Closed-Loop Insulin Dosing via Meal-Based Physiological Calibration
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Solution Overview
Problem
Diabetics face challenges in accurately regulating blood glucose levels due to varying insulin sensitivity and digestive rates, leading to potential hyperglycemia or hypoglycemia, which existing methods fail to address effectively and efficiently.
Innovation Solution
A closed-loop glucose monitoring and insulin dosing system that uses a mobile device to receive food item selections, determine nutritional information, and compute physiological response estimates based on glucose level changes, allowing for precise insulin dosing adjustments through a continuous glucose monitor and insulin pump.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional insulin dosing methods are used, then simplicity and ease of operation are maintained, but measurement precision and reliability of glucose level regulation deteriorate due to varying insulin sensitivity and digestive rates
Solution Approach 1:
The system implements continuous feedback by monitoring glucose levels through a continuous glucose monitor and using this information to automatically adjust insulin dosing. The physiological response estimates are continuously updated based on actual glucose level changes, creating a closed-loop control system that improves precision while managing complexity through automation.
Solution Approach 2:
The system performs self-calibration by automatically computing physiological response estimates from the patient's own glucose level data without requiring manual intervention or complex calibration procedures. The system serves itself by using its own operational data to improve its dosing accuracy over time.
2Reliability
If frequent updates to insulin sensitivity estimates are implemented, then reliability of glucose regulation is improved, but loss of time and computational resources increases
Solution Approach 1:
The system performs physiological response estimate computations periodically based on meal events rather than continuously. Calibration is triggered at specific intervals corresponding to meal consumption, which provides regular updates to improve reliability while avoiding excessive computational burden and time loss.
Solution Approach 2:
The system performs calibration computations during and after meal events when glucose level changes are naturally occurring, rather than requiring separate dedicated calibration sessions. This preliminary action during routine activities minimizes additional time requirements while maintaining reliable estimates.
3Manufacturing precision
If patient-specific physiological response estimates are used, then manufacturing precision of dosing accuracy is improved, but device complexity and difficulty of operation increase
Solution Approach 1:
The system automatically computes and updates patient-specific physiological response estimates without requiring manual input or configuration by the patient. The system serves itself by using its own operational data to personalize dosing, maintaining ease of operation while achieving high precision.
Solution Approach 2:
The system performs all necessary calibration and personalization computations automatically during routine operation, particularly during meal events. This preliminary action eliminates the need for separate setup procedures, maintaining simplicity for the patient while achieving personalized precision dosing.
Data Source
AI summary
Examples of determining and using physiological response estimates with glucose monitoring and insulin dosing systems and methods are disclosed. For example, one method includes receiving a selection of food items from a predefined catalog of food items as being part of a meal associated with a user profile, the catalog including nutritional information corresponding to food items in the catalog; determining nutritional information of the meal based on the nutritional information of each selected food item; receiving a notification from a user interface that a user associated with the user profile has started eating the meal; receiving successive indications of the user's glucose levels; computing a physiological response estimate associated with the user profile and the meal based on the successive indications of the user's glucose levels and the nutritional information of the meal.