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

用于筛查无症状成人糖尿病前期和 2 型糖尿病的移动技术

• 批准号: 10660714
• 负责人: JESSILYN P DUNN
• 金额: $ 63.48万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-05 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10660714
• 关键词: Address; Adult; Affect; Age; American; Arrhythmia; Autonomic Dysfunction; Awareness; Behavioral; Cardiovascular system; Cellular Phone; Characteristics; Chronic; Clinical; Communities; Continuous Glucose Monitor; Data; Detection; Devices; Diabetes Mellitus; Diagnosis; Diagnostic; Digital biomarker; Early Diagnosis; Economic Burden; Electronic Health Record; Food; General Population; Glucose; Glycosylated Hemoglobin; Goals; Guidelines; Habits; Health; Heart Rate; Intervention; Knowledge; Machine Learning; Measures; Methods; Modeling; Monitor; Non-Insulin-Dependent Diabetes Mellitus; Parameter Estimation; Patient Self-Report; Patients; Performance; Persons; Physical activity; Physiological; Pilot Projects; Population; Prediabetes syndrome; Predictive Value; Recommendation; Research; Respiration; Rest; Risk; Sampling; Schedule; Sleep; Technology; Testing; Text Messaging; Time; Training; Translating; Work; cost; diabetes management; digital; fitbit; handheld mobile device; health data; heart rate variability; improved; innovation; interstitial; mHealth; mobile computing; mortality; novel; prevent; response; screening; screening guidelines; sedentary; sensor; smart watch; success; wearable device

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.

项目摘要/摘要 糖尿病前（PD）和2型糖尿病（T2D）影响1.22亿美国人。虽然糖尿病是最昂贵的 美国的慢性病 - 每年的经济伯恩（3270亿美元），严重诊断了 有84％的PD患者和21％的T2D患者不知道其状况。 PD筛选指南 T2D包括阳性预测值低的粗糙自我报告，因此无法 减轻这种严重的诊断差距。这项研究的最终目标是开发和实施创新的， 通过利用使用个人获得的数字数据，实用，可扩展的PD和T2D检测策略 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普遍 在一般人群中，此处开发的技术将直接翻译以立即进行 部署以改善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来检测个性化葡萄糖出发的模型，预测间隙 葡萄糖值，并估计糖化血红蛋白（A1C）的水平，它们都是检测和检测的关键指标 监视PD和T2D。基于可穿戴的模型目前在有限的A1C范围的人身上发挥作用 （糖尿病前期和升高正常）。翻译这项工作并扩大当前筛查的覆盖范围和产量 方法，我们提出以下两个具体目的：（1）验证并将可穿戴模型扩展到 T2D，PD和正常血糖之间的区别； （2）确定我们如何利用智能手机和 智能手表以提高PD和T2D的当前筛选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模型以在更广泛范围内运行的初步工作 血糖变异性，间质葡萄糖和A1C值的质量变异性，并从研究级可穿戴设备转变为 消费级智能手表。在第二个目标中，我们将扩大基于指南的目前的影响力 使用美国糖尿病协会（ADA）“ 60秒风险测试”的短信交付筛选 直接评估并告知患者PD和T2D的风险，目的是增加A1C测试 符合风险标准的患者。我们将通过添加目标智能手表来提高真正积极的收益 和/或智能手机测量（例如体育锻炼，久坐习惯，血糖健康参数估计） 到现有的ADA 60秒风险测试模型。这项拟议的研究的创新可能会改变 通过新颖的方法，用于8100万美国人的PD和T2D检测 实时，连续的移动筛选。成功完成该项目可能最终革新 糖尿病管理通过改善早期检测并实现主动干预以防止或反向 T2D进展。