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Article

How Does Virtual Reality Training Affect Reaction Time and Eye–Hand Coordination? The Impact of Short- and Long-Term Interventions on Cognitive Functions in Amateur Esports Athletes

by
Maciej Lachowicz
1,*,†,
Anna Serweta-Pawlik
2,
Alicja Konopka-Lachowicz
3,
Dariusz Jamro
4 and
Grzegorz Żurek
1,†
1
Department of Biological Foundations of Physical Activity, Wroclaw University of Health and Sport Sciences, 51-612 Wroclaw, Poland
2
Department of Fundamentals of Physiotherapy and Occupational Therapy, Wroclaw University of Health and Sport Sciences, 51-612 Wroclaw, Poland
3
Fizjohome Rehabilitation, 53-201 Wrocław, Poland
4
Department of Physical Education and Sport, General Tadeusz Kosciuszko Military University of Land Forces, 51-150 Wroclaw, Poland
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Appl. Sci. 2025, 15(8), 4346; https://doi.org/10.3390/app15084346
Submission received: 4 March 2025 / Revised: 26 March 2025 / Accepted: 3 April 2025 / Published: 15 April 2025
(This article belongs to the Special Issue Virtual and Augmented Reality: Theory, Methods, and Applications)
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Abstract

:
This study investigates the efficacy of VR-based cognitive training using the game Beat Saber in enhancing cognitive functions in amateur e-athletes. Participants were divided into two groups, undergoing either 8-day or 28-day training. Significant improvements were observed in reaction time (RT) and eye–hand coordination (EHC) for both groups. Notably, cognitive gains in EHC were maintained over time, indicating the durability of training effects. The lack of significant differences between the short-term and long-term training outcomes suggests that even brief, intensive VR training can lead to substantial cognitive improvements, potentially obviating the need for extended training periods. The findings underscore the potential of immersive VR games like Beat Saber as effective tools for cognitive training. This study also highlights the relevance of VR technology beyond entertainment, demonstrating its application in cognitive enhancement. Given the rising popularity of esports and VR, integrating such technologies into cognitive training programs offers a promising avenue for improving cognitive functions in younger populations familiar with virtual environments. The results suggest that VR-based interventions can enhance cognitive functions which are crucial for both competitive esports and general cognitive functioning, making VR a versatile tool in various training contexts. Further research is recommended to explore the generalizability of these findings to other VR games and different populations.

