Innovations in Wearable Glucose Sensors and Integrated Systems for Personalized Type 1 Diabetes Management: Clinical Evidence and Patient Acceptance
Year: 2025
Type: conference-paper
Abstract: Type 1 diabetes (T1D) management is increasingly enhanced by wearable glucose sensors (WGSs) integrated with artificial intelligence (AI) that combine multiple physiological parameters—such as heart rate, galvanic skin response, body temperature, and physical activity—to predict glucose fluctuations more accurately. Noninvasive sensor technologies, including optical and sweat-based methods, show promise in reducing patient discomfort but still require further clinical validation to confirm reliability. Recent clinical data demonstrate significant potential for these advanced WGS technologies, with substantial improvements in glycemic control and overall disease management reported among all surveyed patients. Insulin pumps integrated with continuous glucose monitoring form “artificial pancreas” systems that automatically adjust insulin delivery in real time, improving patient convenience and metabolic outcomes. Despite progress, challenges remain related to response latency, device interoperability, and adaptation to abrupt physiological changes. According to our results, patient acceptance of WGS-based treatments is high, with nearly all individuals willing to adopt these technologies. Initial reluctance is mostly observed during the first weeks, coinciding with the AI algorithm’s calibration and learning phase; however, adherence increases significantly once this period concludes. In conclusion, these integrated technologies represent a practical shift toward personalized, proactive T1D care. Their successful implementation depends on overcoming technical and ethical challenges while addressing psychological factors such as alert fatigue, particularly in vulnerable populations.
Authors: Anxo Carreira-Casais, Antia G. Pereira
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4417492030
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