Abstract: To better understand the impact of glycemic events, some entities have begun examining the relationship between the blood glucose level and another physiological parameter. However, this relationship can be difficult to understand. Introduced here are computer programs and associated computer-implemented techniques for discovering glycemic events in a series of data values representative of blood glucose measurements and then altering the measurement schedule of a sensor capable of generating measurements in a dimension other than blood glucose based on the glycemic events. By altering the measurement schedule of the sensor, a diabetes management platform can better understand how, if at all, dimensions other than blood glucose are related to the glycemic health state of a subject whose blood glucose level is being monitored.

Abstract: Introduced here are diabetes management platforms able to make annotations to physiological data of a subject whose glycemic health state is being monitored. These annotations can be used to classify excursions detected in the physiological data to facilitate the identification of a therapeutic behavior change intended to improve the glycemic health state of the subject. For example, a health coach may review the annotation(s) associated with the subject before generating a recommendation for improving the glycemic health state. As another example, the diabetes management platform can automatically identify an appropriate recommendation based on the annotation(s).

Abstract: To better understand the impact of glycemic events, some entities have begun examining the relationship between the blood glucose level and another physiological parameter. However, this relationship can be difficult to understand. Introduced here are computer programs and associated computer-implemented techniques for discovering glycemic events in a series of data values representative of blood glucose measurements and then altering the measurement schedule of a sensor capable of generating measurements in a dimension other than blood glucose based on the glycemic events. By altering the measurement schedule of the sensor, a diabetes management platform can better understand how, if at all, dimensions other than blood glucose are related to the glycemic health state of a subject whose blood glucose level is being monitored.

Abstract: Introduced here are diabetes management platforms able to examine glucose measurements associated with a subject to discover patterns indicative of similar behaviors/circumstances. More specifically, a diabetes management platform can acquire multiple data series generated by a glucose monitoring device that monitors the blood glucose level of an individual over multiple time intervals. The diabetes management platform can then apply a similarity algorithm to produce a distance measure (also referred to as a “similarity measure”) for each data series. For example, the diabetes management platform may, for each data series, perform dynamic time warping to produce a distance measure with respect to each of the other data series. The diabetes management platform can identify patterns indicative of similar behaviors/circumstances based on these distance measures.

Abstract: Introduced here are diabetes management platforms able to make annotations to physiological data of a subject whose glycemic health state is being monitored. These annotations can be used to classify excursions detected in the physiological data to facilitate the identification of a therapeutic behavior change intended to improve the glycemic health state of the subject. For example, a health coach may review the annotation(s) associated with the subject before generating a recommendation for improving the glycemic health state. As another example, the diabetes management platform can automatically identify an appropriate recommendation based on the annotation(s).