JMIR Publications

ABSTRACT

Background:

The integration of the Internet of Things (IoT) into healthcare is revolutionizing the industry by enhancing acute disease care, managing chronic diseases, and supporting self-health management. The COVID-19 pandemic has accelerated the adoption of IoT devices, particularly wearable medical devices (WMDs), which offer real-time health monitoring and advanced remote health management. Globally, the integration and increased adoption of IoT in healthcare has led to enhanced efficiency, improved patient care, and generated significant economic value.

Objective:

This review aims to conduct a comprehensive meta and weight-analysis synthesizing findings from primarily quantitative articles to identify the most influential predictors and theories explaining the adoption process of IoT in healthcare

Methods:

A keyword search across electronic databases led us to the analysis of 68 papers with 72 datasets. We conducted a weight analysis, to identify the relationships with the most significant results. We also have conducted a meta-analysis by calculating the average beta values and their significance. Finally, we combined the results from both methods to visualize the most used theories.

Results:

A significant portion of studies are conducted in China, South Korea, and the United States. The technology acceptance model (TAM) and unified theory of use and acceptance of technology (UTAUT) were the most extensively used theories. The results highlight the importance of fostering positive perceptions toward IoT healthcare by mitigating perceived risks, emphasizing ease of use, and performance benefits. Leveraging performance impacts, the fun, and enjoyment derived from these technologies, the positive perceptions of family and doctors, and resource and support availability is going to promote intention to use. Promoting IoT healthcare technologies to innovative individuals and those motivated by health is more effective.

Conclusions:

Behavioral intention is the most studied variable, while attitude, performance, effort expectancy, and task-technology fit are less explored, indicating a gap in understanding their predictors. Adoption theories from the information systems field are predominantly used, but integrating health-specific theories can provide deeper insights into individual health motivations and threat perceptions. Future research should focus on understudied variables with conflicting results, predictors with fewer studies, and incorporate qualitative methods to gain deeper insights into the adoption process.