BIOLOGICAL FLUID ANALYSIS AND PERSONALIZED HYDRATION ASSESSMENT SYSTEMS

Abstract

A method of measuring an analyte in a bodily fluid sample and combining measurement data from multiple users may involve initiating a wireless connection between a handheld analyzer and a smart computing device on which an analyte analysis application has been downloaded and inserting a test strip into the handheld analyzer. The method may further involve collecting a sample of a bodily fluid on the test strip, measuring, with the handheld analyzer, a concentration of at least one analyte in the sample, wirelessly communicating the measured concentration from the handheld analyzer to the smart computing device, and displaying the measured concentration on the smart computing device. Finally, the method may involve transmitting the measured concentration to a database and organizing data including the measured concentration and at least one additional measured analyte concentration from at least one additional user on the database.

Claims

1. A method of measuring an analyte in a bodily fluid sample and combining measurement data from multiple users, the method comprising: initiating a wireless connection between a handheld analyzer and a smart computing device on which an analyte analysis application has been downloaded; inserting a test strip into the handheld analyzer; collecting a sample of a bodily fluid on the test strip; measuring, with the handheld analyzer, a concentration of at least one analyte in the sample; wirelessly communicating the measured concentration from the handheld analyzer to the smart computing device; displaying the measured concentration on the smart computing device; transmitting the measured concentration to a database; and organizing data including the measured concentration and at least one additional measured analyte concentration from at least one additional user on the database.

2. The method of claim 1, further comprising initiating a wireless connection between the smart computing device and the Internet, wherein the database is located on a cloud storage location, and wherein transmitting the measured concentration to the database comprises wirelessly transmitting the measured concentration from the smart computing device to the cloud storage location via the Internet.

3. The method of claim 1, wherein the data is organized based upon in groups of multiple users belonging to multiple organizations.

4. The method of claim 1, further comprising initiating the measuring step via the smart computing device, wherein initiating the measuring step comprises: logging into an operator account on the analyte analysis application; selecting a specific source from which the sample will be taken; and confirming the wireless connection between the handheld analyzer and the smart computing device.

5. The method of claim 1, further comprising automatically downloading and storing, on the handheld analyzer, a test strip type and batch data.

6. The method of claim 5, wherein automatically downloading and storing the test strip type and batch data comprises: measuring a resistance-encoded test strip identification on the test strip; and comparing the test strip identification with data in a memory of the handheld analyzer to determine the test strip type and batch data.

7. The method of claim 6, further comprising: communicating the test strip type and batch data to the smart computing device; and alerting a user through an error message on the handheld analyzer and smart computing device if the test strip type is an unknown test strip type.

8. The method of claim 1, further comprising preventing use of a used or faulty test strip by: determining that the test strip has already been used or is faulty; and prompting the user to discard the test strip on at least one of the handheld analyzer or the smart computing device.

9. The method of claim 1, further comprising providing instructions to a user regarding how to collect the sample, using at least one of the application and the handheld analyzer.

10. The method of claim 1, further comprising, using the handheld analyzer: determining an ambient temperature; applying a detection technique based on the ambient temperature; and determining the concentration of the at least one analyte using batch specific calibration coefficients and the ambient temperature.

11. The method of claim 1, further comprising determining, with the handheld analyzer, that a measurement is inaccurate by: measuring a signal inconsistency; and detecting an abnormally high signal or an abnormally low signal for the test strip.

12. The method of claim 1, further comprising using the smart computing device to analyze the measured concentration to assist in user interpretation.

13. The method of claim 1, wherein the smart computing device refers a raw measured concentration to a previously established individual specific reference value.

14. A method of measuring at least one analyte in a bodily fluid sample from a subject, the method comprising: inserting a test strip into a handheld analyzer; collecting the bodily fluid sample on the test strip by bringing the test strip in contact with a body part of the subject where a bodily fluid is present; removing the test strip from contact with the body part after the handheld analyzer indicates that a sufficient amount of the bodily fluid sample has been collected; applying an electrical signal to the test strip; measuring, with the handheld analyzer, a response of a combination of the test strip and the bodily fluid sample to the applied electrical signal; analyzing the response with the handheld analyzer to determine that the bodily fluid sample is a valid sample; measuring a concentration of the at least one analyte in the bodily fluid sample; and at least one of displaying the measured concentration on the handheld analyzer or transferring the measured concentration to another device to at least one of display the measured concentration, generate further calculations or store the measured concentration.

