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Archive > Year 2020, Number 3

Physiological arousal and visual search behavior under intensity exercise demands in elite and non-elite soccer players

The complexity that characterizes the soccer match requires players to exhibit high-levels of performance skills manifested in their physical, technical-tactical and psychological capacities [29]. Players’ physiological responses and perceptual-cognitive skills may vary according to specific match demands, such as the competitive level (e.g., elite and non-elite), task constraints variations (e.g., field dimensions), as well as match duration and physical intensity (high and low) [7,15]. Players’ perceptual cognitive skills have been associated to high performance levels, emerging as fundamental mechanisms used by players to seek and explore relevant information sources in the surrounding environments, which clearly influences and differentiates players’ decision-making and actions in distinct skill levels (e.g., elite vs non-elite) [5,27].
In fact, researchers report that elite/skilled players present better performances under physical and psychological pressure, with a tendency to use more efficient and effective visual search to pick-up relevant sources of information from the competitive environment, comparatively with their counterparts (non-elite/less skilled players) [7,25,26].
Studies have used different methodological approaches to verify and compare how elite and non-elite players’ visual search strategy respond to particular stimuli and sports [1,7,20]. In this regard, research had provided evidence that skilled players consume more time fixating on specific informative areas (e.g. player in ball possession) in order to pick-up significant cues, pivotal to support decision-making and to guide actions [7,16,30].
Roca et al. [21] verified that players (skilled and non-skilled) performed more time fixating in player in ball possession compared with other locations (i.e. ball, attacker, defender and space), specifically in which concerns the utilization of visual search strategies during a video-based simulation of an 11 vs. 11 match. Furthermore, Casanova et al. [7] examined soccer players’ perceptual-cognitive skills during a prolonged intermittent exercise protocol. Authors reported significant differences in physiological responses and visual search strategies between players, once elite players spent smaller time fixating in player in ball possession then non-elite players, as well. Indeed, some researchers have stressed the influence of the physiological demands inherent to official competitive matches in players’ perceptual-cognitive skills underpinning decision making [7,8].
Given that different exercise intensities seems to change players’ visual search strategy in which regards to local fixations, key issues that still need to be addressed are to verify how different intensity demands (high and low) influence physiological responses, as well as to identify which location players from different competitive levels seek information sources [15]. Enlightening such issues may help coaches, practitioners, and performance analysts to enhance understanding on players’ performance regarding the effects induced by different intensity demands on players’ physiological responses and visual search strategies [12,18].
In this sense, research has sought to represent the reality of the match, to stimulate the players’ actions as well as the processes underlying the decision-making, through evaluations using game scenarios and protocol simulations [9]. Thereby, this study aims to examine the physiological arousal and visual search strategy under low- and high-intensity in elite and non-elite soccer players. Based on the study conducted by Casanova et al. [7] we hypothesized that elite players present smaller heart rate (HR) and blood lactate (Lac) concentrations in response to low- and high-intensity exercise than non-elite players. Additionally, we foresee that elite players spend smaller time fixating the ball and the ballplayer, thus spending more time fixating the opponent comparatively with non-elite players [22]. To high-intensity exercise elite players would spend smaller time fixating the ball.

Sixteen soccer players were separated at two groups of eight each at elite (mean age = 24.6 yr., SD = 3.9 yr), who had played at a semiprofessional or professional level for an average of 5.1 yr. (SD = 2.4 yr.), and non-elite (mean age = 26.3 yr., SD = 2.9 yr.), who had only played at an amateur level for an average of 2.1 yr (SD = 2.4 yr). The players reported their visual function as normal levels. The study was carried out with the ethical approval of the lead institution, in agreement with Helsinki Declaration.

It consisted of 40 video clips showing offensive sequences of the soccer formal game (i.e. 11vs.11) validated by Casanova, Garganta, Oliveira, [6]. A panel of four elite-level soccer coaches, who all held the Union of European Football Associations-A license, having at least 10 years’ experience, validated the footage. The level of agreement between observers regarding suitability of the clips was high (α = 0.889). Moreover, four different test films were created, comprising ten different offensive sequences. The duration of each clip was approximately 5 s, with an intertrial period of 5 s. Just before the start of each clip, a small circle surrounding the ball appeared on screen to indicate the area of its first appearance. The clips were all occluded 120 ms before the player in ball possession was about to make a pass or take a shot to goal or maintain ball possession.

To ensure the representativeness of the image regarding the real match play, the screen where the clips were projected consisted of a large screen (2.5 x 2 m) placed 1.5 m in front of participants. The Applied Science Laboratories (ASL®) 3000 eye-movement registration system were wore by participants during whole the protocol test and used for recording the visual search behaviors. The system functions by recording participant’ point of gaze onto a video image of the monocular corneal reflection regarding a helmet-mounted scene camera. The system measures the relative position of the pupil and corneal reflection. These features are used to compute point of gaze by superimposing a crosshair onto the scene image captured by the head mounted camera optics System. Such systems’ accuracy was ± 1º visual angle for both horizontal and vertical directions. The image was analysed frame-by-frame with the software Pinnacle Avid Liquid7.

