Adoption of Internet of Things in Health Care: Weighted and Meta-Analytical Review of Theoretical Frameworks and Predictors.

BACKGROUND: The integration of the Internet of Things (IoT) into health care is transforming the industry by enhancing disease care and management, as well as supporting self-health management. The COVID-19 pandemic has accelerated the adoption of IoT devices, particularly wearable medical devices, which enable real-time health monitoring and advanced remote health management. Globally, the increased adoption of IoT in health care has improved efficiency, enhanced patient care, and generated substantial economic value. OBJECTIVE: This review aims to conduct a comprehensive meta- and weight analysis of quantitative studies to identify the most influential predictors and theoretical frameworks explaining the adoption of IoT in health care. METHODS: We searched databases, including Web of Science and PubMed, for quantitative studies on IoT health care adoption, with the last search conducted in early July 2025. Inclusion criteria comprised peer-reviewed articles written in English that employed a quantitative approach to IoT health care technology adoption. Studies were excluded if they did not report the significance of relationships, involved technologies without IoT features or were outside the scope, or examined target variables irrelevant to the analysis. The weight analysis identified the pathways with the most significant effects. A meta-analysis using a random-effects model was conducted to estimate combined effect sizes and their statistical significance. The results from both methods were then integrated to visualize the most frequently used theoretical frameworks. Risk of bias and heterogeneity were assessed using a funnel plot, Egger regression test, the I2 statistic, and subgroup analysis, which indicated no strong evidence of publication bias but revealed a high level of heterogeneity. RESULTS: Analysis of 115 datasets from 109 papers identified the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology (UTAUT) as the primary frameworks for explaining IoT adoption in health care. Incorporating context-specific variables-such as health consciousness, innovativeness, and trust-into these traditional technology acceptance frameworks enhances the understanding of IoT adoption. Although high heterogeneity suggests a need to refine theoretical models to account for regional contexts, universal adoption drivers such as performance expectancy and effort expectancy remain consistent. CONCLUSIONS: Behavioral intention is the most frequently studied variable in IoT health care adoption, whereas attitude, performance expectancy, effort expectancy, and task-technology fit remain underexplored. While adoption theories from the information systems field, such as the TAM, are predominantly used, integrating context-specific constructs and theories-such as trust and innovativeness-can provide deeper insights into IoT adoption in health care. The strongest and most consistent predictors of behavioral intention were attitude, performance expectancy, habit, self-efficacy, functional congruence, and benefits. Additionally, social influence, facilitating conditions, trust, and aesthetic appeal demonstrated promising or strong effects. By contrast, variables such as privacy and security, barriers, vulnerability, severity, compatibility, financial cost, health, and technology anxiety were generally inconsistent or not statistically significant.