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

mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1309) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (459) Apps, Mobile, Wearables for Diabetes (288) Diabetes Self-Management (463) Glucose Tracking and Self-Monitoring of Blood Glucose (150)

Published on in Vol 7, No 3 (2019): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11933, first published .
Impact of Use Frequency of a Mobile Diabetes Management App on Blood Glucose Control: Evaluation Study

Impact of Use Frequency of a Mobile Diabetes Management App on Blood Glucose Control: Evaluation Study

Impact of Use Frequency of a Mobile Diabetes Management App on Blood Glucose Control: Evaluation Study

Authors of this article:

Josep Vehi1, 2 Author Orcid Image ;   Jordi Regincós Isern1 Author Orcid Image ;   Adrià Parcerisas1 Author Orcid Image ;   Remei Calm1 Author Orcid Image ;   Ivan Contreras1 Author Orcid Image
Article Authors Cited by (17) Tweetations (23) Metrics

Journals

  1. Zhu T, Li K, Kuang L, Herrero P, Georgiou P. An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning. Sensors 2020;20(18):5058 View
  2. Adu M, Malabu U, Malau-Aduli A, Drovandi A, Malau-Aduli B. User Retention and Engagement With a Mobile App Intervention to Support Self-Management in Australians With Type 1 or Type 2 Diabetes (My Care Hub): Mixed Methods Study. JMIR mHealth and uHealth 2020;8(6):e17802 View
  3. Zhang Y, Liu C, Luo S, Huang J, Li X, Zhou Z. Effectiveness of Lilly Connected Care Program (LCCP) App-Based Diabetes Education for Patients With Type 2 Diabetes Treated With Insulin: Retrospective Real-World Study. JMIR mHealth and uHealth 2020;8(3):e17455 View
  4. Chiu C, Yu Y, Du Y, Yang Y, Chen J, Wong L, Tanasugarn C. Comparing a Social and Communication App, Telephone Intervention, and Usual Care for Diabetes Self-Management: 3-Arm Quasiexperimental Evaluation Study. JMIR mHealth and uHealth 2020;8(6):e14024 View
  5. Spaulding E, Marvel F, Piasecki R, Martin S, Allen J. User Engagement With Smartphone Apps and Cardiovascular Disease Risk Factor Outcomes: Systematic Review. JMIR Cardio 2021;5(1):e18834 View
  6. Schindler-Ruwisch J, Peters A. Mobile applications for emerging adults transitioning to independent diabetes monitoring. Informatics for Health and Social Care 2021;46(1):56 View
  7. Jasmin M, Yusuf S, Syahrul S, Abrar E. Validity and Reliability of a Vibration-Based Cell Phone in Detecting Peripheral Neuropathy among Patients with a Risk of Diabetic Foot Ulcer. The International Journal of Lower Extremity Wounds 2023;22(4):687 View
  8. Tsai Y, Hsiao P, Kuo M, Wang S, Chen T, Kung L, Hsiao S, Lin M, Hwang S, Chen H, Chiu Y. Mobile Health, Disease Knowledge, and Self-Care Behavior in Chronic Kidney Disease: A Prospective Cohort Study. Journal of Personalized Medicine 2021;11(9):845 View
  9. Holtz B, Mitchell K, Holmstrom A, Hershey D, Cotten S, Dunneback J, Ellis D, Wood M. The effect of an mHealth intervention for adolescents with Type 1 diabetes and their parents. Journal of Telemedicine and Telecare 2022:1357633X2211258 View
  10. Peart D, Briggs M, Shaw M. Mobile applications for the sport and exercise nutritionist: a narrative review. BMC Sports Science, Medicine and Rehabilitation 2022;14(1) View
  11. Parcerisas A, Contreras I, Delecourt A, Bertachi A, Beneyto A, Conget I, Viñals C, Giménez M, Vehi J. A Machine Learning Approach to Minimize Nocturnal Hypoglycemic Events in Type 1 Diabetic Patients under Multiple Doses of Insulin. Sensors 2022;22(4):1665 View
  12. Liu Z, Wang C, Yang D, Luo S, Ding Y, Xu W, Zheng X, Weng J, Yan J. High engagement in mobile peer support is associated with better glycemic control in type 1 diabetes: A real‐world study. Journal of Diabetes Investigation 2022;13(11):1914 View
  13. Kerr D, Edelman S, Vespasiani G, Khunti K. New Digital Health Technologies for Insulin Initiation and Optimization for People With Type 2 Diabetes. Endocrine Practice 2022;28(8):811 View
  14. Aslan A, Díez J, Laguna Sanz A, Bondia J. On the Use of Population Data for Training Seasonal Local Models-Based Glucose Predictors: An In Silico Study. Applied Sciences 2023;13(9):5348 View
  15. Tolentino D, Costa D, Jiang Y. Determinants of American Adults' Use of Digital Health and Willingness to Share Health Data to Providers, Family, and Social Media. CIN: Computers, Informatics, Nursing 2023;41(11):892 View
  16. Lee J, Kim Y. Evaluation of Mobile Applications for Patients with Diabetes Mellitus: A Scoping Review. Healthcare 2024;12(3):368 View

Books/Policy Documents

  1. Vehi J, Mujahid O, Contreras I. Advanced Bioscience and Biosystems for Detection and Management of Diabetes. View