Effect of social media use and computerized Stroop task on EEG spectral power and physical performance in taekwondo athletes: an experimental randomized trial

Effect of social media use and computerized Stroop task on EEG spectral power and physical performance in taekwondo athletes: an experimental randomized trial | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effect of social media use and computerized Stroop task on EEG spectral power and physical performance in taekwondo athletes: an experimental randomized trial Heloiana Faro, Emerson Franchini, Maicon Albuquerque, Douglas Cavalcante-Silva, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6814478/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Nov, 2025 Read the published version in Experimental Brain Research → Version 1 posted You are reading this latest preprint version Abstract We examined the acute effects of prolonged social media use (SMU) and computerized Stroop Task (MST) on EEG spectral power and physical performance in taekwondo (TKD) athletes. Fifteen athletes underwent cognitive manipulations (SMU, MST, documentary), followed by mental tiredness checks, EEG measurements, an intermittent TKD task, and psychobiological variables (HR, RPE). Only MST increased mental tiredness (p < 0.05). Theta power decreased in the parietal cortex at rest across conditions (p < 0.001). MST induced transient theta, alpha 1, and alpha 2 increases in parietal cortex during the task at 15 minutes (ps < 0.004), diminishing over time. Physical performance declined throughout rounds (p < 0.001), more under MST vs documentary (p = 0.03). RPE increased (p 0.07). High cognitive demand tasks may impair TKD athletes' performance. Mental fatigue physical effort frequency bands combat sports Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Mental fatigue (MF) is defined as a psychobiological state characterized by feelings of tiredness and a lack of energy, resulting from exposure to cognitively demanding tasks, which has been increasingly recognized as a critical factor influencing both cognitive and physical performance (Lopes et al., 2023 ). In modern society, prolonged engagement in mentally demanding tasks, which generates cognitive effort, such as the extensive use of social media on a smartphone or performing cognitively challenging tasks, has become ubiquitous, raising concerns about its potential impact on daily functioning and athletic performance (Montag & Markett, 2023 ). While the effects of MF on physical endurance have been relatively well-documented (Brown et al., 2020a ), its influence on intermittent exercise performance, which involves alternating periods of high-intensity effort and recovery remains less understood. Given the relevance of intermittent exercise patterns in many sports, particularly combat sports like taekwondo (TKD), understanding how MF and cognitive effort could affects such performance is of both theoretical and practical importance. TKD, a striking combat sport, presents an effort/pause ratio ~ 1:1.5(Apollaro et al., 2023 ) and requires athletes to maintain optimal levels of cognitive and physical performance during competition (Campos et al., 2012 ; Russo & Ottoboni, 2019 ). Success in TKD relies not only on physical attributes such as speed, power output, and agility but also on rapid decision-making, focus, and the ability to execute complex motor skills under time and space pressure (Bridge et al., 2014 ; Hsieh et al., 2025 ; Russo & Ottoboni, 2019 ). These demands of effort make athletes particularly vulnerable to MF, which can impair cognitive and physical performance. However, limited research has explored how MF from modern activities, such as prolonged social media use, affects brain activity and sport-specific performance in TKD athletes, nor has it compared these effects to traditional methods like cognitive tests. Although TKD is intermittent, few studies have examined how MF impacts intermittent performance, especially considering the unique motor demands of combat sports. For example, Campos et al.(2019) found no negative impact of MF on a short-duration judo task (< 2 minutes), as such tasks are typically less/no affected by MF (Brown et al., 2020b ; Van Cutsem, Marcora, et al., 2017 ). In contrast, Smith et al.(2015) observed reduced velocities during intermittent running, highlighting the need for tailored research on TKD, as existing studies often overlook its specific intermittency patterns and motor gestures. The scientific literature indicates that MF can affects brain activity (Ishii et al., 2014 ; Tanaka et al., 2014 ; Tran et al., 2020 ; Van Cutsem et al., 2022 ). In this regard, electroencephalography (EEG) has emerged as a valuable tool for assessing brain activity during both cognitive and physical tasks, offering a window into the neural correlates of MF (Chen et al., 2023 ). Neurophysiologically, brain oscillations represent the synchronized post-synaptic activity of groups of neurons (Ishii et al., 2014 ), and these oscillations have been associated with human behavior. Changes in EEG patterns, such as increased theta, alpha, and beta power following prolonged cognitive effort, have been associated with MF and may provide objective markers of its presence (Chikhi et al., 2022 ; Tran et al., 2020 ). However, the effect of MF induced by social media use and cognitive effort on neural oscillations and sport-specific performance remains underexplored, particularly in intermittent exercise. By combining EEG measures with assessments of TKD-specific performance, this study seeks to bridge this gap and provide a more comprehensive understanding of how MF could impacts athletes. In summary, this study investigates the effects of MF induced by prolonged social media use or computerized Stroop word-color task on brain activity, TKD-specific physical performance, and associated psychophysiological responses. We hypothesized that both social media use and the Stroop word-color task would increase spectral power in the theta and alpha frequency bands during resting and task states. Additionally, we hypothesized that both interventions would impair TKD-specific performance without significant differences in psychophysiological responses. By focusing on intermittent exercise, which is highly relevant to many sports, this research aims to advance our understanding of the interplay between MF, brain function, and athletic performance. The findings may have important implications for athletes, coaches, and sports scientists seeking to optimize performance and develop strategies to counteract the negative effects of MF. Methods Study design This study was an experimental crossover and randomized trial that primarily aimed to evaluate the effect of prolonged cognitive effort (i.e., social media use, modified Stroop Task, and documentary) on the resting and task state EEG spectral power and physical performance of TKD athletes. The EEG was measured pre-and post-cognitive effort (i.e., resting state), and during cognitive manipulation (i.e., task state). To measure the resting state, the participant sat in a comfortable chair and looked at a black screen with a fixation cross (+) for three minutes. They were instructed to move their head and muscle face as minimally as possible, blink normally, and avoid stressors thinks. The task state was measured for three minutes after 30-second breaks, at minutes 15, 30, and 45 of cognitive manipulation (Fig. 1 ). They were instructed to keep engaged in the cognitive manipulation while the data were recorded; the intermittent task with TKD kicks was made in the end of experimental sessions. The Institutional Research Ethics Committee approved the research protocol (CAAE: 59010922.0.0000.5188), registered in the Brazilian Register of Clinical Trials (register number: RBR-4rfcfgq), and the study was conducted according to the Declaration of Helsinki. ***Figure here*** Participants Fifteen TKD athletes (11 males; 4 females; age = 19.8 ± 2.3 years; time of experience = 7.6 ± 3.5 years; mean time spent on Instagram during a week = 96 ± 45 minutes/day) were enrolled in the experiment. To be eligible for participation, athletes were required to meet the following inclusion criteria: i) ≥ 5 years of TKD experience; ii) ≥ 5 sessions/week of TKD training; iii) participation in a championship in the last six months; iv) being classified as minimal of tier 3 according to McKay et al ( 2022 ); v) possession of an active profile on Instagram; vi) no self-reported visual impairments that compromise the colors distinctions (i.e., Daltonism) vii) no injuries that could compromise performance in the tests viii) no use of creatine within the last month. Participants were excluded from the study if they failed to complete any experiment phase, sustained an injury that impaired their ability to perform the experimental tasks, or experienced a knockdown or knockout during training or championship. Cognitive manipulation protocols Modified Stroop Task (MST). A computerized modified Word-Color Stroop Task was used to induce high cognitive effort. The task included two stimulus types: (i) incongruent stimuli (words in mismatched ink colors: yellow, blue, green), requiring participants to respond to the ink color while ignoring the word’s meaning; and (ii) switch stimuli (words in red ink), requiring participants to respond to the word’s meaning while ignoring the color. A color-coded keyboard was provided to assist with motor responses. Stimuli remained on-screen until a response was made. After each response, participants received 500 ms of feedback (e.g., “Correct!” or “Incorrect!”), displaying accuracy, percentage accuracy for the block, and mean response time. A 500 ms fixation screen preceded each stimulus, resulting in a 1000 ms interstimulus interval. The task was divided in four blocks of 15 minutes with 30-second break between blocks. Participants were instructed to respond as quickly and accurately as possible. The task was programmed using E-Prime 2.0 (GNU General Public License) and displayed on a 23-inch HP LCD/LED screen (24–94 kHz horizontal frequency; 50–76 Hz vertical refresh rate). Conducted in a quiet, temperature-controlled room (16º–18ºC) with low luminosity, the task was supervised by the lead researcher. Social Media Use (SMU) . In this session, athletes used the social media Instagram ® on the same computer for 60 minutes. They began by