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Citing this Article

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Published on 22.12.17 in Vol 19, No 12 (2017): December

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

Works citing "Consumer Adoption of Future MyData-Based Preventive eHealth Services: An Acceptance Model and Survey Study"

Timo Koivumäki, Saara Pekkarinen, Minna Lappi, Jere Väisänen, Jouni Juntunen, Minna Pikkarainen

J Med Internet Res 2017 (Dec 22); 19(12):e429 

According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.7821):

(note that this is only a small subset of citations)

  1. Harst L, Lantzsch H, Scheibe M. Theories Predicting End-User Acceptance of Telemedicine Use: Systematic Review. Journal of Medical Internet Research 2019;21(5):e13117
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  2. Herrenkind B, Nastjuk I, Brendel AB, Trang S, Kolbe LM. Young people’s travel behavior – Using the life-oriented approach to understand the acceptance of autonomous driving. Transportation Research Part D: Transport and Environment 2019;74:214
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  3. Queiroz MM, Fosso Wamba S, De Bourmont M, Telles R. Blockchain adoption in operations and supply chain management: empirical evidence from an emerging economy. International Journal of Production Research 2020;:1
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  4. Häikiö J, Yli-Kauhaluoma S, Pikkarainen M, Iivari M, Koivumäki T. Expectations to data: Perspectives of service providers and users of future health and wellness services. Health and Technology 2020;10(3):621
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  5. Grundstrom C, Korhonen O, Väyrynen K, Isomursu M. Insurance Customers’ Expectations for Sharing Health Data: Qualitative Survey Study. JMIR Medical Informatics 2020;8(3):e16102
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  6. Herrenkind B, Brendel AB, Nastjuk I, Greve M, Kolbe LM. Investigating end-user acceptance of autonomous electric buses to accelerate diffusion. Transportation Research Part D: Transport and Environment 2019;74:255
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  7. Park HS, Kim KI, Soh JY, Hyun YH, Jang SK, Lee S, Hwang GY, Kim HS. Factors Influencing Acceptance of Personal Health Record Apps for Workplace Health Promotion: Cross-Sectional Questionnaire Study. JMIR mHealth and uHealth 2020;8(6):e16723
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  8. Zhang Y, Liu C, Luo S, Xie Y, Liu F, Li X, Zhou Z. 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 2019;21(8):e15023
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  9. van Velsen L, Evers M, Bara C, Op den Akker H, Boerema S, Hermens H. Understanding the Acceptance of an eHealth Technology in the Early Stages of Development: An End-User Walkthrough Approach and Two Case Studies. JMIR Formative Research 2018;2(1):e10474
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  10. Tao D, Wang T, Wang T, Zhang T, Zhang X, Qu X. A systematic review and meta-analysis of user acceptance of consumer-oriented health information technologies. Computers in Human Behavior 2020;104:106147
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  11. Choi Y, Kim J, Kwon IH, Kim T, Kim SM, Cha W, Jeong J, Lee J. Development of a Mobile Personal Health Record Application Designed for Emergency Care in Korea; Integrated Information from Multicenter Electronic Medical Records. Applied Sciences 2020;10(19):6711
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  12. Yusif S, Hafeez-Baig A, Soar J, Teik DOL. PLS-SEM path analysis to determine the predictive relevance of e-Health readiness assessment model. Health and Technology 2020;10(6):1497
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  13. Mathai N, McGill T, Toohey D. Factors Influencing Consumer Adoption of Electronic Health Records. Journal of Computer Information Systems 2022;62(2):267
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  14. Qi M, Cui J, Li X, Han Y. Perceived Factors Influencing the Public Intention to Use E-Consultation: Analysis of Web-Based Survey Data. Journal of Medical Internet Research 2021;23(1):e21834
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  15. 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
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  16. Pan M, Gao W. Determinants of the behavioral intention to use a mobile nursing application by nurses in China. BMC Health Services Research 2021;21(1)
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  17. Günthner T, Proff H. On the way to autonomous driving: How age influences the acceptance of driver assistance systems. Transportation Research Part F: Traffic Psychology and Behaviour 2021;81:586
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  18. Sora B, Nieto R, Montesano del Campo A, Armayones M. Acceptance and Use of Telepsychology From the Clients’ Perspective: Questionnaire Study to Document Perceived Advantages and Barriers. JMIR Mental Health 2021;8(10):e22199
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  19. . The moderating influence of life events on the acceptance of advanced driver assistance systems in aging societies. Computers in Human Behavior Reports 2022;7:100202
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  20. Xu L, Li P, Hou X, Yu H, Tang T, Liu T, Xiang S, Wu X, Huang C. Middle-aged and elderly users’ continuous usage intention of health maintenance-oriented WeChat official accounts: empirical study based on a hybrid model in China. BMC Medical Informatics and Decision Making 2021;21(1)
