Enhancing Diabetic Patient Outcomes Using Wearable Devices and Data Pipelines

Abstract

This research explores using wearable devices and data systems to improve outcomes for diabetic patients. It utilizes continuous glucose monitors, fitness trackers, and smartwatches to collect real-time data on blood sugar levels, physical activity, and sleep patterns. The data is absorbed, stored, and analyzed using a cloud-based infrastructure, enabling the development of predictive models for personalized care. Machine learning algorithms predict glucose trends and provide tailored recommendations. An initial field test evaluates the system’s impact on patient results, adherence, and satisfaction. The results show significant improvements in glucose control and overall well being. Future research will focus on scaling the system, adding more health metrics, and refining predictive models. This work highlights the potential of combining wearable technology with advanced data analysis to enhance diabetes management and patient quality of life.