COMPUTER-IMPLEMENTED METHOD

Abstract

A computer-implemented method for determining a hydration status of a user, the computer-implemented method. The computer-implemented method comprises acquiring, from sensor on a wearable device worn by a user, data including bodily parameter data related to the user. The computer-implemented method further comprises applying a model to the bodily parameter data to obtain hydration information related to the user. The model derives, from the hydration information, a hydration rank indicative of a hydration status of the user. The hydration rank is a given grade on a hydration rank scale.

Claims

1. A computer-implemented method for determining a hydration 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 hydration information related to the user; wherein the model derives, 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.

2. The computer-implemented method of claim 1 wherein each hydration rank on a hydration ranks scale maps onto a respective output of a standard clinical point of care test.

3. The computer-implemented method of claim 2 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.

4. The computer-implemented method of claim 1 wherein the hydration rank is a hydration index, and wherein the hydration index is a given value on a hydration index scale.

5. The computer-implemented method of claim 4 wherein each hydration index on the hydration index scale maps onto a respective output of a standard clinical point of care test.

6. The computer-implemented method of claim 4 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.

7. The computer-implemented method of claim 1 wherein the hydration rank is a clinical hydration status of the user.

8. The computer-implemented method of claim 6 further comprising: outputting the clinical hydration status of the user.

9. The computer-implemented method of claim 4 further comprising: outputting the hydration index.

10. The computer-implemented method of claim 1 wherein the sensor is an optical sensing module.

11. The computer-implemented method of claim 10 wherein the optical sensing module comprises a laser.

12. The computer-implemented method of claim 11 wherein the optical sensing module comprises a plurality of lasers, each laser of the plurality of lasers operating at a wavelength that is different from the wavelength of the others.

13. The computer-implemented method of claim 12 wherein the optical sensing module is configured to operate each laser one at a time.

14. The computer-implemented method of claim 13 wherein the optical sensing module is configured to operate the plurality of lasers in a cycle according to a pre-determined schedule.

15. The computer-implemented method of claim 10 wherein the bodily parameter data is a body tissue absorption spectrum.

16. The computer-implemented method of claim 1 wherein the model includes a regression model.

17. The computer-implemented method of claim 1, further comprising applying a statistical model to the data acquired from the sensor to validate the data acquired from the sensor.

18. The computer-implemented method of claim 1 further comprising: acquiring other sensor information in addition to the hydration information; and/or, acquiring user input information.

19. The computer-implemented method of claim 18 wherein the other sensor information includes one or more of body temperature information obtained from a temperature sensor, activity information obtained from an accelerometer, heart rate information obtained from a heart rate sensor and blood pressure information obtained from a blood pressure sensor.

20. The computer-implemented method of claim 18 wherein the user input information includes one or more of weight information, activity information, diet information, fluid intake information, illness information and intoxication information.

21. The computer-implemented method of claim 18, further comprising: storing a hydration status cause data table, the hydration status cause data table associating causes of a clinical hydration status with stored other sensor information and/or stored user input information respectively; and when a hydration rank is derived which indicates that the clinical hydration status of the user is a pre-determined clinical hydration status; comparing acquired other sensor information and/or user input information with stored other sensor information and/or stored user input information respectively and, based on this comparison; selecting a cause of a clinical hydration status, and outputting the selected cause of the clinical hydration status to the user.

22. A computer-implemented method for determining a hydration status of a user, the computer-implemented method comprising: applying a model to bodily parameter data obtained from a user to obtain hydration information related to the user; and 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.

23. A computer program which when executed causes one or more processors to perform the method of claim 1.

24. A method for determining a hydration status of a user, the method comprising: providing an optical sensing module on a wearable device worn by a user; providing a processor; and carrying out, by the processor, the computer-implemented method of claim 1, wherein the sensor is the optical sensing module on the wearable device.

