Effectiveness of Digital Versus In-Person Diabetes Prevention Programs

1 项目概要 数百万患有糖尿病前期（未来 2 型糖尿病的高风险状态）的美国成年人没有接受治疗 适当的生活方式咨询可降低患 2 型糖尿病的风险。移动医疗（mHealth）技术 代表了解决这一影响深远的问题的潜在可扩展解决方案。该项目的目标是 将数字糖尿病预防计划 (dDPP) 与标准护理的实际效果进行比较 个人糖尿病预防计划（ipDPP）。这项研究将测试一种新颖的全自动数字健康 平台（Sweetch Health, Ltd.）利用人工智能技术提供及时、自适应的服务 糖尿病前期成人的生活方式改变指导。来自可行性或观察性研究的初步证据 表明 JITAI 通常通过智能手机应用程序提供，因为它们能够提供 持续的自我监控和反馈是有效的。但目前尚不清楚 dDPP 是否 提供 JITAI 的药物在改善糖尿病前期患者的健康结果方面与 ipDPP 一样有效， 能够从此类干预中受益的易感患者群体。 因此，该项目的总体目标是比较 Sweetch 数字化的有效性 糖尿病预防计划 (dDPP) 到现实世界中的个人糖尿病预防计划 (ipDPP) 促进糖尿病前期成人减肥、增加体力活动并降低糖化血红蛋白。的 拟议的研究解决了慢性病预防和健康行为科学中的证据差距 变化，并得到我们团队之前进行的试点试验的有希望的短期结果的支持。 基于我们之前的研究并利用我们多学科研究团队的集体专业知识，我们 将对 382 名 18-75 岁的超重/肥胖、糖尿病前期成年人进行一项随机对照试验，其中 6 岁和 12 岁 月随访：第 1 组 (N=191) 将收到全自动 Sweetch 数字健康套件（“dDPP”组） 第 2 组 (N=191) 将转交给当地 CDC 认可的 ipDPP。双臂都会有身体活动 在试验期间使用活动测定法以基线和 2 个月的间隔进行连续测量。我们假设 dDPP 在减肥、体力活动和降低体重方面比 ipDPP 更有效 6 个月时的血红蛋白 A1C，12 个月时具有持续影响。我们进一步假设总体 dDPP 的参与度和接受度会更高，并且 dDPP 在临床上的优越性 结果将通过该部门的更高参与度来调节。 该项目将通过阐明慢性病预防和行为科学研究 使用人工智能技术的全自动数字干预措施可以在多大程度上提供有效、 可扩展、可持续且具有成本效益的高风险人群健康促进行为改变干预措施 人口。这种全自动方法对糖尿病预防的可扩展性的影响是 深刻。

1 PROJECT SUMMARY Millions of U.S. adults living with prediabetes, a high risk state for future type 2 diabetes, do not receive appropriate lifestyle counseling to lower their risk of type 2 diabetes. Mobile health (mHealth) technologies represent a potential scalable solution to address this far-reaching problem. The objective of this project is to compare the real-world effectiveness of a digital diabetes prevention program (dDPP) to standard of care in- person diabetes prevention programs (ipDPPs). This study will test a novel, fully-automated digital health platform (Sweetch Health, Ltd.) that uses artificial intelligence technology to provide just-in-time and adaptive lifestyle change coaching for prediabetic adults. Preliminary evidence from feasibility or observational studies suggests that JITAIs, which are often delivered via smartphone apps by virtue of their ability to provide continuous self-monitoring and feedback, can be effective. However, it is currently not known whether dDPPs that deliver a JITAI are as effective as ipDPPs in improving health outcomes in patients with prediabetes, a susceptible patient population that is positioned to benefit from such an intervention. The overarching goal of this project, therefore, is to compare the effectiveness of the Sweetch digital diabetes prevention program (dDPP) to real-world in-person diabetes prevention programs (ipDPPs) for promoting weight loss, increasing physical activity, and reducing hemoglobin A1C in prediabetic adults. The proposed study addresses an evidence gap in the science of chronic disease prevention and health behavior change and is supported by promising short-term results from a previous pilot trial conducted by our team. Building on our previous study and leveraging the collective expertise of our multidisciplinary study team, we will conduct a randomized controlled trial of 382 overweight/obese, prediabetic adults ages 18-75 with 6 and 12 month follow-up visits: Arm 1 (N=191) will receive the fully automated Sweetch digital health kit (“dDPP” arm) and Arm 2 (N=191) will be referred to a local CDC-recognized ipDPP. Both arms will have physical activity measured serially during the trial using actimetry at baseline and 2 month intervals. We hypothesize that the dDPP will be more effective than the ipDPP for the outcomes of weight loss, physical activity, and lowering of hemoglobin A1C at 6 months, with sustained effects at 12 months. We further hypothesize that the overall engagement and acceptability will be greater in the dDPP, and that the superiority of the dDPP on clinical outcomes will be mediated by higher engagement in this arm. This project will advance chronic disease prevention and behavioral science research by elucidating the extent to which fully-automated digital interventions using artificial intelligence technology can deliver effective, scalable, sustainable, and cost-effective health-promoting behavioral change interventions in high-risk populations. The implications of this fully-automated approach for scalability in diabetes prevention are profound.