The athletes belonging to the six identified somatotype categories were distributed among the four groups of TBW except for the first group where mesomorph ectomorph, and mesomorphic ectomorph athletes, were not present. As a result, it was possible to explore the association between PhA and morphological features. The endomorphic mesomorph, balanced mesomorph, and ectomorphic mesomorph somatotypes are characterized by a dominant mesomorphy, due to a muscular related body shape. On the contrary, mesomorphic ectomorph and balanced ectomorph categories imply a dominant ectomorphy, and therefore, athletes tend to be taller with a lower muscle mass than the other somatotype categories [25]. Rakovi'c et al. [32] showed how mesomorphic features are linked to individual sports that require higher muscle strength, while ectomorphy is predominant in runners [33], especially those involved in long distance. In previous research [21], it was highlighted how R/H and Xc/H were able to discriminate somatotypes. However, if we consider this new and more individual approach, considering the TBW values, probably some of those athletes needed to be revised, since body fluids have a great influence on the vector position. For this reason, the athletes' somatotypes were analyzed according to groups of body fluids to reduce the differences attributed to TBW, and consequently, to better understand how somatotype is associated with the vector position. Due to this approach, it was possible to observe how the vector position changes based on the morphological features. Indeed, when the athletes were divided into TBW groups, significant differences were found in vector length, but not for PhA, which instead represents the lateral displacement of the vector. This suggests that athletes with a similar PhA could have a greater TBW and, therefore, a different vector position and body composition characteristics. In fact, when comparing the four groups, athletes with a shorter mean vector were those with a higher TBW. A recent literature review on PhA in sports [34] concluded that it was not clear whether PhA differs among athletes engaged in different sports. On the contrary, studies on athletes practicing the same sports, but at different competitive levels, have shown that elite athletes show a higher PhA than those engaged in lower-level categories [4–6]. In particular, Micheli et al. [6] suggested that this is due to a lower R/H in relation to Xc/H and reflects a condition of greater muscularity and body cell mass content in the athletes that compete in the higher levels. In this study, it was shown how the interpretation of the vector position in the R-Xc graph overcomes the limits linked to the interpretation of the PhA alone. In this regard, Reis et al. [35] recently showed that the bioimpedance vector position varied in response to changes in the macrocycle training load in

swimmers of both sexes. The authors identified an accumulation of fluids and, therefore, a shortening of the vector following a first phase characterized by a high training load, and then a subsequent shift of the vector position to the left side as a result of muscle adaptations that occurred after a recovery period. Similarly, Mascherini et al., in 2014 [24], for the first time studied vector changes over the competitive season in soccer players and showed that PhA decreased during the preparatory phase and then increased near the beginning of the competition. In line with this, Nabuco et al. [36] highlighted a moderate and inverse association between PhA and the values obtained from a fatigue assessment test.

The results of the present study add important and useful information for a correct interpretation of BIVA. The careful evaluation and monitoring of body composition allow the athlete to be predisposed to achieving high peak performance [37]. Through BIVA, it is possible to evaluate the progress in the athlete's physical condition during the season [38] in response to a training program or a nutritional intervention [37], avoiding incurring decreases in physical performance. This method, in addition to monitoring body fluids, also allows information about other body composition variables at a whole-body level [39]. In fact, it is not always possible to collect the various anthropometric measures that allow the evaluation of the somatotype; in this regard BIVA, if correctly interpreted, can provide important information, minimizing the need for skinfolds and girths collection by a certified anthropometrist. While this method requires further study, especially concerning monitoring the bioimpedance parameters in the short term, the innovation of this study lies in the fact that it provides useful information for the correct interpretation of the vector position in the R-Xc graph, specifically the role of body water as a mediator of the morphological associations with bioelectrical parameters.