1. Introduction

Esports has rapidly become a major player in the competitive entertainment industry. It all began on 19 October 1972 at Stanford University, with the first-ever computer game tournament featuring “Spacewar!” [1]. This event marked the birth of what is now a multi-billion-dollar industry. According to Statista, esports is expected to generate USD 4.3 billion in revenue by 2024, demonstrating a significant shift from traditional sports to digital competitions [2]. Over the past few decades, technological advancements have driven the transformation of esports into a highly competitive and dynamic field [3]. The early 21st century witnessed a dramatic increase in esports’ popularity and participation, with major corporations and sports clubs (e.g., Ajax Amsterdam, Schalke 04, Paris Saint-Germain) investing in professional teams and international tournaments [4]. Games like “Defense of the Ancients” (DOTA), “League of Legends” (LOL), and “Counter-Strike: Global Offensive” (CS:GO) have become cultural staples [5,6]. Major esports events, such as the League of Legends World Championships, attract audiences rivaling those of traditional sports events like the Super Bowl, with millions of viewers worldwide [7].
The esports industry continues to capture the interest of educational institutions and the scientific community, particularly in cognitive science. Studies have shown that different game genres place unique cognitive demands on players. First-person shooters (FPS) like CS:GO require rapid reflexes and quick reaction times [8]. Real-time strategy (RTS) games like “Starcraft” demand strategic foresight and multitasking [9]. Multiplayer online battle arenas (MOBAs) such as LOL emphasize cognitive flexibility and decision-making [10]. Understanding the cognitive requirements of esports has highlighted the importance of methods to enhance cognitive functions, crucial for both professional and amateur training [11].
As esports continues to parallel traditional sports in both scale and spectacle, the training methods must also evolve to meet the unique demands of digital competition [12,13]. VR emerges as a transformative tool in this landscape, providing not just a method for enhancing cognitive functions critical to esports’ success but also as a way to bridge the gap between traditional athletic training and the digital battlegrounds of esports. By integrating virtual reality (VR) into training regimens, athletes can gain a competitive edge rooted in the ability to quickly process complex scenarios and execute decisions with precision.
A key advantage of VR lies in its capacity to engage multiple cognitive systems simultaneously through immersive, interactive environments that closely replicate the fast-paced, high-stakes nature of esports. Unlike conventional training methods, VR enables real-time adaptation of training difficulty, offering dynamically evolving scenarios that continuously challenge cognitive flexibility. Moon et al. [14,15] demonstrated that VR-based cognitive training enhances both cognitive and emotional adaptability by providing real-time interaction and dynamic feedback, which help users adjust to shifting task demands. Their study highlights that VR encourages spontaneous cognitive restructuring by immersing users in unpredictable situations, forcing them to reassess strategies and process new information efficiently. This constant exposure to variable conditions strengthens cognitive flexibility by reinforcing neural pathways associated with adaptive decision-making. Drigas and Sideraki [16] as well as Zhong et al. [17] explain that by providing immersive and engaging experiences that stimulate neural activity and facilitate adaptive responses, VR-based interventions offer innovative solutions for cognitive neurorehabilitation and cognitive enhancement. Their systematic review highlights that VR-based cognitive training promotes neural activation and synaptic plasticity in relevant brain regions. These changes are associated with an enhanced working memory and cognitive flexibility, supporting the idea that VR can function as an effective tool for long-term cognitive neurorehabilitation. Furthermore, their findings suggest that immersive VR environments trigger neurochemical changes that facilitate more efficient information processing, reinforcing neural circuits involved in executive function and memory consolidation.
Focusing on cognitive enhancement, Lachowicz et al. [18] demonstrated significant improvements in concentration performance and alternating attention after short-term VR training in e-athletes. Further research showed similar effects of short-term VR training on reaction time, motor time, and eye–hand coordination [19].These studies, however, were limited to an 8-day training period, leaving open the question of possible further cognitive improvement after longer training. Additional questions regard the possible persistence of both short- and long-term training. Similarly, Rutkowski et al. [20] have shown that short-term VR training can enhance hand–eye coordination and reaction time. Furthermore, research by Serweta-Pawlik et al. [21] and Szary et al. [22] underscores the broad efficacy of VR technology. The former demonstrated that VR can effectively stimulate cognitive functions, making it a valuable tool for cognitive neurorehabilitation and enhancement, while the latter found that VR significantly increases exercise intensity, offering a novel approach to physical rehabilitation and fitness training. These studies highlight the versatility and significant impact of VR technology, which, much like esports, is transcending its origin and finding success in a myriad of fields including medical and professional training [23,24,25,26,27,28,29,30]. VR also serves as a powerful tool for enhancing motivation and adherence in both training and therapy by leveraging its immersive, interactive nature to transform mundane or challenging tasks into engaging experiences. Research has shown that VR increases adherence through gamification, real-time feedback, and dynamic challenges that sustain user interest [31]. Additionally, VR stimulates reward-related brain pathways, reinforcing intrinsic motivation and promoting long-term engagement in neurorehabilitation and learning programs [32]. Powell et al. [33] demonstrated that VR-based neurorehabilitation enhances patient motivation by fostering a sense of achievement through incremental progress tracking and interactive goal setting. Their findings suggest that the ability to visualize improvements in real time significantly boosts adherence, making therapy more effective. Similarly, Sattar et al. [34] found that VR-based training enhances motivation in educational settings by providing an engaging, exploratory environment that increases learning competency and participation. Their study highlights that the immersive aspects of VR reduce cognitive fatigue and improve focus, leading to higher retention rates and long-term engagement. The sense of autonomy and control provided by VR further enhances adherence, as users can explore and interact with virtual environments at their own pace, fostering self-directed engagement [35]. Moreover, VR’s ability to deliver immediate and meaningful feedback strengthens self-efficacy, a key factor in sustained participation and adherence to training regimens [36]. These findings collectively suggest that VR’s unique ability to blend cognitive stimulation, emotional engagement, and reward-driven reinforcement makes it an ideal medium for increasing motivation and improving long-term adherence in both training and therapeutic interventions.
Evidence suggests that longer VR training sessions lead to greater cognitive and motor improvements compared to shorter interventions. Pelosin et al. [37] found that an extended VR-based cognitive–motor training program resulted in greater improvements in reaction time, motor coordination, and executive function than shorter training programs. Similarly, Sokołowska [38] demonstrated that extended reality (XR) training strengthens cognitive flexibility and decision-making over prolonged exposure, with effects that are significantly more pronounced in longer-duration training groups. This aligns with studies showing that cognitive training in VR induces lasting neural changes, including synaptic plasticity and increased functional connectivity between motor and cognitive regions [39].
During initial training phases, individuals often experience rapid but shallow improvements as they acclimate to new tasks and environments. However, these gains can plateau if the training duration is insufficient, limiting the reinforcement of newly formed neural connections [40]. Extended VR training allows participants to move beyond basic skill acquisition into phases of refinement and automatization, where cognitive and motor skills become deeply ingrained and resistant to decay [41]. Hwang et al. [42] found that VR-based cognitive training combined with locomotor tasks led to more sustained cognitive and motor improvements when training durations were extended, highlighting the need for prolonged exposure to maximize skill retention.
Moreover, VR training’s immersive and interactive nature is particularly conducive to sustained cognitive engagement and long-term learning. By continuously engaging multiple cognitive systems, VR fosters deeper cognitive processing and adaptation, both of which are crucial for esports athletes who must perform complex tasks under pressure. Extended exposure to these environments enables more profound neural adaptation, reinforcing pathways involved in executive control, working memory, and motor coordination. Herold et al. [43] demonstrated that motor–cognitive training is significantly more effective when maintained over a longer period as it allows for better integration of the cognitive and motor functions essential for competitive performance.
Furthermore, VR’s adaptability ensures that training remains optimally challenging, preventing skill plateauing and promoting continuous improvement [44]. Ren et al. [45] highlight that longer VR-based cognitive neurorehabilitation programs lead to more stable improvements in executive function and reaction time, compared to shorter training interventions. Consequently, a longer VR training period is expected to yield more pronounced and durable cognitive improvements in key esports-related functions, such as eye–hand coordination [46].
While considerable research has examined cognitive functions in professional esports athletes and the role of VR in neurorehabilitation, the potential combination of VR training for amateur e-athletes remains largely unexplored. Given that cognitive demands in amateur esports are increasingly mirroring those of professional settings, understanding how VR training can enhance cognitive performance in this group is of particular interest. Building on mentioned insights, we hypothesize that an extended 28-day VR training will result in significantly greater improvements in esports-relevant cognitive skills, including reaction time, eye–hand coordination, and attentional control, compared to a short-term 8-day training program. Additionally, we expect that while cognitive gains will be observed in both conditions, the longer training duration may lead to sustained benefits rather than plateau effects, providing insights into the optimal duration for VR-based cognitive training in amateur e-athletes. By integrating existing research on neuroplasticity, cognitive–motor training, and VR-based cognitive enhancement, this study fills a crucial gap in understanding the optimal duration for VR training interventions in esports performance.

