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

mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1428) Design and Formative Evaluation of Mobile Apps (706) Glucose Tracking and Self-Monitoring of Blood Glucose (166) Apps, Mobile, Wearables for Diabetes (306) Diabetes Self-Management (495) Gestational Diabetes (29)

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

Authors of this article:

Evgenii Pustozerov1, 2 Author Orcid Image ;   Polina Popova2, 3 Author Orcid Image ;   Aleksandra Tkachuk2 Author Orcid Image ;   Yana Bolotko2 Author Orcid Image ;   Zafar Yuldashev1 Author Orcid Image ;   Elena Grineva2, 3 Author Orcid Image
Article Authors Cited by (44) Tweetations (22) Metrics

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
  13. Mennickent D, Ortega-Contreras B, Gutiérrez-Vega S, Castro E, Rodríguez A, Araya J, Guzmán-Gutiérrez E, Jayakody Mudiyanselage S. Evaluation of first and second trimester maternal thyroid profile on the prediction of gestational diabetes mellitus and post load glycemia. PLOS ONE 2023;18(1):e0280513 View
  14. Kytö M, Strömberg L, Tuomonen H, Ruonala A, Koivusalo S, Jacucci G. Behavior Change Apps for Gestational Diabetes Management: Exploring Desirable Features. International Journal of Human–Computer Interaction 2022;38(12):1095 View
  15. Makroum M, Adda M, Bouzouane A, Ibrahim H. Machine Learning and Smart Devices for Diabetes Management: Systematic Review. Sensors 2022;22(5):1843 View
  16. Napolsky I, Popova P. Personalized Nutrition for the Prevention and Treatment of Metabolic Diseases: Opportunities and Perspectives. Russian Journal for Personalized Medicine 2022;2(1):15 View
  17. Kalhori S, Hemmat M, Noori T, Heydarian S, Katigari M. Quality Evaluation of English Mobile Applications for Gestational Diabetes: App Review using Mobile Application Rating Scale (MARS). Current Diabetes Reviews 2021;17(2):161 View
  18. Bertini A, Gárate B, Pardo F, Pelicand J, Sobrevia L, Torres R, Chabert S, Salas R. Impact of Remote Monitoring Technologies for Assisting Patients With Gestational Diabetes Mellitus: A Systematic Review. Frontiers in Bioengineering and Biotechnology 2022;10 View
  19. Daley B, Ni’Man M, Neves M, Bobby Huda M, Marsh W, Fenton N, Hitman G, McLachlan S. mHealth apps for gestational diabetes mellitus that provide clinical decision support or artificial intelligence: A scoping review. Diabetic Medicine 2022;39(1) View
  20. Yuldashev Z. A Remote System for Monitoring the State of Health of People with Chronic Diseases and Predicting Periods of Exacerbation. Biomedical Engineering 2023;56(5):294 View
  21. Pustozerov E, Tkachuk A, Vasukova E, Anopova A, Kokina M, Gorelova I, Pervunina T, Grineva E, Popova P. Machine Learning Approach for Postprandial Blood Glucose Prediction in Gestational Diabetes Mellitus. IEEE Access 2020;8:219308 View
  22. Gambo I, Massenon R, Kolawole B, Ikono R. Analysis and Design Process for Predicting and Controlling Blood Glucose in Type 1 Diabetic Patients. International Journal of Healthcare Information Systems and Informatics 2021;16(4):1 View
  23. Zafar A, Lewis D, Shahid A. Long-Term Glucose Forecasting for Open-Source Automated Insulin Delivery Systems: A Machine Learning Study with Real-World Variability Analysis. Healthcare 2023;11(6):779 View
  24. Rivera-Romero O, Gabarron E, Ropero J, Denecke K. Designing personalised mHealth solutions: An overview. Journal of Biomedical Informatics 2023;146:104500 View
  25. Popova P, Anopova A, Vasukova E, Isakov A, Eriskovskaya A, Degilevich A, Pustozerov E, Tkachuk A, Pashkova K, Krasnova N, Kokina M, Nemykina I, Pervunina T, Li O, Grineva E, Shlyakhto E. Trial protocol for the study of recommendation system DiaCompanion with personalized dietary recommendations for women with gestational diabetes mellitus (DiaCompanion I). Frontiers in Endocrinology 2023;14 View
  26. ÖCAL P. The use of mobile health applications in empowering self-management of type 2 diabetes: a literature review. The European Research Journal 2024;10(1):127 View
  27. Lu H, Lu P, Hirst J, Mackillop L, Clifton D. A Stacked Long Short-Term Memory Approach for Predictive Blood Glucose Monitoring in Women with Gestational Diabetes Mellitus. Sensors 2023;23(18):7990 View
  28. Pato M, Barros M, Couto F. Survey on Recommender Systems for Biomedical Items in Life and Health Sciences. ACM Computing Surveys 2024;56(6):1 View
  29. Qaraqe M, Elzein A, Belhaouari S, Ilam M, Petrovski G. A novel few shot learning derived architecture for long-term HbA1c prediction. Scientific Reports 2024;14(1) View
  30. Lu H, Ding X, Hirst J, Yang Y, Yang J, Mackillop L, Clifton D. Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes. IEEE Reviews in Biomedical Engineering 2024;17:98 View
  31. Yu H, Piri S, Qiu H, Xu R, Miao H. Personalized algorithmic pricing decision support tool for health insurance: The case of stratifying gestational diabetes mellitus into two groups. Information & Management 2024;61(3):103945 View
  32. Chettri L, Rai R. A Perspective Review: State-of-the-Art on m-Health Services. SN Computer Science 2024;5(5) View
  33. Hotta S, Kytö M, Koivusalo S, Heinonen S, Marttinen P, Nunes E. Optimizing postprandial glucose prediction through integration of diet and exercise: Leveraging transfer learning with imbalanced patient data. PLOS ONE 2024;19(8):e0298506 View
  34. Duan B, Zhou Z, Liu M, Liu Z, Zhang Q, Liu L, Ma C, Gou B, Liu W. Development and acceptability of a gestational diabetes mellitus prevention system (Better pregnancy) based on a user-centered approach: A clinical feasibility study. DIGITAL HEALTH 2024;10 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
  2. Xanthis C, Filos D, Chouvarda I. Comprehensive Clinical Approach to Diabetes During Pregnancy. View
  3. Ratul M, Yanoor Bristy T, Sayeed N, Islam A, Chaudhry B. HCI International 2024 – Late Breaking Papers. View