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Relationships Between Cortisol, Optimism, and PerseveranceMeasured in Two Military Settings

Olaf BinschNetherlands Organisation for Applied Scientific

Research, TNO Soesterberg, the Netherlands

Herman Van WietmarschenNetherlands Organisation for Applied Scientific

Research, TNO Zeist, the Netherlands

Fred BuickDefence Research and Development Canada (DRDC), Toronto, Ontario, Canada

Optimism and perseverance are 2 important assets for soldiers to be able to performcompetently in high-risk environments and to complete complex and stressful tasks.Traditionally, optimism is measured using questionnaires and most frequently in aretrospective manner, for example, after a mission is completed. As such, ability topersevere in challenging environments is generally taken into account when it istoo late, that is, when the soldier is showing severe signs of deficiency or failing topersevere. Therefore, there is a need for more objective measurements with which topredict perseverance. We conducted and present 2 studies in which the hormonecortisol seems to play a key role, relating with optimism, and possibly predictingperseverance. In the first study, we measured cortisol levels during a computer-simulated military training mission. A significant correlation between cortisol levelsand optimism measured by the revised Life Orientation Test (LOT-R) Scale was found.In the second study, we followed 29 recruits through a demanding military trainingcourse required for joining the Dutch Air Mobile Brigade. The recruits who perseveredand completed the course had a higher cortisol response to a stressful training eventcompared with those who leave the course. A regression model showed that a combi-nation of the soldiers’ optimism and cortisol response best predicted perseverance. Thisstudy shows that a combination of attitudinal instruments and a biomarker has potentialfor predicting military training course attrition.

Military and other personnel employed inhigh-risk professions are exposed to a range ofsevere stressors during their professional career.For example, during deployment, soldierssometimes operate on a 24-hr schedule while

they are exposed to physical and mental de-mands caused by diverse combat or patrolmissions, complex and critical tasks, harshclimates, sleep deprivation, time pressures, am-biguous information, threats to safety, anddoubts about their physical and mental capabil-ities. In such situations, soldiers and other per-sonnel are prone to negative consequences ofstress, which can lead to decreased professionalperformance (Carlier, Lamberts, & Gersons,1997) and/or mental and/or physical healthproblems (Kolkow, Spira, Morse, & Grieger,2007), and contribute to failure in completingtasks in complex or hostile environments.

Decreased performance and health issues notonly occur during deployed operations, but maybe problems earlier in the military career. Inrecent studies of failure rates in basic trainingand predeployment preparations, up to 68% of

Olaf Binsch, Department of Human Factors, NetherlandsOrganisation for Applied Scientific Research, TNO Soes-terberg, the Netherlands; Herman Van Wietmarschen, De-partment of Microbiology, Netherlands Organisation forApplied Scientific Research, TNO Zeist, the Netherlands;Fred Buick, Defence Research and Development Canada(DRDC), Toronto, Ontario, Canada.

recruits dropped out from basic training in theirefforts to join infantry forces (i.e., mechanizedinfantry, marines, air mobile; Binsch, Banko,Veenstra, & Valk, 2015; Binsch, Jetten, Veen-stra, & Valk, 2014; Binsch, Valk, & Veenstra,2014), and up to 25% left predeployment train-ing where the dropouts lacked mental and phys-ical operational readiness as indicated by lowerlevels of motivation measured by question-naires and interviews, and by low physical per-formance measured by physical fitness tests(Engelhard et al., 2007). Dropout rate is theultimate indicator of failing to persevere intraining or with a task. To make better useof training resources, it would be useful to beable to identify those individuals who are mostlikely to complete rigorous training. One’ssense of optimism is a potential indicator as itappears to be related to perseverance in stressfultasks, training, and/or deployments to hostileenvironments (Cuvelier & Van den Berg,2005). The trait construct optimism has been anelement of retrospective studies of deploymentinvestigating troop morale (Boermans, 2015;Cuvelier & Van den Berg, 2005; Kamphuis etal., 2012), coping styles, and levels of resiliency(Boermans, Kamphuis, Delahaij, Korteling, &Euwema, 2013), and for gaining insight intomotivation to persevere in military tasks andtraining that might be related to failure of per-sonnel (Binsch et al., 2014). In these applica-tions, however, the ability to persevere in chal-lenging environments was studied after the fact,that is, when indications of deficiency or failingto persevere were already evident.

The Psychological Construct of Optimism

Optimism is, according to Carver, Scheier,and Segerstrom (2010), “an individual differ-ence variable, measured by a multi-point scale,that reflects the extent to which people holdgeneralized favorable expectancies for their fu-ture.” It is related to better subjective well-beingin times of adversity or difficulty (Scheier,Carver, & Bridges, 2001). Optimism is associ-ated with taking proactive steps to protect one’shealth (Schou, Ekeberg, & Ruland, 2005),whereas pessimism is associated with health-damaging behaviors (Carver, Lehman, & An-toni, 2003; Carver et al., 2010). Consistent withthese findings, the construct of optimism haspositive relationships to engagement and/or

more perseverance, and negative relationshipsto avoidance, or disengagement (Aspinwall &Taylor, 1997; Scheier et al., 2001; Schou et al.,2005; Solberg Nes & Segerstrom, 2006). Ac-cording to self-regulation theory (Carver &Scheier, 1998), people who face difficulties inmoving toward their goals have two options.They may further engage and persevere to over-come those difficulties or disengage and dropout to avoid those difficulties. Optimism is animportant determinant of viewing goals as at-tainable and of engagement/disengagement de-cisions (Solberg Nes & Segerstrom, 2006). Anincrease in effort results when desired outcomesare seen as attainable, and a decrease in effort isshown when desired outcomes appear unattain-able. Conflicts and difficult or stressful situa-tions therefore lead to perseverance amongthose more optimistic and to drop out among theless optimistic (Aspinwall & Richter, 1999;Carver, Peterson, Follansbee, & Scheier, 1983;Carver & Scheier, 1998; Solberg Nes,Segerstrom, & Sephton, 2005).

A measure of optimism has potential as apredictor for perseverance in different settings(Binsch, Valk, & Veenstra, 2014; Boermans,Delahaij, Euwema, Kamphuis, & Korteling,2012, Boermans et al. 2013; Carbone, Cigrang,Todd, & Fiedler, 1999), but modeling ap-proaches predicting perseverance might be im-proved if this subjectively measured psycholog-ical construct were combined with an objectivepsychophysiological measurement, for exam-ple, a biomarker, indicative of some character-istic related to perseverance.