2. Material and Methods

2.1. Participants

The investigation enrolled 128 amateur e-athletes, consisting of 81 males and 47 females, ranging in age from 18 to 44 years, with a mean age of 23.92 ± 3.55 years. Participants were randomly allocated to one of four groups using a 1:1 randomization procedure executed via www.randomizer.org. This allocation formed two experimental groups, E8 (N = 32) which received eight days of consecutive VR training and E28 (N = 30) which underwent twenty-eight days of consecutive VR training, and two passive control groups, C8 (N = 34) and C28 (N = 32), which did not undergo VR training.
Prior to their inclusion in the study, all participants verified their status as non-professional, active amateur e-athletes. A baseline questionnaire was administered to collect data on their gaming preferences, average daily gaming hours, e-sports experience, and demographic information. Analysis of the questionnaire responses indicated a preference for MOBA and FPS games. There were no significant initial differences between the groups in terms of daily gaming duration, esports experience, age, or baseline cognitive function metrics (all p > 0.575, as detailed in Supplementary Table S1).
The study noted a dropout rate of 27.34%, and only those participants who completed the pre-study, post-intervention, and follow-up assessments were included in the final data analysis. Participant retention and dropout details during subsequent phases are illustrated in Figure 1. Post-randomization exclusions were applied to participants identified with neurological, visual, auditory, or motor impairments that might interfere with VR training, as well as those reporting adverse effects from VR exposure. Participant well-being was continuously monitored using the Simulator Sickness Questionnaire (SSQ) [47] administered before and after each session. Remarkably, no symptoms of virtual reality sickness were reported by any participants throughout the study. Additionally, the implementation of the SSQ facilitated the systematic monitoring of simulator exposure, physiological status, overall health parameters, sleep quality, and alcohol consumption.
The study’s recruitment phase occurred in November and December 2022, while the scientific data were collected in December 2022.

2.2. Measurements

In the E groups, cognitive function assessments were conducted using specific tests administered prior to the initial training session and again 30 min following the last training session. Szpak et al., [48] found that after 50 min of Beat Saber, 40 min of rest was sufficient for SSQ symptoms to return to baseline. Consequently, for our study involving 15 min of VR exposure, we estimated that a 30 min rest period would be appropriate. For the C groups, identical pre- and post-tests were conducted 8 consecutive weekdays apart. Additionally, follow-up assessments were carried out for all groups 31 days after the post-tests to ascertain any enduring effects of the interventions. To assess eye–hand coordination (EHC), the S1 2HAND test from the Vienna Test System (SCHUHFRIED GmbH, Mödling, Austria) was employed. This test requires participants to manipulate two joysticks to navigate a red dot along a predefined track, comprising three sections each demanding varying levels of bilateral coordination. The test records the total mean duration to complete the track, reflecting overall performance, the total percent error duration (TPED), and average error time (AET), which quantifies the accuracy of their EHC. Reaction time (RT) and motor time (MT) were evaluated using the S2 Reaction Test (ReT) from the Vienna Test System, a well-established platform for assessing cognitive and motor abilities. This test involves participants responding to auditory signals by quickly transitioning from pressing one button to another. The time interval from hearing the signal to button release is measured as the RT, and the interval from releasing to pressing the adjacent button is recorded as the MT, with both durations measured in milliseconds.

2.3. Intervention

Participants assigned to the experimental groups underwent either eight or twenty-eight immersive VR training sessions using the rhythm-based virtual reality game Beat Saber, with each lasting 15 min. These sessions were conducted using the Valve Index VR headset. In contrast, individuals in the control groups did not engage in any VR training activities. Beat Saber is a fast-paced, rhythm-based VR game that requires players to slice through incoming blocks in time with music using two virtual lightsabers, as shown in Figure 2. The blocks appear in different positions, are color-coded for each hand, and include directional indicators that dictate the required slicing motion. Players must also avoid obstacles by ducking or sidestepping, further engaging spatial awareness and motor coordination. The cognitive demands of Beat Saber make it a compelling tool for training key esports-related skills. Its gameplay fosters rapid decision-making, reaction time, sustained attention, and cognitive flexibility, as players must process dynamic visual and auditory cues in real-time while executing precise movements.
The key in-game tasks hypothesized to drive cognitive improvements are as follows:
  • Players must quickly recognize and respond to incoming blocks, adjusting their slicing movements in real time. The sequencing and increasing difficulty levels demand continuous cognitive and motor adaptation, reinforcing faster reaction times and decision-making under pressure. Previous research on rhythm-based games, such as Osu!, has demonstrated their effectiveness in improving reaction times, as noted by Hagiwara et al. [49]. Their study found that fast-paced rhythm games require players to process visual stimuli rapidly and execute precise motor responses, making them a valuable tool for enhancing reaction time.
  • The game challenges simultaneous visual tracking and precise hand movements, requiring players to synchronize their actions with the block’s direction, color, and rhythm. This process strengthens sensorimotor integration and visuomotor processing, leading to enhanced coordination and accuracy.
To progressively challenge participants, the difficulty level of the game was escalated systematically; in the E8 group, the difficulty increased every two days, whereas in the E28 group, it increased every seven days. The selection of songs was randomized across the sessions to ensure diversity and mitigate any potential learning or performance biases. This regimen using Beat Saber was designed to offer a dynamic and engaging training environment, ideally suited for enhancing both the cognitive and physical capabilities of e-athletes. The rhythm-based gameplay necessitates swift reflexes, heightened concentration, and the rapid shifting of attention, making it a potent tool for cognitive development [50]. Additionally, the variability in song selection required participants to continually adapt to new auditory patterns and sequences, thereby improving their cognitive flexibility and enhancing auditory processing skills.

2.4. Statistical Analysis

The statistical evaluations were performed using IBM SPSS Statistics V.27. The normality of data distributions was verified using the Shapiro–Wilk test, with a significance threshold established at p ≤ 0.05 for identifying statistically significant results. Initially, mean values and standard deviations were calculated for the ReT and the 2HAND test outcomes.
Subsequently, a detailed 4 × 3 × 5 mixed model ANOVA was conducted to explore the main effects and interactions among time (pre, post, follow-up), measurement (RT, MT, EHC, TPED, AET), and group (E8, E28, C8, C28). A 4 × 3 × 5 mixed model ANOVA was specifically chosen because it allows simultaneous evaluation of both within-subject factors (time and measurement) and the between-subject factor (group). This statistical design aligns closely with our hypotheses, enabling assessment of the overall effectiveness of VR training as well as determination of whether training duration (8-day vs. 28-day) significantly influences cognitive outcomes across multiple testing phases (pre-test, post-test, and follow-up). Furthermore, the mixed model ANOVA facilitates examination of interaction effects, which is critical for identifying how different cognitive functions (RT, MT, EHC, TPED, AET) respond distinctly across training groups and testing points. This approach thus directly addresses our research questions concerning both the durability and specificity of cognitive improvements following VR-based training interventions.
Finally, to discern significant differences between the groups and across different time points, Bonferroni post hoc tests were applied. This analytical approach laid the groundwork for the subsequent statistical findings reported in our study.

