Published on in Vol 6 , No 1 (2018) :January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9236, first published .
Development and Evaluation of a Mobile Personalized Blood Glucose Prediction System for Patients With Gestational Diabetes Mellitus

Development and Evaluation of a Mobile Personalized Blood Glucose Prediction System for Patients With Gestational Diabetes Mellitus

Development and Evaluation of a Mobile Personalized Blood Glucose Prediction System for Patients With Gestational Diabetes Mellitus

Journals

  1. Pustozerov E, Tkachuk A, Vasukova E, Dronova A, Shilova E, Anopova A, Piven F, Pervunina T, Vasilieva E, Grineva E, Popova P. The Role of Glycemic Index and Glycemic Load in the Development of Real-Time Postprandial Glycemic Response Prediction Models for Patients with Gestational Diabetes. Nutrients 2020;12(2):302 View
  2. Wang Y, Li M, Zhao X, Pan X, Lu M, Lu J, Hu Y. Effects of continuous care for patients with type 2 diabetes using mobile health application: A randomised controlled trial. The International Journal of Health Planning and Management 2019;34(3):1025 View
  3. Pustozerov E, Chernykh V, Popova P, Vasyukova E, Tkachuk A, Yuldashev Z. Health Monitoring System for Patients with Gestational Diabetes Mellitus Based on Nutrition Diaries and Fitness Bracelets. Biomedical Engineering 2020;53(5):305 View
  4. Yu Q, Aris I, Tan K, Li L. Application and Utility of Continuous Glucose Monitoring in Pregnancy: A Systematic Review. Frontiers in Endocrinology 2019;10 View
  5. Popova P, Vasilyeva L, Tkachuck A, Puzanov M, Golovkin A, Bolotko Y, Pustozerov E, Vasilyeva E, Li O, Zazerskaya I, Dmitrieva R, Kostareva A, Grineva E. A Randomised, Controlled Study of Different Glycaemic Targets during Gestational Diabetes Treatment: Effect on the Level of Adipokines in Cord Blood and ANGPTL4 Expression in Human Umbilical Vein Endothelial Cells. International Journal of Endocrinology 2018;2018:1 View
  6. Barros M, Moitinho A, Couto F. Using Research Literature to Generate Datasets of Implicit Feedback for Recommending Scientific Items. IEEE Access 2019;7:176668 View
  7. Petry C. Nutrition for Gestational Diabetes—Progress and Potential. Nutrients 2020;12(9):2685 View
  8. Inayama Y, Yamanoi K, Shitanaka S, Ogura J, Ohara T, Sakai M, Suzuki H, Kishimoto I, Tsunenari T, Suginami K. A novel classification of glucose profile in pregnancy based on continuous glucose monitoring data. Journal of Obstetrics and Gynaecology Research 2021;47(4):1281 View
  9. Park J, Kim S, Lee J. Self-Care IoT Platform for Diabetic Mellitus. Applied Sciences 2021;11(5):2006 View
  10. Surendran S, Lim C, Koh G, Yew T, Tai E, Foong P. Women’s Usage Behavior and Perceived Usefulness with Using a Mobile Health Application for Gestational Diabetes Mellitus: Mixed-Methods Study. International Journal of Environmental Research and Public Health 2021;18(12):6670 View
  11. Shang J, Henry A, Zhang P, Chen H, Thompson K, Wang X, Liu N, Zhang J, Liu Y, Jin J, Pan X, Yang X, Hirst J. Chinese women’s attitudes towards postpartum interventions to prevent type 2 diabetes after gestational diabetes: a semi-structured qualitative study. Reproductive Health 2021;18(1) View
  12. De Croon R, Van Houdt L, Htun N, Štiglic G, Vanden Abeele V, Verbert K. Health Recommender Systems: Systematic Review. Journal of Medical Internet Research 2021;23(6):e18035 View

Books/Policy Documents

  1. Pustozerov E, Sachkova N, Tkachuk A, Vasukova E, Dronova A, Pervunina T, Grineva E, Popova P. International Youth Conference on Electronics, Telecommunications and Information Technologies. View