ArticleJournal of diabetes science and technology2022
Impact of Carbohydrate Counting Error on Glycemic Control in Open-Loop Management of Type 1 Diabetes: Quantitative Assessment Through an In Silico Trial.
Article in Journal of diabetes science and technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 16 citations in OpenAlex.
- Efficacy of carbohydrate counting in people with type 1 and type 2 diabetes mellitus: a systematic review and meta-analysis.Diabetology international · 2025Article
- Assessing Carbohydrate Counting Accuracy: Current Limitations and Future Directions.Nutrients · 2024Review
- An automatic deep reinforcement learning bolus calculator for automated insulin delivery systems.Scientific reports · 2024Article
- Development and Theoretical Underpinnings of the PRIORITY Intervention: A Parenting Intervention to Prevent Disordered Eating in Children and Young People With Type 1 Diabetes.Frontiers in clinical diabetes and healthcare · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn the management of type 1 diabetes (T1D), systematic and random errors in carb-counting can have an adverse effect on glycemic control. In this study, we performed an in silico trial aiming at quantifying the impact of different levels of carb-counting error on glycemic control.
methodsThe T1D patient decision simulator was used to simulate 7-day glycemic profiles of 100 adults using open-loop therapy. The simulation was repeated for different values of systematic and random carb-counting errors, generated with Gaussian distribution varying the error mean from -10% to +10% and standard deviation (SD) from 0% to 50%. The effect of the error was evaluated by computing the difference of time inside (∆TIR), above (∆TAR) and below (∆TBR) the target glycemic range (70-180mg/dl) compared to the reference case, that is, absence of error. Finally, 3 linear regression models were developed to mathematically describe how error mean and SD variations result in ∆TIR, ∆TAR, and ∆TBR changes.
resultsRandom errors globally deteriorate the glycemic control; systematic underestimations lead to, on average, up to 5.2% more TAR than the reference case, while systematic overestimation results in up to 0.8% more TBR. The different time in range metrics were linearly related with error mean and SD (
conclusionsThe quantification of carb-counting error impact performed in this work may be useful understanding causes of glycemic variability and the impact of possible therapy adjustments or behavior changes in different glucose metrics.
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