Effects of overnight military training and acute battle stress on the cognitive performance of soldiers in simulated urban combat

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Effects of overnight military training and acute battle stress on the cognitive performance of soldiers in simulated urban combat © 2022 Passi, Lukander, Laarni, Närväinen, Rissanen, Vaara, Pihlainen, Kallinen, Ojanen, Mauno and Pakarinen. Published version Passi, Tomi; Lukander, Kristian; Laarni, Jari; Närväinen, Johanna; Rissanen, Joona; Vaara, Jani, P.; Pihlainen, Kai; Kallinen, Kari; Ojanen, Tommi; Mauno, Saija; Pakarinen, Satu Passi, T., Lukander, K., Laarni, J., Närväinen, J., Rissanen, J., Vaara, J., Pihlainen, K., Kallinen, K., Ojanen, T., Mauno, S., & Pakarinen, S. (2022). Effects of overnight military training and acute battle stress on the cognitive performance of soldiers in simulated urban combat. Frontiers in Psychology, 13, Article 925157. https://doi.org/10.3389/fpsyg.2022.925157 2022 TYPE Original Research PUBLISHED 26 July 2022 DOI 10.3389/fpsyg.2022.925157 OPEN ACCESS EDITED BY Michael B. Steinborn, Julius Maximilian University of Würzburg, Germany REVIEWED BY Pedro F. S. Rodrigues, Infante D. Henrique Portucalense University, Portugal Stephen B. R. E. Brown, Red Deer Polytechnic, Canada *CORRESPONDENCE Tomi Passi tomi.passi@ttl.fi SPECIALTY SECTION This article was submitted to Cognition, a section of the journal Frontiers in Psychology RECEIVED 21 April 2022 ACCEPTED 24 June 2022 PUBLISHED 26 July 2022 CITATION Passi T, Lukander K, Laarni J, Närväinen J, Rissanen J, Vaara JP, Pihlainen K, Kallinen K, Ojanen T, Mauno S and Pakarinen S (2022) Effects of overnight military training and acute battle stress on the cognitive performance of soldiers in simulated urban combat. Front. Psychol. 13:925157. doi: 10.3389/fpsyg.2022.925157 COPYRIGHT ©2022 Passi, Lukander, Laarni, Närväinen, Rissanen, Vaara, Pihlainen, Kallinen, Ojanen, Mauno and Pakarinen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Effects of overnight military training and acute battle stress on the cognitive performance of soldiers in simulated urban combat Tomi Passi1*, Kristian Lukander1, Jari Laarni2, Johanna Närväinen2, Joona Rissanen3, Jani P. Vaara4, Kai Pihlainen5, Kari Kallinen6, Tommi Ojanen6, Saija Mauno7 and Satu Pakarinen1 1Finnish Institute of Occupational Health, Helsinki, Finland, 2VTT Technical Research Centre of Finland Ltd., Espoo, Finland, 3Savox Communications Oy Ab, Espoo, Finland, 4Department of Leadership and Military Pedagogy, National Defence University, Helsinki, Finland, 5Training Division, Defence Command, Helsinki, Finland, 6Finnish Defence Research Agency, Finnish Defence Forces, Tuusula, Finland, 7Department of Psychology, Faculty of Social Sciences (Psychology), and University of Jyväskylä, Tampere University, Jyväskylä, Finland Understanding the effect of stress, fatigue, and sleep deprivation on the ability to maintain an alert and attentive state in an ecologically valid setting is of importance as lapsing attention can, in many safety-critical professions, have devastating consequences. Here we studied the effect of close-quarters battle (CQ battle) exercise combined with overnight military training with sleep deprivation on cognitive performance, namely sustained attention and response inhibition. In addition, the effect of the CQ battle and overnight training on cardiac activity [heart rate and root mean square of the successive differences (RMSSD)] during the cognitive testing and the relationship between cardiac activity and cognitive performance were examined. Cognitive performance was measured with the psychomotor vigilance task (PVT) and the sustained attention to response task (SART). Altogether 45 conscripts participated in the study. The conscripts were divided into control (CON) and experimental (EXP) groups. The CON completed the training day after a night of sleep and the EXP after the overnight military training with no sleep. Results showed that the effect of the overnight training on cognitive performance and the between-group difference in heart rate (HR) and heart rate variability (HRV) depended on the cognitive test. Surprisingly, the cognitive performance was not largely affected by the CQ battle. However, as expected, the CQ battle resulted in a significant decrease in RMSSD and an increase in HR measured during the cognitive testing. Similarly, the HR parameters were related to cognitive performance, but the relationship was found only with the PVT. In conclusion, fatigue due to the overnight training impaired the ability to maintain sufficient alertness level. However, this Frontiers in Psychology 01 frontiersin.org Passi et al. 10.3389/fpsyg.2022.925157 impairment in arousal upregulation was counteracted by the arousing nature of the SART. Hence, the conscripts’ cognitive performance was mainly preserved when performing a stimulating task, despite