Diabetes Management Analyte Data Segmentation
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Solution Overview
Problem
Diabetic patients face challenges in managing their blood-analyte levels, as they often spend excessive time in hyperglycemic or hypoglycemic states due to unawareness of the relationship between their daily routines and deviations from target analyte ranges, leading to negative health outcomes.
Innovation Solution
A system and method that obtain and analyze data points representing a subject's analyte levels over time, determining percentages of time spent within or outside target ranges during different time-of-day ranges, and displaying these values to help patients identify patterns and adjust their routines accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If patients monitor their analyte levels continuously, then they can identify patterns related to their routines, but they remain unaware of the relationship between routines and deviations from target ranges without additional analysis
Solution Approach 1:
The patent segments analyte level data by time-of-day ranges (e.g., morning, afternoon, evening) and calculates time-in-target statistics for each segment. This allows patients to see patterns specific to different times of day without overwhelming complexity, directly addressing the information loss about routine-related deviations.
Solution Approach 2:
The system provides feedback by displaying time-in-target values for different time-of-day ranges, enabling patients to understand the relationship between their routines and analyte level deviations. This feedback loop transforms raw data into actionable insights without requiring complex patient-side analysis.
2Loss of information
If patients are provided with detailed analyte level data, then they can make informed decisions about routine adjustments, but the data becomes difficult to interpret without time-based organization
Solution Approach 1:
The patent divides continuous analyte data into discrete time-of-day ranges and calculates time-in-target percentages for each segment. This segmentation transforms raw, difficult-to-interpret data into organized, time-based statistics that are easy to compare and understand, directly improving data interpretability.
Solution Approach 2:
The system transforms raw analyte level data into derived parameters (time-in-target percentages) that are more meaningful for patient decision-making. This parameter transformation makes the data easier to interpret and act upon without losing important information.
3Reliability
If patients track analyte levels over multiple days, then they can identify consistent patterns, but they cannot determine whether deviations occur at specific times of day without time-based analysis
Solution Approach 1:
The patent segments multi-day analyte data by time-of-day ranges and calculates time-in-target statistics for each segment across multiple days. This segmentation enables reliable pattern identification by showing whether deviations consistently occur at specific times, while keeping the analysis approach simple and systematic.
Data Source
AI summary
Systems and methods for determining an amount of time that a subject spends within a target analyte range by time-of-day. The systems and methods may determine a first time-in-target value indicating a percentage of time within a first time-of-day range that a subject's analyte level was less than a first threshold and greater than a second threshold using at least data points from first and second days. The systems and methods may determine a second time-in-target value indicating a percentage of time within a second time-of-day range that the subject's analyte level was less than the first threshold and greater than the second threshold using at least data points from the first and second days. The systems and methods may display the first time-in-target value and the second time-in-target value.