**1. Introduction**

486 Biomedical Science, Engineering and Technology

Xue, S.C.; Phan-Thien N. and Tanner, R.I. (1995). *Numerical study of secondary flows of* 

Yager P.; Edwards T.; Fu E.; Helton K.; Nelson K.; Tam MR.; Weigl BH.( 2006) *Microfluidic diagnostic technologies for global public health,* pp.412 - 418. Nature 442.

Journal of Non-Newtonian Fluid Mechanics, 59.

*viscoelastic fluid in straight pipes by an implicit finite volume method,* pp. 191-213.

### **1.1 Theoretical background – Outlook to applications**

### **What is stress?**

Although stress may be defined seemingly differently by endocrinologists, physiologists, psychologists, a.s.o. prefering the tools of their own, specialized trades, the general denominator and most important fact to remember is, that stress never ever impacts somehow upon you or threatens you from somewhere. Stress is always and only your individual typical reaction to something beginning with a menace and ending perhaps with a slight challenge typical for everyday life. Your personal reaction to such provocations is called stress. The provocation itself is not at all a stress but is called "stressor". Those two technicalities are regrettably often confused by journalists, so that the word "stress" became an exceedingly wooly term.

But if we once agree upon the reactive nature of stress, further reasoning is simple: If your efforts to remove a provocation turn out to be too feeble, the provocation remains and your unsuccessful efforts become chronic – chronic stress ensues. Although perhaps a bit too feeble to remove the provocation, your stress efforts are still using up more than the portion of energy which you have allotted to the routine running of events. Thus your whole system needs more fuel over a longer time, which in its turn tends to exhaust your energy reserves. Your efforts grow feebler still – burnout threatens.

<sup>\*</sup> Gertrud W. Desch, Harald Gell, Karl Pichlkastner, Reinhard Slanic, Josef Porta, Gerd Korisek, Martin Ecker and Klaus Kisters

*<sup>1</sup> Institute of Applied Stress Research, Judendorf – Strassengel, Austria,* 

*<sup>2</sup> Institute of Pathophysiology, Medical University of Graz, Austria,* 

*<sup>3</sup> Institute of Mathematics and Scientific Computing, KFU Graz, Austria,* 

*<sup>4</sup> Rehabilitation Clinique of the AUVA, Tobelbad, Austria,* 

*<sup>5</sup> Theresianische Militärakademie, Wiener Neustadt, Austria,* 

*<sup>6</sup> St. Anna Hospital, Herne, Germany.* 

It is interesting, that such reasoning describes on the one hand the well known development of chronically increased energy turnover into exhaustion and burnout. On the other hand it shows, that increasing exhaustion also increasingly curtails successful reactions to immanent provocations, meaning that those provocations cannot be fought with adequate reactions any more – an exhausted subject cannot mobilize enough reserves to fend off a challenge – there is not enough stress available to cope successfully. Thus one could appreciate the nonsense of statements repeated in journals over and over again, to "dismantle your stress" or "let your stress phase out". Far from getting rid of a personal reaction which may successfully release you from an impending menace, one has to fight the menace itself, which can be only done by successfully mobilizing ones reserves.

All those different reactions of the organism, due to differing workload intensities and different duration leading to the symptoms just described, can be quantified by a multiple parameter assessment called CSA.

### **Physiological aspects of stress and their utilization for stress assessment**

Stress situations incite changes of Adrenaline and Noradrenaline which, in their turn effect variations in blood pH, CO2, O2, buffer parameters like BE or HCO3, lactate and blood glucose as well as electrolytes like K, Na, Ca and Mg. Since we could show as far back as 1991 (1, 2), that those stress hormone effects do correlate highly significantly with adrenaline and noradrenaline changes themselves, the tedious, costly and time consuming catecholamine determination by HPLC could – at least for the purposes with which we tend to deal in this chapter – be abandoned in favour of a much quicker method for determination of stress induced metabolic changes. Estimation of those metabolic effects has the additional benefit, that the obstacle of the receptor situation, which influences hormonal effects and thus restricts the meaningfulness of catecholamine determination as a tool for assessing stress intensity is avoided, since all of our parameters depict post receptor effects.

Those non hormonal parameters therefore show interdependent stress hormone effects, which, when determined simultaneously, can be laid over an organism like a data net by especially designed online software which can be taken as the basis of an individually adaptable multi parameter stress index.

