Introduction

The recent progression of technology has led to improvements in several areas of life. Concerning leisure time, video games have gained increasing popularity. Nowadays, huge tournaments are organized to compete in video games (also referred to as esports) that are comparable to traditional sports and attract a significant number of participants and viewers (Esport Charts, 2022; Ahn et al., 2020). More specifically, esports can be defined as the organized and competitive playing of video games (Jenny et al., 2017) in which sports activities are conducted via electronic systems (Hamari & Sjöblom, 2017) and where individuals exhibit and develop their mental and physical skills (Wagner, 2006). According to 2020 global data, more than 450 million people attend esports competitions, and prizes of more than 1 billion dollars were awarded to players by esports organizations (Migliore, 2021). In addition to the increasing market value, the number of viewers, and participants, the professionalization of esports opens the possibility of pursuing a career as an esports player. This professionalization of esports also attracted the attention of scientists of different disciplines and triggered attempts to define the physical and cognitive requirements of esports as well as approaches to increase gaming performance (Bányai et al., 2019; Nicholson et al., 2020).

In the context of esports, especially cognitive performance, which refers to specific mental processes including domains such as attention, memory, information processing, and task-switching, plays an important role for esports players (Fisher et al., 2019; Lorenzo Calvo et al., 2021; Mancı, 2022; Toth et al., 2020). Considering the cognitive requirements of esports, successful esports players need well-developed abilities that include, but are not limited to, quick and accurate decisions, strategical thinking, inhibitory control to ignore distracting stimuli, high working memory capacity to remember certain elements in the game, and high problem-solving skills (Bányai et al., 2019; Palaus et al., 2017). Due to the professionalization in esports, players, coaches, and scientists not only show an increasing interest in revealing the relationship between specific cognitive functions and game performance but also in identifying legal strategies to enhance the latter.

Among different lifestyle strategies (e.g., nutrition, sleep, physical exercise) that can be used to improve performance in esports games, physical exercises, which are defined as planned, structured, and repetitive physical activities performed to improve and maintain physical fitness (Caspersen et al., 1985), can play, at least from a theoretical point of view, a prominent role (Toth et al., 2020; Ketelhut et al., 2021). For instance, there is growing evidence that chronic physical exercise (Ludyga et al., 2020) and even an acute bout of physical exercise can improve cognitive performance in domains such as information processing, attention, and memory (Chang et al., 2012; Pontifex et al., 2019) leading to the hypothesis that both acute and chronic physical exercise might benefit performance in esports games (Ketelhut et al., 2021; Toth et al., 2020). The response of cerebral blood flow during physical exercise is related to cognitive improvement after acute physical activity (Pontifex et al., 2019). However, whether such improvements in cognitive performance after an acute bout of physical exercise may translate to better gaming performance in esports games is relatively unclear. To the best of our knowledge, only one study investigated in younger esports players the influence of a short bout of high-intensity interval training on gaming performance and reported exercise-induced improvements (de Las Heras et al., 2020). Thus, although the theoretical foundation for the positive effects of (acute) physical exercise is sound (for review see; Toth et al., 2020), the actual empirical evidence supporting the assumption is relatively scant.

About exercise-induced improvements in cognitive performance, several neurobiological mechanisms are discussed to drive this phenomenon (for review see; Pontifex et al., 2019). Among the several neurobiological mechanisms that might cause the positive effects of acute physical exercise on measures of cognitive performance, the changes in cortical hemodynamics after an acute bout of physical exercise are evinced to play a prominent role because their changes coincide with cognitive improvements (for review see; Herold et al., 2018). Thus, in the current study functional near-infrared spectroscopy (fNIRS), which is a neuroimaging technique that allows continuous measurement of cortical hemodynamics via the recording of concentration changes of oxyhemoglobin and deoxyhemoglobin in the cortical layers of the brain tissue, is applied (Herold et al., 2018, 2020). Compared to other mobile neuroimaging techniques such as electroencephalography, fNIRS offers the advantages of a higher spatial resolution and lower susceptibility to motion-induced artifacts (Herold et al., 2018).

