Download PDF
Exp Clin Endocrinol Diabetes 2022; 130(07): 475-483
DOI: 10.1055/a-1493-2324
Article

Evaluation of Meal Carbohydrate Counting Errors in Patients with Type 1 Diabetes

Sina Buck 
1   Institut für Diabetes-Technologie Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
,
Collin Krauss
1   Institut für Diabetes-Technologie Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
,
Delia Waldenmaier
1   Institut für Diabetes-Technologie Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
,
Christina Liebing
1   Institut für Diabetes-Technologie Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
,
Nina Jendrike
1   Institut für Diabetes-Technologie Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
,
Josef Högel
2   Universitätsklinikum Ulm, Institut für Humangenetik, Ulm, Germany
,
Boris M. Pfeiffer
3   ReferArt, Darmstadt, Germany
,
Cornelia Haug
1   Institut für Diabetes-Technologie Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
,
Guido Freckmann
1   Institut für Diabetes-Technologie Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
› Author Affiliations
› Further Information
Also available at
    Permissions and Reprints

Abstract

Aim Correct estimation of meal carbohydrate content is a prerequisite for successful intensified insulin therapy in patients with diabetes. In this survey, the counting error in adult patients with type 1 diabetes was investigated.

Methods Seventy-four patients with type 1 diabetes estimated the carbohydrate content of 24 standardized test meals. The test meals were categorized into 1 of 3 groups with different carbohydrate content: low, medium, and high. Estimation results were compared with the meals’ actual carbohydrate content as determined by calculation based on weighing. A subgroup of the participants estimated the test meals for a second (n=35) and a third time (n=22) with a mean period of 11 months between the estimations.

Results During the first estimation, the carbohydrate content was underestimated by −28% (−50, 0) of the actual carbohydrate content. Particularly meals with high mean carbohydrate content were underestimated by −34% (−56, −13). Median counting error improved significantly when estimations were performed for a second time (p<0.001).

Conclusions Participants generally underestimated the carbohydrate content of the test meals, especially in meals with higher carbohydrate content. Repetition of estimation resulted in significant improvements in estimation accuracy and is important for the maintenance of correct carbohydrate estimations. The ability to estimate the carbohydrate content of a meal should be checked and trained regularly in patients with diabetes.

