Exploring the Biases in the Decision to Download mHealth Apps: A Discrete Choice Experimental Analysis of Nigerian Healthcare App Users
Article Sidebar
Main Article Content
Abstract
Globally, mobile health (mHealth) tools are becoming more widely available. This provides great benefits for both patients and healthcare providers. However, mHealth adoption is still comparatively low. Furthermore, few studies have examined people's intentions to download mHealth apps. The purpose of this study is to investigate people's propensity to use mobile health apps. The discrete choice experiment method was employed to investigate healthcare application users in Nigeria. Two main features (attributes) of the mHealth applications, subscription cost and data protection, were presented to the 1600 participants in the study. Each feature had two levels: ‘free subscription’ and ‘$20 subscription’ for cost, and ‘data protection’ and ‘no data protection’ for data privacy. The pricing for data protection is $20, equivalent to N39, 058.76 (at the rate of $1 = N1, 502.26), while the free subscription option does not include data protection. A total of 1,600 participants were randomly selected through a web-based survey employing a proportionate stratified sampling technique. Data were analyzed using a conditional logistic regression model in R (Clogit), yielding 43,200 observations (1600 participants’× 18 available choices× 3 alternatives). The results revealed that cost and data protection significantly influenced users’ willingness to download mHealth apps. Specifically, the odds ratio (OR) for paid apps ($20) was 0.498 (95% CI: 0.468–0.530), indicating a reduced likelihood of download compared to free apps, while the OR for data protection was 0.25 (95% CI: 0.232–0.270), suggesting that participants strongly preferred apps that ensured privacy of health data. The model yielded a log-likelihood of –23,165.48 and an R² of 0.113, confirming a good model fit. The findings imply that users are significantly more inclined to adopt mHealth apps that are free and guarantee data protection. Reducing cost barriers and strengthening privacy measures are, therefore, essential strategies for enhancing mHealth adoption in Nigeria and similar contexts.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
References
Silva, B. M. C., Rodrigues, J. J. P. C., de la Torre Díez, I., López-Coronado, M., & K. Saleem, K. (2015). Mobile-health: A review of the current state in 2015. Journal of Biomedical. Informatics, 56, 225. https://doi.org/10.1016/j.jbi.2015.06.003 .
Nweke, H. F., Ikegwu, A. C., Nwafor, C. A., & Ogbaga, I. N. (2024). Smartphone-based Human Activity Recognition using Artificial Intelligence Methods and Orientation Invariant Features, 2024 Nigeria Computer Society International Conference on Technological Solutions for Smart Economy (SmartEco 2024)
Smit, E. S., & Bol, N. (2020). From self-reliant to expert-dependents: Identifying classes based on health-related need for autonomy and need for external control among mobile users. Media Psychology, 23, 414. https://doi.org/10.1080/15213269.2019.1604235 .
Singh, B., Ahmed, M., Staiano, A. E., Gough, C., Petersen, J., Vandelanotte, C., ... & Maher, C. A. (2024). A systematic umbrella review and meta-meta-analysis of eHealth and mHealth interventions for improving lifestyle behaviours. npj Digital Medicine, 7(1), 179. https://doi.org/10.1038/s41746-024-01172-y .
Tomlinson, M., Rotheram-Borus, M. J., Swartz, L., & Tsai, A. C. (2013). Scaling up mHealth: where is the evidence? PLoS medicine, 10(2), e1001382. https://doi.org/10.1371/journal.pmed.1001382
Kumar, S., Nilsen, W. J., Abernethy, A., Atienza, A., Patrick, K., Pavel, M., ... & Swendeman, D. (2013). Mobile health technology evaluation: the mHealth evidence workshop. American journal of preventive medicine, 45(2), 228-236. https://doi.org/10.1016/j.amepre.2013.03.017.
Anastasiadou, D., Folkvord, F., & Lupiañez‐Villanueva, F. (2018). A systematic review of mHealth interventions for the support of eating disorders. European Eating Disorders Review, 26(5), 394-416. https://doi.org/10.1002/erv.2609.
Ng, J. Y., Ntoumanis, N., Thøgersen-Ntoumani, C., Deci, E. L., Ryan, R. M., Duda, J. L., & Williams, G. C. (2012). Self-determination theory applied to health contexts: A meta-analysis. Perspectives on psychological science, 7(4), 325-340. https://doi.org/10.1177/1745691612447309.
Nwogbaga, N. E., Latip, R., Affendey, L. S., Rahiman, A. R. A., Ituma, C., Nweke, H. F., ... & Ikporo, S. C. Canonical Polyadic decomposition-based energy efficient offloading algorithm for mobile edge computing. J. Xi’an Shiyou Univ. Nat. Sci. Ed., 20(2024) 441. [Online]. Available: https://www.xisdxjxsu.asia/viewarticle.php?aid=3131.
Folkvord, F., Bol, N., Stazi, G., Peschke, L., & Lupiáñez-Villanueva, F. (2023). Preferences in the willingness to download an mHealth app: discrete choice experimental study in Spain, Germany, and the Netherlands. JMIR Formative Research, 7(1), e48335. https://doi.org/10.2196/48335.
Ikwunne, T. (2024). A Mobile Health Design Process to Improve User Engagement. ACM SIGACCESS Accessibility and Computing, (138), 1-1. http://dx.doi.org/10.13140/RG.2.2.28483.78880.
He, D., Naveed, M., Gunter, C. A., & Nahrstedt, K. (2014). Security concerns in Android mHealth apps. In AMIA annual symposium proceedings,2014,(645). [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC4419898/.