The Hormone Cortisol

Cortisol has an established history in stressresearch (Hellhammer, Wüst, & Kudielka,2009), and its collection via saliva makes it apractical measurement in field conditions. Cor-tisol is a cholesterol-based, steroid hormone re-leased by the adrenal cortex following activa-tion of the hypothalamic-pituitary sequence bylimbic, cortical, and brainstem signals reactingto psychological and physical stress. Among itsmultiple functions in acute response situations,including an anti-inflammatory role (Guyton &Hall, 2000), is the stimulation of breakdown oflipids and labile proteins, and subsequent glu-cose synthesis in liver cells, believed to beprimarily protecting cerebral glucose-dependent

metabolism (Peters et al., 2004). Stressful con-ditions such as military training for resistance tocaptivity provoke significant cortisol levels(Taylor et al., 2007).

Many factors determine the salivary cortisolresponse to challenges in humans (Kudielka,Hellhammer, & Wüst, 2009). Dickerson andKemeny (2004) concluded from reviewing 208laboratory stress studies that cortisol and adre-nocorticotropin hormone (triggers cortisol re-lease) reliably respond to motivated perfor-mance tasks (tasks requiring a cognitiveresponse) only if those tasks featured uncontrol-lable conditions or the probability of socialevaluation (e.g., performing mental arithmeticunder time constraint or in front of a judgingaudience). The Trier Social Stress Test (TSST)contains such elements and is a common tool instress research (Kirschbaum, Pirke, & Hellham-mer, 1993). In a boot camp training setting,socially dominant army recruits showed highersalivary cortisol responses on exposure to TSSTand to an exercise test compared with sociallysubordinate recruits (Hellhammer, Buchtal,Gutberlet, & Kirschbaum, 1997). Other psycho-social factors are related to more modest sali-vary cortisol responses. For example, higherintensities of personal growth and purpose inlife of elderly persons are associated with flatterslopes of the diurnal change in salivary cortisollevels (Ryff, Singer, & Dienberg Love, 2004).In the clinical domain, greater optimism is re-lated to lower cortisol awakening responseswhen controlled for depression and mood (Laiet al., 2005). This is supported by the findingthat salivary cortisol levels are inversely corre-lated with happiness or positive affect (Steptoe,Wardle, & Marmot, 2005), even when affect ismeasured using momentary assessment tech-niques (Steptoe, Dockray, & Wardle, 2009).

The Purpose of the Study

Research suggests that salivary cortisolshould respond to a physical or psychologicalchallenge and could be directly or indirectlyrelated to optimism and purpose. Therefore, weview the salivary cortisol measurement is a can-didate marker for optimism and perseverance.However, it is difficult to predict the level ofcortisol responding to a challenge in a militarysetting. Although low cortisol levels are relatedto avoiding chronic stress diseases, higher cor-

tisol responses could reflect an increased en-gagement necessary for completing demandingtasks. Therefore, the purpose of the currentstudy was to examine the relationships amongoptimism, cortisol, and perseverance in a mili-tary context and to find what levels of cortisoland optimism are associated with soldiers’ per-severance.

We analyzed different aspects of data fromtwo experiments. The aim of the first experi-ment was to establish cortisol as an objectivemeasure of optimism in a simulated, stressfulmilitary scenario. In the second experiment, weused a different military setting to further inves-tigate the relationship between cortisol and op-timism, and then to determine their associationto perseverance, that is, to attrition in a militarybasic training setting. In the discussion, we re-flect on the significance of this work in themilitary context.

Experiment 1

Military personnel performing predeploy-ment training participated in a video game sce-nario in which mission-deployed peace supportsoldiers encounter stressful situations in thecourse of investigating a violent attack on apolice car in a strife-filled, third-world country.

Method

Subjects. Thirty-five service members (2female and 33 male) with an average age of23.1 years (SD � 2.6) participated in this study.Their rank varied between private and corporal.Thirty-two of the participants were deployedbetween one to three times for at least sixmonths on the North Atlantic Treaty Organiza-tion (NATO) security mission in Afghanistan.The participants had experience as infantry per-sonnel in several patrols on foot or in armoredvehicles, but no one had experience in a com-mander role. Six participants indicated experi-ence with playing recreational (combat) videogames. The study’s protocol was approved bythe Ethics Committee (TCPE) of the Dutch Re-search Institute for Applied Sciences (TNO).

Data collection procedures. The studywas conducted in the briefing room of an infan-try unit in the south of the Netherlands. Theparticipant was seated next to his or her militarytraining partner, acted by a sergeant. Every par-

ticipant worked with this same partner. Thepartner was responsible for introducing thetraining software and acted as a subordinatewithin the played scenario. The data were col-lected over the period from 09:00 to 16:00 hrs.

The participant was instructed to act as ateam commander in a two-man patrol perform-ing one virtual peace mission scenario. In thecommander role, the participant was to lead thedifferent phases in gathering of information atdifferent phases from residents concerning re-cent incidents in the neighborhood. After thestudy goals and procedures were explained, theinformed consent form was signed, and the firstsaliva sample was collected. Then instructionson controlling the avatar in the Virtual Battle-space software (VBS2, Bohemia InteractiveSimulations) were given, after which the secondsaliva sample was collected. Then a prerecordedissue of orders was presented to the participantvia headphones. Subsequently, the participantand partner started the simulated military patroltask by virtually leaving the command post.During the simulation, contact with higher com-mand was maintained that delivered basic in-structions to each participant such as “try again”or “move on.” After completing each phase ofthe single patrol scenario, the third saliva sam-ple was collected, and the participant wasguided to a different room to complete the op-timism questionnaire. Two more saliva sampleswere collected 5 and 10 min later. The experi-ment (see Figure 1) required approximately 60min.

The virtual battlespace scenario consisted ofthe following phases. The issued order was towalk the avatar to the nearest houses outside thecompound to learn what villagers knew aboutlast week’s attack on a police vehicle and aboutother cases of violence involving injury anddeath. The participant first encounters children

seeking candy and gifts, then they meet andinterview a farmer. The command post ordersthe participant to mediate a quarrel between twofarmers that escalates resulting in a beating. Thequarrel turns toward the participant, which isthen aggravated by other villagers throwingrocks from 50 yards. Gunfire creates furtherdisruption after which the participant is orderedto return to the compound. Children approachthem again.