Key words

diabetes - carbohydrate counting - estimation accuracy


Publication History

Received: 09 December 2020
Received: 11 March 2021

Accepted: 13 April 2021

Article published online:
25 May 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Bantle JP, Wylie-Rosett J, Albright AL. et al. Nutrition recommendations and interventions for diabetes: A position statement of the American Diabetes Association. Diabetes Care 2008; 31: 61-78
    CrossrefPubMedGoogle Scholar
  • 2 Sheard NF, Clark NG, Brand-Miller JC. et al. Dietary Carbohydrate (Amount and Type) in the Prevention and Management of Diabetes: A statement by the American Diabetes Association. Diabetes Care 2004; 27: 2266-2271
    CrossrefPubMedGoogle Scholar
  • 3 Gupta L, Khandelwal D, Kalra S. Applied carbohydrate counting. J Pak Med Assoc 2017; 67: 1456-1457
    PubMedGoogle Scholar
  • 4 Bell KJ, Barclay AW, Petocz P. et al. Efficacy of carbohydrate counting in type 1 diabetes: A systematic review and meta-analysis. Lancet Diabetes Endocrinol 2014; 2: 133-140
    CrossrefPubMedGoogle Scholar
  • 5 Vasiloglou MF, Mougiakakou S, Aubry E. et al. A Comparative study on carbohydrate estimation: GoCARB vs. dietitians. Nutrients 2018; 10: 741
    CrossrefPubMedGoogle Scholar
  • 6 Bell KJ, Smart CE, Steil GM. et al. Impact of fat, protein, and glycemic index on postprandial glucose control in type 1 diabetes: implications for intensive diabetes management in the continuous glucose monitoring era. Diabetes Care 2015; 38: 1008-1015
    CrossrefPubMedGoogle Scholar
  • 7 Laurenzi A, Bolla AM, Panigoni G. et al. Effects of carbohydrate counting on glucose control and quality of life over 24 weeks in adult patients with type 1 diabetes on continuous subcutaneous insulin infusion: A randomized, prospective clinical trial (GIOCAR). Diabetes Care 2011; 34: 823-827
    CrossrefPubMedGoogle Scholar
  • 8 The Diabetes Control and Complications Trial Research Group Nutrition interventions for intensive therapy in the Diabetes Control and Complications Trial. J Am Diet Assoc 1993; 93: 768-772
    CrossrefPubMedGoogle Scholar
  • 9 Tascini G, Berioli MG, Cerquiglini L. et al. Carbohydrate counting in children and adolescents with type 1 diabetes. Nutrients 2018; 10: 109
    CrossrefPubMedGoogle Scholar
  • 10 Deeb A, Al Hajeri A, Alhmoudi I. et al. Accurate carbohydrate counting is an important determinant of postprandial glycemia in children and adolescents with type 1 diabetes on insulin pump therapy. J Diabetes Sci Technol 2017; 11: 753-758
    CrossrefPubMedGoogle Scholar
  • 11 Schmidt S, Schelde B, Norgaard K. Effects of advanced carbohydrate counting in patients with type 1 diabetes: A systematic review. Diabet Med 2014; 31: 886-896
    CrossrefPubMedGoogle Scholar
  • 12 Yamada E, Okada S, Nakajima Y. et al. Effect of carbohydrate counting using bolus calculators on glycemic control in type 1 diabetes patients during continuous subcutaneous insulin infusion. Journal of Diabetes Investigation 2017; 8: 496-500
    CrossrefPubMedGoogle Scholar
  • 13 American Diabetes Association Standards of Medical Care in Diabetes—2020. Diabetes Care 2020; 43: S1
    CrossrefPubMedGoogle Scholar
  • 14 Bundesärztekammer, Kassenärztliche Bundesvereinigung, Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften Nationale VersorgungsLeitlinie Therapie des Typ-2-Diabetes – Langfassung. 1 . Auflage Version 3 2014;
    PubMed
  • 15 Mehta SN, Quinn N, Volkening LK. et al. Impact of carbohydrate counting on glycemic control in children with type 1 diabetes. Diabetes Care 2009; 32: 1014-1016
    CrossrefPubMedGoogle Scholar
  • 16 Scavone G, Manto A, Pitocco D. et al. Effect of carbohydrate counting and medical nutritional therapy on glycaemic control in type 1 diabetic subjects: A pilot study. Diabet Med 2010; 27: 477-479
    CrossrefPubMedGoogle Scholar
  • 17 Spiegel G, Bortsov A, Bishop FK. et al. Randomized nutrition education intervention to improve carbohydrate counting in adolescents with type 1 diabetes study: Is more intensive education needed?. J Acad Nutr Diet 2012; 112: 1736-1746
    CrossrefPubMedGoogle Scholar
  • 18 Shapira G, Yodfat O, HaCohen A. et al. Bolus guide: A novel insulin bolus dosing decision support tool based on selection of carbohydrate ranges. J Diabetes Sci Technol 2010; 4: 893-902
    CrossrefPubMedGoogle Scholar
  • 19 Schmidt S, Meldgaard M, Serifovski N. et al. Use of an automated bolus calculator in MDI-treated type 1 diabetes: The BolusCal Study, a randomized controlled pilot study. Diabetes Care 2012; 35: 984-990
