Abstract: Glucose level measurements and additional data regarding a user are obtained over time, such as from a wearable glucose monitoring device being worn by the user. This additional data identifies events or conditions that may affect glucose of the user, such as physical activity engaged in by the user. A glucose prediction system analyzes, for example, activity data of the user and determines when a bout of physical activity occurs. The glucose prediction system predicts what the glucose measurements of the user would have been had the physical activity not occurred, and takes various actions based on the predicted glucose measurements (e.g., provides feedback to the user indicating what their glucose would have been had they not engaged in the physical activity).

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.

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: Feedback regarding diabetes management by a user is generated, such as feedback identifying improvements in glucose measurements for a given time period over previous days, feedback identifying sustained positive patterns, feedback identifying deviations in glucose measurements between time periods, feedback identifying potential behavior modification that a user could take to engage in beneficial diabetes management behavior, feedback identifying what a user's glucose would have been had the particular events or conditions not occurred or not been present, and so forth. A feedback presentation system analyzes the identified feedback and selects feedback based on various rankings, rules and conditions for display to the user. The selected feedback is provided to the user at various times, such as regular reports (e.g., daily or weekly reports), in real time (e.g., notifying the user what his glucose level would have been had he not just taken a walk), and so forth.

Abstract: Glucose measurements are received and features for corresponding time periods over a time window are generated, the features being values indicating whether the user has been engaging in beneficial diabetes management behaviors. Using the aggregated features patterns indicating that beneficial diabetes management behaviors are not being engaged in are identified. Potential behavior modification feedback is generated by including in the potential behavior modification feedback at least one behavior modification feedback, for each of the identified patterns, that a user could take to engage in beneficial diabetes management behavior. At least one of the potential behavior modification feedback is selected and displayed or otherwise presented to the user.

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: Data-stream bridging for sensor transitions is described. A first data stream of glucose measurements is received from a first glucose sensor worn by a user. A termination event for the first glucose sensor is detected when production and/or communication of the first glucose measurements via the first data stream ceases. Next, a second data stream of glucose measurements is received from a second glucose sensor worn by the user that replaces the first glucose sensor. During a warmup period for the second glucose sensor, estimated glucose values are output for the user based on both the first data stream of glucose measurements received from the first glucose sensor prior to the termination event and the second data stream of glucose measurements received from the second glucose sensor.

Abstract: A method for providing clinical data representative of a concentration of a blood analyte in a patient includes receiving a signal from a continuous analyte sensor located within interstitial fluid of the patient and independently modeling two or more factors that influence the signal, the factors arising from individualized characteristics of the sensor and/or individualized physiological characteristics of the patient.