Factors associated with the use of the Indonesia COVID-19 mobile app: What needs to be improved for the future personal mobile health app?
PDF

Keywords

COVID-19
mobile health
public health surveillance
health belief model
mobile app
Indonesia

How to Cite

Umniyatun, Y., Suraya, I., Rachmawati, E., & Nurmansyah, M. I. (2023). Factors associated with the use of the Indonesia COVID-19 mobile app: What needs to be improved for the future personal mobile health app?. Public Health of Indonesia, 9(4), 147–155. https://doi.org/10.36685/phi.v9i4.735

Abstract

Background: Personal mobile health applications, including the COVID-19 mobile app, offer various benefits for enhancing the effectiveness of health programs.

Objective: This study aimed to investigate and compare the factors associated with the utilization of the Peduli Lindungi COVID-19 mobile application, employing the behavioral theory of the Health Belief Model (HBM).

Methods: This cross-sectional survey study, conducted at the Department of Public Health in two universities in South Jakarta, Indonesia, involved 744 respondents. The independent variables were derived from the Health Belief Model (HBM), including components such as perceived vulnerability, severity, benefits, obstacles, and self-efficacy. The dependent variable was the use of the COVID-19 mobile application.

Results: The majority of respondents, with an average age of 19.9 years, were female. Out of the total, 51.9% (386 individuals) used the PL application in the past seven days, albeit not on a daily basis, and 86.6% utilized the application to access public facilities. Non-users primarily cited a lack of necessity due to not having traveled in the last seven days. The variable of perceived usefulness was significantly associated with application use (p <0.001).

Conclusion: There is a need for increased public education regarding the application's benefits. Furthermore, the safe entrance system feature of the app, utilized for accessing public spaces, could be consistently employed to monitor and prevent the transmission of infectious diseases, including COVID-19.

https://doi.org/10.36685/phi.v9i4.735
PDF

References

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

Aji, A. W., & Puspitasari, M. (2022). Penerimaan masyarakat atas kebijakan penggunaan aplikasi pedulilindungi. Jurnal Kebijakan Publik, 13(2), 104-113.

Akinbi, A., Forshaw, M., & Blinkhorn, V. (2021). Contact tracing apps for the COVID-19 pandemic: a systematic literature review of challenges and future directions for neo-liberal societies. Health Information Science and Systems, 9, 1-15. https://doi.org/10.1007/s13755-021-00147-7

Alharbi, N. S., AlGhanmi, A. S., & Fahlevi, M. (2022). Adoption of health mobile apps during the COVID-19 lockdown: a health belief model approach. International Journal of Environmental Research and Public Health, 19(7), 4179. https://doi.org/10.3390/IJERPH19074179

Andriani, J., & Winarno, W. W. (2022). Faktor-faktor yang mempengaruhi penggunaan aplikasi "Pedulilindungi" dengan Technology Acceptance Model (TAM). ZONAsi: Jurnal Sistem Informasi, 4(1), 89-100. https://doi.org/10.31849/ZN.V4I1.9834

Armbruster, B., & Brandeau, M. L. (2007). Contact tracing to control infectious disease: when enough is enough. Health Care Management Science, 10, 341-355. https://doi.org/10.1007/S10729-007-9027-6

Badan Pusat Statistik Republik Indonesia. (2022). Survei perilaku masyarakat pada masa pandemi covid-19. https://malangkota.bps.go.id/

Bi, Q., Wu, Y., Mei, S., Ye, C., Zou, X., Zhang, Z., Liu, X., Wei, L., Truelove, S. A., & Zhang, T. (2020). Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study. The Lancet Infectious Diseases, 20(8), 911-919. https://doi.org/10.1016/S1473-3099(20)30287-5

Dyer, O. (2021). Covid-19: Indonesia becomes Asia’s new pandemic epicentre as delta variant spreads. BMJ, 374. https://doi.org/10.1136/bmj.n1815

Edgar, T. W., & Manz, D. O. (2017). Research methods for cyber security. Syngress. https://doi.org/10.1016/B978-0-12-805349-2.00004-2

Ferary, S. A., Bias, A. B. F., Khoiriyah, K. N., & Fandi, R. S. (2022). Analisis Faktor-Faktor Yang Mempengaruhi Penggunaan Aplikasi Pedulilindungi Dengan Metode UTAUT 2. Prosiding Seminar Nasional Teknologi dan Sistem Informasi,

Ferretti, L., Wymant, C., Kendall, M., Zhao, L., Nurtay, A., Abeler-Dörner, L., Parker, M., Bonsall, D., & Fraser, C. (2020). Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science, 368(6491), eabb6936. https://doi.org/10.1126/science.abb6936

Glasser, J. W., Hupert, N., McCauley, M. M., & Hatchett, R. (2011). Modeling and public health emergency responses: lessons from SARS. Epidemics, 3(1), 32-37. https://doi.org/10.1016/J.EPIDEM.2011.01.001

