Published on in Vol 23, No 6 (2021): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25958, first published .
Preferences for mHealth Technology and Text Messaging Communication in Patients With Type 2 Diabetes: Qualitative Interview Study

Preferences for mHealth Technology and Text Messaging Communication in Patients With Type 2 Diabetes: Qualitative Interview Study

Preferences for mHealth Technology and Text Messaging Communication in Patients With Type 2 Diabetes: Qualitative Interview Study

Journals

  1. Kwan Y, Ong Z, Choo D, Phang J, Yoon S, Low L. A Mobile Application to Improve Diabetes Self-Management Using Rapid Prototyping: Iterative Co-Design Approach in Asian Settings. Patient Preference and Adherence 2023;Volume 17:1 View
  2. Zamanillo-Campos R, Serrano-Ripoll M, Taltavull-Aparicio J, Gervilla-García E, Ripoll J, Fiol-deRoque M, Boylan A, Ricci-Cabello I. Patients’ Views on the Design of DiabeText, a New mHealth Intervention to Improve Adherence to Oral Antidiabetes Medication in Spain: A Qualitative Study. International Journal of Environmental Research and Public Health 2022;19(3):1902 View
  3. Peacock E, Craig L, Krousel-Wood M. Electronic health strategies to improve medication adherence in patients with cardiometabolic disease: current status and future directions. Current Opinion in Cardiology 2022;37(4):307 View
  4. Lauffenburger J, Yom-Tov E, Keller P, McDonnell M, Bessette L, Fontanet C, Sears E, Kim E, Hanken K, Buckley J, Barlev R, Haff N, Choudhry N. REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised trial. BMJ Open 2021;11(12):e052091 View
  5. Brew-Sam N, Desborough J, Parkinson A, Murugappan K, Daskalaki E, Brown E, Ebbeck H, Pedley L, Hannon K, Brown K, Pedley E, Ebbeck G, Tricoli A, Suominen H, Nolan C, Phillips C, Petry C. A user preference analysis of commercial breath ketone sensors to inform the development of portable breath ketone sensors for diabetes management in young people. PLOS ONE 2022;17(7):e0269925 View
  6. Marini C, Cruz J, Payano L, Flores R, Arena G, Mandal S, Leven E, Mann D, Schoenthaler A. Opening the Black Box of an mHealth Patient-Reported Outcome Tool for Diabetes Self-Management: Interview Study Among Patients With Type 2 Diabetes. JMIR Formative Research 2023;7:e47811 View
  7. Bazzano A, Patel T, Nauman E, Cernigliaro D, Shi L. Optimizing Telehealth for Diabetes Management in the Deep South of the United States: Qualitative Study of Barriers and Facilitators on the Patient and Clinician Journey. Journal of Medical Internet Research 2024;26:e43583 View
  8. Lauffenburger J, Yom-Tov E, Keller P, McDonnell M, Crum K, Bhatkhande G, Sears E, Hanken K, Bessette L, Fontanet C, Haff N, Vine S, Choudhry N. The impact of using reinforcement learning to personalize communication on medication adherence: findings from the REINFORCE trial. npj Digital Medicine 2024;7(1) View
  9. Edelman S, Cheatham W, Norton A, Close K. Patient Perspectives on the Benefits and Challenges of Diabetes and Digital Technology. Clinical Diabetes 2024;42(2):243 View