Literature DB >> 28264177

A Randomized Controlled Study of an Insulin Dosing Application That Uses Recognition and Meal Bolus Estimations.

Ewa Pańkowska1, Piotr Ładyżyński2, Piotr Foltyński2, Karolina Mazurczak1.   

Abstract

BACKGROUND: Throughout the insulin pump therapy, decisions of prandial boluses programming are taken by patients individually a few times every day, and, moreover, this complex process requires numerical skills and knowledge in nutrition components estimation. The aim of the study was to determine the impact of the expert system, supporting the patient's decision on meal bolus programming, on the time in range of diurnal glucose excursion in patients treated with continuous subcutaneous insulin infusion (CSII).
METHODS: The crossover, randomized study included 12 adults, aged 19 to 53, with type 1 diabetes mellitus, duration ranging from 7 to 30 years. Patients were educated in complex food counting, including carbohydrate units (CU) and fat-protein units (FPU). Subsequently, they were randomly allocated to the experimental group (A), which used the expert software named VoiceDiab, and the control group (B), using a manual method of meal-bolus estimation.
RESULTS: It was found that 66.7% of patients within the A group statistically reported a relevant increase in the percentage (%) of sensor glucose (SG) in range (TIR 70-180 mg/dl), compared to the B group. TIR (median) reached 53.9% in the experimental group (A) versus 44% within the control group (B), P < .05. The average difference in the number of hypoglycemia episodes was not statistically significant (-0.2%, SD 11.6%, P = .93). The daily insulin requirement in both groups was comparable-the average difference in total daily insulin dose between two groups was 0.26 (SD 7.06 IU, P = .9).
CONCLUSION: The expert system in meal insulin dosing allows improvement in glucose control without increasing the rates of hypoglycemia or the insulin requirement.

Entities:  

Keywords:  bolus calculator; insulin pumps; randomized control trial; type 1 diabetes

Mesh:

Substances:

Year:  2016        PMID: 28264177      PMCID: PMC5375087          DOI: 10.1177/1932296816683409

Source DB:  PubMed          Journal:  J Diabetes Sci Technol        ISSN: 1932-2968


  21 in total

Review 1.  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.

Authors:  Kirstine J Bell; Carmel E Smart; Garry M Steil; Jennie C Brand-Miller; Bruce King; Howard A Wolpert
Journal:  Diabetes Care       Date:  2015-06       Impact factor: 19.112

2.  Higher glucose concentrations following protein- and fat-rich meals - the Tuebingen Grill Study: a pilot study in adolescents with type 1 diabetes.

Authors:  Andreas Neu; Fabian Behret; Regina Braun; Susann Herrlich; Franziska Liebrich; Martina Loesch-Binder; Angelika Schneider; Roland Schweizer
Journal:  Pediatr Diabetes       Date:  2014-10-20       Impact factor: 4.866

3.  Performance of the first combined smartwatch and smartphone diabetes diary application study.

Authors:  Eirik Årsand; Miroslav Muzny; Meghan Bradway; Jan Muzik; Gunnar Hartvigsen
Journal:  J Diabetes Sci Technol       Date:  2015-01-14

4.  Carbohydrate-to-Insulin Ratio in a Mediterranean Population of Type 1 Diabetic Patients on Continuous Subcutaneous Insulin Infusion Therapy.

Authors:  Valeria Alcántara-Aragón; Cintia Gonzalez; Rosa Corcoy; Justa Ubeda; Ana Chico
Journal:  J Diabetes Sci Technol       Date:  2014-12-17

5.  Bolus guide: a novel insulin bolus dosing decision support tool based on selection of carbohydrate ranges.

Authors:  Gali Shapira; Ofer Yodfat; Arava HaCohen; Paul Feigin; Richard Rubin
Journal:  J Diabetes Sci Technol       Date:  2010-07-01

6.  Application of novel dual wave meal bolus and its impact on glycated hemoglobin A1c level in children with type 1 diabetes.

Authors:  Ewa Pańkowska; Agnieszka Szypowska; Maria Lipka; Monika Szpotańska; Marlena Błazik; Lidia Groele
Journal:  Pediatr Diabetes       Date:  2008-10-20       Impact factor: 4.866

7.  Unknown Safety and Efficacy of Smartphone Bolus Calculator Apps Puts Patients at Risk for Severe Adverse Outcomes.

Authors:  Irl B Hirsch; Christopher G Parkin
Journal:  J Diabetes Sci Technol       Date:  2016-06-28

8.  Hypoglycemia Prevention and User Acceptance of an Insulin Pump System with Predictive Low Glucose Management.

Authors:  Pratik Choudhary; Birthe S Olsen; Ignacio Conget; John B Welsh; Linda Vorrink; John J Shin
Journal:  Diabetes Technol Ther       Date:  2016-02-23       Impact factor: 6.118

9.  Dietary fat acutely increases glucose concentrations and insulin requirements in patients with type 1 diabetes: implications for carbohydrate-based bolus dose calculation and intensive diabetes management.

Authors:  Howard A Wolpert; Astrid Atakov-Castillo; Stephanie A Smith; Garry M Steil
Journal:  Diabetes Care       Date:  2012-11-27       Impact factor: 19.112

10.  Predicting the optimal basal insulin infusion pattern in children and adolescents on insulin pumps.

Authors:  Paul-Martin Holterhus; Jessica Bokelmann; Felix Riepe; Bettina Heidtmann; Verena Wagner; Birgit Rami-Merhar; Thomas Kapellen; Klemens Raile; Wulf Quester; Reinhard W Holl
Journal:  Diabetes Care       Date:  2013-02-12       Impact factor: 19.112

View more
  3 in total

1.  Continuous Glucose Monitoring and Insulin Informed Advisory System with Automated Titration and Dosing of Insulin Reduces Glucose Variability in Type 1 Diabetes Mellitus.

Authors:  Marc D Breton; Stephen D Patek; Dayu Lv; Elaine Schertz; Jessica Robic; Jennifer Pinnata; Laura Kollar; Charlotte Barnett; Christian Wakeman; Mary Oliveri; Chiara Fabris; Daniel Chernavvsky; Boris P Kovatchev; Stacey M Anderson
Journal:  Diabetes Technol Ther       Date:  2018-07-06       Impact factor: 6.118

Review 2.  App-Based Insulin Calculators: Current and Future State.

Authors:  Leslie Eiland; Meghan McLarney; Thiyagarajan Thangavelu; Andjela Drincic
Journal:  Curr Diab Rep       Date:  2018-10-04       Impact factor: 4.810

Review 3.  Artificial Intelligence in Decision Support Systems for Type 1 Diabetes.

Authors:  Nichole S Tyler; Peter G Jacobs
Journal:  Sensors (Basel)       Date:  2020-06-05       Impact factor: 3.576

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.