Efficacy and Safety of an Artificial Intelligence Tool for Carbohydrate Counting (Tiabete) in Children and Adults With Type 1 Diabetes Mellitus

NCT07150052 | Recruiting DMC

• 1 other identifier: USaoPauloTiabete
• Study Type: interventional
• Target: 50
• Locations: 1 country
• Sites: 1

Brief Summary

IntroductionType 1 Diabetes Mellitus (T1DM) requires lifelong exogenous insulin therapy, along with self-management strategies, such as carbohydrate counting, to appropriately adjust insulin doses in response to meals. However, many patients face challenges in adhering consistently to carbohydrate counting, compromising glycemic control and increasing the risk of diabetes-related complications. Emerging technologies, such as artificial intelligence (AI), hold significant potential for optimizing disease management by enhancing the accuracy and efficiency of self-care practices. ObjectiveThe primary aim of this study is to evaluate the efficacy and safety of the AI-based tool Tia Bete, designed to assist patients with T1DM in carbohydrate counting and insulin dose adjustment. The tool provides real-time recommendations based on personalized insulin-to-carbohydrate ratios, insulin sensitivity factors, and individualized glycemic goals. MethodsThis is a prospective, longitudinal study involving 40 patients with T1DM, stratified into two cohorts: 20 children and adolescents (6-18 years) and 20 adults (\>18 years), recruited at the Hospital das Clínicas, University of São Paulo (HCFMUSP). Participants will be assessed before and after six months of using the Tia Bete tool. Glycemic control will be evaluated using parameters such as glycated hemoglobin (HbA1c), time in range, and the incidence of hypoglycemia and hyperglycemia. Quality of life and satisfaction with the tool will also be assessed. Overview of the AI Tool Launched in June 2024, Tia Bete is an AI-based digital solution designed to facilitate glycemic control and improve quality of life for patients with T1DM. By offering real-time assistance with carbohydrate counting and insulin dose recommendations, the tool aims to enhance patient autonomy while enabling flexible treatment adherence in collaboration with their multidisciplinary healthcare team. Results and ConclusionsPreliminary data indicate high engagement, with over 35,000 active users interacting with the platform at least four times per week. Initial findings suggest significant improvements in glycemic control, as well as increased confidence in carbohydrate counting and insulin dose adjustments. The dissemination of this project is crucial for advancing T1DM care, offering a scalable, accessible, and effective technological solution. Final results are expected by October 2025.

Conditions

Type 1 Diabetes

Keywords

type 1 diabetes; technology; artificial intelligence; carb counting

Outcome Measures

Primary Outcomes (2)

• Glycated hemoglobin (HbA1c)

  Participants will be assessed before and after six months of using the Tia Bete tool. Glycemic control will be evaluated using : \- Hb1 Ac ( as a percentage of total hemoglobin- real value)

  6 months

• - time in range ( percentage) - hypoglycemia (mean daily percentage of hypo- glycemia less than 70 mg/dl) - hyperglycemia(mean daily percentage of hyper- glycemia above than 180mg/dl)

  Participants will be assessed before and after six months of using the Tia Bete tool. Glycemic control will be evaluated using : * time in range ( percentage) * hypoglycemia (mean daily percentage of hypo- glycemia less than 70 mg/dl) * hyperglycemia(mean daily percentage of hyper- glycemia above than 180mg/dl)

  6 months

Secondary Outcomes (2)

• Quality of life

  6 months

• Satisfaction with the tool

  6 months

Study Arms (1)

Use of Tiabete - AI tool

EXPERIMENTAL

Patients will use the AI tool and we will compare before and after the intervention

Other: Artifical intelligence in whatsapp

Interventions

Artifical intelligence in whatsapp OTHER

AI-based tool Tia Bete, designed to assist patients with T1DM in carbohydrate counting and insulin dose adjustment. The tool provides real-time recommendations based on personalized insulin-to-carbohydrate ratios, insulin sensitivity factors, and individualized glycemic goals.

Use of Tiabete - AI tool

Eligibility Criteria

• Age: 6 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Diagnosis of type 1 diabetes mellitus
• children between 6 and 18 years old and their caregivers; and adults \> 18 years old.
• Glycated Hb between 7.2-10.5% in the last 6 months. Being on a basal-bolus regimen Participating in and understanding the guidelines on how to use the program to be tested in this study Agreeing to the free and informed consent form. Performing periodic monitoring at the DM outpatient clinic at HC-FMUSP

You may not qualify if:

• Not understanding the instructions.
• Being illiterate
• Not attending the visits proposed by the study
• Not using the tool via WhatsApp.
• Not accepting consent.
• Interacting with the tool less than 3 days a week during the study period.

Sponsors & Collaborators

• University of Sao Paulo: lead

Study Sites (1)

University of Sao Paulo

São Paulo, São Paulo, 04026-001, Brazil

RECRUITING

Related Publications (2)

• Cengiz E, Danne T, Ahmad T, Ayyavoo A, Beran D, Ehtisham S, Fairchild J, Jarosz-Chobot P, Ng SM, Paterson M, Codner E. ISPAD Clinical Practice Consensus Guidelines 2022: Insulin treatment in children and adolescents with diabetes. Pediatr Diabetes. 2022 Dec;23(8):1277-1296. doi: 10.1111/pedi.13442. No abstract available.

  PMID: 36537533 BACKGROUND

• Annan SF, Higgins LA, Jelleryd E, Hannon T, Rose S, Salis S, Baptista J, Chinchilla P, Marcovecchio ML. ISPAD Clinical Practice Consensus Guidelines 2022: Nutritional management in children and adolescents with diabetes. Pediatr Diabetes. 2022 Dec;23(8):1297-1321. doi: 10.1111/pedi.13429. Epub 2022 Dec 5. No abstract available.

  PMID: 36468223 BACKGROUND

MeSH Terms

Conditions

Diabetes Mellitus, Type 1

Condition Hierarchy (Ancestors)

Diabetes Mellitus; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases; Autoimmune Diseases; Immune System Diseases

Central Study Contacts

CAROLINE GB PASSONE, ENDOCRINOLOGIST

CONTACT

+5511981467909; carol.passone@gmail.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: SUPPORTIVE CARE
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: HEAD OF PEDIATRIC DIABETES CLINIC - PEDIATRIC ENDOCRINOLOGIST DEPARTMENT

Study Record Dates

• First Submitted: June 28, 2025
• First Posted: September 2, 2025
• Study Start: May 1, 2025
• Primary Completion: October 1, 2025
• Study Completion (Estimated): December 1, 2026
• Last Updated: September 2, 2025

Record last verified: 2025-06

Data Sharing

• IPD Sharing: Will not share

Locations

BR (1)