BIG IDEAs Lab Glycemic Variability and Wearable Device Data

This study aimed to determine the feasibility and effectiveness of wearable devices in detecting early physiological changes prior to the development of prediabetes [1-3]. The study generated digital biomarkers for remote, mHealth-based prediabetes and hyperglycemia risk to classify which individuals should undergo further clinical testing. The primary inclusion criteria were subjects aged 35-65 years, inclusive, including only post-menopausal females, with a point of care A1C measurement between 5.2-6.4%, inclusive. Blood was collected during the study for measurement of glucose, hemoglobin A1C, lipoproteins, and triglycerides. Participants wore a Dexcom 6 continuous glucose monitor (CGM) and an Empatica E4 wristband for 10 days while receiving a standardized breakfast meal every other day. At the end of the 10 days, the participant returned to the clinic for an oral glucose tolerance test (OGTT). Research data collected includes physiological measurements from wearable devices such as heart rate, accelerometry, and electrodermal conductance.

本研究旨在探讨可穿戴设备在糖尿病前期生理变化早期检测中的可行性与有效性（1-3）。研究通过生成远程、基于移动健康（mHealth）的糖尿病前期和血糖升高风险数字生物标志物，以分类识别哪些个体需要接受进一步的临床检测。主要纳入标准为年龄在35-65岁之间（含两端），仅限于绝经后女性，且床边A1C测量值在5.2%-6.4%之间（含两端）。研究期间采集血液样本，用于测量血糖、血红蛋白A1C、脂蛋白和甘油三酯。参与者佩戴Dexcom 6连续葡萄糖监测器（CGM）和Empatica E4腕带，持续10天，并每隔一天接受标准化的早餐餐食。10天后，参与者返回诊所进行口服葡萄糖耐量测试（OGTT）。收集的研究数据包括来自可穿戴设备如心率、加速度计和皮肤电导等生理测量值。