Salivary samples were obtained using aSalivette (Sarsted, Germany), a roll-shapedswab chewed for 30 s, then collected in a la-beled plastic tube and stored in a freezer. Cor-tisol concentrations in the saliva samples wereanalyzed using a radioimmunoassay method(Walker, Riad-Fahmy, & Read, 1978) by U-Di-agnostics, test center of the University Hospitalof Utrecht, the Netherlands.

Optimism was measured after the simulationwith participants completing the Dutch versionof the Life Orientation Test (LOT; Scheier &Carver, 1985). It was originally designed tomeasure dispositional optimism, the general ex-pectation that good things will happen. Therevised Life Orientation Test (LOT-R) consistsof 10 items—3 items assessing optimism, 3pessimism items, and 4 filler items (Scheier,Carver, & Bridges, 1994). Participants scoredeach item against a 5-point Likert scale, rangingfrom 1 strongly disagree to 5 strongly agree.Glaesmer et al. (2012) validated LOT-R in asample of 3,372 subjects across all age groups.The LOT-R has also been applied in differentmilitary settings, for example to study bufferingeffects of dispositional optimism on warzonestress in 2,439 soldiers following deployment toIraq (Thomas, Britt, Odle-Dusseau, & Bliese,2011).

Statistical analysis. The focus of this firststudy was on the relationships between the mea-

Figure 1. Procedures for Study 1.

sure of optimism and changes in cortisol levelsin response to the simulated scenario. Each sub-ject’s LOT-R optimism score was calculated asthe sum of the 3 optimism and 3 pessimism(reverse scored) items (4 filler items excluded).The subjects were ranked according to theirLOT-R optimism score and then divided at themedian optimism score resulting in two groupswith low and high optimism, respectively.

In reaction to time-limited challenges, corti-sol is secreted within minutes, reaches a peaklevel in tens of minutes, then wanes (Kirsch-baum & Hellhammer, 1989), therefore 5 salivasamples were collected to capture the cortisolresponse for each subject. These measurementswere analyzed with the area under the curve(AUC) summary procedure (Hellhammer,Wüst, & Kudielka, 2009; Matthews, Altman,Campbell, & Royston, 1990). This procedure isapplicable for serial data where: (a) successivemeasurements from a participant are correlated,(b) the maximum (or minimum) responseand/or (c) the time to the maximum (or mini-mum) may be different among individuals andgroups, and/or (d) when the overall value of theoutcome variable may be the same in differentgroups but required different time spans to beproduced (Matthews et al., 1990). The AUC fortwo consecutive cortisol measurements y1 andy2 at times t1 and t2 is the product of the timedifference and the average of the two measure-ments. Thus, we computed (t2 – t1) (y1 � y2)/2for AUC between t1 and t2. AUC was calcu-lated for each segment between t2 and t3, t3 andt4, and t4 and t5, and for the four AUC seg-ments combined.

Nonparametric statistics were used for 3 rea-sons: (a) LOT-R optimism was measured withan ordinal Likert scale (rather than intervalscale) in which the 5 points were associatedwith descriptors, for example, strongly agree,for each participant to apply; (b) after splittingthe original participant sample into low andhigh optimism groups at the median optimismscore, we made no assumption about the homo-geneity of the data variance in these groups; and(c) the time segments between cortisol sampleswere not equal. The Mann–Whitney U (MWU)test was conducted with cortisol (AUC) andLOT-R optimism as factors. The MWU wasconducted for the whole AUCcortisol and for the4 single AUCscortisol segments between the 5saliva collection times. Spearman’s rank corre-

lations (Spearman, 1904) were calculated be-tween the LOT-R optimism scores and the cor-tisol measurements at the different time pointsfor the participants as one group.

Results

Cortisol and optimism group, descriptives.Cortisol and LOT-R optimism scores wereavailable for 31 of the 35 participants. TheLOT-R median optimism separation of the par-ticipants resulted in a low optimism group of 14subjects and a high optimism group of 17 sub-jects. The LOT-R optimism scores ranged from2.7 to 4.4.; the group split occurred at the scoreof 3.6. As cortisol has a diurnal cycle, wechecked the potential confound associated withthe timing of salivary collection and the partic-ipants grouping by LOT-R optimism score. Thedistribution of the participants with high versuslow optimism scores was comparable as 8 par-ticipants of the low optimism group and 7 of thehigh optimism group were tested between 09:00and 12:00 hrs; and 6 of the low optimism groupand 10 of the high optimism group were testedbetween 13:00 and 16:00 hrs.

Cortisol and optimism group, Mann–Whitney U. The MWU test comparing thewhole AUCcortisol of the two groups (high [n �17] and low optimism [n � 14]), showed nosignificant difference (U � 104.0, p � .57). Thehigh versus low optimism groups differed sig-nificantly at the AUCcortisol segment betweentime points 4 and 5 (U � 59.5, p � .02 [two-tailed]). The high optimism group had a higher(by 15.9 nmol-min/L higher) cortisol concentra-tion during the last stage of the measurementcompared with the low optimism group (seeFigure 2 and Table 1).

Cortisol and optimism, correlation. TheSpearman’s rank correlations were significantbetween the LOT-R optimism scores and thecortisol time point t5, r � .501, p � .004 andbetween the AUCcortisol segment between timepoints 4 and 5, r � .346, p � .028. Correlationsat the other time points and segments were notsignificant (see Table 2).

Discussion

The aim of this first study was to discover ifcortisol was related to optimism in a simulated,human-in-the-loop, military patrol scenario us-ing virtual battlespace. AUCcortisol post scenario

was significantly greater for the soldiers scoringhigher in optimism compared with the low op-timism group. Optimism scores and cortisol lev-els at the final time segment of the experimentwere also significantly correlated. For infantryunits of the Dutch Ministry of Defense, highattrition rates (varying from 32% to 68%) dur-ing initial training are a persistent problem(Binsch et al., 2014). The reasons for this attri-tion are varied and not yet conclusive (Binsch etal., 2015). With a relationship between cortisoland optimism in a scenario with no outcomesfor the soldiers, that is, no attrition would beexpected; the question raised is whether a sim-ilar association relates to soldier success/failure

in a more complex military setting. Therefore,the aim of the second study was to investigatethe relationship among cortisol, optimism, andperseverance in demanding actual militarytraining.

