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Research Article

Paving the way for increased e-health record use: elaborating intentions of Gen-Z

ORCID Icon & ORCID Icon
Pages 281-298 | Received 03 Mar 2021, Accepted 21 Sep 2022, Published online: 03 Oct 2022

References

  • Alaiad, A., Alsharo, M., & Alnsour, Y. (2019). The determinants of M-Health adoption in developing countries: An empirical investigation. Applied Clinical Informatics, 10(5), 820–840. https://doi.org/10.1055/s-0039-1697906
  • Albar, A. M., & Hoque, M. R. (2019). Patient acceptance of e-health services in Saudi Arabia: An integrative perspective. Telemedicine and E-Health, 25(9), 847–852. https://doi.org/10.1089/tmj.2018.0107
  • Alloghani, M., Hussain, A., Al-Jumeily, D., & Abuelma’Atti, O. (2015). Technology acceptance model for the use of M-health services among health related users in UAE. Proceedings - 2015 International Conference on Developments in ESystems Engineering, DeSE 2015, 213–217. https://doi.org/10.1109/DeSE.2015.58
  • Almazroi, A. A., Mohammed, F., Al-Kumaim, N. H., & Hoque, M. R. (2022). An empirical study of factors influencing e-health services adoption among public in Saudi Arabia. Health Informatics Journal, 28(2), 146045822211023. https://doi.org/10.1177/14604582221102316
  • Alsahafi, Y. A., Gay, V., & Khwaji, A. A. (2022). Factors affecting the acceptance of integrated electronic personal health records in Saudi Arabia: The impact of e-health literacy. Health Information Management Journal, 51(2), 98–109. https://doi.org/10.1177/1833358320964899
  • Ammenwerth, E., Hoerbst, A., Lannig, S., Mueller, G., Siebert, U., & Schnell-Inderst, P. (2019). Effects of adult patient portals on patient empowerment and health-related outcomes: A systematic review. Studies in Health Technology and Informatics, 264(August), 1106–1110. https://doi.org/10.3233/SHTI190397
  • Andreassen, H., Bujnowska-Fedak, M., Chronaki, C., Dumitru, R., Pudule, I., Santana, S., Voss, H., & Wynn, R. (2007). European citizens’ use of E-health services: A study of seven countries. BMC Public Health, 7(1), 53. https://doi.org/10.1186/1471-2458-7-53
  • Andrews, L., Gajanayake, R., & Sahama, T. (2014). The Australian general public’s perceptions of having a personally controlled electronic health record (PCEHR). International Journal of Medical Informatics, 83(12), 889–900. https://doi.org/10.1016/j.ijmedinf.2014.08.002
  • Angst, C. M., & Agarwal, R. (2006). Getting personal about electronic health records: Modeling the beliefs of personal health record users and non-users. SSRN Electronic Journal, May. https://doi.org/10.2139/ssrn.902904
  • Angst, C. M., & Agarwal, R. (2009). Adoption of electronic health records in the presence of privacy concerns: The elaboration likelihood model and individual persuasion. MIS Quarterly, 33(2), 339–370. https://doi.org/10.2307/20650295
  • Asaad Assiri, G. (2022). The Impact of patient access to their electronic health record on medication management safety: A narrative review. Saudi Pharmaceutical Journal, 30(3), 185–194. https://doi.org/10.1016/j.jsps.2022.01.001
  • Aydın, Ş. (2019). Digitization in health and relavant projects. OHSAD Congress: Common Solution Meetings in Health.
