Published on in Vol 5, No 3 (2020): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18660, first published .
The Diabits App for Smartphone-Assisted Predictive Monitoring of Glycemia in Patients With Diabetes: Retrospective Observational Study

The Diabits App for Smartphone-Assisted Predictive Monitoring of Glycemia in Patients With Diabetes: Retrospective Observational Study

The Diabits App for Smartphone-Assisted Predictive Monitoring of Glycemia in Patients With Diabetes: Retrospective Observational Study

Journals

  1. Peeks F, Hoogeveen I, Feldbrugge R, Burghard R, de Boer F, Fokkert‐Wilts M, van der Klauw M, Oosterveer M, Derks T. A retrospective in‐depth analysis of continuous glucose monitoring datasets for patients with hepatic glycogen storage disease: Recommended outcome parameters for glucose management. Journal of Inherited Metabolic Disease 2021;44(5):1136 View
  2. Garzorz-Stark N, Beicht S, Baghin V, Stark S, Biedermann T, Lauffer F. IMPROVE 1.0: Individual Monitoring of Psoriasis Activity by Regular Online App Questionnaires and Outpatient Visits. Frontiers in Medicine 2021;8 View
  3. van Doorn W, Foreman Y, Schaper N, Savelberg H, Koster A, van der Kallen C, Wesselius A, Schram M, Henry R, Dagnelie P, de Galan B, Bekers O, Stehouwer C, Meex S, Brouwers M, Chen C. Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study. PLOS ONE 2021;16(6):e0253125 View
  4. Felizardo V, Machado D, Garcia N, Pombo N, Brandao P. Hypoglycaemia Prediction Models With Auto Explanation. IEEE Access 2022;10:57930 View
  5. Klemme I, Wrona K, de Jong I, Dockweiler C, Aschentrup L, Albrecht J. Integration of the Vision of People With Diabetes Into the Development Process to Improve Self-management via Diabetes Apps: Qualitative Interview Study. JMIR Diabetes 2023;8:e38474 View
  6. Rossi A, Venema A, Haarsma P, Feldbrugge L, Burghard R, Rodriguez-Buritica D, Parenti G, Oosterveer M, Derks T. A Prospective Study on Continuous Glucose Monitoring in Glycogen Storage Disease Type Ia: Toward Glycemic Targets. The Journal of Clinical Endocrinology & Metabolism 2022;107(9):e3612 View
  7. Zhou Y, Gould D, Choong P, Dowsey M, Schilling C. Implementing predictive tools in surgery: A narrative review in the context of orthopaedic surgery. ANZ Journal of Surgery 2022;92(12):3162 View
  8. PALAZ Z, DOĞAN V, KILIÇ V. Smartphone-based Multi-parametric Glucose Prediction using Recurrent Neural Networks. European Journal of Science and Technology 2022 View
  9. Afsaneh E, Sharifdini A, Ghazzaghi H, Ghobadi M. Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review. Diabetology & Metabolic Syndrome 2022;14(1) View
  10. Wolff M, Schaathun H, Fougner A, Steinert M, Volden R. Mobile Software Development Kit for Real Time Multivariate Blood Glucose Prediction. IEEE Access 2024;12:5910 View