Physiological Sensor Data Library for Glucose Variability Monitoring
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Solution Overview
Problem
Existing analyte monitoring systems face challenges due to complexity, data volume, learning curves, and regulatory hurdles, making them less user-friendly and less suitable for non-medical applications, particularly in fitness and wellness contexts, and they often require regulatory approval for each use-case.
Innovation Solution
A software library with a sensor control module and remote management module facilitates communication with physiological sensors, allowing third-party applications to access sensor data without needing regulatory approval for each use-case, providing a unified interface for data interpretation and display across various devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing analyte monitoring systems provide comprehensive sensor data access, then data completeness is improved, but system complexity and regulatory burden increase
Solution Approach 1:
The patent introduces a software library as an intermediary layer between the sensor control device and third-party applications. This library handles the complex regulatory compliance and data access protocols, allowing applications to access sensor data without needing to navigate the regulatory landscape themselves. The library acts as a mediator that simplifies the interface while maintaining data completeness.
Solution Approach 2:
The software library is designed to be universally applicable across multiple applications and use cases. Instead of requiring separate regulatory approval for each application, the library provides a single interface that serves medical, fitness, wellness, and other purposes. This multi-functional approach reduces system complexity while maintaining comprehensive data access.
2Reliability
If third-party applications need regulatory approval for each use-case, then application-specific compliance is improved, but development time and cost increase
Solution Approach 1:
The patent performs the regulatory compliance actions in advance by obtaining FDA clearance for the sensor control device and establishing a pre-approved software library interface. This preliminary action eliminates the need for third-party applications to undergo separate regulatory review for each use case, significantly reducing development time while maintaining compliance reliability.
Solution Approach 2:
The system is segmented into a regulated portion (sensor control device and core software library) that has obtained regulatory approval, and an unregulated portion (third-party applications) that can be developed independently. This segmentation allows the regulated components to be cleared once and reused across multiple applications, eliminating repetitive regulatory review requirements.
3Measurement precision
If existing user interfaces are designed for medical use, then medical accuracy is improved, but usability for non-medical applications decreases
Solution Approach 1:
The patent implements dynamic user interface adaptation where the same sensor data can be presented differently based on the application context. The software library provides a unified data interface that can be customized for medical precision requirements or simplified for wellness applications. This dynamic adaptation allows a single system to serve multiple purposes with appropriate usability for each context.
Data Source
AI summary
Systems and methods for monitoring glucose variability in a subject are described. Data indicative of glucose levels of the subject is received from a sensor control device. A first glucose variability metric of the subject in a first time period is determined. The first glucose variability metric may be compared to a threshold. A first indicator is displayed if the first glucose variability metric does not exceed the threshold and a second indicator is displayed if the first glucose variability metric exceeds the threshold. Additional glucose variability metrics may be determined for subsequent time periods according to a rolling window, and the indicators may be displayed real time or in a report. The glucose variability metric may be a measure of variability compared to a baseline, a difference between a maximum and minimum glucose level, or time in or out of a target range.