Systems and methods for analyzing, interpreting, and acting on continuous glucose monitoring data
Inventors
Liu, Shiping • SHOMALI, Mansur • KUMBARA, Abhimanyu • Iyer, Anand • Peeples, Malinda • DUGAS, Michelle • CROWLEY, Kenyon • GAO, Guodong
Assignees
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Publication Number
US-12279864-B2
Publication Date
2025-04-22
Expiration Date
Abstract
Methods and devices include automated coaching for management of glucose states by receiving a user's glucose levels using a continuous glucose monitoring (CGM) device, determining a time in range (TIR) value, determining a TIR state, receiving a glucose variability (GV) value, determining a GV state, determining a starting state based on the TIR state and the GV state, determining that the starting state corresponds to a non-ideal state, generating an optimized pathway to reach an ideal state based on one or more account vectors such as addressing self-management behavior including food, activity, and medication use. The optimized pathway may further be based on computer detection and classification of significant events of interest over time.
Core Innovation
The disclosure provides a computer-implemented method and system for managing glucose states of a user by reaching an ideal state from a non-ideal state using a plurality of optimization profiles. The ideal state is defined as a first time in range (TIR) state and a first glucose variability (GV) state, while the non-ideal state includes at least one of a second TIR state or a second GV state. The first TIR state is above a threshold TIR value and the second TIR state is below the threshold TIR value, and current states are determined from a user's glucose level.
A current TIR state is determined based on a TIR value of the user's glucose level, where the TIR value is based on an amount of time the user's glucose level is within a threshold band. A current GV state is determined based on a GV value associated with the user's glucose level, where the GV value indicates a standard deviation (SD) of glucose levels or a coefficient of variance (CV). The disclosure identifies an optimized pathway by selecting one of the optimization profiles using one or more user vectors and then using the current TIR state and current GV state.
The optimized pathway includes one or more adjustments to the one or more user vectors, where the adjustments comprise a medication adjustment, a food consumption adjustment, or an exercise value. The optimized pathway further includes a user vector change based on the current TIR state, current GV state, or user attributes, where the user attributes comprise a medical attribute, a user preference, a metabolic attribute, or a user demographic. The optimized pathway is based on a habit index score of the user and is provided to the user.
Claims Coverage
The excerpt includes three independent claim sets covering the same core approach, with inventive features centered on receiving optimization profiles, determining current TIR and GV states, selecting an optimization profile via user vectors, and producing an optimized pathway based on habit index score and user attributes with vector adjustments.
Threshold-based time in range state determination from glucose level
receiving a plurality of optimization profiles for reaching an ideal state from a non-ideal state, the ideal state corresponding to a first time in range (TIR) state and a first glucose variability (GV) state, and the non-ideal state comprising at least one of a second TIR state or a second GV state, wherein the first TIR state is above a threshold TIR value and the second TIR state is below the threshold TIR value; determining a current TIR state based on a TIR value of the user's glucose level, wherein the TIR value is based on an amount of time the user's glucose level is within a threshold band;
Variability-based glucose variability state determination
determining a current GV state based on a GV value associated with the user's glucose level, wherein the GV value indicates a standard deviation (SD) of glucose levels or a coefficient of variance (CV), wherein the CV corresponds to a variability of the user's glucose level;
User-vector-selected optimization profile and optimized pathway with vector adjustments
receiving one or more user vectors for the user; identifying one of the plurality of optimization profiles based on the one or more user vectors; identifying an optimized pathway based on the identified optimization profile, the current TIR state, and the current GV state, the optimized pathway comprising one or more adjustments to the one or more user vectors, wherein the one or more adjustments comprise a medication adjustment, a food consumption adjustment, or an exercise value, wherein the optimized pathway comprises a user vector change, the user vector change being based on the current TIR state, the current GV state, or user attributes, wherein the user attributes comprise a medical attribute, a user preference, a metabolic attribute, or a user demographic; the optimized pathway is based on a habit index score of the user; and providing the optimized pathway to the user.
CV as SD-to-mean ratio over a base time period
wherein the CV value is determined by dividing a standard deviation (SD) of glucose levels of the user over a base period of time by a mean of the user's glucose level over the base time period.
Cgm device obtaining bodily fluid via a skin penetrating component
the CGM device configured to obtain bodily fluid via a skin penetrating component; and wherein the user's glucose levels are determined using a continuous glucose monitoring (CGM) device that obtains bodily fluid via a skin-penetrating component.
Providing an optimized pathway via machine learning model output trained with supervised, unsupervised, or semi-supervised learning
the optimized pathway is provided as a machine learning model output, where the machine learning model is trained using supervised learning, unsupervised learning, or semi-supervised learning.
Across the independent claims, the inventive concept is to determine current TIR and GV states from the user's glucose level using a threshold-band TIR definition and GV variability (SD or CV), select one optimization profile using one or more user vectors, and generate an optimized pathway that adjusts user vectors through medication, food, and/or exercise. The optimized pathway is additionally based on a habit index score and user attributes.
Stated Advantages
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