Flexible Wearable Smart Sensors for Continuous Glucose Monitoring: Original 24-Hour Data Acquisition and Comparative Analysis of Glucose Fluctuations Between Healthy Young Adults and Type 1 Diabetic Patients

¹ Nanjing University of Information Science and Technology, Nanjing, China
² Wenzhou Institute of the Chinese Academy of Sciences, Wenzhou, China

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

This paper focuses on flexible wearable smart sensors for continuous glucose monitoring (CGM). It sorts out the development history of glucose collection and elaborates on the principle, structure, and performance evaluation indicators of minimally invasive CGM. The core original contribution of this work lies in the independent collection and in-depth analysis of original 24-hour CGM data from two distinct cohorts: healthy young adults (22 years old) and patients with Type 1 diabetes. Using MATLAB for data integration and visualization, we revealed novel characteristics of glucose fluctuation. (1) For healthy young individuals, the blood glucose curve is smooth. Postprandial peaks (8.3 mmol/L) occur at 1 hour after meals, and blood glucose returns to normal levels within 2 hours. Notably, there is no significant morning peak (6:00–10:00), which may be related to dietary and work-rest habits. (2) For patients with Type 1 diabetes, an obvious dawn phenomenon was observed between 00:00 and 02:00 (blood glucose increased from 10.2 mmol/L to 16.0 mmol/L). Postprandial blood glucose peaks are extremely high (>18 mmol/L) and decline slowly, taking 4–6 hours. Additionally, late-night near-hypoglycemic episodes were recorded, with the minimum blood glucose reaching 4.8 mmol/L. Furthermore, we compared our data with existing studies on middle-aged healthy populations (45 years old) and supplemented the glucose reference range for young adults. Existing glucose reference ranges are mainly based on middle-aged populations (e.g., average blood glucose: 5.3±0.5 mmol/L). In contrast, the average blood glucose of healthy young adults in this study is slightly lower (4.9±0.4 mmol/L), with no obvious morning peak. This difference provides a targeted baseline for glucose monitoring in young people, which is clinically significant for early identification of abnormal glucose metabolism and personalized health management in this cohort. Meanwhile, our results verify the consistency of CGM data accuracy. Additionally, we explore the theoretical basis, technical pathways, and existing challenges of non-invasive glucose monitoring. Our original CGM data can serve as a reliable reference for calibrating non-invasive sensors and establishing accurate glucose concentration conversion models, bridging the gap between minimally invasive monitoring results and the development of non-invasive technologies.