Glucose Trend Detection Controller for Diabetes Management
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Solution Overview
Problem
Current methods for managing diabetes, such as frequent injections and paper logbooks, are inconvenient and prone to inaccuracies, making it difficult for patients to maintain tight glycemic control and record lifestyle information effectively.
Innovation Solution
A system that combines a glucose monitor with a controller to detect overall and minor trends in glucose levels using statistical analysis, specifically employing a chi-squared test with lower significance values, to provide insightful patterns and annunciate significant trends, thereby improving glycemic control and lifestyle data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple daily injections of rapid and intermediate acting drugs are administered, then glycemic control is improved, but patient convenience and compliance deteriorate due to frequent injections
Solution Approach 1:
The patent segments the glucose monitoring task into automated detection components, separating the monitoring function from manual patient operations. The system automatically detects glucose levels and analyzes patterns without requiring patient intervention in the measurement process itself.
Solution Approach 2:
The system performs self-service by automatically detecting glucose levels and generating trend analyses without requiring patient action. The automated detection and analysis system serves itself by continuously monitoring and processing glucose data independently of patient involvement.
2Reliability
If manual blood analyte monitoring and input is required, then glycemic control can be achieved, but time consumption and patient burden increase
Solution Approach 1:
The patent replaces the mechanical manual process of blood sampling, testing, and data input with an automated detection system. The glucose monitor automatically performs measurements and transmits data electronically, substituting manual mechanical operations with automated instrumental processes.
Solution Approach 2:
The patent introduces an intermediary automated system between the patient and the glycemic control process. The glucose monitor and controller act as intermediaries that automatically collect, process, and analyze glucose data, eliminating the need for direct patient involvement in monitoring tasks.
3Loss of information
If paper logbooks are used for lifestyle recording, then data can be collected, but accuracy and reliability deteriorate due to manual entry errors
Solution Approach 1:
The patent replaces the mechanical paper logbook system with an automated electronic data collection and analysis system. The controller automatically processes glucose data and identifies patterns without requiring manual transcription, substituting error-prone manual operations with reliable automated computational processes.
Solution Approach 2:
The patent creates an automated copy of the data collection process through electronic monitoring and analysis. Instead of manually copying data from paper logs to analysis systems, the automated system continuously captures and processes glucose information directly in digital form, eliminating transcription errors.
4Difficulty of detecting and measuring
If statistical analysis with high significance values is used, then pattern detection sensitivity is improved, but false positive results increase reducing reliability
Solution Approach 1:
The patent changes the statistical parameter from high significance values (which reduce false positives but miss patterns) to lower significance values combined with trend analysis. This parameter change allows detection of subtle glucose patterns while using multiple analytical criteria to filter false positives.
Solution Approach 2:
The patent merges multiple detection approaches by combining statistical significance testing with trend analysis methods. The controller integrates both analytical methods to identify glucose patterns, using the strengths of each approach while compensating for their individual weaknesses.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enhances glycemic control by identifying significant trends and patterns in glucose levels, reducing the need for frequent injections and improving the accuracy of lifestyle data recording, allowing for more effective diabetes management.
Implementation Method 1
measure a glucose concentration based on an enzymatic reaction with physiological fluid in a biosensor that provides an electrical signal representative of the glucose concentration
Data Source
AI summary
Described are methods and systems for determining modal patterns from an overall trend, minor trend or significant trend for various periods during a time of the day modal report which can be utilized to provide insights to the person with diabetes.