Home-Based Telemonitoring for Diabetes: Data Analytics, Clinical Insights, and Patient Lifestyle Integration

Abstract

Home-based telemonitoring for diabetes leverages connected devices, mobile platforms, and digital analytics to improve glycemic control, patient engagement, and personalized care management. This study evaluates the integration of continuous glucose monitoring (CGM), wearable activity sensors, and lifestyle-tracking applications to develop actionable insights for patients and clinicians. A mixed-method approach was used, combining quantitative data analytics with qualitative patient and clinician feedback. The research includes a multi-model assessment using Random Forest, XGBoost, and LSTM algorithms to predict glycemic variability and hypoglycemic events. A six-month pilot involving 180 Type 2 diabetes patients assessed adherence, behavior modification, and clinical outcomes. Findings demonstrate that predictive analytics coupled with lifestyle integration significantly improves blood glucose management, medication adherence, and patient self-efficacy. Additionally, the study highlights the challenges of connectivity, device usability, and integration into existing healthcare workflows. The paper concludes with recommendations for scalable telemonitoring programs and policy considerations for chronic disease management.