Some limitations of this study need to be addressed. First of all, our results are only generalized for male athletes. Secondly, the findings of this study are only applicable to single-frequency BIA equipment. In fact, different results in measuring raw BIA parameters are obtained using devices that work on single- or multi-frequency [40]. On the other hand, the fact that it was ensured that the athletes were in a state of euhydration is a strength of the present work. Future research should study the potential of BIVA patterns as a biomarker of physical condition during the training process, as in specific microcycles. Indeed, as bioimpedance analysis (BIA) equipment provides an easy, simple, and low-cost application, it allows for a frequent assessment to optimize monitoring of the athlete's physical condition.

#### **5. Conclusions**

BIVA provides meaningful information on body composition assessment in athletes. This study showed that body fluid content affects the vector length in the R-Xc graph, while the lateral displacement of the vector can be modified by the morphological characteristics of the athlete. In particular, the mesomorphic component of the somatotype is related to higher PhA, an important marker of cellular integrity and overall physical function.

**Author Contributions:** Conceptualization, A.M.S., C.N.M., J.T., H.L. and S.T.; data curation, F.C. and C.P.M.; formal analysis, F.C., C.P.M.and J.P.N.; investigation, A.P., J.T., and S.T.; methodology, A.M.S., C.N.M., J.P.N., and H.L.; supervision, H.L. and S.T.; visualization, S.T.; writing—original draft, F.C., A.M.S., C.N.M., and J.P.N.; writing—review and editing, F.C., A.M.S, C.N.M., C.P.M., A.P., and S.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors are grateful to all the athletes who took part in this study.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

International Journal of *Environmental Research and Public Health*

## *Article* **E**ff**ects of Aerobic and Anaerobic Fatigue Exercises on Postural Control and Recovery Time in Female Soccer Players**

**Özkan Güler 1 , Dicle Aras 1 , Fırat Akça 1 , Antonino Bianco 2,\* , Gioacchino Lavanco 2 , Antonio Paoli 3 and Fatma Ne¸se ¸Sahin 1**

Received: 4 August 2020; Accepted: 24 August 2020; Published: 28 August 2020

**Abstract:** Sixteen female soccer players (age = 20.19 ± 1.52 years; body mass = 56.52 ± 4.95 kg; body height = 164.81 ± 4.21 cm) with no history of lower extremity injury participated in the study. The Biodex SD Balance system was used to determine the non-dominant single-leg stability. In anaerobic exercise, each subject performed four maximal cycling efforts against a resistance equivalent to 0.075 kg/body mass for 30 s with three-minute rest intervals. In aerobic exercise, subjects performed the Bruce protocol on a motorized treadmill. After each exercise, subjects subsequently performed a single-leg stability test and then repeated the same test for four times with five-minute passive rest periods. In accordance with the results, it was found that the impairment observed right after the aerobic loading was higher (*p* < 0.001) compared to the anaerobic one. However, the time-related deterioration in both aerobic and anaerobic loadings was similar. The B-pre value was lower than Bpost and B5 (*p* < 0.01) and B10 (*p* < 0.05) in both conditions. Subjects could reach the initial balance level at B15 after aerobic and anaerobic loadings. The lactate level did not reach resting value even after 20 min of both fatigue protocols. Although the fatigue after aerobic and aerobic exercise negatively affects a single-leg dynamic balance level, single leg balance ability returns to the baseline status after 10 min of passive recovery duration.

**Keywords:** balance; fatigue; female; support leg; recovery

#### **1. Introduction**

The popularity of soccer among females is increasing each passing day. It is estimated that around 30 million females are actively playing licensed soccer in more than 100 countries around the world. Studies indicate that as the participation of females in soccer increases, the incidence of injury increases at a high rate [1–3]. In a soccer match, soccer players engage in many moves such as high-intensity acceleration, deceleration, sudden change of direction, bounce, and other soccer-oriented movements. Along with these moves, soccer players often experience various injuries when using one leg for stopping and cutting during pressure, while using the other leg to tackle the ball [4]. In addition to these, injuries in soccer are caused by sudden acceleration and deceleration without impact, rapid disorientation, and exposure to high loads while maintaining the stability of the knee joint in unpredictable movements [5–8]. When the injuries experienced in soccer were evaluated according to sex differences, It was reported that female athletes had a higher incidence of anterior cruciate

ligament (ACL) experience in lower extremity injuries due to biomechanical and neuromuscular differences [5,6] than males [9].

