Clinical safety and feasibility of the advanced bolus calculator for type 1 diabetes based on case-based reasoning: A 6-week nonrandomized single-arm pilot study
Abstract
BACKGROUND: The Advanced Bolus Calculator for Diabetes (ABC4D) is an insulin bolus dose decision support system based on case-based reasoning (CBR). The system is implemented in a smartphone application to provide personalized and adaptive insulin bolus advice for people with type 1 diabetes. We aimed to assess proof of concept, safety, and feasibility of ABC4D in a free-living environment over 6 weeks. METHODS: Prospective nonrandomized single-arm pilot study. Participants used the ABC4D smartphone application for 6 weeks in their home environment, attending the clinical research facility weekly for data upload, revision, and adaptation of the CBR case base. The primary outcome was postprandial hypoglycemia. RESULTS: Ten adults with type 1 diabetes, on multiple daily injections of insulin, mean (standard deviation) age 47 (17), diabetes duration 25 (16), and HbA1c 68 (16) mmol/mol (8.4 (1.5) %) participated. A total of 182 and 150 meals, in week 1 and week 6, respectively, were included in the analysis of postprandial outcomes. The median (interquartile range) number of postprandial hypoglycemia episodes within 6-h after the meal was 4.5 (2.0-8.2) in week 1 versus 2.0 (0.5-6.5) in week 6 (P = 0.1). No episodes of severe hypoglycemia occurred during the study. CONCLUSION: The ABC4D is safe for use as a decision support tool for insulin bolus dosing in self-management of type 1 diabetes. A trend suggesting a reduction in postprandial hypoglycemia was observed in the final week compared with week 1.
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The study demonstrated the safety and effectiveness of ABC4D, which made it possible to reduce the occurrence of postprandial hypoglycemia.
“The study demonstrated the safety and effectiveness of ABC4D, which made it possible to reduce the occurrence of postprandial hypoglycemia.”
From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [45]
ABC4D was tested in a six-week-long non-randomized clinical study involving 10 adult subjects with type 1 diabetes.
“ABC4D was tested in a six-week-long non-randomized clinical study, involving 10 adult subjects with T1D [45].”
From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [45]
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Pavan, J., Nass, R. M., Fabris, C., Fathi, A. E., Emory, E. G., Santini, G., Batista, A. M. F. B., Oliveri, M. C., Koravi, C. L. K., Barnett, C. L., et al. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2025.113049 · Cited by 1 · see citing works
Artificial Intelligence and Its Application in Insulin Bolus Calculators
Mansour, A., Amroun, K., & Habbas, Z. (2020) · Springer eBooks · doi:10.1007/978-3-030-57552-6_5
Molecular targets and pharmacodynamics for bactericidal and bacteriostatic activity
Cherukupally, P., Lourenço, C., Farrugia, C., Castillo, D. M., & Sabalić, M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-823994-0.00009-8
Bolusrechner: Entlastung im Alltag und flexible Abstimmung der Insulintherapie mit der Ernährung
Braune, K., Boß, K., & Knoll, C. (2024) · Die Diabetologie · doi:10.1007/s11428-024-01250-5
Baravkar, A., Dhawan, A., Bhondwe, Y. Y., & SHINDE, S. D. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111248
- Authors
- Reddy, M., Pesl, P., Xenou, M., Toumazou, C., Johnston, D., Georgiou, P., Herrero, P., & Oliver, N.
- Affiliations
- Imperial College London (GB)
- Corresponding author
- Reddy, Monika
- ORCID
- Venue
- Diabetes Technology & Therapeutics
- Publisher
- SAGE Publications
- Published
- 2016-08
- Volume
- 18
- Issue
- 8
- Pages
- 487-493
- ISSN
- 1520-9156 (ISSN-L)
- 1557-8593
- OpenAlex
- W2402082249
- Cited by
- 70
- References
- 28
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 7 · 2023: 11 · 2022: 3 · 2021: 5 · 2020: 15 · 2019: 8 · 2018: 9 · 2017: 3
- FWCI
- 6.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Diabetes Technology & Therapeutics (publishedVersion)
- PubMed (publishedVersion)
- Spiral (Imperial College London) (submittedVersion)
- Funders
- National Institute for Health and Care Research (II-LA-0313-20004, II-LA-0214-20008)
- Medical Research Council (MC_PC_12015)
- Engineering and Physical Sciences Research Council (EP/N002474/1)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- type 1 diabetes, case-based reasoning, decision support system, mobile application, postprandial hypoglycemia, multiple daily insulin injections, self-management, clinical safety, feasibility
- MeSH terms
- Smartphone, Adult, Blood Glucose, Diabetes Mellitus, Type 1, Female, Glycated Hemoglobin, Humans, Hypoglycemic Agents, Insulin, Male, Middle Aged, Pilot Projects, Prospective Studies, Self Care, Postprandial Period, Decision Support Systems, Clinical
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