Experiment 2

Method

Participants. Twenty-nine recruits from aDutch Armed Forces Air Mobile Brigade(AMB) School platoon (mean age � 19.2 years,SD � 0.6, age range � 18–21) participatedvoluntarily. The recruits had varied back-

Figure 2. Mean (�SD) cortisol concentrations for high and low optimism groups in Study 1.

Table 1Mean (�SD) Salivary Cortisol Concentration (Nmol/l) and Calculated AreaUnder the Curve Cortisol Values (AUC, Nmol-Min/l) for Time Segments t1– t2,t2 – t3, t3 – t4, t4 – t5, and Total, and Optimism Scores for High and LowOptimism Groups

Groups

High optimism(N � 17)

Low optimism(N � 14)

Cortisol (nmol/l) 1 10.8 � 6.3 12.6 � 3.6Cortisol (nmol/l) 2 10.7 � 5.3 11.7 � 5.1Cortisol (nmol/l) 3 12.4 � 7.0 12.3 � 5.5Cortisol (nmol/l) 4 14.3 � 8.9 10.3 � 3.9Cortisol (nmol/l) 5 14.3 � 9.8 8.8 � 4.7Cortisol (AUC, nmol-min/l) t1 – t2 26.4 � 12.0 27.0 � 13.6Cortisol (AUC, nmol-min/l) t2 – t3 34.8 � 14.1 35.0 � 16.6Cortisol (AUC, nmol-min/l) t3 – t4 40.4 � 23.4 33.1 � 15.2Cortisol (AUC, nmol-min/l) t4 – t5 43.8 � 27.3 27.9 � 13.5Total cortisol (AUC, nmol-min/l) t1 – t5 145.4 � 19.2 123.0 � 14.7Optimism scores (LOT-R) 4.0 � .3 3.1 � .9

grounds (e.g., student, craftsman, military train-ee). They were all considered highly physicallyfit as they had passed the selection and AMBfitness test.

Experimental set-up and procedure.Weeks 6 and 7 of the AMB training programwere used as the experimental setting for thesecond study that was conducted during a mon-itoring research program requested by the DutchMinistry of Defense. Graduates of the 24-weekAMB training program will have masteredshooting with standard rifle and machine gun,day/night orienteering, surviving in the field,and executing group and section-level attackand defensive tactics. In the past five years,course attrition rates ranged from 32% to 68%(Binsch et al., 2014). As the recruits qualifiedfor AMB training based on their physical fit-ness, the main causes of the high attrition ratesare described by military trainers as a “lack ofmotivation,” and/or that the recruits were “in-sufficiently resilient” (Binsch et al., 2015). Ap-proximately 16% of the recruits attrit for med-ical reasons (e.g., injury or disease).

The protocol was approved by the TNO eth-ics committee prior to data collection. In thefirst week of the AMB training, the recruitswere informed that several noninvasive tests,such as saliva collection, and the completion ofdifferent questionnaires would be requestedthroughout the course. The recruits agreed andinformed consent was given.

Daily during the course, the trainees com-pleted an 8-item load questionnaire (LQ—

generally perceived physical and mental de-mands), but only the two LQ items addressingmotivation and optimism to persevere in thetraining course will be described here. As inStudy 1, these two questions originate from theLOT-R (Scheier & Carver, 1985) and wereadapted. The motivational item translates as:“I’m really looking forward to this course.” Theoptimism item was further adapted from theversion used in Study 1 and translates as: “I’moptimistic that I’ll be able to persevere in thistraining course.” Each statement was rated witha 5-point Likert scale ranging from 1 stronglydisagree to 5 strongly agree. Because the re-cruits followed an extremely full training sched-ule (06:00–22:00 hrs daily), we were requiredto use only uncomplicated procedures and ab-breviated questionnaires (i.e., not full LOT-R).Our single optimism item was adapted fromItem #4 of LOT-R. In a two-factor (optimism,pessimism) model (Glaesmer et al., 2012), Item#4 had the highest standardized regression co-efficient (0.796) with the LOT-R 3-item opti-mism subscale. Analysis of data from our firststudy indicate that the equivalent of this singleoptimism item correlated significantly (r � .68,p � .001, two-tailed) with the LOT-R optimismsubscale values from those trainees. Other sin-gle-item optimism measures have also corre-lated significantly (r � .63) with the LOT-Roptimism subscale (Kemper, Kovaleva, Beier-lein, & Rammstedt, 2011). From our first study,that single optimism item correlated moderatelywith cortisol concentration at Time 5 (r � .31,p � .046, one-tailed) and with AUCcortisol timesegment 4 to 5, r � .32, p � 040, one-tailed.

The current study was designed to measuresalivary cortisol in both unloaded (i.e., low-stress training during week 6) and loaded (i.e.,high-stress training during week 7) training set-tings.

Week 6 was characterized by daily marks-manship training at a shooting range. Pressureto perform was low (i.e., not challenging, un-loaded), and the pace was generally relaxed(e.g., periods of waiting in open shelters beforeentering the shooting range). Week 7 comprisedthe field exercise (i.e., challenging, loaded). Theactivities included surviving in the field, build-ing trenches, and executing tactical maneuverssuch as defending positions, withdraws, andattacks. So that evening rest would be similar inboth weeks, on Tuesday and Wednesday eve-

Table 2Values of Spearman’s Rank Correlation Betweenthe LOT-R Optimism Scores and the SalivaryCortisol Concentrations (nmol/l, Time Points, andAUC Time Segments) for Study 1

Correlationcoefficient R p value

Cortisol (nmol/l) 1 .242 .095Cortisol (nmol/l) 2 .045 .405Cortisol (nmol/l) 3 .011 .476Cortisol (nmol/l) 4 .238 .099Cortisol (nmol/l) 5 .501 .004Cortisol (AUC, nmol-min/l) t1 – t2 .045 .405Cortisol (AUC, nmol-min/l) t2 – t3 .033 .429Cortisol (AUC, nmol-min/l) t3 – t4 .149 .212Cortisol (AUC, nmol-min/l) t4 – t5 .346 .028Total Cortisol (AUC, nmol-min/l)

t1 – t5 .147 .216

nings the platoon was excluded from patrollingthe shooting range and field camp. Then, afterconsultation with the commandant and the di-rectly involved drill sergeants, the Wednesdayand Thursday mornings of both weeks wereused to implement the study procedures.

