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: 35774576)

  • 1. Understanding the Drivers of Ghanaian Citizens' Adoption Intentions of Mobile Health Services.
    Mensah IK
    Front Public Health; 2022; 10():906106. PubMed ID: 35774576
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. The behavioral intention to adopt mobile health services: The moderating impact of mobile self-efficacy.
    Mensah IK; Zeng G; Mwakapesa DS
    Front Public Health; 2022; 10():1020474. PubMed ID: 36238232
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Predictive factors of telemedicine service acceptance and behavioral intention of physicians.
    Rho MJ; Choi IY; Lee J
    Int J Med Inform; 2014 Aug; 83(8):559-71. PubMed ID: 24961820
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Continuous usage intention of mobile health services: model construction and validation.
    Nie L; Oldenburg B; Cao Y; Ren W
    BMC Health Serv Res; 2023 May; 23(1):442. PubMed ID: 37143005
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Moderating factors influencing adoption of a mobile chronic disease management system in China.
    Zhu Z; Liu Y; Che X; Chen X
    Inform Health Soc Care; 2018 Jan; 43(1):22-41. PubMed ID: 28068149
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Understanding the Role of Mobile Internet-Based Health Services on Patient Satisfaction and Word-of-Mouth.
    Gu D; Yang X; Li X; Jain HK; Liang C
    Int J Environ Res Public Health; 2018 Sep; 15(9):. PubMed ID: 30201921
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. Predictive factors of physicians' satisfaction with telemedicine services acceptance.
    Kissi J; Dai B; Dogbe CS; Banahene J; Ernest O
    Health Informatics J; 2020 Sep; 26(3):1866-1880. PubMed ID: 31854222
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Understanding Post-Adoption Behavioral Intentions of Mobile Health Service Users: An Empirical Study during COVID-19.
    Jiang Y; Lau AKW
    Int J Environ Res Public Health; 2023 Feb; 20(5):. PubMed ID: 36900918
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Proposing a mobile apps acceptance model for users in the health area: A systematic literature review and meta-analysis.
    Binyamin SS; Zafar BA
    Health Informatics J; 2021; 27(1):1460458220976737. PubMed ID: 33438494
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Healthcare at Your Fingertips: The Acceptance and Adoption of Mobile Medical Treatment Services among Chinese Users.
    Li Q
    Int J Environ Res Public Health; 2020 Sep; 17(18):. PubMed ID: 32967230
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Intention to Use Mobile-Based Partograph and Its Predictors Among Obstetric Health Care Providers Working at Public Referral Hospitals in the Oromia Region of Ethiopia in 2022: Cross-Sectional Questionnaire Study.
    Tilahun KN; Adem JB; Atinafu WT; Walle AD; Mengestie ND; Birhanu AY
    Online J Public Health Inform; 2024 May; 16():e51601. PubMed ID: 38728079
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Measuring Success of Patients' Continuous Use of Mobile Health Services for Self-management of Chronic Conditions: Model Development and Validation.
    Song T; Deng N; Cui T; Qian S; Liu F; Guan Y; Yu P
    J Med Internet Res; 2021 Jul; 23(7):e26670. PubMed ID: 34255685
    [TBL] [Abstract][Full Text] [Related]  

  • 15. User acceptance of mobile health services from users' perspectives: The role of self-efficacy and response-efficacy in technology acceptance.
    Zhang X; Han X; Dang Y; Meng F; Guo X; Lin J
    Inform Health Soc Care; 2017 Mar; 42(2):194-206. PubMed ID: 27564428
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model.
    Hoque R; Sorwar G
    Int J Med Inform; 2017 May; 101():75-84. PubMed ID: 28347450
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Adoption of Health Mobile Apps during the COVID-19 Lockdown: A Health Belief Model Approach.
    Alharbi NS; AlGhanmi AS; Fahlevi M
    Int J Environ Res Public Health; 2022 Mar; 19(7):. PubMed ID: 35409862
    [TBL] [Abstract][Full Text] [Related]  

  • 19. The Determinants of User Acceptance of Mobile Medical Platforms: An Investigation Integrating the TPB, TAM, and Patient-Centered Factors.
    Wang H; Zhang J; Luximon Y; Qin M; Geng P; Tao D
    Int J Environ Res Public Health; 2022 Aug; 19(17):. PubMed ID: 36078473
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Individuals' adoption of smart technologies for preventive health care: a structural equation modeling approach.
    Bettiga D; Lamberti L; Lettieri E
    Health Care Manag Sci; 2020 Jun; 23(2):203-214. PubMed ID: 30684067
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.