the fatigue from the sleep loss of the preceding night and physical activity. KEYWORDS military, stress, cardiac autonomic activity, response inhibition, cognitive performance, vigilance, sustained attention, sleep loss Introduction In safety-critical occupations, such as rescue and recovery, police, and military, maintaining a high level of performance in challenging or even adverse conditions is critical (Krueger, 1989). As the operative duties can vary from long-term passive surveillance or monitoring to active or even hightempo operations, one needs to sustain and adapt one’s focus of attention and decision-making according to the situation and task at hand. Moreover, in demanding situations, these cognitive capabilities can be compromised by either the acute stress evoked by the operative task and/or prolonged stress and strain from continuous physical activity, mental load, energy deficit, and sleep loss (Lieberman et al., 2005; Suárez and Pérez, 2013; Taverniers and De Boeck, 2014; Giessing et al., 2019). This study examined the effects of military overnight training with sleep deprivation and acute stress from close-quarters battle (CQ battle) exercise on the cognitive performance of a soldier. Cognitive performance was assessed using two tasks: a monitoring task resembling, for instance, radar monitoring and an active decision-making task that could be compared to, for example, a decision to shoot or not to shoot. The function of the stress reaction is to energize and increase the psychological and physiological readiness to respond. Stress reaction emerges when demands tax or surpass one’s resources or endanger an individual’s wellbeing (Lazarus and Folkman, 1984). During the stress reaction, the balance in the autonomic nervous system (ANS), which consists of the sympathetic nervous system (SNS) and parasympathetic nervous system (PNS), is altered resulting in multiple hormonal changes and increased arousal. At the fight-or-flight stage (i.e., acute stress stage), catecholamines are secreted in the circulation due to the increased activation of the sympathomedullary pathway (SAM), a part of the SNS (Gunnar and Quevedo, 2007). This results in dilation of pupils, acceleration of the heart and lung activity, and heightened sensitivity to threat-related cues (Chu et al., 2021). When the situation is perceived as safe, the autonomic homeostasis is restored via increased activation of the PNS, while the SNS activation is inhibited. The Yerkes-Dodson law (1908) states that a sufficient vigilance or energetic level is needed to retain an appropriate level of focus on any given task. However, excessive or minimal arousal often negatively affects the performance (Yerkes and Dodson, 1908; Arnsten, 2009). Increased fatigue induced by sleep loss is closely related to a lower vigilance level (Killgore, 2010), which becomes evident even after one night without sleep (Haslam, 1984). According to the state instability hypothesis, sleep loss causes momentary attentional lapses due to variation in an alert state (Doran et al., 2001). In other words, individuals with sleep loss lack the energetic resources to upregulate their alertness to show stable performance. This effect can be described from a neuropsychological perspective. The frontal areas of the brain, critical not only for executive functioning but also for top-down control of the vigilant state, are the first to suffer from sleep loss (O’Connell et al., 2008). Perhaps the most consistent finding is that slowing in processing speed, manifested in prolonged reaction time (RT), is a hallmark of low vigilance caused by sleep loss (Doran et al., 2001). Simulated combat scenarios have been successfully employed to induce high acute operational stress (Lieberman et al., 2005; Suárez and Pérez, 2013; Taverniers and De Boeck, 2014; Giessing et al., 2019). An increase in stress, anxiety, and mental effort, as well as impairment in shooting accuracy, have been found in law-enforcement reality-based training utilizing both low-stress (mannequin) and high-stress (human) targets (Murray, 2004; Taverniers and De Boeck, 2014; Giessing et al., 2019). Military training in harsh environmental conditions that included sleep loss has been shown to result in decreased vigilance, increased fatigue, lowered mood, and decreased higher cognitive abilities (Lieberman et al., 2005). According to Hockey (1997), fatigue, in low controllability and high environmental demand situations, is related to SAM activation due to direct engagement with an acute stressor (i.e., active problem-focused coping). Suárez and Pérez (2013) found increased SNS activation in urban combat training even when physical activity was at a low level. According to the authors, the psychological stress caused by the uncertainty regarding the location of the threats led to fatigue impairing post-combat information processing ability. Sustained attention to response task (SART) and