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.
HbA1c < 9%
Currently following an MDI Bolus-Basal therapy.
Wearing CGMs connected to a mobile phone.
Spanish language proficiency.
HbA1c < 9%.
Not wearing CGMs.
Non-Spanish language proficiency.
Less than one year since the time of diabetes diagnosis
Universidad Complutense de Madrid
Lead sponsor
Bioinspired Intelligence SL
Collaborator
Hospital de Toledo
Collaborator