3. Results

The ReT and 2HAND tests results are displayed as mean values ± standard deviation in Table 1 and Figure 3 and Figure 4.
Table 2 details the outcomes from the utilized 4 × 3 × 5 mixed model concerning main effects and interactions. Significant main effects were identified for group (F = 3.491, p = 0.018, η2p = 0.105, λ = 10.473), measurement (F = 3628.679, p < 0.001, η2p = 0.976, λ = 14,514.716), and time (F = 9.839, p < 0.001, η2p = 0.1, λ = 19.678). The analysis also uncovered significant interactions of time × group (F = 4.531, p < 0.001, η2p = 0.132, λ = 27.186) and measurement × group (F = 2.628, p = 0.002, η2p = 0.081, λ = 31.535). Additionally, significant interactions were found for measurement × time (F = 3.455, p < 0.001, η2p = 0.037, λ = 27.642) and the three-way interaction of measurement × time × group (F = 3.163, p < 0.001, η2p = 0.096, λ = 75.905).
Following the initial analysis, significant within-group changes were explored, as presented in Table 3.
In the E8 group, significant enhancements were observed in EHC (p < 0.001, F = 10.198, η2p = 0.188, λ = 20.395), TPED (p = 0.001, F = 8.068, η2p = 0.155, λ = 16.136), and AET (p = 0.002, F = 6.633, η2p = 0.131, λ = 13.267), indicating that short-term VR training leads to notable improvements in eye–hand coordination and efficiency in cognitive–motor tasks. The moderate effect sizes (η2p = 0.131–0.188) suggest that these improvements were meaningful but not large-scale transformations, aligning with previous research on short-term VR interventions. MT showed a near-significant trend (p = 0.054, F = 3.010, η2p = 0.064, λ = 6.020), while RT did not reach significance (p = 0.479, F = 0.741, η2p = 0.017, λ = 1.483), reinforcing the notion that shorter VR interventions primarily enhance coordination rather than fundamental reaction time improvements.
In the E28 group, significant changes were observed in EHC (p < 0.001, F = 23.193, η2p = 0.345, λ = 46.386), RT (p < 0.001, F = 9.529, η2p = 0.178, λ = 19.059), and MT (p = 0.004, F = 5.933, η2p = 0.119, λ = 11.866), indicating robust cognitive–motor improvements over an extended training period. The large effect size for EHC suggests a substantial impact of VR training on eye–hand coordination, while the moderate effect sizes for RT and MT indicate that reaction time and movement speed improvements were present but less pronounced. AET and TPED did not show significant changes (p = 0.224 and p = 0.126, respectively), suggesting that these functions may either plateau with prolonged training or require different intervention approaches to elicit further improvements.
Conversely, the C groups did not exhibit significant changes in cognitive function, except for a minor significance in AET within C28 (p = 0.046, F = 3.199, η2p = 0.068, λ = 6.399). The small effect size in AET suggests that this improvement was marginal, reinforcing that VR training was the primary driver of cognitive–motor enhancements rather than external factors.
The observed pattern of results supports the notion that short-term VR training is effective at enhancing coordination-based tasks, while prolonged training is more beneficial for reaction time improvements. The large effect size in EHC for E28 indicates that longer training leads to more pronounced gains in sensorimotor integration, while the lack of significance in TPED and AET suggests potential training thresholds where additional exposure does not necessarily yield further gains.
Subsequent Bonferroni post hoc within-group analysis indicated, as shown in Figure 5 and Figure 6 and Table 4, that both the E8 and E28 groups experienced significant improvements after the intervention, with some improvements persisting in pre- vs. follow-up comparisons.
In the E8 group, the pre-test vs. post-test comparison revealed significant improvements in MT (p = 0.047), despite the ANOVA not showing significant overall changes, as well as in EHC (p = 0.003), TPED (p = 0.002) and in AET (p = 0.002). However, RT did not exhibit significant changes (p = 0.693), reinforcing the notion that reaction time may require more prolonged training for measurable improvements. Between pre-test and follow-up, EHC remained significantly improved (p < 0.001), indicating that eye–hand coordination benefits persisted over time, while RT (p = 1.0), MT (p = 1.0), TPED (p = 1.0), and AET (p = 1.0) showed no sustained differences, suggesting that the improvements in these measures may require continued training to maintain long-term effects. Post-test vs. follow-up comparisons revealed a significant change in TPED (p = 0.027), suggesting some degree of performance adjustment over time.
In the E28 group, significant improvements were observed in RT (p < 0.001), MT (p = 0.005), and EHC (p < 0.001) from pre-test to post-test, confirming that extended VR training effectively enhances both motor response time and coordination. Unlike E8, TPED (p = 0.124) and AET (p = 0.553) did not show significant changes, suggesting that these cognitive–motor functions may plateau after a certain training threshold. Comparing pre-test to follow-up, RT (p = 0.007) and EHC (p < 0.001) remained significantly improved, suggesting lasting cognitive–motor adaptations. Post-test vs. follow-up comparisons showed that RT (p = 0.402) and MT (p = 1.0) remained stable, confirming that the observed benefits were maintained over time without further significant shifts.
For the control groups (C8 and C28), there were no significant changes across most comparisons. In C8, there were no significant changes between pre- and post-tests, and comparisons between pre-test and follow-up also yielded non-significant results. In C28, the only significant change occurred in AET between pre- and post-tests (p = 0.041), though the small effect size suggests that this improvement was marginal. The absence of significant differences across most control group comparisons further supports that VR training was the primary driver of cognitive–motor enhancements rather than external influences.
The observed trends highlight that short-term VR training effectively improves coordination-based tasks, whereas extended training is more beneficial for reaction time enhancements. These findings align with previous research on VR-based cognitive–motor training, which suggests that eye–hand coordination improvements can emerge rapidly [51], whereas reaction time adaptations require longer exposure to training [52]. The large effect size for EHC in E28 further supports the idea that prolonged VR training strengthens sensorimotor integration, a crucial skill in esports performance and other cognitively demanding tasks [53].
The final Bonferroni post hoc between-groups comparisons, shown in Table 5, revealed significant differences, indicating the effectiveness of the interventions. In the pre-test phase, comparisons across groups showed no significant differences, presented in Supplementary Table S1, suggesting that the groups were comparable in their performance on these measures before the intervention. This consistency provided a reliable baseline for evaluating the interventions’ impacts.
As the study progressed to the post-test phase, the effects of the interventions became more discernible. In the E8 group, significant differences emerged in specific areas. When compared to the C8 group, E8 showed notable differences in MT (p = 0.003), TPED (p = 0.003), and AET (p = 0.006). In follow-up comparisons, presented in Table 6, interestingly, a new difference appeared in RT (p = 0.016) against C28.
The E28 group also demonstrated significant enhancements post-intervention. Compared to C8, E28 showed substantial improvements in MT (p = 0.037) and EHC (p = 0.009). In comparison with C28, improvements were observed in EHC (p < 0.001). These results were consistently significant in follow-up assessments, particularly in EHC against C8 (p = 0.007).
In contrast, the control groups (C8 and C28), except in one instance where in follow-up tests difference between C28 and E8 regarding RT proved to be significant (p = 0.016), showed no significant differences. This stability indicates that their cognitive functions remained unchanged, underscoring the effectiveness of the VR training interventions in the experimental groups.

