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.本研究旨在明确可穿戴设备在糖尿病前期发病前检测早期生理变化的可行性与有效性[1-3]。本研究生成了用于远程、基于移动健康（mHealth）的糖尿病前期与高血糖风险评估的数字生物标志物，以甄别需接受进一步临床检测的个体。本研究的主要纳入标准为：受试者年龄介于35至65岁（含两端），仅纳入绝经后女性，且床旁糖化血红蛋白（point of care A1C）检测结果处于5.2%至6.4%（含两端）区间。研究期间采集血液样本，用于检测葡萄糖、糖化血红蛋白、脂蛋白及甘油三酯水平。受试者需连续10天佩戴德康（Dexcom）6连续葡萄糖监测仪（CGM）与安帕瓦（Empatica）E4腕带，且每两日进食一份标准化早餐。10天研究周期结束后，受试者返回诊所接受口服葡萄糖耐量试验（OGTT）。本次采集的研究数据涵盖可穿戴设备获取的多项生理测量指标，包括心率、加速度计数据与皮肤电导率。

搜集汇总

背景与挑战

背景概述

该数据集旨在研究可穿戴设备在糖尿病前期早期检测中的应用，包含16名血糖正常但偏高参与者的多模态生理数据，采集自Dexcom G6连续葡萄糖监测仪和Empatica E4腕带，监测周期为8-10天。数据涵盖血糖浓度、心率、加速度计、皮肤电导等7类特征，并附带食物日志，已进行时间偏移处理以确保隐私，适用于血糖变异性与可穿戴设备测量的相关性分析。