Systems and methods for analyzing, interpreting, and acting on continuous glucose monitoring data

The method uses CGM devices to determine TIR and GV values, generating optimized pathways for improved glucose management, addressing the limitations of sporadic glucose monitoring by enhancing treatment recommendations and user-provider interaction.

JP2025143308AActive Publication Date: 2025-10-01WELLDOC INC
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Patent Information

Application Number
JP2025101291
Authority / Receiving Office
JP · JP
Patent Type
Applications
Current Assignee / Owner
Priority Date
2021-01-11
Filing Date
2025-06-17
Publication Date
2025-10-01
Estimated Expiration
2041-03-19

AI Technical Summary

Technical Problem

Existing glucose monitoring methods rely on sporadic measurements, leading to insufficient data for effective treatment recommendations, which can result in misleading medical, dietary, and lifestyle suggestions, and limit the interaction between healthcare providers and users.

Method used

A computer-implemented method using continuous glucose monitoring (CGM) devices to determine time-in-range (TIR) and blood glucose variability (GV) values, generating optimized pathways to improve glucose management by adjusting intake, physical activity, and psychosocial parameters, and providing personalized recommendations.

Benefits of technology

Enhances glucose management by providing personalized, data-driven recommendations to improve glucose control and increase interaction between users and healthcare providers, reducing the burden on providers and improving treatment efficacy.

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Abstract

To provide a method and system for optimizing a glucose state of a user via a mobile application.SOLUTION: The method includes: receiving, using a continuous glucose monitoring (CGM) device, a glucose value of a user; determining a time-in-range (TIR) value; determining a TIR state; receiving a glucose variability (GV) value; determining a GV state; determining an onset state based on the TIR state and the GV state; determining that an onset state corresponds to a non-ideal state; generating an optimization path to reach an ideal state based on one or more account vectors, such as addressing self-managed behavior including meals, activities, and medication use; and providing the optimization path directly to a patient. The optimization path is based on computer detection and classification of important events of interest over time.SELECTED DRAWING: Figure 5A
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