4. Discussion

The aim of this study was to test whether an extended VR training regimen of 28 weekdays would yield more substantial improvements in cognitive functions essential for competitive esports than training lasting 8 weekdays. Studies have demonstrated that consistent and extended practice leads to more significant and lasting changes in brain structure and function as the brain adapts to the demands placed upon it [54,55]. During the initial stages of training, individuals typically experience rapid improvements as they familiarize themselves with new tasks and environments. This phase, often characterized by steep learning curves, is crucial for establishing basic proficiency. As training continues, these initial gains tend to plateau, and further improvements require more sustained and focused effort [40]. This is where the benefits of extended training periods, such as 28 days, become evident.
Such improvements due to extended cognitive training have been linked to structural changes in the brain, particularly in areas responsible for executive function, working memory, and motor coordination. Research shows that prolonged mental engagement promotes increases in gray matter volume, synaptic efficiency, and enhanced connectivity between cognitive and motor control regions [56]. For example, Galetto & Sacco [57] found that long-term cognitive training in neurorehabilitation settings resulted in both functional and structural neural modifications, strengthening prefrontal cortex pathways essential for decision-making and attentional control. Additionally, Caeyenberghs et al. [58] provided evidence that training-dependent neuroplasticity occurs in response to long-term interventions, particularly in tasks requiring visuomotor coordination and complex cognitive processing. Their findings suggest that extended training periods enhance connectivity in the sensorimotor network, reinforcing motor learning and eye–hand coordination. Similarly, Snowball et al. [59] demonstrated that repeated cognitive engagement leads to the long-term modulation of neuroplasticity, facilitating greater adaptability and efficiency in cognitive–motor integration.
Our findings suggest that while the theoretical basis for longer training is sound, the practical benefits of extending VR training from 8 to 28 days may not be as substantial. Both experimental groups showed significant improvements in cognitive performance. Specifically, eight days of VR-based training were sufficient to achieve positive changes in measured cognitive functions. The additional training provided by the longer 28-day regimen induced very similar effects, suggesting a plateau in the benefits gained from extended VR training. This result is consistent with previous studies that reported cognitive improvements following short-term VR interventions. For instance, Jespersen et al. [60] found significant cognitive gains after just one week of VR training. Furthermore, findings from prior research reinforce that VR-based cognitive and motor training can lead to significant short-term improvements, even within limited training periods. Mutasim et al. [61] demonstrated that a gaze-tracking VR system for eye–hand coordination training effectively enhanced reaction accuracy and response time after short-term exposure. Similarly, Batmaz et al. [62] showed that mid-air VR coordination training for sports enhanced movement precision and reaction time, indicating that immersive VR tasks provide rapid cognitive and motor benefits without requiring prolonged exposure. Additionally, Shen et al. [63] found that VR-based neurorehabilitation programs significantly improved eye–hand coordination and dexterity, with notable gains occurring within a relatively short timeframe. Thus, while longer VR training could be theoretically hypothesized to yield more substantial cognitive benefits, our practical findings align with existing research, indicating that the extension from 8 to 28 days does not confer additional cognitive advantages. These results suggest that VR-based cognitive and motor training can rapidly enhance performance, but extended durations may not necessarily lead to continued improvements beyond an initial adaptation phase.
Moreover, our study adds to the understanding of the durability of VR-based cognitive training effects. We observed that some cognitive improvements persisted 31 days after the completion of the VR training sessions, regardless of the training duration. This finding is particularly noteworthy as it is the first study to examine the long-term impact of VR training on cognitive efficiency in amateur e-athletes. The persistence of cognitive enhancements supports the notion that VR-based cognitive training can induce lasting benefits, aligning with the findings of other studies that have suggested the long-term potential of VR interventions [64]. However, the limited number of studies verifying long-term outcomes indicates a need for further research to confirm these findings and establish the robustness of long-term VR training effects.
The durability of cognitive improvements following training is influenced by multiple factors, including the mechanisms of memory consolidation and reinforcement, such as hippocampal activity and prefrontal cortex engagement, which play a crucial role in the retention of trained abilities [65]. Additionally, repeated cognitive engagement strengthens neural pathways, making the acquired skills more resistant to being forgotten. Research indicates that long-term retention is more effective when training includes spaced repetition, real-world application, and multi-domain engagement, as these approaches help integrate cognitive gains into everyday functioning [66]. Without ongoing engagement, neural connections may weaken, leading to gradual skill attrition. Furthermore, some studies suggest that cognitive load during training influences long-term retention, with higher cognitive demands potentially hindering sustained benefits if not well-managed [67].
However, while these mechanisms support sustained cognitive benefits, there is also evidence suggesting that cognitive improvements may plateau over time, limiting the impact of prolonged training. Chapman et al. [68] reported that while cognitive training initially produces strong neural effects, these benefits tend to stabilize as participants approach their individual performance ceilings. Similarly, Gray & Lindstedt [69] highlight that learning curves often exhibit rapid initial improvements followed by a period of stagnation, indicating a diminishing return on cognitive gains. This raises the question of whether extending VR training beyond a certain threshold continues to provide meaningful benefits or merely reinforces existing skills. While reinforcement strategies, such as varied task difficulty and targeted challenges, may help counteract this plateau effect, further research is needed to determine the optimal duration and structure of VR-based cognitive training. Understanding these dynamics could inform the design of more effective interventions that maximize cognitive gains while minimizing unnecessary training exposure.
Our results indicate that the immersive and engaging aspects of Beat Saber significantly contributed to the cognitive benefits observed, supporting the use of immersive VR games for cognitive training. Similarly, Rutkowski et al. [22] utilized a comparable training protocol with young musicians, who participated in five sessions using a commercial VR system. Unlike our findings, their study reported significant improvements in both eye–hand coordination and reaction time for the VR training group compared to the control group, while our study found improvements in RT only in E28 group. This difference might be due to variations in the initial cognitive functions of the participants, as e-athletes are known to have faster reaction times, potentially giving them a cognitive edge [70].
The complex VR environments require users to process multiple types of sensory information simultaneously (visual, auditory, tactile), leading to improved sensory integration skills. This ability to synthesize and respond to multi-sensory inputs is crucial in cognitive tasks that require attention, problem-solving, and multitasking [71]. Recent findings by Kim et al. [72] highlight that stereoscopic objects in VR can impact motor performance, particularly in reaching tasks, by introducing additional depth-processing demands. Their study revealed that young participants exhibited differences in movement smoothness when interacting with 2D versus 3D objects, whereas older participants struggled with predictive control, suggesting that stereoscopic VR environments challenge the brain’s ability to integrate spatial and motor cues effectively. Enhanced sensory integration improves the brain’s capacity to handle complex information and perform tasks that demand high levels of cognitive flexibility and coordination. Furthermore, the immersive nature of VR compels users to remain highly engaged mentally. This sustained mental engagement serves as a form of cognitive exercise, improving overall mental agility and capacity [73]. Engaging in complex VR tasks increases cognitive endurance and flexibility, particularly in executive functions. Tasks in VR often require planning, problem-solving, and strategy, enhancing executive function capabilities crucial for managing information, controlling impulses, and exhibiting flexible thinking [74]. These results underline the potential benefits of incorporating immersive VR games into cognitive training interventions and support the notion that virtual reality can positively impact cognitive functions.
Another explanation for the success of VR training involves the moderate physical activity in games like Beat Saber [75], which can lead to a rise in neurotransmitters such as dopamine and serotonin, enhancing mood and cognitive functions. Dopamine plays a critical role in reward-motivated behavior and learning, while serotonin contributes to mood regulation and cognitive functions such as memory and learning [76,77]. Additionally, VR environments provide instant feedback and rewards, further stimulating the brain’s reward systems and reinforcing learning and task engagement, which are also associated with increased dopamine release [78]. This biochemical change indicates that even a single session of VR training can improve cognitive functions through these neurochemical pathways. The increase in neurotransmitter levels during VR activities enhances synaptic plasticity, facilitating better learning and memory [79]. The dopamine release fosters heightened motivation, which is crucial for maintaining engagement and sustained effort throughout the training period. Motivation, which precedes engagement, fuels the initial desire to participate and persist in the VR-based interventions, thereby amplifying their cognitive benefits.

