Artificial Intelligence for the Prevention and Management of Diabetes: Advances, Opportunities, and Challenges with an Emphasis on Physical Activity

Paper ID : 1556-SPORTCONGRESS

Authors

فاطمه رضائی *1, مهدیه ملانوری شمسی2, رضا قراخانلو2 1دانشجو 2عضو هیئت علمی

Abstract

Introduction: The global prevalence of diabetes-recognized as the second most common non-communicable chronic disease-continues to increase, posing a major health and economic challenge. High mortality rates, the cost of treating complications, and limited access to specialized care contribute to the growing burden. Inadequate self-management, poor medication adherence, and lifestyle changes-especially high-calorie diets and reduced physical activity linked to urbanization-are key drivers of this trend. Physical activity plays a critical role in both the prevention and management of diabetes and other chronic diseases, yet adherence to exercise programs remains low. Methods: This paper explores recent advances in artificial intelligence (AI) and their potential applications in improving diabetes prevention and management. A focused review of AI-based technological solutions related to physical activity monitoring was conducted, examining tools such as wearable sensors, mobile health applications, and machine learning-based analytic systems that support personalized exercise recommendations and patient engagement. Results: AI technologies demonstrate promising capabilities in enhancing diabetes care through continuous monitoring, data-driven insights, and individualized feedback. By integrating lifestyle information-including physical activity, dietary habits, and glucose data-AI systems can predict disease risk, optimize therapy, and support self-management. These approaches improve efficiency in treatment delivery and empower patients to play an active role in managing their health. Conclusion: Artificial intelligence offers significant opportunities to strengthen diabetes prevention and management through intelligent physical activity monitoring and personalized intervention strategies. Nevertheless, challenges related to data quality, privacy, and model transparency remain and must be addressed to ensure reliable and ethical implementation.

Keywords

Keywords: Artificial Intelligence, Diabetes, Physical Activity, Machine Learning, Digital Health

Status: Abstract Accepted (Poster Presentation)