Abstract:
Disclosed are systems and methods for generating graphical displays of analyte data and/or health information. In some implementations, the graphical displays are generating based on a self-referential dataset that are modifiable based on identified portions of the data. The modified graphical displays can indicate features in the analyte data of a host.
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:
The present embodiments provide systems and methods for, among others, tracking sensor insertion locations in a continuous analyte monitoring system. Data gathered from sensor sessions can be used in different ways, such as providing a user with a suggested rotation of insertion locations, correlating data from a given sensor session with sensor accuracy and/or sensor session length, and providing a user with a suggested next insertion location based upon past sensor accuracy and/or sensor session length at that location.
Abstract:
The present embodiments provide systems and methods for, among others, tracking sensor insertion locations in a continuous analyte monitoring system. Data gathered from sensor sessions can be used in different ways, such as providing a user with a suggested rotation of insertion locations, correlating data from a given sensor session with sensor accuracy and/or sensor session length, and providing a user with a suggested next insertion location based upon past sensor accuracy and/or sensor session length at that location.
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 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.