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.

项目概要/摘要 糖尿病前期 (PD) 和 2 型糖尿病 (T2D) 影响着 1.22 亿美国人，尽管糖尿病是造成损失最高的疾病。 美国的慢性病每年造成 3270 亿美元的经济负担，但诊断严重不足， 84% 的帕金森病患者和 21% 的 T2D 患者不知道自己的帕金森病筛查指南。 和 T2D 包括阳性预测值较低的粗略自我报告，因此无法 这项研究的最终目标是开发和实施创新的、 通过利用个人获得的数字数据，实用且可扩展的 PD 和 T2D 检测策略 消费者智能设备（智能手机和智能手表）现在很流行。 在普通人群中，这里开发的技术将可以直接转化为即时应用 为了改善 PD 和 T2D 的检测，我们最近开发了基于可穿戴设备的部署。 使用研究级可穿戴腕带的模型来检测个性化血糖偏差，预测间质性 血糖值，并估计糖化血红蛋白 (A1c) 的水平，这些都是检测和评估的关键指标 PD 监测和 T2D 目前适用于 A1c 范围有限的人。 （糖尿病前期和正常升高）翻译这项工作并扩大当前筛查的范围和产量。 方法，我们提出以下两个具体目标：（1）验证并扩展可穿戴模型 区分 T2D、PD 和血糖正常；以及 (2) 确定我们如何利用智能手机； 智能手表可提高现有 PD 和 T2D 筛查方法的产量和覆盖范围。 将验证并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 A1c 值，并从研究级可穿戴设备转向 在第二个目标中，我们将扩大当前基于指南的范围。 使用美国糖尿病协会 (ADA) 的“60 秒风险测试”短信进行筛查 直接评估并告知患者患 PD 和 T2D 的风险，目的是增加 A1c 检测 我们将通过添加客观的智能手表来提高符合风险标准的患者的真阳性率。 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） 这项拟议研究的创新可能会改变现有的 ADA 60 秒风险测试模型。 通过新方法对 8100 万患有未确诊 PD 和 T2D 的美国人进行 PD 和 T2D 检测 实时、连续的移动筛查的成功完成最终可能会带来革命性的变化。 通过改善早期检测和主动干预来预防或逆转糖尿病的管理 T2D 进展。