Smartphone Glucose Profiling for Activity and Medication Response
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Solution Overview
Problem
Existing glucose monitoring systems fail to accurately predict and respond to fluctuations in glucose levels due to daily activities, meals, and medication intake, leading to inadequate glycemic control and potential hypoglycemic events.
Innovation Solution
A smartphone-based analysis module that integrates with sensors and medication devices to dynamically adjust glucose response patterns based on real-time activity, meal, and medication data, providing personalized therapy recommendations to maintain glycemic stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If glucose monitoring systems provide real-time glucose level information, then glycemic control is improved, but the systems fail to accurately predict and respond to fluctuations due to daily activities, meals, and medication intake
Solution Approach 1:
The patent combines multiple data sources including glucose sensor data, activity monitor data, meal intake data, and medication intake data into a unified monitoring system. This integration allows the system to correlate glucose fluctuations with specific physiological events, improving prediction reliability while maintaining measurement precision.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring glucose levels and comparing them with recorded activities, meals, and medications. The system provides alerts and recommendations based on detected patterns, allowing users to adjust behavior and enabling the system to learn and improve prediction accuracy over time.
2Reliability
If the system integrates multiple data sources for personalized therapy recommendations, then glycemic control is enhanced, but device complexity increases
Solution Approach 1:
The patent employs a smartphone as a universal platform that can handle multiple functions: storing and processing glucose data, tracking activities via built-in sensors, recording meal and medication intake, providing alerts, and delivering therapy recommendations. This multi-functional approach reduces overall system complexity while maintaining reliable glycemic control.
Solution Approach 2:
The system uses a smartphone application as an intermediary layer between various data sources and the user. The app consolidates data from glucose sensors, activity monitors, and user inputs, processes this information using algorithms, and presents simplified recommendations to the user, thereby managing complexity while enhancing control reliability.
3Measurement precision
If the system provides dynamic and personalized glucose response patterns, then medication dose determination accuracy is improved, but the system requires continuous data collection and analysis
Solution Approach 1:
The system performs preliminary analysis by continuously collecting and preprocessing data in the background, establishing baseline patterns of glucose response to activities, meals, and medications. When medication dosing is needed, the system can quickly provide accurate recommendations based on pre-analyzed patterns, reducing the time required at the moment of decision-making.
Solution Approach 2:
The system automatically collects data from integrated sensors and user inputs, performs continuous analysis to identify personal glucose response patterns, and generates therapy recommendations without requiring manual intervention. This automation improves medication dose accuracy while minimizing the time burden on users.
Data Source
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AI summary
Method, device and system for providing consistent and reliable glucose response information to physiological changes and/or activities is provided to improve glycemic control and health management.