Before the beginning of the test the procedure was explained, and the eye-movement system fitted onto the participant’s head. The ASL eye-movement system was calibrated using a 9-point reference grid so that the fixation mark corresponded precisely to the participant´s point of gaze. To ensure that players were familiar with the test procedure, they were presented with six practice trials in the laboratory task environment [28].
To verify the visual search strategy from the test film, players were asked to anticipate their response relative to the three possible responses performed by the player in ball possession: (i) pass, (ii) shot at goal, or (iii) retain ball possession. To simulate the demands of a soccer match it was used the intermittent exercise protocol encompassing different exercise intensities conducted on a motorized treadmill, based on previously reported motion-analysis data (e.g., walking, jogging, running, cruising, and sprinting) [9]. The protocol lasted 119 min and was divided into two halves with the same duration (52 min), interspersed by 15-min rest interval. A static recovery period was included, in which the participants remained stationary on the treadmill (H/P cosmos, Pulsar, Germany). The treadmill velocities used for each activity pattern were as follows: walking 6 km•h-1; jogging 12 km•h-1; running 15 km•h-1; cruising 18 km•h-1; sprinting 23 km•h-1.
Heart rate, blood lactate concentrations and visual search behaviours were collected according to different intensity phases: low-intensity - third and tenth blocks; and high-intensity - seventh and fourteenth blocks. In each assessment, the participants viewed 10 clips presented in a counterbalanced order. The total duration of the experimental protocol, including the period of familiarization with the procedures, lasted approximately 210 minutes (Figure 1).

Figure 1. Representative of protocol experimental design

HR was monitored continuously at 5-s intervals to provide information regarding the circulatory strain (Polar S610i, Finland). We calculated the mean value for each block of the intermittent exercise protocol. A lactate analyzer (Lactate-Pro, Japan) was used to collect the Lac concentration samples. These measures were obtained from the third, seventh, tenth, and fourteenth blocks of the intermittent exercise protocol.

Percentage of the viewing time was defined as the proportion of time spent fixating on each area of the display. The display was divided into five fixation locations: ball; teammate; opposition; player in ball possession (i.e. player in ball possession); and undefined. The undefined category was excluded because the percentage of the viewing time in this location was less than 1% regarding the percentage of the viewing time data.

A Separate Factorial Two-way ANOVAs was conducted with group (elite/non-elite) as the between-participants factor and Intensity Level (Low Intensity / High Intensity) as within-participants factors to analyze differences in heart rate (bpm), lactate concentration (mmol•l-1l) and view time fixating locations (Ball, PlayerBall, Teammate and Opposite). Partial eta squared (ƞp2) values were provided as a measure of effect size for all main effects and interactions. The alpha level of significance was set at p<0.05.

The data revealed significant main effects in Group for HR (F2,188 = 4.99; p = 0.27; ƞ2p = 0.03) and for Lac (F2,188 = 7.59; p = 0.01; ƞ2p = 0.04). In addition, there was statistical significance in intensity level for HR (F2,188 = 38.02; p < 0.001; ƞ2p = 0.67) and Lac (F2,188 = 20.74; p < 0.001; ƞ2p = 0.52). Elite players showed smaller HR (p = 0.031) and Lac concentrations (p = 0.001) in low intensity than non-elite. Both groups increased HR and Lac significantly from low- to high-intensity demands (Elite: p < 0.001; Non-elite: p < 0.001).
There were no statistically significant differences concerning Group * Intensity Level interaction for HR (F2,188 = 1.55; p = 0.215; ƞ2p = 0.01) and Lac (F2,188 = 0.18; p < 0.670; ƞ2p < 0.001) (see Table 1).

Table 1. Means and standard deviations (SD) values for HR and Lac regarding each Group level and Intensity level

Table 2 shows a significant group * intensity level interaction for percentage of Fixation on the PlayerBall, (F1.188 = 9.98; p = 0.002; ƞ2p = 0.05) and Opposition (F1.188 = 7.66; p = 0.01; ƞ2p = 0.04). There were significant differences in Group for Ball (F1.188 = 15.96; p < 0.001; ƞ2p = 0.08), PlayerBall (F1.188 = 8.52; p = 0.004; ƞ2p = 0.04) and Opposition (F1.188 = 13.68; p < 0.001; ƞ2p = 0.07).
Regarding the low intensity level demands, elite players presented significantly smaller time spent fixating the ball (p = 0.004) and playerball (p < 0.001), compared with non-elite. In contrast, elite players spent more time fixating the opposition than non-elite players (p < 0.001). On the other hand, for high intensity level, elite players also displayed a significantly smaller time spent fixating the ball (p = 0.008) than non-elite players.
When fixation percentages were verified within group, elite players increased time spent fixating the ball (p = 0.027) and playerball (p = 0.008). Moreover, elite players decreased time spent fixating the opposition (p < 0.001).