posting a championship photo to increase engagement. All interactions (e.g., stories, likes, comments, reels, direct messages) were permitted, but using other social media platforms simultaneously was prohibited. Every 15 minutes, usage was paused for 30 seconds. Headphones (Lenovo, Beijing) were provided for audio. Documentary (DOC). A low-cognitive-demand documentary, Spirit of Combat (Canal Combate, Brazil), was shown for 57 minutes on a 23-inch screen, with audio delivered via headphones (Lenovo, Beijing, China). The martial arts theme was chosen to maintain attention and minimize boredom or drowsiness. At 15-minute intervals, the documentary was paused for 30 seconds. Electroencephalogram (EEG) Recording. EEG signals were recorded using a 32-channel Ag-AgCl active electrode cap (ActiCAP, Brain Products, Germany), positioned according to the 10–10 international system. The FCz channel was used as the reference electrode, and the AFz channel served as the ground electrode. Electrode impedances were kept below 15 kΩ, and the signals were sampled at a rate of 1000 Hz. The EEG signals were amplified using a BrainAmp DC MR amplifier (Brain Vision, Brain Products, Germany) and recorded via the Brain Vision Recorder software (Brain Products, Germany). To ensure optimal conductivity, approximately 2 ml of conductive gel (SuperVisc, Brain Products, Germany) was applied to each electrode. Preprocessing. The EEG data were processed offline using the EEGLAB toolbox (Delorme & Makeig, 2004 ) following these steps: (a) channel locations were assigned based on the BESA file brain locations (Miltner et al., 1994 ); (b) the data were downsampled to 256 Hz; (c) a bandpass filter (0.1–30 Hz) was applied; (d) the ‘Clean Rawdata’ plugin was used to automatically remove bad channels and correct noisy data segments (Kothe & Makeig, 2013 ); (e) the data were re-referenced to the average reference, and excluded channels were interpolated; (f) Independent Component Analysis (ICA) was performed to decompose the data (Chen et al., 2008 ); (g) artifact-related ICA components, including eye and muscle artifacts with correlations > 0.7, were automatically identified and rejected (Jung et al., 2000 ); (h) a final manual inspection was conducted to remove any remaining artifacts flagged by ICA. Processing. To analyze the frequency bands, the data were decomposed by Fast Fourier Transformation (FFT) and extracted data in theta (4–8 Hz), alpha 1 (8–10 Hz), alpha 2 (10–13 Hz), and beta (13–30 Hz) frequency bands (Li, Huang et al., 2020 ). The Darbeliai plug-in (EEGLAB) was used to process and extract spectral power values (µV 2 /Hz). We used three channel grouping: frontal (Fp1, Fp2, F3, F4, F7, F8, and Fz); central (C3, Cz, and C4); and parietal (P3, Pz, P4, P7, and P8) (Li, Huang et al., 2020 ). Due to the marked differences in visual stimuli between the resting and task states, statistical comparisons were conducted for pre- versus post-manipulation and during the experimental manipulation, both within and between conditions. However, no comparisons were made between the resting and task states. Intermittent Taekwondo Task An intermittent task based on official match time was applied to assess the physical performance of TKD athletes. The athletes performed three rounds of two minutes with one-minute passive interval. To start the task, athletes were positioned in front of a torso-punching bag and, after the signal, performed the maximum possible alternate-leg turning kick (i.e., bandal tchagui ) for 10 seconds followed by 10 seconds of interval. This cycle was repeated six times to complete a round. The number of kicks in each set was recorded and the sum of the kicks in each round was used as the main performance parameter. The task was filmed for counting the kicks. Only the kicks performed during the 10 seconds-set were considered. The fatigue index per round was calculated as follows: Index % = \(\:\frac{NK\:first\:10\text{sec}-\:NK\:last\:10sec}{NK\:first\:10sec}\:x\:100\) , where NK = number of kicks. For the total fatigue index, we used the following formula: Total fatigue index % = , where NK = number of kicks. Heart rate was continuously measured during the task using a Polar H10 HR monitor with a Pro Strap (Polar Electro Oy, Kempele, Finland). A moistened elastic electrode strap was applied below the participant’s chest muscles, and the strap length was fitted to the participant’s chest circumference as described by the manufacturer. The data was continuously transmitted to a smartphone via Bluetooth and registered using the Polar Beat application. The 15-point rating of perceived exertion (RPE) Borg scale (i.e., 6–20 points) was measured immediately after the final of each round. Statistics analysis The Shapiro-Wilk task and histogram inspection were used to analyze the data distribution. Data were expressed as mean and standard derivation. A Gaussian distribution was used for normal data and gamma for non-normal data. The generalized estimated equation was used when normal data distribution was found (i.e., the number of kicks’). The generalized mixed model (GMM) with identity link function and unstructured correlation matrix was used if the data was non-normal and categorical data (i.e., Index %, RPE, HR, and EEG frequencies). The general linear model was used to compare variables with only conditions was comparable (i.e., total index %). For each dependent variable main effects (condition: MST x SMU x DOC; time [rest state]: pre x post; time [task state]: minutes 15 x 30 x 45; rounds: 1 x 2 x 3) and interactions (condition x time, condition x rounds) were analyzed. Individual variability was incorporated as a non-correlated random factor to account for its influence and refine the analysis. The lowest AIC and residual distribution evaluated the model quality. Bonferroni post-hoc was used to identify specific differences. Partial eta squared ( \(\:{{\eta\:}}_{p}^{2}\) ) was used to measure effect size (ES). The magnitude of ES was classified as follows: ƞ 2 p < 0.03 = small, ƞ 2 p ≥ 0.03 = moderate, ƞ 2 p 0.20 = very large (Cohen, 1992 ). Statistical analysis was performed using Jamovi 2.3.21, and p < 0.05 was considered statistically significant. Results In a previous study with primary outcomes was found that perceived mental tiredness increased over time most prominently in the MST condition (main effect of condition [X 2 = 100.3; p < 0.001; ƞ 2 p = 0.14; ES = large]; main effect of time [X 2 = 99.6; p < 0.001; ƞ 2 p = 0.20; ES = very large]; and interaction: X 2 = 49.6; p < 0.001; ƞ 2 p = 0.06; ES = moderate), while the enjoyment level decreased over time on MST condition (main effect of condition [X 2 = 64.18; p < 0.001; ƞ 2 p = 0.10; ES = large]; and interaction [X 2 = 17.31; p = 0.002; ƞ 2 p = 0.009; ES = small]). Those results indicate that only the MST condition induce mental fatigue state (more details on Faro et al. ( 2025 )). Descriptive values of physical task and associated variables and EEG (resting and task state) were found in supplementary tables 1 and 2, respectively. EEG frequency analysis - Resting state Frontal. There were no main effects and interactions for any frequency analyzed (all ps > 0,05; statistical and descriptive values are presented in Supplementary tables 1 and 2). Central. There were no main effects and interactions for any frequency analyzed (all ps > 0,05; statistical and descriptive values are presented in Supplementary tables 1 and 2) Parietal . A main effect of condition was observed for theta (X² = 6.32; p < 0.04; η²p = 0.005; effect size = small), as well as a main effect of time (X² = 14.68; p < 0.001; η²p = 0.01; effect size = small). However, post-hoc analyses did not identifyed any significant differences among conditions. In the time domain, posthoc tests revealed a significant decrease in theta power following all experimental conditions (p < 0.001), as can be seen in Fig. 2 . No interaction effect was found for theta power. For alpha 1, a significant interaction was detected (X² = 12.29; p 0.13). No main effects were observed for alpha 1. Additionally, no main effects or interactions were found for alpha 2 or beta. ***Figure 2 here*** EEG frequency analysis - Task state Frontal. There were no main effects and interactions for any frequency analyzed (all ps > 0.05; statistical and descriptive values are presented in Supplementary tables 1 and 2). Central. There were no main effects and interactions for theta, alpha 1, and alpha 2 (all ps > 0.05; statistical and descriptive values in Supplementary tables 1 and 2). There was a main effect of time for beta (X 2 = 6.27; p < 0.04; ƞ 2 p = 0.01; ES = small), with an increase in power between 15 and 30 minutes for all experimental conditions (p = 0.03). There were no main effects of condition nor interaction for beta power. Parietal. There was an interaction for theta (X 2 = 15.14; p < 0.004; ƞ 2 p = 0.02; ES = small), with a decrease of power from minute 15 to 45 on MST condition (p = 0.02). There were no main effects for theta. There was an interaction for alpha 1 (X 2 = 20.29; p < 0.001; ƞ2p = 0.02; ES = small), with higher power at minute 15 in MST when compared to DOC (p = 0.004) and SMU (p < 0.001). However, values of DOC condition increased over time and were larger than SMU at minute 45 (p = 0.04). There were no main effects for alpha 1. There was an interaction for alpha 2 (X 2 = 12.81; p < 0.01; ƞ2p = 0.01; ES = small), with higher power at minute 15 in MST when compared to SMU (p = 0.01). There were no main effects for alpha 2. There was an interaction for beta (X 2 = 11.52; p 0.05). These results can be seen in Fig. 3 . There were no main effects for beta power. ***Figure 3 here*** Intermittent Taekwondo Task. Number of kicks. Main effects of condition (X 2 = 3.45; p = 0.03; ƞ 2 p = 0.02; ES = small) and round (X 2 = 100.71; p < 0.001; ƞ 2 p = 0.41; ES = very large) were found. The number of kicks dropped over the rounds and this drop was bigger in the MST compared to the DOC condition (p = 0.03; Fig. 4 ). No interaction was found (X 2 = 0.91; p = 0.46; ƞ 2 p = 0.01; ES = small) for the number of kicks. Main effect of round (X 2 = 44.71; p < 0.001; ƞ 2 p = 0.27; ES = very large) was found for Fatigue Index %. No main effect of condition (X 2 = 4.71; p = 0.09; ƞ 2 p = 0.01; ES = small) nor interaction (X 2 = 1.99; p = 0.73; ƞ 2 p = 0.00; ES = small) was found for this variable. There was no