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

26. The device of claim 25 wherein the device is a wearable device.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0208] These and other features and advantages of the present invention will be appreciated and understood with reference to the specification, claims, and appended drawings wherein:

[0209] FIG. 1 depicts an example of hydration information in the form of a hydration rank scale;

[0210] FIG. 2 shows a flowchart of example steps of the computer-implemented method;

[0211] FIG. 3 is a schematic diagram of an optical sensing module that may be configured to carry out the computer-implemented method;

[0212] FIG. 4 shows a flowchart of example steps of the computer-implemented method;

[0213] FIG. 5 is a depiction of a further example of steps of a computer-implemented method;

[0214] FIG. 6 is an example of an output of the computer-implemented invention in the form of a graphical user interface (GUI), for example on a mobile device;

[0215] FIG. 7 shows a further flowchart of example steps of the computer-implemented method;

[0216] FIG. 8A depicts a further example of hydration information in the form of a hydration rank scale; and

[0217] FIG. 8B depicts a further example of hydration information in the form of a hydration rank scale.

DETAILED DESCRIPTION

[0218] The detailed description set forth below in connection with the appended drawings is intended as a description of exemplary embodiments of a computer-implemented method provided in accordance with the present invention and is not intended to represent the only forms in which the present invention may be constructed or utilized. The description sets forth the features of the present invention in connection with the illustrated embodiments. It is to be understood, however, that the same or equivalent functions and structures may be accomplished by different embodiments that are also intended to be encompassed within the spirit and scope of the invention. As denoted elsewhere herein, like element numbers are intended to indicate like elements or features.

[0219] One or more embodiments of the present invention provide a computer-implemented method for determining a hydration status of a user. The computer-implemented method comprises acquiring from a sensor 1101 on a wearable device worn by a user, data including bodily parameter data, for example an optical measurement such as an absorption spectrum related to the user.

[0220] The method further comprises applying a model to the bodily parameter data to obtain hydration information related to the user. This information could take the form, for example of information from the spectrum about how hydrated the user is (e.g. from the location/height of the peak). The model derives, from the hydration information, a hydration rank 1 indicative of a hydration status of the user, wherein the hydration rank 1 is a given grade on a hydration rank scale 12.

[0221] FIG. 1 shows an example of a hydration rank scale 12 according to an embodiment of the present invention. In the embodiment shown in FIG. 1, the hydration rank scale 12 is a scale of hydration indices 10. Each of the hydration indices 1 are integer numbers, and the scale runs from −5 to +5. The model derives a hydration index 1 on this hydration index scale 12.

[0222] As shown in FIG. 1, each hydration index 1 on the hydration index scale 12 is indicative of a clinical hydration status of the user. The hydration index 12 scale is sub-divided into a plurality of sub-ranges 14 of hydration index values 1, each of the plurality of sub-ranges 14 corresponding to a different clinical hydration status of the user. These clinical hydration statuses include severe dehydration, moderate dehydration, mild dehydration, euvolemia, mild overhydration, moderate overhydration and severe overhydration. The sub-range including hydration index −5 corresponds to severe dehydration, the sub-range including hydration indices −3 and −4 correspond to moderate dehydration, the sub-range including hydration indices −1 and −2 correspond to mild dehydration, the sub-range including hydration index 0 corresponds to euvolemia, the sub-range including hydration indices +1 and +2 corresponds to mild overhydration, the sub-range including hydration indices +3 and +4 corresponds to moderate overhydration, and the sub-range including hydration index +5 corresponds to severe overhydration.

[0223] As further shown in FIG. 1, each hydration index 1 on the hydration index scale 12 maps onto respective ranges of outputs of a plurality of standard clinical point of care tests 16a/b/c/d. In the example shown in FIG. 1, the standard clinical point of care tests 16a/b/c/d are tests of urine osmolality 16c, urine specific gravity 16d, fluid loss 16a and serum osmolality 16b.

