Methods for molecular monitoring and reporting of stress

Abstract

Methods and systems of determining a stress score for a subject. The method includes the steps of (1) monitoring the amount or concentration of at least one analyte in a subject, wherein the at least one analyte is an analyte that correlates to stress; (2) detecting a deviation in the amount or concentration of the at least one analyte away from a baseline value of amount or concentration of the at least one analyte; (3) measuring a change in the amount or concentration of the at least one analyte in the subject to a baseline value of amount or concentration of the at least one analyte; and (4) determining a stress score based on the measured change of the amount or concentration of the at least one analyte.

Description

METHODS FOR MOLECULAR MONITORING AND REPORTING OF STRESS CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to, and the benefit of the filing date of, U.S. Provisional Application No. 63 / 754,883, filed on February 6, 2025, the disclosure of which is hereby incorporated by reference herein in its entirety.FIELD OF THE INVENTION

[0002] This invention relates generally to high-frequency sample collection and analysis assays, point-of-care testing devices, and continuous biosensors for molecular monitoring and reporting of stress.BACKGROUND OF THE INVENTION

[0003] This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present invention, which are described and / or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of various aspects of the present invention. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art.

[0004] Continuous molecular sensors are emerging onto the market for analytes such as glucose, ketones, lactate, and ethanol by companies such as Abbott, Dexcom, and Medtronic. These sensors provide value to a user or their doctor or caregiver by reporting an accurate concentration of these molecules vs. time. Continuous molecular sensors are in research and commercial development for additional analytes such as hormones like cortisol.

[0005] These types of molecular sensors provide an additional challenge in that absolute concentrations of the analytes are not the most meaningful measure for users, their doctors, or caregivers. This is because any individual's level of a particular analyte may vary over the course of a day, and may vary relative to other individuals. For example, consider a situation where cortisol is the analyte of interest. Interpreting the physiological meaning of cortisol levels as they are detected in a sample from an individual (e.g., sweat, interstitial fluid, etc.) is a considerable challenge because of cortisol's normal variation cycles, and because external events, individual health, and phenotypical characteristics can considerably alter measured cortisol levels. Cortisol typically experiences two daily cycles of variability: the diurnal cortisol level and the cortisol awakening response.

[0006] With reference to FIG. 1 , diurnal cortisol levels are closely tied to an individual's sleep cycle. Cortisol production and corresponding cortisol blood levels increase during the second halfof an extended sleep (as opposed to a nap), peaking in the early morning. Thereafter, diurnal cortisol levels generally decrease during the waking hours, reaching a nadir, or basal cortisol level, a couple of hours after sleep begins. These blood cortisol levels are generally in the range of 140 to 700 nMol / L in the morning, and 80 to 350 nMol / L at midnight (assuming a night sleep cycle). Diurnal cortisol levels may be significantly affected by the various disorders or chronic conditions affecting cortisol levels generally, but may also vary in response to shorter term circumstances, such as physical or emotional stress, pregnancy, hypoglycemia, food or water intake prior to testing, or taking a course of steroids or corticoids, birth control pills or estrogen.

[0007] Cortisol awakening response (“CAR”) is generally more predictable, and hence potentially more meaningful, than diurnal cortisol level. With reference to FIG. 2, CAR is a sharp increase of about 50% over the then-current diurnal cortisol level, and peaks in blood roughly thirty minutes after an individual wakes from a night’s sleep. The CAR is thought to play a role in activating the body's hypothalamic- pituitary-adrenal (HP A) axis to orient the organism and prepare for the day's demands. CAR is specifically linked to the awakening event, and its magnitude is independent of diurnal cortisol variation. Assessment of an individual's CAR profile may therefore indicate that individual's capacity to activate the HPA axis, and in turn to respond effectively to stressors. An individual's typical CAR value is genetically determined, and remains fairly stable day-to-day, absent short-term factors that are known to affect CAR levels.

[0008] An individual's CAR may be increased or decreased by various factors. CAR is increased by waking earlier in the morning, waking in light as opposed to darkness, awaking for work or an athletic contest as opposed to leisure, having lower socioeconomic status or higher short- term stress. CAR may be reduced, i.e., display a flatter profile, in individuals getting poor sleep or sleep with excess environmental noise, individuals experiencing chronic stress, suffering from pain, or suffering from physical overexertion or over-training. An accurate measurement of CAR, therefore, would indicate an individual's short-term response to physical exertion, mental stress, and sleep. Further, because it is less dependent than the diurnal cortisol profile on longer-terms factors such as physical fitness, chronic stress, depression and disease, CAR has emerged as one of the most informative measures of cortisol in humans.

[0009] Additionally, mental or physical stress events during the day and / or general health and wellness can impact cortisol levels in the day such they also fluctuate over time scales of minutes to hours or even days. These fluctuations are often measurable only while they are occurring (e.g. after a rapid increase in cortisol during a stress event, cortisol will decrease again in a healthy individual, thus later hours after the stress event trying to measure the cortisol increase due to stress is not feasible).[00101 Due to fluctuations in analyte (e.g., cortisol) level, such as those discussed above, a reading of absolute level of analyte may not be helpful - because the baseline level of analyte may differ at any given moment over the course of a day. What is needed are devices and methods for continuously monitoring an analyte, such as a stress hormone such as cortisol or adrenocorticotropic hormone (ACTH) or their precursors or metabolites or other correlative molecules, and providing meaningful measures beyond just absolute concentration or absolute concentration vs. time.SUMMARY OF THE INVENTION

[0011] Certain exemplary aspects of the invention are set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of certain forms the invention might take and that these aspects arc not intended to limit the scope of the invention. Indeed, the invention may encompass a variety of aspects that may not be explicitly set forth below.[0012| Many of the drawbacks and limitations stated above can be resolved by creating novel and advanced interplays of chemicals, materials, sensors, electronics, microfluidics, algorithms, computing, software, systems, and other features or designs, in a manner that affordably, effectively, conveniently, intelligently, or reliably brings sensing technology into proximity with biofluid and analytes.

[0013] As described above, absolute concentrations of an analyte or analytes may not be the most meaningful measure for users, their doctors, or caregivers. This is because any individual’s level of a particular analyte may vary over the course of a day, and may vary relative to other individuals. Various aspects of the present invention address these drawbacks by providing methods, apparatus, and systems that measure the amount or concentration of an analyte of interest in a sample from a subject, and compare that amount or concentration to a separate value in order to determine if the measured amount or concentration is indicative of a physiological response (e.g., a response that may correlate to a physiological event experienced by the subject individual). For example, the analyte of interest may be cortisol, and the measurement of cortisol in a subject may be compared to a separate value, wherein the difference between the measured cortisol and separate vale is indicative of a stress response. Nonlimiting examples of a separate value to which the measured analyte could be compared are (1) a baseline or baseline profile established for a population, (2) a baseline or baseline profile established for the particular individual subject being tested, (3) a change of amount or concentration of analyte over time to establish a rate of change in levels of the analyte of interest, and (4) calculation of an area under the curve (AUG) of a plot of cortisol levels following a stress event.[0014| In general, aspects of the present invention are directed to methods and systems of determining a stress score for a subject. The method includes the steps of (1) monitoring the amount or concentration of at least one analyte in a subject, wherein the at least one analyte is an analyte that correlates to stress; (2) detecting a deviation in the amount or concentration of the at least one analyte away from a baseline value of amount or concentration of the at least one analyte; (3) measuring a change in the amount or concentration of the at least one analyte in the subject to a baseline value of amount or concentration of the at least one analyte; and (4) determining a stress score based on the measured change of the amount or concentration of the at least one analyte .BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The objects and advantages of the disclosed invention will be further appreciated in light of the following detailed descriptions and drawings in which:

[0011] FIG. 1 is graph showing an example profile of an individual’s cortisol levels over the course of a 24 hour period.

[0012] FIG. 2 is a graph showing an example profile of an individual’s cortisol awakening response over a three hour period for weekdays and weekends.

[0013] FIG. 3 is a graph of cortisol concentration versus time, showing examples of healthy and unhealthy responses to a single stress event.

[0014] FIG. 4 is a graph of cortisol concentration versus time, showing an example of a response to a plurality of stress events.

