Continuous Analyte Monitoring for Meal-Impact Time-in-Range
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing analyte monitoring systems struggle to accurately track and represent the impact of meal consumption on blood glucose levels, leading to insufficient data points, manual logging issues, and inadequate detection of meal events, which hinders effective glycemic response analysis and user understanding.
Innovation Solution
Developed systems and methods for detecting and measuring time spent within a target analyte range, ranking meals based on their impact on analyte levels, and providing intuitive graphical user interfaces to visualize and motivate individuals to make healthier food choices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If discrete blood glucose measurements are performed manually, then device complexity is reduced, but measurement precision and data sufficiency deteriorate due to insufficient data points for accurate glycemic response determination
Solution Approach 1:
The patent replaces manual finger-stick blood glucose measurement (mechanical/discrete sampling) with continuous analyte monitoring using an implantable sensor that continuously measures analyte levels in interstitial fluid, providing sufficient data points for accurate glycemic response determination without increasing device complexity for the user
Solution Approach 2:
The patent implements continuous analyte monitoring that continuously measures analyte levels over time, ensuring sufficient data points are captured to accurately determine glycemic response to meals, whereas manual discrete measurements provide intermittent and insufficient data points
2Device complexity
If manual meal logging is required, then device complexity is reduced, but loss of information increases due to reliance on user memory and manual entry accuracy
Solution Approach 1:
The patent uses feedback from continuous analyte monitoring data to automatically detect and confirm meal events by identifying characteristic glucose excursion patterns, thereby reducing reliance on manual user input while improving meal detection accuracy through objective physiological data
Solution Approach 2:
The system performs self-service by automatically detecting meal events through analysis of continuous analyte data patterns, eliminating the need for manual user logging while improving accuracy through algorithmic detection of physiological responses to meals
3Device complexity
If simple glucose rise detection is used for meal event detection, then device complexity is reduced, but reliability deteriorates due to overestimation of meal events and failure to account for prior meal history
Solution Approach 1:
The patent performs preliminary analysis of prior meal history and baseline analyte patterns before detecting new meal events, allowing the system to distinguish between expected post-meal excursions and new meal events, thereby improving detection reliability without requiring complex algorithms
Solution Approach 2:
The patent uses the continuous analyte monitoring data as an intermediary to objectively detect and confirm meal events, replacing subjective or oversimplified detection methods with physiologically-based detection that accounts for individual glycemic responses and meal history
4Measurement precision
If comprehensive analyte data collection is implemented, then measurement precision improves, but loss of time increases due to the challenge of representing and analyzing large data volumes efficiently
Solution Approach 1:
The patent extracts and highlights only the most relevant information from comprehensive analyte data, such as time-in-range metrics and key glycemic response patterns, allowing users to quickly understand the significance of continuous monitoring data without being overwhelmed by raw data volumes
Solution Approach 2:
The patent segments comprehensive analyte data into meaningful time periods and meal-related episodes, organizing continuous data streams into discrete, analyzable segments that facilitate efficient review and understanding of glycemic control patterns
Data Source
AI summary
Systems, devices, and methods for detecting and measuring meal impact and/or an amount of time an individual is within a predetermined analyte range based on analyte measurements. These results and related information are presented to the individual to show the individual an analyte response associated with consumed meals, or change in an analyte level within a predetermined time period after meals are consumed. These results can be organized based on a ranking or scoring system so as to allow the individual to visualize analyte responses and range impact associated with the meals. 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 analyte response.