difference among conditions for Total Index % (F = 0.42; p = 0.65; ; ƞ 2 p = 0.02; ES = small) RPE. A main effect of round (X 2 = 48.26; p < 0.001; ƞ 2 p = 0.08; ES = large), but not for condition (X 2 = 0.75; p = 0.68; ƞ 2 p = 0.00; ES = small), was found for RPE. The RPE responses increased over the rounds (all ps < 0.008). No interaction was found (X 2 = 0.97; p = 0.91; ƞ 2 p = 0.00; ES = small). HR. No main effects of condition (X 2 = 5.22; p = 0.07; ƞ 2 p = 0.01; ES = small), round (X 2 = 0.47; p = 0.78; ƞ 2 p = 0.00; ES = small), nor interaction (X 2 = 2.08; p = 0.72; ƞ 2 p = 0.00; ES = small) were found for HR. ***Figure 4 here*** Discussion The previous study with primary outcomes indicated that the MST condition affected cognitive (e.g., the Stroop task) and subjective responses (e.g., the Visual Analogue Scale), confirming that MF was present in this experiment solely under the MST condition (Faro et al., 2025 ). In this study, we investigated the influence of cognitive effort on the EEG spectral power and specific physical performance of TKD athletes. The results revealed changes in EEG measures in all experimental conditions, mainly in MST condition. The findings showed decreased TKD-specific performance only for ST when compared to DOC experimental condition. Contrary to our initial hypothesis, neither the social media use nor the cognitive task led to an increase in spectral power; notably, only the MST condition exhibited a decline in physical performance that was different from our initial hypothesis. We analyzed neurophysiological responses concerning EEG frequencies from two perspectives: before and after cognitive manipulations (resting state) and during cognitive manipulations (task state). Our findings revealed a decrease in theta power after all cognitive manipulations in the parietal cortex. During the cognitive tasks, the MST condition started with elevated theta power at minute 15 but showed a decline over time in theta, alpha 1, and alpha 2 bands. Based on previous studies (Craig et al., 2012 ; Trejo et al., 2015 ; Wascher et al., 2014 ), we expected an increase in theta power following high-demand cognitive tasks (i.e., SMU and/or MST), particularly in the frontal cortex, as supports by recent meta-analysis (Chikhi et al., 2022 ; Tran et al., 2020 ). However, this expectation was not confirmed in our study. One possible explanation for the discrepancy lies in methodology differences, as prior studies typically compared resting state (e.g., eyes open or closed) with those taken during cognitive tasks. For example, Craig et al. ( 2012 ) observed increased theta power when comparing baseline measures to cognitive effort, while Wascher et al. ( 2014 ) reported similar increases during cognitive task blocks. During cognitive manipulations, visual stimuli might heighten cognitive activity, making direct comparisons with resting states more complex. Conversely, our findings align with Smith et al. ( 2019 ) found no differences in brain frequencies between pre- and post-resting states, even when employing various cognitive tasks. This suggests that neuroelectric changes induced by prolonged cognitive manipulations may only become apparent when comparing task-state data to baseline measures. Unlike the resting state, the task state analysis revealed significant changes in theta, alpha 1, and alpha 2 frequencies in the parietal cortex. Notably, at the 15-minute mark of cognitive effort, there was an increase in the aforementioned frequency bands. These findings confirm that cognitively demanding tasks, such as the Stroop task, which requires attention and inhibitory control, mobilize greater neural resources compared to low-demand tasks (e.g., SMU and DOC). However, contrary to our initial hypothesis, the increase in frequency power under high cognitive demand conditions was not sustained, as power decreased over time during the MST condition. This decline might reflect a phenomenon known as “neural efficiency,” where the brain adapts over time to perform tasks with reduced neural resource expenditure (Li & Smith, 2021 ). Such adaptation allows the brain to meet external demands and performance requirements more efficiently (Del Percio et al., 2011 ), potentially explaining the observed power decrease at the 30-minute mark of cognitive manipulation. Importantly, this study pioneers the investigation of the neurophysiological effects of social media use as a method of inducing MF. Previous research has primarily relied on subjective and behavioral measures to assess the impact of social media on sports performance (Fortes, de Lima-Júnior, et al., 2020 ; Fortes et al., 2019 ; Fortes, Fonseca, et al., 2021 ; Fortes, Gantois, et al., 2021 ; Fortes, Lima-Júnior, et al., 2021 ; Fortes, Nakamura, et al., 2020 ; Fortes et al., 2021 ; Fortes et al., 2022 ); however, this approach is limited, as perception fatigue might not align with actual neurophysiological fatigue (Behrens et al., 2023 ). To address this, we employed a neurophysiological manipulation check to evaluate whether SMU induces MF, a claim not supported by our results. Based on these findings, the use of social media itself does not appear to influence neurophysiological behavior or induce MF. Nonetheless, given the evidence that social media use can cause long-term brain modulation over time (Chun et al., 2017 ; Horvath et al., 2020 ; Lee et al., 2019 ), future studies should explore the neurophysiological effects of extended social media exposure. In the context of physical tasks, only MST conditions negatively affected the physical performance of TKD athletes. These findings align with existing literature indicating that MF impairs physical performance (Brown et al., 2020a ; Habay et al., 2023 ; Van Cutsem, Marcora, et al., 2017 ). However, it contrasts with the expected decline in performance following the SMU condition, as suggested by previous studies (Fortes, Gantois, et al., 2021 ; Fortes, Nakamura, et al., 2020 ; Fortes et al., 2022 ). Since the physical variable most adversely affected was the number of kicks, it can be concluded that the MST condition specifically impairs the physical performance of TKD athletes. This result is consistent with literature suggesting that MF reduces exercise tolerance (Marcora et al., 2009 ). Besides, RPE increased only over the rounds, suggesting that cognitive effort did not directly influence this variable. This contradicts prior studies that reported increased RPE due to MF (Kunasegaran et al., 2023 ; Marcora et al., 2009 ; Van Cutsem, De Pauw, et al., 2017 ). However, several studies have also found no changes in RPE during swimming (Penna et al., 2021 ), cycling (Filipas et al., 2019 ; Silva-Cavalcante et al., 2018 ), soccer (Angius et al., 2022 ) and half marathon (Gattoni et al., 2021 ). This suggests that RPE modulation may not always be the primary mechanism underlying MF-induced performance impairments. Furthermore, the nature of intermittent tasks may influence the RPE response (Kilpatrick et al., 2015 ; Zinoubi et al., 2018 ). Additionally, HR remained unchanged, reinforcing findings from numerous studies that indicate MF does not significantly affect physiological variables (Kunasegaran et al., 2023 ; Roelands et al., 2021 ). Indeed, MF appears to increase the feeling of exhaustion, triggering the activation of inhibitory pathways in the brain and leading to task disengagement (Ishii et al., 2014 ; Schiphof-Godart et al., 2018 ). It is worth noting that athletes repeatedly perform all-out tasks, which likely contributed to a progressive increase in RPE, regardless of prior cognitive effort. This suggests that physical capacity during high-intensity scenarios—where resistance to neuromuscular fatigue is critical—may be negatively impacted by MF. From a practical standpoint, the ability to execute repeated kicks could be a decisive factor in winning a match (Santos et al., 2020 ). Previous studies have shown that proactive behavior and the capacity to directly attack opponents are key characteristics that differentiate winners (Falco et al., 2014 ; Santos & Franchini, 2018 ). Therefore, given that maintaining kicking performance is essential for success in TKD, MF-induced declines in performance may increase the likelihood of defeat. Our study contributes to the literature by investigating the neurophysiological effects of MF and comparing a traditional method of inducing cognitive effort, such as Modified Stroop Task, with a more ecologically valid approach, like social media use, which have been indicated as limited in MF studies (Lam et al., 2025 ). EEG is widely recognized as one of the most effective physiological tools for identifying MF state (Chen et al., 2023 ), was employed to assess brain activity. Additionally, the physical task was designed to closely simulate an official match (Apollaro et al., 2023 ), consisting of three rounds with durations typical of bouts. This all-out task design bridges the gap between laboratory settings and real-world sports contexts, addressing a common limitation in the transferability of results (Lam et al., 2025 ; Russell et al., 2019 ). However, this study has some limitations. The variability of visual stimuli across the different cognitive manipulations restricts direct comparisons between resting and task states. Furthermore, the resting state was assessed only with closed eyes, which precluded the analysis of brain waves without the influence of visual stimuli. Incorporating additional physiological measures—such as heart rate variability, blood lactate, and glucose concentrations—could have provided a more comprehensive understanding of the physical task's demands. Finally, future research should explore the long-term effects of MF, as the cumulative mental load may have a more pronounced detrimental impact and better reflect real-world sports scenarios (Lam et al., 2025 ). Conclusion Our findings indicate that performing the Stroop Task for one hour induced a transient increase in EEG spectral power during the cognitive task—an effect not observed during social media use. Furthermore, neither the Stroop Task nor social media use resulted in significant changes in EEG spectral power from pre- to post-intervention measurements. Finally, only the Stroop Task condition led to a decline in physical performance compared to the low cognitive demand condition, whereas social media use did not produce a similar effect. Psychophysiological measures, on the other hand, remained stable across all experimental conditions. Declarations Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding The author(s) declare that financial support was received for the research from Brazilian Coordination for the Improvement of Higher Education (CAPES). Author Contribution HF, EF, DGSM, and LSF conceived and designed research. MA, EF, DGSM, and LSF critically revised the manuscript. HF and LADM conducted the experiments. DCS and DCP contributed to statistical analysis. HF whore the manuscript. All authors read and approved the manuscript. Acknowledgments To the athletes who patiently contribute to this research. References Angius, L., Merlini, M., Hopker, J., Bianchi, M., Fois, F., Piras, F., Cugia, P., Russell, J. & Marcora, S. M. (2022). Physical and Mental Fatigue Reduce Psychomotor Vigilance in Professional Football Players. 