Anikwe, C. V., Nweke, H. F., Ikegwu, A. C., Onu, U. F, & Alo, U. R. (2022). Exploring the Wearable and Mobile Sensors for Sustainable and Efficient Health Monitoring Systems in Post-COVID-19. Journal of Information Security, Privacy and Digital Forensics (A Publ. Niger. Comptuer Soc., 6(2022) 55. [Online]. Available: https://library.ncs.org.ng/download/exploring-the-wearable-and-mobile-sensors-for-sustainable-and-efficient-health-monitoring-systems-in-post-covid-19/.
Galetsi, P., Katsaliaki, K., & Kumar, S. (2023). Exploring benefits and ethical challenges in the rise of mHealth (mobile healthcare) technology for the common good: An analysis of mobile applications for health specialists. Technovation, 121, 102598. https://doi.org/10.1016/j.technovation.2022.102598.
Bol, N., Helberger, N., & Weert, J. C. (2018). Differences in mobile health app use: a source of new digital inequalities?. The Information Society, 34(3), 183-193. https://doi.org/10.1080/01972243.2018.1438550.
Sax, M., Helberger, N., & Bol, N. (2018). Health as a means towards profitable ends: mHealth apps, user autonomy, and unfair commercial practices. Journal of consumer policy, 41, 103-134. https://doi.org/10.1007/s10603-018-9374-3.
Bernstein, M. L., McCreless, T., & Cote, M. J. (2007). Five constants of information technology adoption in healthcare. Hospital Topics, 85(1), 17-25. https://doi.org/10.3200/HTPS.85.1.17-26.
Zhang, K. Z., Zhao, S. J., Cheung, C. M., & Lee, M. K. (2014). Examining the influence of online reviews on consumers' decision-making: A heuristic–systematic model. Decision support systems, 67, 78-89.
https://doi.org/10.1016/j.dss.2014.08.005.
Ogbaga, I. N., Nweke, H. F., & Ndunagu, J. N. (2023). Deploying a persuasive technology-based model in the prevention and control of malaria in Nigeria to reduce the incidence of deaths. African Scientific Reports, 130-130. https://doi.org/10.46481/asr.2023.2.3.130.
Tam, K. Y., & Ho, S. Y. (2005). Web personalization as a persuasion strategy: An elaboration likelihood model perspective. Information systems research, 16(3), 271-291.https://doi.org/10.1287/isre.1050.0058.
Peschke, L., Peschke, S. G., Ağca, Y. G., Seyfafjehi, S., Dündar, I., & Aydoğdu, Y. (2022). Reward mechanisms in COVID-19 tracking apps and its impact on the voluntary participation of the public in sustainable innovation processes. Türkiye İletişim Araştırmaları Dergisi, (39), 54-72.
https://doi.org/10.17829/turcom.1019006.
Bohner, G., Moskowitz, G. B., & Chaiken, S. (1995). The interplay of heuristic and systematic processing of social information. European review of social psychology, 6(1), 33-68.
https://doi.org/10.1080/14792779443000003.
Chen, S., & Chaiken, S. (2019). The heuristic-systematic model in its broader context. 1999. [Online]. Available: https://psycnet.apa.org/record/1999-02377-003.
Leigh, S., & Ashall-Payne, L. (2019). The role of health-care providers in mHealth adoption. The Lancet Digital Health, 1(2), e58-e59. https://doi.org/10.1016/S2589-7500(19)30025-1.
Bauchner, H., & Fontanarosa, P. B. (2013). Restoring confidence in the pharmaceutical industry. JAMA, 309(6), 607-609. https://doi.org/10.1001/jama.2013.58.
Ogbaga, I. N. (2024). Biases in the Willingness to Download a mHealth App: A Discrete Choices Experimental Study Among Nigerian Healthcare App Subscribers. medRxiv, 2024. https://doi.org/10.1101/2024.01.08.24300983.
Lancsar, E., & Louviere, J. (2008). Conducting discrete choice experiments to inform healthcare decision making: a user’s guide. Pharmacoeconomics, 26, 661-677. https://doi.org/10.2165/00019053-200826080-00004.
Dobra, R., Davies, J., Elborn, S., Kee, F., Madge, S., & Boeri, M. (2024). A discrete choice experiment to quantify the influence of trial features on the decision to participate in cystic fibrosis trials. Journal of Cystic Fibrosis, 23(1), 73-79. https://doi.org/10.1016/j.jcf.2023.04.024.
Ahadzadeh, A. S., Sharif, S. P., Ong, F. S., & Khong, K. W. (2015). Integrating health belief model and technology acceptance model: an investigation of health-related internet use. Journal of medical Internet Research, 17(2), e3564. https://doi.org/10.2196/jmir.3564.
Yuen, K. F., Cai, L., Qi, G., & Wang, X. (2021). Factors influencing autonomous vehicle adoption: An application of the technology acceptance model and innovation diffusion theory. Technology Analysis & Strategic Management, 33(5), 505-519. https://doi.org/10.1080/09537325.2020.1826423.
Champion, V. L., & Skinner, C. S. (2008). The health belief model. Health behavior and health education: Theory, research, and practice, 4, 45-65.
Gagnon, M. P., Ngangue, P., Payne-Gagnon, J., & Desmartis, M. (2016). m-Health adoption by healthcare professionals: a systematic review. Journal of the American Medical Informatics Association, 23(1), 212-220. https://doi.org/10.1093/jamia/ocv052.
Böhm, B., Karwiese, S. D., Böhm, H., & Oberhoffer, R. (2019). Effects of mobile health including wearable activity trackers, to increase physical activity outcomes among healthy children and adolescents: systematic review. JMIR mHealth and uHealth, 7(4), e8298. https://doi.org/10.2196/mhealth.8298.