Glycemic Prediction Interface for Dynamic Meal and Exercise Simulation
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Solution Overview
Problem
Legacy continuous glucose monitoring (CGM) systems fail to estimate future glucose levels and do not allow users to assess the impact of future dietary and exercise choices on their glycemic profile, lacking the ability to dynamically adjust and visualize these changes.
Innovation Solution
A method and system for predicting future glucose levels by integrating a glycemic profile emulation (GPE) subsystem within a CGM interface, allowing users to input future meals and exercise events, and dynamically visualize the impact on their glycemic profile, supported by partner systems via APIs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If legacy CGM systems display only past glucose readings, then the system maintains simplicity and reliability, but the system fails to provide future glucose level estimates and prevent hypoglycemic events
Solution Approach 1:
The system performs preliminary calculations of future glucose levels using physiological models before the actual events occur. By predicting future glucose trajectories based on planned meals, exercises, and insulin doses, the system allows users to take preventive action before hypoglycemic events happen, rather than merely displaying past data.
Solution Approach 2:
The system implements feedback by showing users predicted future glucose levels and allowing them to adjust meal, exercise, and insulin parameters to see how changes affect predictions. This iterative feedback loop enables users to make informed decisions to prevent hypoglycemia while managing diabetes.
2Adaptability or versatility
If the system allows users to input and adjust multiple meal, exercise, and insulin parameters, then the system provides comprehensive glycemic control insights, but the system increases computational complexity and processing requirements
Solution Approach 1:
The system segments the glycemic control problem into distinct modular components: meal inputs, exercise inputs, insulin dose inputs, and physiological response models. Each component can be independently adjusted and calculated, allowing comprehensive control insights while managing computational complexity through modular architecture.
3Loss of information
If the system provides detailed predictions of future glucose levels based on multiple parameters, then the system enhances user decision-making capability, but the system increases data processing time and computational resources
Solution Approach 1:
The system performs preliminary calculations of glucose trajectories using standardized physiological models and typical response curves. By pre-computing baseline predictions and only adjusting for user-specific parameter variations, the system provides comprehensive decision-making information while minimizing additional processing time.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for interconnecting a prediction visualization with user medical data for analyzing the impact of personal choices on future glucose levels. The prediction visualization is configured to generate predictions of glycemic impact based one or more inputs including choices involving diet and exercise and user medical data, such as the user's historical and current glucose levels. The prediction visualization is configured to be adjustable based on user input and the visualization is configured to dynamically update based on user input. The disclosed interface allows the user to adjust the sequencing of these decisions and portion sizes of meal choices and immediately generate new visualizations representing the impact on predicted future glucose levels.