Monitoring Insulin Resistance

• US 20120197622A1
• Filed: 01/31/2011
• Published: 08/02/2012
• Est. Priority Date: 01/31/2011
• Status: Abandoned Application

• Current Assignee: Fujitsu Limited
• Original Assignee: Fujitsu Limited
• Inventors: Jain, Jawahar
• Application Number:
  US13/018,071
• Publication Number:
  US 20120197622A1
• Time in Patent Office: Days
• US Class Current: 703/11
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/60 relating to nutrition control, e.g. diets
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

In particular embodiments, a method includes accessing data streams from stress meters, accelerometers, and continuous glucose monitors affixed to a person'"'"'s body, accessing a baseline insulin-resistance model of the person, analyzing the data streams with respect to the baseline insulin-resistance model, and determining whether the data streams indicate a change in the person'"'"'s insulin resistance.

Claims

1. A method comprising, by one or more computing devices:

accessing one or more data streams from one or more sensors affixed to a person'"'"'s body, the sensors comprising;

one or more stress meters;

one or more accelerometers; and

one or more continuous glucose monitors;

wherein the data streams comprise current stress data of the person from one or more of the stress meters, current accelerometer data of the person from one or more of the accelerometers, and current blood-glucose data of the person from one or more of the continuous glucose monitors;

accessing a baseline insulin-resistance model of the person;

analyzing the data streams with respect to the baseline insulin-resistance model of the person; and

determining based on the analysis whether the data streams indicate a change in the person'"'"'s insulin resistance.

2. The method of claim 1, further comprising:

generating an updated insulin-resistance model of the person based on the data streams and the baseline insulin-resistance model, the updated insulin-resistance model comprising updated stress data, updated accelerometer data, and updated blood-glucose data of the person based on the data streams.

3. The method of claim 1, wherein the baseline insulin-resistance model comprises an algorithm that comprises one or more variables with values based on baseline stress data, baseline accelerometer data, and baseline blood-glucose data of the person.

4. The method of claim 3, wherein:

a first set of the baseline stress data, baseline accelerometer data, and baseline blood-glucose data of the person is collected from the person when the person is not fasting and the person'"'"'s glucocorticoid blood concentration is below a predetermined threshold; and

a second set of the baseline stress data, baseline accelerometer data, and baseline blood-glucose data of the person is collected from the person when the person is engaged in controlled physical activity and the person'"'"'s glucocorticoid blood concentration is below the predetermined threshold.

5. The method of claim 1, wherein the current stress data indicate that the person is substantially unstressed.

6. The method of claim 1, wherein the current stress data indicate that the glucocorticoid blood concentration of the person is less than a predetermined threshold.

7. The method of claim 1, wherein one or more of the stress meters generate current glucocorticoid data based on a biomarker of glucocorticoid.

8. The method of claim 1, wherein:

the sensors further comprise one or more calorie intake monitors; and

the data streams further comprise current calorie intake data of person from one or more of the calorie intake monitors.

9. The method of claim 1, wherein:

the sensors further comprise one or more insulin monitors; and

the data streams further comprise current blood-insulin data of the person from one or more of the insulin monitors.

10. The method of claim 1, wherein:

the sensors further comprise one or more mood sensors; and

the data streams further comprise current mood data of the person from one or more of the mood sensors.

11. The method of claim 1, wherein:

the sensors further comprise one or more behavioral sensors; and

the data streams further comprise current behavioral data of the person from one or more of the behavioral sensors.

12. The method of claim 1, wherein:

the sensors further comprise one or more electromyographs; and

the data streams further comprise current electromyograph data of the person from the one or more electromyographs.

13. The method of claim 1, wherein:

one or more of the stress meters is a glucocorticoid meter; and

the current stress data comprises current glucocorticoid data from one or more of the glucocorticoid meters.

14. One or more computer-readable non-transitory storage media embodying instructions that are operable when executed to:

access one or more data streams from one or more sensors affixed to a person'"'"'s body, the sensors comprising;

one or more stress meters;

one or more accelerometers; and

one or more continuous glucose monitors;

wherein the data streams comprise current stress data of the person from one or more of the stress meters, current accelerometer data of the person from one or more of the accelerometers, and current blood-glucose data of the person from one or more of the continuous glucose monitors;

access a baseline insulin-resistance model of the person;

analyze the data streams with respect to the baseline insulin-resistance model of the person; and

determine based on the analysis whether the data streams indicate a change in the person'"'"'s insulin resistance.