On these mornings of Week 6, the platoon’swaiting time at the shooting range was used toadminister the LQ and collect saliva. Specifi-cally, on Wednesday the LQ survey was com-pleted followed by saliva sampling at 07:00hours. This procedure was repeated on Thurs-day with the addition of saliva collection at07:10, 07:20, and 07:30 hours. In Week 7, againon Wednesday morning before 07:00 hours, theLQ was administered followed by the recruitproviding one saliva sample under no pressureconditions. On Thursday morning of that weekat 06:40 hours, the recruits were instructed bythe drill sergeant to move into foxholes and tomaintain their personal equipment. The LQ sur-vey was completed at 06:55 hrs, and saliva wassampled at 07:00 hrs within this no-pressuresituation. Just 3 min later, four simulated gre-nades were released by the drill sergeant, ex-ploding in the proximity of the foxholes, fol-lowed by the command “defend positions!”After the recruits responded with several roundsof blanks fired in the direction of training targetsplaced some meters in front of them, the sce-nario was concluded, and saliva collected im-mediately afterward at 07:10, 07:20, and 07:30hrs. The recruits were then debriefed by theexperimenters and subsequently continued withfield training.

Design. All 29 recruits participated duringthe shooting training week, that is, the control

(unloaded) condition, and during the field exer-cise week, that is, the experimental (loaded)condition. In both conditions, five saliva sam-ples were collected from each recruit (see Fig-ure 3). Wednesday’s salivary cortisol samplingin both the control and experimental weeks wasmade to establish a routine, that is, that itscollection was not necessarily a prelude forsignificant events. This first sample is displayedin the Results figures, but the actual analyseswas limited to 232 salivary cortisol samples (29recruits � 2 conditions (control, experimen-tal) � 4 salivary cortisol times (2, 3, 4, 5). TheLQ was administered four times. The sameweek, Wednesday and Thursday optimism itemscores were averaged producing a control andan experimental optimism value for each re-cruit. The motivation item was treated identi-cally. Because these optimism and motivationvalues were highly correlated (R � .86, p �.001), only the optimism data set (i.e., 58 aver-aged optimism scores; 29 scores from week 6and 29 from week 7) was used for further anal-yses.

Statistical analysis. Of the 290 cortisolmeasurements obtained from the saliva sam-ples, 15 measurements evenly distributed overthe samples were missing. To avoid the loss ofdata from seven subjects, we used a Fully Con-ditional Specification (FCS) method providedby the Statistical Package for the Social Sci-ences (SPSS) statistics program to impute themissing values (Field, 2013).

The AUCcortisol values (nmol-min/L) werecalculated from the 4 cortisol samples (i.e., t2,t3, t4, t5) per participant for each time framebetween the measurements (i.e., Thursday’s t2 –

Figure 3. Procedures for Study 2. Display of training Week 6 and 7, conditions (i.e., controland experimental), days, times, activities (i.e., LQ-survey, break, or exercise) during stressorapplication (i.e., grenade explosion) and saliva collection.

t3, t3 – t4, and t4 – t5), and per condition (i.e.,control, experimental). Then, the MWU testwas conducted on AUCcortisol with optimismscores and group (perseverance, dropout) asfactors. The optimism scores gathered in Weeks6 and 7 were analyzed in relationship with thecorresponding cortisol data from the sameweek.

To further explore the relationship betweenoptimism and training perseverance, andwhether it was moderated by the cortisolvalue of the recruits, several regression anal-yses and an interaction (moderation) analysis(cf. Baron & Kenny, 1986) were conducted.Because the data included repeated observa-tions within participants (5 salivary cortisolmeasurements during the experimental condi-tion), the effects of cortisol and optimism ontraining perseverance were assessed usinggeneralized estimating equations (GEE; Li-ang & Zeger, 1993). This extension of gener-alized linear models allows misspecificationof the covariance structure, that is, correlateddata (IBM SPSS software).

Following the approach of Baron and Kenny(1986), we used the GEE regression analyses toexamine the role of cortisol as moderator of therelationship between optimism and training per-severance. Prior to the moderator analysis, fourregression analyses were conducted using cor-tisol as a predictor for training perseverance oroptimism, and using optimism as a predictor fortraining perseverance on the data from all sub-jects combined. In the first regression analysis,the relationship between cortisol and trainingperseverance was tested according to:

TP � constant � B1 · C

in which TP is training perseverance (trainingperseverance � 1; training dropout � 0), C iscortisol concentration (nmol/L), and B1 is theregression coefficient.

In the second regression analysis, the cortisolvalues were related to optimism according to:

O � constant � B1 · C

in which O is optimism rating, C is cortisol con-centration, and B1 is the regression coefficient.

In the third regression analysis, the optimismvariable was related to training perseverance ac-cording to:

TP � constant � B2 · O

in which TP is training perseverance (trainingperseverance � 1; training dropout � 0), O isoptimism, and B2 is the regression coefficient.

In the fourth regression analysis, the cortisoland optimism variables were related to trainingperseverance according to:

TP � constant � B1 · C � B2 · O

in which TP is training perseverance (trainingperseverance � 1; training dropout � 0), C iscortisol concentration, O is optimism, and B1and B2 are the regression coefficients, respec-tively.

Finally, the regression analysis described byBaron and Kenny (1986) was conducted to ex-amine whether the previously tested relation-ship between optimism and training persever-ance would be moderated by cortisol accordingto:

TP � constant � B1 · C � B2 · O � B3 · (O · C)

in which TP is training perseverance (trainingperseverance � 1; training dropout � 0), C iscortisol, O is optimism, and B1 and B2 are theregression coefficients, respectively. Modera-tion is identified by the interaction term, thatis, O · C.

For each regression model, the explainedvariance (R2) was computed, that is, the corre-lation coefficient between the real outcome andthe predicted outcome.

Results

Eight recruits left the course of their own voli-tion leading to a group design of training perse-verance [n � 21] versus training dropout [n � 8].The withdrawals occurred after data collectionand in the period Weeks 11–13 (representativeactivities—bivouacking, river crossing exercises,speed marching events, obstacle course runs). Ac-cording to exit interviews, these trainees were nolonger motivated to continue or had different ex-pectations of military service/training.

Cortisol and perseverance. For the con-trol condition, the MWU test conducted ontotal AUCcortisol for the two groups showed nosignificant effect of group (U � 96.0, p � .53;

see Figure 4A). The group effect was signif-icant for total AUCcortisol from the experimen-tal condition (U � 36.0, p � .03 [two-tailed]).Specifically, the group that persevered in thetraining had a higher (by 55.0 nmol-min/L)total AUCcortisol compared with the dropoutgroup (see Figure 4B and Table 3).