psychometric vigilance task (PVT) are commonly used to study psychomotor processing, particularly, sustained attention, vigilance, response inhibition, and arousal (Robertson et al., Frontiers in Psychology 02 frontiersin.org Passi et al. 10.3389/fpsyg.2022.925157 1997; Drummond et al., 2005; McIntire et al., 2014). In PVT, the participant is required to react as fast as possible to a rarely and infrequently presented stimulus. In this rather monotonous task, the performance is highly dependent on the vigilance level of the participant, and not so much on higher cognitive functions (McIntire et al., 2014). In contrast, in go/no-go tasks, such as SART, the aim is to respond to repetitive target stimuli (“GO”) but not to the rarely presented non-target stimuli (“NO-GO”). For this, one needs to overcome the motor response routine through inhibition. This exerts demands on higher-order frontal lobe functions, also known to suffer from the effect of stress and fatigue (Boksem and Tops, 2008; Arnsten, 2009; Lim and Dinges, 2010). Sleep loss has been shown to prolong response times and increase inhibition errors in go/no-go tasks (Chuah et al., 2006; Drummond et al., 2006; Shao et al., 2009; Rabat et al., 2019); also refer Skurvydas et al., 2020 and Hudson et al., 2020a for conflicting findings; Magnuson et al., 2022. Moreover, not only the impairing effects of fatigue but also stress can remain well after the presentation of the stressor. For example, Alomari et al. (2015) showed that acute stress impairs SART performance up to 30 min after stress induction. On the other hand, stress-induced arousal, and the related elevation of the sympathetic tone of the ANS may act as an antagonist for drowsiness- and boredom-related low activation improving the performance in PVT, particularly in individuals suffering from sleep loss (Smith and Nutt, 1996). Psychophysiological assessments offer an objective method to quantify stress and mental effort (Fairclough and Mulder, 2012; Kim et al., 2018). Heart rate variability (HRV) is commonly used to index the alternated activity between the SNS and PNS. Low HRV has been linked to high SNS activation and/or PNS withdrawal (Shaffer et al., 2014). The root mean square of successive differences (RMSSD), a metric of beat-to-beat variability, is often applied to quantify short-term HRV (Munoz et al., 2015). RMSSD depicts the activity of the vagus nerve (Shaffer et al., 2014), that is, the activity of the PNS. Furthermore, RMSSD is relatively free from respiratory effects (Hill et al., 2009). In addition to the HRV indices, heart rate (HR) is connected to the alternating activity of the SNS and PNS so that SNS activation/PNS withdrawal is related to cardiac acceleration (Acharya et al., 2006). The heart (functioning) is closely related to brain activity. According to the neurovisceral integration model (Thayer et al., 2009), the same neural networks involved in autonomic, emotional, and cognitive self-regulation also participate in cardiac autonomic activity control. Thayer et al. (2009) state that vagally mediated HRV is an index of an individual’s ability to self-regulate and that higher HRV relates to greater adaptability and flexibility of responses. The relationship between high HRV and enhanced performance has been shown, especially in tasks requiring higher cognitive functions, such as working memory and attention (Hansen et al., 2003). Furthermore, high HRV has been associated with fast and accurate performance (Thompson et al., 2015). Hence, first, HRV recordings provide information on the regulation of the two branches of the ANS; second, better self-regulation, reflected in higher HRV, is associated with a better performance in tasks demanding complex cognitive abilities; and third, cardiac recordings can be utilized to examine the relationship between the ANS activity and performance in cognitive tests. There is a limited understanding of the overlapping effects of stressors such as sleep loss and physical activity and acute stress on military performance. The operative stressors such as sleep loss and sustained physical activity typically decrease vigilance and performance. On the other hand, acute stress may either energize and increase vigilance or, when excessive, undermine cognition. Therefore, this study examined the effects of acute stress evoked by the CQ battle either with (EXP) or without (CON) sleep loss on arousal, vigilance, and decision-making in a field setting. The cognitive performance was measured using PVT and SART as they capture mental abilities relevant to many occasions in safety-critical fields. Situations can rapidly switch from passive surveillance to a fast tempo-operative activity. The former may require the detection of subtle environmental changes and arousal upregulation while the latter demands quick decision-making. The PVT can be considered a monitoring task, indicating arousal and attentional state (Drummond et al., 2005; McIntire et al., 2014), relevant, for instance, to a radar monitoring task. On the other hand, the SART is more of an active decision-making task, requiring higher cognitive abilities, such as response control and inhibition (Robertson et al., 1997), and comparable to, for instance, a shooting-decision task (gono-go). First, it was hypothesized (H1) that the performance of those participants who completed the overnight training (EXP) would be impaired due to decreased arousal and vigilance and reduced self-regulative capacity, compared with their wellrested counterparts (CON) during the first SART and PVT test (preTest, before the CQ battle). This would become evident during the SART as in prolonged RT and increased percentage of error of commissions (EoC) and during the PVT in prolonged RT and an increased number of attention lapses. Second, the CQ battle was hypothesized (H2) to result in high acute stress during the cognitive testing, impairing the performance of both groups in the SART with the mentioned performance measures (midTest and postTest vs. preTest, i.e., after vs. before the CQ battle). Again, EXP was expected to perform worse than CON during the SART tests after the CQ battle (midTest and postTest). On the other hand, during the PVT (postTest), EXP performance would be protected by battle-induced stress and arousal. Therefore, EXP performance would reach the level of CON measured with PVT RT, and attention lapses after the CQ battle. In addition, it was hypothesized (H3) that the CQ battle-related high acute stress would become evident in HR and RMSSD (i.e., decreased RMSSD and increased HR) during the SART (midTest and postTest vs. preTest) and PVT (postTest vs. Frontiers in Psychology 03 frontiersin.org Passi et al. 10.3389/fpsyg.2022.925157 preTest). Furthermore, EXP participants’ RMSSD was expected to be lower and HR higher than that of CON participants during each SART and PVT tests. Lastly, higher PNS activity was hypothesized (H4) to be associated with enhanced executive functioning, e.g., increased flexibility in responding. On the other hand, low PNS activity was expected to reflect greater alertness and wakefulness (hyperarousal). Therefore, it was hypothesized that higher RMSSD and lower HR would be related to better performance, as indicated by shorter mean RT and decreased EoC during the SART. During the PVT, increased HR and lowered RMSSD was expected to be associated with a shorter median RT and fewer attention lapses. Materials and methods Participants Fifty-five conscripts [49 males and 6 females; (Mage = 20.10, SD =0.96)] from the Finnish Defense Forces gave their informed consent and took part in baseline measurements. Ten participants withdrew from the study (due to illness or suspected illness) before the military training phase. Thus, the final data set consisted of 45 participants who all completed the entire study. The age of these participants varied from 19 to 24 years (Mage = 20.14, SD =1.04). The participants signed the written informed consent. The study was approved by the Ethics Committee of the Hospital District of Helsinki and Uusimaa and the Finnish Defense Forces (AQ9520). The study protocol followed the Declaration of Helsinki. Procedures The study included three consecutive parts: baseline, normal sleep (CON) or overnight military training with sleep loss (EXP), and close-quarters battle exercise (CQ battle; Figure 1A). Baseline measures Before the actual military field training, baseline information was collected. To ensure that the study groups are comparable with respect to the key characteristics that may affect the study results (such as cognitive ability, fitness, age, and cardiac function) or in case differences were found, that can be taken into account in the modeling approach. The baseline measurements collected during the daytime included questionnaires, surveys, and cognitive testing, while the baseline measures for HR were collected during the first night. On the first day of the study, 37 participants filled out surveys and questionnaires and performed several tablet tests, including the SART, as baseline measures in a classroom setting. Due to practicality issues, the remaining 18 participants performed these on the day after. The background questionnaire included basic demographic information (e.g., age, gender, handedness, and education), information on body mass index (BMI), health, and possible medications (for safety and interpretation of physiological data), as well as their selfreported scores in a shooting test and 12-min running test (to evaluate possible differences in the shooting performance and cardiorespiratory fitness). After the baseline tests on day 2, the participants were equipped with MovesenseTM Heart Rate sensors (Suunto Oy, Vantaa, Finland) attached to a chest strap. The sensors were connected to a portable router, which recorded the HR data via Bluetooth. Baseline