In summary, this study aimed to investigate the effects of an acute physical exercise on cognitive performance, game performance, and changes in cortical hemodynamics in the prefrontal cortex (PFC) in esports players and an age-matched control group. Based on previous studies comparing video gamers to non-video gamers (Kowal et al., 2018), we hypothesized that esports players have superior cognitive performance than age-matched controls because computer games requires extensive use of cognitive skills. Furthermore, acute physical exercise has been shown to improve cognitive performance in general (Chang et al., 2012; Pontifex et al., 2019) and gaming performance in particular (de Las Heras et al., 2020), we expected that acute physical exercise improves gaming performance. Providing empirical evidence for the above-mentioned hypotheses will be of practical relevance to provide evidence-based recommendations to esports players who seek to improve their game performance.

Methods

Subjects

Nineteen healthy participants (all maleFootnote 1) aged between 20 and 35 years were recruited for the current study (Table 1). The G-Power 3.1.9.4 software (Heinrich Heine University Düsseldorf, Germany) was used to a-priori determine the required sample size (ANOVA: repeated measures, within-between interaction). Based on a previous study reporting a Cohen’s d of ~ 0.5 for the effects of acute physical exercise on cognitive performance in esports players (de Las Heras et al., 2020), we used an effect size f = 0.25 (i.e., effect size d has been converted to f via (Lenhard & Lenhard, 2016) with an alpha (error) rate of 5% and power of 80% to estimate the required sample size for a repeated measures ANOVA with four different measures of the two groups. The determination in G-Power yielded a total sample size of 24 participants, however, the planned number of participants was not reached because the data collection process coincides with the COVID-19 pandemic. Thus, only a total of 19 participants (i.e., 10 esports players and 9 controls) were included in the study.

Table 1 Overview of the demographic characteristics and measures of physical activity and physical fitness
Full size table

Participants who have been interested in digital gaming (especially those who play FPS games) for at least 5 hours a week for the last 6 months were included in the study as amateur esports players (Bediou et al., 2018). All players had at least about 10 years of experience (10.15 ± 3.17 years) of playing digital video games. The esports players recruited in this study can be classified as Tier 2 representing local-level players or according to Scharkow et al. (2015) and Toth et al. (2021) they can be classified as ‘casual’ players. Participants who had not played esport games before and were not interested in digital games were also included in the study as an age-matched control group. None of the esports players did any specific cognitive training during their daily and routine training seasons. All the enrolled participants met the following general inclusion criteria: (i) aged between 20 and 35 years; (ii) no visual impairment; (iii) absence of depression, (iv) absence of neurologic, psychiatric, orthopedic, or cardiovascular disease and (v) were not taking any medications during the study period. All individuals who did not meet our inclusion criteria were excluded from this study. In the current study, we used announcements (e.g., flyers) and personal contacts (e.g., to esports clubs) to recruit eligible participants.

All measurements, which will be described in more detail in the following sections, were taken between 14:00 and 17:00. Furthermore, the participants were asked to not consume ergogenic foods and drinks (e.g., caffeine, alcohol, vitamin complexes) that could affect their cognitive performance 3 h before the cognitive assessments. All subjects were informed about the procedures, and each gave their written informed consent to participate. The Ethics Committee of the Dokuz Eylül University approved all procedures and the experimental design (GOA 2021/30 − 16). All study procedures are in line with the procedures described in the latest version of the Declaration of Helsinki.

Experimental design

At the beginning of this study, the participants were informed about the study procedures. All participants filled out specific forms containing questions about demographic information and gaming background. In addition, the Turkish Version of the International Physical Activity Questionnaire - Short Form (IPAQ-SF) was used to determine the physical activity levels of the participants (Craig et al., 2003; Saglam et al., 2010). Afterward, the participants conducted the assessments consisting of a Go/No-go test, a Tracking test [(via a computer using the “Psychology Experimental Building Language” (PEBL); (Mueller & Piper, 2014; Piper et al., 2015)], and Valorant Game, respectively. The order of the assessments was randomized for each participant. As done in previous studies of our group, the participants were asked to conduct a Sprint Exercise (SE) consisting of the Wingate Anaerobic Test (WAnT) protocol because we (Mancı et al., 2023) and others (Kujach et al., 2020) have shown that sprint exercises using the WAnT can improve cognitive performance. The cognitive test and game protocol were repeated 5 min (Post-1 measurements) and 30 min (Post-2 measurements) after the cessation of the SE. As shown in Fig. 1, changes in the cortical hemodynamics in the prefrontal cortex (PFC) were recorded during the Valorant game before and after the acute SE (Post-1 and Post-2) via functional near-infrared spectroscopy (fNIRS) (Fig. 1).