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Relationships between rating of perceived exertion, heart rate and blood lactate during continuous and alternated-intensity cycling exercises. Biology of Sport , 35 (1), 29–37. https://doi.org/10.5114/biolsport.2018.70749 Additional Declarations No competing interests reported. Supplementary Files Supplementarytables.docx Cite Share Download PDF Status: Published Journal Publication published 01 Nov, 2025 Read the published version in Experimental Brain Research → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6814478","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469341884,"identity":"ab87097d-e4b3-42d0-9601-49a30e2c8503","order_by":0,"name":"Heloiana Faro","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYBACAwbmBoYEIIO9AUh8AGI2doJaGCFaeA4wMDDOAGlhJkYLA1QLMw+IRUiLOXtj44MHNXfkedjPPnxs82ubPB8zA+OHjzm4tVj2HGw2SDj2zLCHJ93YOLfvtmEbMwOz5MxteBx2I7FNIoHtMON+hjQ26dye24xALWzMvPi03H8I1PLvsH0P/zM2acue2/aEtdxgbJNIbDuc2CMBtIXhx+1EwlrOJDYbJPYdTu6ReMZs2NtwO7mNmbEZv1+OHz748Me3w7Y9/GmMD378uW07v7354IePeLSgAsY2MNlArHoQ+EOK4lEwCkbBKBgpAAAvdVKsNtKGNgAAAABJRU5ErkJggg==","orcid":"","institution":"Federal University of Paraíba","correspondingAuthor":true,"prefix":"","firstName":"Heloiana","middleName":"","lastName":"Faro","suffix":""},{"id":469341885,"identity":"45314aa7-4133-447e-8f0a-b929cff67048","order_by":1,"name":"Emerson Franchini","email":"","orcid":"","institution":"Martial Arts and Combat Sports Research Group, University of São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Emerson","middleName":"","lastName":"Franchini","suffix":""},{"id":469341886,"identity":"ba4587ed-fbb8-4edf-8718-56dff99fc974","order_by":2,"name":"Maicon Albuquerque","email":"","orcid":"","institution":"Federal University of Minas Gerais","correspondingAuthor":false,"prefix":"","firstName":"Maicon","middleName":"","lastName":"Albuquerque","suffix":""},{"id":469341887,"identity":"538f2c45-731e-42af-ab52-cb5344f56aef","order_by":3,"name":"Douglas Cavalcante-Silva","email":"","orcid":"","institution":"Federal University of Paraíba","correspondingAuthor":false,"prefix":"","firstName":"Douglas","middleName":"","lastName":"Cavalcante-Silva","suffix":""},{"id":469341888,"identity":"34fd043e-c5ce-4ef2-9223-bb780e7b26b3","order_by":4,"name":"Daniel Carvalho Pereira","email":"","orcid":"","institution":"Federal University of Paraíba","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"Carvalho","lastName":"Pereira","suffix":""},{"id":469341889,"identity":"9f510aed-4e30-4d74-b8c9-5165667636f2","order_by":5,"name":"Lucas Arthur Duarte de Lima","email":"","orcid":"","institution":"Federal University of Rio Grande do Norte","correspondingAuthor":false,"prefix":"","firstName":"Lucas","middleName":"Arthur Duarte","lastName":"de Lima","suffix":""},{"id":469341890,"identity":"c38d4129-074f-41f8-a7f0-1aaf97e316f4","order_by":6,"name":"Daniel Gomes Silva Machado","email":"","orcid":"","institution":"Federal University of Rio Grande do Norte","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"Gomes Silva","lastName":"Machado","suffix":""},{"id":469341891,"identity":"7acd0747-8e58-4a5c-8b9e-79d246e36e8a","order_by":7,"name":"Leonardo de Sousa Fortes","email":"","orcid":"","institution":"Federal University of Paraíba","correspondingAuthor":false,"prefix":"","firstName":"Leonardo","middleName":"de Sousa","lastName":"Fortes","suffix":""}],"badges":[],"createdAt":"2025-06-03 20:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6814478/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6814478/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00221-025-07185-7","type":"published","date":"2025-11-01T15:58:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84471496,"identity":"c1191efa-158d-4b04-81dd-bf8f67fe8623","added_by":"auto","created_at":"2025-06-12 10:35:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":72752,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6814478/v1/c1ba87522b9f98b5ea178fc6.png"},{"id":84472647,"identity":"1318d962-d324-45ab-af4c-752e0c0e8eb4","added_by":"auto","created_at":"2025-06-12 10:43:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":46153,"visible":true,"origin":"","legend":"\u003cp\u003eTheta values for each experimental condition in parietal cortex\u003c/p\u003e\n\u003cp\u003eNote: MST = Modified Stroop test; SMU = social media use; DOC = documentary; @ = main effect of condition; * = main effect of time\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6814478/v1/4684595a5d457f9b8ff5f16c.png"},{"id":84471497,"identity":"cbe29e4b-eeb9-4f98-80df-a79a27d280ce","added_by":"auto","created_at":"2025-06-12 10:35:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54846,"visible":true,"origin":"","legend":"\u003cp\u003eValues of task state for all conditions on parietal cortex\u003c/p\u003e\n\u003cp\u003eNote: MST = Modified Stroop test; SMU = social media use; DOC = documentary; # = condition x time interaction; ¢ = difference to SMU condition; \u0026amp; = difference to DOC; 1 = difference to minute 15.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6814478/v1/a8159bc30a54afe2ccce09d0.png"},{"id":84471499,"identity":"fb2fe5b0-376e-4af4-8c3d-fc88c9ff0f24","added_by":"auto","created_at":"2025-06-12 10:35:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":59699,"visible":true,"origin":"","legend":"\u003cp\u003eResults of physical task and psychophysiological variables\u003c/p\u003e\n\u003cp\u003eNote: MST = Modified Stroop test; SMU = social media use; DOC = documentary; bpm = beats per minute; RPE = rating of perceived exertion; A.U = arbitrary units; @ = main effect of condition; * = main effect of time; \u0026amp; = difference to DOC.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6814478/v1/37bb69fe44e938463ad40a2e.png"},{"id":95040424,"identity":"5685eb73-747d-435e-9a28-a14b813cb902","added_by":"auto","created_at":"2025-11-03 16:08:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":958277,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6814478/v1/ada19ae9-8b24-47bf-8929-4e7e8f60026d.pdf"},{"id":84472648,"identity":"99c95b86-bf84-470c-8825-4b7762b7502b","added_by":"auto","created_at":"2025-06-12 10:43:50","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":45388,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6814478/v1/97b57b7b93afb51ee86d38d0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effect of social media use and computerized Stroop task on EEG spectral power and physical performance in taekwondo athletes: an experimental randomized trial","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMental fatigue (MF) is defined as a psychobiological state characterized by feelings of tiredness and a lack of energy, resulting from exposure to cognitively demanding tasks, which has been increasingly recognized as a critical factor influencing both cognitive and physical performance (Lopes et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In modern society, prolonged engagement in mentally demanding tasks, which generates cognitive effort, such as the extensive use of social media on a smartphone or performing cognitively challenging tasks, has become ubiquitous, raising concerns about its potential impact on daily functioning and athletic performance (Montag \u0026amp; Markett, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While the effects of MF on physical endurance have been relatively well-documented (Brown et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e), its influence on intermittent exercise performance, which involves alternating periods of high-intensity effort and recovery remains less understood. Given the relevance of intermittent exercise patterns in many sports, particularly combat sports like taekwondo (TKD), understanding how MF and cognitive effort could affects such performance is of both theoretical and practical importance.\u003c/p\u003e \u003cp\u003eTKD, a striking combat sport, presents an effort/pause ratio\u0026thinsp;~\u0026thinsp;1:1.5(Apollaro et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and requires athletes to maintain optimal levels of cognitive and physical performance during competition (Campos et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Russo \u0026amp; Ottoboni, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Success in TKD relies not only on physical attributes such as speed, power output, and agility but also on rapid decision-making, focus, and the ability to execute complex motor skills under time and space pressure (Bridge et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hsieh et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Russo \u0026amp; Ottoboni, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These demands of effort make athletes particularly vulnerable to MF, which can impair cognitive and physical performance. However, limited research has explored how MF from modern activities, such as prolonged social media use, affects brain activity and sport-specific performance in TKD athletes, nor has it compared these effects to traditional methods like cognitive tests. Although TKD is intermittent, few studies have examined how MF impacts intermittent performance, especially considering the unique motor demands of combat sports. For example, Campos et al.(2019) found no negative impact of MF on a short-duration judo task (\u0026lt;\u0026thinsp;2 minutes), as such tasks are typically less/no affected by MF (Brown et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Van Cutsem, Marcora, et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In contrast, Smith et al.