Furthermore, previous studies indicate that female soccer player has the risk of ACL injury nine times greater than males [10]. Several risk factors cause these injuries in female soccer players. These risk factors in female soccer players include a previous history of injury [11], as well as a decline in hip strength [12] due to accumulated fatigue [11] and deterioration of lower extremity dynamic balance [13]. Epidemiological studies have pointed out that 50% of the injuries occur at the end of competitions or sports activities, and 58% of these injuries are due to non-impact conditions. That fatigue is an essential element of sensory-motor changes associated with injury [14,15]. Ekstrand et al. [9] report in their study that traumatic injuries occur more often in the last minutes of both halves of a soccer match [9]. In addition to these, it was reported in another study that lower extremity injuries were commonly seen at the last minutes of competition in sports such as soccer, which includes high-intensity moves and multi-directional sprints [9,16]. Therefore, it can be stated that non-contact injuries caused by fatigue occurred in the last fifteen minutes of play in both the first and second half of games. In non-contact injuries, neuromuscular fatigue is seen as a risk factor [17–20]. Neuromuscular fatigue is divided into two, according to the intensity and duration of exercise, like peripheral and central nervous system fatigue. Long-term activities affect the central nervous system, while short-term high-intensity activities cause peripheral fatigue [21,22]. Peripheral fatigue arises when there is not adequate energy provided to the muscles, despite the increasing energy need [22].

Moreover, it has been reported that muscle fatigue affects both peripheral and central proprioceptive processes [23–25]. Balance is defined as being able to hold the body center of gravity within the center of support [26]. In order to maintain balance visual, vestibular, and proprioceptive systems play a crucial role, and these systems are affected by many factors [27–32]. The proprioceptive system consists of the Golgi tendon organ, the muscle spindle, the Pacini corpuscle, free nerve endings, and the receptors in the joint capsules and skin [32–34]. It ensures maintenance of the balance with the information collected from these structures [32,34]. The proprioceptive system is affected by fatigue, aging, sarcopenia, neurological disease fibromyalgia, cancer, and rheumatological diseases and may result in impaired balance [35–38]. Many researchers have shown that fatigue negatively affects dynamic postural control [39–42]. Fatigue is an essential factor that acutely affects balance ability. In a study in which the center of pressure (COP) was measured before, in halftime and immediately after a soccer match, it was determined that the balance skill of the support leg was impaired in the post-match measurement [41]. The return of balance ability to average values after fatigue depends on many factors. The return of post-fatigue balance ability to initial level depends on the duration, intensity, and type of intensity of the fatigue protocol performed [43]. Deficits in dynamic postural control is a risk factor in experiencing falls and lower extremity injuries [13,42,44–47]. The deterioration of dynamic balance is associated with reactive and compensating movements, and it is stated that this impairment has been linked to falling risk and lower extremity injuries [18,48,49]. Since fatigue increases the rate of injury in athletes [9,16], and the lack of postural control is a lower extremity injury risk factor [13,50,51], it can be expected that the rate of injury as a result of fatigue-induced postural control (fatigue-induced balance deficits) may increase.

Soccer is classified as both an aerobic and an anaerobic sport. In the game, players may experience fatigue from time to time as a result of aerobic and anaerobic activities. There are studies in the literatüre on the effects of anaerobic fatigue on balance performance in soccer players. However, up to date, no previous studies have examined the effects of both types of fatigue in soccer players. Besides, many activities such as passing, kicking, and jumping in soccer are carried out on the support leg. This research will be the first to examine the effects of fatigue on support leg balance performance. Therefore, this study aims to determine the effects of different fatigue protocols on balance performance of the support-leg in female soccer players and to understand the time required for the balance to recover after loading.