5. Conclusions

The study demonstrated that VR-based cognitive training with Beat Saber enhances cognitive functions in amateur e-athletes, with significant improvements in RT and EHC. Notably, EHC gains were sustained over time, highlighting the lasting impact of training. Both eight-day and twenty-eight-day regimens led to improvements, with no significant difference between them, suggesting that even short-term VR training can be highly effective. In esports, where milliseconds matter, even slight improvements in cognitive functions can be crucial to performance. A faster RT and better EHC can influence actions per minute (APM) in strategy games like Starcraft 2, allowing players to execute commands more efficiently. Similarly, enhanced reaction time can determine the outcome of 1v1 clutch scenarios in FPS games like Counter-Strike, where split-second decisions can be the difference between winning or losing a round. These findings support the use of VR games like Beat Saber as practical cognitive training tools, particularly for esports athletes and a younger demographic accustomed to virtual environments. Incorporating such immersive training into cognitive programs could boost both competitive performance and general cognitive function in a broader population.

6. Limitations

Despite our comprehensive research design, there are certain limitations in this study that need to be addressed. A significant constraint is the absence of direct supervision of participants’ activities throughout the study. Although participants were instructed to maintain their regular routines, the absence of continuous monitoring outside the research setting introduces some uncertainty regarding their adherence to these instructions. The monitoring we conducted was part of the SSQ, which included questions regarding simulator usage, well-being, level of physical health, health conditions, sleep, and alcohol consumption. Another limitation stems from the reliance on self-reported measures, especially the questionnaires used for data collection. Self-report methods are susceptible to various biases, including social desirability and recall biases, which can skew participants’ responses and affect the study’s overall validity.
Moreover, the timing of the “post” measurement for the VR group, taken 30 min after their last session, presents a potential confounding factor. This timing could reflect the immediate cognitive and physical stimulation effects, complicating the interpretation of the long-term cognitive benefits of VR training. Another issue is the challenge of measuring the impact of minimal interventions and identifying the minimum intervention necessary to induce cognitive change. Addressing this is crucial for advancing our understanding of the effectiveness and mechanisms of VR-based interventions in cognitive therapy. This investigation is essential for establishing clearer insights into the efficacy of VR-based cognitive training methods.
Another limitation is the specific sample population of amateur e-athletes, which may limit generalizability. Professional players may experience diminished cognitive gains due to prior training, while non-gamers or older individuals may benefit more from VR interventions. Future research should examine VR’s effectiveness across diverse skill levels and demographics to broaden its applicability.
The use of passive control groups is also a limitation, which, while serving as a baseline for comparison, may not fully account for engagement-related factors that could influence cognitive performance. Given the dynamic nature of esports and video games, a more active control condition, such as non-VR cognitive training, could provide additional insight into whether the observed improvements in the VR groups stem from the immersive nature of VR itself or general cognitive engagement. Future research should consider incorporating an active control group to better differentiate the specific benefits of VR-based training from broader training effects.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/app15084346/s1, Table S1: Results of Bonferroni post-hoc comparisons between groups in pre tests presented as p-values.

