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

Usability of Apps and User Perceptions of mHealth (974) Mobile Health (mhealth) (2939) Adoption and Change Management of eHealth Systems (712)

Published on in Vol 8, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17588, first published .
Drivers of Mobile Health Acceptance and Use From the Patient Perspective: Survey Study and Quantitative Model Development

Drivers of Mobile Health Acceptance and Use From the Patient Perspective: Survey Study and Quantitative Model Development

Drivers of Mobile Health Acceptance and Use From the Patient Perspective: Survey Study and Quantitative Model Development

Authors of this article:

Tânia Salgado1 Author Orcid Image ;   Jorge Tavares1 Author Orcid Image ;   Tiago Oliveira1 Author Orcid Image
Article Authors Cited by (35) Tweetations (1) Metrics

Journals

  1. Wattanapisit A, Amaek W, Wattanapisit S, Tuangratananon T, Wongsiri S, Pengkaew P. Challenges of Implementing an mHealth Application for Personalized Physical Activity Counselling in Primary Health Care: A Qualitative Study. International Journal of General Medicine 2021;Volume 14:3821 View
  2. Kajubi P, Parkes-Ratanshi R, Twimukye A, Bwanika Naggirinya A, Nabaggala M, Kiragga A, Castelnuovo B, King R. Perceptions and Attitudes Toward an Interactive Voice Response Tool (Call for Life Uganda) Providing Adherence Support and Health Information to HIV-Positive Ugandans: Qualitative Study. JMIR Formative Research 2022;6(12):e36829 View
  3. Schretzlmaier P, Hecker A, Ammenwerth E. Extension of the Unified Theory of Acceptance and Use of Technology 2 model for predicting mHealth acceptance using diabetes as an example: a cross-sectional validation study. BMJ Health & Care Informatics 2022;29(1):e100640 View
  4. 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
  5. 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
  6. Klaver N, van de Klundert J, van den Broek R, Askari M. Relationship Between Perceived Risks of Using mHealth Applications and the Intention to Use Them Among Older Adults in the Netherlands: Cross-sectional Study. JMIR mHealth and uHealth 2021;9(8):e26845 View
  7. Panch T, Duralde E, Mattie H, Kotecha G, Celi L, Wright M, Greaves F, Lu H. A distributed approach to the regulation of clinical AI. PLOS Digital Health 2022;1(5):e0000040 View
  8. Calegari L, Tortorella G, Fettermann D. Getting Connected to M-Health Technologies through a Meta-Analysis. International Journal of Environmental Research and Public Health 2023;20(5):4369 View
  9. Bajunaied K, Hussin N, Kamarudin S. Behavioral intention to adopt FinTech services: An extension of unified theory of acceptance and use of technology. Journal of Open Innovation: Technology, Market, and Complexity 2023;9(1):100010 View
  10. 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
  11. Korn S, Böttcher M, Busse T, Kernebeck S, Breucha M, Ehlers J, Kahlert C, Weitz J, Bork U. Use and Perception of Digital Health Technologies by Surgical Patients in Germany in the Pre–COVID-19 Era: Survey Study. JMIR Formative Research 2022;6(5):e33985 View
  12. Philippi P, Baumeister H, Apolinário-Hagen J, Ebert D, Hennemann S, Kott L, Lin J, Messner E, Terhorst Y. Acceptance towards digital health interventions – Model validation and further development of the Unified Theory of Acceptance and Use of Technology. Internet Interventions 2021;26:100459 View
  13. Schretzlmaier P, Hecker A, Ammenwerth E. Suitability of the Unified Theory of Acceptance and Use of Technology 2 Model for Predicting mHealth Acceptance Using Diabetes as an Example: Qualitative Methods Triangulation Study. JMIR Human Factors 2022;9(1):e34918 View
  14. Garrett P, White J, Dennis S, Lewandowsky S, Yang C, Okan Y, Perfors A, Little D, Kozyreva A, Lorenz-Spreen P, Kusumi T, Kashima Y. Papers Please - Predictive Factors of National and International Attitudes Toward Immunity and Vaccination Passports: Online Representative Surveys. JMIR Public Health and Surveillance 2022;8(7):e32969 View
