The number of individuals with gestational diabetes has increased substantially within the last 10 years both in Finland and globally. CleverHealth Network is conducting a development project to support the treatment of gestational diabetes with a new digital service model.
In the project there has been developed a mobile application to support the treatment process, which measures and stores in the cloud in real-time the mother’s continuous glucose level, physical activity, nutrition, heart rate, and daily weight. A randomized controlled trial of the project has shown that the mobile application improves self-management of gestational diabetes.
“By influencing lifestyle and nutrition during pregnancy, it is likely possible to reduce the number of mothers developing type 2 diabetes and the health risks transferred to the child, thereby improving the health of future generations. Our studies have shown that the application helps the patient learn how diet and activity affect blood sugar levels and weight development, and thus the course of pregnancy and the health of the newborn,” says Saila Koivusalo, research director of the project and specialist in obstetrics and gynaecology.
The application will forward the data in real time to health care personnel, who can provide guidance and support as needed. This means that the application is integrated into the care pathway instead of being a separate element, which is its greatest benefit compared to other health applications.
“With this service, we can offer even better, modern treatment. The service will also increase the efficiency of the treatment process for women with gestational diabetes, as the number of referrals to special care is decreased. ,” Koivusalo says.
In the next phase of the project, we will make use of machine learning to provide guidance and treatment that are in line with the patient’s risk profile and meet her individual needs. Artificial intelligence also makes it possible to draw up predictions of both the mother’s and the child’s future health.
“This means, for example, that we can predict future glucose levels and propably also the newborn’s weight and adiposity in an unprecedented way. The application uses these predictions to give feedback automatically and advise the mother in making compensatory choices,” Koivusalo explains.
Publications
Määttänen S, Koivusalo S, Ylinen H, Heinonen S, Kytö M. (2025). “Supporting Self-Management in Persons with Gestational Diabetes: The Effect of eMOM Mobile Application on Self-Discovery and Psychological Factors – A Mixed-Methods Study.” JMIR mHealth and uHealth (accepted for publication). DOI: 10.2196/60855
Kytö, M., Hotta, S., Niinistö, S., Marttinen, P., Korhonen, T. E., Markussen, L. T., Jacucci, G., Sievänen, H., Vähä-Ypyä, H., Korhonen, I., Virtanen, S., Heinonen, S., & Koivusalo, S. B. (2024). Periodic Mobile Application (eMOM) With Self-Tracking of Glucose and Lifestyle Improves Treatment of Diet-Controlled Gestational Diabetes Without Human Guidance: A Randomized Controlled Trial. American Journal of Obstetrics and Gynecology. https://doi.org/10.1016/j.ajog.2024.02.303
Hotta, S., Kytö, M., Koivusalo, S., Heinonen, S., & Marttinen, P. (2024). Optimizing postprandial glucose prediction through integration of diet and exercise: Leveraging transfer learning with imbalanced patient data. PLoS ONE, 19(8 August). https://doi.org/10.1371/journal.pone.0298506
Kytö, M., Koivusalo, S., Tuomonen, H., Strömberg, L., Ruonala, A., Marttinen, P., Heinonen, S., & Jacucci, G. (2023). Supporting the Management of Gestational Diabetes Mellitus With Comprehensive Self-Tracking: Mixed Methods Study of Wearable Sensors. JMIR Diabetes, 8, e43979. https://doi.org/10.2196/43979
Kytö, M., Strömberg, L., Tuomonen, H., Ruonala, A., Koivusalo, S. B., & Jacucci, G. (2022). Behavior Change Apps for Gestational Diabetes Management: Exploring Desirable Features. International Journal of Human-Computer Interaction, 38(12), 1095–1112. https://doi.org/10.1080/10447318.2021.1987678
Kytö, M., Koivusalo, S., Ruonala, A., Strömberg, L., Tuomonen, H., Heinonen, S., & Jacucci, G. (2022). Behavior Change App for Self-management of Gestational Diabetes: Design and Evaluation of Desirable Features. JMIR Human Factors, 9(4), e36987. https://doi.org/10.2196/36987
Kytö, M., Markussen, L. T., Marttinen, P., Jacucci, G., Niinistö, S., Virtanen, S. M., Korhonen, T. E., Sievänen, H., Vähä-Ypyä, H., Korhonen, I., Heinonen, S., & Koivusalo, S. B. (2022). Comprehensive self-tracking of blood glucose and lifestyle with a mobile application in the management of gestational diabetes: a study protocol for a randomised controlled trial (eMOM GDM study). BMJ Open, 12(11), e066292. https://doi.org/10.1136/bmjopen-2022-066292
Zhang, G., Ashrafi, R. A., Juuti, A., Pietiläinen, K., & Marttinen, P. (2020). Errors-in-variables modeling of personalized treatment-response trajectories. IEEE Journal of Biomedical and Health Informatics, 25(1), 201-208.
Ashrafi, R. A., Ahola, A. J., Rosengård-Bärlund, M., Saarinen, T., Heinonen, S., Juuti, A., Marttinen, P., & Pietiläinen, K. H. (2021). Computational modelling of self-reported dietary carbohydrate intake on glucose concentrations in patients undergoing Roux-en-Y gastric bypass versus one-anastomosis gastric bypass. Annals of Medicine, 53(1), 1885–1895. https://doi.org/10.1080/07853890.2021.1964035
Project participants
More information
Mikko Kytö, Development manager, Ph.D., Title of docent, mikko.kyto[at]hus.fi
Saila Koivusalo, Specialist in Obstetrics and Gynecology, Ph.D, MD, Title of docent, saila.koivusalo[at]hus.fi
