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

项目摘要/摘要 糖尿病前期(PD)和2型糖尿病(T2D)影响着1.22亿美国人。尽管糖尿病是最昂贵的 在美国，慢性疾病--每年造成3270亿美元的经济负担--被严重低估， 84%的帕金森病患者和21%的T2D患者不知道自己的病情。帕金森病筛查指南 和T2D包括具有低积极预测值的粗略自我报告，因此无法 缓解这一严重的诊断差距。本研究的最终目标是开发和实施一种创新的、 实用、可扩展的PD和T2D检测策略，利用使用个人数据获得的数字数据 消费类智能设备(智能手机和智能手表)。智能手机和智能手表现在很流行 在普通人群中，这里开发的技术将可以直接翻译为即时 部署以改进PD和T2D的检测。为了实现这一目标，我们最近开发了基于可穿戴的 使用研究级可穿戴腕带检测个性化血糖偏差的模型，预测间质 血糖值，并估计糖化血红蛋白(A1c)水平，这些都是检测和 监测PD和T2D。基于可穿戴设备的型号目前适用于A1c范围有限的人 (糖尿病前期和正常升高)。翻译这项工作，扩大目前筛查的范围和产量 方法，我们提出了以下两个具体目标：(1)验证和扩展可穿戴模型以 区分T2D、PD和正常血糖；以及(2)确定我们如何利用智能手机和 智能手表，以提高目前PD和T2D筛查方法的产量和覆盖范围。在第一个目标中，我们 将验证和扩展我们开发可穿戴型号的前期工作，以便在更广泛的范围内发挥作用 血糖变异性、间质血糖和A1c值的变化，并从研究级可穿戴设备转移到 消费级智能手表。在第二个目标中，我们将扩大目前基于指导方针的范围 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 直接评估和告知患者患PD和T2D的风险，目标是增加A1c检测 符合风险标准的患者。我们将通过添加客观智能手表来提高真正积极的结果的产量 和/或智能手机测量(例如，体力活动、久坐习惯、血糖健康参数估计) 现有的ADA 60秒风险测试模型。这项拟议研究的创新可能会改变 通过新方法检测8100万美国未确诊PD和T2D患者的PD和T2D 实时、连续的移动筛查。这个项目的成功完成可能最终会带来革命性的变化 通过改进早期发现和启用主动干预来预防或逆转糖尿病的管理 T2D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.