Fig. 1
figure 1

Experimental design; bpm: beats per minute; fNIRS: functional near-infrared spectroscopy; min: minutes

Full size image

Valorant game protocol

Valorant is a computer game in which two teams of 5 esports players aim to destroy each other or the opponents energy source in a virtual universe. The basic features of Valorant (comparable to other FPS games) demand the esports player (i.e., participant) – as fast and accurate as possible - (i) to perceive and distinguish changing objects by directing the (customizable) target cursor on the screen, (ii) to make appropriate moves, (iii) to track the opponent targets, and (iv) to shoot or not to shoot the opponent. Thus, Valorant requires a broad set of cognitive skills including multiple objects tracking and inhibitory control. In our study, the participant is expected to destroy 50 armored opponent targets that appear and move randomly on the polygon as fast as possible. The destruction of the targets is achieved by a single shot in the head area or two shots in the other areas of the opponent’s body. For our study, a weapon called Guardian and unlimited ammunition were used (Fig. 2a). The time (seconds) to complete the task (i.e., by destroying all targets) and the cortical hemodynamic data were recorded to operationalize the participants’ performance.

Fig. 2
figure 2

Illustration of the a) Valorant Game Scene b) Go/No-go Test and c) Tracking Test

Full size image

Cognitive tests

At the beginning of the cognitive testing, the participants were informed about the procedures of cognitive testing. Each cognitive test was conducted four times. The first three runs of the cognitive tests allowed the participants to appropriately familiarize themselves with the testing procedures and fully understand the test requirements. The performance indices of the fourth run were used in the final statistical analysis. The order of the cognitive tests was randomized for each participant and the participants performed the task in a quiet and well-lit room with a desk, PC screen, mouse, and keyboard in their visual field. All measurements were tested on the same PC and the same monitor was used for cognitive assessments (Philips, Inc. USA, 24″ 59.95 Hz LCD monitor with 1920 × 1200-pixel resolution and 5 ms response time). Orienting on previous studies, the participants were asked to sit at a distance of 70 cm from the 24-inch computer screen (Brush et al., 2016).

Go/No-go test

In this study, the Go/No-go test was used to determine response the performance in inhibitory control and reaction time. The participant was expected to respond as quickly as possible via the computer mouse (Fig. 2b). The Go/No-go test consists of a large square with four small squares on the screen. In any of the squares, the letter ‘P’ or ‘R’ appears randomly. In the first session, the participant was asked to react to the letter ‘R’ and remain unresponsive when the letter ‘P’ appeared on the screen. In the second part of the test, the participant is expected to react to the letter ‘P’, but not to the letter ‘R’. The performance of the Go-No-go test was operationalized via the reaction time and the number of correct responses (Nosek & Banaji, 2001). A faster reaction time and a higher number of correct responses indicated a better inhibitory control.

Tracking test

The Tracking test is based on randomly moving blue circles on the screen that must be followed as closely as possible with the mouse cursor (the dpi setting is up to the participant, see Fig. 2c). Throughout the entire Tracking test, the average distance from the target to the cursor is recorded and a shorter distance to the target indicates a better performance.

