Literature DB >> 29316338

Efficacy of automatic bolus calculator with automatic speech recognition in patients with type 1 diabetes: A randomized cross-over trial.

Piotr Foltynski1, Piotr Ladyzynski1, Ewa Pankowska2, Karolina Mazurczak2.   

Abstract

BACKGROUND: Patients using an insulin pump as part of their diabetes treatment need to calculate insulin bolus doses to compensate for a meal. Some patients do not modify their meal boluses according to changes in the amount and composition of food products in a meal. The lack of correct meal boluses leads to unstable, and therefore harmful, blood glucose levels. The aim of the present study was to test a system supporting bolus determination based on a voice description of a meal.
METHODS: The bolus calculator developed (VoiceDiab) consists of a smartphone application and three remote servers for automatic speech recognition, text analysis, and insulin dosage calculation. Forty-four people with type 1 diabetes (T1D) treated with continuous subcutaneous insulin infusion finished the randomized cross-over study. Patients were randomly allocated to the group in which the VoiceDiab system supported bolus calculation or to an unsupported group, in which patients or their caregivers calculated boluses. After a 14-day washout period, patients from the supported group were switched to the unsupported group, whereas those in the unsupported group were switched to the supported group.
RESULTS: There was a significant difference between the supported and unsupported groups in the percentage of patients with 2-h postprandial glycemia within the 70-180 mg/dL range (58.6% vs 46.6%, respectively; P = 0.031).
CONCLUSIONS: The VoiceDiab system improves postprandial glucose control without increasing the time in hyperglycemia or hypoglycemia. Therefore, it may be useful in the treatment of patients with diabetes on intensive insulin therapy.
© 2018 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd.

Entities:  

Keywords:  1型糖尿病; automatic speech recognition; bolus calculator; insulin; type 1 diabetes; 胰岛素; 自动语音识别; 追加剂量计算器

Mesh:

Substances:

Year:  2018        PMID: 29316338     DOI: 10.1111/1753-0407.12641

Source DB:  PubMed          Journal:  J Diabetes        ISSN: 1753-0407            Impact factor:   4.006


  6 in total

Review 1.  Digital health technology and mobile devices for the management of diabetes mellitus: state of the art.

Authors:  Rongzi Shan; Sudipa Sarkar; Seth S Martin
Journal:  Diabetologia       Date:  2019-04-08       Impact factor: 10.122

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

3.  Technological Ecological Momentary Assessment Tools to Study Type 1 Diabetes in Youth: Viewpoint of Methodologies.

Authors:  Mary Katherine Ray; Alana McMichael; Maria Rivera-Santana; Jacob Noel; Tamara Hershey
Journal:  JMIR Diabetes       Date:  2021-06-03

Review 4.  Effectiveness of Disease-Specific mHealth Apps in Patients With Diabetes Mellitus: Scoping Review.

Authors:  Claudia Eberle; Maxine Löhnert; Stefanie Stichling
Journal:  JMIR Mhealth Uhealth       Date:  2021-02-15       Impact factor: 4.773

5.  Accuracy of Automatic Carbohydrate, Protein, Fat and Calorie Counting Based on Voice Descriptions of Meals in People with Type 1 Diabetes.

Authors:  Piotr Ladyzynski; Janusz Krzymien; Piotr Foltynski; Monika Rachuta; Barbara Bonalska
Journal:  Nutrients       Date:  2018-04-21       Impact factor: 5.717

Review 6.  Smartphones and Apps to Control Glycosylated Hemoglobin (HbA1c) Level in Diabetes: A Systematic Review and Meta-Analysis.

Authors:  María Begoña Martos-Cabrera; Almudena Velando-Soriano; Laura Pradas-Hernández; Nora Suleiman-Martos; Guillermo A Cañadas-De la Fuente; Luis Albendín-García; José L Gómez-Urquiza
Journal:  J Clin Med       Date:  2020-03-04       Impact factor: 4.241

  6 in total

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