SYSTEM AND METHOD FOR EDUCATING USERS, INCLUDING RESPONDING TO PATTERNS

• US 20160328990A1
• Filed: 05/06/2016
• Published: 11/10/2016
• Est. Priority Date: 05/07/2015
• Status: Active Application

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: J., Robert Boock, Johnson, Matthew Lawrence, Simpson, Peter C., DeRenzy, David, Dunn, Laura J., Koehler, Katherine Yerre, Karnath, Apurv Ullas, Pal, Andrew Attila, Price, David, Reihman, Eli, Wu, Mark
• Application Number:
  US15/148,956
• Publication Number:
  US 20160328990A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/1112 Global tracking of patients, e.g. by using GPS
• CPC Class Codes: A61B 5/1118 Determining activity level
• 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 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/4872 Body fat
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/7435 Displaying user selection data, e.g. icons in a graphical user interface
• CPC Class Codes: A61B 5/7475 User input or interface means, e.g. keyboard, pointing device, joystick
• CPC Class Codes: G09B 19/00 Teaching not covered by other main groups of this subclass teaching or practice apparatus for gun-aiming or gun-laying F41G3/26
• CPC Class Codes: G09B 19/0092 Nutrition
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/40 : relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 20/60 : relating to nutrition control, e.g. diets
• CPC Class Codes: G16H 40/40 : for the management of medical equipment or devices, e.g. scheduling maintenance or upgrades
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• 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

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.

Claims

1. A method of alerting a user to a pattern, and providing a program to address the pattern, comprising:

a. evaluating user data to determine a pattern;

a. comparing the pattern against a criterion to determine if the determined pattern is a pattern for which improvement is desired;

b. determining a program to improve the pattern;

c. monitoring and storing analyte concentration data of the user;

d. analyzing the monitored and stored analyte concentration data of the user;

e. evaluating the analyzed analyte concentration data of the user against the determined program; and

f. displaying an output responsive to the evaluating step.

2. The method of claim 1, wherein the determining a program to improve the pattern further comprises:

a. determining a set of potential programs to improve the pattern;

b. displaying a user interface, the user interface including one or more graphical elements respectively representing the set of potential programs;

c. receiving a selection of one of the potential programs from the user interface,

d. wherein the determined program is defined as the received selection.

3. The method of claim 1, wherein the displaying a user interface further comprises analyzing retrospective data of a user and basing the user interface at least in part on the analysis.

4. The method of claim 1, further comprising, after the step of determining a program, displaying an indication on the user interface, the indication representing initial guidance for following the determined program.

5. The method of claim 4, wherein the displaying an indication includes:

a. displaying suggested meals, foods, or recipes, helpful in performing the program; and

/or

b. displaying suggested exercise routines helpful in performing the program.

6. The method of claim 1, wherein the monitoring and storing is performed at least in part by a continuous analyte monitor.

7. The method of claim 1, further comprising monitoring other data about the user, and wherein the analyzing or evaluating steps, or both, are based on the analyte concentration data and the other data.

8. The method of claim 7, wherein the other data includes activity data.

9. The method of claim 8, wherein the activity data is received from an accelerometer or a GPS device.

10. The method of claim 8, wherein the activity data indicates an activity level, and wherein the activity level is selected from the group consisting of:

sleeping, sedentary, light activity, medium activity, or strenuous activity.

11. The method of claim 1, wherein the criteria is received from a cloud connected source.

12. The method of claim 8, wherein the evaluating includes evaluating an effect of the activity data on the analyte concentration data.

13. The method of claim 12, wherein the displaying includes displaying the effect of the activity data on the analyte concentration data.

14. The method of claim 7, wherein the other data include data about other analytes.

15. The method of claim 14, wherein the other analytes are selected from the group consisting of:

ketones, lactic acid, lactate, glycerol, triglycerides, cortisol, and testosterone.

16. The method of claim 15, wherein the analyte concentration data is measured by an analyte sensor, and wherein the data about one or more other analytes is received from one or more other analyte sensors, and wherein the one or more other analyte sensors are calibrated based on a calibration of the analyte sensor.

17. The method of claim 7, wherein the other data include meal data.

18. The method of claim 17, wherein the meal data is received from:

a. a social network;

b. user entry;

c. a food app;

or

d. photographic data.

19. The method of claim 1, wherein the determined program is associated with a difficulty level, and wherein the evaluating against the determined program includes evaluating against the associated difficulty level.

20. The method of claim 19, wherein the difficulty level is set by the program based at least in part on a retrospective history of the patient.

21. The method of claim 19, wherein the difficulty level is selected by the user.

22. The method of claim 1, wherein the output includes a color indicating if a goal associated with the program was met.

23. The method of claim 1, wherein the output includes an avatar indicating if a goal associated with the program was met.

24. The method of claim 1, wherein the output includes a trend graph indicating at least a trace signal representing the analyte concentration value over a time period associated with the program.

25. The method of claim 24, wherein the trend graph further includes a desired analyte concentration value or range of values over the time period.

26. The method of claim 25, wherein the desired analyte concentration value or range of values is based on a modeled, ideal, or predicted analyte concentration value or range of values.

27. The method of claim 25, wherein the determined program is associated with a difficulty level, and wherein the desired analyte concentration value or range of values is further based on the difficulty level.

28. The method of claim 1, wherein the evaluated user data includes retrospective analyte concentration data.

29. The method of claim 1, wherein the evaluated user data includes retrospective meal data.

30. The method of claim 1, wherein the displaying an output includes displaying an indicator of the determined pattern.

31. The method of claim 1, wherein the determined pattern is selected from the group consisting of:

overnight lows, postprandial spikes, a type of discriminated fault, a pattern of high analyte variability, a consistent pattern of weekly highs or lows.

32. The method of claim 1, further comprising determining a baseline analyte concentration pattern for the user, and wherein the determined pattern is a consistent variation from the baseline pattern.

33. The method of claim 1, wherein the evaluating user data to determine a pattern step further comprises initiating a discovery mode, wherein in the discovery mode, one or more questions are posed on the user interface, user responses to the one or more questions constituting additional user data, and wherein the evaluating user data step further comprises evaluating the additional user data along with the monitored and stored analyte concentration data to determine a pattern.

34. The method of claim 33, wherein the additional user data is meal data.

35. The method of claim 33, wherein the additional user data is activity data.

36. The method of claim 1, further comprising transmitting the output to a cloud connected entity.