Evaluation of cognitive load in team sports: literature review Joan Fuster1, Toni Caparrós1,2 and Lluis Capdevila3 1 National Institute of Physical Education of Catalonia (INEFC), Universitat de Barcelona, Barcelona, Spain 2 GRCE Research Group, National Institut of Physical Education of Catalonia (INEFC), Barcelona, Spain 3 Sport Research Institute, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain ABSTRACT In team sports, load management has become one of the most common areas of investigation, given that effective control over load is the key to being able to optimize performance and avoid injuries. Despite the constant evolution and innovation in the latest theories, we can see a clear tendency in load management that focuses on physiological and mechanical aspects and neglects its cognitive character, which generates the variability inherent in the performance of athletes in a changing environment. Indicators of response that inform methods of control over cognitive load can include cognitive, physiological and behavioral indicators. However, limited investigations exist to support the reliability of each indicator regarding cognitive load. For this reason, the objective of this literature review is to present strategies used to manage cognitive load in team sports, as well as the indicators utilized for such a proposition and their relationships in specific contexts. Subjects Neuroscience, Psychiatry and Psychology, Public Health Keywords Sport psychology, Load management, Athlete’s health, Cognitive affordances INTRODUCTION Team sports have a dynamic and complex nature (Lord et al., 2020). The players perform Submitted 6 May 2021 Accepted 3 August 2021 intermittent, high-intensity activities such as repeated sprints, jumps, impacts, fights, Published 8 October 2021 changes of direction, accelerations and decelerations during training and competition Corresponding author (Paulauskas et al., 2019). In this regard, cognitive requirements are also considered crucial Joan Fuster, for optimal performance. These short, intense actions generally last less than 3 s, with joanfuscoll@gmail.com moderate and long recovery periods (Ben Abdelkrim et al., 2007), and should be integrated Academic editor Robert Deaner into training strategies to promote adaptations that efficiently optimize athletic performance (Schelling & Torres-Ronda, 2013) through interaction with the training load. Additional Information and Declarations can be found on Traditionally, planning for training and competition has centered on the management page 21 of physiological and mechanical aspects, defined as internal load (IL) and external load DOI 10.7717/peerj.12045 (EL), respectively. IL and EL are related, the first being defined as the individual Copyright psychophysiological response and the second as the external physical stimulus applied to 2021 Fuster et al. the athlete during training or competition (Soligard et al., 2016). This process can be Distributed under assessed by the control and management of certain indicators that could offer information Creative Commons CC-BY 4.0 about the load effect on the athletes (Soligard et al., 2016). In this context, however, the other elements that can affect the success of the athlete’s training are neglected; for How to cite this article Fuster J, Caparrós T, Capdevila L. 2021. Evaluation of cognitive load in team sports: literature review. PeerJ 9:e12045 DOI 10.7717/peerj.12045 example, the quantification of the cognitive effort (Mujika et al., 2018), defined as the volitional assignment of resources in order to respond to the demands imposed by a task. Depending on the degree of effort involved, cognitive resources can be exhausted which can cause mental fatigue, a psychobiological state provoked by prolonged periods of demanding cognitive activity (Job & Dalziel, 2001). Furthermore, Van Cutsem, Marcora & De Pauw (2017) and Brown, Graham & Innes (2020) conclude in their respective systematic reviews that cognitive effort causes a negative effect on physical performance. Therefore, we consider that the cognitive load will be the relative amount of available mental resources that are invested in the resolution of a task. Cognitive load can be varied by changing the complexity of the assigned task but increasing its complexity will not involve a real increase of the individual’s load if they do not employ enough effort to solve it (Cárdenas, Conde-González & Perales, 2015). For this reason, various authors describe how cognitive load is closely related to the emotional state of the athlete (Camacho et al., 2020; Cárdenas et al., 2013; Cárdenas, Conde-González & Perales, 2015; Rottenberg & Gross, 2003; Vaughan, Laborde & McConville, 2018). The regulation of these emotions involves processes through which individuals influence their emotions. These processes require effort, thereby draining cognitive resources (Schmeichel, 2007). Team athletes perform in a high-entropy environment that causes them to constantly expend mental resources to respond to the demands of the tasks, which requires significant mental effort (Cárdenas, Conde-González & Perales, 2015). Furthermore, deliberate constant evaluation of the possible alternatives during a game can consume the resources of the system and promote fatigue, impairing performance (Marcora, Staiano & Manning, 2009). This cognitive demand combined to the effects of the internal and external load, can cause a maladaptation or rejection of the suggested load. This will directly influence whether the athlete is physically and/or mentally prepared for exposure to another training stimulus, known as the readiness to train/compete (Gabbett et al., 2017). Cognitive load, together with its emotional dimension (Alarcón et al., 2018), requires structured planning and management that is complimentary with that of the physical load, providing information about how the planned sessions are being received. Other authors, such as Camacho et al. (2020), Cárdenas, Conde-González & Perales (2015), Gabbett, Jenkins & Abernethy (2010) and García-Calvo et al. (2019) agree with this reasoning. The main reason is that mismanagement of this load generates not only short-term effects, such as loss of performance (Sansone et al., 2020), reduction of technical abilities (Gantois et al., 2019) or loss of control over the real impact of the session (McLaren et al., 2016a); but also, long-term effects, such overtraining or burnout (Goodger et al., 2007). Including this parameter in training plans will be key to balancing the readiness of the athletes, achieving optimal performance and preventing injuries. The team sports environment requires intense and constant cognitive activity (Figueira et al., 2019), employing various brain mechanisms to adapt to a changing environment (Coutinho et al., 2017). In fact, there is a need to include specific, suitable and objective information for successful planning (Gabbett, Jenkins & Abernethy, 2010), as well as the control of the Fuster et al. (2021), PeerJ, DOI 10.7717/peerj.12045 2/26 cognitive load of training (Gonçalves et al., 2016) and to define properly the variables involved on this process (Soligard et al., 2016). To this end, the current review aims to show, in a practical way, the strategies for the management of cognitive load in team sports, as well as the indicators used to monitor the load and the ways they are related in specific contexts. SURVEY METHODOLOGY Information sources A search of the literature between 1970 and 2020 was performed using the following online databases: Medline (PubMed), SPORTDiscus, PsycINFO and Google Scholar system. The following keywords were used: team sports, cognitive load, mental load, psychological load, workload, mental fatigue, cognitive fatigue, psychological fatigue, together with Boolean operators such as “AND” and “OR”. Furthermore, this literature