Exercise protocol

In the acute physical exercise protocol, the WAnT has been employed using a specific cycling ergometer (Monark 839E, Sweden). At first, the exercise protocol was introduced to the participants, then they were seated on the cycle-ergometer and started a 4-minute warm-up at 50 Watts (W) with a pedaling cadence of 60–70 rpm. During the warm-up period, the participants were requested to sprint twice to determine their maximum pedaling rate for the WAnT and these rates were recorded for each participant. During the WAnT, the participants were asked for “all-out” efforts achieved by cycling with a maximum pedaling speed for 30 s against an individually adjusted flywheel resistance (i.e., 80 gr per kg bodyweight). The peak power (PP) outputs of the participants were recorded during the WAnT.

Functional Near-Infrared Spectroscopy (fNIRS) recordings

As done in previous studies (Mancı et al., 2023), in this study cortical hemodynamics in the PFC were recorded via a portable fNIRS device (fNIRS Devices 1100 LLC, MD, USA). The cortical hemodynamic changes were acquired from 16 channels via a silicon sensor pad, which has four LED light sources and ten light detectors. This sensor pad, whose position corresponds to the prefrontal cortex according to the user manual of the device, was placed on the participants’ forehead as previously described and recommended (Ayaz et al., 2019). In addition, the pad was covered with a black band to reduce the confounding influence of ambient light. Sixteen channels with a 2.5 cm source-detector separation recorded the cortical hemodynamic responses at a frequency of 2 Hz. The amount of light from the light source was transmitted to and sensed by the fNIRS device, and the oxy-hemoglobin (oxy-Hb) and deoxy-hemoglobin (deoxy-Hb) concentration changes in that region are calculated over the amount of light absorption in the cerebral tissues. Total hemoglobin (total-Hb) is obtained from the sum of these two parameters. can be found in. After data processing, which is described in more detail by Mancı et al., 2023, the average of all channel data was determined and used for further statistical analysis.

Statistical analysis

For the statistical analysis and to create the raincloud plots, the JASP 0.16.2. (JASP Team, 2018; https://jasp-stats.org/, accessed on 31 August 2022) software was used. The absence or presence of the normal distribution of the data was assessed using the Shapiro-Wilk test. According to the results of the Shapiro-Wilk test, all variables were normally distributed. Therefore, parametric tests (i.e., independent samples t-tests for between-group analyses) and “repeated measures ANOVA” were used. All results were expressed as means (M) and standard deviations (SD). The significance level was set for all statistical tests at α < 0.05, and the alpha level was adjusted for the post-hoc tests using Bonferroni’s correction method. The effect sizes were reported as partial η2 (for ANOVA) or calculated with Cohen’s d (for independent t-test comparisons). Cohen’s d was rated as follows (with 95% confidence intervals): small effect < 0.2, medium effect ≥ 0.2 to ≤ 0.8, and large effect > 0.8.

Results

Demographic information, body composition, and physiologic parameters

Ten amateur esports players with a mean age of 27.40 ± 3.09 years and nine participants who did not play video games (age-matched controls) with a mean age of 26.55 ± 5.05 years were included in the study. The general demographic and performance characteristics of the participants are displayed in Table 1. For the sake of simplicity, the Results section was divided into subsections to report the findings for the game performance and cognitive performance separately.

As shown in Table 1, there was no statistically significant difference regarding demographic variables and measures of physical fitness (i.e., Peak Power) between the esports group and the control group.

Game performance

The game performance scores showed a significant difference between the two groups in all sessions [F(1,16) = 17.48, p < .001, n²=0.458; Table 2; Fig. 3a,b,c)]. The post-hoc comparisons between the groups revealed that the esports player group was faster in the Pre, Post-1, and Post-2 sessions as compared to the control group (Table 2).

Additionally, we noticed a significant main effect of time regarding the Valorant gaming performance [F(2,32) = 12.34, p < .001, n²=0.050]. As shown in more detail in Table 2, the post hoc analyses revealed that there are significant differences between the Pre - Post-2 and Post-1- Post-2 measurements of the control group (i.e., better performance at Post 2) and between Pre- and Post-1 in the player group (i.e., better performance at Post-1) suggesting that SE has a positive influence on gaming performance.