  • Bachman, E. (2019). Access to healthcare for generational cohorts in a rural community [The College of Saint Scholastica]. ProQuest LLC. https://www.proquest.com/docview/2313355474
  • Baird, A., Raghu, T. S., North, F., & Edwards, F. (2014). When traditionally inseparable services are separated by technology: The case of patient portal features offered by primary care providers. Health Systems, 3(2), 143–158. https://doi.org/10.1057/hs.2013.13
  • Bansal, G., Zahedi, F. M., & Gefen, D. (2010). The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online. Decision Support Systems, 49(2), 138–150. https://doi.org/10.1016/j.dss.2010.01.010
  • Bawack, R. E., & Kala Kamdjoug, J. R. (2018). Adequacy of UTAUT in clinician adoption of health information systems in developing countries: The case of Cameroon. International Journal of Medical Informatics, 109(April2017), 15–22. https://doi.org/10.1016/j.ijmedinf.2017.10.016
  • Ben, A. W., Ben, N. I., Kondrateva, G., & Hikkerova, L. (2021). The role of trust in intention to use the IoT in eHealth: Application of the modified UTAUT in a consumer context. Technological Forecasting and Social Change, 167(February), 120688. https://doi.org/10.1016/j.techfore.2021.120688
  • Bhavnani, V., Fisher, B., Winfield, M., & Seed, P. (2011). How patients use access to their electronic GP record–a quantitative study. Family Practice, 28(2), 188–194. DOI:10.1093/fampra/cmq092.
  • Bonomi, S. (2016). The electronic health record: A comparison of some EuropeanCountries. F. Ricciardi & A. Harfouche Eds. Information and Communication Technologies in Organizations and Society, Past, Present and Future Issues, 15, 33–50. Springer. https://doi.org/10.1007/978-3-319-28907-6_10
  • British Council. (2017). Next Generation Turkey. British Council Next Generation. https://www.britishcouncil.org.tr/sites/default/files/h068_01_next_generation_turkey_report_final_tr.pdf
  • Cajita, M. I., Hodgson, N. A., Lam, K. W., Yoo, S., & Han, H.-R. (2018). Facilitators of and barriers to mHealth adoption in older adults with heart failure. CIN: Computers, Informatics, Nursing, 36(8), 376–382. https://doi.org/10.1097/CIN.0000000000000442
  • Calder, B. J., Phillips, L. W., & Tybout, A. M. (1981). Designing research for application. Journal of Consumer Research, 8(2), 197. https://doi.org/10.1086/208856
  • Cheung, M. L., Leung, W. K. S., & Chan, H. (2020). Driving healthcare wearable technology adoption for Generation Z consumers in Hong Kong. Young Consumers, 22(1), 10–27.DOI:10.1108/YC-04-2020-1123.
  • Chillakuri, B. (2020). Understanding Generation Z expectations for effective onboarding. Journal of Organizational Change Management, 33(7), 1277–1296. https://doi.org/10.1108/JOCM-02-2020-0058
  • Cimperman, M., Makovec Brenčič, M., & Trkman, P. (2016). Analyzing older users’ home telehealth services acceptance behavior-applying an Extended UTAUT model. International Journal of Medical Informatics, 90(Jun), 22–31. DOI:10.1016/j.ijmedinf.2016.03.002.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed. ed.). Erlbaum.
  • Commission, E. (2016). eGovernment in Turkey, February 2016 (Edition 13.0 ed.).
  • Coughlin, J. F., D’Ambrosio, L. A., Reimer, B., & Pratt, M. R. (2007). Older adult perceptions of smart home technologies: Implications for research, policy & market innovations in healthcare. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 1810–1815. https://doi.org/10.1109/IEMBS.2007.4352665
  • Demirhan, H., & Eke, E. (2019). Kuşaklar Bağlamında Tüketici Sağlığı Bilişimine Yönelik Bir Araştırma. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 19(3), 335–358. https://doi.org/10.18037/ausbd.632117
  • Dinev, T., Albano, V., Xu, H., D’Atri, A., & Hart, P. (2016). Individuals’ attitudes towards electronic health records: A privacy calculus perspective. Advances in Healthcare Informatics and Analytics, 19, 19–50. https://doi.org/10.1007/978-3-319-23294-2
  • Dontje, K., Corser, W. D., & Holzman, G. (2014). Understanding patient perceptions of the electronic personal health record. Journal for Nurse Practitioners, 10(10), 824–828. https://doi.org/10.1016/j.nurpra.2014.09.009
  • Dwivedi, Y. K., Shareef, M. A., Simintiras, A. C., Lal, B., & Weerakkody, V. (2016). A generalised adoption model for services: A cross-country comparison of mobile health (m-health). Government Information Quarterly, 33(1), 174–187. https://doi.org/10.1016/j.giq.2015.06.003
  • El-Wajeeh, M., Galal-Edeen, G. H., & Mokhtar, H. (2014). Technology acceptance model for mobile health systems. IOSR Journal of Mobile Computing & Application, 1(1), 21–33. DOI:10.9790/0050-0112133.