Optimism and perseverance. For the con-trol condition, the MWU test conducted on theoptimism scores for the two groups showed nosignificant effect of group (U � 66.5, p � .29).The group effect was significant (U � 43.0, p �.02 [two-tailed]) for the optimism scores fromthe experimental condition. Specifically, thegroup persevering in the training had a greater

(by 0.75) optimism score compared with thedropout group (see Table 3).

Relationships among cortisol, persever-ance, and optimism. Consistent with theMWU results on AUCcortisol from the experi-mental condition, the first regression analysis onthe dataset of the experimental (loaded) week(two averaged optimism scores and 5 cortisolsamples) showed a significant relationship be-tween cortisol and training perseverance (seeTable 4, Regression 1) for the repeated mea-sures dataset. The regression equation was:

TP � 0.36 � 0.26 · C

Figure 4. Mean (�SD) cortisol concentration (nmol/l) for training perseverance [n � 21]and training dropout groups [n � 8] during Week 6 (A: control condition) and Week 7 (B:experimental condition, field exercise).

The constant in this regression comprises theestimated average value of training persever-ance at a cortisol value of zero. Since physio-logical cortisol concentration can never be atthis level, the constant has no purpose, otherthan possible statistical comparison of regres-sions, and will not be considered further.

The relationship between cortisol and opti-mism was not significant (p � .17; Table 4,Regression 2). The relationship between opti-mism and training perseverance was significant(Table 4, Regression 3). The regression equa-tion for this analysis was:

TP � �0.57 � 0.31 · O

The fourth regression analysis used both cor-tisol and optimism variables. The relationshipbetween the optimism scores together with thecortisol concentration, and training persever-ance was significant (Table 4, Regression 4).The regression equation for this analysis was:

TP � �0.60 � 0.14 · C � 0.30 · O

The final equation using the cortisol and op-timism variables includes the interaction term.Cortisol had a significant moderating effect onoptimism (Table 4, Regression 5). Regressionequation 5 was:

TP � �1.88 � 0.11 · C � 0.55 · O

� 0.21 · (O · C)

The explained variance for Regressions 4 and5 were 0.26 and 0.27, respectively (see Table 4).

General Discussion

Because of the resource cost to produce qual-ified armed forces personnel, attrition from mil-itary training courses is problematic (Binsch etal., 2014, 2015). Better methods to screen outthose likely to fail and recruit those likely to

Table 3Data Set for Control (Training Week 6) and Experimental Conditions (TrainingWeek 7)

Groups

Perseverance (N � 21) Dropout (N � 8)

Control conditionCortisol (nmol/l) 1 12.4 � 3.0 13.8 � 4.8Cortisol (nmol/l) 2 9.8 � 3.9 9.4 � 2.9Cortisol (nmol/l) 3 10.8 � 4.2 9.9 � 5.0Cortisol (nmol/l) 4 10.6 � 4.9 9.9 � 5.8Cortisol (nmol/l) 5 10.1 � 4.5 8.9 � 5.3Cortisol (AUC, nmol-min/l) t2 – t3 34.4 � 12.9 32.2 � 12.1Cortisol (AUC, nmol-min/l) t3 – t4 36.1 � 15.0 33.0 � 15.0Cortisol (AUC, nmol-min/l) t4 – t5 34.9 � 15.4 31.2 � 15.4Total cortisol (AUC, nmol-min/l) t2 – t5 105.4 � 14.3 96.4 � 14.2Optimism scores 4.3 � .6 4.0 � .5

Experimental conditionCortisol (nmol/l) 1 11.4 � 5.6 9.2 � 3.6Cortisol (nmol/l) 2 17.0 � 7.2 13.5 � 5.0Cortisol (nmol/l) 3 15.8 � 6.3 10.4 � 2.3Cortisol (nmol/l) 4 14.5 � 5.8 9.9 � 2.3Cortisol (nmol/l) 5 13.9 � 6.0 9.2 � 1.7Cortisol (AUC, nmol-min/l) t2 – t3 57.9 � 18.9 39.9 � 11.8Cortisol (AUC, nmol-min/l) t3 – t4 53.3 � 18.4 33.9 � 7.5Cortisol (AUC, nmol-min/l) t4 – t5 49.5 � 18.8 31.9 � 6.6Total cortisol (AUC, nmol-min/l) t2 – t5 160.7 � 18.7 105.7 � 8.6Optimism scores 4.4 � .5 3.6 � .9

Note. The data displays mean (�SD) for salivary cortisol concentration (Nmol/l), thecalculated area under the curve cortisol values (AUC, Nmol-Min/l) for time segments t2 – t3,t3 – t4, t4 – t5, and total, and optimism scores for training perseverance and dropout groups.

succeed are expected by policy decision makers(General Accounting Office, 1997). Psycholog-ical factors account for much of the trainingcourse outcomes for candidates once medicaland physical fitness factors are removed(Larsson, Broman, & Harms-Ringdahl, 2009;Strickland, 2005), a key feature being optimism(Carbone et al., 1999; Cigrang, Carbone, Todd,& Fiedler, 1998). To better understand humanbehavior, biomarkers from blood-based (e.g.,Kirschbaum et al., 1993) and noninvasive phys-iological (e.g., Binsch et al., 2015) sources arebeing integrated with the insight provided byattitudinal instruments. We measured optimismand salivary cortisol concentration in two sce-narios to investigate a possible relationship withperseverance in military training courses.