15. A handheld analyzer for determining a concentration of one or more analytes in a bodily fluid, the handheld analyzer comprising: a housing; a test strip port in the housing; a display screen on the housing; a temperature sensor in the housing; and multiple electronic components in the housing, the multiple electronic components comprising: at least one of a direct digital synthesis (DDS) chip or a digital-to-analog converter (DAC) chip; an analog-to-digital converter (ADC) chip; a wireless communication chip; processing circuitry; and computer memory.

16. The handheld analyzer of claim 15, wherein the test strip port is configured to accept a test strip selected from the group consisting of analyte specific test strips and test strips capable of measuring multiple analytes.

17. The handheld analyzer of claim 15, wherein the multiple electronic components are configured to automatically transfer test strip configuration settings to the computer memory when the handheld analyzer is connected to a database via a mobile application.

18. The handheld analyzer of claim 15, wherein the handheld analyzer is configured to determine a test strip type and batch data using a resistance-encoded identification on a test strip and data stored in the computer memory.

19. The handheld analyzer of claim 15, wherein the multiple electronic components are configured to automatically adjust a detection method, an excitation waveform and gain settings for multiple types of test strips.

20. The handheld analyzer of claim 15, wherein the temperature sensor is configured to measure an ambient temperature, and wherein the processing circuitry is configured to process the measured ambient temperature using a temperature detection algorithm.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0046] FIG. 1 is a diagram of a biological fluid analysis system, according to one embodiment;

[0047] FIG. 2 is a diagram of a handheld analyzer, according to one embodiment;

[0048] FIG. 3 is a flow chart of a method for using a biological fluid handheld analyzer independently as a point-of-care device, according to one embodiment;

[0049] FIG. 4 is a flow chart of a method for using a biological fluid handheld system, including pairing with a phone or tablet, according to one embodiment;

[0050] FIG. 5 is a diagram of a system for creating a reference dataset for hydration and sweat loss for a human subject, according to one embodiment;

[0051] FIG. 6 is flow chart illustrating a method for assessing sweat loss and recommending a rehydration protocol, according to one embodiment;

[0052] FIG. 7 is flow chart illustrating a method for assessing sweat loss and recommending a rehydration protocol, according to an alternative embodiment;

[0053] FIG. 8 is flow chart illustrating a method for assessing sweat loss and recommending a rehydration protocol, according to another alternative embodiment;

[0054] FIG. 9 is a flow chart illustrating a protocol for rehydrating and monitoring hydration after exercise;

[0055] FIG. 10 is a flow chart illustrating a fluid replacement algorithm based on measurement of salivary osmolarity;

[0056] FIG. 11 is a flow chart illustrating a sodium replacement algorithm based on measurement of sweat sodium concentration;

[0057] FIG. 12 is a flow diagram illustrating a method for assessing sweat loss, recommending a rehydration protocol, and feeding data back to a reference dataset, according to another embodiment;

[0058] FIG. 13 is a system drawing, illustrating a sweat collection and analysis system, according to one embodiment;

[0059] FIGS. 14A-14D illustrate a method for collecting and analyzing sweat, according to one embodiment;

[0060] FIG. 15 is a chart, illustrating correlation between osmolarity and sodium concentration of sweat samples, according to one embodiment; and

[0061] FIG. 16 is a flow diagram illustrating a method for assessing sweat loss, recommending a rehydration protocol, and feeding data back to a reference dataset, according to another embodiment.

DETAILED DESCRIPTION

[0062] The present application describes various embodiments and features of a biological fluid analysis system and method. Referring to FIG. 1, one embodiment of a biological fluid analysis system 10 includes a handheld analyzer 12, multiple test strips 14a-14e, and an application 16 for use on a phone, tablet or other smart computing device. The system 10 may further include a database 18 (or multiple databases) for storing data, which the application 16 may access. The database 18 may be located in the cloud or any other suitable location.

[0063] Test Strips

[0064] In various embodiments, the biological fluid analysis system 10 may include any suitable number and combination of types of test strips 14a-14e. In some embodiments, for example a panel of test strips 14a-14e may be provided, with each strip 14a-14e being chemically sensitive to a specific analyte (e.g., electrolytes, metabolites, hormones) or a panel of analytes (e.g., multiple electrolytes). The test strips 14a-14e are single use, disposable and configured to test for a specific biological sample type (e.g., blood, saliva, sweat, urine) or non-biological sample type (e.g., pool water, wastewater). Test strips 14a-14e may be visually distinguishable in some embodiments and/or may contain a resistor-encoded identification code. Test strips 14a-14e may use one of multiple of a range of detection methods.