[0015] FIG. 5 includes four graphs of diurnal cortisol levels and differing cortisol awakening responses (normal, chronic fatigue, burnout, and chronic stress) obtained from ZRT Laboratory of Beaverton, Oregon (https: / / www.zrtlab.com / landing-pages / diumal-cortisol-curves / ).

[0016] FIG. 6 is a graph representing different cortisol awaking responses for two different groups with differing childhood adversity (with slope score difference between the two groups shown in left insert, and diurnal slope without CAR shown in right insert), based on data obtained from Kuras et al., Blunted Diurnal Cortisol Activity in Healthy Adults with Childhood Adversity, Frontiers in Human Neurosci., Nov. 2017, Vol 11, Article 574, pp. 1-8.

[0017] FIG. 7 is a series of graphs showing relative cortisol levels (as a percentage of a baseline level) for both men and women patients and controls over time for a number of psychiatric disorders [chronic Major Depressive Disorder (cMDD), recurrent Major Depressive Disorder (rMDD), anxiety disorders, and schizophrenia] taken from Zorn et al., Cortisol stress reactivity across psychiatric disorders: A systematic review and meta-analysis , Psychoneuroendocrinology, Vol. 77, March 2017, pp. 25-36.[0018| FIG. 8 is a graph showing cortisol response in two sets of individuals having two different diets: standard protein diets and high protein diets with meals consumed at 8am, 12pm, and 4pm [taken from Slag et al. Meal stimulation of cortisol secretion: A protein induced effect, Metabolism: Clin. And Exp., Vol. 30, Issue 11, Nov. 1981, pp. 1104-1108].

[0019] FIG. 9 is a graph showing the effect of exercise on cortisol response, showing mean cortisol concentration plotted against minutes from wake in individuals pre-exercise intervention and post-exercise intervention [taken from Drogos et al., Aerobic exercise increases cortisol awakening response in older adults, Psychoneuroendocrinology, Vol. 103, May 2019, pp. 241-248],

[0020] FIG. 10 is a graph showing the effect of sleep deprivation on cortisol response, where ‘TSD’ is total sleep deprivation [taken from Vargas I, Lopez-Duran N. The cortisol awakening response after sleep deprivation: Is the cortisol awakening response a “response ” to awakening or a circadian process?, Journal of Health Psychology. 2020;25(7):900-912],

[0021] FIG. 11 is a series of graphs showing the relationship between subjective and other physiological measures of stress (heart rate, visual analogue scale) and cortisol responses [taken from Hellhammer, J. and Schubert, M., The physiological response to Trier Social Stress Test relates to subjective measures of stress during but not before or after the test, Vol. 7, Issue 1, Jan.2012. pp. 119-124],

[0022] FIGS. 12A-D include a series of graphs for hormonal dysregulation after prolonged HPA axis activation showing not only cortisol but a second molecule ACTH which has correlative value with stress and / or cortisol [taken from Karin, O., et al., Hormonal dysregulation after prolonged HPA axis activation can be explained by changes of adrenal and corticotroph masses, Molecular Systems Biology (2020) 16: e9510],

[0023] FIG. 13 is a schematic of an example of a sensor device including a plurality of microneedles.

[0024] FIG. 14 is a schematic of another example of a sensor device including a single microneedle or needle or strip.

[0025] FIG. 15 is a schematic showing a working electrode for an example of a sensor device having a plurality of aptamers bound thereto, and illustrating examples of aptamer conformations with and without analyte bound to the aptamer.

[0026] FIG. 16 is a diagram of an example environment in which systems and / or methods described herein may be implemented.

[0027] FIG. 17 is a schematic showing an example wearable monitoring device according to the present invention.DEFINITIONS

[0028] As used herein, “continuous sensing” with a “continuous sensor” means a sensor that changes in response to changing concentration of at least one solute in a solution such as an analyte. Similarly, as used herein, “continuous monitoring” means the capability of a device to provide multiple measurements of an analyte over time.

[0029] As used herein, the term “about,” when referring to a value or to an amount of mass, weight, time, volume, pl I, size, concentration or percentage is meant to encompass variations of ±20% in some embodiments, ±10% in some embodiments, ±5% in some embodiments, ±1% in some embodiments, ±0.5% in some embodiments, and ±0.1% in some embodiments from the specified amount, as such variations are appropriate to perform the disclosed method.

[0030] As used herein, the term “electrode” means any material that is electrically conductive such as gold, platinum, nickel, silicon, conductive liquid infused materials such as ionic liquids, PEDOTPSS, conductive oxides, carbon, boron-doped diamond, nanotubes or nanowire meshes, or other suitable electrically conducting materials.

[0031] As used herein, the term “blocking layer” means a homogeneous or heterogeneous layer of material or of one or more types of molecules on an electrode which reduce electrochemical background current and / or current due to electrochemical interference, and which may promote proper freedom of movement for the aptamer which is required for creating a measurable response to analyte concentration.

[0032] As used herein, the term “antifouling layer” means a homogeneous or heterogeneous layer of material or of one or more types of molecules on a surface which reduces fouling on a surface compared to if such an antifouling layer was not utilized.

[0033] As used herein, the term “aptamer” means a molecule that undergoes a conformation or binding change as an analyte binds to the molecule, and which satisfies the general operating principles of the sensing method as described herein. Such molecules are, e.g., natural or modified DNA, RNA, or XNA oligonucleotide sequences, spiegelmers, peptide aptamers, and affimers and other affinity-based probes. Modifications may include substituting unnatural nucleic acid bases for natural bases within the aptamer sequence, replacing natural sequences with unnatural sequences, or other suitable modifications that improve sensor function, but which behave analogous to traditional aptamers. Two or more aptamers bound together can also be referred to as an aptamer (i.e., not separated in solution). Aptamers can have molecular weights of at least 1 kDa, at leastlO kDa, or at least 100 kDa.

[0034] As used herein, the term “redox tag” or “redox molecule” means any species such as small or large molecules with a redox active portion that when brought adjacent to an electrodecan reversibly transfer at least one electron with the electrode. Redox tag or molecule examples include methylene blue, ferrocene, quinones, or other suitable species that satisfy the definition of a redox tag or molecule. In some cases, a redox tag or molecule is referred to as a redox mediator. Redox tags or molecules may also exchange electrons or change in behavior when brought into proximity with other redox tags or molecules. Exogenous redox molecules are those added to a device, e.g., they are not endogeneous and provided by the sample fluid to be tested.

[0035] As used herein, the term “change in electron transfer” means a redox molecule whose electron transfer with an electrode has changed in a measurable manner. This change in electron transfer can, for example, originate from availability for electron transfer, distance from an electrode, diffusion rate to or from an electrode, a shift or increase or decrease in electrochemical activity of the redox molecule, or any other embodiment as taught herein that results in a measurable change in electron transfer between the redox molecule and the electrode.

[0036] As used herein, the term “sensing monolayer” means at least a plurality of aptamers on a working electrode, which may also include a plurality of molecules or mixtures of molecules that form a blocking layer or an anti-fouling layer.

[0037] As used herein, the term “analyte” means any solute in a solution or fluid which can be measured using a sensor. Analytes can be small molecules, proteins, peptides, electrolytes, acids, bases, antibodies, molecules with small molecules bound to them, DNA, RNA, drugs, chemicals, pollutants, or other solutes in a solution or fluid.

[0038] As used herein, a “stress monitoring device” comprises at least one continuous sensor for at least one analyte in-side the body that represents stress, including analytes such as cortisol or adrenocorticotropic hormone (ACTH), norepinephrine, or their precursors or metabolites or other correlative molecules that represent stress.

[0039] As used herein, a “stress score” is any representation of stress that can be made using embodiments of the present invention. For example, a stress score could be an absolute concentration measure of cortisol, or an area under the curve for a measures stress event, or a slope of rise or fall in a molecule associated with stress, or number of stress events measure in a day, or a cortisol awaking response measure, or a simple red light / yellow light / green light representation of a stress event or how the stress event is being handled by an individual, or an audible sound or vibration for a wearable device, or a graph, or a table, or any other suitable method or representation of stress experienced by an individual. A stress score may also include a group stress score collected from a plurality of individuals.DETAILED DESCRIPTION OF THE INVENTION

[0040] One or more specific embodiments of the present invention will be described below. In an effort to provide a concise description of these embodiments, all features of an actual implementation may not be described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers’ specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure.