  • Essén, A., Scandurra, I., Gerrits, R., Humphrey, G., Johansen, M. A., Kiergegaard, P., Koskinen, J., Liaw, S. T., Odeh, S., Ross, P., & Ancker, J. S. (2018). Patient access to electronic health records: Differences across ten countries. Health Policy and Technology, 7(1), 44–56. https://doi.org/10.1016/j.hlpt.2017.11.003
  • Farhan, W., El, R. G., Bélanger, C. H., & Razmak, J. (2021). Electronic medical records: Taking young generations of patients and physicians through innovative technology and change management. International Journal of Electronic Healthcare, 11(1), 1. https://doi.org/10.1504/IJEH.2021.10035011
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
  • Francis, T., & Hoefel, F. (2018). True Gen: Generation Z and its implications for companies. McKinsey & Company Insights. https://www.mckinsey.com/industries/consumer-packaged-goods/our-insights/true-gen-generation-z-and-its-implications-for-companies
  • Guo, X., Zhang, X., & Sun, Y. (2016). The privacy-personalization paradox in mHealth services acceptance of different age groups. Electronic Commerce Research and Applications, 16(March–April), 55–65. https://doi.org/10.1016/j.elerap.2015.11.001
  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed. ed.). Sage Publications, Inc.
  • Harbour, J., & Chowdhury, G. G. (2007). Use and outcome of online health information services: A study among Scottish population. Journal of Documentation, 63(2), 229–242. https://doi.org/10.1108/00220410710737196
  • Hearld, K. R., Hearld, L. R., Budhwani, H., McCaughey, D., Celaya, L. Y., & Hall, A. G. (2019). The future state of patient engagement? Personal health information use, attitudes towards health, and health behavior. Health Services Management Research, 32(4), 199–208. https://doi.org/10.1177/0951484819845840
  • Heath, M., & Porter, T. H. (2017). Patient health records: An exploratory study of patient satisfaction. Health Policy and Technology, 6(4), 401–409. https://doi.org/10.1016/j.hlpt.2017.10.002
  • Hemsley, B., Rollo, M., Georgiou, A., Balandin, S., & Hill, S. (2018). The health literacy demands of electronic personal health records (e-PHRs): An integrative review to inform future inclusive research. Patient Education and Counseling, 101 (1), 2–15. Elsevier Ireland Ltd https://doi.org/10.1016/j.pec.2017.07.010
  • Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382
  • Hertzum, M., & Ellingsen, G. (2019). The implementation of an electronic health record: Comparing preparations for Epic in Norway with experiences from the UK and Denmark. International Journal of Medical Informatics, 129(September), 312–317. https://doi.org/10.1016/j.ijmedinf.2019.06.026
  • Honein-Abouhaidar, G. N., Antoun, J., Badr, K., Hlais, S., & Nazaretian, H. (2020). Users’ acceptance of electronic patient portals in Lebanon. BMC Medical Informatics and Decision Making, 20(1), 1–12. https://doi.org/10.1186/s12911-020-1047-x
  • Jose, S. (2021). COVID vaccine and generation Z – A study of factors influencing adoption. Young Consumers, in Press, 23(1). https://doi.org/10.1108/YC-01-2021-1276
  • Jung, M. L., & Loria, K. (2010). Acceptance of Swedish e-health services. Journal of Multidisciplinary Healthcare, 3(November), 55–63. https://doi.org/10.2147/jmdh.s9159
  • Keen, S. M., & Roberts, N. (2017). Preliminary evidence for the use and efficacy of mobile health applications in managing posttraumatic stress disorder symptoms. Health Systems, 6(2), 122–129. https://doi.org/10.1057/hs.2016.2