(2015) observed reduced velocities during intermittent running, highlighting the need for tailored research on TKD, as existing studies often overlook its specific intermittency patterns and motor gestures.\u003c/p\u003e \u003cp\u003eThe scientific literature indicates that MF can affects brain activity (Ishii et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tanaka et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tran et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Van Cutsem et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this regard, electroencephalography (EEG) has emerged as a valuable tool for assessing brain activity during both cognitive and physical tasks, offering a window into the neural correlates of MF (Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Neurophysiologically, brain oscillations represent the synchronized post-synaptic activity of groups of neurons (Ishii et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and these oscillations have been associated with human behavior. Changes in EEG patterns, such as increased theta, alpha, and beta power following prolonged cognitive effort, have been associated with MF and may provide objective markers of its presence (Chikhi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tran et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the effect of MF induced by social media use and cognitive effort on neural oscillations and sport-specific performance remains underexplored, particularly in intermittent exercise. By combining EEG measures with assessments of TKD-specific performance, this study seeks to bridge this gap and provide a more comprehensive understanding of how MF could impacts athletes.\u003c/p\u003e \u003cp\u003eIn summary, this study investigates the effects of MF induced by prolonged social media use or computerized Stroop word-color task on brain activity, TKD-specific physical performance, and associated psychophysiological responses. We hypothesized that both social media use and the Stroop word-color task would increase spectral power in the theta and alpha frequency bands during resting and task states. Additionally, we hypothesized that both interventions would impair TKD-specific performance without significant differences in psychophysiological responses. By focusing on intermittent exercise, which is highly relevant to many sports, this research aims to advance our understanding of the interplay between MF, brain function, and athletic performance. The findings may have important implications for athletes, coaches, and sports scientists seeking to optimize performance and develop strategies to counteract the negative effects of MF.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis study was an experimental crossover and randomized trial that primarily aimed to evaluate the effect of prolonged cognitive effort (i.e., social media use, modified Stroop Task, and documentary) on the resting and task state EEG spectral power and physical performance of TKD athletes. The EEG was measured pre-and post-cognitive effort (i.e., resting state), and during cognitive manipulation (i.e., task state). To measure the resting state, the participant sat in a comfortable chair and looked at a black screen with a fixation cross (+) for three minutes. They were instructed to move their head and muscle face as minimally as possible, blink normally, and avoid stressors thinks. The task state was measured for three minutes after 30-second breaks, at minutes 15, 30, and 45 of cognitive manipulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). They were instructed to keep engaged in the cognitive manipulation while the data were recorded; the intermittent task with TKD kicks was made in the end of experimental sessions. The Institutional Research Ethics Committee approved the research protocol (CAAE: 59010922.0.0000.5188), registered in the Brazilian Register of Clinical Trials (register number: RBR-4rfcfgq), and the study was conducted according to the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e\n\u003cp\u003e***Figure here***\u003c/p\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eFifteen TKD athletes (11 males; 4 females; age\u0026thinsp;=\u0026thinsp;19.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3 years; time of experience\u0026thinsp;=\u0026thinsp;7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5 years; mean time spent on Instagram during a week\u0026thinsp;=\u0026thinsp;96\u0026thinsp;\u0026plusmn;\u0026thinsp;45 minutes/day) were enrolled in the experiment. To be eligible for participation, athletes were required to meet the following inclusion criteria: i)\u0026thinsp;\u0026ge;\u0026thinsp;5 years of TKD experience; ii)\u0026thinsp;\u0026ge;\u0026thinsp;5 sessions/week of TKD training; iii) participation in a championship in the last six months; iv) being classified as minimal of tier 3 according to McKay et al (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); v) possession of an active profile on Instagram; vi) no self-reported visual impairments that compromise the colors distinctions (i.e., Daltonism) vii) no injuries that could compromise performance in the tests viii) no use of creatine within the last month. Participants were excluded from the study if they failed to complete any experiment phase, sustained an injury that impaired their ability to perform the experimental tasks, or experienced a knockdown or knockout during training or championship.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCognitive manipulation protocols\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eModified Stroop Task (MST).\u003c/em\u003e A computerized modified Word-Color Stroop Task was used to induce high cognitive effort. The task included two stimulus types: (i) incongruent stimuli (words in mismatched ink colors: yellow, blue, green), requiring participants to respond to the ink color while ignoring the word\u0026rsquo;s meaning; and (ii) switch stimuli (words in red ink), requiring participants to respond to the word\u0026rsquo;s meaning while ignoring the color. A color-coded keyboard was provided to assist with motor responses. Stimuli remained on-screen until a response was made. After each response, participants received 500 ms of feedback (e.g., \u0026ldquo;Correct!\u0026rdquo; or \u0026ldquo;Incorrect!\u0026rdquo;), displaying accuracy, percentage accuracy for the block, and mean response time. A 500 ms fixation screen preceded each stimulus, resulting in a 1000 ms interstimulus interval. The task was divided in four blocks of 15 minutes with 30-second break between blocks. Participants were instructed to respond as quickly and accurately as possible. The task was programmed using E-Prime 2.0 (GNU General Public License) and displayed on a 23-inch HP LCD/LED screen (24\u0026ndash;94 kHz horizontal frequency; 50\u0026ndash;76 Hz vertical refresh rate). Conducted in a quiet, temperature-controlled room (16\u0026ordm;\u0026ndash;18\u0026ordm;C) with low luminosity, the task was supervised by the lead researcher.\u003c/p\u003e \u003cp\u003e \u003cem\u003eSocial Media Use (SMU)\u003c/em\u003e. In this session, athletes used the social media Instagram\u003csup\u003e\u0026reg;\u003c/sup\u003e on the same computer for 60 minutes. They began by posting a championship photo to increase engagement. All interactions (e.g., stories, likes, comments, reels, direct messages) were permitted, but using other social media platforms simultaneously was prohibited. Every 15 minutes, usage was paused for 30 seconds. Headphones (Lenovo, Beijing) were provided for audio.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDocumentary (DOC).\u003c/em\u003e A low-cognitive-demand documentary, \u003cem\u003eSpirit of Combat\u003c/em\u003e (Canal Combate, Brazil), was shown for 57 minutes on a 23-inch screen, with audio delivered via headphones (Lenovo, Beijing, China). The martial arts theme was chosen to maintain attention and minimize boredom or drowsiness. At 15-minute intervals, the documentary was paused for 30 seconds.\u003c/p\u003e\n\u003ch3\u003eElectroencephalogram (EEG)\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eRecording.\u003c/em\u003e EEG signals were recorded using a 32-channel Ag-AgCl active electrode cap (ActiCAP, Brain Products, Germany), positioned according to the 10\u0026ndash;10 international system. The FCz channel was used as the reference electrode, and the AFz channel served as the ground electrode. Electrode impedances were kept below 15 kΩ, and the signals were sampled at a rate of 1000 Hz. The EEG signals were amplified using a BrainAmp DC MR amplifier (Brain Vision, Brain Products, Germany) and recorded via the Brain Vision Recorder software (Brain Products, Germany). To ensure optimal conductivity, approximately 2 ml of conductive gel (SuperVisc, Brain Products, Germany) was applied to each electrode.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePreprocessing.\u003c/em\u003e The EEG data were processed offline using the EEGLAB toolbox (Delorme \u0026amp; Makeig, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) following these steps: (a) channel locations were assigned based on the BESA file brain locations (Miltner et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1994\u003c/span\u003e); (b) the data were downsampled to 256 Hz; (c) a bandpass filter (0.1\u0026ndash;30 Hz) was applied; (d) the \u0026lsquo;Clean Rawdata\u0026rsquo; plugin was used to automatically remove bad channels and correct noisy data segments (Kothe \u0026amp; Makeig, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); (e) the data were re-referenced to the average reference, and excluded channels were interpolated; (f) Independent Component Analysis (ICA) was performed to decompose the data (Chen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e); (g) artifact-related ICA components, including eye and muscle artifacts with correlations\u0026thinsp;\u0026gt;\u0026thinsp;0.7, were automatically identified and rejected (Jung et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2000\u003c/span\u003e); (h) a final manual inspection was conducted to remove any remaining artifacts flagged by ICA.\u003c/p\u003e \u003cp\u003e \u003cem\u003eProcessing.