Abstract:
Methods and systems for encouraging interactions with a glucose monitoring system include incrementing a score and/or providing a reward based on a variety of different interactions with the glucose monitoring system. The interactions which improve the score may include initiating or changing displays, downloading data, setting operational parameters and other interactions that are independent of a user's glucose measurements. Encouraging these interactions can enhance success in maintaining healthy glucose concentrations.
Abstract:
Systems and methods are described that provide a dynamic reporting functionality that can identify important information and dynamically present a report about the important information that highlights important findings to the user. The described systems and methods are generally described in the field of diabetes management, but are applicable to other medical reports as well. In one implementation, the dynamic reports are based on available data and devices. For example, useless sections of the report, such as those with no populated data, may be removed, minimized in importance, assigned a lower priority, or the like.
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:
Systems and methods disclosed here provide ways to discriminate fault types encountered in analyte sensors and systems and further provide ways to process such discriminated faults responsively based on sensor data, clinical context information, and other data about the patient or patient's environment. The systems and methods thus employ clinical context in detecting and/or responding to errors or faults associated with an analyte sensor system, and discriminating the type of fault, and its root cause, particularly as fault dynamics can appear similar to the dynamics of physiological systems, emphasizing the importance of discriminating the fault and providing appropriate responsive processing. Thus, the disclosed systems and methods consider the context of the patient's health condition or state in determining how to respond to the fault.
Abstract:
Systems and methods disclosed here provide ways to discriminate fault types encountered in analyte sensors and systems and further provide ways to process such discriminated faults responsively based on sensor data, clinical context information, and other data about the patient or patient's environment. The systems and methods thus employ clinical context in detecting and/or responding to errors or faults associated with an analyte sensor system, and discriminating the type of fault, and its root cause, particularly as fault dynamics can appear similar to the dynamics of physiological systems, emphasizing the importance of discriminating the fault and providing appropriate responsive processing. Thus, the disclosed systems and methods consider the context of the patient's health condition or state in determining how to respond to the fault.
Abstract:
“Zero-click” viewing of sensor data without any user input is provided. A display with sensor data may be “always on,” and may enable discrete viewing of sensor data without significant user hassle. Also, a system may be configured to display only current data, and/or to display the most current data for only a set interval. Also, one device in a continuous analyte monitoring system may be designated as a primary device, or hub, for receiving sensor data, and may control the flow of information and/or alerts to other devices in the system. Sensor data and/or alerts may be sent to a hierarchy of devices and/or persons in a designated order.
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:
The present invention relates generally to systems and methods for measuring an analyte in a host. More particularly, the present invention relates to systems and methods for transcutaneous measurement of glucose in a host.
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:
Systems and methods for dynamically and intelligently estimating analyte data from a continuous analyte sensor, including receiving a data stream, selecting one of a plurality of algorithms, and employing the selected algorithm to estimate analyte values. Additional data processing includes evaluating the selected estimative algorithms, analyzing a variation of the estimated analyte values based on statistical, clinical, or physiological parameters, comparing the estimated analyte values with corresponding measure analyte values, and providing output to a user. Estimation can be used to compensate for time lag, match sensor data with corresponding reference data, warn of upcoming clinical risk, replace erroneous sensor data signals, and provide more timely analyte information encourage proactive behavior and preempt clinical risk.