Accuracy and Feasibility of Using a Smartphone Application for Carbohydrate Counting Versus Traditional Carbohydrate Counting for Adults With Insulin-Treated Diabetes

Author:

Shehab Mohammad1ORCID,Cohen Robert M.23,Brehm Bonnie1,Bakas Tamilyn1ORCID

Affiliation:

1. University of Cincinnati College of Nursing, Cincinnati, OH, USA

2. University of Cincinnati College of Medicine, Cincinnati, OH, USA

3. Cincinnati Veterans Affairs Medical Center, Cincinnati, OH, USA

Abstract

Background: Patients with insulin-treated diabetes struggle with performing accurate carbohydrate counting for proper blood glucose control. Little is known about the comparative accuracy and feasibility of carbohydrate counting methods. Purpose: The purpose of this study was to determine whether carbohydrate counting using a smartphone application is more accurate and feasible than a traditional method. Theoretical/conceptual framework: Based on a conceptual model derived from the Technology Acceptance Model, feasibility was defined as usefulness, ease of use, and behavioral intention to use each method. Methods: A standardized meal was presented to 20 adults with insulin-treated diabetes who counted carbohydrates using traditional and smartphone methods. Accuracy was measured by comparing carbohydrate counting estimates with the standardized meal values. Perceived feasibility (usefulness, ease of use, behavioral intention) was measured using rating forms derived from the Technology Acceptance Model. Results: The number of training and estimation minutes were significantly higher for the traditional method than the smartphone method ( Z = −3.83, P < .05; Z = −2.30, P < .05). The traditional method took an additional 1.4 minutes for estimation and 12.5 minutes for training. There were no significant differences in accuracy between traditional and smartphone methods for carbohydrate counting (Wilcoxon signed-rank test, Z = −1.10, P = .28). There were no significant differences between traditional and smartphone methods for feasibility (usefulness, Z = −.10, P = .95; ease of use, Z = −.36, P = .72; or behavioral intention, Z = −.94, P = .35). Conclusion: While both traditional and smartphone methods were found to be similar in terms of accuracy and feasibility, the smartphone method took less time for training and for carbohydrate estimation.

Publisher

SAGE Publications

Reference26 articles.

1. Centers for Disease Control and Prevention. National diabetes statistics report. https://www.cdc.gov/diabetes/data/statistics-report/index.html. Accessed January 19, 2024.

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

3. In children using intensive insulin therapy, a 20-g variation in carbohydrate amount significantly impacts on postprandial glycaemia

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