System and method for educating users, including responding to patterns

• US 10,410,538 B2
• Filed: 05/06/2016
• Issued: 09/10/2019
• Est. Priority Date: 05/07/2015
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Simpson, Peter C., Boock, Robert J., DeRenzy, David, Dunn, Laura J., Johnson, Matthew Lawrence, Koehler, Katherine Yerre, Kamath, Apurv Ullas, Pal, Andrew Attila, Price, David, Reihman, Eli, Wu, Mark
• Primary Examiner(s): Natnithithadha, Navin
• Application Number:
  US15/148,757
• Publication Number:
  US 20160324463A1
• Time in Patent Office: 1,222 Days
• Field of Search: 600309, 600345-365
• 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 optimizing a diabetes therapy involving a medicament, comprising:

receiving data about a first medicament to be ingested by a user in a first regimen, wherein the first regimen indicates a course of treatment for the user including ingestion of the first medicament;

monitoring and storing glucose concentration data of the user;

receiving data from a cloud connected source, the data pertaining to medicaments and/or regimens attempted by a plurality of other users, the plurality of other users determined by having similar demographics to the user;

analyzing the monitored and stored glucose concentration data of the user, wherein the analyzing is based at least in part on the data pertaining to medicaments and/or regimens attempted by the plurality of users;

evaluating the analyzed glucose concentration data of the user against the first regimen; and

displaying an output responsive to the evaluating.

2. The method of claim 1, the similar demographics including one or more similar demographic parameters selected from the group consisting of:

age, ethnicity, weight, BMI, and type of diabetes.

3. The method of claim 1, wherein the evaluating includes determining either the first medicament or the first regimen to be non-optimal for the user, and further comprising:

determining a second medicament or a second regimen, or both, for the user; and

displaying an output indicating the second medicament, or the second regimen, or both.

4. The method of claim 1, wherein the regimen includes data about a dosage of a respective medicament and the timing of ingestion of the respective medicament.

5. 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 glucose concentration data and the other data.

6. The method of claim 5, wherein the other data includes activity data.

7. The method of claim 5, wherein the other data include data about other analytes.

8. The method of claim 5, wherein the other data include meal data.

9. The method of claim 8, wherein the meal data is received from:

a social network;

user entry;

a food app;

or

photographic data.

10. The method of claim 1, further comprising determining user compliance with the regimen.

11. The method of claim 1, further comprising transmitting an indication of the output to a cloud connected entity, along with demographic data about the user.