AIDCare: A Technical Proof-of-Concept for Integrating Personalized Nutrition, Physical Activity, and AI-Assisted Care in Diabetes Lifestyle Management

Background/Objectives: Medical nutrition therapy and structured lifestyle self-management are fundamental to diabetes care, yet conventional mobile health tools typically track diet, physical activity, and glucose measurements in isolation, with limited connection to clinical oversight. This study presents AIDCare, an integrated mobile health platform that connects camera-based nutritional assessment, wearable physical activity tracking, physiological monitoring, and clinician-supervised conversational artificial intelligence (AI) to support personalized dietary and lifestyle self-management in diabetes. Methods: AIDCare links a patient mobile application with wearable sensors, backend analytical services, and an interactive clinician portal. Dietary assessment uses smartphone camera imaging to identify foods and estimate meal contents, specifically carbohydrates, protein, and dietary fiber, against nutritional reference data. Physical activity and energy expenditure are captured through smartphone and smartwatch motion sensors. To support contextual lifestyle guidance, an open-access medical conversational language model was adapted using 24,612 structured digital health and diabetes educational articles. A clinician-in-the-loop mechanism allows healthcare providers to inspect AI-generated recommendations and adjust personalization parameters. System feasibility was evaluated through benchtop and system-level testing without patient recruitment, using synthetic patient profiles across 400 data-acquisition pipeline trials and an intent-classification benchmark spanning nutrition, activity, glucose monitoring, and physician consultation. Results: Across the 400 software-pipeline trials, the platform achieved data acquisition, transmission, and processing completion rates of 97.5%, 96.5%, and 95.5%, respectively, with backend response times under 1.8 s. In the recommendation-intent benchmark, the medical language model achieved 81.0% accuracy, 83.5% precision, 79.0% recall, and an F1-score of 81.2%. The platform successfully linked image-based meal nutrient estimates (carbohydrate, protein, and dietary fiber) with physical activity metrics and physiological tracking within a unified interface under clinician supervision. Conclusions: AIDCare establishes the technical feasibility of a unified digital platform that integrates dietary nutrient estimation, physical activity tracking, and clinician-guided AI decision support for diabetes self-management. By bridging daily nutritional intake with metabolic tracking and professional oversight, the architecture provides a functional framework for personalized lifestyle care. Prospective clinical trials in patient cohorts are required to establish nutritional estimation accuracy against weighed food records, long-term user adherence, and clinical efficacy on glycemic control.