\u003c/em\u003e To analyze the frequency bands, the data were decomposed by Fast Fourier Transformation (FFT) and extracted data in theta (4\u0026ndash;8 Hz), alpha 1 (8\u0026ndash;10 Hz), alpha 2 (10\u0026ndash;13 Hz), and beta (13\u0026ndash;30 Hz) frequency bands (Li, Huang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The Darbeliai plug-in (EEGLAB) was used to process and extract spectral power values (\u0026micro;V\u003csup\u003e2\u003c/sup\u003e/Hz). We used three channel grouping: frontal (Fp1, Fp2, F3, F4, F7, F8, and Fz); central (C3, Cz, and C4); and parietal (P3, Pz, P4, P7, and P8) (Li, Huang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Due to the marked differences in visual stimuli between the resting and task states, statistical comparisons were conducted for pre- versus post-manipulation and during the experimental manipulation, both within and between conditions. However, no comparisons were made between the resting and task states.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIntermittent Taekwondo Task\u003c/h2\u003e \u003cp\u003eAn intermittent task based on official match time was applied to assess the physical performance of TKD athletes. The athletes performed three rounds of two minutes with one-minute passive interval. To start the task, athletes were positioned in front of a torso-punching bag and, after the signal, performed the maximum possible alternate-leg turning kick (i.e., \u003cem\u003ebandal tchagui\u003c/em\u003e) for 10 seconds followed by 10 seconds of interval. This cycle was repeated six times to complete a round. The number of kicks in each set was recorded and the sum of the kicks in each round was used as the main performance parameter. The task was filmed for counting the kicks. Only the kicks performed during the 10 seconds-set were considered. The fatigue index per round was calculated as follows: Index % = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{NK\\:first\\:10\\text{sec}-\\:NK\\:last\\:10sec}{NK\\:first\\:10sec}\\:x\\:100\\)\u003c/span\u003e\u003c/span\u003e, where NK\u0026thinsp;=\u0026thinsp;number of kicks. For the total fatigue index, we used the following formula: Total fatigue index % = \u003cimg src=\"data:image/png;base64,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\" width=\"313\" height=\"39\"\u003e, where NK\u0026thinsp;=\u0026thinsp;number of kicks. Heart rate was continuously measured during the task using a Polar H10 HR monitor with a Pro Strap (Polar Electro Oy, Kempele, Finland). A moistened elastic electrode strap was applied below the participant\u0026rsquo;s chest muscles, and the strap length was fitted to the participant\u0026rsquo;s chest circumference as described by the manufacturer. The data was continuously transmitted to a smartphone via Bluetooth and registered using the Polar Beat application. The 15-point rating of perceived exertion (RPE) Borg scale (i.e., 6\u0026ndash;20 points) was measured immediately after the final of each round.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistics analysis\u003c/h3\u003e\n\u003cp\u003eThe Shapiro-Wilk task and histogram inspection were used to analyze the data distribution. Data were expressed as mean and standard derivation. A Gaussian distribution was used for normal data and gamma for non-normal data. The generalized estimated equation was used when normal data distribution was found (i.e., the number of kicks\u0026rsquo;). The generalized mixed model (GMM) with identity link function and unstructured correlation matrix was used if the data was non-normal and categorical data (i.e., Index %, RPE, HR, and EEG frequencies). The general linear model was used to compare variables with only conditions was comparable (i.e., total index %). For each dependent variable main effects (condition: MST \u003cem\u003ex\u003c/em\u003e SMU \u003cem\u003ex\u003c/em\u003e DOC; time [rest state]: pre \u003cem\u003ex\u003c/em\u003e post; time [task state]: minutes 15 \u003cem\u003ex\u003c/em\u003e 30 \u003cem\u003ex\u003c/em\u003e 45; rounds: 1 \u003cem\u003ex\u003c/em\u003e 2 \u003cem\u003ex\u003c/em\u003e 3) and interactions (condition \u003cem\u003ex\u003c/em\u003e time, condition \u003cem\u003ex\u003c/em\u003e rounds) were analyzed. Individual variability was incorporated as a non-correlated random factor to account for its influence and refine the analysis. The lowest AIC and residual distribution evaluated the model quality. Bonferroni post-hoc was used to identify specific differences. Partial eta squared (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\eta\\:}}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e) was used to measure effect size (ES). The magnitude of ES was classified as follows: ƞ\u003csup\u003e2\u003c/sup\u003ep \u0026lt; 0.03\u0026thinsp;=\u0026thinsp;small, ƞ\u003csup\u003e2\u003c/sup\u003ep \u0026ge; 0.03\u0026thinsp;=\u0026thinsp;moderate, ƞ\u003csup\u003e2\u003c/sup\u003ep \u0026lt; 0.10\u0026thinsp;=\u0026thinsp;large, and ƞ\u003csup\u003e2\u003c/sup\u003ep \u0026gt; 0.20\u0026thinsp;=\u0026thinsp;very large (Cohen, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Statistical analysis was performed using Jamovi 2.3.21, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn a previous study with primary outcomes was found that perceived mental tiredness increased over time most prominently in the MST condition (main effect of condition [X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;100.3; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ƞ\u003csup\u003e2\u003c/sup\u003ep = 0.14; ES\u0026thinsp;=\u0026thinsp;large]; main effect of time [X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;99.6; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ƞ\u003csup\u003e2\u003c/sup\u003ep = 0.20; ES\u0026thinsp;=\u0026thinsp;very large]; and interaction: X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;49.6; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ƞ\u003csup\u003e2\u003c/sup\u003ep = 0.06; ES\u0026thinsp;=\u0026thinsp;moderate), while the enjoyment level decreased over time on MST condition (main effect of condition [X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;64.18; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ƞ\u003csup\u003e2\u003c/sup\u003ep = 0.10; ES\u0026thinsp;=\u0026thinsp;large]; and interaction [X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;17.31; p\u0026thinsp;=\u0026thinsp;0.002; ƞ\u003csup\u003e2\u003c/sup\u003ep = 0.009; ES\u0026thinsp;=\u0026thinsp;small]). Those results indicate that only the MST condition induce mental fatigue state (more details on Faro et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)). Descriptive values of physical task and associated variables and EEG (resting and task state) were found in supplementary tables 1 and 2, respectively.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEEG frequency analysis - Resting state\u003c/h2\u003e \u003cp\u003e \u003cem\u003eFrontal.\u003c/em\u003e There were no main effects and interactions for any frequency analyzed (all ps\u0026thinsp;\u0026gt;\u0026thinsp;0,05; statistical and descriptive values are presented in Supplementary tables 1 and 2).\u003c/p\u003e \u003cp\u003e \u003cem\u003eCentral.\u003c/em\u003e There were no main effects and interactions for any frequency analyzed (all ps\u0026thinsp;\u0026gt;\u0026thinsp;0,05; statistical and descriptive values are presented in Supplementary tables 1 and 2)\u003c/p\u003e \u003cp\u003e \u003cem\u003eParietal\u003c/em\u003e. A main effect of condition was observed for theta (X\u0026sup2; = 6.32; p\u0026thinsp;\u0026lt;\u0026thinsp;0.04; η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.005; effect size\u0026thinsp;=\u0026thinsp;small), as well as a main effect of time (X\u0026sup2; = 14.68; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.01; effect size\u0026thinsp;=\u0026thinsp;small). However, post-hoc analyses did not identifyed any significant differences among conditions. In the time domain, posthoc tests revealed a significant decrease in theta power following all experimental conditions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. No interaction effect was found for theta power.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor alpha 1, a significant interaction was detected (X\u0026sup2; = 12.29; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.001; effect size\u0026thinsp;=\u0026thinsp;small) but the post-hoc analyses failed to identify any specific differences (all ps\u0026thinsp;\u0026gt;\u0026thinsp;0.13). No main effects were observed for alpha 1. Additionally, no main effects or interactions were found for alpha 2 or beta.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003cp\u003e***Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here***\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eEEG frequency analysis - Task state\u003c/h2\u003e \u003cp\u003e \u003cem\u003eFrontal.\u003c/em\u003e There were no main effects and interactions for any frequency analyzed (all ps\u0026thinsp;\u0026gt;\u0026thinsp;0.05; statistical and descriptive values are presented in Supplementary tables 1 and 2).\u003c/p\u003e \u003cp\u003e \u003cem\u003eCentral.\u003c/em\u003e There were no main effects and interactions for theta, alpha 1, and alpha 2 (all ps\u0026thinsp;\u0026gt;\u0026thinsp;0.05; statistical and descriptive values in Supplementary tables 1 and 2). There was a main effect of time for beta (X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;6.27; p\u0026thinsp;\u0026lt;\u0026thinsp;0.04; ƞ\u003csup\u003e2\u003c/sup\u003ep = 0.01; ES\u0026thinsp;=\u0026thinsp;small), with an increase in power between 15 and 30 minutes for all experimental conditions (p\u0026thinsp;=\u0026thinsp;0.03). There were no main effects of condition nor interaction for beta power.\u003c/p\u003e \u003cp\u003e \u003cem\u003eParietal.