#### **2. Materials and Methods**

Sixteen sub-elite female soccer players (with mean age of 20.19 ± 1.52 years, body mass 56.52 ± 4.95 kg, body height 164.81 ± 4.21 cm, percent body fat 22.63 ± 2.42%, and maxVO2 52.33 ± 5.74 mL.kg.−1min−1 ) participated in the study voluntarily. Players who suffered lower extremity injuries for the last six months were not included in the study. Participants were instructed not to perform exercises that may cause exhaustion 48 h before the tests and not to use stimulants such as alcohol, caffeine, or drugs in the last 24 h before the study. The study was conducted according to the Declaration of Helsinki and was approved by the Ethics Committee of Ankara University, Approval code 21-1300-17, released in December 2017.

#### *2.1. Balance Test*

The participants were invited to participate in the Biodex SD Balance System (Biodex, Shirley, NY, USA) athletic single-leg testing protocol [52]. As noted, the Biodex Balance System (BBS) uses a circular platform that is free to move in the anterior-posterior and medial-lateral axes simultaneously. The BBS measures, in degrees, the tilt about each axis during dynamic conditions and calculates an overall stability index (OSI). A high score in the OSI indicates poor balance. The platform stability ranges from 1–12, with 1 representing the most significant instability.

The familiarization protocol was implemented before the experiments. All participants were instructed to perform the balance test on five different days of the targeted week.

The athletic single leg test protocol consisted of 3 trials of 20 s of upright stance on support-leg with 10 s of rest intervals between trials. Participants were asked to place their feet with the malleolar axis aligned with the midpoint of the platform over the center dot of the platform in a comfortable position. An athletic single-leg test was conducted on BBS with the platform set at level 4. Balance tests were carried out on the non-dominant leg. The same test protocol was performed before (Bpre) and right after (Bpost) both aerobic and anaerobic fatigue protocols, and repeated at the 5th (B5), 10th (B10), 15th (B15), and 20th (B20) minutes. The participants were allowed to rest passively during the 5 min of recovery periods. There was a 2-day period between aerobic and anaerobic fatigue protocols (Figure 1).

#### *2.2. Aerobic Fatigue Protocol*

The Bruce protocol was performed on a motorized treadmill (Cosmed, Rome, Italy) in order to create aerobic fatigue in soccer players [53]. The participants continued the test until they were exhausted. At the end of the test, the maxVO2 consumption values of the participants were calculated and recorded. Rating of perceived exertion (RPE) was obtained using the 6–12 point Borg scale at the end of every load [54]. MaxVO2 was defined as the highest 30 s average in oxygen uptake and maximal heart rate (HRmax) as the highest every 10 s average during the Bruce protocol. A test was considered maximal when four of the following criteria were completed: VO2 plateau at peak exercise, respiratory exchange ratio ≥ 1.10 greater age-predicted maximal heart rate (220-age), and an indication of 18–20 rating on the Borg RPE scale [55].

#### *2.3. Anaerobic Loading Protocol*

Anaerobic fatigue protocol was performed using a bicycle ergometer (Monark Erogomedic 894 E Peak Bike Vansbro Sweden). The Wingate test protocol was used for anaerobic fatigue. In the Wingate protocol, participants were asked to pedal at maximal speed for 30 s. As the intensity of the training, a weight equivalent to 7.5% of the participants' body mass was placed on the load scale. Once the participants started pedaling, the scale dropped when the bike's wheel revolution went up to 150 rpm and the maximal pedaling for 30 s. Soccer players were verbally motivated during training. Participants repeated the Wingate test protocol a total of 6 times with intervals of 4 min.

Basel, Switzerland).

