Mobile technologies to screen for prediabetes and type 2 diabetes in asymptomatic adults

项目总结/摘要 糖尿病前期（PD）和2型糖尿病（T2 D）影响着1.22亿美国人。虽然糖尿病是最昂贵的 在美国，慢性病每年的经济负担为3270亿美元，但诊断严重不足， 84%的PD患者和21%的T2 D患者不知道自己的病情。PD筛查指南 和T2 D包括具有低阳性预测值的粗略自我报告，因此无法 缓解这种严重的诊断差距。本研究的最终目标是开发和实施一种创新的， 通过利用使用个人数据获取的数字数据， 消费者智能设备（智能手机和智能手表）。智能手机和智能手表现在很流行 在一般人群中，这里开发的技术将直接翻译为即时 部署以改善PD和T2 D的检测。为了实现这一目标，我们最近开发了基于可穿戴设备的 模型使用研究级可穿戴腕带检测个性化的血糖偏差，预测间质性 葡萄糖值，并估计糖化血红蛋白（A1 c）的水平，这些都是检测和 监测PD和T2 D。可穿戴模型目前适用于A1 c范围有限的人 （糖尿病前期和正常升高）。翻译这项工作，扩大目前筛选的范围和产量 方法，我们提出了以下两个具体目标：（1）扩展和扩展可穿戴模型， 区分T2 D、PD和正常;（2）确定我们如何利用智能手机， 智能手表，以提高目前PD和T2 D筛查方法的产量和覆盖范围。在第一个目标中，我们 我将验证和扩展我们开发可穿戴模型的初步工作，使其在更广泛的范围内发挥作用 血糖变异性，间质葡萄糖和A1 c值，并从研究级可穿戴设备转向 消费级智能手表在第二个目标中，我们将扩大目前以准则为基础的 使用美国糖尿病协会（ADA）“60秒风险测试”的短信传递进行筛查， 直接评估并告知患者他们患PD和T2 D的风险，目的是增加A1 c检测， 符合风险标准的患者。我们将通过增加客观的智能手表来提高真阳性的产量 和/或智能手机措施（例如，体力活动、久坐习惯、血糖健康参数估计） ADA 60秒风险测试模型这项研究的创新可能会改变 通过新方法检测8100万未确诊PD和T2 D的美国人的PD和T2 D， 实时、连续的移动的筛查。这个项目的成功完成将彻底改变 糖尿病管理，通过改善早期发现和积极干预，以预防或逆转 T2 D进展。

PROJECT SUMMARY/ABSTRACT Prediabetes (PD) and type 2 diabetes (T2D) affect 122 million Americans. Although diabetes is the most costly chronic condition in the US—with an annual economic burden of $327 billion—it is severely underdiagnosed, with 84% of those with PD and 21% of those with T2D unaware of their condition. Screening guidelines for PD and T2D include coarse self-reports with low positive predictive value, and therefore have been unable to mitigate this severe diagnostic gap. The ultimate goal of this research is to develop and implement an innovative, practical, and scalable PD and T2D detection strategy by leveraging digital data obtained using personal consumer smart devices (smartphones and smartwatches). Smartphones and smartwatches are now prevalent in the general population, and the technology developed here will be directly translatable for immediate deployment to improve the detection of PD and T2D. Toward this goal, we recently developed wearable-based models using a research-grade wearable wristband to detect personalized glucose deviations, predict interstitial glucose values, and estimate the level of glycated hemoglobin (A1c), which are all key metrics for detecting and monitoring PD and T2D. The wearable-based models currently function on people with a limited A1c range (prediabetic and elevated normal). To translate this work and expand the reach and yield of current screening methods, we propose the following two Specific Aims: (1) Validate and extend the wearable models to distinguish between T2D, PD, and normoglycemia; and (2) Determine how we can leverage smartphones and smartwatches to improve the yield and reach of present screening methods for PD and T2D. In the first Aim, we will validate and extend our preliminary work developing the wearable models to function across a wider range of glycemic variability, interstitial glucose, and A1c values and to move from research-grade wearables to consumer-grade smartwatches. In the second Aim, we will expand the reach of current guideline-based screening using text message delivery of the American Diabetes Association (ADA) “60-second Risk Test” to directly assess and inform patients about their risk for PD and T2D with the goal of increasing A1c testing in patients that meet the risk criteria. We will increase the yield of true positives by adding objective smartwatch and/or smartphone measures (e.g., physical activity, sedentary habits, glycemic health parameter estimations) to the existing ADA 60-second Risk Test model. The innovations from this proposed research could transform PD and T2D detection for the 81 million Americans with undiagnosed PD and T2D through novel methods for real-time, continuous mobile screening. Successful completion of this project could ultimately revolutionize diabetes management by improving early detection and by enabling proactive intervention to prevent or reverse T2D progression.