[0065] Test strips 14a-14e may significantly differ in size, shape and design, but share common design elements allowing for compatibility with a single analyzer 12. In one embodiment, a test strip 14a-14e includes a sampling port and four untreated carbon electrodes, three of which are used for impedimetric measurement and the fourth of which is the resistance-encoded identification code describing the test strip type and batch. In another embodiment, the test strip 14a-14e includes a sampling port and three carbon electrodes, two of which are configured to allow for potentiometric measurement of an analyte, and the third of which is the resistance-encoded identification code describing the test strip type and batch. Common to both of these embodiments of test strips 14a-14e is the electrode structure for interfacing with the handheld analyzer 12 and test strip identification. All other features are configured for a given analyte (or set of analytes) and sample type.

[0066] In the embodiment illustrated in FIG. 1, four types of test strips 14a-14e are shown as part of the biological fluid analysis system 10. (Multiple strips of each type may be provided, and alternatively any other suitable number of test strip types may be provided.) In this embodiment, the analysis system includes a test strip for analyte #1 14a, a strip for analyte #2 14b, a strip for analyte #3 14c, a strip for analytes #1 and #2 14d, and a strip for analytes #1 and #3 14e. Of course, analytes #1, #2 and #3 may be any suitable analytes, according to various embodiments.

[0067] Analyzer

[0068] In some embodiments, the analyzer 12 of the biological fluid analysis system 10 is a handheld, point-of-care analyzer 12 capable of signal generation, measurement and processing. The handheld analyzer 12 may automatically determine the type of test strip 14a-14e inserted into it, for example by reading a resistor-encoded identification code on the test strip 14a-14e. The handheld analyzer 12 may use the identification information to configure the detection method, excitation waveform and gain settings, and to apply a batch specific calibration curve when processing the raw measurement data.

[0069] Referring now to FIG. 2, one embodiment of the handheld analyzer 12 is illustrated in detail. In this embodiment, the handheld analyzer 12 includes a micro-controller 20 with an onboard Bluetooth low energy chip 22 for wireless communication and an analog-to-digital converter (ADC) chip 24 for measurement of analog signals. The analyzer further includes: an antenna 26 for wireless communication; a real time clock 28; memory 30 for storage of test-strip identification and batch calibration data; a user interface 32 including a screen, buttons and LED; a temperature sensor 34 for monitoring of ambient temperature; a direct digital synthesis chip 36 for digital signal generation; a digital-to-analog converter chip 38 for analog signal generation; a low-noise analog switch 40 for regulating the transfer of signal generation to sensor circuitry 42; and gain setting circuitry 44.

[0070] The handheld analyzer may also include three multiplexers (MUX) 46, 48, 50. The first multiplexer 46 is configured to regulate the connection between the sensor circuitry 42 and a sensor port 54. The second multiplexer 48 is configured to regulate the connection between the sensor circuitry 42 and a high-resolution ADC 52. The third multiplexer 50 is configured to regulate the connection between the sensor circuitry 42 and the micro-controller 20.

[0071] The features of this embodiment of the handheld analyzer 12 allow a diverse range of analytical techniques to be employed with a single analyzer 12. Specifically, the multiplexing of signal generation, gain settings and a second high-resolution ADC allow for impedimetric, amperometric and potentiometric analysis.

[0072] FIG. 3 illustrates one exemplary method 100 for using the handheld analyzer 12 independently, as a point-of-care analysis device. In this embodiment, the method begins by starting (e.g., powering on) the analyzer 12 in step 102. In step 104, the user is prompted to insert a test strip. The user inserts a test strip into the analyzer 12 in step 106, and the handheld analyzer 12 automatically detects, in steps 108 and 110, the test strip type and batch to ensure that the test strip is not used or faulty. For example, in one embodiment, the impedance of a test strip is used to determine if it is used or faulty. In another embodiment, a one-way fuse is used to ensure a test strip is not reused.

[0073] An error detection algorithm may used to identify suspect readings. In one embodiment, a periodic stimulus signal is applied and consistency between measurements used to assess accuracy of results. In another embodiment, this periodic signal is applied at multiple distinct frequencies and these are investigated. A reference range of values may also be used to determine whether a result is of an appropriate range for a given analyte or sample type.