[0224] Each of these standard clinical point of care tests 16a/b/c/d defines ranges of outputs of the standard clinical point of care test, wherein each of the ranges corresponds to a respective clinical hydration status.

[0225] In the example shown in FIG. 1, a hydration index value of −5, which indicates severe dehydration, corresponds to USG values of over 1.030, which also indicates severe dehydration. A sub-range including hydration index values of −3 or −4, which indicate moderate dehydration, corresponds to USG values of between 1.020 and 1.030, which also indicate moderate dehydration. A sub-range including hydration index values of −1 or −2, which indicate mild dehydration, corresponds to USG values of approximately 1.020, which also indicates mild dehydration. A sub-range including hydration index values of −2, −1, 0, 1 or 2, which indicate a hydration status between mild dehydration and mild overhydration, corresponds to USG values of between 1.005 and 1.020, which also indicate a hydration status between mild dehydration and mild overhydration. A hydration index value of 0, which indicates euvolemia corresponds to USG values of approximately 1.010, which also indicates euvolemia. A sub-range including hydration index values of +1 or +2, which indicate mild overhydration, corresponds to a USG value of approximately 1.005, which also indicates mild overhydration. A sub-range including hydration index values of +1, +2, +3, or +4, which indicates a hydration status between mild and moderate overhydration, corresponds to USG values of between 1.002 and 1.005, which also indicates a hydration status between mild and moderate overhydration. A hydration index value of +5, which indicates severe overhydration, corresponds to USG values of less than 1.002, which also indicates severe overhydration.

[0226] In other embodiments, the hydration rank scale 12 may be a scale of clinical hydration statuses. In this case, the model derives a clinical hydration status on this clinical hydration status scale. In such embodiments, the clinical hydration statuses (severe dehydration, moderate dehydration, mild dehydration, euvolemia, mild overhydration, moderate overhydration and severe overhydration) shown in FIG. 1, which map onto respective ranges of outputs of the plurality of standard clinical point of care tests, 16a/b/c/d, are directly derived by the model.

[0227] FIG. 2 shows a flow chart 15 setting out steps 18, 20, 22, 24, 26, 28 of the computer-implemented method. Computer-implemented methods according to other embodiments of the present invention may include some, but not all, of the steps shown in FIG. 2. Computer-implemented methods according to other embodiments of the present invention may include additional steps to the steps shown in FIG. 2. The first step 18 of the computer-implemented method shown in FIG. 2 is acquiring data from a sensor on the wearable device.

[0228] An example of an optical sensing module 1101 will now be described with reference to FIG. 3. The optical sensing module is typically located on the wearable device which acquires the data including the bodily parameter data (e.g. absorption spectrum) related to the user.

[0229] The optical sensing module 1101 includes a transmitter photonic integrated circuit (PIC) 4 located on a substrate 2. The PIC 4 includes a plurality of lasers (not visible in FIG. 3), each laser of the plurality of lasers operating at a wavelength that is different from the wavelength of the others. The optical sensing module 1101 is configured to drive the plurality of lasers one at a time. Light from the plurality of lasers exits the PIC 4 and therefore the optical sensing module 101 via one or more optical output ports. A mirror 10 is present to take the light from the plane of the PIC 4 and translate it into a direction more suitable for interrogating the surface. The direction is orthogonal or substantially orthogonal to the plane of the PIC 4.

[0230] The plurality of lasers emit light in a wavelength band which is sensitive to changes in water concentration within the interstitial space. The plurality of lasers may emit light in the infrared wavelength band. The plurality of lasers may emit light in the near-infrared wavelength band. The plurality of lasers may emit light in the short wavelength infrared wavelength band. A laser within the plurality of lasers may emit light at 970 nm, 1200 nm, 1450 nm, 1950 nm, 2766 nm, 2898 nm, or 6097 nm, which correspond to water absorption peaks.

[0231] In other embodiments, the optical sensing module 1101 may include LEDs in addition to or instead of the lasers.

