Trial protocol for the study of recommendation system DiaCompanion with personalized dietary recommendations for women with gestational diabetes mellitus (DiaCompanion I)

• Published in: Frontiers in endocrinology (Lausanne) Vol. 14; p. 1168688
• Main Authors: Popova, Polina, Anopova, Anna, Vasukova, Elena, Isakov, Artem, Eriskovskaya, Angelina, Degilevich, Andrey, Pustozerov, Evgenii, Tkachuk, Alexandra, Pashkova, Kristina, Krasnova, Natalia, Kokina, Maria, Nemykina, Irina, Pervunina, Tatiana, Li, Olga, Grineva, Elena, Shlyakhto, Evgeny
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 07.06.2023
• Subjects: Artificial Intelligence; Blood Glucose; Blood Glucose Self-Monitoring; Diabetes, Gestational; Diet; Endocrinology; Female; gestational diabetes mellitus; Humans; Infant, Newborn; Insulin; mHealth; personalized nutrition; postprandial glycaemic response prediction; Pregnancy; Randomized Controlled Trials as Topic; trial protocol; mHealth; trial protocol; gestational diabetes mellitus; postprandial glycaemic response prediction; personalized nutrition
• ISSN: 1664-2392; 1664-2392
• DOI: 10.3389/fendo.2023.1168688

Gestational diabetes mellitus (GDM) is a common complication of pregnancy associated with serious adverse outcomes for mothers and their offspring. Achieving glycaemic targets is the mainstream in the treatment of GDM in order to improve pregnancy outcomes. As GDM is usually diagnosed in the third trimester of pregnancy, the time frame for the intervention is very narrow. Women need to get new knowledge and change their diet very quickly. Usually, these patients require additional frequent visits to healthcare professionals. Recommender systems based on artificial intelligence could partially substitute healthcare professionals in the process of educating and controlling women with GDM, thus reducing the burden on the women and healthcare systems. We have developed a mobile-based personalized recommendation system DiaCompanion I with data-driven real time personal recommendations focused primarily on postprandial glycaemic response prediction. The study aims to clarify the effect of using DiaCompanion I on glycaemic levels and pregnancy outcomes in women with GDM. Women with GDM are randomized to 2 treatment groups: utilizing and not utilizing DiaCompanion I. The app provides women in the intervention group the resulting data-driven prognosis of 1-hour postprandial glucose level every time they input their meal data. Based on the predicted glucose level, they can adjust their current meal so that the predicted glucose level falls within the recommended range below 7 mmol/L. The app also provides reminders and recommendations on diet and lifestyle to the participants of the intervention group. All the participants are required to perform 6 blood glucose measurements a day. Capillary glucose values are retrieved from the glucose meter and if not available, from the woman's diary. Additionally, data on glycaemic levels during the study and consumption of major macro- and micronutrients will be collected using the mobile app with electronic report forms in the intervention group. Women from the control group receive standard care without the mobile app. All participants are prescribed with insulin therapy if needed and modifications in their lifestyle. A total of 216 women will be recruited. The primary outcome is the percentage of postprandial capillary glucose values above target (>7.0 mmol/L). Secondary outcomes include the percentage of patients requiring insulin therapy during pregnancy, maternal and neonatal outcomes, glycaemic control using glycated hemoglobin (HbA1c), continuous glucose monitoring data and other blood glucose metrics, the number of patient visits to endocrinologists and acceptance/satisfaction of the two strategies assessed using a questionnaire. We believe that the approach including DiaCompanion I will be more effective in patients with GDM for improving glycaemic levels and pregnancy outcomes. We also expect that the use of the app will help reduce the number of clinic visits. ClinicalTrials.gov, Identifier NCT05179798.