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Modeling Carbohydrate Counting Error in Type 1 Diabetes Management.

ABSTRACT: Background: The error in estimating meal carbohydrates (CHO) amount is a critical mistake committed by type 1 diabetes (T1D) subjects. The aim of this study is both to investigate which factors, related to meals and subjects, affect the CHO counting error most and to develop a mathematical model of CHO counting error embeddable in T1D patient decision simulators to conduct in silico clinical trials. Methods: A published dataset of 50 T1D adults is used, which includes a patient's CHO count of 692 meals, dietitian's estimates of meal composition (used as reference), and several potential explanatory factors. The CHO counting error is modeled by multiple linear regression, with stepwise variable selection starting from 10 candidate predictors, that is, education level, insulin treatment duration, age, body weight, meal type, CHO, lipid, energy, protein, and fiber content. Inclusion of quadratic and interaction terms is also evaluated. Results: Larger errors correspond to larger meals, and most of the large meals are underestimated. The linear model selects CHO (P?

SUBMITTER: Roversi C

PROVIDER: S-EPMC7594710 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Modeling Carbohydrate Counting Error in Type 1 Diabetes Management.

Roversi Chiara C Vettoretti Martina M Del Favero Simone S Facchinetti Andrea A Sparacino Giovanni G

Diabetes technology & therapeutics 20200924 10

Background: The error in estimating meal carbohydrates (CHO) amount is a critical mistake committed by type 1 diabetes (T1D) subjects. The aim of this study is both to investigate which factors, related to meals and subjects, affect the CHO counting error most and to develop a mathematical model of CHO counting error embeddable in T1D patient decision simulators to conduct in silico clinical trials. Methods: A published dataset of 50 T1D adults is used, which includes ...[more]

PMID: 32223551

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Project description:BackgroundCarbohydrate counting is an important component of diabetes management, but it is challenging, often performed inaccurately, and can be a barrier to optimal diabetes management. iSpy is a novel mobile app that leverages machine learning to allow food identification through images and that was designed to assist youth with type 1 diabetes in counting carbohydrates.ObjectiveOur objective was to test the app's usability and potential impact on carbohydrate counting accuracy.MethodsIterative usability testing (3 cycles) was conducted involving a total of 16 individuals aged 8.5-17.0 years with type 1 diabetes. Participants were provided a mobile device and asked to complete tasks using iSpy app features while thinking aloud. Errors were noted, acceptability was assessed, and refinement and retesting were performed across cycles. Subsequently, iSpy was evaluated in a pilot randomized controlled trial with 22 iSpy users and 22 usual care controls aged 10-17 years. Primary outcome was change in carbohydrate counting ability over 3 months. Secondary outcomes included levels of engagement and acceptability. Change in HbA1c level was also assessed.ResultsUse of iSpy was associated with improved carbohydrate counting accuracy (total grams per meal, P=.008), reduced frequency of individual counting errors greater than 10 g (P=.047), and lower HbA1c levels (P=.03). Qualitative interviews and acceptability scale scores were positive. No major technical challenges were identified. Moreover, 43% (9/21) of iSpy participants were still engaged, with usage at least once every 2 weeks, at the end of the study.ConclusionsOur results provide evidence of efficacy and high acceptability of a novel carbohydrate counting app, supporting the advancement of digital health apps for diabetes care among youth with type 1 diabetes. Further testing is needed, but iSpy may be a useful adjunct to traditional diabetes management.Trial registrationClinicalTrials.gov NCT04354142; https://clinicaltrials.gov/ct2/show/NCT04354142.

Project description:IntroductionClinical guidelines recommend that patients with type 1 diabetes (T1D) learn carbohydrate counting or similar methods to improve glycaemic control. Although systematic educating in carbohydrate counting is still not offered as standard-of-care for all patients on multiple daily injections (MDI) insulin therapy in outpatient diabetes clinics in Denmark. This may be due to the lack of evidence as to which educational methods are the most effective for training patients in carbohydrate counting. The objective of this study is to compare the effect of two different educational programmes in carbohydrate counting with the usual dietary care on glycaemic control in patients with T1D.Methods and analysisThe study is designed as a randomised controlled trial with a parallel-group design. The total study duration is 12 months with data collection at baseline, 6 and 12 months. We plan to include 231 Danish adult patients with T1D. Participants will be randomised to one of three dietician-led interventions: (1) a programme in basic carbohydrate counting, (2) a programme in advanced carbohydrate counting including an automated bolus calculator or (3) usual dietary care. The primary outcome is changes in glycated haemoglobin A1c or mean amplitude of glycaemic excursions from baseline to end of the intervention period (week 24) between and within each of the three study groups. Other outcome measures include changes in other parameters of plasma glucose variability (eg, time in range), body weight and composition, lipid profile, blood pressure, mathematical literacy skills, carbohydrate estimation accuracy, dietary intake, diet-related quality of life, perceived competencies in dietary management of diabetes and perceptions of an autonomy supportive dietician-led climate, physical activity and urinary biomarkers.Ethics and disseminationThe protocol has been approved by the Ethics Committee of the Capital Region, Copenhagen, Denmark. Study findings will be disseminated widely through peer-reviewed publications and conference presentations.Trial registration numberClinicalTrials.gov Registry (NCT03623113).

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Modeling Carbohydrate Counting Error in Type 1 Diabetes Management.

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Modeling Carbohydrate Counting Error in Type 1 Diabetes Management.

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