review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (Stewart et al., 2015). Criteria of inclusion of the study The titles and abstracts of all the articles were analyzed to determine the relevance of the publications for inclusion. Selection criteria for the articles were followed. The complete text of the publication was obtained to determine if it met the criteria for inclusion. In addition, the bibliographies of the selected articles were analyzed to find other relevant articles. Finally, for the current review, only those studies that are centered on the management of cognitive, mental or psychological load (in relation to the workload) and/ or the ones that analyzed cognitive, mental or psychological fatigue (the effect on work and performance) in the specific context of team sports, were included. Therefore, those articles that try to quantify the individual cost of mental resources, given some capabilities, while achieving a given level of performance in a task with specific demands were included. Criteria of exclusion of the study Individual sports and studies in situations far from sports reality were excluded, as well as analyses in laboratory situations. Duplicate articles were eliminated and summaries, non-peer reviewed works, book chapters and opinion articles were also excluded. As a first step, the titles, abstracts and key words screening of the literature was carried out by the authors. In the second step, full-text articles of the relevant studies were screened while in the third step, the reference lists of the suitable articles and the review articles on the management of cognitive, mental or psychological load and/or fatigue were searched for additional articles. Any disagreement was discussed until consensus was reached. RESULTS The initial literature search found 28,851 articles related to cognitive load, but only 1,947 were selected based on their title and abstract. After determining the content of the complete text, 1,926 articles were excluded for being unrelated or incompatible with the inclusion criteria, or both (Fig. 1). A total of 22 studies were included, which described the Fuster et al. (2021), PeerJ, DOI 10.7717/peerj.12045 3/26 Figure 1 Flowchart. Full-size  DOI: 10.7717/peerj.12045/fig-1 management of cognitive load in different situations. These, following the classification of indicators described by Capdevila (2001), were classified depending on the type of indicator of response analyzed, discriminating between cognitive (Table 1), a set of subjective indicators collected in a sporting situation, usually from written tests such as questionnaires, tests or self-reports; physiological (Table 2), indicators obtained with the aid of apparatus allowing the analysis of physiological or biochemical parameters; and behavioral (Table 3), indicators of the analysis of the observable behavior of the athlete, usually external motor behavior or verbal behavior. DISCUSSION The authors use different methods to manage cognitive load, analyze specific and applicable information, exercise better control over training and optimize performance. Fuster et al. (2021), PeerJ, DOI 10.7717/peerj.12045 4/26 Table 1 Managing of cognitive load with cognitive indicators. Author Year n. Objective Task Cognitive load Internal load External load Measurement Results Conclusion variables variables variables period Camacho 2020 22 university To explore the 4 sessions of 3 × 3 Mental load Rating of Ratio of During the Statistical analysis Restrictions imposed et al. (2020) students with less impact of four task SSG using the (NASA-TLX, perceived successful task: %HR showed that on SSG had a than 2 years of restrictions on full court (5 × 2′, a.u.), affective exertion offensive peak, restrictions B, C, and significant impact experience mental load and 2′ rec.) with response (RPE) Borg possessions Edwards D produced more on subjective playing their consequences restrictions: (PANAS scale 6–20 a. (total num. of HRZ, CE mental load, perceptions and basketball for individual and (A) Normal questionnaire, u., %HR peak, made shots/ Variables provoking differences cognitive and (20.81 ± 1.76) team performance game a.u.) Edwards’ total num. of both in affective emotional in small-sided (B) Temporal heart rate offensive After the response and RPE, responses. basketball games restriction (7″ to zones possessions), task: NASA- and consequently The analyzed (SSG). score) ratio of TLX, impairing restrictions are seen (C) Restriction successful PANAS, performance. as an efficient way of passes per passes RPE No significant to increase the possession (successful differences were decision-making (D) Conditions 2 passes/total found in regards to difficulty and, Fuster et al. (2021), PeerJ, DOI 10.7717/peerj.12045 and 3 num. of the Internal Load of consequently, the simultaneously passes), ratio the sessions, extracted mental load. It is of rebounds from the HR. valid to consider (num. of the collective captured performance, rather rebounds/ than the individual, num. of off and as a way to generate def missed a greater mental shots) load Kunrath et al. 2020 18 amateur football To examine how Two sessions were Mental fatigue Peripheral System of tactical Before and MF diminished MF decreases (2020) players mental fatigue carried out: (MF) 100-mm perception ( ) assessment in after the peripheral perception. peripheral (21.8 ± 2.5) (MF) has an Control: Control VAS a.u. football (FUT- Stroop task: Also, it induced a perception influence on visualization SAT), tracking VAS MF, reduction in precision provoking more peripheral followed by SSG systems: peripheral of tactical actions: errors for most perception, tactical test task Intensity zones perception offensive coverage, tactical actions. behavior and “Goalkeeper + 3 in the distance offensive unity, The MF condition physical vs 3 + covered, total During the balance, damages the performance Goalkeeper” distance SSG: concentration, cognitive and during a SSG in MF condition: covered, FUT-SAT mobility, and tactical aspects of football Stroop task average V and and tracking defensive depth and the players’ followed by SSG max. V system unity. Under MF behavior, causing a test task variables conditions, players compensatory “Goalkeeper + 3 had elevated values increase in the vs 3 + for total distance, physical Goalkeeper” average V and time performance. running at a moderate velocity. Greater high-intensity efforts were observed. (Continued ) 5/26 Table 1 (continued ) Author Year n. Objective Task Cognitive load Internal load External load Measurement Results Conclusion variables variables variables period Sansone et al. 2020 12 semi- To assess the 4 half-court 3x3 Mental fatigue RPE with Quantitative Right before Offensive tasks require The short SSG (2020) professional technical-tactical, SSG sessions: (MF) 100-mm centiMax technical- the task: greater ME in induced a greater basketball players perceptive and (1) Long attack VAS a.u., scale a.u., tactical video VAS MF comparison with volume of offensive (21 ± 2 years) mental demands (3 × 4′, 2′ rec.) Mental Perceived analysis defensive ones. play and different of SSG in (2) Short attack exertion (ME) enjoyment During the Short SSG induce tech-tac behaviors, basketball. (6×2′, 1′ rec.) 100-mm VAS 100-mm VAS task: T-T better RPE and ME (number of (3) Long defense a.u. a.u. video responses, as well as possessions and (3 × 4′, 2′ rec.) analysis an increase in the individual or (4) Short defense demands of the game collective actions). (6 × 2′, 1′ rec.) 5′ after the (possession, dribbling The offensive task is task: RPE, and shots). more demanding VAS MF, RPE is significantly than the individual VAS ME, associated with one, in terms of VAS variations of mental perceived effort and Enjoyment fatigue provoked by mental effort. the training exercises. Fuster et al. (2021), PeerJ, DOI 10.7717/peerj.12045 García-Calvo 2019 40 professional To examine the 4 