COMPUTER-IMPLEMENTED METHOD

1 . A computer-implemented method for deriving a physiological rank indicative of a physiological status of a user, the computer-implemented method comprising:

acquiring, from a sensor on a wearable device worn by a user, data including bodily parameter data related to the user; and,

applying a model to the bodily parameter data to obtain physiological information related to the user; and,

deriving, from the physiological information, a physiological rank indicative of a physiological status of the user wearing the device, wherein the physiological rank is a given value on a physiological rank scale.

2 . The computer-implemented method of claim 1 further comprising:

acquiring other sensor information in addition to the physiological information; and/or,

acquiring user input information.

3 . The computer-implemented method of claim 2 further comprising:

storing a notification data table, the notification data table associating notifications with stored physiological ranks; and,

comparing the derived physiological rank with the stored physiological ranks and, based on this comparison,

selecting a notification to output to the user; and,

outputting the selected notification to the user.

4 . The computer-implemented method of claim 3 , wherein the notification data table further associates notification with stored other sensor information and/or stored user input information, and wherein the method further comprises:

comparing acquired other sensor information and/or acquired user input information with the stored other sensor information and/or the stored user input information,

wherein the selecting the notification to output to the user is further based upon this comparison.

5 . The computer-implemented method of claim 1 wherein the physiological information is temperature information, and wherein the physiological rank is a temperature rank of the user.

6 . The computer-implemented method of claim 5 wherein the temperature rank is an output of a temperature value or a temperature status.

7 . The computer-implemented method of claim 5 wherein the bodily parameter data is a body tissue absorption spectrum.

8 . The computer-implemented method of claim 5 further comprising:

acquiring other sensor information in addition to the physiological information; and/or,

acquiring user input information,

wherein the other sensor information includes one or more of hydration information obtained from a hydration sensor, heart rate information obtained from a heart rate sensor, blood pressure information obtained from a blood pressure sensor, activity information obtained from an accelerometer, and climate information obtained from a climate sensor.

9 . The computer-implemented method of claim 5 , further comprising:

acquiring other sensor information in addition to the physiological information; and/or,

acquiring user input information,

wherein the user input information includes one or more of cervical mucous status and date of menstruation.

10 . A computer-implemented method for deriving a physiological rank indicative of a physiological status of a user, the computer-implemented method comprising:

applying a model to bodily parameter data acquired from a user to obtain physiological information related to the user; and,

deriving, from the physiological information, a physiological rank indicative of a physiological status of the user wearing the device, wherein the physiological rank is a value on a physiological rank scale.

11 . The computer-implemented method of claim 5 , further comprising a step of determining a hydration status of a user, the computer-implemented method further comprising:

acquiring, from a sensor on a wearable device worn by a user, data including additional bodily parameter data related to the user; and,

applying a model to the bodily parameter data to obtain hydration information related to the user; wherein,

the model deriving, from the hydration information, a hydration rank indicative of a hydration status of the user, wherein the hydration rank is a given grade on a hydration rank scale.

12 . The computer-implemented method of claim 11 wherein each hydration rank on the hydration ranks scale maps onto a respective output of a standard clinical point of care test.

13 . The computer-implemented method of claim 12 wherein the standard clinical point of care test is a test of urine osmolality, urine specific gravity, fluid gain, fluid loss, increases or decreases in body weight or mass representing fluid gain or fluid loss, respectively, or serum osmolality.

14 . The computer-implemented method of claim 11 wherein the hydration rank is a hydration index, and wherein the hydration index is a given value on a hydration index scale.

15 . The computer-implemented method of claim 14 wherein each hydration index on the hydration index scale maps onto a respective output of a standard clinical point of care test.

16 . The computer-implemented method of claim 14 wherein the hydration index scale is sub-divided into a plurality of sub-ranges of hydration index values, each of the plurality of sub-ranges corresponding to a different clinical hydration status of the user, and wherein the method further comprises:

determining which sub-range of the plurality of sub-ranges the hydration index value falls within.

17 . The computer-implemented method of claim 11 wherein the hydration rank is a clinical hydration status of the user.

18 . The computer-implemented method of claim 17 further comprising:

outputting the clinical hydration status of the user, or the hydration index.

19 . The computer-implemented method of claim 11 wherein the bodily parameter data includes a water absorption spectrum.

20 . The computer-implemented method of claim 11 wherein the model includes a regression model.

21 . The computer-implemented method of claim 5 , further comprising a determination of how well a user's body is regulating an analyte, the computer-implemented method further comprising:

acquiring, from a sensor on a wearable device worn by the user, data including bodily parameter data related to the user; and,

applying a model to the bodily parameter data to obtain analyte concentration information related to the user, wherein,

the model derives, from the analyte concentration information, a health score indicative of how well the user's body is regulating the analyte, wherein the health score is a given grade on a health score scale.

22 . A wearable device comprising a processor, the processor configured to carry out the computer-implemented method of claim 1 .