Participants' blood lactate values were determined immediately after the fatigue protocol and during the recovery period (Lpre) just before the balance tests at 0th (Lpost), 5th (L5), 10th (L10), 15th (L15), and 20th (L20) minutes. During the lactate test, participants' fingertips were wiped with alcoholbased tissue paper, and their capillary blood samples were taken with a lancet pen. Blood lactate

**Figure 1.** One subject during the data collection phase. Female soccer player. **Figure 1.** One subject during the data collection phase. Female soccer player.

#### *2.5. Statistical Analysis 2.4. Lactate Testing*

In all statistical analyses, SPSS version 20 was used (SPSS Inc., Chicago, IL, USA). First, because the number of participants was below 50, the normality of the data was analyzed with the Shapiro-Wilk test. Depending on the distribution, lactate and balance values obtained at different times following aerobic and anaerobic fatigue protocols were compared by the Paired Sample t-Test or Wilcoxon Test. For the intergroup analyzes, either the Repeated Measurements Analysis of Variance (Aerobic Lpre, L5, L10, L15, L20; Bpre, B5, B15, B20; Anaerobic Lpost, L5, L10, L15, L20; Bpre, B5, B15, Participants' blood lactate values were determined immediately after the fatigue protocol and during the recovery period (Lpre) just before the balance tests at 0th (Lpost), 5th (L5), 10th (L10), 15th (L15), and 20th (L20) minutes. During the lactate test, participants' fingertips were wiped with alcohol-based tissue paper, and their capillary blood samples were taken with a lancet pen. Blood lactate levels of the participants were determined by an Accutrend Plus lactate device (Roche Diagnostics, Basel, Switzerland).

#### B20) or the Friedman test (Aerobic Lpost, Bpost, B10; Anaerobic Lpre, Bpost, B10) was used again depending on the distribution. In the case of the dataset exhibited both non-normally distributed and *2.5. Statistical Analysis*

norma distributed data, we proceeded with the non-parametric analysis. In all statistical analyses, the alpha value was considered to be 0.05. **3. Results**  The following variables were shown to be not normally distributed (Aerobic Lpost, Bpost, B10; Anaerobic Lpre, Bpost, B10). The lactate values obtained from the participants before, during, and after aerobic and anaerobic fatigue protocols are shown in Table 1. In all statistical analyses, SPSS version 20 was used (SPSS Inc., Chicago, IL, USA). First, because the number of participants was below 50, the normality of the data was analyzed with the Shapiro-Wilk test. Depending on the distribution, lactate and balance values obtained at different times following aerobic and anaerobic fatigue protocols were compared by the Paired Sample t-Test or Wilcoxon Test. For the intergroup analyzes, either the Repeated Measurements Analysis of Variance (Aerobic Lpre, L5, L10, L15, L20; Bpre, B5, B15, B20; Anaerobic Lpost, L5, L10, L15, L20; Bpre, B5, B15, B20) or the Friedman test (Aerobic Lpost, Bpost, B10; Anaerobic Lpre, Bpost, B10) was used again depending on the distribution. In the case of the dataset exhibited both non-normally distributed and norma distributed data, we proceeded with the non-parametric analysis. In all statistical analyses, the alpha value was considered to be 0.05.

#### **3. Results**

The following variables were shown to be not normally distributed (Aerobic Lpost, Bpost, B10; Anaerobic Lpre, Bpost, B10).

The lactate values obtained from the participants before, during, and after aerobic and anaerobic fatigue protocols are shown in Table 1.

**Table 1.** Mean comparisons of participants with lactate and balance, which vary depending on aerobic and anaerobic loading. In horizontal, the repeated measures *p* values, in vertical the *p* values for aerobic vs. anaerobic comparisons.

L: Lactate; B: Balance; \* *p* < 0.05; \*\* *p* < 0.01.