    CrossrefPubMedGoogle Scholar
  • 20 Samadi S, Turksoy K, Hajizadeh I. et al. Meal detection and carbohydrate estimation using continuous glucose sensor data. IEEE J Biomed Health Inform 2017; 21: 619-627
    CrossrefPubMedGoogle Scholar
  • 21 Rohling M, Martin T, Wonnemann M. et al. Determination of postprandial glycemic responses by continuous glucose monitoring in a real-world setting. Nutrients 2019; 11: 2305
    CrossrefPubMedGoogle Scholar
  • 22 Zeevi D, Korem T, Zmora N. et al. Personalized nutrition by prediction of glycemic responses. Cell 2015; 163: 1079-1094
    CrossrefPubMedGoogle Scholar
  • 23 DuBose SN, Li Z, Sherr JL. et al. Effect of exercise and meals on continuous glucose monitor data in healthy individuals without diabetes. J Diabetes Sci Technol. 2020: 1932296820905904 Epub ahead of print
    PubMedGoogle Scholar
  • 24 Kawamura T. The importance of carbohydrate counting in the treatment of children with diabetes. Pediatr Diabetes 2007; 8: 57-62
    CrossrefPubMedGoogle Scholar
  • 25 Kawamura T, Takamura C, Hirose M. et al. The factors affecting on estimation of carbohydrate content of meals in carbohydrate counting. Clin Pediatr Endocrin 2015; 24: 153-165
    CrossrefPubMedGoogle Scholar
  • 26 Mehta SN, Haynie DL, Higgins LA. et al. Emphasis on carbohydrates may negatively influence dietary patterns in youth with type 1 diabetes. Diabetes Care 2009; 32: 2174-2176
    CrossrefPubMedGoogle Scholar
  • 27 Smart CE, Ross K, Edge JA. et al. Children and adolescents on intensive insulin therapy maintain postprandial glycaemic control without precise carbohydrate counting. Diabet Med 2009; 26: 279-285
    CrossrefPubMedGoogle Scholar
  • 28 Smart CE, Ross K, Edge JA. et al. Can children with Type 1 diabetes and their caregivers estimate the carbohydrate content of meals and snacks?. Diabet Med 2010; 27: 348-353
    CrossrefPubMedGoogle Scholar
  • 29 Brazeau AS, Mircescu H, Desjardins K. et al. Carbohydrate counting accuracy and blood glucose variability in adults with type 1 diabetes. Diabetes Res Clin Pract 2013; 99: 19-23
    CrossrefPubMedGoogle Scholar
  • 30 Roversi C, Vettoretti M, Del Favero S. et al. Modeling carbohydrate counting error in type 1 diabetes management. Diabetes Technol Ther 2020; 22: 749-759
    CrossrefPubMedGoogle Scholar
  • 31 Meade LT, Rushton WE. Accuracy of carbohydrate counting in adults. Clin Diabetes 2016; 34: 142-147
    CrossrefPubMedGoogle Scholar
  • 32 Koschinsky T, Heckermann S, Heinemann L. Parameters affecting postprandial blood glucose: effects of blood glucose measurement errors. J Diabetes Sci Technol 2008; 2: 58-66
    CrossrefPubMedGoogle Scholar
  • 33 Smart CE, King BR, McElduff P. et al. In children using intensive insulin therapy, a 20-g variation in carbohydrate amount significantly impacts on postprandial glycaemia. Diabet Med 2012; 29: e21-e24
    CrossrefPubMedGoogle Scholar
  • 34 Graff MR, Gross TM, Juth SE. et al. How Well Are individuals on intensive insulin therapy counting carbohydrates?. Diabetes Res Clin Pract 2000; 50: 238-239
    CrossrefPubMedGoogle Scholar
  • 35 Jensen KD, Jensen B, Lervang HH. et al. Diabetes patients' ability to estimate dietary carbohydrate content for use in a decision support system. Stud Health Technol Inform 2002; 90: 649-654
    PubMedGoogle Scholar
  • 36 Rabasa-Lhoret R, Garon J, Langelier H. et al. Effects of meal carbohydrate content on insulin requirements in type 1 diabetic patients treated intensively with the basal-bolus (ultralente-regular) insulin regimen. Diabetes Care 1999; 22: 667-673
    CrossrefPubMedGoogle Scholar
  • 37 Rhyner D, Loher H, Dehais J. et al. Carbohydrate Estimation by a mobile phone-based system versus self-estimations of individuals with type 1 diabetes mellitus: A comparative study. J Med Internet Res 2016; 18: e101
    CrossrefPubMedGoogle Scholar
  • 38 Bishop FK, Maahs DM, Spiegel G. et al. The Carbohydrate Counting in Adolescents With Type 1 Diabetes (CCAT) Study. Diabetes Spectr 2009; 22: 56-62
    CrossrefPubMedGoogle Scholar
  • 39 Domhardt M, Tiefengrabner M, Dinic R. et al. Training of carbohydrate estimation for people with diabetes using mobile augmented reality. J Diabetes Sci Technol 2015; 9: 516-524
    CrossrefPubMedGoogle Scholar
  • 40 Rovner AJ, Nansel TR, Mehta SN. et al. Development and validation of the type 1 diabetes nutrition knowledge survey. Diabetes Care 2012; 35: 1643-1647
    CrossrefPubMedGoogle Scholar