Determinants of patients' intention to adopt diabetes self-management applications

Health; Surveys; Consumer health informatics; Consumer healths; Diabetes self-management; Facilitating conditions; Privacy and security; Quantitative approach; Research instruments; Technology acceptance; Human computer interaction

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Bibliographic Details
Main Authors: Maniam A., Dhillon J.S., Baghaei N.
Other Authors: 57203390778
Format: Conference Paper
Published: Association for Computing Machinery 2023
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spelling my.uniten.dspace-222542023-05-29T13:59:52Z Determinants of patients' intention to adopt diabetes self-management applications Maniam A. Dhillon J.S. Baghaei N. 57203390778 7003949854 14020983900 Health; Surveys; Consumer health informatics; Consumer healths; Diabetes self-management; Facilitating conditions; Privacy and security; Quantitative approach; Research instruments; Technology acceptance; Human computer interaction Despite significant advances in medicine, diabetes mellitus remains a major health problem among diabetes patients (diabetics). Diabetes in Malaysia has become increasingly critical along with diabetes complications. Diabetes Self-Management Applications (DSMA) are impactful patient-centered tools that has immense potential in enabling diabetics to manage their health conditions and thereby prevent complications. This study identifies factors that influence the intention to adopt DSMA by diabetics in Malaysia. The aim is to develop a research model to represent the adoption of DSMA amongst diabetics in Malaysia. Previous work is reviewed to develop the proposed model which comprised of constructs from established models and othper constructs from the literature. To test the developed model, a quantitative approach was employed and established questionnaires were administered as research instrument for data collection. The findings indicate that Perceived Financial Risk, Perceived Privacy and Security Risk, Technology Anxiety and Facilitating Conditions have significantly positive relationship with the intention to adopt DSMA. Findings from this study serve as a guideline for DSMA developers in understanding the core factors that influence the adoption and use of diabetes health applications by diabetics. � 2015 ACM International Conference Proceeding Series. All rights reserved. Final 2023-05-29T05:59:52Z 2023-05-29T05:59:52Z 2015 Conference Paper 10.1145/2808047.2808059 2-s2.0-85010370887 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010370887&doi=10.1145%2f2808047.2808059&partnerID=40&md5=1667a516b8a3ce199bfbab93592872ec https://irepository.uniten.edu.my/handle/123456789/22254 43 50 Association for Computing Machinery Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Health; Surveys; Consumer health informatics; Consumer healths; Diabetes self-management; Facilitating conditions; Privacy and security; Quantitative approach; Research instruments; Technology acceptance; Human computer interaction
author2 57203390778
author_facet 57203390778
Maniam A.
Dhillon J.S.
Baghaei N.
format Conference Paper
author Maniam A.
Dhillon J.S.
Baghaei N.
spellingShingle Maniam A.
Dhillon J.S.
Baghaei N.
Determinants of patients' intention to adopt diabetes self-management applications
author_sort Maniam A.
title Determinants of patients' intention to adopt diabetes self-management applications
title_short Determinants of patients' intention to adopt diabetes self-management applications
title_full Determinants of patients' intention to adopt diabetes self-management applications
title_fullStr Determinants of patients' intention to adopt diabetes self-management applications
title_full_unstemmed Determinants of patients' intention to adopt diabetes self-management applications
title_sort determinants of patients' intention to adopt diabetes self-management applications
publisher Association for Computing Machinery
publishDate 2023
_version_ 1806428221757980672
score 13.214268