Table 2 Overview of the valorant game performance of esports players and control groups concerning the between and within group comparisons
Full size table

Cognitive performance

Tracking tests

A significant main effect of group was observed [(F(1,16) = 7.06, p = .017, n²=0.276; Fig. 3d,e,f)], but no main effect of time was noticed [F(2,32) = 0.92, p > .05, n²=0.005]. The post-hoc comparisons between both groups showed that the esports player group more successfully tracked the target cursor with the mouse in the Pre, Post-1, and Post-2 sessions as compared to the control group.

Go/No-go tests

There were neither statistically significant differences between the two groups at the different time points of assessment when the results of response accuracy in the Go/No-go Test were analyzed [F(1,16) = 2.84, p > .05, n²=0.100; Fig. 3g,h,j] nor did we observe a significant time effect in the control group [F(2,32) = 0.23, p > .05, n²=0.003]. In the esports player group, a significant difference was observed between Pre - Post-2 [MD: -0.02 (acc), p < .05, d: -0.90] and Post-1 – Post-2 [MD: -0.03 (acc), p < .01, d: -1.09].

Regarding the reaction time, we observed a statistically significant main effect of the group indicating that the esports group responded faster as compared to the control group [F(1,16) = 5.41 p = .033, n²=0.210; Fig. 3k,l,m]. However, the statistical significance did not survive the Bonferroni correction in the post-hoc analyses. Additionally, no significant main effect of time was observed in both groups [F(2,32) = 2.44, p > .05, n²=0.020].

Fig. 3
figure 3

Visualization of the Valorant gaming performance and Tracking and Go/No-go test result between groups. (a) Valorant Pre-session results (-Pre), (b) Valorant 5 min after the cessation after the acute bout of SE (Post-1), (c) Valorant 30 min after the cessation after the acute bout of SE (Post-2), (d) Tracking Pre-session results, (e) Tracking Post-1 session results, (f) Tracking Post-2 session results, (g) Go/No-go Acc. Pre-session results, (h) Go/No-go Post-1 Acc. session results, j) Go/No-go Acc. Post-2 session results, k) Go/No-go RT. Pre-session results, l) Go/No-go Post-1 RT. session results, m) Go/No-go RT., Post-2 session results SE: Sprint Exercise, Acc: Accuracy, RT: Reaction Time; *p < .05

Full size image

Hemodynamic parameters during game protocol

Oxy-hemoglobin results (oxy-Hb)

The oxy-Hb values of the players and the control group during the Valorant game did not show a statistically significant difference [F(1,16) = 0.46, p > .05, η2=,016]. Although it was observed on descriptive level that oxy-Hb values gradually decreased in the esports player group as an effect of time (Pre-M: 2.49 ± 2.12; Post-1 M: 1.45 ± 2.75 and Post-2 M: 0.94 ± 2.04 µ/umol), no statistically significant effects of time were observed for both groups [F(2,32) = 0.26, p > .05, η2 = 0.007; Fig. 4a].

Fig. 4
figure 4

Oxy-Hb values during Valorant performance (a) oxy-Hb, (b) deoxy-Hb, (c) total-Hb

Full size image

De-oxyhemoglobin results (deoxy-Hb)

There was no statistically significant difference between the deoxy-Hb values of the players and the control group during the Valorant game [F(1,16) = 0.78, p > .05, η2=,019]. Although we observed on descriptive level that deoxy-Hb values gradually increased in the player group as an effect of time (Pre M: -0.03 ± 0.77; Post-1 M: 0.29 ± 2.68 and Post-2 M: 0.62 ± 2.15 µ/umol), no statistically significant effects of time were observed for both groups [F(2,32) = 0.77, p > .05, η2 = 0.026; Fig. 4b].

Total-hemoglobin results (total-Hb)

The total-Hb values of the players and control group during the Valorant game did not show a statistically significant difference [F(1,16) = 0.006, p > .05, η2 = 2.01]. Although total-Hb values in the player group gradually decreased on descriptive level as an effect of time (Pre M: 2.45 ± 2.14; Post-1 M: 1.75 ± 3.99 and Post-2 M: 1.55 ± 3.74 µ/umol), no statistically significant effects of time were observed for both groups[F(2,32) = 0.61, p > .05, η2 = 0.015; Fig. 4c].