  • Khan, I., Xitong, G., Ahmad, Z., & Shahzad, F. (2019). Investigating factors impelling the adoption of e-Health: A perspective of African expats in China. SAGE Open, 9(3), 3. https://doi.org/10.1177/2158244019865803
  • Kijsanayotin, B., Pannarunothai, S., & Speedie, S. M. (2009). Factors influencing health information technology adoption in Thailand’s community health centers: Applying the UTAUT model. International Journal of Medical Informatics, 78(6), 404–416. https://doi.org/10.1016/j.ijmedinf.2008.12.005
  • Kilit, D. Ö., & Eke, E. (2019). Bireylerin Sağlık Bilgisi Arama Davranışlarının Değerlendirilmesi: Isparta İli Örneği. Hacettepe Sağlık İdaresi Dergisi, 22(2), 401–436. https://dergipark.org.tr/tr/download/article-file/812538
  • Koulayev, S., & Simeonova, E. (2015). Can health IT adoption reduce health disparities? Health Systems, 4(1), 55–63. https://doi.org/10.1057/hs.2014.10
  • Lallmahomed, M. Z. I., Lallmahomed, N., & Lallmahomed, G. M. (2017). Factors influencing the adoption of e-Government services in Mauritius. Telematics and Informatics, 34(4), 57–72. https://doi.org/10.1016/j.tele.2017.01.003
  • Laugesen, J., & Hassanein, K. (2017). Adoption of personal health records by chronic disease patients: A research model and an empirical study. Computers in Human Behavior, 66(January), 256–272. https://doi.org/10.1016/j.chb.2016.09.054
  • Lee, Y. P., Tsai, H. Y., & Ruangkanjanases, A. (2020). The determinants for food safety push notifications on continuance intention in an e-appointment system for public health medical services: The perspectives of utaut and information system quality. International Journal of Environmental Research and Public Health, 17(21), 1–15. https://doi.org/10.3390/ijerph17218287
  • Liu, T., & Xiao, X. (2021). A framework of AI-based approaches to improving eHealth literacy and Combating infodemic. Frontiers in Public Health, 9. https://doi.org/10.3389/fpubh.2021.755808
  • Mat Zain, N. H., Johari, S. N., Abdul Aziz, S. R., Ibrahim Teo, N. H., Ishak, N. H., & Othman, Z. (2021). Winning the needs of the Gen Z: Gamified health awareness campaign in defeating COVID-19 pandemic. Procedia Computer Science, 179, 974–981. https://doi.org/10.1016/j.procs.2021.01.087
  • Mensah, I. K. (2019). Factors influencing the intention of university students to adopt and use E-government services: An empirical evidence in China. SAGE Open, 9(2), 2. https://doi.org/10.1177/2158244019855823
  • Moll, J., Rexhepi, H., Cajander, Å., Grünloh, C., Huvila, I., Hägglund, M., Myreteg, G., Scandurra, I., & Åhlfeldt, R. M. (2018). Patients’ experiences of accessing their electronic health records: National patient survey in Sweden. Journal of Medical Internet Research, 20(11), 1–13. https://doi.org/10.2196/jmir.9492
  • Mwachofi, A. K., Khaliq, A. A., Carrillo, E. R., & Winfree, W. (2016). Technology versus humanism: How patients perceive the use of electronic health records in physicians’ offices—a qualitative study. Health Communication, 31(3), 257–264. https://doi.org/10.1080/10410236.2014.947467
  • Nayak, B., Bhattacharyya, S. S., Kumar, S., & Jumnani, R. K. (2022). Exploring the factors influencing adoption of health-care wearables among generation Z consumers in India. Journal of Information, Communication and Ethics in Society, 20(1), 150–174. https://doi.org/10.1108/JICES-07-2021-0072
  • Nicholas, A. J. (2020). Preferred learning methods of generation Z. Northeast Business & Economics Association 46th Annual Conference, 1–12.
