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Bolus Calculators

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Schmidt, Signe, Nørgaard, Kirsten·2014·Journal of Diabetes Science and Technology

DOI 10.1177/1932296814532906PMC PMC4455388

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Abstract

Matching meal insulin to carbohydrate intake, blood glucose, and activity level is recommended in type 1 diabetes management. Calculating an appropriate insulin bolus size several times per day is, however, challenging and resource demanding. Accordingly, there is a need for bolus calculators to support patients in insulin treatment decisions. Currently, bolus calculators are available integrated in insulin pumps, as stand-alone devices and in the form of software applications that can be downloaded to, for example, smartphones. Functionality and complexity of bolus calculators vary greatly, and the few handfuls of published bolus calculator studies are heterogeneous with regard to study design, intervention, duration, and outcome measures. Furthermore, many factors unrelated to the specific device affect outcomes from bolus calculator use and therefore bolus calculator study comparisons should be conducted cautiously. Despite these reservations, there seems to be increasing evidence that bolus calculators may improve glycemic control and treatment satisfaction in patients who use the devices actively and as intended.

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The Effect of Intensive Treatment of Diabetes on the Development and Progression of Long-Term Complications in Insulin-Dependent Diabetes Mellitus
(1993) · New England Journal of Medicine · doi:10.1056/nejm199309303291401 · Cited by 24,667
Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes
(2005) · New England Journal of Medicine · doi:10.1056/nejmoa052187 · Cited by 5,138
Standards of medical care in diabetes—2014
American Diabetes Association (2014) · Diabetes Care · doi:10.2337/dc14-s014 · Cited by 4,572
Training in flexible, intensive insulin management to enable dietary freedom in people with type 1 diabetes: dose adjustment for normal eating (DAFNE) randomised controlled trial
(2002) · BMJ · doi:10.1136/bmj.325.7367.746 · Cited by 916
Association of Numeracy and Diabetes Control
Cavanaugh, K. L., Huizinga, M. M., Wallston, K. A., Gebretsadik, T. S., Shintani, A., Davis, D., Gregory, R., Fuchs, L. S., Malone, R., Cherrington, A., et al. (2008) · Annals of Internal Medicine · doi:10.7326/0003-4819-148-10-200805200-00006 · Cited by 420
The Diabeo Software Enabling Individualized Insulin Dose Adjustments Combined With Telemedicine Support Improves HbA1c in Poorly Controlled Type 1 Diabetic Patients
Charpentier, G., Benhamou, P.-Y., Dardari, D., Clergeot, A., Franc, S., Schaepelynck-Belicar, P., Catargi, B., Melki, V., Chaillous, L., Farret, A., et al. (2011) · Diabetes Care · doi:10.2337/dc10-1259 · Cited by 292
The Role of Diet Behaviors in Achieving Improved Glycemic Control in Intensively Treated Patients in the Diabetes Control and Complications Trial
Delahanty, L. M., & Halford, B. N. (1993) · Diabetes Care · doi:10.2337/diacare.16.11.1453 · Cited by 246
Both dietary protein and fat increase postprandial glucose excursions in children with type 1 diabetes, and the effect is additive
Smart, C. E. M., Evans, M., O’Connell, S. M., McElduff, P., Lopez, P. E., Jones, T. W., Davis, E. A., & King, B. R. (2013) · Diabetes Care · doi:10.2337/dc13-1195 · Cited by 240
Dietary Fat Acutely Increases Glucose Concentrations and Insulin Requirements in Patients With Type 1 Diabetes
Wolpert, H. A., Atakov-Castillo, A., Smith, S. A., & Steil, G. M. (2012) · Diabetes Care · doi:10.2337/dc12-0092 · Cited by 199
Using Carbohydrate Counting in Diabetes Clinical Practice
Gillespie, S. J., Kulkarni, K. D., & Daly, A. E. (1998) · Journal of the American Dietetic Association · doi:10.1016/s0002-8223(98)00206-5 · Cited by 172

Show all 36 references

Research works printed but not linked: 2

Walsh PA, 2006, Pumping Insulin
Schmidt S, 2014, Diabetes Technol Ther, 15, A-1

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Authors: Schmidt, S., & Nørgaard, K.
Affiliations: Hvidovre Hospital (DK)
Corresponding author: Schmidt, Signe
ORCID: Schmidt, Signe
Nørgaard, Kirsten
Venue: Journal of Diabetes Science and Technology
Publisher: SAGE Publications
Published: 2014-05-19
Volume: 8
Issue: 5
Pages: 1035-1041
ISSN: 1932-2968 (ISSN-L)
DOI: 10.1177/1932296814532906
PMC: PMC4455388
OpenAlex: W4241093605
Cited by: 170
References: 36
Citations by year: 2026: 12 · 2025: 22 · 2024: 17 · 2023: 22 · 2022: 10 · 2021: 12 · 2020: 18 · 2019: 16 · 2018: 14 · 2017: 12 · 2016: 8 · 2015: 7
FWCI: 1.43
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Journal of Diabetes Science and Technology (publishedVersion, cc-by)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
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 › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
Keywords: bolus calculator, type 1 diabetes, insulin pump, mobile application, glycemic control, treatment satisfaction, patient decision support
MeSH terms: Blood Glucose, Diabetes Mellitus, Type 1, Humans, Hypoglycemic Agents, Insulin, Drug Dosage Calculations

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