Discussion

This study investigated the differences between and effects of acute sprint exercise on the gaming performance and cognitive performance of esports players and age-matched controls. Our results suggest that esports players outperformed the controls in gaming performance regardless of the session, and (ii) that a single bout of SE can lead to an improvement in gaming performance in both groups while benefiting specific aspects of cognitive performance in the esports players group (i.e., accuracy score in the Go/No-go task 30 min after cessation of the SE). However, in both groups, the improvements in gaming performance were not accompanied by changes in cortical hemodynamics in the PFC.

Gaming performance

In the literature, there is growing evidence that acute physical exercise can improve cognitive performance (for review see Chang et al., 2012; Pontifex et al., 2019), however, there is a limited number of studies that investigated the effects of acute physical exercise on gaming performance by contrasting esports players and age-matched controls (de Las Heras et al., 2020; Toth et al., 2020). In this study, in-game performances at 5 and 30 min after the cessation of an acute bout of SE were examined. Regardless of the time of the assessment (i.e., Pre, Post-1, and Post-2), the esports player group showed a better gaming performance as compared to the control group. Additionally, it was observed that the Valorant game scores of both the player and the control group were better in the Post- 30 min after the cessation of the acute bout of SE (Post-2, see Fig. 4; Table 2). In general, this observation is consistent with the evidence that the largest effect sizes regarding cognitive improvements (i.e., gaming performance) after acute bouts of strenuous physical exercises (i.e., SIT) are observed after a certain delay after the cessation of the physical exercises (Chang et al., 2012) although it has to be acknowledged that our knowledge concerning specific dose-response effects is not exhaustive (Pontifex et al., 2019), especially about follow-up assessments of cognitive performance after SE.

The observed improvements in gaming performance are probably related to exercise-induced changes in specific neurobiological mechanisms. About the latter, several neurobiological mechanisms explaining cognitive improvements after acute physical exercise are discussed in the literature (for review see Pontifex et al., 2019) but the exact drivers of exercise-induced cognitive performance improvements are still relatively unknown. Among those neurobiological mechanisms, the changes in cortical hemodynamics (e.g., assessed via fNIRS in the PFC) might play an important role (for review see Herold et al., 2018). To elucidate whether the latter is related to the observed changes in gaming performance, we assessed in the current study cortical hemodynamics in the PFC via fNIRS (see section Cortical Hemodynamic Responses for a detailed discussion).

Based on the findings of our study, SE that is performed by esports players between games or during training might exert after a certain delay a positive effect on their gaming performance in the next session. However, to provide more robust evidence for this assumption, further studies considering a potential dose-response relationship (e.g., different exercise intensities and durations) are needed.

Cognitive performance

A recent systematic review reported that there is a limited number of studies that investigated the cognitive characteristics of esports players and advocated for future research in this direction (Toth et al., 2020; Pedraza-Ramirez et al., 2020). In this context, our study adds to the literature that, as compared to the age-matched and non-gaming control group, our cohort of younger esports players showed better tracking skills and faster reaction times in the Go/No-go task but did not have a higher accuracy in the latter. This finding is at least partly in line with the observation of previous studies reporting that esports players outperformed the control groups concerning working memory and reaction times (Kang et al., 2020; Wechsler et al., 2021). Moreover, our findings mirrored the findings of (i) Kowal et al. (2018) who evaluated visual tracking skills via the Trail Making Test (TMT) and noticed a faster completion time of esports players in the TMT (Kowal et al., 2018), and (ii) Wechsler et al. (2021) who observed that esports players showed more advanced moving object tracking performance than the general population (Wechsler et al., 2021).

The superior performance of esports players in the tracking task as compared to the control group might mirror a higher neural efficiency of esports players because this ability is required for successful gameplay and, in turn, probably trained by a regular engagement in esports. Another fact that perhaps explains the superior tracking performance of esports players is the more efficient utilization of ocular function (Khromov et al., 2019). Schenk et al. (2020) reported that esports players detect stimuli with a more centralized perspective and can perform an early perceptual analysis (Schenk et al., 2020) which might contribute to a more efficient visual tracking performance.

