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

Evaluation and Research Methodology for mHealth (193)

Published on in Vol 4, No 3 (2016): Jul-Sept

Expert Involvement Predicts mHealth App Downloads: Multivariate Regression Analysis of Urology Apps

Expert Involvement Predicts mHealth App Downloads: Multivariate Regression Analysis of Urology Apps

Expert Involvement Predicts mHealth App Downloads: Multivariate Regression Analysis of Urology Apps

Authors of this article:

Nuno Pereira-Azevedo1, 2 Author Orcid Image ;   Luís Osório2 Author Orcid Image ;   Vitor Cavadas2 Author Orcid Image ;   Avelino Fraga2 Author Orcid Image ;   Eduardo Carrasquinho3 Author Orcid Image ;   Eduardo Cardoso de Oliveira3 Author Orcid Image ;   Miguel Castelo-Branco4 Author Orcid Image ;   Monique J Roobol1 Author Orcid Image
Article Authors Cited by (28) Tweetations (4) Metrics

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  11. Wang Y, Wang Y, Greene B, Sun L. An analysis and evaluation of quality and behavioral change techniques among physical activity apps in China. International Journal of Medical Informatics 2020;133:104029 View
  12. Pereira-Azevedo N, Osório L, Fraga A, Roobol M. Rotterdam Prostate Cancer Risk Calculator: Development and Usability Testing of the Mobile Phone App. JMIR Cancer 2017;3(1):e1 View
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  14. Pinheiro M, Serra M, Pereira-Azevedo N. Predictors of the Number of Installs in Psychiatry Smartphone Apps: Systematic Search on App Stores and Content Analysis. JMIR Mental Health 2019;6(11):e15064 View
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  16. Sales R, Silva R. mHealth na prevenção das Infecções Sexualmente Transmissíveis (IST). Ciência & Saúde Coletiva 2020;25(11):4315 View
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  19. Zhang R, Zou D, Xie H. Spaced repetition for authentic mobile-assisted word learning: nature, learner perceptions, and factors leading to positive perceptions. Computer Assisted Language Learning 2022;35(9):2593 View
  20. Wang E, Breyer B, Lee A, Rios N, Oni-Orisan A, Steinman M, Sim I, Kenfield S, Bauer S. Perceptions of Older Men Using a Mobile Health App to Monitor Lower Urinary Tract Symptoms and Tamsulosin Side Effects: Mixed Methods Study. JMIR Human Factors 2021;8(4):e30767 View
  21. Hayman M, Alfrey K, Waters K, Cannon S, Mielke G, Keating S, Mena G, Mottola M, Evenson K, Davenport M, Barlow S, Budzynski-Seymour E, Comardelle N, Dickey M, Harrison C, Kebbe M, Moholdt T, Moran L, Nagpal T, Schoeppe S, Alley S, Brown W, Williams S, Vincze L. Evaluating Evidence-Based Content, Features of Exercise Instruction, and Expert Involvement in Physical Activity Apps for Pregnant Women: Systematic Search and Content Analysis. JMIR mHealth and uHealth 2022;10(1):e31607 View
  22. Brammall B, Garad R, Boyle J, Hayman M, de Jersey S, Teede H, Hong Q, Carrandi A, Harrison C. Assessing the Content and Quality of Digital Tools for Managing Gestational Weight Gain: Systematic Search and Evaluation. Journal of Medical Internet Research 2022;24(11):e37552 View
  23. García-Sánchez S, Somoza-Fernández B, de Lorenzo-Pinto A, Ortega-Navarro C, Herranz-Alonso A, Sanjurjo M. Mobile Health Apps Providing Information on Drugs for Adult Emergency Care: Systematic Search on App Stores and Content Analysis. JMIR mHealth and uHealth 2022;10(4):e29985 View
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  25. Praus F, Krzowski B, Walther T, Gratzke C, Balsam P, Miernik A, Pohlmann P. Smartphone Apps for Managing Antithrombotic Therapy: Scoping Literature Review. JMIR Cardio 2022;6(1):e29481 View
  26. Westerlinck P, Coucke P. Review of interactive digital solutions improving health literacy of personal cancer risks in the general public. International Journal of Medical Informatics 2021;154:104564 View
  27. Chen T, Lee S, Ma W. Attributes, Quality, and Downloads of Dementia-Related Mobile Apps for Patients With Dementia and Their Caregivers: App Review and Evaluation Study. JMIR Formative Research 2024;8:e51076 View
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