Type 1 Diabetes Publications
Type 1 Diabetes Research
Publications focused on Type 1 Diabetes management, blood glucose monitoring, continuous glucose monitoring (CGM), hypoglycaemia detection, and diabetes technology.
17 publications | ← Back to all publications
2025
Gasca Garcia, D. et al. (2025). “Toward a Personalized Basal Tuner for Detecting Basal Rate Inaccuracies in Type 1 Diabetes Mellitus Without Meal Data: Algorithm Development and Retrospective Validation Study”. JMIR Diabetes, 10, e72769–e72769. DOI: 10.2196/72769.
Lubasinski, N. et al. (2025). “Coefficient of Variation to Assess the Reproducibility of Meal-Induced Glycemic Responses: Development of a Clustering Algorithm”. JMIR Diabetes, 10, e68821–e68821. DOI: 10.2196/68821.
Shekh khalil, N. et al. (2025). “Exploring Location Data as a Predictor for Blood Glucose in Type 1 Diabetes: A Systematic Review (Preprint)”. DOI: 10.2196/preprints.87181.
Kongdee, R. et al. (2025). “Glucose interpretation meaning and action: enhancing type 1 diabetes decision-making with textual descriptions”. Therapeutic Advances in Endocrinology and Metabolism, 16. DOI: 10.1177/20420188251362089.
Kongdee, R. et al. (2025). “Glucose interpretation meaning and action (GIMA): Insights to blood glucose user interface interpretation in type 1 diabetes”. Digital Health, 11. DOI: 10.1177/20552076251332580.
2024
Lubasinski, N. et al. (2024). “Coefficient of Variation to Assess the Reproducibility of Meal-Induced Glycemic Responses (Preprint)”. DOI: 10.2196/preprints.68821.
Lubasinski, N. et al. (2024). “Blood Glucose Prediction from Nutrition Analytics in Type 1 Diabetes: A Review”. Nutrients, 16(14). DOI: 10.3390/nu16142214.
Lubasinski, N., Thabit, H., Nutter, P. (2024). “What Is the Tech Missing? Nutrition Reporting in Type 1 Diabetes”. Nutrients, 16(11). DOI: 10.3390/nu16111690.
2023
Worth, C., Nutter, P., Estebanez, S. (2023). “The behaviour change behind a successful pilot of hypoglycaemia reduction with HYPO-CHEAT”. Digital Health, 9. DOI: 10.1177/20552076231192011.
Worth, C., Hoskyns, L., Estebanez, S. (2023). “Continuous glucose monitoring for children with hypoglycaemia: Evidence in 2023”. Frontiers in Endocrinology, 14. DOI: 10.3389/fendo.2023.1116864.
2022
Worth, C., Dunne, M., Estebanez, S. (2022). “The hypoglycaemia error grid: A UK-wide consensus on CGM accuracy assessment in hyperinsulinism”. Frontiers in Endocrinology, 13. DOI: 10.3389/fendo.2022.1016072.
Worth, C. et al. (2022). “HYPO-CHEAT’”. Digital Health, 8, 1–22. DOI: 10.1177/20552076221129712.
Auckburally, S., Worth, C., Estebanez, S. (2022). “Families’”. Frontiers in Endocrinology. DOI: 10.3389/fendo.2022.894559.
2021
Worth, C. et al. (2021). “Timing of Hypoglycaemia in Patients with Hyperinsulinism (HI”. Journal of Medical Internet Research. DOI: 10.2196/26957.
Worth, C., Harper, S., Estebanez, S. (2021). “Timing of Hypoglycaemia in Patients with Hyperinsulinism (HI): Extension of the Digital Phenotype”. JOURNAL OF MEDICAL INTERNET RESEARCH.
Worth, C. et al. (2021). “Timing of Hypoglycaemia in Patients with Hyperinsulinism (HI”. DOI: 10.2196/preprints.26957.
2020
Worth, C. et al. (2020). “Continuous glucose monitoring for hypoglycaemia in children: Perspectives in 2020”. Pediatric Diabetes, 21(5), 697–706. DOI: 10.1111/pedi.13029.
Research Themes
Blood Glucose Prediction & Monitoring
Developing algorithms and machine learning models to predict blood glucose levels from nutrition analytics, activity data, and continuous glucose monitoring.
Continuous Glucose Monitoring (CGM)
Evaluating CGM accuracy, developing error grids for hypoglycaemia assessment, and improving glucose monitoring technology for children and adults.
Hypoglycaemia Detection & Prevention
Creating systems to detect, predict, and prevent hypoglycaemic events, particularly in children with hyperinsulinism and congenital conditions.
Diabetes Technology & User Interfaces
Designing and evaluating user interfaces for glucose monitoring devices, investigating interpretation challenges, and exploring nutrition reporting technologies.
Digital Phenotyping for Diabetes
Using digital biomarkers and smartphone data to understand diabetes self-management behaviours and personalise interventions.