[0041] Certain embodiments of the disclosed invention show sensors as simple individual elements. It is understood that many sensors require two or more electrodes, reference electrodes, or additional supporting technology or features which are not captured in the description herein. Sensors can be in duplicate, triplicate, or more, to provide improved data and readings. Sensors may provide continuous or discrete data and / or readings. Certain embodiments of the disclosed invention show sub-components of what would be sensing devices with more sub-components needed for use of the device in various applications, which are known (e.g., a reference or counter electrode, a battery, antenna, adhesive, an electronic display), and for purposes of brevity and focus on inventive aspects, such components may not be explicitly shown in the diagrams or described in the embodiments of the disclosed invention. All ranges of parameters disclosed herein include the endpoints of the ranges.

[0042] As described above, absolute concentrations of an analyte or analytes may not be the most meaningful measure for users, their doctors, or caregivers. This is because any individual’s level of a particular analyte may vary over the course of a day, and may vary relative to other individuals. Various aspects of the present invention address these drawbacks by providing methods, apparatus, and systems that measure the amount or concentration of an analyte of interest in a sample from a subject, and compare that amount or concentration to a separate value in order to determine if the measured amount or concentration is indicative of a physiological response (e.g., a response that may correlate to a physiological event experienced by the subject individual). For example, the analyte of interest may be cortisol, and the measurement of cortisol in a subject may be compared to a separate value, wherein the difference between the measured cortisol and separate vale is indicative of a stress response. Nonlimiting examples of a separate value to which the measured analyte could be compared are (1) a baseline or baseline profile established for a population, (2) a baseline or baseline profile established for the particular individual subject being tested, (3) a change of amount or concentration of analyte over time to establish a rate of changein levels of the analyte of interest, and (4) calculation of an area under the curve (AUC) of a plot of cortisol levels following a stress event.

[0043] In general, aspects of the present invention are directed to methods and systems of determining a stress score for a subject. The method includes the steps of (1) monitoring the amount or concentration of at least one analyte in a subject, wherein the at least one analyte is an analyte that correlates to stress; (2) detecting a deviation in the amount or concentration of the at least one analyte away from a baseline value of amount or concentration of the at least one analyte; (3) measuring a change in the amount or concentration of the at least one analyte in the subject to a baseline value of amount or concentration of the at least one analyte; and (4) determining a stress score based on the measured change of the amount or concentration of the at least one analyte.

[0044] With references to embodiments of the present invention the following examples first teach embodiments of the present invention at a high level from monitoring data obtained from at least one molecule in the body that responds to stress. Terms such as “determined” may mean using a mathematical function, algorithm, or other automated software or calculations to determine a data a value or measurement from continuous monitoring data. Terms such as “reported” may mean using a electronics, computing, software, antennas, receivers, or other suitable methods to provide the determined data or value or measurement data to the user, to storage, or to another location such that later it may be utilized as needed.

[0045] Turning now to FIG. 3, a graph 100 of cortisol concentration versus time is shown, including examples of healthy and unhealthy responses to a single stress event. Principles and embodiments of the present invention will be described with respect to using such information to monitor and detect changes in analyte(s) in a subject in order to provide a stress score. In one embodiment, at least one stress score can be reported based on monitoring at least one molecule in a subject that correlates with a single physiological stress response based on a single physiological event.

[0046] The graph 100 provides examples of cortisol responses for a healthy stress response 110 versus an unhealthy stress response 112. Healthy stress responses generally exhibit rapid increase in cortisol from a baseline level 110a to a peak concentration 1 lOd (the peak concentration llOd can be determined by looking for zero slope in a curve fit to data points obtained by measuring analyte in a subject followed by a decrease back to or near to baseline 110g). A magnitude of stress response can then be determined (and may be reported) using a simple linear baseline modeling illustrated by line 120 and looking at the change in cortisol concentration over that baseline at point 1 lOd, or other suitable methods. The baseline is established by a slope line extending from a data point of analyte amount or concentration (cortisol in FIG. 3) just prior to the increase of analyte amount or concentration. And thus, the magnitude of stress response (for FIG.3) would be the change between a baseline value (which is the amount or concentration of analyte at the baseline) and the peak amount or concentration of analyte in the curve. In the exemplary graph 100 of FIG. 3, this change, and thus the magnitude of stress response, is approximately 10 nM of unbound cortisol in the blood.

[0047] Still referring to FIG. 3, an increase of analyte (cortisol) and stress response can be determined (and may be reported) through a linear fit between points on the curve that are less than the total distance between baseline and peak amounts / concentrations. In other words, the measured increase to be used may be less than the magnitude of stress response (i.e., may only represent a portion of the magnitude of stress response). For example, in FIG. 3, the increase may be measured by a line between points 110b and 110c. The portion of the magnitude of stress response that can be used maybe 10% to 90%, 25% to 75%, 40% to 60% or other percentages of the total magnitude of stress response.

[0048] Similar to an increase in analyte (cortisol), a decrease of analyte (cortisol) and stress response can be determined (and may be reported) through a linear fit between points on the curve that are on the down slope of the curve. For example, in FIG. 3, the decrease may be measured by a line between points IlOe and IlOf, which can 10% to 90%, 25% to 75%, 40% to 60% or other percentages of the total magnitude of stress response. The analyte increase or decrease measures may be reported in units such as % change of cortisol over baseline vs. time (% / s) or change in cortisol concentration vs. time (nM / time) or other suitable units.

[0049] Additionally, and still referring to FIG. 3, a total stress value may be determined and reported for the stress event shown in plot 100 by integrating the total area under the curve between 110a to 110g vs. baseline 120 in units of nM-s or other suitable methods or units. For example, if the area under the curve 110 was interpreted as a triangle connected by measures at 50 min, 95 min, and 120 min, the area under the curve would be one half base times height or approximately Vi * (95 min - 55 min) * 14 nM = 280 nM-min. Curve 112 is comparatively a generally a less healthy stress response because baseline cortisol concentrations 122 are higher, and / or because the peak stress response is smaller, and / or because the return to baseline (fall in cortisol and stress) is slower. The present invention may therefore assign a simple score to a stress response, for example in terms of fall in cortisol or stress in nM / s or % / s, area under the curve nM-s, or other suitable measures. Using the above techniques based on percentages the present invention is able to report at least one measure of stress without knowing the absolute concentration of at least one molecule such as cortisol (e.g. the vertical axis in plot 100 could be unit-less) which is beneficial for sensors that have difficulty maintaining calibration over time to absolute cortisol or molecule concentration in the body (e.g. like a glucose monitor that could only report percentage changes in glucose like the earliest commercial glucose monitors did in hospital use cases).[00501 In making determinations, such as those described above, and below, one may need to know whether a particular reading is at baseline. In one embodiment, this may be accomplished via a change in cortisol concentration vs. time that is at least one of <1%, <5%, <10%, <30%, or <50% change in concentration for at least 15 minutes."

[0051] Turning now to FIG. 4, another graph 200 of cortisol concentration versus time is shown. However, this graph shows an example of how the methods, systems, and apparatus described herein can measure (and may report) at least one stress score based on a plurality of physiological stress responses and monitoring of at least one molecule in the body that correlates with those stress responses based on multiple physiological events. The stress score can be for example half of the area of the curve, which can be measured using area defined between lines 220a and portion of curve 210 between time points 210a and 210b, which for example could be calculated by mathematical integration to obtain the area under the curve, where points 210a and 210b are determined by a rapid change in slope followed by zero slope, respectively. Moving average or other statistical methods may be applied to a continuous measurement such as curve 210 to aid determination of a stress score that can then be reported as it is being determined or after it is being determined.

[0052] Curve 210 can also be used to determine a stress score for a second (or any number of) physiological events, such as second event that is shown in graph 200 as occurring between time points 210c and 210d (such event could be the eating of a meal, or a stress event, or other physiological event). Methods for determining such stress core or scores could be those taught for FIG. 3 (described above). Curve 210 also exhibits a potential double physiological event from hours 9 to 10, and a physiological event near hour 12 which could indicate the eating of a meal. The double event could be determined by recognizing the feature of two distinct peaks between hours 9 and 10, or the event could be determined as a single event depending on software, moving averaging, thresholding, or other methods used to measure or analyze curve 210.

