This paper is in the following e-collection/theme issue:

Use and User Demographics of mHealth (311) Adoption and Change Management of eHealth Systems (670)

Published on in Vol 7, No 8 (2019): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13491, first published .
How Resource Scarcity and Accessibility Affect Patients’ Usage of Mobile Health in China: Resource Competition Perspective

How Resource Scarcity and Accessibility Affect Patients’ Usage of Mobile Health in China: Resource Competition Perspective

How Resource Scarcity and Accessibility Affect Patients’ Usage of Mobile Health in China: Resource Competition Perspective

Authors of this article:

Qing Ye1, 2 Author Orcid Image ;   Zhaohua Deng2 Author Orcid Image ;   Yanyan Chen1 Author Orcid Image ;   Jiazhi Liao3 Author Orcid Image ;   Gang Li3 Author Orcid Image ;   Yaobin Lu4 Author Orcid Image
Article Authors Cited by (24) Tweetations (3) Metrics

Journals

  1. Han Y, Lie R, Guo R. The Internet Hospital as a Telehealth Model in China: Systematic Search and Content Analysis. Journal of Medical Internet Research 2020;22(7):e17995 View
  2. Ramdani B, Duan B, Berrou I. Exploring the Determinants of Mobile Health Adoption by Hospitals in China: Empirical Study. JMIR Medical Informatics 2020;8(7):e14795 View
  3. Lu W, Wu H. How Online Reviews and Services Affect Physician Outpatient Visits: Content Analysis of Evidence From Two Online Health Care Communities. JMIR Medical Informatics 2019;7(4):e16185 View
  4. Fu L, Xu K, Liu F, Liang L, Wang Z. Regional Disparity and Patients Mobility: Benefits and Spillover Effects of the Spatial Network Structure of the Health Services in China. International Journal of Environmental Research and Public Health 2021;18(3):1096 View
  5. Li D, Hu Y, Pfaff H, Wang L, Deng L, Lu C, Xia S, Cheng S, Zhu X, Wu X. Determinants of Patients’ Intention to Use the Online Inquiry Services Provided by Internet Hospitals: Empirical Evidence From China. Journal of Medical Internet Research 2020;22(10):e22716 View
  6. Kumm A, Viljoen M, de Vries P. The Digital Divide in Technologies for Autism: Feasibility Considerations for Low- and Middle-Income Countries. Journal of Autism and Developmental Disorders 2022;52(5):2300 View
  7. Fan G, Deng Z, Ye Q, Wang B. Machine learning-based prediction models for patients no-show in online outpatient appointments. Data Science and Management 2021;2:45 View
  8. Zhu Z, Liu Y, Cao X, Dong W. Factors Affecting Customer Intention to Adopt a Mobile Chronic Disease Management Service. Journal of Organizational and End User Computing 2021;34(4):1 View
  9. Tian X, Wu R. Determining Factors Affecting the Users’ Participation of Online Health Communities: An Integrated Framework of Social Capital and Social Support. Frontiers in Psychology 2022;13 View
  10. Ye Q, Wu H. Patient's decision and experience in the multi-channel appointment context: An empirical study. Frontiers in Public Health 2022;10 View
  11. Jacob C, Sezgin E, Sanchez-Vazquez A, Ivory C. Sociotechnical Factors Affecting Patients’ Adoption of Mobile Health Tools: Systematic Literature Review and Narrative Synthesis. JMIR mHealth and uHealth 2022;10(5):e36284 View
  12. Tian X, Wu R. Determinants of the Mobile Health Continuance Intention of Elders with Chronic Diseases: An Integrated Framework of ECM-ISC and UTAUT. International Journal of Environmental Research and Public Health 2022;19(16):9980 View
  13. Cui W, Zhu W, Li X, Wu D, He P, Yu G. Attitudes and perspectives of 534 Chinese pediatricians toward internet hospitals. Frontiers in Pediatrics 2022;10 View
  14. Liu J, Li C, Huang Y, Han J. An intelligent medical guidance and recommendation model driven by patient-physician communication data. Frontiers in Public Health 2023;11 View
  15. Qin M, Zhu W, You C, Li S, Qiu S. Patient's behavior of selection physician in online health communities: Based on an Elaboration likelihood model. Frontiers in Public Health 2022;10 View
  16. Cao L, Chongsuvivatwong V, McNeil E. The Sociodemographic Digital Divide in Mobile Health App Use Among Clients at Outpatient Departments in Inner Mongolia, China: Cross-sectional Survey Study. JMIR Human Factors 2022;9(2):e36962 View
  17. Cao L, Chongsuvivatwong V, McNeil E. The Association between mHealth App Use and Healthcare Satisfaction among Clients at Outpatient Clinics: A Cross-Sectional Study in Inner Mongolia, China. International Journal of Environmental Research and Public Health 2022;19(11):6916 View
  18. Guo X, Zeng S, Namaiti A, Zeng J. Evaluation of Supply–Demand Matching of Public Health Resources Based on Ga2SFCA: A Case Study of the Central Urban Area of Tianjin. ISPRS International Journal of Geo-Information 2023;12(4):156 View
  19. 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
  20. Fan G, Deng Z, Liu L. Understanding the antecedents of patients’ missed appointments: the perspective of attribution theory. Data Science and Management 2023;6(4):247 View
  21. Li J, Lu Y, Peng Z, Yen D. A Chinese case to satisfy the patient journey: optimal service configuration for online health service platforms. Journal of Information Technology Case and Application Research 2024;26(1):4 View
  22. Wei H, Jiang K, Zhao Y, Pu C. Equity of health resource allocation in Chongqing, China, in 2021: a cross-sectional study. BMJ Open 2024;14(1):e078987 View
  23. Tao D, Liu K. How do self-motivation and social motivation contribute to consumers’ acceptance of m-health services?. Health Policy and Technology 2024;13(3):100878 View

Books/Policy Documents

  1. Yang M, Guo Y. Human-Computer Interaction. View