Author Contributions

Conceptualization, M.L., A.S.-P., D.J. and G.Ż.; Methodology, M.L., A.K.-L. and G.Ż.; Software, M.L. and G.Ż.; Validation, M.L., A.K.-L., D.J. and G.Ż.; Formal analysis, M.L., A.S.-P. and G.Ż.; Investigation, M.L., D.J. and G.Ż.; Resources, M.L., A.S.-P., A.K.-L., D.J. and G.Ż.; Data curation, M.L., A.S.-P. and G.Ż.; Writing—original draft, M.L., A.K.-L., D.J. and G.Ż.; Writing—review & editing, M.L. and G.Ż.; Visualization, M.L., A.S.-P. and G.Ż.; Supervision, M.L., A.K.-L., D.J. and G.Ż.; Project administration, M.L., A.S.-P., A.K.-L., D.J. and G.Ż.; Funding acquisition, M.L. and G.Ż. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was carried out in full accordance with the ethical standards of the Helsinki Declaration of 1964 and its subsequent amendments. Approval for the study’s methods and protocol was granted by the Research Ethics Committee of the Wroclaw University of Health and Sport Sciences (approval no. 19/2022). All participants gave their written informed consent before taking part in the study, in line with ethical guidelines.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Due to the European Union General Data Protection Regulation, all data, analysis scripts, and research materials are not accessible online but are available from the corresponding author on reasonable request.