[0053] In a situation where multiple physiological events (potential stress events) occur over the course of the time period be measured, there may be multiple ways of measuring or reporting a stress score. For example, certain embodiments may include a stress score that is total area under the curve for multiple events (e.g. all events, or a subset of events in a given day). Other embodiments of the present invention may include a stress score that is the total number of events per day. Additionally, as noted above, there may be events that cause a change in amount or concentration of an analyte (such as cortisol) but which may not be a true stress event (such as the eating of a meal). Thus, certain embodiments may allow for a determination - based on measured data - as to which events are to be considered stress events for purposes of the stress score, and which are not. For example, an event may be determined to be a stress event anytime levels of(e.g., cortisol or another analyte) increases by >5% in less than 30 minutes, or increases by >5% in less than 30 minutes and is sustained over that >5% threshold over a period of at least 20 minutes. Other embodiments may include a stress score that is the total number of events per day, calculated for example by a rising threshold that is at least one of a >5%, >10%, >20%, >40% increase in cortisol or another analyte that is correlated to stress (or is correlated to another molecule that itself is correlated to stress).

[0054] Next, as noted above, determining a stress score by the methods of the present invention includes a comparison of a measured increase in analyte amount or concentration to a baseline value that allows one to calculate a stress score (and / or to identify stress events, or a response to stress events) based on magnitude of change from the baseline value, or rate of change from baseline value, or time a change is sustained over the baseline value, or rate of return to or near baseline value, or some combination of one, or more, or all of these factors.

[0055] In various embodiments of the present invention, then, there are multiple ways that the baseline value can be calculated, determined, or otherwise identified. Each of these methods generally fall into one of two overarching categories of baseline values: (1) those that are calculated based on the subject being monitored himself or herself, or (2) baselines that are generated from data obtained from an individual or populations of individuals external to the particular individual being monitored.

[0056] In the first category (baseline values generated based on data from the individual being monitored) are baselines such as those described above with respect to FIG. 3. In that embodiment, the baseline may be established by a zero slope line extending from a data point of analyte amount or concentration (cortisol in FIG. 3) just prior to the increase of analyte amount or concentration (in FIG. 3, this would be a line extending from point 110a). And a magnitude of stress response (for FIG. 3) would then be the change between a baseline value (which in the amount or concentration of analyte at the baseline) and the peak amount or concentration of analyte in the curve.

[0057] Another method for providing a baseline based on data generated by the individual themselves, would include the generation of a baseline profile individual to the subject being monitored. For example, as described above in the Background section, cortisol is an analyte having a concentration that fluctuates over the course of any given day. As noted above, cortisol typically experiences two daily cycles of variability: the diurnal cortisol level and the cortisol awakening response.

[0058] In that regard, diurnal cortisol levels are closely tied to an individual's sleep cycle. Cortisol production and corresponding cortisol blood levels increase during the second half of an extended sleep (as opposed to a nap), peaking in the early morning. Thereafter, diurnal cortisollevels generally decrease during the waking hours, reaching a nadir, or basal cortisol level, a couple of hours after sleep begins. Diurnal cortisol levels may be significantly affected by the various disorders or chronic conditions affecting cortisol levels generally, but may also vary in response to shorter term circumstances, such as physical or emotional stress, pregnancy, hypoglycemia, food or water intake prior to testing, or taking a course of steroids or corticoids, birth control pills or estrogen.

[0059] Cortisol awakening response is generally more predictable, and hence potentially more meaningful, than diurnal cortisol level. As described above, CAR is a sharp increase of about 50% over the then-current diurnal cortisol level, and peaks in blood roughly thirty minutes after an individual wakes from a night's sleep. An individual’s typical CAR value is genetically determined, and remains fairly stable day-to-day, absent short-term factors that are known to affect CAR levels (for example, CAR response is increased by waking earlier in the morning, waking in light as opposed to darkness, awaking for work or an athletic contest as opposed to leisure, having lower socioeconomic status or higher short- term stress; and CAR may be reduced, i.e., display a flatter profile, in individuals getting poor sleep or sleep with excess environmental noise, individuals experiencing chronic stress, suffering from pain, or suffering from physical overexertion or over-training).

[0060] Additionally, the amount and or concentration of cortisol, and the amount by which it fluctuates, may vary from individual to individual.

[0061] And so, a baseline profile for the subject being monitored may be created via use of the device being used to calculate the stress score via the methods of the present invention, or via use of another device. This can be done by monitoring the levels of the analyte of interest for a period of time sufficient to create a useful profile. For example, the individual may be monitored for a period of one day, multiple days, one week, multiple weeks, one month, or multiple months in order to create a baseline profile of the analyte in the individual. Such monitoring to create a profile may be performed using a device worn by the individual that periodically records the amount or concentration of analyte in the individual. As mentioned, this may be recorded over a period of time sufficient to develop a baseline analyte profile for the individual.

[0062] As noted above, in another category, the baseline profile may be created from an individual or population of individuals that do not include the subject being monitored. For example, in some embodiments, the baseline profile may be estimated based on relevant information about the individual, such as the person's age, weight, sleep habits, physical fitness, stress level, medications, etc. The individual's information may then be compared to aggregated data about other individuals in a database, and a baseline profile from a comparable individual or population of individuals would be selected. Alternatively (or additionally) an external baselineprofile may selected from an individual or population of individuals who share at least one characteristic with the subject (from biological characteristics, like shared race or sex, or external characteristics, like shared profession or socioeconomic class).

[0063] Once a profile is created or selected, that would be used as the baseline profile for comparing any monitored amount or concentration of analyte. As analyte (such as cortisol) fluctuates over the course of the day, one would monitor amounts or concentrations of the analyte compared to the amounts or concentrations shown on the profile at the same time of day as the monitoring occurs (e.g., if a reading of analyte is taken at 10:00am, that result would be compared to the amount or concentration shown on the baseline profile at 10:00am).

[0064] As described above, a use of the method of the present invention is to develop stress scores by monitoring at least one analyte that correlates to stress. As discussed previously, cortisol is one such molecule. Under normal circumstances, cortisol prepares the organism to respond to external circumstances by stimulating glucose production, increasing blood pressure, and reducing swelling and pain. Cortisol's role in an individual's risk-taking behavior is related to this stress management function. Under exposure to short-duration stressors, cortisol prepares the organism to respond to external circumstances by activating the body's sympathetic nervous system, the so-called "fight or flight" mode. Under exposure to long-term or chronic stress, cortisol levels can remain elevated for extended periods. Because of its close relationship to alert and resting states, cortisol variations due to stress and other causes can also disrupt sleep cycles, and in turn, sleep deprivation or sleep apnea may increase blood cortisol levels. Cortisol's normal function in metabolism is to counteract insulin and promote insulin resistance, thereby creating a temporary hyperglycemic state. If this hyperglycemic state remains in place due to chronically high cortisol levels, diabetes can result. Cortisol normally acts to regulate bone and connective tissue formation by inhibiting collagen production, transporting potassium out of cells and inhibiting the uptake of calcium by the small intestine. In excessive amounts, therefore, cortisol promotes osteoporosis and hypokalemia. Cortisol’s role in maintaining the body's water / electrolyte balance is to promote sweating, diuresis, sodium retention, and potassium excretion. Along with its vaso-constrictive effects, excessive cortisol may therefore lead to hypernatremia, increased high blood pressure and cardiovascular disease. Cortisol is also instrumental in human immune response, acting to suppress cell-mediated immune response, thereby reducing inflammation, and activating humoral immunity, which promotes the mobilization of antibodies. In excess amounts, cortisol can degrade effective immune response, and slow wound healing. Cortisol in excess also may impair learning and memory recall.

[0030] Excess cortisol levels may be caused by chronic stress levels, pituitary or adrenal disorder, kidney or liver disease, excessive use of cortisol-like drugs, and tumors. Conversely, deficientcortisol levels may be caused by pituitary or adrenal disorder, steroid use, autoimmune disorders, and tumors. Deficient cortisol may manifest in hypoglycemia, dehydration, low blood pressure, and other conditions

[0065] And so, the at least one analyte being monitored may be cortisol. By so doing, and by being able to determine and report a stress score or scores based on changing levels of cortisol, one may be able to determine if an individual is at risk of myriad health issues, including those described above.

