Abstract: Systems and methods are disclosed which provide for a “factory-calibrated” sensor. In doing so, the systems and methods include predictive prospective modeling of sensor behavior, and also include predictive modeling of physiology. With these two correction factors, a consistent determination of sensitivity can be achieved, thus achieving factory calibration.

Abstract: Systems and methods are disclosed that provide smart alerts to users, e.g., alerts to users about diabetic states that are only provided when it makes sense to do so, e.g., when the system can predict or estimate that the user is not already cognitively aware of their current condition, e.g., particularly where the current condition is a diabetic state warranting attention. In this way, the alert or alarm is personalized and made particularly effective for that user. Such systems and methods still alert the user when action is necessary, e.g., a bolus or temporary basal rate change, or provide a response to a missed bolus or a need for correction, but do not alert when action is unnecessary, e.g., if the user is already estimated or predicted to be cognitively aware of the diabetic state warranting attention, or if corrective action was already taken.

Abstract: Systems and methods described provide dynamic and intelligent ways to change the required level of user interaction during use of a monitoring device. The systems and methods generally relate to real time switching between a first or initial mode of user interaction and a second or new mode of user interaction. In some cases, the switching will be automatic and transparent to the user, and in other cases user notification may occur. The mode switching generally affects the user's interaction with the device, and not just internal processing. The mode switching may relate to calibration modes, data transmission modes, control modes, or the like.

Abstract: Systems and methods are provided to calibrate an analyte concentration sensor within a biological system, generally using only a signal from the analyte concentration sensor. For example, at a steady state, the analyte concentration value within the biological system is known, and the same may provide a source for calibration. Similar techniques may be employed with slow-moving averages. Variations are disclosed.

Abstract: Systems and methods for processing sensor data and end of life detection are provided. In some embodiments, a method for determining the end of life of a continuous analyte sensor includes evaluating a plurality of risk factors using an end of life function to determine an end of life status of the sensor and providing an output related to the end of life status of the sensor. The plurality of risk factors may be selected from the list including the number of days the sensor has been in use, whether there has been a decrease in signal sensitivity, whether there is a predetermined noise pattern, whether there is a predetermined oxygen concentration pattern, and error between reference BG values and EGV sensor values.

Abstract: An amount of glycemic dysfunction associated with mis-timing (e.g., delay) of meal boluses based on replay analysis is determined. The amount of dysfunction of historical or estimated bolusing as compared to an optimally timed bolus based on the replay analysis is quantified and visualized. Inferences may be made about diabetes meal management regarding inputs from a patient.

Abstract: Systems and methods are disclosed that provide smart alerts to users, e.g., alerts to users about diabetic states that are only provided when it makes sense to do so, e.g., when the system can predict or estimate that the user is not already cognitively aware of their current condition, e.g., particularly where the current condition is a diabetic state warranting attention. In this way, the alert or alarm is personalized and made particularly effective for that user. Such systems and methods still alert the user when action is necessary, e.g., a bolus or temporary basal rate change, or provide a response to a missed bolus or a need for correction, but do not alert when action is unnecessary, e.g., if the user is already estimated or predicted to be cognitively aware of the diabetic state warranting attention, or if corrective action was already taken.

Abstract: Systems and methods described provide dynamic and intelligent ways to change the required level of user interaction during use of a monitoring device. The systems and methods generally relate to real time switching between a first or initial mode of user interaction and a second or new mode of user interaction. In some cases, the switching will be automatic and transparent to the user, and in other cases user notification may occur. The mode switching generally affects the user's interaction with the device, and not just internal processing. The mode switching may relate to calibration modes, data transmission modes, control modes, or the like.

Abstract: An amount of glycemic dysfunction associated with mis-timing (e.g., delay) of meal boluses based on replay analysis is determined. The amount of dysfunction of historical or estimated bolusing as compared to an optimally timed bolus based on the replay analysis is quantified and visualized. Inferences may be made about diabetes meal management regarding inputs from a patient.

Abstract: Systems and methods for analyte monitoring, particularly systems and methods for monitoring and managing life of a battery in an analyte sensor system worn by a user, are provided.

Abstract: Systems and methods are provided that address the need to frequently calibrate analyte sensors, according to implementation. In more detail, systems and methods provide a preconnected analyte sensor system that physically combines an analyte sensor to measurement electronics during the manufacturing phase of the sensor and in some cases in subsequent life phases of the sensor, so as to allow an improved recognition of sensor environment over time to improve subsequent calibration of the sensor.

Abstract: An amount of glycemic dysfunction associated with mis-timing (e.g., delay) of meal boluses based on replay analysis is determined. The amount of dysfunction of historical or estimated bolusing as compared to an optimally timed bolus based on the replay analysis is quantified and visualized. Inferences may be made about diabetes meal management regarding inputs from a patient.

Abstract: Systems and methods described provide dynamic and intelligent ways to change the required level of user interaction during use of a monitoring device. The systems and methods generally relate to real time switching between a first or initial mode of user interaction and a second or new mode of user interaction. In some cases, the switching will be automatic and transparent to the user, and in other cases user notification may occur. The mode switching generally affects the user’s interaction with the device, and not just internal processing. The mode switching may relate to calibration modes, data transmission modes, control modes, or the like.

Abstract: Systems and methods for analyte monitoring, particularly systems and methods for monitoring and managing life of a battery in an analyte sensor system worn by a user, are provided.

Abstract: Systems and methods for processing sensor data and end-of-life detection are provided. In some embodiments, a method for determining the end-of-life of a continuous analyte sensor includes receiving a sensor signal from an analyte sensor. A plurality of risk factors associated with end-of-life symptoms of analyte sensors is evaluated. The risk factors include a downward drift in sensor sensitivity over time, an amount of non-symmetrical, non-stationary noise and a duration of noise. An end-of-life status of the analyte sensor is determined based at least in part on the evaluating. An output related to the end-of-life status of the analyte sensor is provided.

Abstract: Systems and methods are provided that address the need to frequently calibrate analyte sensors, according to implementation. In more detail, systems and methods provide a preconnected analyte sensor system that physically combines an analyte sensor to measurement electronics during the manufacturing phase of the sensor and in some cases in subsequent life phases of the sensor, so as to allow an improved recognition of sensor environment over time to improve subsequent calibration of the sensor.

Abstract: Systems and methods are provided to calibrate an analyte concentration sensor within a biological system, generally using only a signal from the analyte concentration sensor. For example, at a steady state, the analyte concentration value within the biological system is known, and the same may provide a source for calibration. Similar techniques may be employed with slow-moving averages. Variations are disclosed.

Abstract: Systems and methods are disclosed that provide smart alerts to users, e.g., alerts to users about diabetic states that are only provided when it makes sense to do so, e.g., when the system can predict or estimate that the user is not already cognitively aware of their current condition, e.g., particularly where the current condition is a diabetic state warranting attention. In this way, the alert or alarm is personalized and made particularly effective for that user. Such systems and methods still alert the user when action is necessary, e.g., a bolus or temporary basal rate change, or provide a response to a missed bolus or a need for correction, but do not alert when action is unnecessary, e.g., if the user is already estimated or predicted to be cognitively aware of the diabetic state warranting attention, or if corrective action was already taken.

Abstract: Systems and methods are provided to calibrate an analyte concentration sensor within a biological system, generally using only a signal from the analyte concentration sensor. For example, at a steady state, the analyte concentration value within the biological system is known, and the same may provide a source for calibration. Similar techniques may be employed with slow-moving averages. Variations are disclosed.

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.