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  21. Choi W, Chang S, Yang Y, Jung S, Lee S, Chun J, Kim D, Lee W, Choi IY. Study of the factors influencing the use of MyData platform based on personal health record data sharing system. BMC Medical Informatics and Decision Making 2022;22(1)
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  22. Mensah IK, Zeng G, Mwakapesa DS. The behavioral intention to adopt mobile health services: The moderating impact of mobile self-efficacy. Frontiers in Public Health 2022;10
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  23. Tang J, Howell M, Roger S, Wong G, Tong A. Perspectives of Kidney Transplant Recipients on eHealth: Semistructured Interviews. Transplantation Direct 2022;8(12):e1404
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  24. 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)
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  25. Trkman M, Popovič A, Trkman P. The roles of privacy concerns and trust in voluntary use of governmental proximity tracing applications. Government Information Quarterly 2023;40(1):101787
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  26. Leväsluoto J, Kohl J, Sigfrids A, Pihlajamäki J, Martikainen J. Digitalization as an Engine for Change? Building a Vision Pathway towards a Sustainable Health Care System by Using the MLP and Health Economic Decision Modelling. Sustainability 2021;13(23):13007
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  27. Azimi S, Estai M, Patel J, Silva D. The feasibility of a digital health approach to facilitate remote dental screening among preschool children during COVID‐19 and social restrictions. International Journal of Paediatric Dentistry 2023;33(3):234
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  28. Sumaedi S, Sumardjo , Saleh A, Syukri AF. Factors influencing millennials' online healthy food information-sharing behaviour during the Covid-19 pandemic. British Food Journal 2022;124(9):2772
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  29. 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
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  30. Mukherjee S, Baral MM, Lavanya BL, Nagariya R, Singh Patel B, Chittipaka V. Intentions to adopt the blockchain: investigation of the retail supply chain. Management Decision 2023;61(5):1320
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  31. Pagé I, Roos M, Collin O, Lynch SD, Lamontagne M, Massé-Alarie H, K. Blanchette A. UTAUT2-based questionnaire: cross-cultural adaptation to Canadian French. Disability and Rehabilitation 2023;45(4):709
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  32. Devkota B, Montalvo F, McConnell DS, Smither JA. Combined Model of Technology Use and Medical Adherence in eHealth Technology Implementation. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2021;65(1):776
    CrossRef
  33. Kalańska-Łukasik B, Gładyś A, Jadczyk T, Gruz-Kwapisz M, Wojakowski W, Kowalska M. Readiness for Telemedical Services in Patients With Cardiovascular Diseases: Cross-sectional Study. JMIR Formative Research 2022;6(10):e33769
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  34. Phibbs CL, Rahman SSM. A Synopsis of “The Impact of Motivation, Price, and Habit on Intention to Use IoT-Enabled Technology: A Correlational Study”. Journal of Cybersecurity and Privacy 2022;2(3):662
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  35. Shao H, Liu C, Tang L, Wang B, Xie H, Zhang Y. Factors Influencing the Behavioral Intentions and Use Behaviors of Telemedicine in Patients With Diabetes: Web-Based Survey Study. JMIR Human Factors 2023;10:e46624
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  36. Priyank H, Verma A, Zama Khan DU, Prakash Rai N, Kalburgi V, Singh S. Comparative Evaluation of Dental Caries Score Between Teledentistry Examination and Clinical Examination: A Systematic Review and Meta-Analysis. Cureus 2023;
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  37. Busch-Casler J, Radic M. Trust and Health Information Exchanges: Qualitative Analysis of the Intent to Share Personal Health Information. Journal of Medical Internet Research 2023;25:e41635
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  38. Memenga P, Baumann E, Luetke Lanfer H, Reifegerste D, Geulen J, Weber W, Hahne A, Müller A, Weg-Remers S. Intentions of Patients With Cancer and Their Relatives to Use a Live Chat on Familial Cancer Risk: Results From a Cross-Sectional Web-Based Survey. Journal of Medical Internet Research 2023;25:e45198
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  39. Altawaiha I, Atan R, Yaakob RB, Abdullah RBH. A three-step SEM-Bayesian network approach for predicting the determinants of CloudIoT-based healthcare adoption. International Journal of Information Technology 2024;
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  40. . The Use of Online Medical Record Functionalities in Older Adulthood: The Role of Use Encouragement and Access Frequency. Journal of Technology in Human Services 2024;42(1):65
    CrossRef
  41. Nguyen HTT, Tapanainen T, Hubona G. Behavioral responses resulting from e-health services and the role of user satisfaction: the case of the online diabetes test. Journal of Systems and Information Technology 2024;
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/jmir.7821):

  1. Parra L, Rocher J, Sendra S, Lloret J. Energy Conservation for IoT Devices. 2019. Chapter 5:111
    CrossRef
  2. . Making Connected Mobility Work. 2021. Chapter 46:737
    CrossRef