Methods and systems for promoting glucose management

• US 10,537,678 B2
• Filed: 12/16/2015
• Issued: 01/21/2020
• Est. Priority Date: 03/27/2009
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Kamath, Apurv Ullas, Pryor, Jack, Carlton, Alexandra Lynn, Cote, Kristin Koenekamp, Bowman, Leif N., Mensinger, Michael Robert
• Primary Examiner(s): Cheng, Jacqueline
• Assistant Examiner(s): Tran, Tho Q
• Application Number:
  US14/971,886
• Publication Number:
  US 20160101232A1
• Time in Patent Office: 1,497 Days
• CPC Class Codes: A61B 5/0004 characterised by the type of physiological signal transmitted
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14546 for measuring analytes not otherwise provided for, e.g. ions, cytochromes
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: A61B 5/4833 Assessment of subject's compliance to treatment
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/743 Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• CPC Class Codes: A61B 5/744 Displaying an avatar, e.g. an animated cartoon character
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61B 5/7475 User input or interface means, e.g. keyboard, pointing device, joystick
• CPC Class Codes: A61M 2205/3303 Using a biosensor
• CPC Class Codes: A61M 2205/502 User interfaces, e.g. screens or keyboards
• CPC Class Codes: A61M 2205/581 by audible feedback
• CPC Class Codes: A61M 2205/582 by tactile feedback
• CPC Class Codes: A61M 2205/583 by visual feedback
• CPC Class Codes: A61M 2230/06 Heartbeat rate only
• CPC Class Codes: A61M 2230/201 Glucose concentration
• CPC Class Codes: A61M 2230/63 Motion, e.g. physical activity
• CPC Class Codes: A61M 5/142 Pressure infusion, e.g. using pumps
• CPC Class Codes: A61M 5/1723 using feedback of body parameters, e.g. blood-sugar, pressure measurement of body parameters A61B5/00
• CPC Class Codes: G16H 20/17 : delivered via infusion or injection
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: G16H 40/67 : for remote operation
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders
• CPC Class Codes: Y02A 90/10 : Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

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.

Claims

1. A non-transitory computer-readable medium, containing instructions for causing a computing environment to perform a method of operating a customized bolus calculator, the method including learning a personalized meal interpretation for a host, the method comprising:

receiving data about a meal consumed by the host during a meal time period;

receiving data about insulin provided to the host during the meal time period, wherein the host insulin data comprises a first insulin data point from a start of the meal time period and a second insulin data point from an end of the meal time period;

receiving data about a host glucose concentration during the meal time period, wherein the host glucose concentration data comprises a first glucose concentration data point from the start of the meal time period and a second glucose concentration data point from the end of the meal time period;

learning a personalized meal interpretation for the host based on the host meal data, host insulin data, and host glucose concentration data, wherein the learning comprises comparing the first insulin data point and the first glucose concentration data point to the second insulin data point, second glucose concentration data point, and the host meal data to determine the personalized meal interpretation;

customizing an insulin bolus calculation based on the personalized meal interpretation; and

transmitting the customized insulin bolus calculation to an insulin pump or pen.

2. The medium of claim 1, wherein the received meal data comprises a meal size, and wherein the meal size corresponds to small, medium, or large.

3. The medium of claim 1, wherein the received meal data comprises a meal diversity and/or makeup.

4. The medium of claim 3, wherein the meal diversity and/or makeup includes data about amounts of carbohydrates, fats, and proteins.

5. The medium of claim 1, wherein the customizing is performed over a number of meals, and further comprising developing a relationship of a personalized meal interpretation to a customized bolus amount, such that upon host entry of meal data into the bolus calculator, the bolus calculator provides an insulin bolus amount based on the mapping.

6. The medium of claim 5, wherein the developing a relationship further comprises determining a typical meal size for the host.

7. The medium of claim 5, wherein the developing a relationship further comprises determining a typical meal diversity and/or makeup for the host.

8. The medium of claim 1, wherein the customizing further comprises customizing a time for bolus delivery, the time for bolus delivery based on at least one of a meal time and an insulin delivery time.

9. The medium of claim 1, further comprising:

receiving exercise data for determining whether the host has exercised, the data received from one or more external or internal devices, and wherein the customizing a bolus calculation is further based on the received exercise data.

10. The medium of claim 9, wherein the receiving exercise data includes receiving data from a GPS device, accelerometer, or heart rate sensor.

11. The medium of claim 1, further comprising displaying a graphic if display criteria are met.

12. The medium of claim 11, wherein the graphic comprises a reward indication, and further comprising displaying the graphic if reward criteria are met.

13. The medium of claim 12, wherein the reward criteria include maintaining a blood glucose level within a predetermined range over a certain time period.

14. The medium of claim 12, wherein the graphic comprises a host selectable character or avatar.

15. The medium of claim 14, further comprising displaying a therapeutic suggestion or motivational message in response to the current glucose value, wherein the host selectable character or avatar delivers the therapeutic suggestion or motivational message.

16. The medium of claim 11, wherein the graphic comprises a prompt for a host to enter data corresponding to meal, insulin, or exercise, and following the prompted-for data being entered, the customizing is further based on the entered data.

17. The medium of claim 11, wherein the graphic comprises a prompt for a host to perform an action.

18. The medium of claim 1, wherein the receiving insulin data includes receiving data from a pump.

19. An insulin pump, programmed to provide a customized bolus calculator, the bolus calculator incorporating learned patient data, the programming performing a method comprising:

during a meal time period, receiving host data about a meal consumed during the meal time period, receiving data about insulin provided to the host during the meal time period, and receiving data about the host glucose concentration during the meal time period, wherein the host insulin data is continuous insulin data over the meal time period and the host glucose concentration data is continuous glucose concentration data over the meal time period;

associating the host meal data with the insulin data and the glucose data during the meal time period, wherein the associating comprises determining a host glucose response based on the continuous insulin data and the continuous glucose concentration data, and associating the host glucose response with the host meal data;

learning a personalized meal interpretation for the host based on the host meal data and the associated host glucose response;

customizing an insulin bolus calculation based on the personalized meal interpretation; and

injecting an amount of insulin corresponding to the customized insulin bolus calculation into the host.

20. The pump of claim 19, wherein the customizing a bolus calculation further comprises customizing a time to action of the delivery of the amount of insulin.