Conflicts of Interest

Author Alicja Konopka-Lachowicz was employed by the company Fizjohome Rehabilitation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Applsci 15 04346 g001
Figure 1. Participant distribution and retention.
Figure 1. Participant distribution and retention.
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Figure 2. First-person in-game perspective of a player holding two virtual sabers (red on the left, blue on the right). Incoming blocks colored red and blue approach the player, indicating that they must be sliced with the corresponding colored saber. Arrows on each block specify the required slicing direction. Transparent rectangular obstacles are also visible, requiring players to physically dodge them by moving their bodies. On-screen indicators include the current combo streak and total score, as well as a multiplier representing score bonuses achieved by consecutive successful hits.
Figure 2. First-person in-game perspective of a player holding two virtual sabers (red on the left, blue on the right). Incoming blocks colored red and blue approach the player, indicating that they must be sliced with the corresponding colored saber. Arrows on each block specify the required slicing direction. Transparent rectangular obstacles are also visible, requiring players to physically dodge them by moving their bodies. On-screen indicators include the current combo streak and total score, as well as a multiplier representing score bonuses achieved by consecutive successful hits.
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Figure 3. Results of ReT for all groups across three measurement points.
Figure 3. Results of ReT for all groups across three measurement points.
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Figure 4. Results of 2HAND for all groups across three measurement points.
Figure 4. Results of 2HAND for all groups across three measurement points.
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Figure 5. Significant within-groups comparisons for ReT, p < 0.05 *, p < 0.01 **, p < 0.001 ***.
Figure 5. Significant within-groups comparisons for ReT, p < 0.05 *, p < 0.01 **, p < 0.001 ***.
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Figure 6. Significant within-groups comparisons for 2HAND, p < 0.05 *, p < 0.01 **, p < 0.001 ***.
Figure 6. Significant within-groups comparisons for 2HAND, p < 0.05 *, p < 0.01 **, p < 0.001 ***.
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Table 1. Test results presented as mean values +/− standard deviation.
Table 1. Test results presented as mean values +/− standard deviation.
GroupVariablePre-Test Mean ± SDPost-Test Mean ± SDFollow-Up-Test Mean ± SD
E8RT (ms)276.54 ± 37.79259.77 ± 28.96267.08 ± 29.92
MT (ms)112.85 ± 18.2594.31 ± 11.90107.12 ± 23.85
EHC (s)23.81 ± 9.7319.70 ± 7.7916.78 ± 6.64
TPED (%)6.33 ± 4.683.36 ± 2.055.92 ± 4.48
AET (ms)1.16 ± 0.670.57 ± 0.330.97 ± 0.88
E28RT (ms)299.30 ± 44.47259.75 ± 35.89279.00 ± 37.01
MT (ms)126.05 ± 30.7999.85 ± 22.70108.10 ± 27.99
EHC (s)22.26 ± 7.7013.00 ± 4.5213.75 ± 5.79
TPED (%)6.15 ± 3.103.58 ± 1.454.62 ± 2.54
AET (ms)1.16 ± 0.490.91 ± 0.660.78 ± 0.59
C8RT (ms)279.04 ± 51.62281.77 ± 71.44305.54 ± 69.40
MT (ms)119.00 ± 40.93120.00 ± 36.00111.88 ± 35.77
EHC (s)20.57 ± 7.6119.23 ± 6.3720.69 ± 8.95
TPED (%)8.21 ± 6.257.40 ± 6.856.86 ± 6.06
AET (ms)1.43 ± 0.901.22 ± 1.011.12 ± 0.84
C28RT (ms)294.62 ± 46.97301.19 ± 65.78315.14 ± 63.49
MT (ms)118.67 ± 25.50103.81 ± 17.41121.76 ± 47.71
EHC (s)21.50 ± 4.9622.12 ± 6.1123.38 ± 5.23
TPED (%)7.46 ± 2.165.89 ± 2.406.05 ± 4.30
AET (ms)1.46 ± 0.381.01 ± 0.551.29 ± 1.00
Note: RT—reaction time; MT—motor time; EHC—eye–hand coordination; TPED—total percent error duration; AET—average error time.
Table 2. Results of mixed model ANOVA for main effects and interactions.
Table 2. Results of mixed model ANOVA for main effects and interactions.
Factor/InteractionF-Valuedfp-Valueηp2λ
Group3.49130.0180.10510.473
Measurement3628.6794<0.0010.97614,514.716
Time9.8392<0.0010.119.678
Time × Group4.5316<0.0010.13227.186
Measurement × Group2.628120.0020.08131.535
Measurement × Time3.4558<0.0010.03727.642
Measurement × Time × Group3.16324<0.0010.09675.905
Table 3. Results of mixed model ANOVA for within-groups changes.
Table 3. Results of mixed model ANOVA for within-groups changes.
GroupVariablep-ValuesF-Valueη2pλ
E8RT (ms)0.4790.7410.0171.483
MT (ms)0.0543.010.0646.02
EHC (s)<0.00110.1980.18820.395
TPED (%)0.0018.0680.15516.136
AET (ms)0.0026.6330.13113.267
E28RT (ms)<0.0019.5290.17819.059
MT (ms)0.0045.9330.11911.866
EHC (s)<0.00123.1930.34546.386
TPED (%)0.1262.1230.0464.246
AET (ms)0.2241.520.0333.041
C8RT (ms)0.0652.8230.065.647
MT (ms)0.5010.6960.0161.392
EHC (s)0.3980.930.0211.86
TPED (%)0.4930.7130.0161.425
AET (ms)0.2691.3340.0292.669
C28RT (ms)0.3571.0420.0232.084
MT (ms)0.0892.4830.0534.966
EHC (s)0.5760.5550.0121.11
TPED (%)0.2621.3590.032.718
AET (ms)0.0463.1990.0686.399
Note: RT—reaction time; MT—motor time; EHC—eye–hand coordination; TPED—total percent error duration; AET—average error time.
Table 4. Results of Bonferroni post hoc within-groups comparisons between measurement points presented as p-values.
Table 4. Results of Bonferroni post hoc within-groups comparisons between measurement points presented as p-values.
GroupRTMTEHCTPEDAET
Pre vs. PostPre vs. Follow-UpPost vs. Follow-UpPre vs. PostPre vs. Follow-UpPost vs. Follow-UpPre vs. PostPre vs. Follow-UpPost vs. Follow-UpPre vs. PostPre vs. Follow-upPost vs. Follow-UpPre vs. PostPre vs. Follow-UpPost vs. Follow-Up
E80.6931.01.00.0471.00.4130.003<0.0010.1170.0021.00.0270.0021.00.155
E28<0.0010.0070.4020.0050.0711.0<0.001<0.0011.00.1240.7621.00.5530.3361.0
C81.00.1930.1081.00.9030.9000.8091.00.8951.00.7521.00.6010.4281.0
C281.00.5870.7930.1841.00.1231.00.8871.00.3070.8351.00.0411.00.647
Note: RT—reaction time; MT—motor time; EHC—eye–hand coordination; TPED—total percent error duration; AET—average error time.
Table 5. Results of Bonferroni post hoc comparisons between groups in post-tests presented as p-values.
Table 5. Results of Bonferroni post hoc comparisons between groups in post-tests presented as p-values.
GroupRTMTEHCTPEDAET
E8E28C8C28E8E28C8C28E8E28C8C28E8E28C8C28E8E28C8C28
E8x1.00.8730.063x1.00.0031.0x0.0041.01.0x1.00.0030.210x0.6100.0060.210
E281.0x1.00.0961.0x0.0381.00.004x0.009<0.0011.0x0.0450.9870.610x0.8121.0
C80.8731.0x1.00.0030.038x0.1521.00.009x0.7710.0030.045x1.00.0060.812x1.0
C280.0630.0961.0x1.01.00.152x1.0<0.0010.771x0.2100.9871.0x0.2101.01.0x
Note: RT—reaction time; MT—motor time; EHC—eye–hand coordination; TPED—total percent error duration; AET—average error time.
Table 6. Results of Bonferroni post hoc comparisons between groups in follow-up tests presented as p-values.
Table 6. Results of Bonferroni post hoc comparisons between groups in follow-up tests presented as p-values.
GroupRTMTEHCTPEDAET
E8E28C8C28E8E28C8C28E8E28C8C28E8E28C8C28E8E28C8C28
E8x1.00.620.016x1.01.00.916x0.8680.2730.10x1.01.01.0x1.01.01.0
E281.0x0.5730.1891.0x1.01.00.868x0.007<0.0011.0x0.6481.01.0x1.00.350
C80.620.573x1.01.01.0x1.00.2730.007x1.01.00.648x1.01.01.0x1.0
C280.0160.1891.0x0.9161.01.0x0.10<0.0011.0x1.01.01.0x1.00.3501.0x
Note: RT—reaction time; MT—motor time; EHC—eye–hand coordination; TPED—total percent error duration; AET—average error time.
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Lachowicz, M.; Serweta-Pawlik, A.; Konopka-Lachowicz, A.; Jamro, D.; Żurek, G. How Does Virtual Reality Training Affect Reaction Time and Eye–Hand Coordination? The Impact of Short- and Long-Term Interventions on Cognitive Functions in Amateur Esports Athletes. Appl. Sci. 2025, 15, 4346. https://doi.org/10.3390/app15084346

AMA Style

Lachowicz M, Serweta-Pawlik A, Konopka-Lachowicz A, Jamro D, Żurek G. How Does Virtual Reality Training Affect Reaction Time and Eye–Hand Coordination? The Impact of Short- and Long-Term Interventions on Cognitive Functions in Amateur Esports Athletes. Applied Sciences. 2025; 15(8):4346. https://doi.org/10.3390/app15084346

Chicago/Turabian Style

Lachowicz, Maciej, Anna Serweta-Pawlik, Alicja Konopka-Lachowicz, Dariusz Jamro, and Grzegorz Żurek. 2025. "How Does Virtual Reality Training Affect Reaction Time and Eye–Hand Coordination? The Impact of Short- and Long-Term Interventions on Cognitive Functions in Amateur Esports Athletes" Applied Sciences 15, no. 8: 4346. https://doi.org/10.3390/app15084346

APA Style

Lachowicz, M., Serweta-Pawlik, A., Konopka-Lachowicz, A., Jamro, D., & Żurek, G. (2025). How Does Virtual Reality Training Affect Reaction Time and Eye–Hand Coordination? The Impact of Short- and Long-Term Interventions on Cognitive Functions in Amateur Esports Athletes. Applied Sciences, 15(8), 4346. https://doi.org/10.3390/app15084346

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