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

Focus Groups and Qualitative Research for Human Factors Research (781) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1317) Mobile Health (mhealth) (2777) Diabetes (154) Diabetes Self-Management (464) Usability of Apps and User Perceptions of mHealth (902) Adoption and Change Management of eHealth Systems (677) mHealth for Wellness, Behavior Change and Prevention (2754) Theories, Models, and Frameworks in Human Factors (63)

Published on in Vol 9, No 1 (2022): Jan-Mar

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34918, first published .
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

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

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

Authors of this article:

Patrik Schretzlmaier1 Author Orcid Image ;   Achim Hecker2, 3 Author Orcid Image ;   Elske Ammenwerth1 Author Orcid Image
Article Authors Cited by (10) Tweetations (4) Metrics

Journals

  1. 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
  2. Alhur M, Alshamari S, Oláh J, Aldreabi H. Unsupervised Machine Learning to Identify Positive and Negative Themes in Jordanian mHealth Apps. International Journal of E-Services and Mobile Applications 2022;14(1):1 View
  3. Thérouanne P, Hayotte M, Halgand F, d'Arripe-Longueville F. The Acceptability of Technology-Based Physical Activity Interventions in Postbariatric Surgery Women: Insights From Qualitative Analysis Using the Unified Theory of Acceptance and Use of Technology 2 Model. JMIR Human Factors 2023;10:e42178 View
  4. Edo O, Ang D, Etu E, Tenebe I, Edo S, Diekola O. Why do healthcare workers adopt digital health technologies - A cross-sectional study integrating the TAM and UTAUT model in a developing economy. International Journal of Information Management Data Insights 2023;3(2):100186 View
  5. Conway A, Ryan A, Harkin D, Mc Cauley C. “It’s Another Feather in My Hat”-Exploring Factors Influencing the Adoption of Apps With People Living With Dementia. Dementia 2023;22(7):1487 View
  6. Jibb L, Sivaratnam S, Hashemi E, Chu C, Nathan P, Chartrand J, Alberts N, Masama T, Pease H, Torres L, Cortes H, Zworth M, Kuczynski S, Fortier M, Ayatollahi H. Parent and clinician perceptions and recommendations on a pediatric cancer pain management app: A qualitative co-design study. PLOS Digital Health 2023;2(11):e0000169 View
  7. Zolfaqari Z, Ayatollahi H, Ranjbar F, Abasi A. Motivating and inhibiting factors influencing the application of mhealth technology in post-abortion care: a review study. BMC Pregnancy and Childbirth 2024;24(1) View
  8. Huang W, Ong W, Wong M, Ng E, Koh T, Chandramouli C, Ng C, Hummel Y, Huang F, Lam C, Tromp J. Applying the UTAUT2 framework to patients’ attitudes toward healthcare task shifting with artificial intelligence. BMC Health Services Research 2024;24(1) View
  9. Mosleh S, Alsaadi F, Alnaqbi F, Alkhzaimi M, Alnaqbi S, Alsereidi W. Examining the association between emotional intelligence and chatbot utilization in education: A cross-sectional examination of undergraduate students in the UAE. Heliyon 2024;10(11):e31952 View
  10. Roy P. What drives the adoption of data analytics at Australian university libraries in the perspective of UTAUT2?. The Journal of Academic Librarianship 2024;50(5):102927 View