Glycemic Excursion Alarm Characterization for Personalized Glucose Monitoring
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Solution Overview
Problem
Existing glucose monitoring systems fail to effectively characterize glycemic excursion events, leading to inadequate alarm settings that do not account for the frequency and severity of these events, which can compromise diabetes management.
Innovation Solution
A system and method for determining the rate of occurrence and frequency of glycemic excursion events, setting alarm parameters based on these metrics, and using analyte sensors to monitor glucose levels, enabling personalized alarm settings for timely intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If alarm parameters are set using fixed thresholds, then the system is simple to operate, but it cannot accurately reflect the frequency and severity of glycemic excursion events
Solution Approach 1:
The alarm parameters are made dynamic by automatically adjusting them based on the user's historical glycemic data and alarm response patterns. The system continuously learns from user behavior and modifies alarm thresholds, frequencies, and notification methods to optimize effectiveness for each individual user's glycemic profile and response patterns.
Solution Approach 2:
The system incorporates feedback loops that monitor user responses to alarms and glycemic trends. By analyzing whether users respond to alarms as intended and adjusting parameters accordingly, the system refines its alarm characterization to better match actual user needs and glycemic patterns over time.
2Reliability
If the system monitors and responds to all glycemic excursions, then diabetes management is improved, but false alarms may increase causing user fatigue
Solution Approach 1:
The system changes alarm parameters dynamically based on the context of each glycemic excursion, including the rate of change of glucose levels, the duration of the excursion, and the user's historical response patterns. This allows the system to differentiate between clinically significant excursions requiring alarm and transient variations that may not require user intervention.
Solution Approach 2:
Different alarm characteristics are applied to different types of glycemic excursions based on their specific properties. The system customizes alarm parameters such as notification method, frequency, and urgency level according to the local characteristics of each glycemic event, ensuring appropriate response without unnecessary alarms.
3Measurement precision
If alarm frequency is increased to improve detection, then glycemic excursions are detected more often, but user attention is overwhelmed
Solution Approach 1:
The system uses periodic alarm strategies where notifications are spaced according to user response patterns and glycemic trends. Rather than continuous or frequent alarms, the system employs timed intervals that allow user processing and response, adjusting the periodicity based on historical data to optimize both detection and user responsiveness.
Solution Approach 2:
Alarm frequency and notification intensity are dynamically adjusted based on the urgency and pattern of detected glycemic excursions. The system modulates alarm characteristics in real-time based on the situation, using lower frequency for stable trends and higher frequency only when rapid changes are detected, preventing user overload while maintaining effective monitoring.
Data Source
AI summary
Methods and apparatus including determining a rate of occurrence of a glycemic excursion event, determining a frequency of an alarm activation associated with the glycemic excursion event, determining an analyte level associated with the alarm activation, and setting an alarm parameter based on one or more of the determined rate of occurrence of the glycemic excursion event, the frequency of the alarm activation associated with the glycemic excursion event or the determined analyte level are provided.