Baumgartner, Michelle; Kuhn, Christian; Nakas, Christos T.; Herzig, David; Bally, Lia (2024). Carbohydrate Estimation Accuracy of Two Commercially Available Smartphone Applications vs Estimation by Individuals With Type 1 Diabetes: A Comparative Study. (In Press). Journal of diabetes science and technology, p. 19322968241264744. Sage 10.1177/19322968241264744
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BACKGROUND
Despite remarkable progress in diabetes technology, most systems still require estimating meal carbohydrate (CHO) content for meal-time insulin delivery. Emerging smartphone applications may obviate this need, but performance data in relation to patient estimates remain scarce.
OBJECTIVE
The objective is to assess the accuracy of two commercial CHO estimation applications, SNAQ and Calorie Mama, and compare their performance with the estimation accuracy of people with type 1 diabetes (T1D).
METHODS
Carbohydrate estimates of 53 individuals with T1D (aged ≥16 years) were compared with those of SNAQ (food recognition + quantification) and Calorie Mama (food recognition + adjustable standard portion size). Twenty-six cooked meals were prepared at the hospital kitchen. Each participant estimated the CHO content of two meals in three different sizes without assistance. Participants then used SNAQ for CHO quantification in one meal and Calorie Mama for the other (all three sizes). Accuracy was the estimate's deviation from ground-truth CHO content (weight multiplied by nutritional facts from recipe database). Furthermore, the applications were rated using the Mars-G questionnaire.
RESULTS
Participants' mean ± standard deviation (SD) absolute error was 21 ± 21.5 g (71 ± 72.7%). Calorie Mama had a mean absolute error of 24 ± 36.5 g (81.2 ± 123.4%). With a mean absolute error of 13.1 ± 11.3 g (44.3 ± 38.2%), SNAQ outperformed the estimation accuracy of patients and Calorie Mama (both P > .05). Error consistency (quantified by the within-participant SD) did not significantly differ between the methods.
CONCLUSIONS
SNAQ may provide effective CHO estimation support for people with T1D, particularly those with large or inconsistent CHO estimation errors. Its impact on glucose control remains to be evaluated.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Endocrinology, Diabetology and Clinical Nutrition 04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Institute of Clinical Chemistry |
UniBE Contributor: |
Nakas, Christos T., Herzig, David, Bally, Lia Claudia |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
1932-2968 |
Publisher: |
Sage |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
29 Jul 2024 16:18 |
Last Modified: |
30 Jul 2024 00:17 |
Publisher DOI: |
10.1177/19322968241264744 |
PubMed ID: |
39058316 |
Uncontrolled Keywords: |
carbohydrate counting decision support diabetes mHealth smartphone application |
BORIS DOI: |
10.48350/199287 |
URI: |
https://boris.unibe.ch/id/eprint/199287 |