205 related articles for article (PubMed ID: 35262493)
1. 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.
Schretzlmaier P; Hecker A; Ammenwerth E
JMIR Hum Factors; 2022 Mar; 9(1):e34918. PubMed ID: 35262493
[TBL] [Abstract][Full Text] [Related]
2. 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.
Schretzlmaier P; Hecker A; Ammenwerth E
BMJ Health Care Inform; 2022 Nov; 29(1):. PubMed ID: 36379608
[TBL] [Abstract][Full Text] [Related]
3. Predicting mHealth Acceptance Using the UTAUT2 Technology Acceptance Model: A Mixed-Methods Approach.
Schretzlmaier P; Hecker A; Ammenwerth E
Stud Health Technol Inform; 2023 May; 301():26-32. PubMed ID: 37172148
[TBL] [Abstract][Full Text] [Related]
4. Applying an Extended UTAUT2 Model to Explain User Acceptance of Lifestyle and Therapy Mobile Health Apps: Survey Study.
Schomakers EM; Lidynia C; Vervier LS; Calero Valdez A; Ziefle M
JMIR Mhealth Uhealth; 2022 Jan; 10(1):e27095. PubMed ID: 35040801
[TBL] [Abstract][Full Text] [Related]
5. The role of trust and habit in the adoption of mHealth by older adults in Hong Kong: a healthcare technology service acceptance (HTSA) model.
Liu JYW; Sorwar G; Rahman MS; Hoque MR
BMC Geriatr; 2023 Feb; 23(1):73. PubMed ID: 36737712
[TBL] [Abstract][Full Text] [Related]
6. Factors affecting physicians using mobile health applications: an empirical study.
Wu P; Zhang R; Luan J; Zhu M
BMC Health Serv Res; 2022 Jan; 22(1):24. PubMed ID: 34983501
[TBL] [Abstract][Full Text] [Related]
7. Comparing the Acceptance of Mobile Hypertension Apps for Disease Management Among Patients Versus Clinical Use Among Physicians: Cross-sectional Survey.
Breil B; Salewski C; Apolinário-Hagen J
JMIR Cardio; 2022 Jan; 6(1):e31617. PubMed ID: 34989683
[TBL] [Abstract][Full Text] [Related]
8. Factors Influencing Continued Usage Behavior on Mobile Health Applications.
Wu P; Zhang R; Zhu X; Liu M
Healthcare (Basel); 2022 Jan; 10(2):. PubMed ID: 35206823
[TBL] [Abstract][Full Text] [Related]
9. Patient Acceptance of Prescribed and Fully Reimbursed mHealth Apps in Germany: An UTAUT2-based Online Survey Study.
Uncovska M; Freitag B; Meister S; Fehring L
J Med Syst; 2023 Jan; 47(1):14. PubMed ID: 36705853
[TBL] [Abstract][Full Text] [Related]
10. 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.
Thérouanne P; Hayotte M; Halgand F; d'Arripe-Longueville F
JMIR Hum Factors; 2023 Jan; 10():e42178. PubMed ID: 36689255
[TBL] [Abstract][Full Text] [Related]
11. Usage Behaviour and Adoption Criteria for Mobile Health Solutions in Patients with Chronic Diseases in Gastroenterology.
Wiest IC; Sicorello M; Yesmembetov K; Ebert MP; Teufel A
Visc Med; 2024 Apr; 40(2):61-74. PubMed ID: 38584857
[TBL] [Abstract][Full Text] [Related]
12. Acceptance of mHealth Apps for Self-Management Among People with Hypertension.
Breil B; Kremer L; Hennemann S; Apolinário-Hagen J
Stud Health Technol Inform; 2019 Sep; 267():282-288. PubMed ID: 31483283
[TBL] [Abstract][Full Text] [Related]
13. Evaluating an mHealth App for Health and Well-Being at Work: Mixed-Method Qualitative Study.
de Korte EM; Wiezer N; Janssen JH; Vink P; Kraaij W
JMIR Mhealth Uhealth; 2018 Mar; 6(3):e72. PubMed ID: 29592846
[TBL] [Abstract][Full Text] [Related]
14. Assessment of the Intention to Use Mobile Health Applications Using a Technology Acceptance Model in an Israeli Adult Population.
Shemesh T; Barnoy S
Telemed J E Health; 2020 Sep; 26(9):1141-1149. PubMed ID: 31930955
[No Abstract] [Full Text] [Related]
15. Smartphone Users' Persuasion Knowledge in the Context of Consumer mHealth Apps: Qualitative Study.
Joo E; Kononova A; Kanthawala S; Peng W; Cotten S
JMIR Mhealth Uhealth; 2021 Apr; 9(4):e16518. PubMed ID: 33847596
[TBL] [Abstract][Full Text] [Related]
16. Mobile phone applications and their use in the self-management of Type 2 Diabetes Mellitus: a qualitative study among app users and non-app users.
Jeffrey B; Bagala M; Creighton A; Leavey T; Nicholls S; Wood C; Longman J; Barker J; Pit S
Diabetol Metab Syndr; 2019; 11():84. PubMed ID: 31636719
[TBL] [Abstract][Full Text] [Related]
17. Utilizing Health Behavior Change and Technology Acceptance Models to Predict the Adoption of COVID-19 Contact Tracing Apps: Cross-sectional Survey Study.
Tomczyk S; Barth S; Schmidt S; Muehlan H
J Med Internet Res; 2021 May; 23(5):e25447. PubMed ID: 33882016
[TBL] [Abstract][Full Text] [Related]
18. Determinant Factors of Public Acceptance of Stress Management Apps: Survey Study.
Apolinário-Hagen J; Hennemann S; Fritsche L; Drüge M; Breil B
JMIR Ment Health; 2019 Nov; 6(11):e15373. PubMed ID: 31697243
[TBL] [Abstract][Full Text] [Related]
19. Identifying Mobile Health Engagement Stages: Interviews and Observations for Developing Brief Message Content.
Burns K; Nicholas R; Beatson A; Chamorro-Koc M; Blackler A; Gottlieb U
J Med Internet Res; 2020 Sep; 22(9):e15307. PubMed ID: 32960181
[TBL] [Abstract][Full Text] [Related]
20. Factors Predicting Older People's Acceptance of a Personalized Health Care Service App and the Effect of Chronic Disease: Cross-Sectional Questionnaire Study.
Koo JH; Park YH; Kang DR
JMIR Aging; 2023 Jun; 6():e41429. PubMed ID: 37342076
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