[0232] In use, emitted light from the plurality of lasers is transmitted towards the skin 30 of a user.

[0233] Back-scattered light from the surface of the skin 30, and from within a volume below the surface of the skin, returns to the optical sensing module 1101.

[0234] A photodetector array comprising photodetector pixels 1106, which collect the backscattered light, forms part of the optical sensing module 1101. In the example shown in FIG. 3, the photodetector array is located on the substrate 2 but is not part of the PIC 4.

[0235] An ASIC or microcontroller 11 is located on the substrate 2 of the optical sensing module 1101.

[0236] The wearable device carries out the computer-implemented method according to the present invention on a processor (e.g., on a processor of the microcontroller 11 of the wearable device). In other embodiments, an external device such as a mobile phone carries out the computer-implemented method according to the present invention on a processor of the external device.

[0237] When the data is acquired from optical sensing module 1110, or from other optical sensing modules, the bodily parameter data is a body tissue absorption spectrum where the absorption is in the water band. The hydration information is a quantitative value associated with the absorption spectrum, for example a wavelength shift of a peak of the absorption spectrum, a height of a peak of the absorption spectrum, or a width of a peak of the absorption spectrum.

[0238] In this way, an indication of clinical hydration status of the user can be provided, as the hydration information is sensitive to concentration changes of water within the skin sub-corneal interstitial fluid. As water in the dermis diminishes, the concentration of solutes become higher, thereby changing the degree of water absorption.

[0239] Returning to the flow chart 15 shown in FIG. 2, the second and third steps 20, 22 of the computer-implemented method shown in FIG. 2 respectively include carrying out data regression analysis 20 and determining a hydration index value 22. These steps are shown in more detail in FIG. 4.

[0240] FIG. 4 shows that the second and third steps 20, 22 of the computer-implemented method shown in FIG. 2, include firstly receiving the spectral data 200. Subsequently, pre-processing steps 202, 204 are applied to the data. The first pre-processing step 202 validates the data to determine whether the data has been acquired from a human user, or to determine whether the data includes outlying data. The second pre-processing step 204 applies a baseline correction to the data. This step will be described in more detail below with reference to FIG. 5. Subsequently, the spectra is fed into an offline model 206 and a hydration index value is calculated 208. These steps will be described in more detail below with reference to FIG. 5.

[0241] FIG. 5 shows the pre-processing step 204 of applying a baseline correction to the data in more detail. FIG. 5 shows that applying the baseline correction 204 to the data 40 comprises subtracting baseline data 42 from the data 40. The baseline data 42 is the average data acquired from the user over a long time period, for example over 24 hours, 1 week, or 1 month. A delta spectrum 44 results from the baseline correction 42 pre-processing step.

[0242] FIG. 5 further shows the step 206 of applying an offline model to the delta spectrum 44 to derive the hydration rank 1. In the example shown in FIG. 5, the offline model derives, as the hydration rank 1, both a hydration index and a hydration status of the user. The offline model includes an index model 46 which derives the hydration index, and a status model 48 which derives the hydration status.

[0243] The status model 48 comprises a PCA model and a logistic regression model, where the PCA model is applied to the delta spectrum 44, and the logistic regression model is applied to the output of the PCA model.

[0244] The index model 46 is a PLS regression model. The PLS regression model is applied to the delta spectrum.

[0245] Further details 210 of how the offline model can generated can be understood with reference to FIG. 4. Each of the index model 46 and the status model 48 of the offline model are generated or trained using training data. The index model and/or the status model may be machine learning models.

[0246] A first step 212 outputs of standard clinical point of care tests are received, and in a second step 214 a training dataset is received. A third step 216, is a pre-processing step in which the training dataset is validated to determine whether the training data has been acquired from a human user, or to determine whether the training dataset includes outlying data. In a fourth step 218, the training dataset is mapped to the outputs of the standard clinical point of care tests. In a fifth step 220, a hydration index value is calculated for the training dataset. In a sixth step 222, the offline model is developed.

