Effectiveness of Digital Versus In-Person Diabetes Prevention Programs

1项目概要 数以百万计的美国成年人患有糖尿病前期，这是未来2型糖尿病的高风险状态， 适当的生活方式咨询，以降低2型糖尿病的风险。移动的保健技术 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）的实际效果与标准护理进行比较- 个人糖尿病预防计划（ipDPP）。这项研究将测试一种新颖的、全自动的数字健康 平台（Sweetch Health，Ltd.）它使用人工智能技术来提供及时和自适应的 为糖尿病前期成年人提供生活方式改变指导。可行性或观察性研究的初步证据 JITAI通常通过智能手机应用程序提供， 持续的自我监控和反馈，才能有效。然而，目前尚不清楚dDPP是否 在改善糖尿病前期患者的健康状况方面， 易受影响的患者群体，其被定位为受益于这种干预。 因此，该项目的首要目标是比较Sweetch数字 糖尿病预防计划（dDPP）到现实世界的糖尿病预防计划（ipDPP）， 促进体重减轻，增加体力活动，降低糖尿病前期成人的血红蛋白A1 C。的 一项拟议的研究解决了慢性病预防和健康行为科学中的证据缺口 我们的团队进行了先前的试点试验，并得到了有希望的短期结果的支持。 基于我们以前的研究，并利用我们多学科研究团队的集体专业知识，我们 将对382名年龄在18-75岁的超重/肥胖、糖尿病前期成人进行随机对照试验， 月随访访视：第1组（N=191）将接受全自动Sweetch数字健康套件（“dDPP”组） 组2（N=191）将被转诊至当地CDC认可的ipDPP。两个手臂都会有体力活动 在试验期间，使用活动测定法在基线和2个月间隔连续测量。我们假设 dDPP比ipDPP更有效地减轻体重、进行体力活动和降低 6个月时的血红蛋白A1 C，12个月时的持续效应。我们进一步假设， dDPP的参与度和可接受性将更高，并且dDPP在临床上的优越性 结果将通过更多地参与这一部门的工作来调节。 该项目将通过阐明慢性疾病的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可以在多大程度上提供有效， 可扩展的，可持续的，具有成本效益的促进健康的行为改变干预措施， 人口数量。这种全自动方法对糖尿病预防可扩展性的影响是 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.