drills (12′): Mental load CR-10 RPE a.u., N sprints, V avg During the The experimental task The modified scoring et al. (2019) soccer players effect of modifying (1) SSG, reduced (adapted HR avg, HR (m/s) task: HR, involves a significant system significantly (17.32 ± 1.21 the scoring system game 5v5 NASA -TLX, max. HR max, increase of the load affected the mental years) on the cognitive (2) SSG 5vs5 a.u.), 100-mm Sprints, V and the mental load and mental load perceived by with VAS mental avg fatigue, in respect fatigue, with a football players psycho-tactical fatigue a.u. At the end of with the control task; stronger effect on due to training load (scoring the task: having more impact the SSG than the tasks system) NASA-TLX, on the SSG than in possession drill. (3) Possession 5 VAS MF the possession drills. The physical and vs 5 The physical load and mental load (4) Possession 30′ later: the mental load behaved differently. with RPE behave in a different psycho-tactical way: control load (scoring situations, greater PL; system) experimental situations, greater ML. Reina et al. 2019 Adult amateur To quantify the Different training External % Maximum SIATE All averages The subjective load The use of the SIATE (2019) female basketball training load via tasks were subjective load Heart Rate (Integrated were measurement ecological system to team (>18 years) three types of analyzed (a total test items System of measured maintains a register tasks gives instruments and of 120 tasks) (STATE): Training Task during statistically significant us the same establish possible competitive Analysis) training correlation with the information as relationships load and (Opposition variables of objective other more between the cognitive degree, task load and %MHR. expensive measurements involvement density, field of The SIATE task is a information utilized (scale from play and reliable method of systems, such as 0–5) a.u. percentage of analysis to track the inertial devices or performers), load of the players. HR bands. Player load 6/26 Table 1 (continued ) Author Year n. Objective Task Cognitive load Internal load External load Measurement Results Conclusion variables variables variables period Alarcón et al. 2018 28 semiprofessional To advance the A football-specific Mental load Anxiety level Qualitative During the The mental fatigue is The presence of the (2018) football players study of the task with (adapted (STAI passing task: Passing significantly greater in physical load (20.07 ± 0.23 relationship acoustic signals NASA -TLX, questionnaire, precision precision the ML condition simultaneous to the years) between mental that alternates a.u.), a.u.) (observational) After than in the ACM. mental load had a and physical load sprints of 12 m, Emotional repetition: This difference negative effect on during athletic jogging state (Self- NASA-TLX, provokes a decrease precision. activities and see displacements assessment SAM and of the precision. Both The emotional how this and passes. manikin scale, STAI frustration and states in ML interaction Objectives of SAM, a.u.) subjective anxiety condition predicted influences athletic passing predict passing the performance in performance. precision. performance. the motor task. Counterbalanced There is a tendency design between for the performance two conditions: to improve over the without mental course of the test. load (AML) and with mental load (ML) 2×(3 × Fuster et al. (2021), PeerJ, DOI 10.7717/peerj.12045 3′–2′ pause). Barrett et al. 2018 32 professional To investigate the The entire season dRPE CR-100, a. dRPE CR-100, Playing position 15′–30′ post The wings reported a Significant differences (2018) football players effects of playing was analyzed (38 u.: RPE-T a.u.: RPE, and level of game: dRPE substantially higher in dRPE were (25 ± 8 years) position and games in total) (cognitive RPE-B opponents dRPE in comparison observed between contextual factors demand) (dyspnea), to the rest of the different playing on the internal RPE-L positions. positions, with the load experienced (muscular Substantially higher wings being the during games, effort of the RPE-T were players who assume measured via lower body) registered in the the greatest load. differential ratings games played against The RPE-T of perceived the best teams in increases when the exertion (dRPE) comparison to the game is against the rest. top teams in the league. Coutinho 2018 10 amateur football To examine the Three situations of Mental fatigue CR-10 RPE a.u. GPS Variables: During the Mental fatigue increases The improvement of et al. (2018) players effect of additional 5 × 5 SSG, (MF) 100-mm Physical task: GPS the low ratio, the the levels of (13.7 ± 0.5 years) mental and random order (3 VAS a.u. variables units number of acc. and longitudinal muscular fatigue × 6′–3′ pause, for (Total the longitudinal sync synchronization in physical and each condition): distance, High Pre-post and provokes a after muscular tactical behavior in (1) Normal ratio, Low task: VAS decrease of the fatigue, suggests the football. condition ratio, Acc., MF, RPE distance covered at use of tactical tasks (2) Additional Dec.) and high intensities. after intense muscular fatigue position The physical profile exercises. Mental (previous task of variables during the task is fatigue is the kind repeated (Dyads more affected under of fatigue that most direction distance, mental fatigue decreases the changes, RCOD) ApEn, Stretch conditions. Mental physical (3) Additional Index, fatigue provokes a performance of the mental fatigue Longitudinal decrease of the task, so high (Stroop color- Sync and longitudinal sync, intensity training is word task of 30’ Lateral Sync) stretch index and directly affected. before the SSG) dyads distance. (Continued ) 7/26 Table 1 (continued ) Author Year n. Objective Task Cognitive load Internal load External load Measurement Results Conclusion variables variables variables period Alarcón, 2017 18 semiprofessional To analyze the Three sets of 10 Mental load Degree of Free throw During the Statistically significant There is a negative Ureña & basketball players effects of mental free throws were (NASA-TLX anxiety (STAI efficiency task: Free differences in the influence of mental Cárdenas (21.35 ± 2.48 fatigue on free performed, with adaptation, a. questionnaire, (num. of shots, throw average NASA-TLX fatigue on precision (2017) years) throw efficiency in active recovery u.) a.u.) scores/failed efficiency scores were found abilities, such as the basketball players. between them, throws and % between the control basketball free and carrying out scores) At the end of group and throw. the following the task: experimental group. task: NASA-TLX The participants in (a) Oddball and STAI the experimental control task group exhibited worse (b) Experimental free throw Fuster et al. (2021), PeerJ, DOI 10.7717/peerj.12045 task: 2-back task performance than the (Cognitive Load participants in the medium-high) control group. Arruda et al. 2017 12 professional To examine the Three games at CSAI-2 Testosterone Before the The concentration of T The results suggest (2017) basketball players effect of playing home against cognitive concentration start of the (pre) is significantly that playing against (18.6 ± 0.5 years) games against three rivals of anxiety, a.u. (T), Cortisol game/ lower in the training a high level different rivals on different concentration training: situations than in the opponent can cause pre-game salivary competitive (C), CSAI-2 Saliva games. A significant a higher level of testosterone levels were somatic samples (T change of C (pre and psychobiological concentration (T), analyzed anxiety a.u. and C) and post) in all conditions stress, probably precompetitive (Difficult, CSAI-2 was observed. A because the level of anxiety and pre- 1st–3rd ranked, higher C (pre) in HM the opponent can and post-game HM; Medium, After the was also observed, be perceived as a cortisol 4th - 6th, MM; game/ compared to TS and threat to the social concentration (C). Easy, 7th–10th; training: EM, as well as a status of a EM) and a Saliva higher HM C (post) determined training situation samples (T in comparison to EM, hierarchy. (TS) and C) and MM and training. CSAI-2 The level of anxiety is lower in EM than in MM or HM. The cognitive anxiety increases exponentially, more than the somatic, depending on the rival. 