[0074] If step 108 or step 110 indicate a bad strip has been inserted, the analyzer provides the user with an error code or other prompt (steps 112 and 114), so that the user knows to remove the test strip from the analyzer 12 and start the method again. If the test strip is accepted by the analyzer 12, the user receives a prompt in step 116 to apply a fluid sample to the test strip. Instructions for sample collection and error messages may be relayed to the user on a built-in display screen. In step 118, the analyzer 12 applies a fluid detection algorithm to the fluid sample to determine if the sample is adequate. If the sample is insufficient, the user receives another prompt (repeating step 116) to add more fluid to the test strip. If the sample is sufficient, the analyzer may provide another prompt to the indicate that fluid was detected and to please wait for results (step 120). Data processing is performed on the handheld analyzer 12 in step 122, and an error detection algorithm is applied in step 124. If the analyzer 12 detects an error, the user is prompted accordingly in step 126, and the test strip is removed and replaced with a new strip to start the method again. If the error detection algorithm confirms a good reading of the fluid sample, the results of the measurement are displayed to the user on the analyzer and/or a smart device coupled with the analyzer in step 128. In some embodiments, the handheld analyzer 12 may include integrated cellular or wireless capability. In such embodiments, the handheld analyzer 12 can upload measurement results for storage in a cloud server or other database.

[0075] The handheld analyzer 12 may record the number of measurements performed on its internal memory. Using this information, the user is prompted to perform routine maintenance at specific milestones. In one embodiment the user is prompted to replace the test-strip port after a certain number of measurements have been performed. In some embodiments, the handheld analyzer also contains an internal reference load, which may be used to perform start-up calibration and account for manufacturing variability.

[0076] Phone/Tablet Application

[0077] Referring back to FIG. 1, the application 16 provided with the biological fluid analysis system 10 may be configured for use on a smart phone, tablet and/or other computing device. The application 16 facilitates step-by-step operator instructions, subject selection and measurement interpretation. Measurements made by the handheld analyzer 12 may be automatically uploaded to a cloud database 18 via the application 16.

[0078] Referring to FIG. 4, a method 200 of operation of the phone/tablet application with the handheld analyzer 12 is illustrated. In this method, application is started 202 and the user logs in 204. The user selects a test subject 203. The app prompts the user to turn on the analyzer 206. The user starts the analyzer 224, which prompts the user to turn on the app 226. Obviously, the order of these steps may be reversed, and the prompts are optional. The app checks to see if it is paired with the analyzer 208, and the analyzer checks to see if it is paired with eh app 228, using a data logging application. Upon pairing, any new type/batch data may be transferred to the handheld analyzer memory (steps 210 and 230). The user then selects a profile to initiate a measurement.

[0079] Next the user is prompted to insert a test strip into the handheld analyzer (steps 212 and 232), the user inserts the test strip, and the analyzer walks the user through steps similar to or the same as those described in relation to FIG. 3 (steps 234, 236, 238, 240, 242, 244, 246, 248). Instructions may be displayed on the handheld analyzer and/or phone/tablet, instructing the user as to how to collect a sample. (See, for example, steps 216, 218, 220 regarding display on app). Sample analysis and data processing are performed on the handheld analyzer. Ambient temperature may be used to adjust the measurement technique or assist in processing of data. Results are communicated to the application (step 250), displayed on the phone/tablet (step 220) and logged to the user profile. Interpretive information may be displayed on the phone application screen. Results may be automatically uploaded to a cloud server (step 222), if an Internet connection is available and/or stored locally for upload when a connection is available. Finally, the user may be prompted to remove the used test strip 252.

[0080] In some embodiments, a user specific reference panel may be previously established through a protocol or set of protocols. This information may be used to provide user specific interpretive information.

[0081] Database

[0082] Referring again to FIG. 1, the biological fluid analysis system 10 may also include one or more databases 18 for storing any suitable data relevant for the system 10 and method. In some embodiments, the system 10 may include a database 18 located in the cloud. The database 18 may be decentralized and may store information for operation of the system 10, including test strip characteristics (type, batch, calibration coefficient), operator profiles, subject profiles and test results. The cloud database 18 can be accessed by operators through a web browser to review, analyze and export test results. Users with necessary permissions may view test results collected by multiple users and/or multiple analyzers for multiple tests.

[0083] The present application describes various embodiments and features of a hydration assessment system and method, for determining a human subject's level of hydration and recommending a hydration protocol. Although the following disclosure focuses on the analysis of sweat and/or saliva, the embodiments described below, or variations of those embodiments, may be used for analysis of any other bodily fluid, such as blood, urine or the like.

