Efficacy of automatic bolus calculator with automatic speech recognition in patients with type 1 diabetes: A randomized cross‐over trial: 具有自动语音识别功能的自动剂量计算器在1型糖尿病患者中的应用:一项随机交叉试验
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.
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Of the 27 papers, 7 focused on T1DM, with 308 patients in the intervention groups and 129 in the control groups.
“Of the 27 papers, 7 were focused on T1DM (308 patients in the intervention groups and 129 patients in the control groups) [43-49]”
From Effectiveness of Disease-Specific mHealth Apps in Patients With Diabetes Mellitus: Scoping Review (2021) · cites this paper as [44]
Foltynski et al found a significant 12% difference in 2-hour postprandial time in range (TIR) in favor of the periods with app support (P=.031).
“Foltynski et al [44] found a significant 12% difference in 2-hour postprandial time in range (TIR) in favor of the periods with app support (P=.031).”
From Effectiveness of Disease-Specific mHealth Apps in Patients With Diabetes Mellitus: Scoping Review (2021) · cites this paper as [44]
Foltynski et al did not find significant differences regarding TIR, time ≤70 mg/dL, and time ≥180 mg/dL.
“However, they did not find significant differences regarding TIR (P=.764), time ≤70 mg/dL (P=.764), and time ≥180 mg/dL (P=.883) [44].”
From Effectiveness of Disease-Specific mHealth Apps in Patients With Diabetes Mellitus: Scoping Review (2021) · cites this paper as [44]
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“However, they did not find significant differences regarding TIR ( P= .764), time ≤ 70 mg/dL ( P= .764), and time ≥ 180 mg/dL ( P= .883) [44].”
- Authors
- Foltynski, P., Ladyzynski, P., Pankowska, E., & Mazurczak, K.
- Affiliations
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences Warsaw Poland
- Department of Pediatrics Institute of Mother and Child Warsaw Poland
- Instytut Matki i Dziecka (PL)
- Venue
- Journal of Diabetes
- Publisher
- Wiley
- Published
- 2018-02-09
- Volume
- 10
- Issue
- 7
- Pages
- 600-608
- ISSN
- 1753-0393 (ISSN-L)
- 1753-0407
- OpenAlex
- W2784226803
- Cited by
- 24
- References
- 25
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 2 · 2023: 2 · 2022: 1 · 2021: 2 · 2020: 4 · 2019: 4 · 2018: 4
- FWCI
- 2.78
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Diabetes (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Narodowe Centrum Badań i Rozwoju (PBS1/B9/13/2012)
- 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 › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- type 1 diabetes, speech recognition, insulin pump therapy, insulin pump, postprandial glucose control, intensive insulin therapy, mobile application, hypoglycemia, hyperglycemia
- MeSH terms
- Adolescent, Blood Glucose, Diabetes Mellitus, Type 1, Female, Follow-Up Studies, Glycated Hemoglobin, Humans, Hyperglycemia, Hypoglycemia, Hypoglycemic Agents, Insulin, Insulin Infusion Systems, Male, Prognosis, Speech, Biomarkers, Cross-Over Studies, Postprandial Period, Speech Recognition Software, Mobile Applications
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