Conference Abstract | Volume 9 (ConfPro8): Abstract 114 | Published: 14 Oct 2026
1Department of Health Policy, Management and Economics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
&Corresponding author: Stephen Bandoma, Department of Health Policy, Management and Economics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana, Email: stephenbandoma7@gamil.com, ORCID: https://orcid.org/0000-0003-1791-2674
Received: 11 Mar 2026, Accepted: 30 Aug 2026, Published: 14 Oct 2026
Domain: Infectious Disease Epidemiology
Keywords: Mobile health, non-communicable diseases, healthcare professionals, technology adoption
©Stephen Bandoma et al. Journal of Interventional Epidemiology and Public Health (ISSN: 2664-2824). This is an Open Access article distributed under the terms of the Creative Commons Attribution International 4.0 License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article: Stephen Bandoma et al., Determinants of healthcare professionals’ adoption of mobile health interventions for non-communicable disease management in the Kumasi metropolis, Ghana. Journal of Interventional Epidemiology and Public Health. 2026;9(ConfPro8):114. https://doi.org/10.37432/JIEPH-CONFPRO8-00114
Mobile health (mHealth) has the potential to strengthen the delivery and continuity of care for non-communicable diseases (NCDs). However, empirical evidence on factors associated with its adoption among healthcare professionals in Ghana remains limited. This study examined determinants of healthcare professionals’ adoption of mHealth interventions for NCD management in the Kumasi Metropolis, guided by an extended Unified Theory of Acceptance and Use of Technology framework.
A quantitative cross-sectional survey was conducted among 442 healthcare professionals selected through multistage sampling. Data were collected using a structured self-administered questionnaire and analysed in SmartPLS 4 using structural equation modelling to examine direct, indirect, and moderating relationships among the study constructs.
Performance expectancy (β=0.319, p=0.001), trust (β=0.180, p=0.001), facilitating conditions (β=0.140, p=0.001), technology anxiety (β=0.102, p=0.001), effort expectancy (β=0.089, p=0.001), technology self-efficacy (β=0.060, p=0.022), and social influence (β=0.052, p=0.020) were positively associated with attitudes towards mHealth adoption. Perceived risk and digital literacy were not significantly associated with attitude. Attitude was positively associated with mHealth adoption (β=0.563, p=0.001; 95% CI: 0.519–0.603) and mediated the associations between seven determinants and adoption. The hypothesised moderating effects of digital literacy and technology self-efficacy were not supported. The model explained 47.3% of the variance in attitude and 31.9% of the variance in mHealth adoption.
Healthcare professionals’ attitudes were strongly associated with mHealth adoption and mediated the associations between several technological, organisational, and individual factors and adoption. Strategies to enhance mHealth adoption for NCD management should focus on demonstrating clinical utility, strengthening trust, improving ease of use, and ensuring adequate organisational and technical support. These findings provide empirical evidence to inform the design and implementation of context-responsive mHealth interventions within Ghana’s health system.
Menu