[0247] Thus, training data is collected which comprises a plurality of training datasets, each training dataset being a dataset which comprises bodily parameter information. Each training dataset is associated with a clinical label, where each clinical label is associated with a respective plurality of outputs of standard clinical point of care tests. The clinical data which is used to derive the outputs of the standard clinical point of care tests are acquired at a similar time to the acquisition of the corresponding training dataset, for example within the same 5 minute interval, 15 minute interval or 1 hour interval.

[0248] In this way, the offline model effectively maps the data to outputs of standard point of care tests.

[0249] Returning to the computer-implemented method 15 shown in FIG. 2, the fourth step 24 of the computer-implemented method is integrating the data with other sensor information, user-input information or learnt metrics.

[0250] At this step, 24 the computer-implemented method comprises acquiring other sensor information. The other sensor information may include, for example, body temperature obtained from a temperature sensor, activity information obtained from an accelerometer, heart rate information obtained from a heart rate sensor and blood pressure information obtained from a blood pressure sensor.

[0251] User input information may be acquired from a user input into the wearable device or from a user input into an external device such as a mobile phone. The user input information may include, for example, weight information, activity information, diet information, fluid intake information, illness information and intoxication information.

[0252] The computer-implemented method may further comprise a step of acquiring a learnt basal hydration rank or basal bodily parameter data of the user. The basal hydration rank or basal bodily parameter data of the user may be learnt in a calibration period of the computer-implemented method using a machine learning model. The training data for the machine learning model may comprise a plurality of training basal datasets comprising bodily parameter data acquired from the wearable device, and a respective plurality of context labels. Each training basal dataset may be associated with a respective one of the context labels. Each of the context labels may include training other sensor information and/or training user input information. The training other sensor information or user input information is acquired at the same time as or at a similar time to the acquisition of the corresponding training basal dataset, for example within the same 5 minute interval, 15 minute interval or 1 hour interval.

[0253] Returning to the computer implemented method 15 shown in FIG. 2, the fifth step 26 of the computer-implemented method is determining a user prompt.

[0254] The user prompt which is determined may include a cause of a hydration status, a recommendation, a type of rehydration fluid to consume, a volume of rehydration fluid for the user to consume, a rehydration schedule for the user to follow, time-correlated hydration information, an indication that the user's hydration status is outside of a pre-determined basal hydration range, or an indication that the user remains dehydrated or overhydrated after a reassessment time. How each of these user prompts is determined will now be described.

[0255] To determine the cause of a hydration status, the computer-implemented method may comprise storing a hydration status cause data table which associates causes of a hydration status with stored other sensor information and/or stored user input information. The computer-implemented method may comprise, when a hydration rank is determined which indicates that the user's clinical hydration status is a pre-determined clinical hydration status, comparing acquired other sensor information and/or user input information with stored other sensor information and/or user input information respectively, and based on this comparison, selecting a cause of a clinical hydration status.

[0256] The pre-determined clinical hydration status may be dehydration or overhydration.

[0257] For example, the derived hydration index may indicate that the user is dehydrated, and the other sensor information may include temperature information which indicates that the user has a high temperature. The cause of dehydration associated with this temperature information may be, for example, that the user is ill, or that the user is overheated.

[0258] To determine a recommendation, the computer-implemented method comprises storing a recommendation data table which associates recommendations with stored hydration ranks. The computer-implemented method comprises comparing the derived hydration rank with the stored hydration ranks respectively and, based on this comparison, selecting a recommendation to output to the user.

[0259] For example, the derived hydration rank may indicate that the user is dehydrated. The recommendation associated with this hydration rank may be for the user to drink water.