[0066] Other analytes that may be monitored include, but are not limited to, adrenocorticotropic hormone, Na+, C1-, K+, glucose, norepinephrine, epinephrine, dopamine, serotonin, estrogen, and testosterone. Analytes may also include precursors and metabolites of molecules such as those recited herein.

[0067] Further, the fact that an analyte like cortisol is known to fluctuate in individuals for many various reasons shows why it is useful to have a method that can compensate for these fluctuations either via operation of an internal baseline value, or via the use of a profile that takes fluctuations into account. For example, the data shown in FIGS. 5-12D show many examples of cortisol responses to factors (both internal and external) that may be experienced by an individual. With reference to FIG. 5, data is show representing different cortisol awaking responses, and the data obtained from: https: / / www.zrtlab.com / landing-pages / diumal-cortisol-curves / . In particular, there are four graphs of diurnal cortisol levels and differing cortisol awakening responses (normal, chronic fatigue, burnout, and chronic stress) obtained from ZRT Laboratory of Beaverton, Oregon. As detailed by ZRT Laboratory, the curves in these graphs are each derived from taking four daily saliva collections - typically upon waking, before lunch, before dinner, and before bed - and charting them on a 24-hour graph to plot cortisol levels throughout the day. The first graph (Diurnal Cortisol - Normal) shows a normal rise in cortisol production within 30 minutes of waking for the day, and cortisol levels then drop throughout the remainder of the day, reaching their lowest point at bedtime. However, individuals with certain issues (such as adrenal gland dysfunction) will exhibit irregularities in their curves. These are shown in the remaining three graphs of FIG. 3. The Diurnal Cortisol - Chronic Stress graph shows higher than normal cortisol production throughout the day (such as may result from prolonged stress). The Diurnal Cortisol - Chronic Fatigue graph shows elevated cortisol levels in the morning, which then drop off rapidly during the day. And the Diurnal Cortisol - Burnout graph show low overall cortisol levels throughout the day. Thus, the Cortisol Awakening Response - also called CAR - reveals more detailed clues that help in assessing adrenal hormone / HPA Axis dysfunction. This testing is often useful for cases of PTSD, major depression, chronic fatigue syndrome and other severe stress conditions. During a normalcortisol awakening response, adrenal hormone levels typically may increase 50% in the first 30 minutes after waking for the day and then begin to progressively drop through the afternoon and evening. To capture this response, at least three - rather than one - cortisol measures are needed to properly chart the diurnal cortisol curve.

[0068] With reference to FIG. 6, data is shown representing different cortisol awaking responses for two different groups with differing childhood adversity, and the data obtained from: Kuras et al., Blunted Diurnal Cortisol Activity in Healthy Adults with Childhood Adversity, Frontiers in Human Neuroscience, Nov. 2017, Vol. 11, Article 574, pgs. 1-8. In the data shown in FIG. 6 (using an ANCOVA), Kuras et al found that, controlling for age, sex, and body fat, those with a history of childhood adversity had a less negative slope and, in testing for group differences within each subscale, Kuras et al found (1) a significant difference in diurnal cortisol slope in those with a history of childhood physical neglect, and (2) a nonsignificant, but trend-level difference in those with emotional abuse, but not any other subscale [see Kuras et al., p. 5], Kuras used a repeated measures ANCOVA, including average cortisol at each of the saliva collection time points. Controlling for age, sex, and body fat, the repeated measures ANCOVA revealed significant differences between those with and without childhood adversity and diurnal cortisol slope [see Kuras et al., p. 5], Repeated measures ANCOVA revealed a significant difference in diurnal cortisol slope between those with and without physical neglect, but did not find this to be the case with any of the other subscales; it can be observed that those with low-to-moderate childhood adversity had a less steep, or blunted diurnal cortisol slope [see Kuras et al., p. 5].

[0069] With reference to FIG. 7, data is shown representing different cortisol responses vs. phsychiatric disorders, and the data is shown as a series of graphs showing relative cortisol levels (as a percentage of a baseline level) for both men and women patients and controls over time for a number of psychiatric disorders (cMDD, rMDD, anxiety disorders, and schizophrenia) taken from Zorn et al., Cortisol stress reactivity across psychiatric disorders: A systematic review and meta-analy is, Psychoneuroendocrinology, Vol. 77, March 2017, pp. 25-36.

[0070] With reference to FIG. 8, data is shown representing cortisol response to meals and the types of meals, and the data is shown as a graph showing cortisol response in two sets of individuals having two different diets: standard protein diets and high protein diets with meals consumed at 8am, 12pm, and 4pm [taken from Slag ct al. Meal stimulation of cortisol secretion: A protein induced effect, Metabolism: Clin. And Exp., Vol. 30, Issue 11, Nov. 1981, pp. 1104- 1108],

[0071] With reference to FIG. 9, data is shown representing the effect of exercise on cortisol response, and the data is shown as a graph showing mean cortisol concentration plotted against minutes from wake in individuals pre-exercise intervention and post-exercise intervention [takenfrom Drogos et al., Aerobic exercise increases cortisol awakening response in older adults, Psychoneuroendocrinology, Vol. 103, May 2019, pp. 241-248], with the data from the Drogos study providing support for the conclusion that aerobic exercise is a potential mechanism for improving HPA axis function in otherwise healthy older adults.

[0072] With reference to FIG. 10, data is shown representing the effect of sleep deprivation on cortisol response, where ‘TSD’ is total sleep deprivation, and the data obtained from: Vargas I, Lopez-Duran N. The cortisol awakening response after sleep deprivation: Is the cortisol awakening response a “response” to awakening or a circadian process?, Journal of Health Psychology. 2020;25(7):900-912. More specifically, the study tested whether the CAR is dependent upon the transition from sleep, or whether it is independent of awakening; participants in the sleep condition demonstrated traditional CAR, where are sleep deprived participants showing no significant increases in morning cortisol (consistent with the idea that CAR may only be activated when awakening occurs or is anticipated [taken from Vargas I, Lopez-Duran N. The cortisol awakening response after sleep deprivation: Is the cortisol awakening response a “response ” to awakening or a circadian process?, Journal of Health Psychology. 2020;25(7):900-912],

[0073] With reference to FIG. 11, data is shown for the relationship between subjective measures of stress and other physiological measures and cortisol responses to stress, and the data is shown as a series of graphs showing the relationship between subjective and other physiological measures of stress (heart rate, visual analogue scale) and cortisol responses [taken from Hellhammer, J., Schubert, M., The physiological response to Trier Social Stress Test relates to subjective measures of stress during but not before or after the test, Vol. 7, Issue 1, Jan. 2012, pp.119-124], This study used the Trier Social Stress Test (TSST) to induce stress in human subjects, which leads to a physiological response of the hypothalamus-pituitary-adrenal axis (HPAA) and the autonomous nervous system (ANS) with common biomarkers being cortisol levels and heart rate (and the test also triggers a psychological response such as an increase in perceived stress, anxiety and emotional insecurity). The study assessed perceived stress, anxiety and emotional insecurity before, during and after the test using visual analogue scales - and cortisol levels and heart rates were assessed as well. Data shown in FIG. 11, demonstrating that stress perception, anxiety and emotional insecurity were significantly higher during the TSST as compared to post-TSST ratings, and a covariance of the psychological stress response during the TSST and the physiological stress responses (cortisol and heart rate) for stress perception though the explained variance was small (this observation was not found for pre- and post-TSST ratings).