[0084] The Reference Dataset

[0085] FIG. 5 shows a hydration measurement system 300 for creating a reference dataset 302 of information related to sweat loss and hydration of a human subject. The reference dataset 302 includes a panel of measured parameters collected across multiple measurement sessions 312, using one or more measurement devices. The parameters may include, but are not limited to, environmental conditions 304 (e.g., temperature, humidity, wind-speed, elevation), body mass changes and/or salivary osmolarity changes 306, sweat electrolyte content 208, and/or one or more physical exertion measurements 310 (e.g., heart rate, respiratory rate, etc.). Each of the various parameters 304, 306, 308, 310 may be measured using its own, separate measurement device or, in some cases, using one measurement device for measuring multiple parameters. For example, as illustrated diagrammatically in FIG. 5, a smart phone may be used for collecting environmental condition data 304, a saliva measurement device may be used for measuring body mass changes and/or salivary osmolarity 306, a sweat measurement device may be used for measuring sweat osmolarity/sweat sodium content 308, and a heart rate monitor may be used for measuring physical exertion 310. In an alternative embodiment, for example, saliva measurements 306 and sweat measurements 308 may be collected using the same handheld measurement device and the same or different test strips. These types of devices are described in more detail in the Incorporated References.

[0086] Environmental condition data 304 may be collected, for example, by manually logging parameter data from external equipment, automated data logging from a purpose-built testing system, or automated logging of these parameters from a weather monitoring service based on time and location data. The degree of physical exertion 310 may be determined, for example, through self-reporting on a standard rating of perceived exertion (RPE) rating scale and/or may be inferred from one or more measured biomarkers of physical exertion (e.g., activity, heart rate, VO2 max, lactic acid concentration in blood, sweat or saliva). These measurements may be manually reported and/or automatically logged with a personal monitoring device, such as a fitness watch or heart rate chest-strap. The percentage body mass loss 306 may be established, for example, through direct measurement of body mass before and after a period of exertion, accounting for any fluid ingested or lost through urination or inferred from a biomarker of change in body mass (e.g., increased salivary osmolarity or salinity, urine osmolarity or urine specific gravity). The sweat sodium content 308 may be established through direct measurement of the sweat sodium content 308 with a chemical analysis system or estimated from the conductivity or osmolarity of a sweat sample.

[0087] Thus, the hydration assessment system 300 may include multiple data capturing devices (or programs or applications on devices), to gather, for example, the environmental conditions 304, the body mass loss 306, the sweat sodium content 308 and/or the heart rate or other measure of exertion 310. Each measurement device is used over multiple measurement sessions 312 to collect the various types of data 304, 306, 308, 310, and provide the data to the reference dataset 302, which may be located in a database stored on a computer or in the cloud. The reference dataset 302 may be generated, for example, by following a set of predefined exercise protocols that specify duration and intensity of exertion prior to collection of measurements or by taking measurements over time during regular activity. In the illustrated example, data is collected over four trial measurement sessions 312 (trials 1-4). The human subject runs for 60 minutes during each session, at different outdoor temperatures and with different RPEs. Collected data from all four measurement sessions 312 feeds into the reference dataset 302. The reference dataset 302 may then be used to establish an algorithm, which may be used to estimate sweat rate and/or sweat sodium composition of the human subject under various conditions.

[0088] In the embodiment of FIG. 5, a set of four exercise trial measurement sessions 312 is defined, outlining environmental conditions (cool and hot) and intensity (RPE 3-4 (Light-Moderate Activity) and RPE 7-8 (Vigorous Activity). Environmental conditions 304 are manually logged by the user into a phone application. Salivary osmolarity 306 is measured before and after exercise with a handheld testing device (such as the device described in the Incorporated Applications), which wirelessly communicates the results to the phone application. The change in salivary osmolarity 306 may be used to estimate a change in body mass during each trial. Sweat may be collected with an adhesive patch (or other collection device in alternative embodiments) during each trial. Sweat osmolarity 308 may be measured with a handheld testing device (such as the device described in the Incorporated Applications), which wirelessly communicates the results to the phone application. Sweat osmolarity 308 or conductivity may be used to estimate sweat sodium content. Heart rate may be monitored with a chest strap monitor and wirelessly communicated to the phone application. Average heart rate may be used to determine the intensity of exertion 310.

[0089] Fluid Replacement Guidelines

[0090] Referring now to FIG. 6, a method 320 is outlined for using the reference dataset 302 to generate a rehydration protocol 324 including fluid replacement guidelines for the human subject. These guidelines may assist in offsetting fluid and sodium losses during heat stress or physical exertion and to replenish and recover fluid and sodium back to rest levels following exertion or heat stress. Once the reference dataset 302 is established, the method 320 may involve using an algorithm 322 to predict sweat sodium loss and then using the predicted sweat sodium loss to generate a rehydration protocol 324. The instructions in the rehydration protocol 324 may define the volume of fluid required to replace losses, the sodium composition of fluid required to replace losses, and/or the time over which these fluids should be ingested. The algorithm may be implemented 322, and the rehydration protocol 324 may be generated, via a processor stored in a computer, an application stored on a smart phone or smart tablet, a processor in the cloud, or the like. An application may provide alerts to assist in following this protocol.