  15. Schomakers E, Lidynia C, Vervier L, Calero Valdez A, Ziefle M. Applying an Extended UTAUT2 Model to Explain User Acceptance of Lifestyle and Therapy Mobile Health Apps: Survey Study. JMIR mHealth and uHealth 2022;10(1):e27095 View
  16. Kela N, Eytam E, Katz A. Supporting Management of Noncommunicable Diseases With Mobile Health (mHealth) Apps: Experimental Study. JMIR Human Factors 2022;9(1):e28697 View
  17. Hasan N, Sultana R, Bao Y. Re-Conceptualizing the Drivers Toward mHealth Adoption in a Least Developing Country: A Qualitative Exploration. Sage Open 2022;12(2) View
  18. Esber A, Teufel M, Jahre L, in der Schmitten J, Skoda E, Bäuerle A. Predictors of patients’ acceptance of video consultation in general practice during the coronavirus disease 2019 pandemic applying the unified theory of acceptance and use of technology model. DIGITAL HEALTH 2023;9 View
  19. Ha J, Park J, Lee S, Lee J, Choi J, Kim J, Cho S, Jeon G. Predicting Habitual Use of Wearable Health Devices Among Middle-aged Individuals With Metabolic Syndrome Risk Factors in South Korea: Cross-sectional Study. JMIR Formative Research 2023;7:e42087 View
  20. Calegari L, R.D. B, Fettermann D. A meta-analysis of a comprehensive m-health technology acceptance. International Journal of Lean Six Sigma 2024;15(1):1 View
  21. Robinson R, Liday C, Lee S, Williams I, Wright M, An S, Nguyen E. Artificial Intelligence in Health Care—Understanding Patient Information Needs and Designing Comprehensible Transparency: Qualitative Study. JMIR AI 2023;2:e46487 View
  22. Anthony Jnr B. Examining the adoption of telehealth during public health emergencies based on technology organization environment framework. Journal of Science and Technology Policy Management 2023 View
  23. Terhorst Y, Weilbacher N, Suda C, Simon L, Messner E, Sander L, Baumeister H. Acceptance of smart sensing: a barrier to implementation—results from a randomized controlled trial. Frontiers in Digital Health 2023;5 View
  24. 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 View
  25. Lin S, Chuang M, Huang C, Liu C. Nursing Staff’s Behavior Intention to Use Mobile Technology: An Exploratory Study Employing the UTAUT 2 Model. Sage Open 2023;13(4) View
  26. Ning Y, Dong Z, Jia Z, Zhao W, Ding Y, Wang Q, Zhu R, Han S. Development of mobile health-based interventions to promote physical activity in patients with head and neck cancer: a qualitative study. Frontiers in Public Health 2023;11 View
  27. Shaw Jr G, Castro B, Gunn L, Norris K, Thorpe Jr R. The Association of eHealth Literacy Skills and mHealth Application Use Among US Adults With Obesity: Analysis of Health Information National Trends Survey Data. JMIR mHealth and uHealth 2024;12:e46656 View
  28. Wiest I, Sicorello M, Yesmembetov K, Ebert M, Teufel A. Usage Behaviour and Adoption Criteria for Mobile Health Solutions in Patients with Chronic Diseases in Gastroenterology. Visceral Medicine 2024;40(2):61 View
  29. Asiri A, Al-Somali S, Maghrabi R. The Integration of Sustainable Technology and Big Data Analytics in Saudi Arabian SMEs: A Path to Improved Business Performance. Sustainability 2024;16(8):3209 View
  30. Knauer J, Baumeister H, Schmitt A, Terhorst Y. Acceptance of smart sensing, its determinants, and the efficacy of an acceptance-facilitating intervention in people with diabetes: results from a randomized controlled trial. Frontiers in Digital Health 2024;6 View
  31. Aini Q, Manongga D, Rahardja U, Sembiring I, Li Y. Understanding Behavioral Intention to Use of Air Quality Monitoring Solutions with Emphasis on Technology Readiness. International Journal of Human–Computer Interaction 2024:1 View
  32. Fettermann D, Philipi Calegari L. ACEPTACIÓN DE LAS TECNOLOGÍAS DE E-SALUD:UN ESTUDIO DE METAANÁLISIS. Ciência da Informação 2024;52(2) View
  33. Fettermann D, Philipi Calegari L. E-HEALTH TECHNOLOGY ACCEPTANCE: A META-ANALYSIS. Ciência da Informação 2024;52(2) View

Books/Policy Documents

  1. Idachaba F, Idachaba E. Proceedings of the Future Technologies Conference (FTC) 2020, Volume 3. View