O-25: AI-integrated biosensor wearables for Real-Time monitoring and personalized risk mitigation in adolescents with type 1 diabetes and early diabetic kidney disease
V Sharma, S SharmaAbstract
Introduction
Adolescents with type 1 diabetes mellitus (T1DM) are at increased risk of early diabetic kidney disease (DKD). Conventional clinic-based monitoring often fails to detect early physiological changes such as autonomic imbalance and glycemic variability that contribute to disease progression
Objectives
To evaluate the role of AI-integrated wearable biosensors in continuous monitoring and personalized risk mitigation in adolescents with early DKD
Methods
In this 30-day observational study, 260 adolescents with T1DM and early DKD were equipped with wearable biosensors measuring heart rate variability (HRV), electrodermal activity, physical activity, and sleep patterns. An AI-driven platform analyzed real-time data to detect subclinical stress markers and deliver personalized behavioral recommendations, including hydration, glycemic control, and stress reduction strategies
Results
The system effectively identified early physiological deviations such as reduced nocturnal HRV and sleep disturbances. Participants adhering to AI-generated recommendations showed significant improvement in autonomic balance and hemodynamic stability (p<0.05). Early detection of fatigue and sleep fragmentation enabled timely intervention. Adherence to renal-protective behaviors improved, accompanied by better mental well-being and reduced anxiety
Conclusions
AI-enabled wearable biosensors provide a proactive and personalized approach to managing early DKD in adolescents with T1DM. By enabling continuous monitoring and real-time interventions, this model enhances patient engagement, stabilizes physiological parameters, and represents a promising advancement in precision-based diabetes care