Menstrual Cycle-Aware Insulin Dosing for Glucose Control
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Solution Overview
Problem
Conventional medicament delivery devices do not account for variations in medicament dosage needs due to the menstrual cycle of the user, leading to increased risks of hyperglycemia or hypoglycemia.
Innovation Solution
A medicament delivery device that adjusts medicament dosages based on the user's menstrual cycle phase using sensors and a machine learning model to determine insulin sensitivity, allowing for real-time adjustments of basal and bolus dosages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional fixed dosages are delivered, then device complexity is reduced, but blood glucose control reliability deteriorates due to menstrual cycle variations
Solution Approach 1:
The system transitions from fixed dosages to dynamic dosages that automatically adjust based on detected menstrual cycle phase. The control system modifies basal and bolus insulin dosages in real-time according to cycle phase, resolving the contradiction by making the delivery parameters adaptive rather than static.
Solution Approach 2:
The system incorporates feedback loops where sensor data (temperature, heart rate, activity) continuously monitors user state, the machine learning model determines menstrual cycle phase, and the control system adjusts dosages accordingly. This closed-loop feedback mechanism improves reliability while managing complexity through automated decision-making.
Solution Approach 3:
The system uses the user's own physiological data (temperature, heart rate, activity patterns) to automatically determine menstrual cycle phase and adjust dosages without external intervention. The machine learning model learns individual patterns and enables self-adjustment, reducing the need for manual user input while improving control reliability.
2Measurement precision
If menstrual cycle phase detection is implemented, then medicament dosage accuracy is improved, but device complexity increases due to additional sensors and machine learning model
Solution Approach 1:
The system uses existing sensors (temperature, heart rate, activity) that serve multiple purposes: monitoring general health status and detecting menstrual cycle phase. This multi-functionality approach improves dosage accuracy without adding dedicated hardware, thereby limiting the increase in device complexity.
Solution Approach 2:
The machine learning model creates a virtual representation or 'copy' of the user's menstrual cycle patterns based on sensor data. Instead of directly measuring hormones or cycle phase, the system infers phase from behavioral and physiological patterns, achieving accurate dosage adjustment without complex direct measurement devices.
3Reliability
If real-time dosage adjustments are made, then hyperglycemia and hypoglycemia risks are reduced, but ease of operation deteriorates due to automated control complexity
Solution Approach 1:
The system automatically performs all dosage adjustment operations without requiring user intervention. The user simply provides basic information (menstrual cycle start date), and the system autonomously monitors sensor data, determines cycle phase, and adjusts dosages. This self-service approach maintains ease of operation while achieving reliable blood glucose control.
Solution Approach 2:
The system performs preliminary actions by pre-calculating dosage adjustments based on predicted menstrual cycle phase. Rather than reacting to glucose excursions, the system proactively adjusts dosages before hyperglycemia or hypoglycemia can occur, reducing the need for corrective user actions and maintaining operational simplicity.
Data Source
AI summary
Exemplary embodiments account for differing needs of a user over the menstrual cycle of the user to better control the blood glucose concentration of the user. The exemplary embodiments may be realized in control systems for medicament delivery devices that deliver medicaments, such as medicaments that regulate blood glucose concentration levels. Examples of such medicaments that regulate blood glucose concentration levels include insulin, glucagon, and glucagon peptide-1 (GLP-1) agonists. The exemplary embodiments are able to better tailor the dosages of the medicament delivered to the user with the medicament delivery device to reduce the risk of hyperglycemia and hypoglycemia and help reduce blood glucose concentration excursions.