8/26 Table 1 (continued ) Author Year n. Objective Task Cognitive load Internal load External load Measurement Results Conclusion variables variables variables period Coutinho 2017 12 amateur football To identify the Four situations of 6 Mental fatigue CR-10 RPE a.u. CMJ (cm), Pre task cont./ After the experimental The results showed et al. (2017) players (15.9 ± effects of mental vs 6 SSG, with (MF) 100-mm positional and exp.: VAS task, higher values that MF affects 0.8 years) fatigue and the previous work VAS a.u. physical data MF, RPE were found in MF, both the capacity to modification of (control task or with GPS (m, and CMJ VAS and RPE than in use environmental additional motor m/s) the control condition. information and reference lines in coordination Post task MEN showed an the position of the the physical and work to induce cont./exp. increase of RPE in players. tactical mental fatigue). (pre SSG): comparison to CTR. The additional performance of Comparison of VAS MF, The CTR condition reference lines the players during the performance RPE and provokes an could have SSG of football. in 4 conditions: CMJ improvement if GPS improved the use of Fuster et al. (2021), PeerJ, DOI 10.7717/peerj.12045 1) MF vs no MF, positional variables in less relevant normal field During SSG: comparison to MEN. information to (MEN) 2) MF vs GPS Adding the reference guide actions MF, field with lines to the field during the #MEN additional Post SSG: provokes a condition. reference lines VAS MF and deterioration of the The motor (#MEN) 3) no RPE physical GPS coordination task MF vs MF, variables and an induces a similar normal field increase in RPE. MF to the levels (CTR) 4) no MF Furthermore, adding reported in vs no MF, field them in MF situations previous studies with additional provokes a using Stroop tasks. reference lines deterioration of the (#CTR) positional GPS variables. Vallés, 2017 12 professional To analyze the All trainings VSC test items: CR-10 RPE a.u. VSC test items: During Except for the degree of The results revealed Fernández- basketball players technical-tactical during the last 2 competitive and TQR Degree of training: obstacle of the task, that the VSC scale Ozcorta & (21.91 ± 4.81 training load months of load and obstacle, task subjective the VSC scale shows a shows a high Suero years) assessment (VSC) competition cognitive density, num. assessment high correlation with correlation with the (2017) and its connection were analyzed implication of participants of the load the RPE. There are no RPE, but not with to the parameters (Women’s (scale 0–5) a.u. and game field. 20′ post correlations between the TQR. This of internal load. league). training: the VSC scale and the indicates that the RPE TQR. The task VSC can be a good The day density explains tool to calculate the after 91,4% of the RPE. internal load in training: basketball. TQR (Continued ) 9/26 Table 1 (continued ) Author Year n. Objective Task Cognitive load Internal load External load Measurement Results Conclusion variables variables variables period Veness et al. 2017 10 professional To investigate the Three specific Mental fatigue CR-10 RPE a.u., Results obtained Before the Significant differences The mental fatigue (2017) cricket players acute effects of a physical tests (MF) 100-mm Motivation in the specific task: VAS in the perception of induced by a Stroop (21 ± 8 years) mentally were carried out VAS a.u. 100-mm VAS technical and MF, VAS mental fatigue were task significantly exhausting task on in three different a.u. physical tests motivation observed after the affects cricket-relevant sessions: Cricket treatment. cricket-relevant physical and run-two test, After the The specific physical technical and technical tasks. Batak lite task: RPE, Yo-Yo-IR1 and physical reaction time test VAS MF Cricket run-two tests, performance. and Yo-Yo-IR1 as well as the RPE, test; preceded for were affected by MF. 30′ by a control In the case of the Fuster et al. (2021), PeerJ, DOI 10.7717/peerj.12045 task or a Stroop Batak lite test, the task (induces change was MF) moderated, but their performance was not affected. Badin et al. 2016 20 professional To assess the effects Two sessions of 5 Mental fatigue %HR max, CR- GPS Variables: Before the The MF and ME Mental fatigue did (2016) football players of mental fatigue vs 5 SSG without (MF) 100-mm 10 RPE a.u., Velocity zones, task: VAS increased significantly not affect physical (17.8 ± 1 years) on the technical goalkeepers (2 × VAS a.u., Motivation total distance, MF, VAS PF after carrying out the performance, and physical 7′, 1′ rec), with a Mental Effort (M) 100-mm repeated Stroop task, while despite the increase performance in preceding (ME) 100-mm VAS a.u., sprints and After the motivation was in perceived effort. SSG of football. exposure to 30′ VAS a.u. Physical accelerations. task: VAS similar in both In contrast, mental of control or Fatigue (PF) % passing MF, VAS conditions. The RPE fatigue impaired Stroop task 100-mm VAS precision ME, VAS was greater in the offensive and (induces MF). a.u. (successful/ PF, VAS M, condition of mental defensive technical total), % RPE fatigue, although the performance. effective HR was greater in the challenges During SSG: control situation. (successful/ %HR max, The mental fatigue total), % GPS does not have a very possessions variables clear effect on most of (positive/ the variables of negative) and After the physical performance, % SSG: VAS but it damages the participations MF, VAS technical (positive/ PF, VAS performance negative) ME, RPE variables. 10/26 Table 1 (continued ) Author Year n. Objective Task Cognitive load Internal load External load Measurement Results Conclusion variables variables variables period McLaren 2016 29 professional To investigate the The trainings of an sRPE CR-100, a. sRPE CR-100, a. Classification of 15’ post Differences in the sRPE The dRPE can et al. (2016) rugby players application of intensive 6-week u.: RPE-T u.: RPE, the training session: between sessions are provide a detailed (24 ± 3 years) dRPE in the period were (cognitive RPE-B typology: sdRPE observed depending quantification of training for team monitored. A demands) (dyspnea), HIIT (High- on the training the internal load sports. linear increase of RPE-L intensity typology. The dRPE during training the load was (muscular intervals loads combined to activities. produced in the effort in the training, RHIE explain the 66-91% of The knowledge of first 3 weeks and lower body), (Repeated high variance of the sRPE the connections after that it was RPE-U intensity loads. The strongest between sRPE and gradually (muscular efforts), associations are found dRPE can isolate reduced. effort in the SkCond between: sRPE-L for the specific Fuster et al. (2021), PeerJ, DOI 10.7717/peerj.12045 upper body) (Conditioning HIIT, sRPE-B for perceptive demands based on RHIE, sRPE-U for RT of the different skills), Skills, and sRPE-T for training methods. RT (resistance Velocity and Skills. task) and URT (Upper body resistance physical task) Smith et al. 2016 14 amateur football To investigate the Two sessions of Mental fatigue Motivation (M) Results obtained Before the MF and ME increased Mental fatigue (2016) players effects of mental football-specific (MF) 100-mm 100-mm VAS in the football- task: VAS significantly after damages (Study 2) (19.6 ± 3.5 years) fatigue on technical tests: VAS a.u., a.u. specific