Abstract: Glucose level measurements of a user are obtained over time, such as from a wearable glucose monitoring device being worn by the user. These glucose level measurements can be produced substantially continuously, such that the device may be configured to produce the glucose level measurements at regular or irregular intervals of time, responsive to establishing a communicative coupling with a different device, and so forth. These glucose level measurements are analyzed to detect deviations from past glucose measurements, such as glucose measurements received earlier in the day or glucose measurements received at corresponding times of one or more preceding days. Indications of detected deviations are provided to the user or communicated elsewhere, such as to a healthcare professional.

Abstract: User interfaces for glucose insight presentation are leveraged. A glucose monitoring application is configured to process glucose measurements to determine one or more glucose insights, e.g., about a user's glucose. The glucose measurements, for example, may be obtained from a glucose monitoring device that collects glucose measurements of the user at predetermined intervals, e.g., every five minutes. The glucose monitoring application configures a user interface, based on configuration data, to present one or more visual elements representative of the one or more glucose insights. For example, the glucose monitoring application may configure the user interface to include a visual element in the form of a color field which represents whether the user's current glucose measurement (e.g., the most recent glucose measurement obtained from the glucose monitoring device) is below, within, or above a glucose range.

Abstract: In implementations of systems for determining a similarity of sequences of glucose values, a computing device implements a similarity system to receive input data describing a sequence of user glucose values measured by a continuous glucose monitoring (CGM) system. The similarity system computes similarity scores for a plurality of sequences of glucose values by comparing each glucose values included in the sequence of user glucose values with ever glucose value included in each sequence of the plurality of sequences. A particular sequence of glucose values that is associated with a highest similarity score is identified. The similarity system determines an externality associated with the particular sequence. The similarity system generates an indication of the externality for display in a user interface.

Abstract: User interfaces for glucose insight presentation are leveraged. A glucose monitoring application is configured to process glucose measurements to determine one or more glucose insights, e.g., about a user's glucose. The glucose measurements, for example, may be obtained from a glucose monitoring device that collects glucose measurements of the user at predetermined intervals, e.g., every five minutes. The glucose monitoring application configures a user interface, based on configuration data, to present one or more visual elements representative of the one or more glucose insights. For example, the glucose monitoring application may configure the user interface to include a visual element in the form of a color field which represents whether the user's current glucose measurement (e.g., the most recent glucose measurement obtained from the glucose monitoring device) is below, within, or above a glucose range.

Abstract: In implementations of systems for determining a similarity of sequences of glucose values, a computing device implements a similarity system to receive input data describing a sequence of user glucose values measured by a continuous glucose monitoring (CGM) system. The similarity system computes similarity scores for a plurality of sequences of glucose values by comparing each glucose values included in the sequence of user glucose values with ever glucose value included in each sequence of the plurality of sequences. A particular sequence of glucose values that is associated with a highest similarity score is identified. The similarity system determines an externality associated with the particular sequence. The similarity system generates an indication of the externality for display in a user interface.

Abstract: Glucose measurement and glucose-impacting event prediction using a stack of machine learning models is described. A CGM platform includes stacked machine learning models, such that an output generated by one of the machine learning models can be provided as input to another one of the machine learning models. The multiple machine learning models include at least one model trained to generate a glucose measurement prediction and another model trained to generate an event prediction, for an upcoming time interval. Each of the stacked machine learning models is configured to generate its respective output when provided as input at least one of glucose measurements provided by a CGM system worn by the user or additional data describing user behavior or other aspects that impact a person's glucose in the future. Predictions may then be output, such as via communication and/or display of a notification about the corresponding prediction.

Abstract: Glucose prediction using machine learning (ML) and time series glucose measurements is described. Given the number of people that wear glucose monitoring devices and because some wearable glucose monitoring devices can produce measurements continuously, a platform providing such devices may have an enormous amount of data. This amount of data is practically, if not actually, impossible for humans to process and covers a robust number of state spaces unlikely to be covered without the enormous amount of data. In implementations, a glucose monitoring platform includes an ML model trained using historical time series glucose measurements of a user population. The ML model predicts upcoming glucose measurements for a particular user by receiving a time series of glucose measurements up to a time and determining the upcoming glucose measurements of the particular user for an interval subsequent to the time based on patterns learned from the historical time series glucose measurements.

Abstract: Glucose measurement and glucose-impacting event prediction using a stack of machine learning models is described. A CGM platform includes stacked machine learning models, such that an output generated by one of the machine learning models can be provided as input to another one of the machine learning models. The multiple machine learning models include at least one model trained to generate a glucose measurement prediction and another model trained to generate an event prediction, for an upcoming time interval. Each of the stacked machine learning models is configured to generate its respective output when provided as input at least one of glucose measurements provided by a CGM system worn by the user or additional data describing user behavior or other aspects that impact a person's glucose in the future. Predictions may then be output, such as via communication and/or display of a notification about the corresponding prediction.

Abstract: Certain aspects of the present disclosure relate to methods and systems for optimized delivery of communications including content to users of a software application. The method also includes obtaining, by a customer engagement platform (CEP), a set of cohort selection criteria for identifying a user cohort to deliver the content; identifying, by a data analytics platform (DAP), the user cohort to communicate with in accordance with the set of cohort selection criteria; identifying, by the DAP, one or more communication configurations for communicating with one or more sub-groups within the user cohort; and to each user of the user cohort, transmitting one or more communications based on the content and a corresponding communication configuration for a sub-group that may include the corresponding user; and measuring engagement outcomes associated with usage of the corresponding one or more communication configurations in communication with each of the sub-groups.

