Integrated delivery device for continuous glucose sensor

1. A method for monitoring and treating diabetes, the method using a device configured to receive glucose concentration data from a glucose sensor, the glucose sensor configured to continuously monitor glucose concentration in a user over time, the device further configured for outputting data including personalized therapy recommendations, the method comprising:

receiving sensor data from the glucose sensor;

evaluating the sensor data to determine one or more signal parameters, the one or more signal parameters including at least one of a glucose concentration, a glucose concentration rate of change, or a glucose concentration acceleration;

evaluating the sensor data to adaptively learn one or more patterns occurring in the sensor data over time;

determining an output including at least one of an optimum time, amount, or type of medicament based on at least one determined signal parameter and at least one adaptively learned pattern; and

repeating, over time, evaluating the sensor data to adaptively learn one or more patterns,

wherein the output provides a personalized therapy recommendation for the user, and the personalized therapy recommendation adaptively adjusts over time based on adaptively learned patterns.

2. The method of claim 1 , further comprising displaying the output on a user interface associated with the device or in signal communication with the device.

3. The method of claim 1 , wherein:

the device is in RF communication with, and is configured for at least semi-automated integration with, a medicament delivery device; and

the medicament delivery device is a pen or a pump, for delivery of at least one of insulin or glucagon.

4. The method of claim 3 , further comprising prompting the user to enter a validation of the personalized therapy recommendation prior to providing the output to the pen or the pump.

5. The method of claim 4 , wherein:

the validation of the personalized therapy recommendation includes data about at least one of exercise, food, medicament intake, or rest; and

evaluating the sensor data to adaptively learn one or more patterns is based at least in part on the data of the validation.

6. The method of claim 3 , wherein the pen or the pump sends medicament delivery data including at least one of an amount, type, or time of medicament delivery to the device.

7. The method of claim 6 , wherein evaluating the sensor data to adaptively learn one or more patterns further comprises evaluating the medicament delivery data from the pen or pump, and evaluating the sensor data to adaptively learn one or more patterns is based at least in part on a determined optimum time, optimum amount, or optimum type of medicament.

8. The method of claim 1 , further comprising adaptively learning a user response to at least one of a time of medicament dispensing, an amount of medicament dispensed, or a type of medicament dispensed, the user response adaptively learned based on evaluating the sensor data to adaptively learn one or more patterns, and further based on repeating, over time, evaluating the sensor data to adaptively learn one or more patterns.

9. The method of claim 8 , wherein adaptively learning the user response is performed in a startup or learning period.

10. The method of claim 8 , wherein:

adaptively learning the user response is performed iteratively over a time period; and

the personalized therapy recommendation of the output is adaptively adjusted over time based on iterations of adaptively learning the user response performed in association with newly-learned user responses.

11. The method of claim 10 , wherein a learned user response includes insulin sensitivity.

12. The method of claim 1 , wherein evaluating the sensor data to adaptively learn one or more patterns and determining the output are performed in a startup or learning period.

13. The method of claim 1 , wherein:

evaluating the sensor data to determine the one or more signal parameters, evaluating the sensor data to adaptively learn the one or more patterns, and determining the output are performed, iteratively; and

adaptively adjusting the personalized therapy recommendations over time is based on iterations.

14. A non-transitory computer readable medium, including instructions for causing a computing environment to perform a method for monitoring and treating diabetes, the instructions configuring a device to receive glucose concentration data from a glucose sensor, the instructions further configuring the device to output data including personalized therapy recommendations, the instructions causing the computing environment to:

receive sensor data from the glucose sensor;

evaluate the sensor data to determine one or more signal parameters, the one or more signal parameters including at least one of a glucose concentration, a glucose concentration rate of change, or a glucose concentration acceleration;

evaluate the sensor data to adaptively learn one or more patterns in the sensor data over time;

determine an output including at least one of an optimum time, amount, or type of medicament based on at least one determined signal parameter and at least one adaptively learned pattern; and

repeat, over time, evaluation of the sensor data to adaptively learn one or more patterns,

wherein the output provides a personalized therapy recommendation for a user, and the personalized therapy recommendation adaptively adjusts over time based on adaptively learned patterns.

15. A kit for monitoring and treating diabetes, comprising:

a port for downloading the instructions of claim 14 to a computing device; and

a glucose sensor.

16. The kit of claim 15 , further comprising sensor electronics, the sensor electronics configured to be in signal communication with the glucose sensor and to transmit sensor data from the glucose sensor to the computing device.