[0260] The recommendation data table may further associate recommendations with stored other sensor information and/or user input information. The computer-implemented method may comprise comparing acquired other sensor information and/or acquired user input information with stored other sensor information and/or stored user input information respectively. Selecting the recommendation to output to the user may be further based upon this comparison.

[0261] For example, the derived hydration rank may indicate that the user is dehydrated, and activity information obtained from an accelerometer may indicate that the user is doing/has just finished doing exercise. The recommendation associated with this activity information for this hydration rank may be to rest, or to consume electrolyte fluid.

[0262] To determine a type of rehydration fluid to consume, the computer-implemented method comprises storing a rehydration fluid type data table. The rehydration fluid type data table associates types of rehydration fluids with stored other sensor information and/or stored user input information.

[0263] The computer-implemented method comprises, when a hydration rank is derived which indicates that the clinical hydration status of the user is dehydration, comparing acquired other sensor information and/or acquired user input information with stored other sensor information and/or stored user input information respectively and, based on this comparison, selecting a type of rehydration fluid.

[0264] For example, the derived hydration rank may indicate that the user is dehydrated, and activity information obtained from an accelerometer may indicate that the user is doing/has just finished doing exercise. The rehydration fluid selected in this case may be an electrolyte fluid.

[0265] To determine a volume of rehydration fluid for the user to consume, the computer-implemented method comprises storing a rehydration fluid volume data table which associates volumes of rehydration fluid with stored hydration ranks.

[0266] The computer-implemented method comprises, when a hydration rank is derived which indicates that the clinical hydration status if the user is dehydration, comparing a derived hydration rank with stored hydration ranks and, based on this comparison, selecting a volume of rehydration fluid.

[0267] The rehydration fluid volume data table may further associate volumes of rehydration fluid with stored other sensor information and/or user input information.

[0268] The computer-implemented method may comprise, when a hydration rank is derived which indicates that the clinical hydration status of the user is dehydration, comparing acquired other sensor information with stored other sensor information and, based on this comparison, selecting a volume of rehydration fluid. Alternatively, or in addition, the computer-implemented method may comprise, when a hydration rank is derived which indicates that the clinical hydration status of the user is dehydration, comparing acquired user input information with stored user input information and, based on this comparison, selecting a volume of rehydration fluid.

[0269] To determine a rehydration schedule for the user to follow, the computer-implemented method may comprise storing a rehydration schedule data table which associates a schedule by which rehydration fluid should be consumed with stored hydration ranks.

[0270] The computer-implemented method may comprise, when a hydration rank is derived which indicates that the clinical hydration status of the user is dehydration, comparing a derived hydration rank with stored hydration ranks and based on this comparison, selecting a schedule by which rehydration fluid should be consumed.

[0271] The rehydration fluid volume data table may further associate a schedule by which rehydration fluid should be consumed with stored user input information and/or other sensor information.

[0272] The computer-implemented method may comprise, when a hydration rank is derived which indicates that the clinical hydration status of the user is dehydration, comparing acquired other sensor information or user input information with stored other sensor information or user input information respectively and, based on this comparison, selecting a schedule by which rehydration fluid should be consumed.

[0273] For example, the hydration rank may indicate that the user is moderately dehydrated and temperature information acquired from a temperature sensor may indicate that the user is overheated. The selected schedule by which rehydration fluid should be consumed may be selected based on these factors. The schedule may include a type of rehydration fluid to consume, an overall volume of rehydration fluid to consume, sub-volumes of the overall volume of rehydration fluid to consume and times at which the sub-volumes of rehydration fluid should be consumed.

[0274] To determine time-correlated hydration information, the computer-implemented method may comprise a step of storing the hydration rank and storing the time at which the hydration rank is derived. The computer-implemented method thus comprises obtaining time-correlated hydration rank information from previously derived stored hydration ranks and their corresponding stored times.