MF, VAS M, carrying out the football-specific technical LSPT Mental Effort technical tests VAS ME Stroop task, although technical performance (Loughborough (ME) 100-mm motivation was performance. evaluated via specific pass test) VAS a.u. After the similar in both football-specific and LSST task: VAS conditions. tests (Loughborough MF, VAS The performance specific shot ME, Test time in LSPT did not test). These were results vary between preceded by two conditions, but the 30’ situations, a penalization time control and a (errors) increased Stroop task significantly in the (induces mental condition of MF. MF fatigue) also provokes a decrease of the velocity of throw and precision in LSST. (Continued ) 11/26 Table 1 (continued ) Author Year n. Objective Task Cognitive load Internal load External load Measurement Results Conclusion variables variables variables period Gabbett, 2010 16 professional To investigate the Two 8′ 8 vs 8 SSG Cognitive RPE a. Heart Rate and GPS variables: During the "Off-side" games have a The results show that Jenkins & rugby players physiological, sessions, in u. RPE-10 a.u. total distance, task: GPS greater amount of "off-side" games Abernethy (17.3 ± 0.9 years) cognitive and skill random order. relative variables, involvements, passes provide a greater (2010) demands of “on- - “off-side” SSG distance, technical and effective passes. physiological and side” and “off- (passing the ball accelerations variables Moreover, they also skills demand than side” games in elite to a player that is (in three and HR provoke a greater "on-side" games. rugby players offside is levels), velocity total covered distance, For this reason, allowed) - “on- (in five levels) When the and increase minor they are a good tool side” SSG (the and recovery task ends: and moderate to develop physical ball can only be (time, in three RPE and accelerations as well conditioning and passed to the levels) Cog.RPE as the distance technical skills. players that are Technical covered at low, Even so, "on-side" onside) variables (well medium and high games show a or poorly velocities. Significant greater cognitive executed): differences in disposal load. Fuster et al. (2021), PeerJ, DOI 10.7717/peerj.12045 passes, efficiency were not receptions, found. In "off-side" disposal games recovery is efficiency and lower. Cog. RPE is involvements lower for "on-side" (objective of games. the game) Farrow, Pyne 2008 13 amateur To compare the Two training RPE Cognitive a. Heart rate, GPS variables During the Two of the three open The open exercises & Gabbett Australian physiological, sessions in two u. blood lactate and technical task: GPS exercises were more were generally (2008) football players cognitive and skill days. and RPE-10 a. variables variables, demanding in terms more cognitively (16.7 ± 0.5 years) components in The training u. technical of the total covered and physically open and closed tasks used were variables distance, RPE and demanding than exercises, within 3-man weave, and HR relative intensity. the closed ones, game-based drills square kicking At the end of All the open exercises commonly used in used in Australian drill and the task: had more efforts of Australian football. football. diamond kicking RPE, lactate moderate velocity. Open exercises drill. These use and Cog. Differences in the should be open exercises in RPE concentration of prescribed to obtain one session and lactate were not greater cognitive closed exercises found. The HR was and physical in the other one. greater in the open training loads in a format of the third game-specific exercise. The open context. exercises were more technically demanding. Higher cognitive load scores were obtained in the open exercises. 12/26 Table 1 (continued ) Author Year n. Objective Task Cognitive load Internal load External load Measurement Results Conclusion variables variables variables period Mashiko et al. 2004 37 university rugby To know the Every game of the POMS: Profile of Biochemical All data was Differences were not The exercise load (2004) players relationship competitive mood states parameters in collected observed in the differs depending (20.3 ± 1.5 years) between physical season was questionnaire the blood and twice: in the changes in the on the position and mental fatigue analyzed (to evaluate opsonic morning on biochemical played during the in rugby players mental activity in the the day of parameters, except for rugby game. It can Fuster et al. (2021), PeerJ, DOI 10.7717/peerj.12045 after a game, based fatigue) a.u. serum the match ureic nitrogen in cause important on their playing (fasting blood, between differences in the position condition) forwards and 3/4. connection between and right In regards with the mental and physical after the correlation between fatigue. match mental and physical fatigue, in the forwards, the changes in the POMS scores show a positive correlation with the levels of enzymes of musculoskeletal origin. In the 3/4 the changes in the POMS scores show a positive correlation with the changes in the levels of the parameters related to the lipids. 13/26 Table 2 Managing of cognitive load with physiological indicators. Author Year n. Objective Task Cognitive load Internal load External Measurement Results Conclusion variables variables load period variables Gantois 2019 20 To analyze Three different Heart rate Urine Index of Pre/Post There are no The mental et al. professional the effect tests were carried variability, osmolality Passing Stroop Task: significant fatigue induced (2019) football of mental out over 3 weeks HRV (Armstrong’s Decision- Urine, TQR, differences by the Stroop players fatigue on (1 week between (RMSSD, scale), TQR making HRV, RPE between HRV, task damages (22.6 ± 3.3 decision- sessions): “Stroop SDNN and questionnaire (PDM) TQR, Urine, the decision years) making in task”, followed by pNN50) and (total quality (%) During SSG: sRPE and the making of the football a warm-up and a reaction time, of recovery, PDM, TR different Stoop football players players SSG. RT (s) a.u.), CR-10 30′ post SSG: tasks. and therefore Stroop tasks: sRPE a.u. sRPE All participants their control (CON), showed a performance. 15′ ST (15ST) decrease of PDM and 30′ ST in conditions of (30ST) 30ST in comparison to 15ST and CON. The RT increases significantly with mental fatigue. To do so, they try to quantify, through indicators, the individual cost of mental resources, given certain capacities, while reaching a certain level of performance in a task with specific demands. Cognitive load management methods can be used before, during or after task performance. These cognitive load management methods are based on the analysis of these response indicators, usually classified as cognitive, physiological and behavioral indicators (Capdevila, 2001), so this review will address all three types. Cognitive indicators NASA-TLX The NASA-TLX questionnaire has proven sensitive to the mental load in a variety of cognitively demanding tasks, such as piloting an aircraft or laboratory tasks (Luque- Casado et al., 2016). This scale analyses the subjective perceived work according to six dimensions: mental demand (perceptive and mental effort), physical demand (degree of physical activity), temporal demand (perceived pressure related to decision-making speed), effort (the combination of the mental and physical effort needed) and frustration (the negative emotions perceived). This offers an overall score of the workload (from 0 to 100 points, a.u.) based on the average of the six dimensions. In all studies analysed, the NASA-TLX questionnaire was significantly sensitive to changes in the cognitive load in different team sports situations. In tasks that involve mental fatigue, significant differences are shown between the control and experimental groups (Alarcón, Ureña & Cárdenas, 2017; Alarcón et al., 2018). In the study by Camacho et