Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment

Cappon, Giacomo; Acciaroli, Giada; Vettoretti, Martina; Facchinetti, Andrea; Sparacino, Giovanni

doi:10.3390/electronics6030065

Cited by 183 publications

(84 citation statements)

References 79 publications

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“…DSSs are software tools designed to help people with diabetes to improve blood glucose (BG) control in their daily routine. For instance, typical a DSS consist of alerts notifying the user of potential future adverse events, such as hypoglycaemia and hyperglycaemia [8]. They might also suggest the administration of meal insulin boluses or corrective insulin boluses to mitigate hyperglycaemia [9,10,11], recommend the intake of carbohydrates (CHO) to tackle hypoglycaemia (rescue CHO) [12,13,14], or provide suggestions to prevent exercise-induced hypoglycaemia [15].…”

Section: Introductionmentioning

confidence: 99%

Predicting Quality of Overnight Glycaemic Control in Type 1 Diabetes Using Binary Classifiers

Güemes

Cappon

Hernandez

et al. 2020

IEEE J. Biomed. Health Inform.

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In type 1 diabetes management, maintaining nocturnal blood glucose within target range can be challenging. Although semi-automatic systems to modulate insulin pump delivery, such as low-glucose insulin suspension and the artificial pancreas, are starting to become a reality, their elevated cost and performance below user expectations is hindering their adoption. Hence, a decision support system that helps people with type 1 diabetes, on multiple daily injections or insulin pump therapy, to avoid undesirable overnight blood glucose fluctuations (hyper-or hypoglycaemic) is an attractive alternative. In this paper, we introduce a novel data-driven approach to predict the quality of overnight glycaemic control in people with type 1 diabetes by analyzing commonly gathered data during the day-time period (continuous glucose monitoring data, meal intake and insulin boluses). The proposed approach is able to predict whether overnight blood glucose concentrations are going to remain within or outside the target range, and therefore allows the user to take the appropriate preventive action (snack or change in basal insulin). For this purpose, a number of popular established machine learning algorithms for binary classification were evaluated and compared on a publicly available clinical dataset (i.e. OhioT1DM). Although there is no clearly superior classification algorithm, this study indicates that, by using commonly gathered data in type 1 diabetes management, it is possible to predict the quality of overnight glycaemic control with reasonable accuracy (AUC-ROC=0.7).

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Section: Introductionmentioning

confidence: 99%

Predicting Quality of Overnight Glycaemic Control in Type 1 Diabetes Using Binary Classifiers

Güemes

Cappon

Hernandez

et al. 2020

IEEE J. Biomed. Health Inform.

Self Cite

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“…Diabetes mellitus [1] is one of the pandemics characterized by chronic hyperglycemia [2], which seriously threatens human physical and mental health and quality of life. Since no cure has been developed for this disease, maintaining euglycemia through diet, exercise and pharmacological management is a way to prevent diabetic complications [3][4][5]. Currently, continuous glucose monitoring systems (CGMS) are increasingly popular, since they can reflect fluctuation of blood glucose (BG), easily perceive asymptomatic hypoglycemia and relieve the pain of sticking fingers frequently for people with diabetes [2,6,7].…”

Section: Introductionmentioning

confidence: 99%

Design of a Sandwich Hierarchically Porous Membrane with Oxygen Supplement Function for Implantable Glucose Sensor

et al. 2020

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This study aims to develop an oxygen regeneration layer sandwiched between multiple porous polyurethanes (PU) to improve the performance of implantable glucose sensors. Sensors were prepared by coating electrodes with platinum nanoparticles, Nafion, glucose oxidase and sandwich hierarchically porous membrane with an oxygen supplement function (SHPM-OS). The SHPM-OS consisted of a hierarchically porous structure synthesized by polyethylene glycol and PU and a catalase (Cat) layer that was coated between hierarchical membranes and used to balance the sensitivity and linearity of glucose sensors, as well as reduce the influence of oxygen deficiency during monitoring. Compared with the sensitivity and linearity of traditional non-porous (NO-P) sensors (35.95 nA/mM, 0.9987, respectively) and single porous (SGL-P) sensors (45.3 nA /mM, 0.9610, respectively), the sensitivity and linearity of the SHPM-OS sensor was 98.45 nA/mM and 0.9989, respectively, which was more sensitive with higher linearity. The sensor showed a response speed of five seconds and a relative sensitivity of 90% in the first 10 days and remained 78% on day 20. This sensor coated with SHPM-OS achieved rapid responses to changes of glucose concentration while maintaining high linearity for long monitoring times. Thus, it may reduce the difficulty of back-end hardware module development and assist with effective glucose self-management for people with diabetes.

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“…Portability is an important factor when we are following up on elderly people. The noninvasive sensors attached to a miniature electronic mechanism allow for better follow up [14]. Sensors of the chronoamperometric type and ASIC (application-specific integrated circuit) designs make up a small battery-free device [15].…”

Section: Related Workmentioning

confidence: 99%

An IoT-Based Glucose Monitoring Algorithm to Prevent Diabetes Complications

et al. 2020

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Diabetes mellitus (DM) is a metabolic disorder characterized by blood glucose levels above normal limits. The impact of this disease on the population has increased in recent years. It is already a public health problem worldwide and one of the leading causes of death. Recently, several proposals have been developed for better and regular monitoring of glucose. However, theses proposals do not discard erroneous readings and they are not able to anticipate a critical condition. In this work, we propose an algorithm based on the double moving average supported by an IoT architecture to prevent possible complications in elderly patients. The algorithm uses historical readings to construct a series. Given a number of periods, it is possible to calculate averages of different subsets and trends for the next periods and, in this way, the prognosis is obtained. With the prognosis, it is possible to notify the doctor and relatives in advance about a possible critical condition in the patient. The aim of our work is to validate the architecture and prognosis algorithm used for elderly persons. Tests of the algorithm and the architecture were performed with different readings and it was shown that the system generated corresponding notifications before the glucose values were higher than those defined by the WHO (World Health Organization), thus avoiding unnecessary alarms.

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Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment

Cited by 183 publications

References 79 publications

Predicting Quality of Overnight Glycaemic Control in Type 1 Diabetes Using Binary Classifiers

Predicting Quality of Overnight Glycaemic Control in Type 1 Diabetes Using Binary Classifiers

Design of a Sandwich Hierarchically Porous Membrane with Oxygen Supplement Function for Implantable Glucose Sensor

An IoT-Based Glucose Monitoring Algorithm to Prevent Diabetes Complications

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