A Review of Emerging Technologies for the Management of Diabetes Mellitus

Abstract

Objective High prevalence of diabetes mellitus (DM) along with the poor health outcomes and the escalated costs of treatment and care poses the need to focus on prevention, early detection and improved management of the disease. The aim of this paper is to present and discuss the latest accomplishments in sensors for glucose and lifestyle monitoring along with clinical decision support systems (CDSSs) facilitating self-disease management and supporting healthcare professionals in decision making. Methods A critical literature review analysis is conducted focusing on advances in: 1) sensors for physiological and lifestyle monitoring, 2) models and molecular biomarkers for predicting the onset and assessing the progress of DM, and 3) modeling and control methods for regulating glucose levels. Results Glucose and lifestyle sensing technologies are continuously evolving with current research focusing on the development of noninvasive sensors for accurate glucose monitoring. A wide range of modeling, classification, clustering, and control approaches have been deployed for the development of the CDSS for diabetes management. Sophisticated multiscale, multilevel modeling frameworks taking into account information from behavioral down to molecular level are necessary to reveal correlations and patterns indicating the onset and evolution of DM. Conclusion Integration of data originating from sensor-based systems and electronic health records combined with smart data analytics methods and powerful user centered approaches enable the shift toward preventive, predictive, personalized, and participatory diabetes care. Significance The potential of sensing and predictive modeling approaches toward improving diabetes management is highlighted and related challenges are identified.

Sampled-data Observer-based Glucose Control for the Artificial Pancreas

“…This heterogeneous collection of devices, techniques, technology and theory, is known under the umbrella term "Artificial Pancreas" (AP). The main approach (though not the only one) to the regulation of the levels of glucose in blood has been the administration of carefully titrated amounts of the hormone insulin [1][2][3], which is notably completely lacking in Type-1 Diabetes Mellitus (T1DM) patients. Insulin promotes the uptake of glucose by peripheral tissues (particularly muscle and adipose tissue) and inhibits the release of autonomously synthesized glucose (from glycogen or other precursors) in the liver and kidney.…”

Special issue on emerging technologies for the management of diabetes mellitus

“…The rapidly growing number of patients with chronic diseases like DM, along with the escalated costs of treatment and care, the predictable number of deaths and medical errors, poses the urgent need to improve chronic disease management by focusing on preventive, personalized and citizen-centered healthcare systems [16]. Optimal management of DM requires deep understanding of the risk factors associated with the disease, prevention with appropriate As more and more data are gathered, data processing and interpretation become more crucial in order to transform the acquired raw data into clinically meaningful knowledge with the ultimate goal to support diabetes decision making.…”

“…Optimal management of DM requires deep understanding of the risk factors associated with the disease, prevention with appropriate As more and more data are gathered, data processing and interpretation become more crucial in order to transform the acquired raw data into clinically meaningful knowledge with the ultimate goal to support diabetes decision making. The application of advanced modeling, control, classification and clustering methodologies on different combinations of datasets has led to the development of a range of clinical decision support systems (CDSS) [16]. Reliable computational models able to predict glucose profile in response to various stimuli and insulin infusion advisory algorithms able to produce personalized recommendations of insulin rates/injections are assuming to provide valuable tools within the context of achieving tight glycemic control in patients with DM [15].…”

“…Multi-parametric monitoring systems combined with intelligent interoperable communication platforms enable remote monitoring, context awareness, integrative risk assessment, as well as automated closed-loop insulin delivery [11,16]. Within the same context, artificial pancreas (AP) is one of the major challenges for the management of insulin-treated patients with DM.…”

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lifestyle interventions, early diagnosis and treatment of diabetes before the occurrence of complications, and tight glycemic control [14,16]. The ultimate goal is to focus on applying science and technology to enable the creation of new and cost-effective tools to help people with DM.

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