HR was collected during the night following the first study day. The overnight HR measures were filtered to include data only when the participant was in a supine position. This was based on Link GT9X (ActiGraph LLC, Pensacola, FL, USA) posture data; thus the duration of the measure varied from individual to individual (range: 4.35–8.5 h). In cases with missing Link GT9X-data, the duration of the overnight HR was collected from self-reported hours in bed. The course of the experiment The final set of participants, 45 conscripts, were pseudorandomized into two groups (i.e., EXP and CON) by a drill instructor. The participants were further divided into three-person drill teams. CON (7 teams) performed the CQ battle the day after all the baseline measures were conducted. Both groups (15 teams) began an overnight training the same evening when CON had completed the CQ battle. The distance marched during the overnight training from 10:00 p.m. to 06:00 a.m. was approximately 30 km. EXP (8 teams) performed the CQ battle after completing the overnight march (Figure 1A). Using this arrangement, all participants had similar training. The only difference was that CON had an uninterrupted overnight sleep before the CQ battle while EXP completed the CQ battle sleep-deprived after the overnight military training. Moreover, as the study was conducted as a part of the urban combat military field training within the military service, the drill teams participated in various training activities in addition to the CQ battle. Thus, all the drill teams had mentally and physically similar activities during the test day when not in the CQ battle. The provided activities also ensured that the participants stayed active, preventing any opportunity for sleep before the CQ battle. Close-quarters battle exercise The main tasks of the CQ battle were to break into a building, notice any wounded and give first aid, clear an urban building from the enemies, and command and capture Frontiers in Psychology 04 frontiersin.org Passi et al. 10.3389/fpsyg.2022.925157 FIGURE 1 (A) The temporal order of the main events throughout the whole study. (B) A three-person drill team’s test day procedure. The sustained attention to response task (SART) and psychometric vigilance task (PVT) were performed in the order displayed in the picture with a comparative in-between task duration. individuals who did not show resistance. The CQ battle consisted of two simulated urban buildings (Figure 2). The first track (Track A; the duration ∼15 min) was considered more demanding than the second track (Track B; the duration ∼10min). Both the soldiers and the enemies were armed with Glock FXTM training pistols with short-range color-marking training ammunition. The use of these weapons increased the realism of the CQ battle as the projectiles likely caused some sensations of pain when hitting the target. Although the conducted simulation was not physically demanding per se, the CQ battle setting induced high acute stress for the participants. The soldiers progressed from one room to another at a slow or very slow walking pace. Therefore, changes in heart rate during the training scenarios were considered to arise predominantly from the stressfulness of the simulated situation. The CON started the battle day at approximately 8:30 a.m., and the EXP started at 7:30 a.m. The drill teams entered the first testing (preTest) in groups of two- or three-person drill teams and started the testing and the CQ battle stepwise. Each drill team performed the first battery of cognitive tests and surveys, after which they made the required preparations before entering the building (e.g., preparing the equipment and receiving instructions from a drill team leader) (duration ∼40min). After mounting the equipment, the drill team entered Track A. When the CQ battle, including the three testing sessions, was performed, the drill team unmounted unnecessary equipment and left the training area as a team (Figure 1B). Simultaneously, when another drill team entered Track A, the following drill team proceeded to the preTest. The drill team from the preTest was allowed to enter Track A after the drill team ahead had left the midTest, and Track A was ensured to be empty. This sequence went through until all the drill teams had performed each section included in the test day. The SART was performed before, in the middle of, and after the CQ battle, and the PVT before and after the CQ battle. During the testing, the participants sat supervised around a table, and instructions for each test and questionnaires were given. The participants in each drill team were instructed to begin each test simultaneously. The SART and PVT, and two questionnaires (Karolinska Sleepiness Scale [KSS; Shahid et al., 2011] and NASA Task Load Index questionnaire concerning SART [NASA-TLX; Hart and Staveland, 1988]); duration ∼3min), were completed using a tablet during the preTest (duration ∼20 min). The midTest (duration ∼15min) consisted of the SART (starting ∼10min after Track A) and four questionnaires (Two NASA Frontiers in Psychology 05 frontiersin.org Passi et al. 10.3389/fpsyg.2022.925157 FIGURE 2 Overview of the main activities and the urban buildings included in the experiment. Task Load Index questionnaires, one concerning Trac kA and one SART [NASA-TLX; Hart and Staveland, 1988], Karolinska Sleepiness Scale [KSS; Shahid et al., 2011], and self-assessment Manikin (SAM) test (Bradley and Lang, 1994); duration ∼ 5min). After the midTest, the drill teams were prepared for Track B. In the postTest (duration ∼45 min), the SART began after approximately 15min of the testing, and the PVT was performed as the last task during the postTest. In addition to the SART and PVT, postTest consisted of one other test (Visual Search Task [Motter and Simoni, 2008]; duration ∼5min) and nine questionnaires (A questionnaire considering details of the track, Two NASA task load index questionnaires, one concerning Track B and one SART [NASATLX; Hart and Staveland, 1988], Karolinska Sleepiness Scale [KSS; Shahid et al., 2011], self-assessment Manikin [SAM] test (Bradley and Lang, 1994), perceptual and memory distortions questionnaire [created on the basis of Grossman, 2008], mission awareness rating scale [MARS; (Matthews and Beal, 2002)], team workload assessment scale [TWAS; (Lin et al., 2011)], and self-evaluation of participant’s own performance (constructed in close collaboration with a military expert); duration ∼20 min). The results of those tests and questionnaires other than SART and PVT are not reported here. The SART and visual search task as well as all questionnaires were implemented in the Inquisit software package (Millisecond Software, Seattle, WA, USA) and performed using a tablet computer with a USB keyboard attached for responses. In SART, one is to respond to frequently presented digits from 1 to 9 appearing in a random sequence (go response) and refrain from reacting to infrequently presented targets (digit 3; no-go response). The importance of speed and accuracy was stressed. Participants were presented with an on-screen stimulus for 250ms. After the stimulus was presented, there was a circle with an X on the screen for 900 ms during which the response had to be given. Hence, the interstimulus interval was 1,150 ms. The task was to respond by pressing a keyboard spacebar when a digit from 1 to 9 (excluding three) was presented (go) and refrain from responding when digit 3 was presented (no-go). The inability to refrain from pressing when digit 3 is presented is termed as an EoC. Each testing session included 200 go and 25 no-go stimuli. The EoC (%) and mean RT for the correct go-response (ms) were calculated for each participant from the SART data. The PVT was performed using a simple handheld apparatus that included the Movesense button and LED light (Suunto Oy MoveSense, Vantaa, Finland). Participants were instructed to press the button as quickly as possible each time the LED light lit up. Interstimulus intervals varied randomly from 1 to 10s. The duration of the task was 10 min. Median RT was used to index the level of vigilance and RTs exceeding 500ms indexed attention lapses indicating attentional disengagement. The PVT responses were recorded on the Actilink wrist devices via Bluetooth connection. Heart rate measurement Heart rate was measured throughout the whole experiment with the Suunto Movesense sensor (Suunto MoveSense Oy. Vantaa, Finland). The data was streamed to a combatant’s portable Savox BS router via Bluetooth connection. The threeperson drill team leader was carrying the router inside a combat vest throughout the study. In addition to the HR, the sensor registered a movement, skin temperature, and body position. Frontiers in Psychology 06 frontiersin.org Passi et al. 10.3389/fpsyg.2022.925157 Statistical analysis Group comparisons in baseline All statistical tests were conducted using R version 4.0.5. (R Core Team, 2021). The between-group comparisons were conducted regarding age, gender, skill level, education, a 12-min running test, the SART (EoC and mean RT), the PVT (median RT and attentional lapses), and heart rate parameters (RMSSD and HR) to determine whether the groups were comparable in these terms. Due to the non-normality of the data, age, the SART, PVT, and the 12-min running test were compared using the unpaired two-sample Wilcoxon Signed Rank test for independent samples. The Chi-square test was applied to compare the groups regarding gender, skill level, and education. The SART and PVT, in the baseline, were tested on the day before the first battle exercise day. Similarly, the HR parameters for the between-group comparison were taken from the first night, over which both groups had a regular sleep. The p-values of the tests were Bonferroni corrected according to the number of tests conducted. The battle