Abstract:
Methods and apparatus, including computer program products, are provided for processing analyte data. In some example implementations, a method may include generating glucose sensor data indicative of a host's glucose concentration using a glucose sensor; calculating a glycemic variability index (GVI) value based on the glucose sensor data; and providing output to a user responsive to the calculated glycemic variability index value. The GVI may be a ratio of a length of a line representative of the sensor data and an ideal length of the line. Related systems, methods, and articles of manufacture are also disclosed.
Abstract:
A system is provided for monitoring analyte in a host, including a continuous analyte sensor that produces a data stream indicative of a host's analyte concentration and a device that receives and records data from the data stream from the continuous analyte sensor. In one embodiment, the device includes a single point analyte monitor, from which it obtains an analyte value, and is configured to display only single point analyte measurement values, and not any analyte measurement values associated with data received from the continuous analyte sensor. Instead, data received from the continuous analyte sensor is used to provide alarms to the user when the analyte concentration and/or the rate of change of analyte concentration, as measured by the continuous analyte sensor, is above or below a predetermined range. Data received from the continuous analyte sensor may also be used to prompt the diabetic or caregiver to take certain actions, such as to perform another single point blood glucose measurement. In another embodiment, the device provides for toggling between two modes, with one mode that allows for display of glucose concentration values associated with the continuous glucose sensor and a second mode that prevents the display of glucose concentration values associated with the continuous glucose sensor.
Abstract:
Methods and apparatus, including computer program products, are provided for remote monitoring. In some example implementations, there is provided a method. The method may include receiving, at a remote monitor, a notification message representative of an event detected, by a server, from analyte sensor data obtained from a receiver monitoring an analyte state of a host; presenting, at the remote monitor, the notification message to activate the remote monitor, wherein the remote monitor is configured by the server to receive the notification message to augment the receiver monitoring of the analyte state of the host; accessing, by the remote monitor, the server, in response to the presenting of the notification message; and receiving, in response to the accessing, information including at least the analyte sensor data. Related systems, methods, and articles of manufacture are also disclosed.
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:
Provided are systems and methods using which users may learn and become familiar with the effects of various aspects of their lifestyle on their health, e.g., users may learn about how food and/or exercise affects their glucose level and other physiological parameters, as well as overall health. In some cases the user selects a program to try; in other cases, a computing environment embodying the system suggests programs to try, including on the basis of pattern recognition, i.e., by the computing environment determining how a user could improve a detected pattern in some way. In this way, users such as type II diabetics or even users who are only prediabetic or non-diabetic may learn healthy habits to benefit their health.
Abstract:
Provided are systems and methods using which users may learn and become familiar with the effects of various aspects of their lifestyle on their health, e.g., users may learn about how food and/or exercise affects their glucose level and other physiological parameters, as well as overall health. In some cases the user selects a program to try; in other cases, a computing environment embodying the system suggests programs to try, including on the basis of pattern recognition, i.e., by the computing environment determining how a user could improve a detected pattern in some way. In this way, users such as type II diabetics or even users who are only prediabetic or non-diabetic may learn healthy habits to benefit their health.
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:
A system is provided for monitoring analyte in a host, including a continuous analyte sensor that produces a data stream indicative of a host's analyte concentration and a device that receives and records data from the data stream from the continuous analyte sensor. In one embodiment, the device includes a single point analyte monitor, from which it obtains an analyte value, and is configured to display only single point analyte measurement values, and not any analyte measurement values associated with data received from the continuous analyte sensor. Instead, data received from the continuous analyte sensor is used to provide alarms to the user when the analyte concentration and/or the rate of change of analyte concentration, as measured by the continuous analyte sensor, is above or below a predetermined range. Data received from the continuous analyte sensor may also be used to prompt the diabetic or caregiver to take certain actions, such as to perform another single point blood glucose measurement. In another embodiment, the device provides for toggling between two modes, with one mode that allows for display of glucose concentration values associated with the continuous glucose sensor and a second mode that prevents the display of glucose concentration values associated with the continuous glucose sensor.
Abstract:
Methods and apparatus, including computer program products, are provided for processing analyte data. In some example implementations, a method may include generating glucose sensor data indicative of a host's glucose concentration using a glucose sensor; calculating a glycemic variability index (GVI) value based on the glucose sensor data; and providing output to a user responsive to the calculated glycemic variability index value. The GVI may be a ratio of a length of a line representative of the sensor data and an ideal length of the line. Related systems, methods, and articles of manufacture are also disclosed.
Abstract:
Diabetes prediction using glucose measurements and machine learning is described. In one or more implementations, the observation analysis platform includes a machine learning model trained using historical glucose measurements and historical outcome data of a user population to predict a diabetes classification for an individual user. The historical glucose measurements of the user population may be provided by glucose monitoring devices worn by users of the user population, while the historical outcome data includes one or more diagnostic measurements obtained from sources independent of the glucose monitoring devices. Once trained, the machine learning model predicts a diabetes classification for a user based on glucose measurements collected by a wearable glucose monitoring device during an observation period spanning multiple days. The predicted diabetes classification may then be output, such as by generating one or more notifications or user interfaces based on the classification.