[0074] With reference to FIGS. 12A-D, data is shown for hormonal dysregulation after prolonged HPA axis activation showing not only cortisol but a second molecule ACTH which hascorrelative value with stress and / or cortisol, and the data is shown as a series of graphs Itaken from Karin, O., et al., Hormonal dysregulation after prolonged HPA axis activation can be explained by changes of adrenal and corticotroph masses, Molecular Systems Biology (2020) 16: e9510]. These graphs show that, after prolonged stress, ACTH response is blunted for weeks after cortisol response normalizes. More specifically, the graphs of FIG. 12A show schema of the classic HPA axis, and that CRH causes the secretion of ACTH and cortisol. The graphs of FIG. 12B show that, in the CRH test, the secretion of these hormones is measured after CRH administration. The graphs of FIG. 12C show that patients suffering from major depressive disorder (MDD) show a blunted ACTH response to CRH (black line, N = 10), compared with controls (gray line, N = 10) — data from (von Bardeleben et al, 1988), shown are mean ± SEM. And the graphs of FIG. 12D show that patients suffering from anorexia and admitted to treatment show a blunted ACTH response and hypercortisolemia, which resolves within 6-24 months after weight normalization — data from Gold et al (1986a). However, 3-4 weeks after weight normalization, cortisol dynamics are normal whereas ACTH dynamics are blunted. Pregnancy is associated with elevated cortisol levels due to CRH secretion by the placenta. 3 weeks after delivery, cortisol levels and dynamics return to normal, whereas ACTH dynamics are blunted — data from Magiakou et al (1996). After 12 weeks, ACTH dynamics normalize as well. Individuals recovering from alcohol abuse show hypercortisolemia and blunted ACTH response after admission — data from von Bardeleben et al (1989). After 2-6 weeks, these individuals show normal cortisol dynamics, but blunted ACTH responses persist. In all panels, control patient data are denoted by thin gray line (Anorexia: N = 13. Pregnancy: N was unspecified. Alcohol abuse disorder: N = 11), and case data by a thicker black line (Anorexia: left panel, N = 9, center panel, N = 5, right panel, N = 6. Pregnancy: N = 17. Alcohol abuse disorder: N = 20). Shown are mean ± SEM for all panels. [See Karin et all, 2020]

[0075] Various devices may be used for monitoring the analyte in the subject in order to determine a stress score for the subject. Below are examples of embodiments and configurations that such devices (or systems including such devices) may take. And so, they are not intended to be limiting on the particular structure or type of device to be used (or components of the device or of a system including a device).

[0076] With reference to FIG. 13, an example of a sensor device 100 as placed initially in a sample fluid 130 such as dermal interstitial fluid of skin 12 is shown. The device includes a microneedle support 110 that can be made of metal, semiconductor, or plastic for example, and at least one working electrode 120 such as gold, carbon, platinum, or other suitable electrode material. Counter and references electrodes are not shown and may be included on feature 110. The device may also comprise electronics for reading the sensor 120 and communicating data to a user or smart phone (not shown). Sensor 120 may also be an aptamer sensor comprising atleast one blocking layer a plurality of molecules such as mercaptohexanol that are thiol bonded to the electrode, and at least one aptamer 124 that is responsive to binding to an analyte and which contains a redox tag such as methylene blue. The working electrode(s) 120 are typically for the same analyte, such as cortisol for example, and are embedded through the epidermis 12a and in the dermis 12b, and potentially into the hypodermis 12c. The depth of penetration into skin 12 by the device 100 is typically 100’s of pm (often 500-700 pm) for microneedle arrays as shown in FIG. 13. With further reference to FIG. 13, the device 100 may also use hollow microneedles and a sensor which is inside the hollow microneedles or which is outside the body (not shown), as taught by, Friedel M, Werbovetz B, Drexelius A, Watkins Z, Bali A, Plaxco KW, Heikenfeld J. Continuous molecular monitoring of human dermal interstitial fluid with microneedle-enabled electrochemical aptamer sensors. Lab Chip. 2023 Jul 12;23(14):3289-3299. doi: 10.1039 / d31c00210a. PMID: 37395135.

[0077] With reference to FIG. 14, where like numerals refer to like features, a conventional prior art sensor device 200 includes a single needle element with a working electrode 220 embedded in the hypodermis 12c. This arrangement is commonly employed in continuous glucose monitors. The working electrode 220 is embedded deeply enough with adequate penetrating depth (e.g. ~5 mm) such that for most users of the device 200 the working electrode 220 will always be securely in the hypodermis 12c during use of the device 200. Hence, the prior art has at least one approach where the working electrode depth of penetration into tissue is predetermined.

[0078] With reference to FIG. 15, where like numerals refer to like features, for embodiments of the present invention additional details are provided on example working electrode construction and operation (with particular reference to a device that is an affinitybased sensor - such as an aptamer sensor). The working electrode 320 is comprised of an electrode material 358 such as gold. The gold is then incubated with aptamers 350 via thiol attachment to the gold electrode, and the aptamer includes a redox tag such as methylene blue 352. In between the aptamers the gold is further incubated with a protective monolayer 356 such as mercaptohexanol, mercaptoocotanol, or other suitable chemistry. A protective membrane (not shown) such as polybetaine or other suitable material may be added to prevent fouling of the monolayer surface. The working electrode 320 may be preserved in a preservative such as trehalose to enable dry storage. In a non-limiting but specific example, binding of aptamer 350 to target 354 causes a shape confirmation change which alters the distance bewteen the redox tag 352 and the electrode 358 resulting in a change in electron transfer (a change in electrical current) - such as, for example, an increase in electrical current. As concentration of target 354 increases, more binding of target 354 to aptamer 350 occurs, and more electron transfer occurs(more measurable electrical current). As concentration of target 354 decreases, conversely electrical current decreases. Devices as taught herein can insert the supports carrying the working electrodes into skin using one or methods such as those commonly deployed for the insertion of glucose sensors needles for continuous glucose monitors (such as a slotted insertion guide or other methods). An example of device fabrication and testing is as follows:

[0079] In such examples of devices, electrochemical measurements can be performed with a miniaturized potentiostat or performed by a benchtop CHI 620E potentiostat (Austin, Texas) connected to a 64-channel multiplexer in a standard three-electrode system with aptamer / alkylthiolate functionalized electrodes serving as working electrodes. The counter and reference electrodes can be inserted into the skin using a platinum counter electrode, and a Ag / AgCl reference electrode, or alternately the counter and reference electrodes can be a large gel-electrode-pad electrode on the surface of the skin as taught in PCT / US21 / 51972 - ‘APTAMER SENSORS WITH REFERENCE AND COUNTER VOLTAGE CONTROL’. Cyclic voltammograms can be recorded in a window from -0.1 V to -0.5 V at a scan rate of 100 mV / s. Square-wave voltammetry can be performed in a potential window from -0.1 V to -0.5 V at 25 mV amplitude at the optimal frequency of measurement for each aptamer.

[0080] Devices used in the method of the present invention are not limited to specific examples as taught herein.

[0081] With a basic understanding of the above-described sensors in place, reference is now drawn to FIG. 16, which illustrates a general diagram of a computer system 800 according to various aspects of the present disclosure. Like numerals in FIG. 16 not necessarily refer to like features like that in the other figures. The computer system 800 comprises a plurality of hardware processing devices (designated generally by the reference 802) that are linked together by one or more network(s) (designated generally by the reference 804).

[0082] The network(s) 804 provides communications links between the various processing devices 802 and may be supported by networking components 806 that interconnect the processing devices 802, including for example, routers, hubs, firewalls, network interfaces, wired or wireless communications links and corresponding interconnections, cellular stations and corresponding cellular conversion technologies (e.g., to convert between cellular and TCP / IP, etc.). Moreover, the nctwork(s) 804 may comprise connections using one or more intranets, extranets, local area networks (LAN), wide area networks (WAN), wireless networks (Wi-Fi), the Internet, including the world wide web, cellular and / or other arrangements for enabling communication between the processing devices 802, in either real time or otherwise (e.g., via time shifting, batch processing, etc.).[00831 A processing device 802 can be any device capable of communicating with another processing device 802, e.g., via Bluetooth, Ultrawide band, near field communication (NFC), via one or more radio frequencies (RF) or via any other form of wired or wireless communication, over the network 804, or combinations thereof.

[0084] Some examples of processing devices 802 are cellular devices (including cellular mobile telephones (i.e., smartphones)), tablet computers, netbook computers, notebook computers, personal computers, servers, cloud devices, edge devices, etc.

[0085] Also, in certain contexts and roles, a processing device 802 is intended to be a wearable monitoring device. Examples of a wearable monitoring device include a purpose-driven appliance, Internet of Things (loT) device, special purpose device, etc. A processing device 802 implemented as a wearable monitoring device is schematically illustrated in FIG. 16 as a wearable device mounted to a patient’s arm solely for convenience of illustration. In practical applications, the wearable monitoring device can attach to other parts of a patient's body.