\u003c/em\u003e There was an interaction for theta (X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;15.14; p\u0026thinsp;\u0026lt;\u0026thinsp;0.004; ƞ\u003csup\u003e2\u003c/sup\u003ep = 0.02; ES\u0026thinsp;=\u0026thinsp;small), with a decrease of power from minute 15 to 45 on MST condition (p\u0026thinsp;=\u0026thinsp;0.02). There were no main effects for theta. There was an interaction for alpha 1 (X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;20.29; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ƞ2p\u0026thinsp;=\u0026thinsp;0.02; ES\u0026thinsp;=\u0026thinsp;small), with higher power at minute 15 in MST when compared to DOC (p\u0026thinsp;=\u0026thinsp;0.004) and SMU (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, values of DOC condition increased over time and were larger than SMU at minute 45 (p\u0026thinsp;=\u0026thinsp;0.04). There were no main effects for alpha 1. There was an interaction for alpha 2 (X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;12.81; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ƞ2p\u0026thinsp;=\u0026thinsp;0.01; ES\u0026thinsp;=\u0026thinsp;small), with higher power at minute 15 in MST when compared to SMU (p\u0026thinsp;=\u0026thinsp;0.01). There were no main effects for alpha 2. There was an interaction for beta (X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;11.52; p\u0026thinsp;\u0026lt;\u0026thinsp;0.02; ƞ\u003csup\u003e2\u003c/sup\u003ep = 0.01; ES\u0026thinsp;=\u0026thinsp;small), but the post hoc did not find any punctual difference (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These results can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. There were no main effects for beta power.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003cp\u003e***Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here***\u003c/p\u003e \u003cp\u003e \u003cem\u003eIntermittent Taekwondo Task.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNumber of kicks.\u003c/em\u003e Main effects of condition (X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;3.45; p\u0026thinsp;=\u0026thinsp;0.03; ƞ\u003csup\u003e2\u003c/sup\u003ep = 0.02; ES\u0026thinsp;=\u0026thinsp;small) and round (X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;100.71; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ƞ\u003csup\u003e2\u003c/sup\u003ep = 0.41; ES\u0026thinsp;=\u0026thinsp;very large) were found. The number of kicks dropped over the rounds and this drop was bigger in the MST compared to the DOC condition (p\u0026thinsp;=\u0026thinsp;0.03; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). No interaction was found (X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.91; p\u0026thinsp;=\u0026thinsp;0.46; ƞ\u003csup\u003e2\u003c/sup\u003ep = 0.01; ES\u0026thinsp;=\u0026thinsp;small) for the number of kicks. Main effect of round (X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;44.71; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ƞ\u003csup\u003e2\u003c/sup\u003ep = 0.27; ES\u0026thinsp;=\u0026thinsp;very large) was found for Fatigue Index %. No main effect of condition (X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4.71; p\u0026thinsp;=\u0026thinsp;0.09; ƞ\u003csup\u003e2\u003c/sup\u003ep = 0.01; ES\u0026thinsp;=\u0026thinsp;small) nor interaction (X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.99; p\u0026thinsp;=\u0026thinsp;0.73; ƞ\u003csup\u003e2\u003c/sup\u003ep = 0.00; ES\u0026thinsp;=\u0026thinsp;small) was found for this variable. There was no difference among conditions for Total Index % (F\u0026thinsp;=\u0026thinsp;0.42; p\u0026thinsp;=\u0026thinsp;0.65; ; ƞ\u003csup\u003e2\u003c/sup\u003ep = 0.02; ES\u0026thinsp;=\u0026thinsp;small)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eRPE.\u003c/em\u003e A main effect of round (X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;48.26; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ƞ\u003csup\u003e2\u003c/sup\u003ep = 0.08; ES\u0026thinsp;=\u0026thinsp;large), but not for condition (X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.75; p\u0026thinsp;=\u0026thinsp;0.68; ƞ\u003csup\u003e2\u003c/sup\u003ep = 0.00; ES\u0026thinsp;=\u0026thinsp;small), was found for RPE. The RPE responses increased over the rounds (all ps\u0026thinsp;\u0026lt;\u0026thinsp;0.008). No interaction was found (X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.97; p\u0026thinsp;=\u0026thinsp;0.91; ƞ\u003csup\u003e2\u003c/sup\u003ep = 0.00; ES\u0026thinsp;=\u0026thinsp;small).\u003c/p\u003e \u003cp\u003e \u003cem\u003eHR.\u003c/em\u003e No main effects of condition (X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;5.22; p\u0026thinsp;=\u0026thinsp;0.07; ƞ\u003csup\u003e2\u003c/sup\u003ep = 0.01; ES\u0026thinsp;=\u0026thinsp;small), round (X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.47; p\u0026thinsp;=\u0026thinsp;0.78; ƞ\u003csup\u003e2\u003c/sup\u003ep = 0.00; ES\u0026thinsp;=\u0026thinsp;small), nor interaction (X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2.08; p\u0026thinsp;=\u0026thinsp;0.72; ƞ\u003csup\u003e2\u003c/sup\u003ep = 0.00; ES\u0026thinsp;=\u0026thinsp;small) were found for HR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003cp\u003e***Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e here***\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe previous study with primary outcomes indicated that the MST condition affected cognitive (e.g., the Stroop task) and subjective responses (e.g., the Visual Analogue Scale), confirming that MF was present in this experiment solely under the MST condition (Faro et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In this study, we investigated the influence of cognitive effort on the EEG spectral power and specific physical performance of TKD athletes. The results revealed changes in EEG measures in all experimental conditions, mainly in MST condition. The findings showed decreased TKD-specific performance only for ST when compared to DOC experimental condition. Contrary to our initial hypothesis, neither the social media use nor the cognitive task led to an increase in spectral power; notably, only the MST condition exhibited a decline in physical performance that was different from our initial hypothesis.\u003c/p\u003e \u003cp\u003eWe analyzed neurophysiological responses concerning EEG frequencies from two perspectives: before and after cognitive manipulations (resting state) and during cognitive manipulations (task state). Our findings revealed a decrease in theta power after all cognitive manipulations in the parietal cortex. During the cognitive tasks, the MST condition started with elevated theta power at minute 15 but showed a decline over time in theta, alpha 1, and alpha 2 bands. Based on previous studies (Craig et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Trejo et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wascher et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), we expected an increase in theta power following high-demand cognitive tasks (i.e., SMU and/or MST), particularly in the frontal cortex, as supports by recent meta-analysis (Chikhi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tran et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, this expectation was not confirmed in our study. One possible explanation for the discrepancy lies in methodology differences, as prior studies typically compared resting state (e.g., eyes open or closed) with those taken during cognitive tasks. For example, Craig et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) observed increased theta power when comparing baseline measures to cognitive effort, while Wascher et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) reported similar increases during cognitive task blocks. During cognitive manipulations, visual stimuli might heighten cognitive activity, making direct comparisons with resting states more complex. Conversely, our findings align with Smith et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found no differences in brain frequencies between pre- and post-resting states, even when employing various cognitive tasks. This suggests that neuroelectric changes induced by prolonged cognitive manipulations may only become apparent when comparing task-state data to baseline measures.\u003c/p\u003e \u003cp\u003eUnlike the resting state, the task state analysis revealed significant changes in theta, alpha 1, and alpha 2 frequencies in the parietal cortex. Notably, at the 15-minute mark of cognitive effort, there was an increase in the aforementioned frequency bands. These findings confirm that cognitively demanding tasks, such as the Stroop task, which requires attention and inhibitory control, mobilize greater neural resources compared to low-demand tasks (e.g., SMU and DOC). However, contrary to our initial hypothesis, the increase in frequency power under high cognitive demand conditions was not sustained, as power decreased over time during the MST condition. This decline might reflect a phenomenon known as \u0026ldquo;neural efficiency,\u0026rdquo; where the brain adapts over time to perform tasks with reduced neural resource expenditure (Li \u0026amp; Smith, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such adaptation allows the brain to meet external demands and performance requirements more efficiently (Del Percio et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), potentially explaining the observed power decrease at the 30-minute mark of cognitive manipulation. Importantly, this study pioneers the investigation of the neurophysiological effects of social media use as a method of inducing MF. Previous research has primarily relied on subjective and behavioral measures to assess the impact of social media on sports performance (Fortes, de Lima-J\u0026uacute;nior, et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fortes et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fortes, Fonseca, et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fortes, Gantois, et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fortes, Lima-J\u0026uacute;nior, et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fortes, Nakamura, et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fortes et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fortes et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); however, this approach is limited, as perception fatigue might not align with actual neurophysiological fatigue (Behrens et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To address this, we employed a neurophysiological manipulation check to evaluate whether SMU induces MF, a claim not supported by our results. Based on these findings, the use of social media itself does not appear to influence neurophysiological behavior or induce MF. Nonetheless, given the evidence that social media use can cause long-term brain modulation over time (Chun et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Horvath et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), future studies should explore the neurophysiological effects of extended social media exposure.