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This paper is in the following e-collection/theme issue:

Formative Studies and eHealth/mHealth Development (687) Wearable Devices and Sensors (1521) Glucose Tracking and Self-Monitoring of Blood Glucose (236) Apps, Mobile, Wearables for Diabetes (437) Machine Learning (3051) Artificial Intelligence in Diabetes Care and Prevention (103)

Published on in Vol 15 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/99626, first published .
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An AI-Assisted Tool to Predict Continuous Glucose Monitor Adherence in Children With Type 1 Diabetes in Oman: Protocol for a Multiphase Mixed Methods Translational Study

An AI-Assisted Tool to Predict Continuous Glucose Monitor Adherence in Children With Type 1 Diabetes in Oman: Protocol for a Multiphase Mixed Methods Translational Study

Authors of this article:

Thamra Al Ghafri1, 2 Author Orcid Image ;   Saud Al Harthi3 Author Orcid Image ;   Asma Bait Ishaq1 Author Orcid Image ;   Huwaida Al Harthi1 Author Orcid Image ;   Maryam Al Muaini4 Author Orcid Image ;   Rahma Al-Ghadani4 Author Orcid Image ;   Ahmed Aljufaili5 Author Orcid Image ;   Mohamed Al Harb5 Author Orcid Image ;   Abdullah Al Saadi5 Author Orcid Image ;   Abdelhamid Abdessalem2 Author Orcid Image ;   Noushath Shaffi2 Author Orcid Image ;   Mohamed Ahmed6 Author Orcid Image ;   Said Jaboob7 Author Orcid Image ;   Nadia Al Maqbali8 Author Orcid Image ;   Moza Al-Shehhi9 Author Orcid Image ;   Talib Al Kalbani10 Author Orcid Image ;   Amal Al Shukaili11 Author Orcid Image ;   Jannat Al Harthi12 Author Orcid Image
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