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

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.