\u003c/p\u003e \u003cp\u003eIn the context of physical tasks, only MST conditions negatively affected the physical performance of TKD athletes. These findings align with existing literature indicating that MF impairs physical performance (Brown et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Habay et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Van Cutsem, Marcora, et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, it contrasts with the expected decline in performance following the SMU condition, as suggested by previous studies (Fortes, Gantois, et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fortes, Nakamura, et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fortes et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Since the physical variable most adversely affected was the number of kicks, it can be concluded that the MST condition specifically impairs the physical performance of TKD athletes. This result is consistent with literature suggesting that MF reduces exercise tolerance (Marcora et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Besides, RPE increased only over the rounds, suggesting that cognitive effort did not directly influence this variable. This contradicts prior studies that reported increased RPE due to MF (Kunasegaran et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Marcora et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Van Cutsem, De Pauw, et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, several studies have also found no changes in RPE during swimming (Penna et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), cycling (Filipas et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Silva-Cavalcante et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), soccer (Angius et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and half marathon (Gattoni et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This suggests that RPE modulation may not always be the primary mechanism underlying MF-induced performance impairments. Furthermore, the nature of intermittent tasks may influence the RPE response (Kilpatrick et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zinoubi et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, HR remained unchanged, reinforcing findings from numerous studies that indicate MF does not significantly affect physiological variables (Kunasegaran et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Roelands et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIndeed, MF appears to increase the feeling of exhaustion, triggering the activation of inhibitory pathways in the brain and leading to task disengagement (Ishii et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Schiphof-Godart et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is worth noting that athletes repeatedly perform all-out tasks, which likely contributed to a progressive increase in RPE, regardless of prior cognitive effort. This suggests that physical capacity during high-intensity scenarios\u0026mdash;where resistance to neuromuscular fatigue is critical\u0026mdash;may be negatively impacted by MF. From a practical standpoint, the ability to execute repeated kicks could be a decisive factor in winning a match (Santos et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Previous studies have shown that proactive behavior and the capacity to directly attack opponents are key characteristics that differentiate winners (Falco et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Santos \u0026amp; Franchini, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, given that maintaining kicking performance is essential for success in TKD, MF-induced declines in performance may increase the likelihood of defeat.\u003c/p\u003e \u003cp\u003eOur study contributes to the literature by investigating the neurophysiological effects of MF and comparing a traditional method of inducing cognitive effort, such as Modified Stroop Task, with a more ecologically valid approach, like social media use, which have been indicated as limited in MF studies (Lam et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). EEG is widely recognized as one of the most effective physiological tools for identifying MF state (Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), was employed to assess brain activity. Additionally, the physical task was designed to closely simulate an official match (Apollaro et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), consisting of three rounds with durations typical of bouts. This all-out task design bridges the gap between laboratory settings and real-world sports contexts, addressing a common limitation in the transferability of results (Lam et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Russell et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, this study has some limitations. The variability of visual stimuli across the different cognitive manipulations restricts direct comparisons between resting and task states. Furthermore, the resting state was assessed only with closed eyes, which precluded the analysis of brain waves without the influence of visual stimuli. Incorporating additional physiological measures\u0026mdash;such as heart rate variability, blood lactate, and glucose concentrations\u0026mdash;could have provided a more comprehensive understanding of the physical task's demands. Finally, future research should explore the long-term effects of MF, as the cumulative mental load may have a more pronounced detrimental impact and better reflect real-world sports scenarios (Lam et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings indicate that performing the Stroop Task for one hour induced a transient increase in EEG spectral power during the cognitive task\u0026mdash;an effect not observed during social media use. Furthermore, neither the Stroop Task nor social media use resulted in significant changes in EEG spectral power from pre- to post-intervention measurements. Finally, only the Stroop Task condition led to a decline in physical performance compared to the low cognitive demand condition, whereas social media use did not produce a similar effect. Psychophysiological measures, on the other hand, remained stable across all experimental conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe author(s) declare that financial support was received for the research from Brazilian Coordination for the Improvement of Higher Education (CAPES).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHF, EF, DGSM, and LSF conceived and designed research. MA, EF, DGSM, and LSF critically revised the manuscript. HF and LADM conducted the experiments. DCS and DCP contributed to statistical analysis. HF whore the manuscript. All authors read and approved the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eTo the athletes who patiently contribute to this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAngius, L., Merlini, M., Hopker, J., Bianchi, M., Fois, F., Piras, F., Cugia, P., Russell, J. \u0026amp; Marcora, S. M. (2022). Physical and Mental Fatigue Reduce Psychomotor Vigilance in Professional Football Players. \u003cem\u003eInternational Journal of Sports Physiology and Performance\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(9), 1391\u0026ndash;1398. https://doi.org/10.1123/ijspp.2021-0387\u003c/li\u003e\n\u003cli\u003eApollaro, G., Sarmet Moreira, P. V., Herrera-Valenzuela, T., Franchini, E. \u0026amp; Falc\u0026oacute;, C. (2023). 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Relationships between rating of perceived exertion, heart rate and blood lactate during continuous and alternated-intensity cycling exercises. \u003cem\u003eBiology of Sport\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(1), 29\u0026ndash;37. https://doi.org/10.5114/biolsport.2018.70749\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mental fatigue, physical effort, frequency bands, combat sports","lastPublishedDoi":"10.21203/rs.3.rs-6814478/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6814478/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe examined the acute effects of prolonged social media use (SMU) and computerized Stroop Task (MST) on EEG spectral power and physical performance in taekwondo (TKD) athletes. Fifteen athletes underwent cognitive manipulations (SMU, MST, documentary), followed by mental tiredness checks, EEG measurements, an intermittent TKD task, and psychobiological variables (HR, RPE). Only MST increased mental tiredness (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Theta power decreased in the parietal cortex at rest across conditions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). MST induced transient theta, alpha 1, and alpha 2 increases in parietal cortex during the task at 15 minutes (ps\u0026thinsp;\u0026lt;\u0026thinsp;0.004), diminishing over time. Physical performance declined throughout rounds (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), more under MST vs documentary (p\u0026thinsp;=\u0026thinsp;0.03). RPE increased (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); HR remained stable (ps\u0026thinsp;\u0026gt;\u0026thinsp;0.07). High cognitive demand tasks may impair TKD athletes' performance.\u003c/p\u003e","manuscriptTitle":"Effect of social media use and computerized Stroop task on EEG spectral power and physical performance in taekwondo athletes: an experimental randomized trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-12 10:35:45","doi":"10.21203/rs.3.rs-6814478/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"34cfb8f9-7700-4a96-9b62-a2881d8ea226","owner":[],"postedDate":"June 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T16:04:08+00:00","versionOfRecord":{"articleIdentity":"rs-6814478","link":"https://doi.org/10.1007/s00221-025-07185-7","journal":{"identity":"experimental-brain-research","isVorOnly":false,"title":"Experimental Brain Research"},"publishedOn":"2025-11-01 15:58:57","publishedOnDateReadable":"November 1st, 2025"},"versionCreatedAt":"2025-06-12 10:35:45","video":"","vorDoi":"10.1007/s00221-025-07185-7","vorDoiUrl":"https://doi.org/10.1007/s00221-025-07185-7","workflowStages":[]},"version":"v1","identity":"rs-6814478","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6814478","identity":"rs-6814478","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}