IoTMindCare: An Integrative Reference Architecture for Safe and Personalized IoT-Based Depression Management

Depression affects millions of people worldwide. Traditional management relies heavily on periodic clinical assessments and self-reports, which lack real-time responsiveness and personalization. Despite numerous research prototypes exploring Internet of Things (IoT)-based mental health support, almost none have translated into practical, mainstream solutions. This gap stems from several interrelated challenges, including the absence of robust, flexible, and safe architectural frameworks; the diversity of IoT device ownership; the need for further research across many aspects of technology-based depression support; highly individualized user needs; and ongoing concerns regarding safety and personalization. We aim to develop a reference architecture for IoT-based safe and personalized depression management. We introduce <i>IoTMindCare</i>, integrating current best practices while maintaining the flexibility required to incorporate future research and technology innovations. A structured review of contemporary IoT-based solutions for depression management is used to establish their strengths, limitations, and gaps. Then, following the Attribute-Driven Design (ADD) method, we design <i>IoTMindCare</i>. The Architecture Trade-off Analysis Method (ATAM) is used to evaluate the proposed reference architecture. The proposed reference architecture features a modular, layered logical view design with cross-layer interactions, incorporating expert input to define system components, data flows, and user requirements. Personalization features, including continuous, context-aware feedback and safety-related mechanisms, were designed based on the needs of stakeholders, primarily users and caregivers, throughout the system architecture. ATAM evaluation shows that <i>IoTMindCare</i> supports safety and personalization significantly better than current designs. This work provides a flexible, safe, and extensible architectural foundation for IoT-based depression management systems, enabling the construction of optimal solutions that integrate the most effective current research and technology while remaining adaptable to future advancements. <i>IoTMindCare</i> provides a unifying, aggregation-style reference architecture that consolidates design principles and operational lessons from multiple prior IoT mental-health solutions, enabling these systems to be instantiated, compared, and extended rather than directly competing with any single implementation.