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

Journals

  1. Biviji R, Vest J, Dixon B, Cullen T, Harle C. Content analysis of behavior change techniques in maternal and infant health apps. Translational Behavioral Medicine 2021;11(2):504 View
  2. Biviji R, Vest J, Dixon B, Cullen T, Harle C. Factors Related to User Ratings and User Downloads of Mobile Apps for Maternal and Infant Health: A Cross-Sectional Study. JMIR mHealth and uHealth 2020;8(1):e15663 View
  3. Vaccari N, da Silveira L, Bortolini M, Haddad J, Baracat E, Ferreira E. Content and functionality features of voiding diary applications for mobile devices in Brazil: a descriptive analysis. International Urogynecology Journal 2020;31(12):2573 View
  4. Bondaronek P, Alkhaldi G, Slee A, Hamilton F, Murray E. Quality of Publicly Available Physical Activity Apps: Review and Content Analysis. JMIR mHealth and uHealth 2018;6(3):e53 View
  5. Pan Y, Chen H, Chen H, Jin X, Zhu Y, Chen G. Is electronic follow-up using a mobile phone application after mid-urethral sling placement feasible and efficient?. World Journal of Urology 2021;39(3):863 View
  6. Vaggers S, Puri P, Wagenlehner F, Somani B. A Content Analysis of Mobile Phone Applications for the Diagnosis, Treatment, and Prevention of Urinary Tract Infections, and Their Compliance with European Association of Urology Guidelines on Urological Infections. European Urology Focus 2021;7(1):198 View
  7. Naderi H, Etminani K. What Android mHealth Apps in Iranian App Store ‘Cafebazaar’ Have More Chance of Download. Shiraz E-Medical Journal 2018;In Press(In Press) View
  8. Tabi K, Randhawa A, Choi F, Mithani Z, Albers F, Schnieder M, Nikoo M, Vigo D, Jang K, Demlova R, Krausz M. Mobile Apps for Medication Management: Review and Analysis. JMIR mHealth and uHealth 2019;7(9):e13608 View
  9. Maheu M, Nicolucci V, Pulier M, Wall K, Frye T, Hudlicka E. The Interactive Mobile App Review Toolkit (IMART): a Clinical Practice-Oriented System. Journal of Technology in Behavioral Science 2016;1(1-4):3 View
  10. Chen J, Lieffers J, Bauman A, Hanning R, Allman-Farinelli M. Designing Health Apps to Support Dietetic Professional Practice and Their Patients: Qualitative Results From an International Survey. JMIR mHealth and uHealth 2017;5(3):e40 View
  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
  13. Pulier M, Daviss S. A Call for a Global Digital Health Consortium. Journal of Technology in Behavioral Science 2016;1(1-4):16 View
  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
  15. Panatto D, Domnich A, Gasparini R, Bonanni P, Icardi G, Amicizia D, Arata L, Carozzo S, Signori A, Bechini A, Boccalini S. An eHealth Project on Invasive Pneumococcal Disease: Comprehensive Evaluation of a Promotional Campaign. Journal of Medical Internet Research 2016;18(12):e316 View
  16. Sales R, Silva R. mHealth na prevenção das Infecções Sexualmente Transmissíveis (IST). Ciência & Saúde Coletiva 2020;25(11):4315 View
  17. Mehta P, Moore S, Bull S, Kwan B. Building MedVenture – A mobile health application to improve adolescent medication adherence – Using a multidisciplinary approach and academic–industry collaboration. DIGITAL HEALTH 2021;7:205520762110198 View
  18. Aldughayfiq B, Sampalli S. A framework to lower the risk of medication prescribing and dispensing errors: A usability study of an NFC-based mobile application. International Journal of Medical Informatics 2021;153:104509 View
  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 2021:1 View