In the simplest case of a physical workload e.g., we find workload dependent change in blood glucose, increase in lactate, accompanied by adaptive changes in pCO2 and/or HCO3 and baseexcess, softening the lactate impact upon pH. Moreover, typical shifts in Ca, Mg and K provide us with information about the intensity and duration of stress. When e.g. more sensitive parameters like pCO2 or less sensitive ones like HCO3 and blood glucose, are determined at the same time in the same sample, they can give a good idea about the duration of the stress, depending upon the relative involvement of the said parameters.

Likewise, change of electrolytes can tell – together with either pH or pCO2 about the momentary inclination to sportive performance, about the intensity of symapthoadrenal expectation situations or even about the individually felt efforts of competition and – in the long run – even help to diagnose and quantify a possible state of exhaustion. Chronic sympathoadrenal impact upon metabolism could also be detectable in psychopathological diseases like depressive disorders, where the question arises, whether the patients' mental exhaustion may not affect metabolic processes too.

It is interesting, that such reasoning describes on the one hand the well known development of chronically increased energy turnover into exhaustion and burnout. On the other hand it shows, that increasing exhaustion also increasingly curtails successful reactions to immanent provocations, meaning that those provocations cannot be fought with adequate reactions any more – an exhausted subject cannot mobilize enough reserves to fend off a challenge – there is not enough stress available to cope successfully. Thus one could appreciate the nonsense of statements repeated in journals over and over again, to "dismantle your stress" or "let your stress phase out". Far from getting rid of a personal reaction which may successfully release you from an impending menace, one has to fight the menace itself, which can be only done by successfully mobilizing ones

All those different reactions of the organism, due to differing workload intensities and different duration leading to the symptoms just described, can be quantified by a multiple

Stress situations incite changes of Adrenaline and Noradrenaline which, in their turn effect variations in blood pH, CO2, O2, buffer parameters like BE or HCO3, lactate and blood glucose as well as electrolytes like K, Na, Ca and Mg. Since we could show as far back as 1991 (1, 2), that those stress hormone effects do correlate highly significantly with adrenaline and noradrenaline changes themselves, the tedious, costly and time consuming catecholamine determination by HPLC could – at least for the purposes with which we tend to deal in this chapter – be abandoned in favour of a much quicker method for determination of stress induced metabolic changes. Estimation of those metabolic effects has the additional benefit, that the obstacle of the receptor situation, which influences hormonal effects and thus restricts the meaningfulness of catecholamine determination as a tool for assessing stress intensity is avoided, since all of our parameters depict post

Those non hormonal parameters therefore show interdependent stress hormone effects, which, when determined simultaneously, can be laid over an organism like a data net by especially designed online software which can be taken as the basis of an individually

In the simplest case of a physical workload e.g., we find workload dependent change in blood glucose, increase in lactate, accompanied by adaptive changes in pCO2 and/or HCO3 and baseexcess, softening the lactate impact upon pH. Moreover, typical shifts in Ca, Mg and K provide us with information about the intensity and duration of stress. When e.g. more sensitive parameters like pCO2 or less sensitive ones like HCO3 and blood glucose, are determined at the same time in the same sample, they can give a good idea about the duration of the stress, depending upon the relative involvement of the said

Likewise, change of electrolytes can tell – together with either pH or pCO2 about the momentary inclination to sportive performance, about the intensity of symapthoadrenal expectation situations or even about the individually felt efforts of competition and – in the long run – even help to diagnose and quantify a possible state of exhaustion. Chronic sympathoadrenal impact upon metabolism could also be detectable in psychopathological diseases like depressive disorders, where the question arises, whether the patients' mental

**Physiological aspects of stress and their utilization for stress assessment** 

reserves.

receptor effects.

parameters.

parameter assessment called CSA.

adaptable multi parameter stress index.

exhaustion may not affect metabolic processes too.

Also, those interconnections of multiple metabolic effects can be used to uncover hitherto less well understood parameter interactions in metabolic diseases like the metabolic syndrome or even diabetes. There the quantity of electrolyte deficiency and its relation to the idiosyncratic behaviour of a chronically affected metabolism may open new aspects of diagnosis and therapy. Finally, diagnosis of mental and physical load leading to exhaustive stress can not only be used in managers and sports persons, but also to the purpose of being better able to judge upon correct treatment of livestock.

In most cases one has to take pains to collect pre- and post workload data.

The hardware used for such an assessment consists of well established determination systems, implemented in most ICUs all over the world. Due to the easy transportability, at least of those two examples shown below in fig.1 and fig.2., they have been used in assessment campaigns, ranging from determination of psychical workload of teachers in schools or managers in industrial plants to the evaluation of fitness of professional ski racing teams in mountain ranges and assessment of the impact of sleep deprivation in soldiers far away from human habitations.