al. (2020) performance on training tasks under temporal and quantity-of-technical-movement restrictions was analysed and the most specific management tool for the increased cognitive load was the NASA-TLX. Likewise, García-Calvo et al. (2019) also used this tool to identify the increase of the cognitive load as a function of the modification of the scoring system. Measurement Period: After the session/competition. Fuster et al. (2021), PeerJ, DOI 10.7717/peerj.12045 14/26 Table 3 Managing of cognitive load with behavioral indicators. Author Year n. Objective Task Cognitive load Internal load External Measurement Results Conclusion variables variables load period variables Gantois 2019 20 professional To analyze the Three different Heart Rate Urine osmolality Index of Pre/Post There are no significant The mental fatigue induced et al. football effect of tests were carried Variability, (Armstrong’s Passing Stroop Task: differences between by the Stroop task (2019) players mental out over 3 weeks HRV scale), TQR Decision- Urine, TQR, HRV, TQR, Urine, sRPE damages the decision (22.6 ± 3.3 fatigue on (1 week between (RMSSD, questionnaire making HRV, RPE and the different Stoop making of the football years) decision- sessions): “Stroop SDNN and (Total Quality of (PDM) (%) tasks. players and therefore making in task”, followed by pNN50) and Recovery, a.u.), During SSG: All participants showed a their performance. football a warm-up and a reaction time, CR-10 sRPE a.u. PDM, TR decrease of PDM in players SSG. RT (seconds) 30′ post SSG: conditions of 30ST in Stroop tasks: sRPE comparison to 15ST and control (CON), CON. The RT increases 15′ ST (15ST) significantly with mental and 30′ ST fatigue. (30ST) Fuster et al. (2021), PeerJ, DOI 10.7717/peerj.12045 Moreira 2018 32 high level To examine the Two 4 × 4 SSG Reaction time, RPE session (CR- Performance Pre task cont./ The RT decreased Mental fatigue induced by a et al. basketball effect of trainings, RT (s) 10, a.u.), HR, parameters exp.: Saliva significantly during the Stroop task affects the (2018) players mental effort separated by a saliva samples: through During the test, except during the performance of the task (15.2 ± 1.2 on week (4 × 2′30–1′ concentration of video task cont./ last 5′. There’s a direct negatively. This also years) physiological pause). They are testosterone (T), analysis. exp.: RT correlation between the provokes a modulation of changes and preceded by two cortisol (C) and Post task number of turnovers and the endocrine response technical situations: alpha-amylase cont./exp. the experimental task. (it alleviates the performance (1) Control: 30′ (sAA) (pre SSG): concentrations of T and in SSG. control treatment Saliva Thera are an increase of sAA, in comparison to (2) Experimental: During SSG: the concentrations of T the control condition). 30′ Stroop Task Performance and sAA from the Between sRPE and HR parameters pre-control treatment to we do not see very clear and HR the post SSG. There is differences. Post SSG: little difference between Saliva (15′ the treatments regarding post) and the sRPE and the HR. RPE (30′ post) Scanlan 2013 12 basketball To determine Three basketball- Reaction time 5, 10, and 20 All measures The simple and stepwise The cognitive measures et al. players the influence specific tests were and decision- m sprint were carried regression analyses had the greatest influence (2013) (25.9 ± 6.7 of physical carried out: making time times (s), out during determined the on the reactive agility years) and cognitive multiple sprint (s) max. V (m/ the individual influence of performance of the factors in the test, Change of s), reactive basketball- each predictive variable basketball players development Direction Speed agility time specific tests and the best predictive of reactive Test and Reactive (s), model for reactive agility agility in Agility Test closed-skill time. Through the basketball agility time stepwise model, the players. (s) reaction time is identified as the only predictive variable. 15/26 100 mm VAS (MF and ME) The VAS scale (Visual Analogue Scale) offers specific information about a characteristic or attitude which is identified along a continuum of values and cannot easily be directly measured (Gould et al., 2001). It has a unidimensional format, charted in a 100 mm straight line, with limits identified as the perceived minimum (0) and maximum (100). The subjects only need to make a mark on the line that indicates their relative perception of their current situation. The distance will be measured in millimeters (from left to right) and this will be the subjective reference value. This tool has been used frequently in the bibliography to describe the fatigue and lack of energy caused by a cognitively demanding activity (mental fatigue, MF) and/or the degree of effort employed to perform a cognitively demanding activity (mental effort, ME). It has been proved that the scale is a valid, reliable method to measure both MF and ME (Lee, Hicks & Nino-Murcia, 1991). The first investigations demonstrated that the increase of perceived indicator VAS MF damaged specific technical performance (Smith et al., 2016; Badin et al., 2016). In Badin’s study (2016), effects of VAS MF on physical abilities were not found, in contrast to subsequent studies which related VAS MF to the loss of both technical and physical abilities (Veness et al., 2017; Coutinho et al., 2017; Coutinho et al., 2017). Sansone et al. (2020) adds that within this loss of abilities, offensive tasks cause more significant VAS MF and ME than defensive tasks. Differing a bit from what has been previously described, Kunrath et al. (2020) shows that VAS MF reduces technical, tactical and cognitive abilities, provoking a compensatory improvement of the performance. Measurement Period: After the session/competition. CSAI-2 CSAI-2 (Competitive State Anxiety Inventory 2) is a test that aims to estimate the cognitive and somatic anxiety levels of the players, as well as their levels of self-confidence. Cognitive anxiety refers to the negative feelings that the subject has about his or her performance and the consequences of the outcome. Somatic anxiety, on the other hand, refers to the perception of physiological indicators of anxiety such as muscle tension, increased heart rate, sweating and stomach discomfort. It has a questionnaire format consisting of 27 items, with nine items for each subscale. Each of them is classified on a four-point Likert-type scale, which yields scores from nine to 36 in each subscale. A higher score related to cognitive and somatic anxiety indicates a higher level of anxiety (Martens et al., 1990). Arruda et al. (2017) study suggests that cognitive anxiety increases as a function of the level of the opponent, causing more psychological stress. Furthermore, it highlights that cognitive anxiety increases exponentially depending on the rival, more than somatic anxiety. This information could offer an approximation to the concept of cognitive load, dissociating the somatic state from the cognitive one. Measurement Period: After the session/competition. Fuster et al. (2021), PeerJ, DOI 10.7717/peerj.12045 16/26 POMS The POMS (Profile of Mood States) questionnaire was proposed by McNair, Losr & Droppleman (1971) with the aim of evaluating the mental fatigue related to physical effort. The questionnaire is classified on a Likert scale and it contains 65 items that provide measures of six specific mood states: tension, depression, anger, vigor, fatigue and confusion. These factors can be combined to create a compound measurement of mood state by adding the five negative factors and subtracting the positive factor of vigor. Further, a 100-point baseline score is added to prevent negative scores (Raglin, Morgan & O’Connor, 1991). It has been proved that POMS is a reliable and valid questionnaire to measure affective features, mood state and emotions (Lin, Hsiao & Wang, 2014). That is why Mashiko et al. (2004) use it as a test of mood state in order to evaluate mental fatigue. In their study they could observe significant changes between different game positions in rugby games, obtaining important differences between the relation of mental and physical fatigue. Measurement Period: After the session/competition. Cognitive RPE The RPE (rating of perceived exertion) is a valid method for quantification of the effort expended in an athletic training activity during a wide variety of exercise types (Foster et al., 2001). Depending on the question the athlete is asked, distinctive subjective variables can be measured. These differential ratings of perceived exertion (dRPE) can provide additional information to that obtained by a single measurement (Gil-Rey, Lezaun & Los Arcos, 2015). In this review, we focus on cognitive RPE (RPE-T in the classification of dRPE), which is the one that answers the question “How much mental effort and decision-making has this task required?” To quantify it, two scales are used, CR-100, also known as “centiMax”, (Borg & Borg, 2001), scored from 0 to 100 and CR-10 (Borg, Hassmen. & Langerstorm, 1985), varying from no exertion (0) to maximum effort, such as a competition (10). The important difference between them is that the CR-10 uses whole numbers corresponding to verbal anchors and the CR-100 does not (Fanchini et al., 2016). In Farrow, Pyne & Gabbett (2008) study it was shown that cognitive RPE (CR-10) was sensitive to tasks that involved increased decision-making in game-like situations. Having followed this line of research and found results that agree with the mentioned study, Gabbett, Jenkins & Abernethy (2010) justify the use of cognitive RPE as valid for control of cognitive load, but since it is a subjective measurement it would require more objective methods to be compared and measured. In order to relate the subjective data obtained to some objective data, McLaren et al. (2016b) finds strong associations between RPE-T multiplied by the session time (sRPE-T) and the tasks focused on speed and specific abilities, the latter being open tasks focused on the game. This RPE-T increases significantly when the game is against the top teams of the league (Barrett et al., 2018). Therefore, we can say that according to the results of these four studies, cognitive RPE Fuster et al. (2021), PeerJ, DOI 10.7717/peerj.12045 17/26 is sensitive to open tasks that seek to replicate the competitive game, creating an uncertain environment. Measurement Period: After the session (recommended 15–30′ later). SIATE/VSC SIATE (Sistema Integral de Analisis de Tareas de Entrenamiento, from its Spanish initials) and VSC (Valoración Subjetiva de la Carga, from its Spanish initials) are ecologic systems for quantifying the load of a basketball training session (Vallés, Fernández-Ozcorta & Suero, 2017; Reina et al., 2019). They are intended to provide a holistic view of training, taking into account more than just internal and external load. Ibáñez, Feu & Cañadas (2016), in order to quantify training using direct observations, designed a methodological system to register and subsequently analyze different factors that are relevant to the athletic process. For this purpose, they created an observational survey known as Integrated System of Training Task Analysis (SIATE). This survey records six variables: degree of opposition, density of task, percentage of simultaneous participants, competitive load, space of play and cognitive involvement. The variables are scored from one (minimum load) to five (maximum load). The sum of the scores provides a measurement of the total load of the task. Coque (2009) suggests a tool that, similarly to the previous one, aims to evaluate the training load using a direct observation. The analysis system is the Subjective Evaluation of the technical-tactical training load (VSC), in which six variables are recorded: degree of obstacle, density of the task, percentage of simultaneous executions, competitive load, field of play and cognitive involvement. The variables are scored from one (minimum load) to four (maximum load). The sum of the scores provides a measurement of the total load of the task. Both of them can be used to calculate the total load of the task, which would be the total of the assessed values, and the load weighted for the length of the task, which would be calculated by multiplying the total load by the useful time of the task in minutes. The latter shows more precision in the real load of the task. Reina et al. (2019) shows that the SIATE organic system gives us the same information as that recorded by inertial devices or HR monitors. Also, Vallés, Fernández-Ozcorta & Suero (2017) show that VSC has a strong correlation with RPE. Studies that analyze correlations between individual variables to see the percentage of the variability as a function of the cognitive load have not been found. Measurement Period: After the session/competition. PHYSIOLOGICAL INDICATORS Heart rate variability (HRV) Heart Rate Variability is defined as the temporal variation of the heart rate during a specified time period (Capdevila & Niñerola, 2006). In the simplest way, HRV has been analysed within the time domain, but more complex evaluations include an analysis within the frequency domain and nonlinear methods (García Manso, 2013). Fuster et al. (2021), PeerJ, DOI 10.7717/peerj.12045 18/26 The Autonomic Nervous System (ANS) is responsible for the regulation of the HRV through parasympathetic and sympathetic modulation (Bricout, DeChenaud & Favre, 2010), the balance of which is disrupted after changes in the training load (Pichot, Busso & Roche, 2002). This is the reason why many studies define HRV analysis as a useful, non-invasive method to evaluate the function of the ANS (Bellenger et al., 2016; Bosquet, Merkari & Arvisais, 2008; Hynynen et al., 2006; Parrado et al., 2010). The findings in the Thayer et al. (2009) study suggest an important relationship between cognitive performance and HRV, reaffirming the relevance of this information to measure the effect of cognitive load. Even though the literature in control situations suggests that HRV is very sensitive to demands on the cognitive load (Luque-Casado et al., 2016), no significant differences have been found in HRV comparing HRV control situations and mental overload (Gantois et al., 2019). Measurement period: Before the session. BEHAVIORAL INDICATORS Reaction time or response time Reaction time is calculated as the time from the beginning of the stimulus until the corresponding response of the participant (Gabbett, Kelly & Sheppard, 2009). This indicator depends mainly on, but may be affected by age, gender or duration of the stimulus (Der & Deary, 2006), on cognitive processes, which means that mental fatigue could be inhibitory (Huijgen et al., 2015) and could increase the response time. It should be noted, however, that beyond a certain duration and/or intensity of exercise, muscle fatigue induces an increase in reaction time which may be due to a decrease in cognitive performance. fatigue induces an increase in reaction time that may be due to a decrease in cognitive performance (Chmura, Nazar & Kaciuba-Uscilko, 1994). In Scanlan et al. (2013) response time is identified as the only variable that predicts the time of reactive agility in the phased model. This serves us to determine the influence of physical and cognitive factors on the development of reactive agility in basketball players. Gantois et al. (2019) found a significant increase in reaction time in relation to increased mental fatigue induced by the Stroop task. This increased reaction time impairs the decision making of soccer players and thus their performance. Coinciding with these results, Moreira et al. (2018) saw an increase in reaction time caused by mental fatigue in a Stroop task. Reaction