[0275] The computer-implemented method may further comprise a step of storing the other sensor information and/or user input information. The computer-implemented method may thus comprise obtaining time-correlated other sensor information and/or time correlated user input information from previously derived stored other sensor information and/or user input information and their corresponding stored times.

[0276] To determine whether the user's hydration status is outside of a pre-determined basal hydration range, the computer-implemented method may comprise a step of applying a basal hydration model to the bodily parameter data, or the derived hydration rank.

[0277] In one or more embodiments, the basal hydration model takes the learnt basal bodily parameter data or learnt hydration rank as an input. The basal hydration model may derive whether the derived bodily parameter data, or derived hydration rank is more than a pre-determined threshold away from a user's basal bodily parameter data or basal hydration rank respectively.

[0278] To determine whether the user remains dehydrated or overhydrated after a reassessment time, the computer-implemented method comprises re-deriving the hydration rank after a reassessment time. The reassessment time may be a pre-determined reassessment time, a user-input reassessment time, or a reassessment time based upon the initially derived hydration rank.

[0279] Returning to the method 15 shown in FIG. 2, the sixth step 28 of the computer-implemented method shown in FIG. 2 is delivery of an output to the user.

[0280] The output may be displayed to the user on the wearable device, or on an external device such as a mobile phone.

[0281] FIG. 6 shows an example of an output being displayed to a user on a mobile device which is a mobile phone 50. The output includes a derived hydration index 1 of −4, and a derived clinical hydration status 52 of mildly dehydrated.

[0282] The output further includes a recommended rehydration schedule 54 which is to drink 500 ml of electrolyte fluid at a current time, 300 ml of water after half an hour, and another 300 ml of water after an hour.

[0283] The output also includes time-correlated hydration information 56 which shows that the user's hydration index has fallen from 0 to −4 over a period of time.

[0284] FIG. 6 also shows that a user input may be received on a portion 58 of the user interface of the mobile device which displays “Start Rehydration Timer” to the user to cause the computer-implemented method to re-derive the hydration status or the hydration index of the user after a pre-determined reassessment time. The computer-implemented method may comprise, when a hydration status or a hydration index, derived after the reassessment time, indicates that the user remains dehydrated after the reassessment time, outputting an alert to the user.

[0285] FIG. 7 shows a flow chart 60 for a method which includes embodiments of the computer-implemented method of the present invention. At a first step 62, the user wears a wearable device such that it is in contact with their skin. At a second step 64, data is acquired from sensors of the wearable device. At a third step 66, other sensor information, user input data, and learnt parameters are acquired. At a fourth step 68, an output is provided to the user. At a fifth step 70, the user makes a choice based on the output. At a sixth step 72, data is re-acquired from the sensors. The third step 66 to the sixth step 72 are carried out in a loop. At a seventh step 74, which follows the fifth step, time-correlated information and learnt parameters are acquired.

[0286] FIGS. 8A and 8B show examples of different hydration index scales 12 to that shown in FIG. 1. As shown in FIGS. 1, 8A and 8B, the hydration index scale 12, and how its hydration indices 1 map onto output of standard clinical point of care tests 16a/b/c/d, may depend on the use case for the computer-implemented method. For example, FIG. 1 shows a hydration index scale 12 running from −5 to +5 which may be used for critical care patients. FIG. 8A shows a hydration index scale 12 running from −9 to 0 which may be used for both healthy and vulnerable users. FIG. 8B shows a hydration index scale 12 running from −5 to +5 which may be used for healthy, high-risk users such as athletes. Populations that may benefit from the present invention include elderly populations, endurance athletes, those travelling to altitude or hot climates, and those in occupations with a high risk of dehydration and overheating.

[0287] Although exemplary embodiments of a computer-implemented method have been specifically described and illustrated herein, many modifications and variations will be apparent to those skilled in the art. Accordingly, it is to be understood that a computer-implemented method constructed according to principles of this invention may be embodied other than as specifically described herein. The invention is also defined in the following claims, and equivalents thereof.