Factors influencing adoption model of continuous glucose monitoring devices for internet of things healthcare

Continuous Glucose Monitoring Systems (CGMs) device is the most developed technology, which has reshaped manual diabetes management with smart features having sensor, transmitter and monitor. However, the number of users for CGMs device is still very low compared to existing manual systems although this device provides a smart landmark in blood glucose monitoring for diabetes management. Consequently, the aspire of the assessment is to explore the factors that influence users' intention to adopt CGMs device on the Internet of Things (IoT) based healthcare. This paper provides an adoption model for CGMs device by integrating some factors from different theories in existing studies of wearable healthcare devices. The proposed adoption model also examines current factors as a guideline for the users to adopt the CGMs device. We have collected data from 97 actual CGMs device users. Partial least square and structural equation modelling were involved for measurement and structural model assessment of this study. The experiential study specifies that interpersonal influence and trustworthiness are the strong predictors of attitude toward a wearable device, which shows significant relationships to use for CGMs device's adoption. Personal innovativeness shows no significant relationship with attitude toward a wearable device. Besides, self-efficacy has no direct influence on a person's health interest where heath interest directly influences users' intention to use CGMs device. Moreover, perceived value is not found to be significant for measuring intention to use CGMs devices. The results from this research provide suggestions for the developers to ensure users' intention to adopt CGMs device. (C) 2020 Published by Elsevier B.V.

Automated summary

The study explores factors influencing users' adoption of Continuous Glucose Monitoring Systems (CGMs) device, providing an adoption model. It found that interpersonal influence and trustworthiness are strong predictors of attitude and intention to use, while personal innovativeness and self-efficacy do not have a direct impact. Additionally, perceived value is not significant in measuring intention.