System and method for educating users, including responding to patterns

• US 10,424,219 B2
• Filed: 05/06/2016
• Issued: 09/24/2019
• 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): Coburn, Corbett B
• Application Number:
  US15/148,976
• Publication Number:
  US 20160328991A1
• Time in Patent Office: 1,236 Days
• 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 automatically determining a therapy for a user, the method comprising:

accessing, by a computer system, first continuous glucose monitor (CGM) data describing glucose levels for the user over a first period of time;

detecting, by the computer system, a characteristic signature within the CGM data, the characteristic signature indicating a defect of the user, wherein detecting the characteristic signature comprises;

identifying at least one curve feature in the CGM data; and

assigning the at least one curve feature to a classification associated with the defect of the user;

selecting, by the computer system, a therapy based on the indicated defect indicated by the characteristic signature, wherein the therapy describes a medicament plan including a first medicament associated with the defect;

determining, by the computer system, a type or dosage of the first medicament based at least in part on the first CGM data;

accessing, by the computer system, second CGM data describing glucose values for the user over a second time period, the second time period following treatment of the user with the medicament plan;

titrating, by the computer system, at least one of type of the first medicament or a dose of the first medicament based at least in part on the second CGM data, the titrating comprising machine learning, by the computer system, a modified medicament plan based on the first CGM data, the medicament plan, and the second CGM data; and

displaying, by the computer system, a user interface comprising an indication of the titrating and an indication of at least a portion of the second CGM data.

2. The method of claim 1, further comprising receiving, by the computer system, second analyte data, the second analyte data describing at least one level of a second analyte for the user, the second analyte selected from the group consisting of:

ketones, lactic acid, lactate, glycerol, triglycerides, cortisol, and testosterone, wherein the determining that the CGM data comprises the characteristic signature or the selecting the therapy is based at least in part on the second analyte data.

3. The method of claim 1, wherein the first medicament is metformin.

4. The method of claim 1, wherein the therapy also describes an exercise or meal plan.

5. The method of claim 1, further comprising receiving, by the computer system, event data describing at least one event associated with the user, wherein the determining that the CGM data comprises the characteristic signature or the selecting the therapy is based at least in part on the event data.

6. The method of claim 5, wherein the at least one event comprises a meal of the user or exercise by the user.