Fig. 1. and 2. Two types of transportable ICU analyzers (dimensions estimable by the syringe in the foreground)

### **1.2 Practical implementation**

### **Sampling and sample determination – Single persons**

Thus, a persons' or an animals' workload, stress compatibility, duration of stress and also the intensity and the kind of stress can be determined within 3 minutes by collecting about 100 microliters of capillary blood, usually from the finger tip. The sample is routinely analyzed for pH, pCO2, pO2, O2saturation, ionized magnesium, ionized potassium, ionized calcium and ionized sodium, lactate, blood glucose, baseexcess and HCO3 (optionally hemoglobine and hematocrit) using a CCX (Crital Care Express, fig.1)) analyzer (NOVA Biomedical) or a Phox – M (fig.2) of the same producer (NOVA Biomedical), with about the same functions but smaller and even easier transportable and CSA (Clinical Stress Assessment) software. Both devices are widely applied all over the world in Intensive Care Units (ICUs), the software for online data evaluation and interpretation however has been developed by an Austrian corporation (PLK, Judendorf - Strassengel).

Healthy persons are usually checked before and after a standardized ergometric workload, mainly 80 Watts during 8 minutes, or before and after absolving sporting activities, or before and after a standardized psychical load like a Shapiro – test or any kind of training routine the impact of which stands in question. Additionally, the effects upon a persons' metabolism by so called wellness activities - from steam bath to sauna baths, massages etc. can be investigated, applying the same protocol. In other fields of application even single determinations can be useful e.g. in the course of daily glucose profiles from diabetic patients in rehabilitation hospitals.

### **Sampling and sample determination – Groups**

Such determinations can therefore characterize the reaction of a single person but they also can do the same with a whole group of people. In the latter case the group reaction may serve as a mirror of the typical demands of a certain task on a sample of persons. Thus information about e.g. the usefulness of a bout of training for defined purposes can be collected. All data won from the 100 microliter sample are analysed simultaneously within two minutes, so that total measuring time from blood sampling until the printout of the online processed data takes no more than 3 minutes. Calculations of averages, standard errors of means (SEM), delta values between the groups and linear correlations between all sampled parameters with their regression coefficients are software immanent. This means, that basic group statistics are available immediately after the testing of the last group member. The automatic correlation of every single parameter with all others comes in useful in many ways as we will see during the progress of the chapter. It allows us to look behind the equalizing group averages, thus enabling us to quantify the position of every group member from the point of view of a certain parameter combination. Moreover, the automatically emphasized numbers of significant regression coefficients in the correlation table creates a quickly recognizable pattern of typical group behaviour, as explained in fig. 3. Seen for the first time, this coefficient tables seem somehow crowded with data, but after a short acclimatisation one appreciates the quick oversight over all relevant interconnections of the measured parameters under different conditions:

In the first table (correlations day 1 before load) there are 7 significant linear correlations between stress related parameters. In the second table, describing the correlative situation of the same parameters and the same group, but this time after workload (military obstacle run – HIB), the number of correlations more than doubles to 15. Especially the increase of correlations with lactate and pCO2 after workload and the occasional difference in plus/minus signs are noteworthy. As we will endeavour to demonstrate, such correlative views can show at a glance, whether the present workload of a group still allows overcompensation (see below) or whether the group seems to be on the brink of exhaustion already. The usefulness of this tool in preventive medicine is obvious. The results of more than 2000 patients and experiments have been recorded in our data banks and published in about 60 papers and printed abstracts, thus providing comparative material for easier interpretation of results.

hemoglobine and hematocrit) using a CCX (Crital Care Express, fig.1)) analyzer (NOVA Biomedical) or a Phox – M (fig.2) of the same producer (NOVA Biomedical), with about the same functions but smaller and even easier transportable and CSA (Clinical Stress Assessment) software. Both devices are widely applied all over the world in Intensive Care Units (ICUs), the software for online data evaluation and interpretation however has been

Healthy persons are usually checked before and after a standardized ergometric workload, mainly 80 Watts during 8 minutes, or before and after absolving sporting activities, or before and after a standardized psychical load like a Shapiro – test or any kind of training routine the impact of which stands in question. Additionally, the effects upon a persons' metabolism by so called wellness activities - from steam bath to sauna baths, massages etc. can be investigated, applying the same protocol. In other fields of application even single determinations can be useful e.g. in the course of daily glucose profiles from diabetic