15. The media of claim 14, the media embodying instructions that are further operable when executed to:

generate an updated insulin-resistance model of the person based on the data streams and the baseline insulin-resistance model, the updated insulin-resistance model comprising updated stress data, updated accelerometer data, and updated blood-glucose data of the person based on the data streams.

16. The media of claim 14, wherein the baseline insulin-resistance model comprises an algorithm that comprises one or more variables with values based on baseline stress data, baseline accelerometer data, and baseline blood-glucose data of the person.

17. The media of claim 16, wherein:

a first set of the baseline stress data, baseline accelerometer data, and baseline blood-glucose data of the person is collected from the person when the person is not fasting and the person'"'"'s glucocorticoid blood concentration is below a predetermined threshold; and

a second set of the baseline stress data, baseline accelerometer data, and baseline blood-glucose data of the person is collected from the person when the person is engaged in controlled physical activity and the person'"'"'s glucocorticoid blood concentration is below the predetermined threshold.

18. The media of claim 14, wherein the current stress data indicate that the person is substantially unstressed.

19. The media of claim 14, wherein the current stress data indicate that the glucocorticoid blood concentration of the person is less than a predetermined threshold.

20. The media of claim 14, wherein one or more of the stress meters generate current glucocorticoid data based on a biomarker of glucocorticoid.

21. The media of claim 14, wherein:

the sensors further comprise one or more calorie intake monitors; and

the data streams further comprise current calorie intake data of person from one or more of the calorie intake monitors.

22. The media of claim 14, wherein:

the sensors further comprise one or more insulin monitors; and

the data streams further comprise current blood-insulin data of the person from one or more of the insulin monitors.

23. The media of claim 14, wherein:

the sensors further comprise one or more mood sensors; and

the data streams further comprise current mood data of the person from one or more of the mood sensors.

24. The media of claim 14, wherein:

the sensors further comprise one or more behavioral sensors; and

the data streams further comprise current behavioral data of the person from one or more of the behavioral sensors.

25. The media of claim 14, wherein:

the sensors further comprise one or more electromyographs; and

the data streams further comprise current electromyograph data of the person from the one or more electromyographs.

26. The media of claim 14, wherein:

one or more of the stress meters is a glucocorticoid meter; and

the current stress data comprises current glucocorticoid data from one or more of the glucocorticoid meters.

27. An apparatus comprising:

a memory comprising instructions executable by one or more processors; and

one or more processors coupled to the memory and operable to execute the instructions, the one or more processors being operable when executing the instructions to;

access one or more data streams from one or more sensors affixed to a person'"'"'s body, the sensors comprising;

one or more stress meters;

one or more accelerometers; and

one or more continuous glucose monitors;

wherein the data streams comprise current stress data of the person from one or more of the stress meters, current accelerometer data of the person from one or more of the accelerometers, and current blood-glucose data of the person from one or more of the continuous glucose monitors;

access a baseline insulin-resistance model of the person;

analyze the data streams with respect to the baseline insulin-resistance model of the person; and

determine based on the analysis whether the data streams indicate a change in the person'"'"'s insulin resistance.

28. The apparatus of claim 27, the apparatus further operable when executing instructions to:

generate an updated insulin-resistance model of the person based on the data streams and the baseline insulin-resistance model, the updated insulin-resistance model comprising updated stress data, updated accelerometer data, and updated blood-glucose data of the person based on the data streams.

29. The apparatus of claim 27, wherein the baseline insulin-resistance model comprises an algorithm that comprises one or more variables with values based on baseline stress data, baseline accelerometer data, and baseline blood-glucose data of the person.

30. The apparatus of claim 29, wherein:

a first set of the baseline stress data, baseline accelerometer data, and baseline blood-glucose data of the person is collected from the person when the person is not fasting and the person'"'"'s glucocorticoid blood concentration is below a predetermined threshold; and

a second set of the baseline stress data, baseline accelerometer data, and baseline blood-glucose data of the person is collected from the person when the person is engaged in controlled physical activity and the person'"'"'s glucocorticoid blood concentration is below the predetermined threshold.