[0091] In the embodiment illustrated in FIG. 6, environmental conditions 304 are monitored with a portable weather monitoring system 334 and wirelessly communicated to a phone application. Exertion level 310 during an exercise session is monitored with a chest-strap heart rate monitor 340 and wirelessly communicated to a phone application. Salivary osmolarity 306 is measured before and after exercise with a handheld testing device 336, which wirelessly communicates the results to the phone application. The change in salivary osmolarity 306 is used to estimate change in body mass during exercise. The reference dataset 302 is used to predict the sweat composition 322 based on the recorded information. The phone application generates a rehydration protocol 324 outlining what and how much to drink to assist in rehydration and recovery after exercise.

[0092] FIG. 7 illustrates another embodiment of a method 350 for assessing hydration and providing a rehydration protocol 366. In this embodiment, environmental conditions 354 are determined using an online weather monitoring service 356 and the user's location. Exertion level 360 during an exercise session is monitored with a heart rate monitor 362, for example a wrist-based monitor integrated into a smart watch, and wirelessly communicated to a phone application. The reference dataset 352 is used to predict changes in body mass and sweat composition 364, based on the recorded and measured information. The phone application generates a rehydration protocol 366 outlining what to drink to assist in recovery after exercise and periodically alerts the user through mobile notifications to reinforce drinking behavior. Again, the sweat composition predicting step 364 and the generating a rehydration protocol step 366 may be performed by any suitable processor, such as an application for a smart device, a processor in a computing device, a cloud processor or the like.

[0093] FIG. 8 illustrates another embodiment of a method 370 for assessing hydration and providing a rehydration protocol 392. In this embodiment, environmental conditions 374 are determined using an online weather monitoring service 384 and the user's location. Exertion levels 380 are predicted by the user specifying 382 anticipated intensity of exercise (e.g., race day/high intensity). Salivary osmolarity 376 is measured before exercise with a handheld testing device 386, which wirelessly communicates the results to the phone application. Salivary osmolarity 376 is used to estimate starting hydration status. The reference dataset 372 is used to predict changes in body mass and sweat composition 390, based on the recorded information. The phone application generates a rehydration protocol 392 outlining what to drink to assist in maintaining hydration during exercise and assist in rehydration and recovery after exercise. The user is periodically alerted during exercise to drink to assist in maintaining a hydrated state and to offset fluid losses.

[0094] In another embodiment, salivary osmolarity is measured post-exercise. The reference dataset together with final salivary osmolarity, or changes of salivary osmolarity before and after the event, or difference between an individuals optimal hydration zone, is used to predict changes in body mass. Sodium and electrolyte loss are estimated from sweat composition based on the recorded information. The phone application generates a protocol outlining what to drink, how much to drink and when to drink assist in hydration recovery after exercise.

[0095] In another embodiment, the user may be periodically alerted during post-exercise recovery to measure their salivary osmolarity. These other salivary measurements are used to estimate the effectiveness of the initial hydration protocol and to permit the hydration protocol to be adapted in order to increase its effectiveness and to return the individual to a desirable hydration status.

[0096] In another embodiment, the user may be periodically alerted during post-exercise recovery to measure their salivary osmolarity. The time period between prompts is based upon how far the individual salivary osmolarity and hydration status is from their desirable hydration status.

[0097] FIG. 9 illustrates one embodiment a rehydration protocol 400 (or recovery protocol). In this embodiment salivary osmolarity is measured before exercise 402 and after exercise 404, the change in osmolarity is calculated 406, and that change is used to estimate body mass loss 408 using a fluid replacement algorithm 450, such as the algorithm 450 illustrated in FIG. 10. Sweat sodium is either directly measured 410 or estimated from the reference panel using environment and/or exertional parameters, and a protocol for salt replacement generated 412 using a sweat replacement algorithm 470, such as the algorithm 470 illustrated in FIG. 11. Salivary osmolarity is used to monitor rehydration until the individual has appropriately achieved their desirable hydration status.