Such determinations can therefore characterize the reaction of a single person but they also can do the same with a whole group of people. In the latter case the group reaction may serve as a mirror of the typical demands of a certain task on a sample of persons. Thus information about e.g. the usefulness of a bout of training for defined purposes can be collected. All data won from the 100 microliter sample are analysed simultaneously within two minutes, so that total measuring time from blood sampling until the printout of the online processed data takes no more than 3 minutes. Calculations of averages, standard errors of means (SEM), delta values between the groups and linear correlations between all sampled parameters with their regression coefficients are software immanent. This means, that basic group statistics are available immediately after the testing of the last group member. The automatic correlation of every single parameter with all others comes in useful in many ways as we will see during the progress of the chapter. It allows us to look behind the equalizing group averages, thus enabling us to quantify the position of every group member from the point of view of a certain parameter combination. Moreover, the automatically emphasized numbers of significant regression coefficients in the correlation table creates a quickly recognizable pattern of typical group behaviour, as explained in fig. 3. Seen for the first time, this coefficient tables seem somehow crowded with data, but after a short acclimatisation one appreciates the quick oversight over all

relevant interconnections of the measured parameters under different conditions:

In the first table (correlations day 1 before load) there are 7 significant linear correlations between stress related parameters. In the second table, describing the correlative situation of the same parameters and the same group, but this time after workload (military obstacle run – HIB), the number of correlations more than doubles to 15. Especially the increase of correlations with lactate and pCO2 after workload and the occasional difference in plus/minus signs are noteworthy. As we will endeavour to demonstrate, such correlative views can show at a glance, whether the present workload of a group still allows overcompensation (see below) or whether the group seems to be on the brink of exhaustion already. The usefulness of this tool in preventive medicine is obvious. The results of more than 2000 patients and experiments have been recorded in our data banks and published in about 60 papers and printed abstracts, thus providing comparative material for easier

developed by an Austrian corporation (PLK, Judendorf - Strassengel).

patients in rehabilitation hospitals.

interpretation of results.

**Sampling and sample determination – Groups** 

### Correlations from Day 1 Before Load **pH** -0,2504 0,481831 0,333402 0,433029 0,583413 -0,5074 0,25455 -0,01633 -0,32217 **-0,63423** -0,24493 **pCO2 0,726923 0,828486** -0,20617 -0,29714 -0,04356 -0,09519 -0,06547 -0,35569 -0,06392 0,035502 **BE 0,986333** 0,139369 0,161857 -0,40704 0,102724 -0,06267 -0,53472 -0,49239 -0,12316 **HCO**<sup>3</sup> 0,069993 0,063736 -0,33665 0,05957 -0,06223 -0,52028 -0,41705 -0,0929 n= **pO**<sup>2</sup> **0,964941** -0,42371 0,190862 0,006544 0,192812 0,168397 0,594032 p<0.05 0,6 **O2sat** -0,44238 0,220152 0,015527 0,143388 0,047916 0,439431 p<0.01 0,6411 **Na** -0,15078 0,436357 0,348535 0,322723 -0,30305 p<0.001 0,7604 **Ca 0,762729** 0,159733 -0,16922 -0,1855 **Mg** 0,225335 -0,02046 -0,28541 \*\*\* Blue cell indicates correlation always seen **K** 0,289389 0,289389 **Lactate 0,642421 BS**  Correlations from Day 1 After Load **pH -0,63133** 0,222844 -0,00479 -0,13636 0,296601 -0,21557 -0,36304 -0,28804 0,228092 **-0,62068** 0,000522 **pCO2 0,648703 0,811668** -0,45956 **-0,66746 0,506397** 0,1739 0,223456 -0,23483 0,026883 0,160047 **BE 0,9723 -0,6818** -0,49137 0,401231 -0,15568 -0,02538 -0,02401 **-0,55603** 0,21133 **HCO**<sup>3</sup> **-0,65535 -0,55363** 0,45704 -0,06946 0,033726 -0,05444 -0,41068 0,207617 n= **pO**<sup>2</sup> **0,853721** -0,33658 0,133764 0,009547 0,210286 **0,760339** -0,12479 p<0.05 0,5 **O2sat** -0,43265 -0,09908 -0,22285 0,449271 0,459362 0,060453 p<0.01 0,6411 **Na 0,773358 0,843156** 0,019402 -0,25188 -0,18683 p<0.001 0,7604 **Ca 0,914917** 0,123339 0,085249 -0,37858 **Mg** 0,111536 -0,03408 -0,41609 **K** -0,07662 0,282364 **Lactate** -0,08128 **BS**

### Data correlation before and after workload

Outprint of CSA – software immanent automatic correlations of all the parameters measured. Upper triangle: before workload,

Lower triangle: after workload

Red numbers: significant correlation coefficients.

Fig. 3. Example of two tables of regression coefficients of a group before and after workload, useful for quick overall estimation of the changes of interparametric dynamics.
