Assessing Mealtime Macronutrient Content: Patient Perceptions Versus Expert Analyses via a Novel Phone App.

Area of Science:

• Endocrinology
• Nutrition Science
• Biomedical Engineering

Background:

• Accurate estimation of meal macronutrient content is crucial for individuals with type 1 diabetes to manage blood glucose levels.
• While carbohydrate counting is standard, protein and fat also significantly influence postprandial glycemia.
• Current methods for estimating meal composition may have limitations in accuracy.

Purpose of the Study:

• To evaluate the accuracy of a novel phone application in estimating the macronutrient content of meals among individuals with type 1 diabetes.
• To compare participant estimations with expert analysis using the Remote Food Photography Method (RFPM©).
• To explore the relationship between estimation accuracy and glycemic response.

Main Methods:

• Participants (n=48) used a phone app to photograph meals and estimate carbohydrate content, categorizing meals by protein and fat levels.
• Expert nutritionists analyzed meal composition using the RFPM©.
• Continuous glucose monitoring (CGM) data were collected to assess glycemic response.

Main Results:

• The app combined with RFPM© captured 88% of estimated total energy expenditure.
• Accurate classification was high for low-protein (70%) and low-fat (70%) meals, but low for high-protein (22%) and high-fat (17%) meals.
• Carbohydrate overestimation occurred in meals <30g (49%), while underestimation was common in meals ≥60g (64%).

Conclusions:

• Existing tools show limitations in accurately estimating protein and fat content, and carbohydrate quantities in meals for type 1 diabetes management.
• Development of improved tools to enhance macronutrient estimation skills is recommended.
• Accurate total macronutrient estimation could enhance insulin decision support and closed-loop systems.