Barriers for User Acceptance of Mobile Health Applications for Diabetic Patients: Applying the UTAUT Model

Abstract

The literature illustrates that technology will widen health disparity if its use is restricted to patients who are already motivated and demonstrate good self-management behaviours. Additionally, despite the availability of free mobile health (m-health) applications for diabetes self-management, usage is low. There are also limited studies of m-health acceptance in South Africa. This research is delineated to the Western Cape, South Africa. The populace suffers from increasing numbers of diabetic patients. Segments of the population also suffer from technological forms of exclusion, such as limited internet access. Therefore, the objective of this study was to identify challenges for user acceptance that discourages the use of m-health applications. This study analysed 130 semi-structured interviews, using thematic content analysis. Respondents were predominantly female with type 2 diabetes, older than 50, residing in the Western Cape. It used key constructs from the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The results confirmed that all four UTAUT constructs; performance expectancy ("the degree to which an individual believes that using the system will help him or her to attain gains in performance"), effort expectancy ("the degree of ease associated with the use of the system", social influence ("the degree to which an individual perceives that important others believe he or she should use the new system") and facilitating conditions ("the degree to which an individual believes that an organisational and technical infrastructure exists to support the use of the system"), explains the challenges for m-health acceptance in low socio-economic areas. Factors such as technology anxiety, resistance to change and a lack of trust in the use of devices for self-management need to be considered when implementing future interventions.

Factors That Influence Use of a Patient Portal by Health Professionals

“…However, researchers have adapted the model outside this context. This has included applying the UTAUT to characterise factors in the literature that enable adoption of EMRs [ 21 ], to deductively analyse qualitative interview data [ 22 , 23 ] and to design semi-structured interview guides based on UTAUT constructs. Refer to Figure 1 to see a process diagram showing how the study data was collected and analysed…”

Acceptance and Influencing Factors of Social Virtual Reality in the Urban Elderly

“…The UTAUT Model adopted in this study is the acceptance and evaluation tool widely used in the application of science and technology to the users, and the calculation method and evaluation data adopted in this study are in accordance with the standard framework of UTAUT Model [45,46], and there is no difference. The UTAUT Model used in this study is only adjusted according to the elements of the structure, and uses the experimental procedures and methods set by the previous researchers [47,48]. The sample population of the experts and the elderly are required to master the reliability and validity to improve the performance of the study.…”

Predicting Big Data Adoption in Companies With an Explanatory and Predictive Model

“…Previous studies have shown that some of the UTAUT variables are losing significance, while others endow the model with greater explanatory power. Among these variables, perceived risk (Al-Saedi et al, 2020 ; Arfi et al, 2021 ) and resistance to use (Dwivedi et al, 2020 ; Petersen et al, 2020 ) are particularly worthy of note. For this reason, to extend the UTAUT and achieve a greater explanatory capacity to the Big Data adoption, we added the variables Resistance to Use (Polites and Karahanna, 2012 ; Lapointe and Rivard, 2017 ) and Perceived Risk (Featherman and Pavlou, 2003 ; Jia et al, 2016 ).…”