31. The apparatus of claim 27, wherein the current stress data indicate that the person is substantially unstressed.

32. The apparatus of claim 27, wherein the current stress data indicate that the glucocorticoid blood concentration of the person is less than a predetermined threshold.

33. The apparatus of claim 27, wherein one or more of the stress meters generate current glucocorticoid data based on a biomarker of glucocorticoid.

34. The apparatus of claim 27, wherein:

the sensors further comprise one or more calorie intake monitors; and

the data streams further comprise current calorie intake data of person from one or more of the calorie intake monitors.

35. The apparatus of claim 27, wherein:

the sensors further comprise one or more insulin monitors; and

the data streams further comprise current blood-insulin data of the person from one or more of the insulin monitors.

36. The apparatus of claim 27, wherein:

the sensors further comprise one or more mood sensors; and

the data streams further comprise current mood data of the person from one or more of the mood sensors.

37. The apparatus of claim 27, wherein:

the sensors further comprise one or more behavioral sensors; and

the data streams further comprise current behavioral data of the person from one or more of the behavioral sensors.

38. The apparatus of claim 27, wherein:

the sensors further comprise one or more electromyographs; and

the data streams further comprise current electromyograph data of the person from the one or more electromyographs.

39. The apparatus of claim 27, wherein:

one or more of the stress meters is a glucocorticoid meter; and

the current stress data comprises current glucocorticoid data from one or more of the glucocorticoid meters.

40. A system comprising:

means for accessing one or more data streams from one or more sensors affixed to a person'"'"'s body, the sensors comprising;

one or more stress meters;

one or more accelerometers; and

one or more continuous glucose monitors;

wherein the data streams comprise current stress data of the person from one or more of the stress meters, current accelerometer data of the person from one or more of the accelerometers, and current blood-glucose data of the person from one or more of the continuous glucose monitors;

means for accessing a baseline insulin-resistance model of the person;

means for analyzing the data streams with respect to the baseline insulin-resistance model of the person; and

means for determining based on the analysis whether the data streams indicate a change in the person'"'"'s insulin resistance.

41. The system of claim 40, further comprising:

means for generating an updated insulin-resistance model of the person based on the data streams and the baseline insulin-resistance model, the updated insulin-resistance model comprising updated stress data, updated accelerometer data, and updated blood-glucose data of the person based on the data streams.

42. The system of claim 40, wherein the baseline insulin-resistance model comprises an algorithm that comprises one or more variables with values based on baseline stress data, baseline accelerometer data, and baseline blood-glucose data of the person.

43. The system of claim 42, wherein:

a first set of the baseline stress data, baseline accelerometer data, and baseline blood-glucose data of the person is collected from the person when the person is not fasting and the person'"'"'s glucocorticoid blood concentration is below a predetermined threshold; and

a second set of the baseline stress data, baseline accelerometer data, and baseline blood-glucose data of the person is collected from the person when the person is engaged in controlled physical activity and the person'"'"'s glucocorticoid blood concentration is below the predetermined threshold.

44. The system of claim 40, wherein the current stress data indicate that the person is substantially unstressed.

45. The system of claim 40, wherein the current stress data indicate that the glucocorticoid blood concentration of the person is less than a predetermined threshold.

46. The system of claim 40, wherein one or more of the stress meters generate current glucocorticoid data based on a biomarker of glucocorticoid.

47. The system of claim 40, wherein:

the sensors further comprise one or more calorie intake monitors; and

the data streams further comprise current calorie intake data of person from one or more of the calorie intake monitors.

48. The system of claim 40, wherein:

the sensors further comprise one or more insulin monitors; and

the data streams further comprise current blood-insulin data of the person from one or more of the insulin monitors.

49. The system of claim 40, wherein:

the sensors further comprise one or more mood sensors; and

the data streams further comprise current mood data of the person from one or more of the mood sensors.

50. The system of claim 40, wherein:

the sensors further comprise one or more behavioral sensors; and

the data streams further comprise current behavioral data of the person from one or more of the behavioral sensors.

51. The system of claim 40, wherein:

the sensors further comprise one or more electromyographs; and

the data streams further comprise current electromyograph data of the person from the one or more electromyographs.

52. The system of claim 40, wherein:

one or more of the stress meters is a glucocorticoid meter; and

the current stress data comprises current glucocorticoid data from one or more of the glucocorticoid meters.