Hendarmin, L. A., Rosyidah, I., & Nurmansyah, M. I. (2021). Pesantren during the pandemic: resilience and vulnerability. https://doi.org/10.36712/sdi.v28i3.24994

Indonesia Baik. (2023). Tampilan baru aplikasi. https://indonesiabaik.id/infografis/tampilan-baru-aplikasi-pedulilindungi

Jones, K., & Thompson, R. (2021). To use or not to use a COVID-19 contact tracing app: Mixed methods survey in Wales. JMIR mHealth and uHealth, 9(11), e29181. https://doi.org/10.2196/29181

Kementerian Kesehatan Republik Indonesia. (2021). Sertifikat vaksin & data bermasalah? ini solusinya. https://sehatnegeriku.kemkes.go.id/baca/rilis-media/20210814/5838287/sertifikat-vaksin-data-bermasalah-ini-solusinya/

Kementerian Kesehatan Republik Indonesia. (2023). Apakah SATUSEHAT platform adalah aplikasi baru? https://faq.kemkes.go.id/faq/apakah-satusehat-platform-adalah-aplikasi-baru

Ki, H. K., Han, S. K., Son, J. S., & Park, S. O. (2019). Risk of transmission via medical employees and importance of routine infection-prevention policy in a nosocomial outbreak of Middle East respiratory syndrome (MERS): a descriptive analysis from a tertiary care hospital in South Korea. BMC pulmonary medicine, 19(1), 1-12. https://doi.org/10.1186/S12890-019-0940-5

Kondylakis, H., Katehakis, D. G., Kouroubali, A., Logothetidis, F., Triantafyllidis, A., Kalamaras, I., Votis, K., & Tzovaras, D. (2020). COVID-19 mobile apps: a systematic review of the literature. Journal of Medical Internet Research, 22(12), e23170. https://doi.org/10.2196/23170

Matt, C., Teebken, M., & Özcan, B. (2022). How the introduction of the COVID-19 tracing apps affects future tracking technology adoption. Digital Transformation and Society, 1(1), 95-114. https://doi.org/10.1108/DTS-05-2022-0015

Melzner, J., Heinze, J., & Fritsch, T. (2014). Mobile health applications in workplace health promotion: an integrated conceptual adoption framework. Procedia Technology, 16, 1374-1382. https://doi.org/10.1016/J.PROTCY.2014.10.155

Nurmansyah, M. I., Rosidati, C., Yustiyani, Y., & Nasir, N. M. (2022). Measuring the success of pedulilindungi application use for supporting COVID-19 prevention: a case study among college students in Jakarta, Indonesia. Kesmas: Jurnal Kesehatan Masyarakat Nasional (National Public Health Journal), 17(sp1). https://doi.org/10.21109/KESMAS.V17ISP1.6057

Orji, R., Vassileva, J., & Mandryk, R. (2012). Towards an effective health interventions design: an extension of the health belief model. Online Journal of Public Health Informatics, 4(3). https://doi.org/10.5210/ojphi.v4i3.4321

Peduli Lindungi. (2021). Ini manfaat aplikasi pedulilindungi yang belum banyak diketahui https://covid19.go.id/p/berita/ini-manfaat-aplikasi-pedulilindungi-yang-belum-banyak-diketahui

Perrotta, F., Corbi, G., Mazzeo, G., Boccia, M., Aronne, L., D’Agnano, V., Komici, K., Mazzarella, G., Parrella, R., & Bianco, A. (2020). COVID-19 and the elderly: insights into pathogenesis and clinical decision-making. Aging Clinical and Experimental Research, 32, 1599-1608. https://doi.org/10.1007/s40520-020-01631-y

Shahroz, M., Ahmad, F., Younis, M. S., Ahmad, N., Boulos, M. N. K., Vinuesa, R., & Qadir, J. (2021). COVID-19 digital contact tracing applications and techniques: A review post initial deployments. Transportation Engineering, 5, 100072. https://doi.org/10.1016/J.TRENG.2021.100072

Singh, H. J. L., Couch, D., & Yap, K. (2020). Mobile health apps that help with COVID-19 management: scoping review. JMIR Nursing, 3(1), e20596. https://doi.org/10.2196/20596

Sujarwoto, S., Augia, T., Dahlan, H., Sahputri, R. A. M., Holipah, H., & Maharani, A. (2022). COVID-19 mobile health apps: an overview of mobile applications in Indonesia. Frontiers in Public Health, 10, 879695. https://doi.org/10.3389/FPUBH.2022.879695/FULL

Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48, 1273-1296. https://doi.org/10.1007/S11165-016-9602-2/TABLES/1

Walrave, M., Waeterloos, C., & Ponnet, K. (2020). Adoption of a contact tracing app for containing COVID-19: A health belief model approach. JMIR Public Health and Surveillance, 6(3), e20572. https://doi.org/10.2196/20572

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Copyright (c) 2023 Yuyun Umniyatun, Izza Suraya, Emma Rachmawati, Mochamad Iqbal Nurmansyah

Downloads

Download data is not yet available.