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

mHealth in a Clinical Setting (615) User-centred Design Case Studies (596) Apps, Mobile, Wearables for Diabetes (298) Diabetes Education and Elearning for Patients (114) Diabetes Self-Management (484) Diabetes-specific EHR Improvements (33)
Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/10206, first published .
Diabetes Educators’ Insights Regarding Connecting Mobile Phone– and Wearable Tracker–Collected Self-Monitoring Information to a Nationally-Used Electronic Health Record System for Diabetes Education: Descriptive Qualitative Study

Diabetes Educators’ Insights Regarding Connecting Mobile Phone– and Wearable Tracker–Collected Self-Monitoring Information to a Nationally-Used Electronic Health Record System for Diabetes Education: Descriptive Qualitative Study

Diabetes Educators’ Insights Regarding Connecting Mobile Phone– and Wearable Tracker–Collected Self-Monitoring Information to a Nationally-Used Electronic Health Record System for Diabetes Education: Descriptive Qualitative Study

Authors of this article:

Jing Wang1 Author Orcid Image ;   Chin-Fun Chu1 Author Orcid Image ;   Chengdong Li2 Author Orcid Image ;   Laura Hayes3 Author Orcid Image ;   Linda Siminerio4 Author Orcid Image
Article Authors Cited by (14) Tweetations (8) Metrics

Journals

  1. Rodriguez S, Hwang K, Wang J. Connecting Home-Based Self-Monitoring of Blood Pressure Data Into Electronic Health Records for Hypertension Care: A Qualitative Inquiry With Primary Care Providers. JMIR Formative Research 2019;3(2):e10388 View
  2. Kulzer B. Ist die elektronische Patientenakte bei Diabetes Fluch oder Segen?. Info Diabetologie 2020;14(2):29 View
  3. Wang J, Gephart S, Mallow J, Bakken S. Models of collaboration and dissemination for nursing informatics innovations in the 21st century. Nursing Outlook 2019;67(4):419 View
  4. Alwashmi M, Hawboldt J, Davis E, Fetters M. The Iterative Convergent Design for Mobile Health Usability Testing: Mixed Methods Approach. JMIR mHealth and uHealth 2019;7(4):e11656 View
  5. Mulvaney S, Vaala S, Carroll R, Williams L, Lybarger C, Schmidt D, Dietrich M, Laffel L, Hood K. A mobile app identifies momentary psychosocial and contextual factors related to mealtime self-management in adolescents with type 1 diabetes. Journal of the American Medical Informatics Association 2019;26(12):1627 View
  6. Karampela M, Isomursu M, Porat T, Maramis C, Mountford N, Giunti G, Chouvarda I, Lehocki F. The Extent and Coverage of Current Knowledge of Connected Health: Systematic Mapping Study. Journal of Medical Internet Research 2019;21(9):e14394 View
  7. Alenazi H, Jamal A, Batais M. Identification of Type 2 Diabetes Management Mobile App Features and Engagement Strategies: Modified Delphi Approach. JMIR mHealth and uHealth 2020;8(9):e17083 View
  8. Lüscher J, Kowatsch T, Boateng G, Santhanam P, Bodenmann G, Scholz U. Social Support and Common Dyadic Coping in Couples' Dyadic Management of Type II Diabetes: Protocol for an Ambulatory Assessment Application. JMIR Research Protocols 2019;8(10):e13685 View
  9. Wendrich K, van Oirschot P, Martens M, Heerings M, Jongen P, Krabbenborg L. Toward Digital Self-monitoring of Multiple Sclerosis. International Journal of MS Care 2019;21(6):282 View
  10. Abdolkhani R, Gray K, Borda A, DeSouza R. Patient-generated health data management and quality challenges in remote patient monitoring. JAMIA Open 2019;2(4):471 View
  11. Karimi N, Opie R, Crawford D, O’Connell S, Hamblin P, Steele C, Ball K. Participants’ and Health Care Providers’ Insights Regarding a Web-Based and Mobile-Delivered Healthy Eating Program for Disadvantaged People With Type 2 Diabetes: Descriptive Qualitative Study. JMIR Formative Research 2023;7:e37429 View
  12. Fontecha J, González I, Barragán A, Lim T, Pitocco D. Use and Trends of Diabetes Self-Management Technologies: A Correlation-Based Study. Journal of Diabetes Research 2022;2022:1 View
  13. Willms A, Rhodes R, Liu S. The Development of a Hypertension Prevention and Financial-Incentive mHealth Program Using a “No-Code” Mobile App Builder: Development and Usability Study. JMIR Formative Research 2023;7:e43823 View
  14. Li S, Du Y, Meireles C, Song D, Sharma K, Yin Z, Brimhall B, Wang J. Decoding heterogeneity in data-driven self-monitoring adherence trajectories in digital lifestyle interventions for weight loss: a qualitative study. BMC Digital Health 2024;2(1) View