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-11564600-B2
Publication Date
2023-01-31
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 invention provides a computer-implemented method for managing glucose states of a user by reaching an ideal state from a non-ideal state using optimization profiles. The ideal state corresponds to a first time in range (TIR) state and a first glucose variability (GV) state, while the non-ideal state comprises at least one of a second TIR state or a second GV state.
The method determines a current TIR state based on a TIR value of the user’s glucose level over a first period of time, where the TIR value is based on an amount of time the glucose level is within a threshold band over a base time period. The method also determines a current GV state 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 method receives one or more user vectors and identifies one of the optimization profiles based on the one or more user vectors and one or more user attributes. An optimized pathway is then identified based on the optimization profile, the current TIR state, and the current GV state, and the optimized pathway comprises one or more adjustments to the user vectors, including a medication adjustment, a food consumption adjustment, or an exercise value.
The optimized pathway is further based on a habit index score of the user, determined based on a cohort of users with one or more user attributes in common with the user, and the method provides the optimized pathway to the user. The disclosure further supports a system and non-transitory computer-readable medium performing these operations, and refined embodiments include using a CGM device obtained bodily fluid and outputting the optimized pathway as a machine learning model output.
Claims Coverage
The partial content includes three independent claims. The independent claims center on determining current TIR and GV states, selecting an optimization profile using user vectors and user attributes, and identifying an optimized pathway that includes adjustments to user vectors and is further based on a habit index score derived from a cohort with shared user attributes.
Optimization profiles to reach an ideal glucose state
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 a second TIR state is below the threshold TIR value.
Current TIR and GV state determination
Determining a current TIR state based on a TIR value of the user's glucose level over a first period of time, wherein the TIR value is based on an amount of time the user's glucose level is within a threshold band over a base time period and the current TIR state is one of a first current TIR state or a second current TIR state; and 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 is variability of the user's glucose level in view of a standard deviation of the glucose level over the base time period.
User vector and attribute-based optimization profile selection
Receiving one or more user vectors for the user and identifying one of the optimization profiles based on the one or more user vectors and one or more user attributes.
Optimized pathway selection and vector adjustments guided by habit index
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, and the optimized pathway is further based on a habit index score of the user determined based on a cohort of users with one or more user attributes in common with the user, and providing the optimized pathway to the user.
CGM device configured to obtain bodily fluid via a skin penetrating component
Providing a continuous glucose monitoring (CGM) device configured to obtain bodily fluid via a skin penetrating component.
Machine learning model output for the optimized pathway
Providing the optimized pathway as a machine learning model output, where the machine learning model is trained using supervised, unsupervised, or semi-supervised learning.
Multiple optimization profiles mapped to optimized pathways via potential TIR/GV states
Associating multiple optimization profiles with multiple optimized pathways, where each optimized pathway is identified based on one or more of a potential TIR state or a potential GV state.
Across the independent claims, the inventive coverage centers on current TIR and GV state determination, optimization profile selection using user vectors and user attributes, and an optimized pathway with adjustments to user vectors that is further based on a habit index score. The partial content also includes refinements describing CGM bodily fluid acquisition, machine-learning-model output, and linking optimization profiles to optimized pathways using potential TIR and/or potential GV states.
Stated Advantages
Not explicitly described in patent.
Documented Applications
Managing glucose states of a user using optimization profiles selected from user vectors and user attributes to provide an optimized pathway.
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