  • Norman, C. D., & Skinner, H. A. (2006). eHEALS: The eHealth literacy scale. Journal of Medical Internet Research, 8(4), 1–7. https://doi.org/10.2196/jmir.8.4.e27
  • Nunes, A., Limpo, T., & Castro, S. L. (2019). Acceptance of mobile health applications: Examining key determinants and Moderators. Frontiers in Psychology, 10(December), 1–9. https://doi.org/10.3389/fpsyg.2019.02791
  • Nymberg, V. M., Bolmsjö, B. B., Wolff, M., Calling, S., Gerward, S., & Sandberg, M. (2019). ‘Having to learn this so late in our lives … ’ Swedish elderly patients’ beliefs, experiences, attitudes and expectations of e-health in primary health care. Scandinavian Journal of Primary Health Care, 37(1), 41–52. https://doi.org/10.1080/02813432.2019.1570612
  • Papp-Zipernovszky, O., Horváth, M. D., Schulz, P. J., & Csabai, M. (2021). Generation gaps in digital health literacy and their impact on health information seeking behavior and health empowerment in Hungary. Frontiers in Public Health, 9. https://doi.org/10.3389/fpubh.2021.635943
  • Patel, V. N., Dhopeshwarkar, R. V., Edwards, A., Barrón, Y., Sparenborg, J., & Kaushal, R. (2012). Consumer support for health information exchange and personal health records: A regional health information organization survey. Journal of Medical Systems, 36(3), 1043–1052. https://doi.org/10.1007/s10916-010-9566-0
  • Platt, J., & Kardia, S. (2015). Public trust in health information sharing: Implications for biobanking and electronic health record systems. Journal of Personalized Medicine, 5(1), 3–21. https://doi.org/10.3390/jpm5010003
  • Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. The Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
  • Portz, J. D., Bayliss, E. A., Bull, S., Boxer, R. S., Bekelman, D. B., Gleason, K., & Czaja, S. (2019). Using the technology acceptance model to explore user experience, intent to use, and use behavior of a patient portal among older adults with multiple chronic conditions: Descriptive Qualitative study. Journal of Medical Internet Research, 21(4), e11604. https://doi.org/10.2196/11604
  • Pywell, J., Vijaykumar, S., Dodd, A., & Coventry, L. (2020). Barriers to older adults’ uptake of mobile-based mental health interventions. DIGITAL HEALTH, 6, 205520762090542. https://doi.org/10.1177/2055207620905422
  • Rahman, M. S., Lakshmikanth, G. S., Chavarria, J. A., Smith, D., Hoque, M. R., Senn, W. D., & Flores, J. (2021). Toward understanding the technology trust calculus in healthcare: A generation Z and millennial view. Proceedings of the Annual Hawaii International Conference on System Sciences, 2020-Janua, 3514–3523. https://doi.org/10.24251/hicss.2021.426
  • Rasche, P., Wille, M., Bröhl, C., Theis, S., Schäfer, K., Knobe, M., & Mertens, A. (2018). Prevalence of health app use among older adults in Germany: National survey. JMIR MHealth and UHealth, 6(1), e26. https://doi.org/10.2196/mhealth.8619
  • Sarstedt, M., Henseler, J., & Ringle, C. M. (2011). Multigroup analysis in Partial Least Squares (PLS) path modeling: Alternative methods and empirical results. In C. R. T. Marko Sarstedt & M. Schwaiger (Eds.), Measurement and research methods in international marketing advances in international marketing (pp. 195–218). Emerald Group Publishing Ltd. https://doi.org/10.1108/S1474-7979(2011)0000022012
  • Schulenkorf, T., Krah, V., Dadaczynski, K., & Okan, O. (2021). Addressing health literacy in schools in Germany: concept analysis of the mandatory digital and media literacy school curriculum. Frontiers in Public Health, 9. https://doi.org/10.3389/fpubh.2021.687389
  • Shapiro, L. M., & Kamal, R. N. (2021). Implementation of electronic health records during global outreach: A necessary next step in measuring and improving quality of care. The Journal of Hand Surgery, 47(3), 279–283. In press. https://doi.org/10.1016/j.jhsa.2021.09.016
  • Showell, C., & Turner, P. (2013). Personal health records are designed for people like Us. Studies in Health Technology and Informatics, 192(1–2), 1037. DOI:10.3233/978-1-61499-289-9-1037.
