Published on in Vol 4, No 1 (2016): Jan-Mar

This is a member publication of Centre for Global eHealth Innovation, Toronto, ON, Canada

Uptake of a Consumer-Focused mHealth Application for the Assessment and Prevention of Heart Disease: The <30 Days Study

Uptake of a Consumer-Focused mHealth Application for the Assessment and Prevention of Heart Disease: The <30 Days Study

Uptake of a Consumer-Focused mHealth Application for the Assessment and Prevention of Heart Disease: The <30 Days Study

Journals

  1. Pham Q, Cafazzo J, Feifer A. Adoption, Acceptability, and Effectiveness of a Mobile Health App for Personalized Prostate Cancer Survivorship Care: Protocol for a Realist Case Study of the Ned App. JMIR Research Protocols 2017;6(10):e197 View
  2. Matthews P, Topham P, Caleb-Solly P. Interaction and Engagement with an Anxiety Management App: Analysis Using Large-Scale Behavioral Data. JMIR Mental Health 2018;5(4):e58 View
  3. Reading M, Merrill J. Converging and diverging needs between patients and providers who are collecting and using patient-generated health data: an integrative review. Journal of the American Medical Informatics Association 2018;25(6):759 View
  4. Pham Q, Graham G, Carrion C, Morita P, Seto E, Stinson J, Cafazzo J. A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer mHealth Apps for Chronic Conditions: Scoping Review. JMIR mHealth and uHealth 2019;7(1):e11941 View
  5. Mitchell M, White L, Oh P, Alter D, Leahey T, Kwan M, Faulkner G. Uptake of an Incentive-Based mHealth App: Process Evaluation of the Carrot Rewards App. JMIR mHealth and uHealth 2017;5(5):e70 View
  6. Pham Q, Wiljer D, Cafazzo J. Beyond the Randomized Controlled Trial: A Review of Alternatives in mHealth Clinical Trial Methods. JMIR mHealth and uHealth 2016;4(3):e107 View
  7. Liao G, Chien Y, Chen Y, Hsiung H, Chen H, Hsieh M, Wu W. What to Build for Middle-Agers to Come? Attractive and Necessary Functions of Exercise-Promotion Mobile Phone Apps: A Cross-Sectional Study. JMIR mHealth and uHealth 2017;5(5):e65 View
  8. Mitchell M, White L, Lau E, Leahey T, Adams M, Faulkner G. Evaluating the Carrot Rewards App, a Population-Level Incentive-Based Intervention Promoting Step Counts Across Two Canadian Provinces: Quasi-Experimental Study. JMIR mHealth and uHealth 2018;6(9):e178 View
  9. Morita P, Yeung M, Ferrone M, Taite A, Madeley C, Stevens Lavigne A, To T, Lougheed M, Gupta S, Day A, Cafazzo J, Licskai C. A Patient-Centered Mobile Health System That Supports Asthma Self-Management (breathe): Design, Development, and Utilization. JMIR mHealth and uHealth 2019;7(1):e10956 View
  10. Reiners F, Sturm J, Bouw L, Wouters E. Sociodemographic Factors Influencing the Use of eHealth in People with Chronic Diseases. International Journal of Environmental Research and Public Health 2019;16(4):645 View
  11. Brower J, LaBarge M, White L, Mitchell M. Examining Responsiveness to an Incentive-Based Mobile Health App: Longitudinal Observational Study. Journal of Medical Internet Research 2020;22(8):e16797 View
  12. Shah L, Yang W, Demo R, Lee M, Weng D, Shan R, Wongvibulsin S, Spaulding E, Marvel F, Martin S. Technical Guidance for Clinicians Interested in Partnering With Engineers in Mobile Health Development and Evaluation. JMIR mHealth and uHealth 2019;7(5):e14124 View
  13. Kabeza C, Harst L, Schwarz P, Timpel P. A qualitative study of users’ experiences after 3 months: the first Rwandan diabetes self-management Smartphone application “Kir’App”. Therapeutic Advances in Endocrinology and Metabolism 2020;11:204201882091451 View
  14. Chin W, Kurowski A, Gore R, Chen G, Punnett L. Use of a Mobile App for the Process Evaluation of an Intervention in Health Care: Development and Usability Study. JMIR Formative Research 2021;5(10):e20739 View
  15. Shah L, Ding J, Spaulding E, Yang W, Lee M, Demo R, Marvel F, Martin S. Sociodemographic Characteristics Predicting Digital Health Intervention Use After Acute Myocardial Infarction. Journal of Cardiovascular Translational Research 2021;14(5):951 View
  16. Bevens W, Gray K, Neate S, Nag N, Weiland T, Jelinek G, Simpson-Yap S. Characteristics of mHealth app use in an international sample of people with multiple sclerosis. Multiple Sclerosis and Related Disorders 2021;54:103092 View
  17. Wu D, An J, Yu P, Lin H, Ma L, Duan H, Deng N. Patterns for Patient Engagement with the Hypertension Management and Effects of Electronic Health Care Provider Follow-up on These Patterns: Cluster Analysis. Journal of Medical Internet Research 2021;23(9):e25630 View
  18. Mbotwa C, Kazaura M, Moen K, Leshabari M, Metta E, Leyna G, Mmbaga E. Predictors of mHealth use in promoting adherence to pre-exposure prophylaxis among female sex workers: an evaluation of the Jichunge intervention in Dar es Salaam, Tanzania. BMC Health Services Research 2022;22(1) View
  19. Elnaggar A, von Oppenfeld J, Whooley M, Merek S, Park L. Applying Mobile Technology to Sustain Physical Activity After Completion of Cardiac Rehabilitation: Acceptability Study. JMIR Human Factors 2021;8(3):e25356 View
  20. Wu D, Huyan X, She Y, Hu J, Duan H, Deng N. Exploring and Characterizing Patient Multibehavior Engagement Trails and Patient Behavior Preference Patterns in Pathway-Based mHealth Hypertension Self-Management: Analysis of Use Data. JMIR mHealth and uHealth 2022;10(2):e33189 View
  21. Kay M, Miller H, Askew S, Spaulding E, Chisholm M, Christy J, Yang Q, Steinberg D. Patterns of Engagement With an Application-Based Dietary Self-Monitoring Tool Within a Randomized Controlled Feasibility Trial. AJPM Focus 2022;1(2):100037 View
  22. Rodríguez-Fernández J, Danies E, Hoertel N, Galanter W, Saner H, Franco O. Telemedicine Readiness Across Medical Conditions in a US National Representative Sample of Older Adults. Journal of Applied Gerontology 2022;41(4):982 View

Books/Policy Documents

  1. Morita P. Design for Health. View
  2. Dorronzoro-Zubiete E, Rivera-Romero O, Nuñez-Benjumea F, Cervera-Torres S. Personalized Health Systems for Cardiovascular Disease. View