In Study 1 following a simulated militarypatrol scenario, the group of soldiers scoring

higher in optimism showed a significantlylarger area under the cortisol curve at the end ofthe training. The optimism scores and cortisolconcentrations were also significantly corre-lated. This suggested that cortisol might be anobjective marker for optimism in this setting. Inthe training challenge phase of the secondstudy, the perseverance group compared withthe dropout group had higher optimism scoresand showed higher cortisol concentration. Cor-tisol and optimism across the high and lowoptimism groups were not correlated in thiscase, but each was significantly and positivelyassociated with perseverance in the demandingtraining course. When optimism and cortisolwere combined, approximately one quarter ofthe variance in training perseverance was ex-plained. The Optimism � Cortisol interactionterm was significant, consistent with our reason-

Table 4Results of Regression Analyses Concerning Relationships Among TrainingPerseverance, Cortisol Concentration, and Optimism Score

Constant

1 Cortisolconcentration(nmol/l) B1

2 Optimismscore B2

3 CortisolConcentration �Optimism Score

B3 R2

Regression 1Training perseverance

Coefficient .36 .26 .12SE .18 .01p value .48 .02

Regression 2Optimism

Coefficient 3.98 .18 (.05)SE .21 .01p value .00 .17

Regression 3Training perseverance

Coefficient �.57 .31 .18SE .32 .08p value .08 .00

Regression 4Training perseverance

Coefficient �.60 .14 .30 .26SE .32 .06 .07p value .06 .04 .00

Regression 5Training perseverance

Coefficient �1.88 .11 .55 �.21 .27SE .90 .04 .22 .01p value .04 .02 .01 .05

Note. The actual values, standard errors (SE), and corresponding p values are presented forthe constants and the regression coefficients B1–B3. The values of (nonadjusted) R2 representthe proportion of the explained variance in comparing predictions by the GEE model andactual measured values.

ing that cortisol concentration might moderatethe relationship between optimism and trainingperseverance. While Regression 5 coefficientsare positive for both cortisol and optimism, thenegative coefficient of the interaction termshows that, for a portion of the total TP vari-ance, the effect of optimism on training perse-verance decreases as the effect of cortisol in-creases.

What could explain the discrepant results inthe optimism-cortisol correlations in the twostudies? Possibilities concern validity of the op-timism measure, cortisol validity, the scenariosin the two studies, and the interaction/indepen-dence of the two variables.

That we found a linear relationship betweencortisol and optimism in the first study but notin the second study was perhaps because thefull, 6-item LOT-R scale (Scheier, Carver, &Bridges, 1994) was used in the former study butonly one LOT-R item was used in the latter.Also, the single LOT-R item was modified witha context specific ending (i.e., “to persevere thetraining course”), which could have altered con-struct validity. The LOT-R measures disposi-tional optimism whereas our single item moreaddresses specific optimism (Schmitt, Gielnik,Zacher, & Klemann, 2013). The modified andreduced optimism scale was employed as atime-conserving strategy in this operational set-ting.

Studies reviewing earlier research investigatingpsycho-endocrine relationships have noted the fre-quency of inconsistent results (Dickerson & Ke-meny, 2004; Hellhammer et al., 2009; Kudielka etal., 2009). Among the explanations, including po-tential confounders which were accounted forhere, are that cortisol is not a perfect biomarker forpsychological states, and that measures quantify-ing such states, as well as traits, are themselves notperfect (Dickerson & Kemeny, 2004; Hellhammeret al., 2009; Kudielka et al., 2009). In consider-ation of clinical diagnoses, Aronson (2005) dis-cusses features of an ideal biomarker that can beapplied to the current work: (a) biomarker is spe-cific for the psychologic state; (b) the psychologicstate occurs through the biomarker; (c) the psy-chologic state and biomarker operate in parallelbut connected pathways; (d) the psychologic stateand biomarker covary in time, and with consis-tency and strength of association; and (e) the as-sociation between the psychologic state and bio-marker are biologically or conceptually plausible.

It is unlikely that features (a) and (b) apply tocortisol as used in the current work. Evidence insupport of feature (d) above are the finding ofcortisol’s correlation with optimism in Study 1,and the cortisol differences between the optimismgroups in both Studies. With respect to biomarkerfeatures (c) and (e), which overlap in applicationto the current work, other research demonstratesthat the optimism-cortisol association is plausible.1. Cortisol increases as a result of emotional acti-vation, heightened arousal which could reflect in-tensely positive or negative moods and an antici-pation of imminent activity (Brown, Sirota,Niaura, & Engebretson, 1993; Mason, 1968). 2.Cortisol increases from the extra engagement andeffort optimists expend toward accomplishing dif-ficult goals that they see as reachable. Further,individuals combining high optimism and highself-consciousness demonstrated the best task per-severance (Solberg Nes et al., 2005). 3. In antici-pation of competition, skilled judoists showed in-creased cortisol concentration, and winners hadgreater cortisol levels before and after the compe-tition compared with losers. Both groups had sim-ilar motivation to win, but on ability to win, win-ners had higher self-ratings (Suay et al., 1999).

The cortisol concentrations themselves arereliable. The baseline cortisol concentrations inboth studies are consistent with the morningvalues reported by others (Kirschbaum & Hell-hammer, 1989). Further, the cortisol concentra-tion responses lie within the range observed forpsychological stressors and are expectedly lessthan those seen in heavy exercise (Kirschbaum& Hellhammer, 1994) and in military survivaltraining (Taverniers et al., 2010; Taylor et al.,2007). It was important to collect several serialsamples of cortisol because it has a consistentresponse pattern of rising within minutes(Kirschbaum & Hellhammer, 1989; Kirsch-baum & Hellhammer, 1994) and reaching apeak value in tens of minutes (Dickerson &Kemeny, 2004; Kirschbaum & Hellhammer,1989; Kirschbaum & Hellhammer, 1994) afteran acute stress exposure. Only serial samplingwill reliably capture the peak value.

A possible limitation relevant to the postchal-lenge cortisol levels in Study 2 is their elevationin concentration just before the experimentalchallenge. Between cortisol collections at pre-challenge 1 and prechallenge 2, all traineeswere instructed to move to foxholes with theirequipment. With already moderate intensity ex-

ercise a known stimulator of this hormone (Hillet al., 2008), this physical exertion likely causedthe parallel increase in cortisol concentration atprechallenge 2 for both groups (Figure 4B).This elevated cortisol could have influenced thesubsequent cortisol response to the challenge,that is, the simulated grenade attack.

Other possible reasons for the dissimilar cor-relation results between the studies are differ-ences in participant population and setting. Thehigh optimism group had the greater cortisolconcentration each time, but the trainees in thefirst study were more experienced soldiers com-pared with the recently joined recruits in thesecond study. In addition, the first study used asimulated setting with the stressor being theassigned command of a virtual military patrol,whereas the participants of the second studywere active members undertaking real-life, spe-cial forces training. The superior fitness ofStudy 2 participants likely means that the rela-tionship among training perseverance, opti-mism, and cortisol is not generalizable to train-ing courses unless they require high fitnessentry standards. The timing of the collection ofthe optimism scores in the studies may alsohave affected the correlation between optimismand cortisol. In the first study, the optimismquestionnaire was administered after the train-ing event, but in the second study, it was con-ducted prior to experiencing the cortisol-stimulating event. Study 1 participants’experience with the just-performed virtual pa-trol scenario could have influenced their self-report of optimism, thus creating a potentialfollow-up effect.