  • Suwannapusit, U. (2021). The UTAUT model analysis in the technology use of generation-Z users in Cambodia during COVID-19 situation. International Journal of Current Science Research and Review, 04(7), 07. https://doi.org/10.47191/ijcsrr/V4-i7-06
  • Szymkowiak, A., Melović, B., Dabić, M., Jeganathan, K., & Kundi, G. S. (2021). Information technology and Gen Z: The role of teachers, the internet, and technology in the education of young people. Technology in Society, 65(May), 101565. https://doi.org/10.1016/j.techsoc.2021.101565
  • Tatar, M., Mollahaliloǧlu, S., Sahin, B., Aydin, S., Maresso, A., & Hernández-Quevedo, C. (2011). Turkey. Health system review. Health Systems in Transition, 13(6), 1–184. https://pubmed.ncbi.nlm.nih.gov/22455830/
  • Tavares, J., & Oliveira, T. (2016). Electronic health record patient portal adoption by health care consumers: An acceptance model and survey. Journal of Medical Internet Research, 18(3), 3. https://doi.org/10.2196/jmir.5069
  • Tieu, L., Sarkar, U., Schillinger, D., Ralston, J. D., Ratanawongsa, N., Pasick, R., & Lyles, C. R. (2015). Barriers and facilitators to online portal use among patients and caregivers in a safety net health care system: A qualitative study. Journal of Medical Internet Research, 17(12), e275. https://doi.org/10.2196/jmir.4847
  • Tulu, B., Trapp, A. C., Strong, D. M., Johnson, S. A., Hoque, M., Trudel, J., & Garber, L. (2016). An analysis of patient portal utilization: What can we learn about online patient behavior by examining portal click data? Health Systems, 5(1), 66–79. https://doi.org/10.1057/hs.2015.5
  • Turkish Republic Ministry of Health. (2019). 10 million people are using e-pulse. https://sbsgm.saglik.gov.tr/TR,52960/10-milyon-kisi-e-nabiz-kullaniyor.html
  • Turkish Statistical Institute. (2021). Younger generation statistics, 2020. https://data.tuik.gov.tr/Bulten/Index?p=Istatistiklerle-Genclik-2020-37242
  • Uslu, D., & İpek, K. (2022). Bireylerin E-Sağlık Okuryazarlık Düzeyinin E-nabız Sisteminin Kullanımına Yönelik Algılarına Etkisi. Hacettepe Sağlık İdaresi Dergisi, 25(1), 69–86. https://dergipark.org.tr/tr/pub/hacettepesid/issue/69083/948159
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
  • Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly: Management Information Systems, 36(1), 157–178. https://doi.org/10.2307/41410412
  • Verkijika, S. F., & De Wet, L. (2018). E-government adoption in sub-Saharan Africa. Electronic Commerce Research and Applications, 30(July–August), 83–93. https://doi.org/10.1016/j.elerap.2018.05.012
  • Warraich, H. J., Califf, R. M., & Krumholz, H. M. (2018). The digital transformation of medicine can revitalize the patient-clinician relationship. Npj Digital Medicine, 1(1), 49. https://doi.org/10.1038/s41746-018-0060-2
  • Wei, J., Vinnikova, A., Lu, L., & Xu, J. (2020). Understanding and predicting the adoption of fitness mobile apps: Evidence from China. Health Communication, 36 (8) , 950–961. https://doi.org/10.1080/10410236.2020.1724637
