These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

191 related articles for article (PubMed ID: 36901379)

  • 1. Getting Connected to M-Health Technologies through a Meta-Analysis.
    Calegari LP; Tortorella GL; Fettermann DC
    Int J Environ Res Public Health; 2023 Feb; 20(5):. PubMed ID: 36901379
    [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. 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]  

  • 4. Empirical Investigation of Factors Influencing Consumer Intention to Use an Artificial Intelligence-Powered Mobile Application for Weight Loss and Health Management.
    Huang CY; Yang MC
    Telemed J E Health; 2020 Oct; 26(10):1240-1251. PubMed ID: 31971883
    [No Abstract]   [Full Text] [Related]  

  • 5. Anatomy in the metaverse: Exploring student technology acceptance through the UTAUT2 model.
    Kalınkara Y; Özdemir O
    Anat Sci Educ; 2024 Mar; 17(2):319-336. PubMed ID: 37942914
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Understanding consumer acceptance of healthcare wearable devices: An integrated model of UTAUT and TTF.
    Wang H; Tao D; Yu N; Qu X
    Int J Med Inform; 2020 Jul; 139():104156. PubMed ID: 32387819
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Technology Experience of Solid Organ Transplant Patients and Their Overall Willingness to Use Interactive Health Technology.
    Vanhoof JMM; Vandenberghe B; Geerts D; Philippaerts P; De Mazière P; DeVito Dabbs A; De Geest S; Dobbels F;
    J Nurs Scholarsh; 2018 Mar; 50(2):151-162. PubMed ID: 29193654
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Adoption of Mobile Health Apps in Dietetic Practice: Case Study of Diyetkolik.
    Akdur G; Aydin MN; Akdur G
    JMIR Mhealth Uhealth; 2020 Oct; 8(10):e16911. PubMed ID: 33006566
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Unveiling the adoption of metaverse technology in Bangkok metropolitan areas: A UTAUT2 perspective with social media marketing and consumer engagement.
    Sritong C; Sawangproh W; Teangsompong T
    PLoS One; 2024; 19(6):e0304496. PubMed ID: 38848432
    [TBL] [Abstract][Full Text] [Related]  

  • 11. The impact of users' trust on intention to use the mobile medical platform: Evidence from China.
    He J
    Front Public Health; 2023; 11():1076367. PubMed ID: 37026131
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Development of a tripolar model of technology acceptance: Hospital-based physicians' perspective on EHR.
    Beglaryan M; Petrosyan V; Bunker E
    Int J Med Inform; 2017 Jun; 102():50-61. PubMed ID: 28495348
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Consumer Adoption of Future MyData-Based Preventive eHealth Services: An Acceptance Model and Survey Study.
    Koivumäki T; Pekkarinen S; Lappi M; Väisänen J; Juntunen J; Pikkarainen M
    J Med Internet Res; 2017 Dec; 19(12):e429. PubMed ID: 29273574
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Keep Using My Health Apps: Discover Users' Perception of Health and Fitness Apps with the UTAUT2 Model.
    Yuan S; Ma W; Kanthawala S; Peng W
    Telemed J E Health; 2015 Sep; 21(9):735-41. PubMed ID: 25919238
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Wearable Technology Acceptance in Health Care Based on National Culture Differences: Cross-Country Analysis Between Chinese and Swiss Consumers.
    Yang Meier D; Barthelmess P; Sun W; Liberatore F
    J Med Internet Res; 2020 Oct; 22(10):e18801. PubMed ID: 33090108
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Application and extension of the UTAUT2 model for determining behavioral intention factors in use of the artificial intelligence virtual assistants.
    García de Blanes Sebastián M; Sarmiento Guede JR; Antonovica A
    Front Psychol; 2022; 13():993935. PubMed ID: 36329748
    [TBL] [Abstract][Full Text] [Related]  

  • 18. What Factors Affect a User's Intention to Use Fitness Applications? The Moderating Effect of Health Status: A Cross-Sectional Study.
    Kim B; Lee E
    Inquiry; 2022; 59():469580221095826. PubMed ID: 35580021
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Technology anxiety and resistance to change behavioral study of a wearable cardiac warming system using an extended TAM for older adults.
    Tsai TH; Lin WY; Chang YS; Chang PC; Lee MY
    PLoS One; 2020; 15(1):e0227270. PubMed ID: 31929560
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 10.