Abstract: Glucose prediction using machine learning (ML) and time series glucose measurements is described. Given the number of people that wear glucose monitoring devices and because some wearable glucose monitoring devices can produce measurements continuously, a platform providing such devices may have an enormous amount of data. This amount of data is practically, if not actually, impossible for humans to process and covers a robust number of state spaces unlikely to be covered without the enormous amount of data. In implementations, a glucose monitoring platform includes an ML model trained using historical time series glucose measurements of a user population. The ML model predicts upcoming glucose measurements for a particular user by receiving a time series of glucose measurements up to a time and determining the upcoming glucose measurements of the particular user for an interval subsequent to the time based on patterns learned from the historical time series glucose measurements.

Abstract: Certain aspects of the present disclosure relate to methods and systems for optimized delivery of communications including content to users of a software application. The method also includes obtaining, by a customer engagement platform (CEP), a set of cohort selection criteria for identifying a user cohort to deliver the content; identifying, by a data analytics platform (DAP), the user cohort to communicate with in accordance with the set of cohort selection criteria; identifying, by the DAP, one or more communication configurations for communicating with one or more sub-groups within the user cohort; and to each user of the user cohort, transmitting one or more communications based on the content and a corresponding communication configuration for a sub-group that may include the corresponding user; and measuring engagement outcomes associated with usage of the corresponding one or more communication configurations in communication with each of the sub-groups.

Abstract: Glucose prediction using machine learning (ML) and time series glucose measurements is described. Given the number of people that wear glucose monitoring devices and because some wearable glucose monitoring devices can produce measurements continuously, a platform providing such devices may have an enormous amount of data. This amount of data is practically, if not actually, impossible for humans to process and covers a robust number of state spaces unlikely to be covered without the enormous amount of data. In implementations, a glucose monitoring platform includes an ML model trained using historical time series glucose measurements of a user population. The ML model predicts upcoming glucose measurements for a particular user by receiving a time series of glucose measurements up to a time and determining the upcoming glucose measurements of the particular user for an interval subsequent to the time based on patterns learned from the historical time series glucose measurements.

Abstract: Techniques for enhancing sharing of a user's analyte data and techniques for enhancing social support provided to users for managing users' health are disclosed.

Abstract: Systems, devices, and methods for determining user-specific hyperparameters for decision support models are provided.

Abstract: Systems, devices, and methods for determining decision support outputs using user-specific analyte level criteria for improving patients' health outcomes are provided. In one embodiment, a non-transitory computer readable storage medium storing a program is provided, the program comprising instructions that, when executed by at least one processor of a computing device, cause the at least one processor to perform operations including receiving sensor data generated by an analyte sensor configured to monitor at least one analyte, determining at least one analyte level criteria for the user for the at least one analyte; determining, using a decision support model, at least one decision support output based on the at least one analyte level criteria, and providing the at least one decision support output to the user.

Abstract: Certain aspects of the present disclosure relate to methods and systems for optimized delivery of communications including content to users of a software application. The method also includes obtaining, by a customer engagement platform (CEP), a set of cohort selection criteria for identifying a user cohort to deliver the content; identifying, by a data analytics platform (DAP), the user cohort to communicate with in accordance with the set of cohort selection criteria; identifying, by the DAP, one or more communication configurations for communicating with one or more sub-groups within the user cohort; and to each user of the user cohort, transmitting one or more communications based on the content and a corresponding communication configuration for a sub-group that may include the corresponding user; and measuring engagement outcomes associated with usage of the corresponding one or more communication configurations in communication with each of the sub-groups.

Abstract: Certain aspects of the present disclosure relate to methods and systems for optimized delivery of communications including content to users of a software application. The method also includes obtaining, by a customer engagement platform (CEP), a set of cohort selection criteria for identifying a user cohort to deliver the content; identifying, by a data analytics platform (DAP), the user cohort to communicate with in accordance with the set of cohort selection criteria; identifying, by the DAP, one or more communication configurations for communicating with one or more sub-groups within the user cohort; and to each user of the user cohort, transmitting one or more communications based on the content and a corresponding communication configuration for a sub-group that may include the corresponding user; and measuring engagement outcomes associated with usage of the corresponding one or more communication configurations in communication with each of the sub-groups.

Abstract: Systems, devices, and methods for data collection and development as well as providing user interaction policies are provided. In one embodiment, a method includes collecting contextual data for a first subset of a plurality of users. The method further includes generating a first set of contextual profiles for the first subset of the plurality of users based on the collected contextual data. Additionally, the method includes training one or more imputation models to develop the contextual data for the second subset of the plurality of users. The method also includes generating the contextual data for the second subset of the plurality of users using the one or more imputation models. Further, the method includes generating a second set of contextual profiles for the second subset of the plurality of users based on the generated contextual data for the second subset of the plurality of users.

Abstract: Glucose monitoring over phases and corresponding phased information display is described. A multi-phase glucose monitoring program that includes at least a first phase and a second phase is initiated. First glucose data of a user is obtained during the first phase of the multi-phase glucose monitoring program. The output of the first glucose data in a glucose monitoring user interface is prevented during the first phase of the multi-phase glucose monitoring program. Second glucose data of the user is then obtained during a second phase of the multi-phase glucose monitoring program. The second glucose data is output, in real-time, in the glucose monitoring user interface during the second phase of the multi-phase glucose monitoring program.

Abstract: Glucose level measurements or other data regarding a user are obtained over time, such as from a wearable glucose monitoring device being worn by the user. These glucose level measurements or other data are analyzed based on various rules to determine time periods during a day of, for example, good diabetes management by the user and provide feedback indicating such to the user. Good diabetes management is identified in various manners, such as by identifying improvements in glucose measurements for a given time period over one or more previous days, identifying a time period of the day during which glucose measurements were the best, identifying sustained positive patterns (e.g., good diabetes management for a same time period in each of multiple days), and so forth.