exercise day The unpaired two-sample Wilcoxon Signed Rank test for independent samples was applied to study the differences between the groups in the preTest (SART, PVT, HR) before the CQ battle for all measures. Second, we investigated the effect of the CQ battle on SART and PVT performance, and HR parameters at the midTest and postTest. Furthermore, we inspected if the groups differed in these parameters overall and in each testing session. The effect was studied with linear mixed models using lmerTest package (Kunzetsova et al., 2017). Three models were built separately for each response variable. In the first model, the testing session was added as a fixed effect and the participant as a random effect (random intercept model). The second model was built to study if there were between-group differences in these parameters. It included the group and testing session as fixed effects and the participant as a random effect (random intercept model). The third model was built to inspect if the group and the testing session interaction was present to find out if the groups differed during the testing sessions in the parameters of interest. In this model, the testing session, group, and their interaction were added as fixed effects and the participant as a random effect (random intercept model). Satterthwaite approximation (ANOVA function of the stats package) was applied to compare the three models. If the second model did not improve the fit significantly, the interaction was not investigated further. If the interaction improved the fit significantly in comparison to the second model, it was left as the final model. Post-hoc pairwise comparisons on the marginal means were conducted using the Tukey method implemented in the emmeans package in R (Lenth, 2021). When a significant main effect only of the testing session or both the main effects of the group and testing were present, the differences between the testing sessions were studied post hoc. When the group and testing session interaction was a significant addition to the model with both main effects, the differences between the groups during the testing sessions and the difference between the testing sessions within groups were studied post hoc. The SART—reaction time and errors of commission Since speed-accuracy tradeoff is a well-known phenomenon in the SART (Peebles and Bothell, 2004; Helton, 2009; Seli et al., 2013; Dang et al., 2018), this effect was further explored between the groups to understand our results better. The effects of RT and group on EoC were examined with linear mixed models using lmerTest package (Kunzetsova et al., 2017). Mean RT, group, and their interaction were set as fixed effects and the participant as a random effect (random intercept model). HR parameters and sustained attention Lastly, the effect of the HR parameters on the SART and PVT was examined with linear mixed models using lmerTest package (Kunzetsova et al., 2017) utilizing the whole sample. Since group was a significant predictor of the HR and RMSSD, the effect of the group was controlled in the analyses. First, all the participants were split into high and low HR and high and low RMSSD groups based on the median HR/RMSSD value of the three testing sessions of both groups. During the SART, the median HR was 87 bpm and the RMSSD was 30 ms. During the PVT, the median HR was 80 bpm and the median RMSSD was 50 ms. In the model, the HR parameter group (low/high) and group (CON/EXP) were fixed effects, and the participant was a random effect (random intercept model). Second, through visual scrutiny, it seemed that EXP participants in the high HRV and low HR groups had the longest RTs after the CQ battle. For this reason, we further investigated post-hoc to test if the effect was due to EXP in the third testing session or solely due to EXP using linear mixed models. We built two separate models employing backward elimination, and the fit was analyzed using Satterthwaite approximation (ANOVA function of the stats package). In the first model, group, HR parameter group, and testing session and their three-way interaction were the fixed effects and participants, a random effect. In the second model, group and HR parameter group and their interaction, and in addition, testing session were fixed effects and participants, a random effect. In case significant interaction effects were found, pairwise comparisons on the marginal means were conducted Frontiers in Psychology 07 frontiersin.org Passi et al. 10.3389/fpsyg.2022.925157 using the Tukey method implemented in the emmeans package (Lenth, 2021). Results Group comparisons in baseline The groups did not differ in age, gender, skill level, education, or self-reported 12-minrunning test result after the p-value adjustment. However, before adjusting the p-value, the difference between the groups in the 12-min running test was significant (<0.01, r =0.40; moderate effect size). The median distance