Evaluation of a Mobile AI-powered Decision Support System for Insulin Dosing and Glucose Prediction in Type 1 Diabetes: The glUCModel Clinical Trial Protocol

NCT07304778 | Recruiting DMC

• 4 other identifiers: glUCModel-HUTPDC2022-133429-I00PID2021-125549OB-I00PID2024-158129OB-I00
• Study Type: interventional
• Target: 34
• Locations: 1 country
• Sites: 2

Brief Summary

The goal of this clinical trial is to evaluate the safety and efficacy of integrating predictive models into insulin therapy management via the user-centered glUCModel mobile app in People with Type 1 Diabetes Mellitus following Multiple Insulin Dosing therapy. Participants will be aged 18 to 65 years. The main questions it aims to answer are: Does using the app improve glycaemic control, as measured by time in range? Does using the app reduce the number of episodes of hyperglycaemia and hypoglycaemia? Are the app's design and functionality adequate? The study will comprise four phases:ses}):

• Screening phase: Informed consent, collection of sociodemographic and clinical data, and baseline Pittsburg, IFIS, and DTSQ questionnaires.
• Run-in phase: 2 weeks of standard care with CGM. Data will be used to generate personalized predictive models in the intervention group.
• Active treatment phase: Participants continue MDI therapy. The intervention group will additionally use the glUCModel mobile app. CGM data from the final 2 weeks will be analyzed.
• Evaluation and analysis phase: Participants will complete the uMARS, Pittsburgh, and DTSQ questionnaires. Statistical analysis and correlations among outcomes will be processed.

Conditions

Diabetes (Insulin-requiring, Type 1 or Type 2)

Keywords

Diabetes; Bolus recomendation; Artificial Intelligence

Outcome Measures

Primary Outcomes (2)

• Time in Range (TIR)

  Time in Range (TIR), defined as the percentage of time that interstitial glucose is between 70-180 mg/dL during the final 2 weeks of the intervention phase

  During the last 2 weeks of intervention

• Usability and adherence

  Patient-reported outcomes on usability and adherence through uMARS. The usability of the app will be evaluated using the Spanish Version of the User Version of the Mobile Application Rating Scale (uMARS). This scale provides a comprehensive and objective measure of app usability and consists of 20 items. Each item is rated on a 5-point scale, ranging from 1 (inadequate) to 5 (excellent).

  Two weeks

Secondary Outcomes (13)

• Frequency of Level 1 hypoglycemia

  During the last 2 weeks of intervention

• Frequency of Level 2 hypoglycemias

  During the last 2 weeks of intervention

• Frequency of Level 1 hyperglycemias

  During the last 2 weeks of intervention

• Frequency of Level 2 hyperglycemias

  During the last 2 weeks of the intervention

• Duration of Level 1 hypoglycemias

  During the last 2 weeks of intervention

Other Outcomes (2)

• Sleep Quality before the intervention

  The day of the start of the intervention

• Sleep Quality after the intervention

  Two weeks after the day of the end of the intervention

Study Arms (2)

Intervention

EXPERIMENTAL

Participants continue MDI therapy. The intervention group will additionally use the glUCModel app. CGM data from the final 2 weeks will be analyzed

Other: glUCModel app

Control

NO INTERVENTION

Participants continue MDI therapy. CGM data from the final 2 weeks will be analyzed

Interventions

glUCModel app OTHER

The intervention consists on using glUCModel, an application designed to help people with diabetes. It features a suite of artificial intelligence tools and statistical techniques for capturing and managing key information that people with diabetes need to track, as well as for predicting glucose values to aid users in informed decision-making.

Intervention

Eligibility Criteria

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

You may qualify if:

• HbA1c \< 9%
• Currently following an MDI Bolus-Basal therapy.
• Wearing CGMs connected to a mobile phone.
• Spanish language proficiency.
• Willingness to participate in the trial.
• At least one year since the time of diabetes diagnosis.
• Ability to use a mobile application like glUCModel.
• Own a mobile phone running Android or iOS operating system.
• Ability to follow a Portion-controlled diet for diabetes.
• Educated to do an active management of insulin dosing

You may not qualify if:

• HbA1c \< 9%.
• Not wearing CGMs.
• Non-Spanish language proficiency.
• Less than one year since the time of diabetes diagnosis
• Unable to use a mobile application like glUCModel
• Unable to follow a Portion-controlled diet for diabetes
• Unable to do an active management of insulin dosing.
• Diagnosed with a significant psychiatric disorder.
• Subjects in treatment with corticoids
• Patients who have required hospitalization or surgery in the last six months.
• Pregnancy or planning a pregnancy

Sponsors & Collaborators

• Universidad Complutense de Madrid: lead
• Bioinspired Intelligence SL: collaborator
• Hospital de Toledo: collaborator

Study Sites (2)

Universidad Complutense de Madrid

Madrid, Madrid, 280240, Spain

NOT YET RECRUITING

Hospital Universitario de Toledo

Toledo, Toledo, 45007, Spain

RECRUITING

MeSH Terms

Conditions

Diabetes Mellitus

Condition Hierarchy (Ancestors)

Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases

Central Study Contacts

Ignacio Hidalgo, PhD

CONTACT

+34679562712; hidalgo@dacya.ucm.es

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Full Professor

Study Record Dates

• First Submitted: November 30, 2025
• First Posted: December 26, 2025
• Study Start: July 11, 2025
• Primary Completion: February 1, 2026
• Study Completion (Estimated): June 1, 2026
• Last Updated: January 6, 2026

Record last verified: 2025-12

Locations

ES (2)