Effectiveness of Digital Versus In-Person Diabetes Prevention Programs

1个项目摘要 数百万患有糖尿病前期的美国成年人没有收到 适当的生活方式咨询，以降低他们患2型糖尿病的风险。移动医疗(MHealth)技术 代表了一个潜在的可扩展解决方案，以解决这一影响深远的问题。这个项目的目标是 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、全自动化的数字健康 Platform(Sweetch Health，Ltd.)它使用人工智能技术提供及时和自适应的 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 建议JITAI，通常通过智能手机应用程序交付，因为它们有能力提供 持续的自我监控和反馈，才能见效。然而，目前尚不清楚dDPP是否 在改善糖尿病前期患者的健康结局方面，JITAI与ipDPP一样有效， 能够从这种干预中受益的易受影响的患者群体。 因此，该项目的首要目标是比较Sweetch Digital的有效性 糖尿病预防计划(DDPP)到现实世界中的糖尿病预防计划(IpDPP) 促进减肥，增加体力活动，降低糖尿病前期成年人的血红蛋白A1C。这个 拟议的研究解决了慢性病预防和健康行为科学中的一个证据空白 变化，并得到了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。 在我们先前研究的基础上，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随机对照试验，分别为6岁和12岁 一个月的随访：ARM 1(N=191)将接受全自动Sweetch数字健康试剂盒(“DDPP”ARM) ARM 2(N=191)将被称为当地疾控中心认可的ipDPP。两只胳膊都会有体力活动 在试验期间，在基线和2个月的间隔内使用活动测量进行连续测量。我们假设 在减肥、体力活动和降低体重方面，DDPP将比ipDPP更有效 血红蛋白A1C在6个月时，并在12个月时持续影响。我们进一步假设，总体上 DDPP的参与度和接受度将会更高，DDPP在临床上的优越性将会更大 结果将通过在这一领域更高的参与度来调节。 该项目将通过阐明慢性疾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可以在多大程度上提供有效、 高危人群中可扩展、可持续且经济高效的健康促进行为改变干预措施 人口。在糖尿病预防中，这种全自动化方法对可扩展性的影响是 很深刻。

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.