[0098] In the embodiment illustrated in FIG. 9, the rehydration protocol first involves the step of determining whether the fluid loss was less than 500 milliliters 414. If less than 500 mL, then the protocol instructs the user to replace fluids as a single bolus in the amount of 150% of fluid lost 416. If the fluid loss was greater than or equal to 500 mL, the user is instructed to replace fluids up to 800 mL/hour, up to 150% of fluid lost 418. The protocol next involves waiting approximately 10 minutes after fluid replacement and measuring salt osmolarity 420. If the osmolarity is still elevated compared to the pre-exercise measurement 422, then the method returns to step 406. If the osmolarity has returned to the pre-exercise level 424, then the rehydration protocol stops 426. Of course, this is just one example.

[0099] FIG. 10 illustrates one embodiment of a fluid replacement algorithm 450 that may be used as part of the rehydration protocol generation methods described herein. In this embodiment, the fluid replacement algorithm 450 calculates the change in saliva and/or sweat osmolarity 406, or alternatively the algorithm 450 may receive the change on osmolarity, after it has been calculated. The algorithm then categorizes the change in osmolarity into one of four categories 452, based on the size of the change. Then, the change is multiplied by a multiplier 454 and a percent of body mass loss is calculated 456. The body mass loss is multiplied by the user's body weight 458 to calculate fluid loss 460, and the fluid loss 460 is used to generate a fluid replacement protocol 462.

[0100] FIG. 11 illustrates one embodiment of a salt replacement algorithm 470 that may be used as part of the rehydration protocol generation methods described herein. In this embodiment, the salt replacement algorithm 470 may start with environment data and/or exertion data 472 and sweat data from a reference panel 476. Alternatively, it may start with measured sweat 474. The algorithm 470 calculates salt loss 478 based on fluid loss and sweat data. The algorithm 470 then divides the salt loss across a fluid replacement time 480 and determines whether salt loss has occurred at greater than 1200 mg/hour or less than or equal to 1200 mg/hour 482. The algorithm 470 then generates a salt replacement protocol 484, based on the amount of salt loss.

[0101] Testing Method

[0102] Currently, chemical analysis of sweat is performed using laboratory tools. These are bulky and expensive and require large samples for analysis. As the reference dataset described above requires multiple sweat measurements to be performed across multiple training sessions, laboratory-style analysis of samples may be impractical. To facilitate the methods described herein for hydration assessment and hydration recommendations, this application also describes a method of rapid assessment of sweat sodium content through measurement of sweat conductivity, impedance or osmolarity, using a handheld portable testing system.

[0103] Referring now to the diagrammatic flow chart of FIG. 12, one embodiment of a method 500 for measuring a sweat sample is illustrated. In this embodiment, the method 500 involves a user 502 affixing an adhesive, absorbent patch 503 to the skin. The user then exercises 504 (or otherwise exerts himself/herself) and collects sweat with the patch 503. After exercise, sweat 506 is extracted from the patch 503, for example by squeezing the sweat 506 out of the patch 506 and into a collection receptacle 507. A small sample of the sweat 506 is then collected from the collection receptacle 507, using a single-use disposable test strip 508. Next, the test strip 508 is inserted into a portable, handheld testing system 510 (such as the system described in the Incorporated Applications). The testing system 510 then calculates sweat sodium concentration from impedance (or osmolarity) of the sweat sample 506. In alternative embodiments, the test strip 508 may be inserted into the handheld testing system 510 before applying the sweat 506 to the test strip 508. Once the testing system 510 has been used to generate any desired data pertaining to the sweat 506, some or all of the data may be sent (wirelessly, for example) to the reference dataset 512 for the user.

[0104] FIG. 13 is an illustration of one embodiment of a sweat measurement system 520, which may be used on its own or as part of any of the method embodiments described above. In this embodiment, the sweat measurement system 520 includes a sweat collection kit 521, which may be provided in a pouch, tray or any other suitable packaging. The kit 521 includes one or more adhesive sweat collection patches 522 (e.g., adhesive gauze pad), one or more alcohol wipes 524 (or other disinfectant skin wipes) for cleaning the skin before applying the patch 522, one or more sweat collection trays 526 and/or sweat collection storage tubes 530, a syringe 528, and three (or alternatively any other number of) sweat test strips 532. The sweat collection tray 526 may be used if sweat will be tested immediately or very soon after collection. The sweat collection tube 530 may be used if the sweat will be tested later, stored and/or transported. In some embodiments, the adhesive patch 522 may be placed into the barrel of the syringe 528, and the plunger of the syringe 528 may be used to squeeze the sweat out of the adhesive patch 522 and into the collection tray 526 or tube 530. The designated end of a test strip 532 may then be placed into the collection tray 526 or tube 530 to collect a sufficient sweat sample on the end of the test strip 532. The system 520 may include any number of test strips 532, where one end of each strip 532 is used to collect the sweat sample, and the other end is inserted into a handheld measurement device 534, which is also part of the system 520. Finally, the system 520 includes a computer application 536 for a smart phone 538, tablet or the like. The handheld measurement device 534, which is described further in the Incorporated References, analyzes the sweat sample and transmits sweat sample data wirelessly to the computer application 536, which may conduct further analysis and may generate further data, such as a rehydration protocol.

