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

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.

[1]  H. Lucas,et al.  eHealth and mHealth initiatives in Bangladesh: A scoping study , 2014, BMC Health Services Research.

[2]  Majid Dadgar,et al.  The Role of Information and Communication Technology in Self-Management of Chronic Diseases: An Empirical Investigation through Value Sensitive Design , 2018, J. Assoc. Inf. Syst..

[3]  T. Gary-Webb,et al.  Innovative strategies to improve diabetes outcomes in disadvantaged populations , 2016, Diabetic medicine : a journal of the British Diabetic Association.

[4]  J. Raubenheimer,et al.  Development of a health dialogue model for patients with diabetes: A complex intervention in a low-/middle income country , 2018 .

[5]  Mu Li,et al.  Can Mobile Phone Apps Influence People’s Health Behavior Change? An Evidence Review , 2016, Journal of medical Internet research.

[6]  Jennifer Y. Liu,et al.  Low Socioeconomic Status is Associated with Increased Risk for Hypoglycemia in Diabetes Patients: The Diabetes Study of Northern California (DISTANCE) , 2014, Journal of health care for the poor and underserved.

[7]  A. Sheikh,et al.  Barriers and facilitators to health information exchange in low- and middle-income country settings: a systematic review. , 2016, Health policy and planning.

[8]  Clara B. Aranda-Jan,et al.  Systematic review on what works, what does not work and why of implementation of mobile health (mHealth) projects in Africa , 2014, BMC Public Health.

[9]  D. Ross-Degnan,et al.  Uptake and use of a diabetes management program with a mobile glucometer. , 2019, Primary care diabetes.

[10]  Zhiguang Zhou,et al.  Factors Influencing Patients’ Intentions to Use Diabetes Management Apps Based on an Extended Unified Theory of Acceptance and Use of Technology Model: Web-Based Survey , 2019, Journal of medical Internet research.

[11]  Golam Sorwar,et al.  Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model , 2017, Int. J. Medical Informatics.

[12]  Dorothea Kleine,et al.  POLICY ARENA ICT4WHAT?— USING THE CHOICE FRAMEWORK TO OPERATIONALISE THE CAPABILITY APPROACH TO DEVELOPMENT , 2010 .

[13]  P. Zanaboni,et al.  Information and communications technologies in low and middle-income countries: Survey results on economic development and health , 2016 .

[14]  E. Rogers,et al.  Diffusion of innovations , 1964, Encyclopedia of Sport Management.

[15]  M. Marshall Sampling for qualitative research. , 1996, Family practice.

[16]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[17]  A. Bergland,et al.  The Effectiveness of Smartphone Apps for Lifestyle Improvement in Noncommunicable Diseases: Systematic Review and Meta-Analyses , 2018, Journal of medical Internet research.

[18]  A. Müller Behavioural mHealth in Developing Countries: What About Culture? , 2016 .

[19]  Josip Car,et al.  Clinical relevance of smartphone apps for diabetes management: A global overview , 2018, Diabetes/metabolism research and reviews.

[20]  Liisa von Hellens,et al.  Qualitative Research in Information Systems , 2007, Australas. J. Inf. Syst..

[21]  L. Giddings Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 2d ed , 2005 .

[22]  Lisa P. Newmark,et al.  Many Mobile Health Apps Target High-Need, High-Cost Populations, But Gaps Remain. , 2016, Health affairs.

[23]  Jina Huh,et al.  Answers to Health Questions: Internet Search Results Versus Online Health Community Responses , 2016, Journal of medical Internet research.

[24]  Kevin B. Johnson,et al.  Disparities in the use of a mHealth medication adherence promotion intervention for low-income adults with type 2 diabetes , 2016, J. Am. Medical Informatics Assoc..

[25]  John W. Creswell,et al.  Research Design: Qualitative, Quantitative, and Mixed Methods Approaches , 2010 .

[26]  Anthony Deacon,et al.  Mobile applications for diabetes mellitus self-management: A systematic narrative analysis , 2017 .

[27]  Yvonne O'Connor,et al.  A ground-up approach to mHealth in Nigeria: A study of primary healthcare workers’ attitude to mHealth adoption , 2017 .

[28]  Jesse Coleman,et al.  The clinic-level perspective on mHealth implementation: a South African case study , 2018, Inf. Technol. Dev..

[29]  Wilhelm Kirch,et al.  Acceptance Factors of Mobile Apps for Diabetes by Patients Aged 50 or Older: A Qualitative Study , 2015, Medicine 2.0.

[30]  J. Coetzer Application of HCI Design Principles in overcoming Information Illiteracy: Case of a M-Health Application for a Rural Community in South Africa , 2018, 2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC).

[31]  J. Hewitt,et al.  Mobile phone applications and self‐management of diabetes: A systematic review with meta‐analysis, meta‐regression of 21 randomized trials and GRADE , 2018, Diabetes, obesity & metabolism.

[32]  S. Pather,et al.  Challenges for the adoption of ICT for diabetes self‐management in South Africa , 2019, Electron. J. Inf. Syst. Dev. Ctries..

[33]  Emma M. Macdonald,et al.  Enablers and barriers to using two-way information technology in the management of adults with diabetes: A descriptive systematic review , 2017, Journal of telemedicine and telecare.

[34]  A. Gillwald,et al.  The State of ICT in South Africa , 2018 .