Concerning inhibitory control (i.e., measured by the Go/No-go task) our findings add to the rather mixed evidence in this direction. For instance, several studies did not find compelling evidence that esports players have superior inhibition control (Colzato et al., 2013; Kowal et al., 2018). More specifically, Kowal et al. (2018) reported that esports players showed faster reaction times but had lower accuracy in the Stroop task as compared to the non-gaming control group (Kowal et al., 2018). However, in our study, we observed that esports players have faster reaction times in the Go/No-go test but did not outperform the controls concerning accuracy rate. Thus, our findings favor the interpretation that esports players have a more efficient inhibitory control although our findings should be treated cautiously based on the small sample size of the current study. An explanation for the above-mentioned phenomenon of faster reaction time and lower/comparable accuracy is perhaps related to the fact that action video gamers such as esports players focus more on reaction time than accuracy because the latter is often not of critical importance for successful actions within the game (McDermott et al., 2014). Although our study findings add some new evidence to the literature, future rigorous research is necessary to gain more solid knowledge on the cognitive performance of esports players in general and domain-specific differences in particular (Pedraza-Ramirez et al., 2020).

Cortical hemodynamic responses

In general, there is a limited number of studies that examine cortical hemodynamics during gaming using fNIRS (Izzetoglu et al., 2004; Nagamitsu et al., 2006). To the best of our knowledge, cross-sectional comparisons of esports players and non-gaming controls are scant since the available studies focused, for instance, on the influence of gaming difficulty and reported that a higher gaming difficulty is accompanied by an increase in the activation of the dorsolateral prefrontal cortex (Izzetoglu et al., 2004). In another study, Nagamitsu et al. (2006) evaluated participants’ cortical hemodynamic responses during gaming with fNIRS and noticed that they observed an increase in total hemoglobin levels in four of six adults (Nagamitsu et al., 2006). However, in the current study, we neither observed a statistically significant difference between esports players and age-matched controls nor changes in response to the acute bout of SE in both groups. Especially, latter finding is somewhat surprising because the findings of several studies suggest that an acute bout of physical exercise is associated with a post-exercise increase in oxy-Hb in the PFC which coincides with better cognitive performance (for review see Herold et al., 2018). Although on the descriptive level, after the SE (during the post-1 session) both the oxy- and deoxy-Hb values were decreased in the control group while oxy-Hb was decreased and deoxy-Hb was increased in the esports player, these changes did not reach statistical significance. The absence of statistically significant effects is perhaps related to the high inter individual variability of the cortical hemodynamics and the rather low sample size in the current study. Thus, future studies should seek to verify (or refute) our observations using a higher number of participants.

Strength and limitation

Some limitations should be considered when interpreting the findings of the current study. Firstly, the number of required amateur esports players that need to be included in this study to achieve the predetermined level of power has not been reached due to COVID-19 conditions. Secondly, our fNIRS montage was limited to the PFC and thus future studies are advised to use a whole-head montage to better understand whether our findings can be generalized to the other brain areas. Thirdly, the implementation of a control condition (e.g., seated rest) would allow more solid conclusions concerning the effects of time (e.g., learning/ practice effects caused by the repetitive performance of the cognitive test) (Pontifex et al., 2019). Finally, the esports players were familiar with the Valorant game while the control group was completely unaware of that game. Their tendency to play the game was not evaluated in the current scope of MS. In future studies, this motivation can also be measured with various tools.

Conclusion

In the present study, we observed that amateur esports players showed superior performance in both game performance and tracking ability regardless of the sessions as compared to age-matched controls. Moreover, we noticed that a single session of SE positively influences the gaming performance of both groups and leads to an improvement in accuracy score in the Go/No-go task in amateur esports players 30 min after SE.

Taken together, our findings suggest (i) that engaging in esports can improve specific aspects of cognition, and (ii) that amateur esports players and controls can benefit from an acute bout of SE before competitions or training in terms of their gaming performance. However, future research is necessary to substantiate these promising findings and their generalizability (e.g., to professional esports players).