[0086] In some embodiments, the wearable monitoring device can communicate locally (e.g., to a smart phone) via Bluetooth, ultrawide band, via one or more radio frequencies (RF) or via any other form of wired or wireless communication. In other embodiments, the wearable monitoring device can communicate across a network, e.g., via Wi-Fi and / or communicate locally to another processing device 802.

[0087] The illustrative computer system 800 also includes a processing device implemented as a server 812 (e.g., a web server, file server, and / or other processing device) that supports an analysis engine 814 and corresponding data sources (collectively identified as data sources 816). The analysis engine 814 and data sources 816 provide the resources to implement and store data related to collecting and aggregating data from wearable monitoring devices, captured events, combinations thereof, etc., as described in greater detail herein.

[0088] In an exemplary implementation, the data sources 816 are implemented by a collection of databases that store various types of information. Solely by way of example, the data sources 816 can include device data 818, e.g., data related to wearable monitoring devices, including configuration data, version data, software versioning and control, data generated from wearing a wearable monitoring device, etc. The data sources 816 can also include medical data 820, e.g., medical research, etc., used to calibrate, tune, design, modify, etc., wearable monitoring devices. The data sources 816 can also optionally include user data, e.g., data regarding the patients that are wearing the wearable monitoring devices, where such data is collected. As yet further examples, the data sources 816 can include platform data 824, e.g., data used by the analysis engine 814, e.g., computer drivers, GUI information, algorithms forprocessing physiological conditions, etc. As yet a further example, the data sources 816 can optionally include miscellaneous data 826, e.g., any data needed by the analysis engine 814 that is not otherwise accounted for above.

[0089] Considering FIG. 16 as an environment used by wearable monitoring devices, in some embodiments, the processing of physiological data of a corresponding patient wearing the wearable monitoring device (e.g., biochemical sensing with additional sensing modalities that enhance patient care or health and wellness) can be carried out entirely on a processing device 802 (such as a wearable monitoring device itself); on a processing device 802 such as a smartphone, by the analysis engine 814, or via combinations thereof (e.g., by distributing processing tasks among two or more processing devices).

[0090] With specific regard to a processing device 802 implemented as a wearable monitoring device (see processing device 802 schematically attached to a patient’s arm), it may be desirable to carry out all of the processing on the wearable monitoring device itself. In this regard, a corresponding device such as a smartphone can optionally provide a graphical user interface for displaying dashboard measurement results, but all processing is carried out on the wearable monitoring device itself.

[0091] In other embodiments, the smart phone can carry out some processing, e.g., to compare computed data to dashboard thresholds, to carry out algorithms, rules, or other processing, as described more fully herein.

[0092] In still other embodiments, the analysis engine 814 can collect data from each wearable monitoring device, e.g., for trend analysis of patient data, for device state of health monitoring (e.g., to detect faults in the wearable devices themselves), for battery charge level monitoring, for versioning (such as to carry out software updates), etc.

[0093] In some embodiments, the analysis engine 814 is controlled by a third party, e.g., the manufacturer of the wearable monitoring devices that are implemented in the environment.

[0094] In some embodiments, the analysis engine 814 schematically represents integration into an electronic health record system, e.g., to connect a patient to the patient’s doctor so that the doctor can access the electronic data generated by a corresponding wearable monitoring device.

[0095] Referring now to FIG. 17, an example wearable monitoring device 900 is schematically illustrated, according to aspects of the present disclosure. Like numerals in FIG. 17 not necessarily refer to like features like that in the other figures. The wearable monitoring device 900 can represent an example embodiment of a processing device 802 (FIG. 16), e.g., a wearable monitoring device as previously described.[0096| The wearable monitoring device 900 includes a housing 910 that attaches to a patient. The housing can attach to the patient via an adhesive 904, a strap, or other securement.

[0097] The wearable monitoring device 900 also includes at least a first working electrode 920 and may include a second working electrode 922 and further may include a third working electrode 923 or even more working electrodes. In some embodiments, one or more electrodes include an analyte detecting material, e.g., aptamers, such that continuous sensing can be carried out. Electrode 950, as previously described may be a gel electrode pad and serve the roles of a reference and counter electrode.

[0098] In practical applications, the housing 910 is couplable to the electrodes 920, 922, 924. As used herein, "couplable" is to be construed broadly to mean any one of permanently coupled, detachably coupled, temporarily coupled, user attachable, user detachable, user attachable and detachable, factory attachable, factory detachable, user attachable, factory attachable and detachable, or any combination thereof, unless specifically noted otherwise.

[0099] As illustrated, the housing 910 includes a potentiostat 991 that is communicably coupled to the electrodes 920, 922, 924 (or a combination thereof) using an optional multiplexer 990, or alternatively each of electrodes 920, 922, 924 can receive a direct dedicated connection to a potentiostat 991. In practical applications, the term “potentiostat” is to be interpreted broadly, and is not limited to any particular number of sensors. For instance, the potentiostat can be implemented as a bipotentiostat, polypotentiostat, etc., depending upon the sensor configuration provided by the wearable monitoring device 900.

[0100] Additionally, wearable monitoring device 900 includes a controller 993 that is communicably coupled to memory 992. The controller 993 is also communicably coupled to a communication interface 994.

[0101] The controller 993 includes necessary electronics that enable the controller 993 to carry out the intended functionality of the wearable monitoring device. For instance, the controller 993 can include a processor, bus interface, ports, registers, memory, etc., that enables the wearable monitoring device 900 to carry out the functionality described more fully herein.

[0102] Also, as illustrated, the controller 992 is communicably coupled to one or more of the optional multiplexer 990, potentiostat 991, the memory 992, the transceiver(s) 994, optional miscellaneous sensors 995, optional display / output 996, combinations thereof, etc.

[0103] The communication interface 994 may comprise, for example, at least one transceiver that communicates via Bluetooth, Wi-Fi, Ultrawideband, near field communication, combinations thereof, etc.[00104| The optional display / output 996 can comprise a display screen, a dimensionally limited display screen, a touch screen, a haptic output, a light output, a speaker / alarm, or combinations thereof.

[0105] The controller 993 uses the potentiostat 991 to collect measurements from electrodes 920a, 920b, 920c, and stores the collected measurements in the memory 992. The controller 993 may further provide filtering, analysis, control, authorization, authentication, and other controller specific functions. The communication interface 994 facilitates coupling the wearable monitoring device 900 with an external computing device, e.g., a smartphone, a cloud computer, etc. In this regard, the communication interface 994 can include one or more modalities, each with different data and / or authorizations. For instance, a patient may access data from the wearable monitoring device on a smartphone, whereas a doctor may be able to access more detailed information from a cloud server and / or through electronic health records (see FIG. 17). In this regard, multiple modalities of communication may be utilized with wearable monitoring device 900.

[0106] In some embodiments, the adhesive 904 of the wearable device 900 is, or includes, a gel electrode 950 that is connected to at least one of the potentiostat 991, the controller 993, or the sensor 995. For example, a gel electrode 950 could be the counter or reference electrode for the electrodes 920, 922, 924.

[0107] Examples of cortisol sensors that may be used with the various methods, systems, devices, and components of the present invention may be found in Wang, et al., Wearable aptamerfield-effect transistor sensing system for noninvasice cortisol monitoring, Science Advances, Vol.8(1), January 2022, pp. 1-15 and Thompson, et al., An antibody-based molecular switch for continuous small-molecule biosensing. Sci Adv. 2023 Sep 22;9(38):eadh4978. doi: 10.1126 / sciadv.adh4978. Epub 2023 Sep 22. PMID: 37738337; PMCID: PMC10516488 - each of which are incorporated by reference herein.

[0108] With respect to embodiments of the present invention, software, the sensor, an app, the cloud, or other suitable methods can further measure, calculate, predict, or report a stress score of severity of stress events or cumulative stress events throughout a period of time (days, weeks, months, etc.) and provide the user with an overall stress experienced score weighted upon such measures.

[0109] With respect to further embodiments of the present invention, software, the sensor, an app, the cloud, or other suitable methods can further measure, calculate, predict, or report a score of stress health for individual or multiple stress events (such as averaging individual scores) for example as measure of or adapted from percent cortisol change per time (especially for thedecrease in cortisol following an event), and provide the user with an overall stress score weighted up upon such measures.

