Factors influencing adoption of wearable continuous glucose monitoring systems devices in internet of things healthcare

Abstract

In smart healthcare system, Continuous Glucose Monitoring Systems (CGMs) devices are very developed system to measure blood glucose which is sensor-based. The number of users for this device is low in usage though this device provides some features for blood glucose monitoring. The adoption rate for this device is also lower than 20%, whereas other wearable smart devices are used more in developed countries and developing countries as well. The aspire of this assessment is to investigate the factors, that make user?s intention to use wearable CGMs device in the Internet of Things (IoT) based healthcare to monitor blood glucose measuring. This study has set off research in IoT healthcare, focusing on CGMs based on previous studies about some wearable devices. The key aim of the study is to deliver an adoption model to find the current factors as a guideline for the user in adopting wearable devices in smart healthcare. From this adoption model, developers can be helped by taking the proper suggestion to make sure users? intention to adopt this device. The identified factors for adoption model of CGMs devices are Interpersonal Influence, Self- Efficiency, Personal Innovativeness, Attitude Toward Wearable Device, Health Interest, Perceived Value, Trustworthiness and Intention to Use. For factor identification, the weight of each factor was measured to get the most cited factor and more weight ratio valued factor. Based on the identified factors an adoption model is developed for measuring users? intention to use the CGMs device. Here, content validity index and content validity ratio methods were also used to measure the content validity of each construct after expert validation. In order to evaluate the model, a questionnaire has been developed and distributed to the respondents, who have the knowledge and experience of using wearable CGMs device for their own blood glucose monitoring. Based on the collected data from 97 respondents, Smart PLS software is used to analyse the data. The results show that interpersonal influence, attitude toward a wearable device, trustworthiness and health interest have significant impact whereas personal innovativeness, self-efficiency and perceived value have no significant influence on measuring intention to use wearable CGMs device.