1C方面的初步效力，导致了充分的确认 有效性研究。