Adolescents with Type 1 Diabetes Mellitus (T1DM) are at elevated risk of developing hypertension and related complications, requiring ongoing biometric surveillance and tailored health education. This study investigates the application of AI-powered biosensor wearables as a diagnostic and educational tool to enhance real-time physiological monitoring and promote lifestyle adherence in adolescent patients.

In a 30-day observational study conducted in Agra City, 260 adolescents diagnosed with both T1DM and hypertension were enrolled. Participants wore biosensor-based wearable devices embedded with photoplethysmography (PPG), electrodermal activity (EDA) sensors, accelerometers, and skin temperature sensors. The devices continuously tracked critical physiological and behavioral parameters including heart rate variability (HRV), systolic/diastolic blood pressure, sleep architecture (REM, light, deep stages), step count, caloric expenditure, skin temperature, and BMI variations. AI algorithms were employed to interpret the multivariate biometric data, generate personalized insights, and deliver educational prompts via a connected mobile interface.

Data analytics identified early markers of autonomic dysregulation and poor glycemic control, such as decreased HRV, elevated blood pressure, irregular sleep patterns, and suboptimal physical activity levels. Individuals with consistent step counts above baseline showed statistically significant improvements in HRV and blood pressure (p < 0.05), correlating with better glycemic trends and sleep regularity. The AI-generated feedback enhanced adherence to personalized diabetes education plans, emphasizing dietary regulation, physical activity scheduling, and stress management. Notably, the devices also facilitated remote monitoring of mental health parameters including anxiety levels and fatigue indices, allowing for timely educational and behavioral interventions.

From a laboratory medicine perspective, AI-integrated biosensor wearables represent a significant advancement in adolescent diabetes care. The combination of high-resolution physiological data collection with AI-based interpretation enables early detection of cardiometabolic risk patterns and real-time behavior modification. These systems not only enhance disease monitoring but also foster personalized health education and proactive disease management. Further longitudinal studies are recommended to validate the clinical impact of such tools on long-term outcomes in T1DM and comorbid hypertension in youth.