Table 2. Means and standard deviations (SD) for fixation percentage of view time (ball, Playerball, teammate and opposition) regarding each Group level and Intensity exercise level

We hypothesized that elite players would present smaller HR and Lac concentration values in response to low intensity than non-elite players. Also, we have predicted that elite players would spend smaller time fixating the ball and playerball, as well as more time fixating the opponent comparatively with non-elite players. In which regards the high-intensity level, elite players would spend smaller time fixating the ball when compared with non-elite players.
Indeed, the main findings of our study reported differences between elite and non-elite players regarding the physiological demands and visual search strategy demonstrated for both low- and high-intensity levels. Therefore, such results are in line with other studies, once elite players displayed smaller physiological arousal than their counterparts [2, 20].
Moreover, independently of the task constraints or stimuli applied by researchers in 11 vs. 11 match, elite players performed different visual search strategies for picking-up information sources from the environment when compared with non-elite players [19,22,28].
This study applied an intermittent protocol with the purpose of evaluating the physical demands, allowing to simulate exercise intensities in treadmill with duration similar to the soccer match [7,9]. Although this protocol had not included soccer specific activities (e.g., ball contacts, physical tackles) our results showed differences between groups (i.e., elite, and non-elite) for HR and Lac, specifically in low-intensity levels. The elite players have shown smaller values of HR and Lac when compared with non-elite players. When we compared low- and high-intensity levels within groups (i.e., elite and non-elite), players have increased significantly their HR and Lac values, possibly because high-intensity levels simulated the activities at the end of the match [3,14]. Such differences were also observed in real competitive matches, where players´ fatigue occurred, occasionally, during the match and near the end, regardless of competitive level [15].
Our results have also revealed differences for the visual search strategy displayed by elite and non-elite players. Previous studies have confirmed such differences by suggesting that skilled players search information sources in more relevant places than their counterparts (i.e. less-skilled players, thus altering their visual search in relation to the variations of task constraints, like the manipulation of “far and near” aiming task [see, 22,30] constraint in 11 vs.11 matches [4,23]. Indeed, elite players picked-up more relevant information from the surrounding environment to resolve problems that emerged in competitive performance [8,24]. Notwithstanding, Casanova et al. [7] investigated the perceptual-cognitive skills of elite and non-elite soccer players during intermittent exercise protocol. The authors reported that when soccer players are submitted to physiological wear that characterizes the soccer game demands, the ability of elite and non-elite players to make better decisions could be compromised due to an increase of the search time for relevant information to carry out the actions.
Concerning the search time needed to pick-up information from the environment, in this study elite players have shown to spend less time fixating the ball and playerball, despite having spent more time fixating the oppositions when compared with non-elite players, in low-intensity level. Moreover, analysing the visual search strategy in high-intensity exercise level, we verified that elite players also spent less time fixating the ball comparatively to non-elite players. Williams et al. [27] claimed that expert players dedicated more time in seeking information in more places of the field, such as the opponents´ movements, through a greater number of short fixations compared to less skilled players, which in turn tended to seek information’s according to the ball trajectory by exhibiting longer fixations [21]. Hence, elite players spend more time directing their fixations towards informative areas (e.g. opponents) trying to perceive key information sources through observation of opponents’ actions to support decision-making [7,19].
Even when we observed the differences within groups for the visual search according to distinct intensity levels (e.g., high and low intensity), elite players displayed changes in three different locations (i.e., ball; playerball and opposite), while non-elite players have shown differences just in one place (i.e., teammate). Thereby, it is possible to highlight that players may need to adapt their behaviours during different match intensity demands, since high performance players are able to adapt their visual search for balancing strategy allocated in the search of relevant information at high pressure [7,10,13].
Thus, our findings agree with Martins et al. [15] where authors found that when elite players were subjected to high-intensity exercises, they exhibited alterations in visual search strategy [cf., 11]. However, the authors have not identified where players focused their visual search strategy.
Furthermore, to better understand the perceptual-cognitive skills underpinning decision-making, we emphasise that future researches should examine the effects of workload over mental effort and visual search conducted on-field or by using video simulations. Moreover, another interesting question is to ascertain the effects induced by tactical behaviour and positional roles over players’ perceptual-cognitive skills.

The elite soccer players demonstrated better responses under both low- and high-intensity exercise demands due to HR and Lac arousal. Moreover, elite players’ performance was sustained by their ability to alternate and adapt their perceptual-cognitive skills according to different exercise intensities. Finally, soccer players can be confronted with practical interventions aiming to achieve superior levels of performance, such interventions could improve the physical and perception abilities of both elite and non-elite players. For instance, training sessions can be organised and structured by focusing on tactical and strategical match situations performed under different physical levels demanding’s (e.g., high- and low-intensity levels).