A Minimized Measurement Scheme for Predicting HbA1c Using Discrete Self-Monitoring Blood Glucose Data Within 4 Weeks for People with Type 2 Diabetes.

To establish an accurate and robust calculation model for predicting hemoglobin A1c (HbA1c) for people with type 2 diabetes (T2D) by using the fewest discrete blood glucose values according to an irregular data set and propose an appropriate cost-effective and scientific scheme for routine blood glucose monitoring. By using two data sets obtained from 2017 to 2022, which involved 2432 people with T2D, ∼420,000 irregular blood glucose values, and 10,000 HbA1c values, multiple blood glucose monitoring schemes were designed and compared to find the optimal one. The data were structured and then fitted using a regularized extreme learning machine, and the results were evaluated on the basis of indicators such as mean absolute error (MAE), root mean square error, and the relevance analysis (R) value; the optimal scheme for routine blood glucose monitoring was determined by combining the accuracy and the cost and was compared with previous studies in terms of accuracy and stability.

Evaluating the Clinical Accuracy of a Non-invasive Single-Fasting-Calibration Glucometer in Patients with Diabetes: A Multicentre Study.

Introduction: The aim of this study was to evaluate the stability and accuracy of glucose measurements determined using the metabolic heat conformation (MHC)-based non-invasive glucometer in a multicentre, self-controlled clinical trial. This device is the first to obtain a medical device registration certificate awarded by the National Medical Products Administration of China (NMPA).

Methods: The multicentre clinical study was conducted at three sites and enrolled 200 subjects whose glucose was measured with a non-invasive glucometer (the Contour Plus blood glucose monitoring system) and by venous plasma glucose (VPG) measurements, in a fasted state and at 2 and 4 h after meals.

Noninvasive Continuous Glucose Monitoring Using a Multisensor-Based Glucometer and Time Series Analysis.

Daily continuous glucose monitoring is very helpful in the control of glucose levels for people with diabetes and impaired glucose tolerance. In this study, a multisensor-based, noninvasive continuous glucometer was developed, which can continuously estimate glucose levels via monitoring of physiological parameter changes such as impedance spectroscopy at low and high frequency, optical properties, temperature and humidity. Thirty-three experiments were conducted for six healthy volunteers and three volunteers with diabetes.