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

mHealth in a Clinical Setting (624) Decision Support for Health Professionals (1210) Hypertension Prevention and Treatment (229)

Published on in Vol 7, No 3 (2019): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9869, first published .
Development and Evaluation of a Mobile Decision Support System for Hypertension Management in the Primary Care Setting in Brazil: Mixed-Methods Field Study on Usability, Feasibility, and Utility

Development and Evaluation of a Mobile Decision Support System for Hypertension Management in the Primary Care Setting in Brazil: Mixed-Methods Field Study on Usability, Feasibility, and Utility

Development and Evaluation of a Mobile Decision Support System for Hypertension Management in the Primary Care Setting in Brazil: Mixed-Methods Field Study on Usability, Feasibility, and Utility

Authors of this article:

Daniel Vitório Silveira1, 2, 3 Author Orcid Image ;   Milena Soriano Marcolino1, 2, 3 Author Orcid Image ;   Elaine Leandro Machado2 Author Orcid Image ;   Camila Gonçalves Ferreira2 Author Orcid Image ;   Maria Beatriz Moreira Alkmim1, 2, 3 Author Orcid Image ;   Elmiro Santos Resende2, 4 Author Orcid Image ;   Bárbara Couto Carvalho2 Author Orcid Image ;   André Pires Antunes2, 5 Author Orcid Image ;   Antonio Luiz Pinho Ribeiro1 Author Orcid Image
Article Authors Cited by (26) Tweetations (4) Metrics

Journals

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  2. Santo K, Redfern J. The Potential of mHealth Applications in Improving Resistant Hypertension Self-Assessment, Treatment and Control. Current Hypertension Reports 2019;21(10) View
  3. Daud M, Ramli A, Abdul-Razak S, Isa M, Yusoff F, Baharudin N, Mohamed-Yassin M, Badlishah-Sham S, Nikmat A, Jamil N, Mohd-Nawawi H. The EMPOWER-SUSTAIN e-Health Intervention to improve patient activation and self-management behaviours among individuals with Metabolic Syndrome in primary care: study protocol for a pilot randomised controlled trial. Trials 2020;21(1) View
  4. Marcolino M, Oliveira J, Cimini C, Maia J, Pinto V, Sá T, Amancio K, Coelho L, Ribeiro L, Cardoso C, Ribeiro A. Development and Implementation of a Decision Support System to Improve Control of Hypertension and Diabetes in a Resource-Constrained Area in Brazil: Mixed Methods Study. Journal of Medical Internet Research 2021;23(1):e18872 View
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  7. Hankins J, Shah N, DiMartino L, Brambilla D, Fernandez M, Gibson R, Gordeuk V, Lottenberg R, Kutlar A, Melvin C, Simon J, Wun T, Treadwell M, Calhoun C, Baumann A, Potter M, Klesges L, Bosworth H. Integration of Mobile Health Into Sickle Cell Disease Care to Increase Hydroxyurea Utilization: Protocol for an Efficacy and Implementation Study. JMIR Research Protocols 2020;9(7):e16319 View
  8. Diao X, Huo Y, Yan Z, Wang H, Yuan J, Wang Y, Cai J, Zhao W. An Application of Machine Learning to Etiological Diagnosis of Secondary Hypertension: Retrospective Study Using Electronic Medical Records. JMIR Medical Informatics 2021;9(1):e19739 View
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  10. Tabaeeian R, Hajrahimi B, Khoshfetrat A. A systematic review of telemedicine systems use barriers: primary health care providers' perspective. Journal of Science and Technology Policy Management 2024;15(3):610 View
  11. Xiong S, Lu H, Peoples N, Duman E, Najarro A, Ni Z, Gong E, Yin R, Ostbye T, Palileo-Villanueva L, Doma R, Kafle S, Tian M, Yan L. Digital health interventions for non-communicable disease management in primary health care in low-and middle-income countries. npj Digital Medicine 2023;6(1) View
  12. Moon K, Son C, Lee J, Park M. The development of a web-based app employing machine learning for delirium prevention in long-term care facilities in South Korea. BMC Medical Informatics and Decision Making 2022;22(1) View
  13. Dorr D, Richardson J, Bobo M, D'Autremont C, Rope R, Dunne M, Kassakian S, Samal L. Provider Perspectives on Patient- and Provider-Facing High Blood Pressure Clinical Decision Support. Applied Clinical Informatics 2022;13(05):1131 View
  14. Akinsulore A, Aloba O, Oginni O, Oloniniyi I, Ibigbami O, Seun-Fadipe C, Opakunle T, Owojuyigbe A, Olibamoyo O, Mapayi B, Okorie V, Adewuya A. Developing an mHealth Intervention to Reduce COVID-19–Associated Psychological Distress Among Health Care Workers in Nigeria: Protocol for a Design and Feasibility Study. JMIR Research Protocols 2022;11(11):e36174 View
  15. Cimini C, Maia J, Pires M, Ribeiro L, Pinto V, Batchelor J, Ribeiro A, Marcolino M. Pandemic-Related Impairment in the Monitoring of Patients With Hypertension and Diabetes and the Development of a Digital Solution for the Community Health Worker: Quasiexperimental and Implementation Study. JMIR Medical Informatics 2022;10(3):e35216 View
  16. Randine P, Sharma A, Hartvigsen G, Johansen H, Årsand E. Information and communication technology-based interventions for chronic diseases consultation: Scoping review. International Journal of Medical Informatics 2022;163:104784 View
  17. Zha H, Liu K, Tang T, Yin Y, Dou B, Jiang L, Yan H, Tian X, Wang R, Xie W. Acceptance of clinical decision support system to prevent venous thromboembolism among nurses: an extension of the UTAUT model. BMC Medical Informatics and Decision Making 2022;22(1) View
  18. Hogg H, Al-Zubaidy M, Talks J, Denniston A, Kelly C, Malawana J, Papoutsi C, Teare M, Keane P, Beyer F, Maniatopoulos G. Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence. Journal of Medical Internet Research 2023;25:e39742 View
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  20. Prieto-Díaz M, Méndez-Rodríguez E, Murillo-García D, Escobar-Cervantes C, Pallarés-Carratalá V, Díaz-Rodríguez A, Escribano-Serrano J, Polo-García J, Cinza-Sanjurjo S. Utilidad de una aplicación web interactiva en la mejora del control de los factores de riesgo cardiovascular. Proyecto Control-RCV. Medicina de Familia. SEMERGEN 2022;48(6):411 View
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