time underwent a significant decrease during the test, except for the last 5 min in which it was maintained Measurement period: During a task previous to training/competition. Decision-making time Making a successful decision depends on the ability of the player to identify, select and then execute the correct action in response to the postural signs of opponents or teammates, recognizing significant patterns in the game and determining the situational probabilities (Roca et al., 2013; Williams et al., 2011). Decision-making time is determined as the time interval between the first identifiable contact of the stimulus-player and the Fuster et al. (2021), PeerJ, DOI 10.7717/peerj.12045 19/26 first identifiable contact that initiates the participant’s response (Gabbett, Kelly & Sheppard, 2009). This cognitive ability to make fast and precise decisions is fundamental for success in football (Smith et al., 2016), an observation that can be applied to the rest of the team sports. In Scanlan et al. (2013) decision-making time is identified as a variable which is highly associated with agility time. This cognitive measurement, together with reaction time, was the one that influenced the basketball players the most. Gantois et al. (2019) showed that induced mental fatigue damaged the decision-making process, provoking a decrease in performance. These findings agree with those of Smith et al. (2016), determining that mental fatigue affected the decision-making precision and time of the football players. Measurement period: During the task. CONCLUSIONS Relation with internal and external load Cognitive factors interact with physiological and mechanical factors which are present during training and competition. For this reason, distinguishing their connections can be a significant element in the research of load management in sport (Soligard et al., 2016). In this context, the loads that the athletes assume involve stress and provoke changes in their physical and psychological well-being. Furthermore, understanding the interaction between these loads, perceptive well-being and readiness for training or competition will provide us with significant individual training prescriptions (Gabbett et al., 2017). The IL measured through RPE is significantly connected to variations in cognitive load imposed by the training exercises, as evaluated by cognitive indicators such as the NASA-TLX questionnaire (Camacho et al., 2020) or the VAS MF scale (Badin et al., 2016; Sansone et al., 2020; Veness et al., 2017). This increase in cognitive fatigue has a direct impact on the internal load, provoking a modulation of the endocrine response attenuating the concentration of testosterone and alpha-amylase, markers of the activity of the mesolimbic pathways and the sympathetic nervous system, respectively (Moreira et al., 2018). There were no studies showing significant correlations between the internal load taken from the HR and the cognitive load taken from the NASA-TLX questionnaire (Badin et al., 2016), except for the study conducted by Farrow, Pyne & Gabbett (2008) which showed increases in the RPE-Cog and the HR during open exercises. Moreover, the EL recorded through tracking systems shows that players under high cognitive load conditions (VAS MF) register higher values for total distance, average speed and run time at a moderate speed, demonstrating higher intensity efforts (Coutinho et al., 2017; Kunrath et al., 2020). This condition of mental fatigue damages aspects of the tactical behavior of the players, causing a compensatory increase in the physical performance (Kunrath et al., 2020). Position variables, taken from GPS analysis, are also affected in high cognitive load conditions, provoking a decrease of the longitudinal synchronization (coordination of longitudinal movements of the players during the game), of the stretch index (represented by the average distances of each player from the geometric gravity centers of the team) and of the distance between dyads (distance between Fuster et al. (2021), PeerJ, DOI 10.7717/peerj.12045 20/26 a pair of players that share the same environment and intend to reach the same team objective) (Coutinho et al., 2018). This deterioration of physical performance (physical variables) and elapsed synchronization time (position variables) should alert us that cognitive load should be considered a variable that can be controlled with the objective of improving collective behavior. The models of training load control that take into account cognitive load such as the CSE or VSC can offer the same information as more difficult methods (Reina et al., 2019; Vallés, Fernández-Ozcorta & Suero, 2017). However, we must take into account that they are both subjective recording systems. Practical applications To optimize performance and exert more control over training processes and/or competition in team sports, it is necessary to have load control strategies which include cognitive load in the monitoring cycles of the athlete. However, there is limited literature related to this concept. NASA-TLX, VAS MF or Cognitive-RPE analysis strategies are considered valid to measure the impact of the cognitive load in team sports. Other control methods like VFC, reaction time or decision-making time need scientific evidence. In this sense, there is a clear lack of studies which use objective tools to measure the cognitive load. The evaluation of external, internal and cognitive demands in an isolated way would be potentially problematic, because we would only obtain information on the provided stimulus, the processed stimulus or the players responses without examining the inherent connections. Even though it is accepted that external, internal and cognitive demands are separate constructs, they must be analyzed and interpreted in the same context, and they must be considered as a whole in order to optimize performance and prevent injuries. Processing systems will produce an alteration in the stimulus that generates physiological responses, so the manipulation of one of these three constructs will provoke changes in the others. With this objective, future studies that delve into this paradigm will be justified. ADDITIONAL INFORMATION AND DECLARATIONS Funding This study was funded by the Ministry of Science and Innovation (Ministerio de Ciencia e Innovación) of the Spanish government (Grant Number: PID2019-107473RB-C21). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Grant Disclosures The following grant information was disclosed by the authors: Ministry of Science and Innovation (Ministerio de Ciencia e Innovación): PID2019- 107473RB-C21. Fuster et al. (2021), PeerJ, DOI 10.7717/peerj.12045 21/26 Competing Interests The authors declare that they have no competing interests. Author Contributions  Joan Fuster conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft.  Toni Caparrós conceived and designed the experiments, performed the experiments, authored or reviewed drafts of the paper, and approved the final draft.  Lluis Capdevila conceived and designed the experiments, authored or reviewed drafts of the paper, and approved the final draft. Data Availability The following information was supplied regarding data availability: No code was used in this Literature Review. REFERENCES Alarcón F, Castillo-Díaz A, Madinabeitia I, Castillo-Rodríguez A, Cárdenas D. 2018. La carga mental deteriora la precisión del pase en jugadores de fútbol. Journal of Sport Psychology 27(2):155–164. Alarcón F, Ureña N, Cárdenas D. 2017. La fatiga mental deteriora el rendimiento en el tiro libre en baloncesto. Revista De Psicologia Del Deporte 26(1):33–36. Arruda A, Aoki M, Paludo A, Moreira A. 2017. Salivary steroid response and competitive anxiety in elite basketball players: effect of opponent level. Physiology & Behavior 177:291–296 DOI 10.1016/j.physbeh.2017.05.017. 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