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

Md Ismail, Hossain
Ahmad Fadhil, Yusof
Ab Razak, Che Hussin
Noorminshah, A lahad
Sadiq, Ali Safaa
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Abstract
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.
Citation
Hossain, M.I., Yusof, A.F. et al. (2021) Factors influencing adoption model of continuous glucose monitoring devices for internet of things healthcare, Internet of Things, 15, Article No. 100353. https://doi.org/10.1016/j.iot.2020.100353
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Journal article
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en
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This is an accepted manuscript of an article published by Elsevier in Internet of Things on 18/01/2021, available online at: https://doi.org/10.1016/j.iot.2020.100353 The accepted version of the publication may differ from the final published version
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2542-6605
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