  • Wen, K. Y., Kreps, G., Zhu, F., & Miller, S. (2010). Consumers’ perceptions about and use of the Internet for personal health records and health information exchange: Analysis of the 2007 health information national trends survey. Journal of Medical Internet Research, 12(4), 4. https://doi.org/10.2196/jmir.1668
  • WHO. (2016). From innovation to implementation: eHealth in the WHO European Region. https://doi.org/10.1016/j.jacc.2014.10.008
  • Wilson, J., Heinsch, M., Betts, D., Booth, D., & Kay-Lambkin, F. (2021). Barriers and facilitators to the use of e-health by older adults: A scoping review. BMC Public Health, 21(1), 1–12. https://doi.org/10.1186/s12889-021-11623-w
  • Windle, J. R., Windle, T. A., Shamavu, K. Y., Nelson, Q. M., Clarke, M. A., Fruhling, A. L., & Tcheng, J. E. (2021). Roadmap to a more useful and usable electronic health record. Cardiovascular Digital Health Journal, 2(6), 301–311. https://doi.org/10.1016/j.cvdhj.2021.09.007
  • Witten, N. A., & Humphry, J. (2018). The electronic health literacy and utilization of technology for health in a remote Hawaiian community: Lana’i. Hawaii Journal of Medicine & Public Health: A Journal of Asia Pacific Medicine & Public Health, 77(3), 51–59. http://www.ncbi.nlm.nih.gov/pubmed/29541550%0Ahttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC5845020
  • Yousef, C. C., Thomas, A., Alenazi, A. O., Elgadi, S., Abu Esba, L. C., AlAzmi, A., Alhameed, A. F., Hattan, A., Almekhloof, S., AlShammary, M. A., Alanezi, N. A., Alhamdan, H. S., Eldegeir, M., Abulezz, R., Khoshhal, S., Masala, C. G., & Ahmed, O. (2020). Adoption of a personal health record in the digital Age: cross-sectional study. Journal of Medical Internet Research, 22(10), e22913. https://doi.org/10.2196/22913
  • Yuan, S., Ma, W., Kanthawala, S., & Peng, W. (2015). Keep using my health apps: Discover users’ perception of health and fitness apps with the UTAUT2 model. Telemedicine and E-Health, 21(9), 735–741. https://doi.org/10.1089/tmj.2014.0148
  • Zhang, Y., Liu, C., Luo, S., Xie, Y., Liu, F., Li, X., & Zhou, Z. (2019). 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. Journal of Medical Internet Research, 21(8), 1–17. https://doi.org/10.2196/15023
  • Zhao, Y., Ni, Q., & Zhou, R. (2018). What factors influence the mobile health service adoption? A meta-analysis and the moderating role of age. International Journal of Information Management, 43(December2016), 342–350. https://doi.org/10.1016/j.ijinfomgt.2017.08.006
  • Zibrik, L., Khan, S., Bangar, N., Stacy, E., Novak Lauscher, H., & Ho, K. (2015). Patient and community centered eHealth: Exploring eHealth barriers and facilitators for chronic disease self-management within British Columbia’s immigrant Chinese and Punjabi seniors. Health Policy and Technology, 4(4), 348–356. https://doi.org/10.1016/j.hlpt.2015.08.002