[0105] FIGS. 14A-14D illustrate a method for using the system 520 illustrated in FIG. 13 to collect and analyze a sweat sample. As illustrated in FIG. 14A, step 550 involves the test subject (or user) wiping down either of her inner forearms with the alcohol wipe 524 from the sweat collection kit 521. Then, as illustrated in FIG. 14B, step 552 involves the user applying the adhesive sweat collection patch 522 to the forearm in the cleaned area. The user will then exercise for a suitable amount of time to collect sweat in the patch 522, for example around 30-60 minutes in one embodiment. (Alternatively, shorter or longer periods of time may be sufficient.) Next, the user removes the sweat collection patch 522, places it in the barrel of the syringe 528, and, as shown in FIG. 14C, step 554, the user depresses the plunger of the syringe 528 to squeeze sweat out of the patch 522 and into the collection tray 526 (or alternatively the collection tube 530). As illustrated in FIG. 14D, the user then places the collection end (or free end) of one of the test strips 532 into the sweat sample that resides in the collection tray 526. In this embodiment, the opposite end of the test strip 532 has already been inserted into the handheld measurement device 534, so the user holds the handheld device 534 while inserting the free end of the test strip 532 into the collected sweat sample. Alternatively, the test strip 532 may be inserted into the sweat sample and then into the handheld device 534.

[0106] After the measurement device 534 takes a measurement from the first test strip 532, the user removes the first test strip 532, inserts a second test strip 532 into the handheld measurement device 534, inserts the free end of the test strip 532 into the sweat sample, and takes a second measurement. These last steps of the method are repeated for a third test strip 532. In alternative embodiments, fewer than three test strips 532 or more than three test strips 532 may be used for one measurement set. It may be advantageous to use three strips 532, to allow for averaging of three measurements and thus increase accuracy of the test results as compared to using only one or two test strips 532. The handheld measurement device 534 may provide measurements of sweat sodium concentration, sweat osmolarity and/or other sweat characteristics. Measurement data may then be used in any of the methods and algorithms described herein. For example, sweat osmolarity and/or sodium concentration may be used to help the test subject determine how much fluid to consume and what type of fluid (e.g., what quantity and type of electrolytes).

[0107] FIG. 15 illustrates the correlation between osmolarity and sodium concentration of various sweat samples. The handheld sweat measurement device 534 wirelessly communicates this result to a phone application for integration into the reference dataset.

[0108] Referring now to FIG. 16, another embodiment of a sweat sample collection method 560 is illustrated. In this embodiment, the user first applies a single-use patch 562 to the forearm or other collection area on the skin. The patch 562 in this embodiment includes integrated microfluidics and electrodes. In one embodiment, for example, the single-use patch may include a test strip, such as or the same as the test strips 532 illustrated in FIG. 13. Alternative embodiments may include a different configuration. The user then engages in exercise or other exertion 564, and sweat is collected by the patch 562. For the next step 566, during or after exercise, while still affixed to the test-subject's body, the patch 562 (for example including a test strip 532) is connected to a handheld sweat measurement device 534 to perform measurement(s) of sweat osmolarity, conductivity and/or the like. The handheld device 534 then wirelessly communicates sweat data to a phone application for integration into the reference dataset 568. In some embodiments, the handheld device 534 may also be used to perform other measurements for establishing the reference dataset 568, such as but not limited to saliva osmolarity change and blood lactate concentration.

[0109] In another embodiment, a saliva sample is collected directly from the tongue with integrated microfluidics and electrodes. The handheld device 534 wirelessly communicates saliva data to a phone application for integration into the reference dataset. This same system may be capable of performing other measurements for establishing the reference dataset, such as but not limited to saliva osmolarity change and blood lactate concentration.

[0110] In another embodiment, a saliva sample is collected by a test subject providing a saliva sample into a receptacle. The sample is then analyzed with integrated microfluidics and electrodes. The handheld device 534 wirelessly communicates saliva data to a phone application for integration into the reference dataset. This same system may be capable of performing other measurements for establishing the reference dataset, such as but not limited to saliva osmolarity change and blood lactate concentration.

[0111] Although the above description is believed to be complete and accurate, various changes to any of the embodiments and features described herein may be made, without departing from the scope of the invention.