[0110] With respect to further embodiments of the present invention, software, the sensor, an app, the cloud, or other suitable methods can help and individual or a group of individuals better manage their stress. For example, an embodiment could be a a continuous cortisol sensor that takes in other wearable data such as glucose, respiration rate, activity, steps taken, heart rate, sleep monitoring, blood oxygenation, or other suitable measures and automatically correlate these other measures to stress handling score or stress experienced score to help an individual have a more complete picture of how they manage their stress. For example, glucose spikes followed by cortisol spikes would indicate a potential eating disorder in response to stress events. An further example could be a system that notifies and individual on their mobile phone screen that: “stress sensor predicts that for every X steps you take per day your stress handling score and stress experienced scores improve by X% ” or “Congrats, after taking a 15 minute breathing or walking break, your stress is back to baseline and you did it X times faster compared to when you didn 't take a break’’, or “based on heart rate data and blunted cortisol awakening responses you may be overtraining at the gym ”, or “your improved sleep scores appear to be correlating with improved cortisol awakening response ”, or other potential examples achieved using embodiments of the present invention.

[0111] Although not described in detail herein, other steps which are readily interpreted from or incorporated along with the disclosed embodiments shall be included as part of the invention. The embodiments that have been described herein provide specific examples to portray inventive elements, but will not necessarily cover all possible embodiments commonly known to those skilled in the art.

Claims

WHAT IS CLAIMED IS:

1. A method of determining a stress score for a subject, the method comprising:monitoring the amount or concentration of at least one analyte in a subject, wherein the at least one analyte is an analyte that correlates to stress;detecting a deviation in the amount or concentration of the at least one analyte away from a baseline value of amount or concentration of the at least one analyte;measuring a change in the amount or concentration of the at least one analyte after detecting the deviation away from the baseline value of amount or concentration of the at least one analyte; anddetermining a stress score based on the measured change of the amount or concentration of the at least one analyte.

2. The method of claim 1 , wherein determining the stress score further comprises determining a magnitude of stress response, wherein the magnitude of stress response is the change in the amount or concentration of the at least one analyte between the baseline value and a peak amount or concentration.

3. The method of claim 2, wherein determining the stress score further comprises measuring the change in the amount or concentration of the at least one analyte over a portion of the magnitude of stress response.

4. The method of claim 3, wherein the portion of the magnitude of stress response measured is chosen from at least 10% to 90% of the magnitude of stress response, at least 25% to 75% of the magnitude of stress response, and at least 40% to 60% of the magnitude of stress response.

5. The method of claim 1, wherein the measured change of the amount or concentration of the at least one analyte in the subject is an increase in the amount or concentration of the at least one analyte in the subject.

6. The method of claim 1, wherein the measured change of the amount or concentration of the at least one analyte in the subject is a decrease in the amount or concentration of the at least one analyte in the subject.

7. I’he method of claim 1, wherein detecting a deviation away from the baseline value in the amount or concentration of the at least one analyte further comprises plotting a curve following an increase in the amount or concentration of the at least one analyte from a baseline value to a peak amount or concentration, and to a decrease in in the amount or concentration of the at least one analyte from the peak amount or concentration back to or near the baseline value; and wherein determining the stress score further comprises measuring the area under the curve.

8. The method of claim 1, wherein detecting a deviation in the amount or concentration of the at least one analyte further comprises plotting a curve following an increase in the amount or concentration of the at least one analyte from a baseline value to a peak amount or concentration; andwherein determining the stress score further comprises measuring half of the area under the curve.

9. The method of claim 1, wherein the measured change of the amount or concentration of the at least one analyte in the subject is measured as a percent change.

10. The method of claim 1, wherein the measured change of the amount or concentration of the at least one analyte in the subject is measured as a numerical value.

11. The method of claim 1, wherein detecting a deviation in the amount or concentration of the at least one analyte further comprises detecting a deviation in the amount or concentration of the at least one analyte in the subject versus a period of time over which the deviation occurs.

12. The method of claim 1, wherein the stress score is based on a single physiological stress response resulting in the change of the amount or concentration of the at least one analyte in the subject.

13. The method of claim 1, wherein the stress score is based on a plurality of physiological stress responses to a plurality of physiological events resulting in the change of the amount or concentration of the at least one analyte in the subject.

14. The method of claim 13, wherein the stress score is the number of physiological events in a 24 hour period.

15. The method of claim 13, wherein each physiological event of the plurality of physiological events is defined as a percentage change in the amount or concentration of the at least one analyte over the baseline value.

16. The method of claim 15, wherein the percentage change is chosen from greater than 5%, greater than 10%, greater than 20%, and greater than 40%.

17. The method of claim 13, wherein each physiological event of the plurality of physiological events is defined as a percentage change in the amount or concentration of the at least one analyte over the baseline value, wherein the change occurs over a certain period of time.

18. The method of claim 17, wherein the percentage change is greater than 5%, and the period of time is less than 30 minutes.

19. The method of claim 13, wherein each physiological event of the plurality of physiological events is defined as a percentage change in the amount or concentration of the at least one analyte over the baseline value, wherein the change occurs over a certain period of time, and wherein the change is sustained over a certain second period of time.

20. The method of claim 1 , wherein the percentage change is greater than 5%, the period of time is less than 30 minutes, and the second period of time is at least 20 minutes.

21. The method of claim 1, wherein the baseline value is the measured amount or concentration of the at least one analyte that occurs just prior to a change in amount or concentration of the at least one analyte.

22. The method of claim 1, wherein the baseline value is obtained from a baseline profile of amount or concentration of the at least one analyte.

23. The method of claim 1, wherein the baseline profile is the average amount or concentration of the at least one analyte over a period of time.

24. The method of claim 23, wherein the average amount or concentration of the at least one analyte over a period of time is calculated from measured amounts or concentrations of the at least one analyte in the subject over the period of time.

25. The method of claim 23, wherein the average amount or concentration of the at least one analyte over a period of time is calculated from measured amounts or concentrations of the at least one analyte in a population of individuals over the period of time.

26. The method of claim 23, wherein the period of time is chosen from 1 hour, 4 hours, 12 hours, 24 hours, 2 days, 1 week, 2 weeks, and more than 2 weeks.

27. The method of claim 22, wherein the baseline profile is a plot of measured amounts or concentrations of the at least one analyte per time of day of the at least one analyte in the subject.

28. The method of claim 27, wherein at least a portion of the plot of the baseline profile includes at least one of a cortisol awakening response and diurnal cortisol.

29. The method of claim 27, wherein the baseline profile is created by plotting measured amounts or concentrations of the at least one analyte in the subject per time of day over a period of time.

30. The method of claim 27, wherein the baseline profile is created by plotting measured amounts or concentrations of the at least one analyte in a population of individuals per time of day over a period of time.

31. The method of claim 29, wherein the period of time is 1 day, 2 days, 1 week, 1 month, or more than one month.

32. The method of claim 27, wherein the baseline value is the amount or concentration of the at least one analyte in the plot at the same time of day as the detected change in the amount or concentration of the at least one analyte in the subject.

33. The method of claim 1, wherein the at least one analyte is chosen from cortisol, adrenocorticotropic hormone, precursors of same, and metabolites of same.

34. The method of claim 1, wherein the at least one analyte comprises two or more analytes.

35. The method of claim 34, wherein the two or more analytes includes at least two of cortisol, adrenocorticotropic hormone, Na+, C1-, K+, glucose, norepinephrine, epinephrine, dopamine, serotonin, estrogen, and testosterone.

36. The method of claim 1, wherein the subject is a member of a plurality of subjects, wherein the monitoring, detecting, and measuring steps are performed for each subject in the plurality of subjects, such that the stress score that is determined is a group stress score from the plurality of subjects.

37. The method of claim 1, wherein the monitoring the amount or concentration of at least one analyte in a subject is performed by a device including a sensor having at least one electrode.

38. The method of claim 37, wherein the sensor is an affinity-based sensor.

39. The method of claim 38, wherein the device includes a plurality of aptamers having affinity for the at least one analyte.

40. The method of claim 39, wherein each aptamer of the plurality of aptamers is bound, directly or indirectly, to a surface of the at least one electrode.

41. The method of claim 39, further comprising a plurality of redox tags, wherein a redox tag of the plurality of redox tags is associated with each aptamer of the plurality of aptamers.

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