Meal Detection From Continuous Analyte Monitoring for Glycemic Feedback
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Solution Overview
Problem
Existing systems for tracking meal consumption and correlating it to analyte data, such as blood glucose levels, are inadequate due to reliance on inconvenient blood glucose measurements, insufficient data points, and failure to account for prior meal history, leading to inaccurate meal detection and difficulty in understanding the impact of meals on analyte levels.
Innovation Solution
Systems and methods for detecting and classifying meals based on analyte measurements, using in vivo monitoring systems, with improved graphical user interfaces that provide intuitive and timely feedback, allowing users to understand the impact of meals on their analyte responses and enabling adjustments to dietary habits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If discrete blood glucose measurements are used for meal tracking, then analyte data can be obtained, but the measurements are inconvenient and uncomfortable requiring finger stick tests
Solution Approach 1:
The patent replaces the mechanical finger-stick blood glucose measurement system with an in vivo analyte monitoring system that uses a sensor implanted in the body to continuously monitor analyte levels. This substitution eliminates the need for repeated manual blood draws while providing continuous analyte data for meal correlation analysis.
2Loss of information
If manual logging of meals is required, then meal information can be collected, but the process is time-consuming and relies on user compliance
Solution Approach 1:
The system enables automatic meal detection by analyzing patterns in the continuous analyte data without requiring manual user input. The algorithm automatically identifies meal events based on analyte level changes, eliminating the need for users to manually log meals while maintaining complete meal information for correlation analysis.
Solution Approach 2:
The system provides feedback to users about detected meals and their correlation with analyte responses, allowing users to verify and adjust detections. This feedback mechanism ensures accurate meal information collection while minimizing manual input requirements.
3Productivity
If simple glucose rise detection is used for meal event detection, then meal events can be identified, but prior meal history is not accounted for leading to overestimation
Solution Approach 1:
The system performs preliminary analysis of analyte data patterns and establishes baseline expectations based on individual user characteristics before detecting meal events. This preliminary action enables the algorithm to distinguish between genuine meal-related glucose rises and other physiological variations, improving detection accuracy while maintaining speed.
Solution Approach 2:
The meal detection algorithm dynamically adjusts its sensitivity and parameters based on the user's individual response patterns, meal history, and contextual information. This dynamic adaptation prevents overestimation by learning from past detections and adjusting to the user's specific physiological characteristics.
4Productivity
If insufficient data points are collected, then analysis can be performed quickly, but adequate glycemic response determination is not achieved
Solution Approach 1:
The system implements continuous analyte monitoring that collects data points at regular intervals without interruption. This continuous data collection ensures sufficient data points are available for accurate glycemic response determination while enabling real-time analysis and immediate feedback to users.
Data Source
AI summary
Systems, devices, and methods for detecting, measuring and classifying meals for an individual based on analyte measurements. These results and related information can be presented to the individual to show the individual which meals are causing the most severe analyte response. These results can be organized and categorized based on preselected criteria or previous meals and results so as to organize and present the results in a format with reference to glucose as the monitored analyte. Various embodiments disclosed herein relate to methods, systems, and software applications intended to engage an individual by providing direct and timely feedback regarding the individual's meal-related glycemic response.