User acquisition and profile of COVID-19’s health education website: a descriptive study

https://doi.org/10.22146/jcoemph.57050

Avinindita Nura Lestari(1*), Tiara Putri Leksono(2), Reyfal Khaidar(3), Ekky Novriza Alam(4), Lutfan Lazuardi(5)

(1) Faculty of Medicine, Universitas Islam Bandung, Bandung, Indonesia
(2) Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
(3) Faculty of Medicine, Universitas Islam Negeri Syarif Hidayatullah Jakarta, Jakarta, Indonesia
(4) Information System Department, School of Industrial Engineering, Telkom University Bandung, Indonesia
(5) Department of Health Policy and Management, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Indonesia
(*) Corresponding Author

Abstract


In early of 2020, China had identified a new etiology of pneumonia which was later called Coronavirus Disease 2019 (COVID-19) by the World Health Organization (WHO) and the condition declared as pandemic. In this emergency state of affair, people will seek information from websites disseminating health information online, including Indonesia. Since there is currently no vaccine or specific antiviral treatment, the application of preventive measures has been essential. The hygiene and health measures can be easily spread widely as there’s been fast & numerous information spreading in the media, but that is not usually the case with underprivileged people with little access to technology. False news and lack of credible sources are also a threat. A health startup in Bandung, Indonesia, made initiatives to educate people about COVID-19 prevention through downloadable script and audio in the form of Public Service Announcement provided with 19 local languages through their website.  This study aims to know the characteristics of profile users accessing the website through descriptive observational approach. The data came from the website automatically analysed by Google Analytics. We look into the audience data, comprising demographics and geographical distribution. Additionally, we observe the acquisition data that helps us in seeing website traffic. The significant difference found in this study is seen in the age group, meanwhile the gender group did not have a significant difference, which has 8% of disparity. By geographical distribution, 60% of top users are located in cities located in Java Island. Direct traffic, interestingly, made up almost 86 percent of all traffic. Twitter ranked the top for the social media traffic in our case. In conclusion, it is necessary to promote credible information in COVID-19 preventive measures and help maintain the accessibility of information.


Keywords


COVID-19; descriptive study; user; web traffic

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References

  1. Kementrian Kesehatan. Pedoman Pencegahan dan Pengendalian Coronavirus Disease (COVID-19). Pedoman Pencegah dan Pengendali Coronavirus Dis. 2020;4(Revisi ke-4):1–125
  2. Pan L, Mu M, Yang P, Sun Y, Wang R, Yan J, et al. Clinical characteristics of COVID-19 patients with digestive symptoms in Hubei, China. Am J Gastroenterol. 2020;115(5):766–73.
  3. Siswanta. Informasi Kesehatan di Media Online. Jurnal Ilmu Komunikasi UNISRI Surakarta. 2015;13(3):210-223.
  4. Puspitasari L, Ishii K. Digital divides and mobile Internet in Indonesia: impact of smartphones. Telemat Informatics. 2016;
  5. Global Connectivity Index: Country profile Indonesia [Internet]. www.huawei.com. 2019 [cited 18 June 2020]. Available from: https://www.huawei.com/minisite/gci/en/country-profile-id.htm
  6. Asosiasi Penyelenggara Jasa Internet Indonesia. Laporan survei penetrasi & profil perilaku pengguna internet di Indonesia 2018. Asosiasi Penyelenggara Jasa Internet Indonesia. 2018.
  7. Hilbert M. The end justifies the definition: the manifold outlooks on the digital divide and their practical usefulness for policy-making. Telecomm Policy. 2011;35(8):715
  8. Basch CH, Hillyer GC, Meleo-Erwin ZC, Jaime C, Mohlman J, Basch CE. Preventive behaviors conveyed on YouTube to mitigate transmission of COVID-19: cross-sectional study. JMIR Public Heal Surveill. 2020;
  9. Project HE and E. No Title [Internet]. 2020 [cited 2020 Jun 11]. p. 1. Available from: https://www.talenthouse.com/i/1839/submission/424125/38c866e1
  10. Bridging the language divide in health [Internet]. World Health Organization. 2015 [cited 18 June 2020]. Available from: http://dx.doi.org/10.2471/BLT.15.020615
  11. Mahendradhata Y, Trisnantoro L, Listyadewi S, Soewondo P, Marthias T, Harimurti P et al. The Republic of Indonesia health system review. Health Systems in Transition. 2017;7(1):1-8.
  12. Hargittai E. Second-level digital divide: differences in people’s online skills. FM [Internet]. 2002Apr.1 [cited 2020Jun.18];7(4). Available from: https://journals.uic.edu/ojs/index.php/fm/article/view/942
  13. Lukito D Tuwo. Rencana pembangunan broadband nasional. 2013;(September).
  14. Vota W. Should Indonesia be teaching ICT in schools? - ICTworks [Internet]. ICTworks. 2014 [cited 18 June 2020]. Available from: https://www.ictworks.org/should-indonesia-be-teaching-ict-in-schools/
  15. Chakrabortty K, Jose E. Relationship analysis between website traffic, domain age and Google indexed pages of e-commerce websites. IIM Kozhikode Soc Manag Rev. 2018;7(2):171–7.
  16. Duklan M, Bahuguna H. Search engine optimization techniques for organic search : a theoretical approach. Uttaranchal Bus Rev. 2016;(April):45–55.
  17. Fox S. Health Topics: 80% of Internet users look for health information online. Pew Internet & American Life Project. 2011.
  18. Park HW, Park S, Chong M. Conversations and medical news frames on Twitter: infodemiological study on COVID-19 in South Korea. J Med Internet Res. 2020.



DOI: https://doi.org/10.22146/jcoemph.57050

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