Artificial intelligence (AI) technology can assist medical teams in remote monitoring and continuing education of women with gestational diabetes (GDM), potentially improving adherence to interventions and impacting outcomes. An AI remote monitoring model called "monitoring model for women with GDM using pharmacological therapy," created by the ChamouDr technical team, will be analyzed focusing on disease education, glycemic control monitoring, and therapeutic interventions. Women diagnosed with GDM are invited to participate in the study and sign a free and informed consent form. The AI tool is installed on the pregnant woman's cell phone, who receives instructions to collect capillary blood glucose 6 times a day according to the protocol, at home, and report the results via WhatsApp to the study tool. Algorithm generated by the AI model based on self monitoring of blood glucose (SMBG) informs about diabetes control in the last week. The dashboard is accessible via a web browser, and signals: in green and red for patients with satisfactory and unsatisfactory control, respectively. Thus, the AI model optimizes the team's time in analyzing and treating patients appropriately in a simple, cost-effective, and accessible way.
AI technology can assist medical teams in remote monitoring and continuing education of women with GDM. Objective: To analyze the results of using an AI model in remote monitoring and continuing education of women with GDM and pharmacological treatment, correlating them with clinical outcomes for the mother-fetus binomial. Methods: prospective, longitudinal, interventional clinical study approved by the local ethics committee. Patients signed a consent form to participate. An AI remote monitoring model called "monitoring model for women with GDM using pharmacological therapy," created by the ChamouDr technical team, will be analyzed focusing on disease education, glycemic control monitoring, and therapeutic interventions. The modell uses WhatsApp®, through a structured chatbot and AI resources, to communicate with the participant. Comparative analyses will be conducted between two groups of 100 pregnant women with GDM on insulin therapy, followed in the high-risk prenatal clinic of the Obstetrics Department of a tertiary hospital: case group using the AI model versus control group, composed of patients previously monitored under conventional in-person supervision, without the use of this technology. Algorithm generated by the AI model based on SMBG informs about diabetes control in the last week. The dashboard is accessible via a web browser, and signals: in green and red for patients with satisfactory and unsatisfactory control, respectively. Thus, the AI model optimizes the team's time in analyzing and treating patients appropriately in a simple, cost-effective, and accessible way.
Study Type
INTERVENTIONAL
Allocation
NON_RANDOMIZED
Purpose
TREATMENT
Masking
NONE
Enrollment
100
Artificial Intelligence modell through WhatsApp® to remote monitoring gestacional diabetes in insulin treatment, focusing on disease education, glycemic control monitoring, and therapeutic interventions.
Fundação Faculdade Regional de Medicina de São José do Rio Preto
São José do Rio Preto, São Paulo, Brazil
fetal death
Fetal death resulting from metabolic changes caused by gestational diabetes
Time frame: From the moment of randomization to delivery (until 40 weeks of pregnancy)
Fetal birth weight
Fetal weight at birth assessed using a precision scale.
Time frame: From the moment of randomization to delivery (until 40 weeks of pregnancy)
neonatal hypoglycemia
Neonatal hypoglycemia is the abnormal reduction of glucose in the newborn's blood to levels considered insufficient to meet the metabolic needs of the brain and other tissues. Plasma glucose parameters: \< 40 mg/dL in the first 4 hours of life, \< 45 mg/dL between 4 and 24 hours of life, After 24 hours, values \< 50-60 mg/dL
Time frame: From the moment of randomization to delivery (until 40 weeks of pregnancy), and Assessment of neonatal blood glucose levels from birth up to 48 hours post-birth.
glycemic control
Glycemic control will be evaluated according to capillary glucose measurements that are taken 6 times a day: fasting, before and 1 hour after meals, following the target ranges of 70 to 95 mg/dL fasting; 70 ton 100 mg/dL pre-prandial; and 100 to 140 mg/dL post-prandial.
Time frame: From the moment of randomization to delivery (until 40 weeks of pregnancy)
admission of the newborn to the intensive care unit
The need for the newborn to be admitted to an intensive care unit due to metabolic disorders associated with poor maternal glycemic control.
Time frame: From the moment of randomization to delivery (until 40 weeks of pregnancy), and from birth to 48 hours postpartum
mother weight gain
Maternal weight gain assessed during the gestational follow-up period up to delivery.
Time frame: From the moment of randomization to delivery (until 40 weeks of pregnancy).
gestational age at delivery
Gestational age at the time of natural childbirth or cesarean section in weeks
Time frame: From the moment of randomization to delivery (until 40 weeks of pregnancy).
route of delivery
Description of whether it was a natural birth or a cesarean section.
Time frame: From the moment of randomization to delivery (until 40 weeks of pregnancy).
Blood pressure
Evaluate if hypertension is present and assess blood pressure levels during pregnancy and up to delivery.
Time frame: From the moment of randomization to delivery (until 40 weeks of pregnancy).
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