Exercise Glucose Indexing for Quantifying Performance Impact
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Solution Overview
Problem
Existing systems fail to effectively quantify the relationship between blood glucose management and exercise performance, limiting the ability to optimize exercise-related activities based on glucose levels.
Innovation Solution
A computerized method and system that utilizes time-based data from continuous glucose monitors to determine an exercise-related index by combining blood glucose data from event and non-event periods, incorporating additional data such as pre-event, post-event, and meal data, to provide a composite index that quantifies the impact of glucose management on exercise performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous glucose monitoring data is collected and analyzed, then the precision of exercise performance measurement is improved, but the complexity of the monitoring system increases
Solution Approach 1:
The monitoring system segments the observation period into distinct event periods (exercise) and non-event periods (rest), analyzing blood glucose data separately for each segment. This segmentation allows the system to focus computational resources on specific time windows, improving measurement precision for exercise performance while managing system complexity through structured data processing.
Solution Approach 2:
The system transforms raw blood glucose measurements into a composite index that incorporates temporal dimensions (event vs non-event periods, pre-event, post-event) and contextual dimensions (meal data, sleep data). This dimensional transformation consolidates multiple data streams into a single interpretable metric, improving measurement precision without proportionally increasing system complexity.
2Adaptability or versatility
If multiple data types (blood glucose, event data, meal data, sleep data) are integrated, then the comprehensiveness of exercise analysis is improved, but the difficulty of data processing increases
Solution Approach 1:
The system merges multiple data types (blood glucose measurements, event data, meal data, sleep data) into a unified composite index. By combining these diverse data sources through a consistent computational framework, the system achieves comprehensive exercise analysis while simplifying the processing complexity through standardized integration procedures.
Solution Approach 2:
The computational module is designed with universal functionality to handle multiple data types through a single processing pipeline. The same algorithmic framework processes blood glucose data, event data, meal data, and sleep data, transforming them all into contributions toward the composite index. This multi-functionality improves analysis comprehensiveness while reducing the need for separate processing procedures.
3Measurement precision
If blood glucose data from multiple time periods is analyzed, then the accuracy of exercise performance quantification is improved, but the amount of data to be processed increases
Solution Approach 1:
The system segments the time series data into meaningful periods (event periods, non-event periods, pre-event, post-event) and applies targeted analysis to each segment. This segmentation allows the system to process data in manageable chunks while capturing the temporal dynamics of exercise performance, improving quantification accuracy without requiring processing of all data uniformly.
Solution Approach 2:
The system transforms the quantity of raw data into a condensed composite index that preserves temporal and contextual information across multiple periods. By aggregating data from event and non-event periods, pre-event and post-event measurements into a single indexed value, the system reduces data volume while maintaining the precision needed for accurate exercise performance quantification.
Data Source
AI summary
A method for determination of an event-related index for a human user, where:one provides time-based data for an observation period, with:time-based blood glucose data for the totality of the observation period, said time-based blood glucose data being derived from a continuous glucose monitor,time-based event data for the event period,a computerized index determination module determines said event-related index as a composite index of the blood glucose data related to the event period and the blood glucose data outside the event period.