Published on in Vol 9, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26845, first published .
Relationship Between Perceived Risks of Using mHealth Applications and the Intention to Use Them Among Older Adults in the Netherlands: Cross-sectional Study

Relationship Between Perceived Risks of Using mHealth Applications and the Intention to Use Them Among Older Adults in the Netherlands: Cross-sectional Study

Relationship Between Perceived Risks of Using mHealth Applications and the Intention to Use Them Among Older Adults in the Netherlands: Cross-sectional Study

Journals

  1. ALsharif A. Attitudes of Patients with Chronic Diseases toward Management eHealth Applications Systems in Post-COVID-19 Times. International Journal of Environmental Research and Public Health 2022;19(7):4289 View
  2. Pan J, Dong H. mHealth Adoption Among Older Chinese Adults: A Conceptual Model With Design Suggestions. International Journal of Human–Computer Interaction 2023;39(5):1072 View
  3. Liu J, Sorwar G, Rahman M, Hoque M. The role of trust and habit in the adoption of mHealth by older adults in Hong Kong: a healthcare technology service acceptance (HTSA) model. BMC Geriatrics 2023;23(1) View
  4. van Elburg F, Klaver N, Nieboer A, Askari M. Gender differences regarding intention to use mHealth applications in the Dutch elderly population: a cross-sectional study. BMC Geriatrics 2022;22(1) View
  5. Wu Y, Wen J, Wang X, Wang Q, Wang W, Wang X, Xie J, Cong L. Associations between e-health literacy and chronic disease self-management in older Chinese patients with chronic non-communicable diseases: a mediation analysis. BMC Public Health 2022;22(1) View
  6. Uncovska M, Freitag B, Meister S, Fehring L. Patient Acceptance of Prescribed and Fully Reimbursed mHealth Apps in Germany: An UTAUT2-based Online Survey Study. Journal of Medical Systems 2023;47(1) View
  7. Vincent W. Developing and Evaluating a Measure of the Willingness to Use Pandemic-Related mHealth Tools Using National Probability Samples in the United States: Quantitative Psychometric Analyses and Tests of Sociodemographic Group Differences. JMIR Formative Research 2023;7:e38298 View
  8. Wang X, Lee C, Jiang J, Zhu X. Factors Influencing the Aged in the Use of Mobile Healthcare Applications: An Empirical Study in China. Healthcare 2023;11(3):396 View
  9. Pan J, Dong H, Bryan-Kinns N. Perception and Initial Adoption of Mobile Health Services of Older Adults in London: Mixed Methods Investigation. JMIR Aging 2021;4(4):e30420 View
  10. Marshall-McKenna R, Kotronoulas G, Kokoroskos E, Granados A, Papachristou P, Papachristou N, Collantes G, Petridis G, Billis A, Bamidis P. A multinational investigation of healthcare needs, preferences, and expectations in supportive cancer care: co-creating the LifeChamps digital platform. Journal of Cancer Survivorship 2023;17(4):1094 View
  11. Tu R, Park S, Ding Y. Travel intentions of travelers in the COVID-19 context: The moderation of fear of COVID-19. Frontiers in Psychology 2023;14 View
  12. Wong A, Wong F, Bayuo J, Chow K, Wong S, Lee A. A randomized controlled trial of an mHealth application with nursing interaction to promote quality of life among community-dwelling older adults. Frontiers in Psychiatry 2022;13 View
  13. Ge F, Qian H, Lei J, Ni Y, Li Q, Wang S, Ding K. Experiences and Challenges of Emerging Online Health Services Combating COVID-19 in China: Retrospective, Cross-Sectional Study of Internet Hospitals. JMIR Medical Informatics 2022;10(6):e37042 View
  14. Wang X, Lee C, Jiang J, Zhang G, Wei Z. Research on the Factors Affecting the Adoption of Smart Aged-Care Products by the Aged in China: Extension Based on UTAUT Model. Behavioral Sciences 2023;13(3):277 View
  15. Rho M, Park J. An Investigation of Factors Influencing the Postponement of the Use of Distributed Research Networks in South Korea: Web-Based Users’ Survey Study. JMIR Formative Research 2023;7:e40660 View
  16. Schroeder T, Dodds L, Georgiou A, Gewald H, Siette J. Older Adults and New Technology: Mapping Review of the Factors Associated With Older Adults’ Intention to Adopt Digital Technologies. JMIR Aging 2023;6:e44564 View
  17. Wang X, Luo R, Liu Y, Chen P, Tao Y, He Y. Revealing the complexity of users’ intention to adopt healthcare chatbots: A mixed-method analysis of antecedent condition configurations. Information Processing & Management 2023;60(5):103444 View
  18. Tandon U, Ertz M, Shashi . Continued Intention of mHealth Care Applications among the Elderly: An Enabler and Inhibitor Perspective. International Journal of Human–Computer Interaction 2023:1 View
  19. Zhang M, Zhang H, Zhu R, Yang H, Chen M, Wang X, Li Z, Xiong Z. Factors affecting the willingness of patients with type 2 diabetes to use digital disease management applications: a cross-sectional study. Frontiers in Public Health 2023;11 View
  20. Ramdowar H, Khedo K, Chooramun N. A comprehensive review of mobile user interfaces in mHealth applications for elderly and the related ageing barriers. Universal Access in the Information Society 2023 View
  21. Kamaruzaman K, Hussein Z, Fikry A. Factors Affecting Behavioural Intention to Use Mobile Health Applications among Obese People in Malaysia. European Journal of Business Science and Technology 2023;9(1):92 View
  22. Li Z, Ge J, Zhang C, Peng X, Wu Q, You H. Information-Motivation-Behavioral Skills Model Supplemented With the Moderated-Mediation Path: A Framework for Interpreting Patients’ Online Medical Services Utilization. American Journal of Health Promotion 2023;37(7):924 View
  23. Bakker C, Wyatt T, Breth M, Gao G, Janeway L, Lee M, Martin C, Tiase V. Nurses’ Roles in mHealth App Development: Scoping Review. JMIR Nursing 2023;6:e46058 View
  24. van Elburg F, van de Klundert J, Nieboer A, Askari M. The intention to use mHealth applications among Dutch older adults prior and during the COVID pandemic. Frontiers in Public Health 2023;11 View
  25. Sujarwoto S, Maharani A. Facilitators and barriers to the adoption of mHealth apps for COVID-19 contact tracing: a systematic review of the literature. Frontiers in Public Health 2023;11 View
  26. Ullman A, Larsen E, Gibson V, Binnewies S, Ohira R, Marsh N, Mcbride C, Winterbourn K, Boyte F, Cunninghame J, Dufficy M, Plummer K, Roberts N, Takashima M, Cooke M, Byrnes J, Rickard C, Kleidon T. An mHealth application for chronic vascular access: A multi‐method evaluation. Journal of Clinical Nursing 2024;33(5):1762 View
  27. Zou X, Na Y, Lai K, Liu G. Unpacking public resistance to health Chatbots: a parallel mediation analysis. Frontiers in Psychology 2024;15 View
  28. Chun M, Yu H, Jung H. A Deep Learning–Based Rotten Food Recognition App for Older Adults: Development and Usability Study. JMIR Formative Research 2024;8:e55342 View
  29. Harun Z, Muhammad N, Hussein Z, Fikri A, Abdul A. Factors influencing patients’ intention to use the Health Clinic Online Appointment System app. Information Management and Business Review 2024;16(2(I)):53 View
  30. Goel A, Singh A, Taneja U, Jain S. Consumer adoption of digital health services: A systematic literature review and research agenda. International Journal of Consumer Studies 2024;48(4) View