Personalized Meal Input Interface for Insulin Dosing Accuracy
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Solution Overview
Problem
Current insulin delivery systems for diabetes management rely solely on carbohydrate counting for meal compensation, neglecting the overall macronutrient profile of meals, leading to inaccurate bolus insulin dosing and timing, which can result in suboptimal blood glucose control.
Innovation Solution
A personalized meal input interface that allows users to enter dietary preferences and macronutrient information, using filters for meal suggestions and nutrient profiling, which can adjust bolus insulin dosing based on the meal's macronutrient composition and historical data, integrated with a drug delivery device for precise insulin delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If only carbohydrate counting is used for meal compensation, then the system is simple and easy to operate, but the insulin dosing accuracy deteriorates because it ignores macronutrient profile effects on glucose absorption
Solution Approach 1:
The meal input process is segmented into multiple levels: basic carbohydrate counting for quick entry, and optional detailed macronutrient breakdown (fat, protein, carbohydrates) for comprehensive analysis. This allows users to choose the appropriate level of detail based on their needs, maintaining simplicity while enabling precision when required.
Solution Approach 2:
The system dynamically adjusts the meal input interface based on user preferences, historical data, and contextual information. The interface can transition between simple carbohydrate-only entry and comprehensive macronutrient profiling, adapting to different user needs and situations to balance ease of operation with dosing accuracy.
2Measurement precision
If detailed macronutrient information is collected from users, then the insulin dosing accuracy improves by considering fat and protein effects, but the device complexity increases due to additional input requirements
Solution Approach 1:
The system performs preliminary actions by automatically suggesting macronutrient profiles based on user preferences, historical meal data, and contextual information. This preliminary suggestion reduces the cognitive load on users and simplifies the input process while still capturing detailed macronutrient information needed for accurate dosing.
Solution Approach 2:
The system serves itself by automatically analyzing meal compositions, calculating macronutrient breakdowns, and generating bolus recommendations based on the collected data. This self-service capability reduces the need for complex manual calculations and simplifies the user interface while maintaining high dosing accuracy.
3Measurement precision
If macronutrient profiling is implemented, then the bolus split calculation improves by matching glucose absorption timing, but the computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary calculations of expected glucose absorption patterns based on macronutrient composition before the meal is consumed. By pre-calculating absorption curves and bolus split recommendations, the system reduces real-time processing requirements and enables rapid insulin delivery decisions while maintaining high timing accuracy.
Solution Approach 2:
The system changes parameters by using simplified absorption models and pre-defined macronutrient profiles for common food items. This parameter optimization allows the system to process complex macronutrient data rapidly without sacrificing bolus timing accuracy, reducing the computational burden while maintaining precision.
4Loss of information
If comprehensive meal descriptors are collected and stored in a catalog, then the nutritional analysis and blood glucose correlation improve, but the data management complexity and storage requirements increase
Solution Approach 1:
The system creates simplified copies or representations of meal data in the catalog, storing essential macronutrient profiles and key characteristics rather than complete detailed information. This copying approach maintains the nutritional information needed for analysis while reducing data management complexity and storage requirements.
Solution Approach 2:
Instead of storing complete detailed meal information and then filtering, the system inverts the approach by storing pre-processed, essential macronutrient profiles and characteristics. This inversion simplifies data management while preserving the nutritional information needed for comprehensive analysis and blood glucose correlation.
Data Source
AI summary
The disclosed embodiments are directed to an automatic drug delivery (ADD) system device configured to provide bolus dosing of insulin. The embodiments include a system and method for providing an improved meal input interface for the user as well as methods for the use of the information provided by the user to both improve the post-prandial bolus dosing of insulin and to advise the user on meals that will lead to improved blood glucose control for the user.