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Advances in diabetes technology: clinical implications, limitations, and future perspectives
Maria Apostolopoulou1,2, Grigorios Effraimidis3,4
1Faculty of Medicine, School of Health Sciences, University of Thessaly, Larissa, Greece. maria-apostolopoulou@hotmail.com.
Diabetes technology like continuous glucose monitoring (CGM) and automated insulin delivery (AID) systems have revolutionized diabetes care. These advancements offer significant benefits but still face challenges, with future innovations focusing on personalization and AI integration.
Area of Science:
- Endocrinology and Metabolic Diseases
- Biomedical Engineering
- Digital Health
Background:
- Diabetes management has been transformed by technological advancements over the last two decades.
- Continuous glucose monitoring (CGM) and automated insulin delivery (AID) systems are central to modern diabetes care, especially for type 1 diabetes (T1D).
- CGM offers continuous insights into glucose trends and variability, often replacing traditional self-monitoring of blood glucose.
Purpose of the Study:
- To review the evolution of diabetes technologies, focusing on CGM and insulin delivery systems.
- To summarize evidence on the application of these technologies in various clinical contexts.
- To explore emerging developments and future directions in diabetes technology.
Main Methods:
- Narrative review of randomized controlled trials and real-world studies.
- Analysis of evidence on the evolution of CGM technology and insulin delivery strategies.
- Exploration of technological applications in specific clinical settings.
Main Results:
- CGM and AID systems provide significant metabolic and psychosocial benefits for individuals with diabetes.
- Limitations include delayed insulin pharmacokinetics, sensor lag, and user-dependent factors.
- Emerging technologies include fully closed-loop systems, dual-hormone systems, and AI for data analysis.
Conclusions:
- Diabetes technology has significantly improved glycemic control and quality of life.
- Addressing current limitations is crucial for further advancements.
- Future research should focus on personalization, technology integration, and predictive/proactive care using AI.
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