Perhaps it is possible that the optimism-cortisol correlation of Study 1 is a coincidentalfinding. We showed earlier that this associationis plausible (i.e., Aronson’s features (c) and (e)),but with the evidence being only indirect, theassociation is not strong. Study 2 showed nooptimism-cortisol correlation, but each was sig-nificantly related with training perseverance.When they were combined, the explained vari-ance increased considerably. This suggests thateach variable is contributing something differ-ent to the measurement of training persever-ance.

The indirect evidence highlighted in refer-ence to Aronson’s features (c) and (e) suggeststhat individuals with high optimism and highconcentrations of stimulated cortisol associated

with completing the training course are recruitswith high expectations and confidence in theirabilities, who were motivated and engaged inthe training activities, were emotionally chargedby the simulated grenades event, and were theones most prepared to exert full effort and per-severance toward attaining a valued goal.

Even though the regression with the interac-tion term (moderation) accounted for slightlymore of the training perseverance variance thanthe regression without it, of more importance isthat any regression with optimism and cortisolbetter explained training perseverance variancethan optimism or cortisol alone. We are awareof only two other studies that have combinedmeasurements of psychological factors and bio-markers in efforts to explain military trainingcourse attrition. In one study (Moran et al.,2012), bone quality, hemoglobin, and minerals,vitamins and proteins in blood were measuredon the first day of training while self-confi-dence, motivation, and stress coping were mea-sured at the beginning, and at 2 and 4 months.Perceived commander’s appreciation and self-confidence were found to contributed most tothe attrition prediction model (Moran et al.,2012). In the second study (Kalns et al., 2011),the Emotional Quotient Inventory test was partof a pretraining battery of assessments that in-cluded physical fitness, aptitude, and two sali-vary proteins generating a biomarker fatigueindex. Two indicators of fitness and the proteinsbest explained training success (Kalns et al.,2011). In our work, different variables weremeasured, and our biomarker was collected inresponse to the training activity rather than be-fore training.

Low scores on the single optimism-persever-ance item predicted those recruits who wouldeventually quit the training course over the fol-lowing four weeks. This item is now perma-nently part of the load questionnaire and mon-itored weekly. This and other interventionsbased on the outcome of the results of the loadquestionnaire have significantly reduced the at-trition rate of the recruits’ training course in theDutch Armed Forces.

As salivary cortisol and the optimism itemare potentially able to predict specific persever-ance, or rather lack thereof, it would be ofinterest to know the duration of this mental statebefore dropout occurs. For the eight participantsof Study 2, the dropouts occurred in the middle

of the training course. Given that attrition is afeature of demanding military courses, it wouldbe useful to discover dropout likelihood as earlyas possible. Our second study observed no re-lationship between salivary cortisol and opti-mism scores in the unchallenging phase butthese were relevant in the challenging segment.This suggests that a challenging environmentpermits the optimism-cortisol-perseverance re-lationship to materialize because it exposes therecruit’s most vulnerable condition.

Finally, while training perseverance was sig-nificantly associated with increased optimismscores and cortisol concentration, three-quartersof the variance in training perseverance remainsto be explained. This calls for continued effortsto improve measurement methods and investi-gate other potential factors affecting dropoutfrom rigorous, specialized, military training. Asour optimism scale is still in the developmentalstage, future research studying cortisol and op-timism should make efforts toward establishingvalidity. Variations in psychoneuroendocrino-logical response profiles to challenging situa-tions within a military context should be ex-plored further. A broader set of markers wouldalso reveal a more detailed picture of the dif-ferences among service members. The power ofoptimism and cortisol to predict perseveranceshould be explored in military settings with abroader range of challenging conditions in lon-gitudinal studies. Similar work could be ex-tended to entire units and possibly explain whysome individuals perform better than others.

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1. ORIGINATOR (The name and address of the organization preparing the document. Organizations for whom the document was prepared, e.g., Centre sponsoring a contractor's report, or tasking agency, are entered in Section 8.) DRDC – Toronto Research Centre Defence Research and Development Canada 1133 Sheppard Avenue West Toronto, Ontario M3K 2C9 Canada

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3. TITLE (The complete document title as indicated on the title page. Its classification should be indicated by the appropriate abbreviation (S, C or U) in parentheses after the title.) Relationships Between Cortisol, Optimism, and Perseverance Measured in Two Military Settings

4. AUTHORS (last name, followed by initials – ranks, titles, etc., not to be used) Olaf Binsch; Herman Van Wietmarschen; Fred Buick

5. DATE OF PUBLICATION (Month and year of publication of document.) September 2017

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12. ABSTRACT (A brief and factual summary of the document. It may also appear elsewhere in the body of the document itself. It is highly desirable that the abstract of classified documents be unclassified. Each paragraph of the abstract shall begin with an indication of the security classification of the information in the paragraph (unless the document itself is unclassified) represented as (S), (C), (R), or (U). It is not necessary to include here abstracts in both official languages unless the text is bilingual.)

Optimism and perseverance are 2 important assets for soldiers to be able to perform competently in high-risk environments and to complete complex and stressful tasks. Traditionally, optimism is measured using questionnaires and most frequently in a retrospective manner, for example, after a mission is completed. As such, ability to persevere in challenging environments is generally taken into account when it is too late, that is, when the soldier is showing severe signs of deficiency or failing to persevere. Therefore, there is a need for more objective measurements with which to predict perseverance. We conducted and present 2 studies in which the hormone cortisol seems to play a key role, relating with optimism, and possibly predicting perseverance. In the first study, we measured cortisol levels during a computersimulated military training mission. A significant correlation between cortisol levels and optimism measured by the revised Life Orientation Test (LOT-R) Scale was found. In the second study, we followed 29 recruits through a demanding military training course required for joining the Dutch Air Mobile Brigade. The recruits who persevered and completed the course had a higher cortisol response to a stressful training event compared with those who leave the course. A regression model showed that a combination of the soldiers’ optimism and cortisol response best predicted perseverance. This study shows that a combination of attitudinal instruments and a biomarker has potential for predicting military training course attrition.___________________________________________________________________________

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attrition, bio marker, hormones, military training, infantry