Impact of Interfering Factors on a Glucose Sensor Model

Management of diabetes often involves monitoring blood glucose levels through Blood Glucose Monitoring (BGM) or Continuous Glucose Monitoring (CGM) systems. CGM systems, preferred for their noninvasive nature and ability to track glucose trends, face challenges in accuracy due to sensor drift. In fact, it is well known that all sensor systems are subject to different types of change that alter the measurement and increase the inaccuracy in detecting analytes. This paper proposes a model that integrates environmental conditions, physiological parameters, and sensor characteristics to model their impact on the accuracy of CGM systems. The study examines the impact of various interfering variables, including physiological changes, such as sweating or physical exertion; environmental conditions, such as high temperatures; and sensor-specific changes, such as adhesive properties and aging. Each factor is modeled and analyzed to understand its influence on the sensor's accuracy and reliability. This model seeks to enhance the repeatability and reliability of the system by offering a method for predicting interferences and their effect on measurements. Consequently, it enables the development of reliable countermeasures.