run by the EXP was 2,725 m (IQR =241), while the median in the CON group was 2,600 m (IQR =400). Furthermore, the groups did not differ in PVT median RT, attention lapses, or in SART errors of commission, and mean RT. Similar findings were found considering the HR parameters (RMSSD and HR) measured over the first night. The groups were comparable with respect to the HR measures. Since there was an indication of possible group differences concerning the 12-minrunning test, we inspected whether it was associated with the HR parameters, SART, and PVT during the baseline. No association between the 12-minrunning test and the mentioned variables was found. Furthermore, we used the 12-min running test as a covariate in the linear mixed models to explore if it affected the results that were obtained. The result showed that the 12-min running test as a covariate did not affect the results significantly (i.e., if an independent variable was a significant predictor, the significance did not become nonsignificant and vice versa). Hence, the results are provided without the covariate. The sustained attention to response task Errors of commission There were no differences between the groups during preTest EoC. Adding group did not improve the model fit; thus, the model with one predictor (testing session) was the final model. The testing session was not a significant predictor in the model. The EoC percentage of the groups was at a comparable level during each testing session, and in addition, no change in EoC was observed over the three testing sessions (Figure 3). Mean RT There were no differences between the groups during preTest in mean RT. Adding group did not improve the model fit; thus, the model with one predictor (testing session) was the final model. A significant effect of the testing session on mean RT was observed in the simpler model [F(2, 75.44) =3.32, p<0.05]. There was 38.03 ms (±14.91, 95% CI [8.81, 67.24]) slowing in mean RT during postTest compared with the preTest. The mean RT of the midTest did not differ from the preTest RT (Figure 3). The SART mean RT and errors of commission The mean RT had a highly significant effect on EoC [F(1, 111.55) =50.03, p<0.001]. The more there were EoC, the shorter was the mean RT (b= −0.09, ±0.04, 95% CI [- 0.14,−0.03]). In addition, a significant mean RT and group interaction was present [F(1, 111.55) =5.34, p<0.05]. The slope of EXP between mean RT and EoC was significantly steeper than that of CON. (b= −0.08, ±0.04, 95% CI [−0.15, −0.01]). The psychomotor vigilance task Median RT The groups differed in median RT during preTest (p<0.001, r=0.54; large effect size). CON was significantly faster (Mdn = 269.0 ms [IQR =30.0]) in comparison to EXP (Mdn =300.0 ms [IQR =56.8]). The model fit improved significantly after adding group in the model, χ²(1) =11.10, p<0.01. However, adding the interaction term did not further improve the model fit. Group was a significant predictor in the model [F(1, 41.32) = 12.03, p<0.01]. EXP was 47.04 ms (±13.56, 95% CI [20.46, 73.63]) slower than CON during both testing sessions (Figure 4). Attentional lapses The groups differed in the number of attentional lapses during preTest (p<0.01, r=0.47; moderate effect size). EXP had significantly more (Mdn =8.5 [IQR =14.8]) attentional lapses than the CON (Mdn =1.0 [IQR =2.0]). The model fit improved significantly after adding group in the model, χ²(1) =9.33, p<0.01. However, adding the interaction term did not further improve the model fit. Group was a significant predictor in the model [F(1, 41.48) =9.91, p<0.01]. EXP had 8.10 (± 2.57, 95% CI [3.05–13.13) attentional lapses more than CON during both testing sessions (Figure 4). HR and HRV during the SART and PVT HR during the SART CON had a lower HR (Mdn =74.7 bpm [IQR =17.1]; p< 0.05, r=0.34; moderate effect size) during preTest than EXP (Mdn =87.5 bpm [IQR =10.2]). Adding group into the model did not improve the model fit. The model with testing session as the only fixed effect was the final model. The testing session had a highly significant effect on the mean HR [F(2, 81.65) = 28.82, p<0.001]. Mean HR was 9.26 bpm higher during the midTest (±1.25, 95% CI [6.81, 11.71]) and 3.36 bpm during the postTest (±1.25, 95% CI [0.91, 5.80]) compared with the preTest (Figure 5).Post hoc pairwise comparisons suggested that there was a 5.90 bpm (±1.19, 95% CI [3.05, 8.76]) decrease in HR from the midTest to the postTest. Frontiers in Psychology 08 frontiersin.org Passi et al. 10.3389/fpsyg.2022.925157 Publisher’s note All claims expressed in this article are solely those of the author and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. References Åkerstedt, T., and Folkard, S. (1997). The three-process model of alertness and its extension to performance, sleep latency, and sleep length. Chronobiol. Int. 14, 115–123. doi: 10.3109/07420529709001149 Acharya, U. 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