JMIR Med Inform. 2026 Sep 30;14:e89189. doi: 10.2196/89189.
ABSTRACT
BACKGROUND: Smart home technologies integrated with technology-enhanced health care (TEH) systems are transforming residential care by supporting independent living, continuous health monitoring, and remote clinical interventions. The Internet of Medical Things, wearable biosensors, and AI-driven analytics enable proactive health care delivery and personalized interventions, particularly for older adults and individuals with chronic conditions.
OBJECTIVE: This review synthesizes literature on TEH integration within smart homes, examining global deployment patterns, technological maturity, biomedical sensor integration, machine learning applications, and health outcomes. It also identifies implementation challenges and disparities to improve digital health care strategies.
METHODS: A scoping review was conducted across PubMed, Scopus, Web of Science, ScienceDirect, and IEEE Xplore for peer-reviewed studies published between January 2005 and February 2025. Following screening of 6276 records, 169 studies were included, covering experimental, qualitative, and system design methodologies. Data were extracted on geographic deployment, sensor types, TEH architectures, machine learning algorithms, clinical outcomes, and adoption barriers.
RESULTS: TEH adoption is concentrated in Europe, East and Southeast Asia, and higher-income countries, with potential emerging initiatives in West Asia in lower-income regions. Smart home maturity ranges from foundational systems with basic automation to connected ecosystems with centralized Internet of Things coordination, and intelligent systems with data-driven adaptive monitoring. The literature was synthesized across thematic domains, including sensor technologies, smart home infrastructure, predictive analytics, telehealth integration, and ethical and regulatory considerations, and interpreted through a 3-level maturity taxonomy of foundational, connected, and intelligent smart home systems. Integration of biomedical sensors can enable continuous monitoring of cardiovascular, respiratory, neurological, metabolic, and mobility parameters, while machine learning algorithms can support early disease detection, predictive health analytics, activity recognition, and personalized interventions. Evidence from current literature indicates remote monitoring improves early detection of health issues, chronic disease management, medication adherence, and psychological well-being. Several studies reported that remote monitoring systems improved early detection of health deterioration, chronic disease management, medication adherence, and patient well-being. Adoption barriers include interoperability challenges, data privacy, digital literacy gaps, social and economic disparities, and long-term sustainability concerns.
CONCLUSIONS: Unlike previous work, this review emphasizes practical adoption barriers, interoperability challenges, and equity considerations alongside sensor performance and system integration. The key significance in this scoping review is how it highlights technological trends and implementation patterns while mapping global deployments of TEH smart homes through encompassing wearable and environmental systems. It demonstrates realistic integration scenarios and how it enhances independent living, preventive care, and personalized health management while reducing hospitalizations and health care costs. It demonstrates how widespread implementation requires standardized evaluation frameworks, robust interoperability, adaptable design, equitable access, and clinically friendly integration. By addressing technical, social, and regulatory challenges, smart home systems can achieve scalable, sustainable, and effective digital health care delivery.
PMID:42814874 | DOI:10.2196/89189