According to the results, there was no significant difference between participants' resting lactate concentration values obtained before aerobic and anaerobic loading (*p* > 0.05). However, the lactate values obtained immediately after, 5th, 10th, 15th, and 20th min were statistically significant (*p* < 0.01) according to the fatigue conditions. After anaerobic loading, lactate values were found to be higher. Besides, it was understood that 20 min was not sufficient for the recovery, regardless of the type of loading.

Lactate values obtained at six different phases of the aerobic fatigue protocol were significantly different (*p* < 0.01). The resting lactate value was determined to be lower than all others reached after loading. Also, a significant difference was found between Lpost and L10, L15 and L20; L5 and L10, Lpost L15, and L20; L10 and L15 and L20 (*p* < 0.01); and with L5 and L20 (*p* < 0.05).

In lactate values measured after anaerobic loading, the resting value was determined to be significantly lower than in all other measurements. The difference between Lpost and L5 was not significant, similar to the aerobic one. However, a significant difference at the level of *p* < 0.01 was found between Lpost and L10, L15, and L20, between L5 and L10, L15, and L20 and between L15 and L20.

When the results related to the balance were examined, a significant difference was found at the level of *p* < 0.01 between the balance values obtained immediately after aerobic and anaerobic loading and at the 5th minute. It was understood that there is more deterioration in the balance after aerobic loading. No significant difference was observed in the values obtained after 10th, 15th, and 20th min, depending on the type of loading (Figure 2).

When the values obtained due to aerobic loading are taken into account, it was understood that the differences between Bpre and Bpost, B5 (*p* < 0.01), and between Bpre and B10 were (*p* < 0.05) statistically significant. However, there was no significant difference with the values reached between B15, B20, and Bpre. Accordingly, it can be stated that recovery takes place after aerobic load in the state of balance between 10th and 15th minutes. In the other results, on the other hand, a significant difference was found between Bpost and B5, B10, B15, and B20 (*p* < 0.01). The measurement of B5 was found to be significantly higher than the measurements of B10, B15 and B20 (*p* < 0.01).

*Diagnostics* **2020**, *10*, x FOR PEER REVIEW 6 of 11

**Figure 2.** Overall stability index across the experimentation. Aerobic condition vs. anaerobic **Figure 2.** Overall stability index across the experimentation. Aerobic condition vs. anaerobic condition.

#### condition. **4. Discussion**

When the values obtained due to aerobic loading are taken into account, it was understood that the differences between Bpre and Bpost, B5 (*p* < 0.01), and between Bpre and B10 were (*p* < 0.05) statistically significant. However, there was no significant difference with the values reached between B15, B20, and Bpre. Accordingly, it can be stated that recovery takes place after aerobic load in the state of balance between 10th and 15th minutes. In the other results, on the other hand, a significant difference was found between Bpost and B5, B10, B15, and B20 (*p* < 0.01). The measurement of B5 was found to be significantly higher than the measurements of B10, B15 and B20 (*p* < 0.01). **4. Discussion**  This study aimed to investigate the acute effects of aerobic and anaerobic exercises on dynamic balance skill and recovery time in female soccer players. In order to control the fatigue level, lactate concentrations of the subjects were also collected. According to the lactate test results, both fatigue protocols were found to be successful in creating fatigue. Although it was higher after the anaerobic exercise, the lactate level did not return to the initial level within 20 min after both fatigue conditions. Besides, as a result of strenuous aerobic or anaerobic exercise, female soccer players' ability to balance the support leg was affected negatively. After both aerobic and anaerobic loading, the recovery time of balance skill lasted about 10 min. The deterioration in the athletic single-leg stability test was observed to be higher after the aerobic fatigue protocols in all of the measurements. Furthermore, the difference was statically significant in Bpost (*p* < 0.01) and B5 (*p* < 0.05) values. In the literature, many studies suggest that aerobic and anaerobic fatigue negatively affect balance ability [41–43,56–61]. There are similar studies on this subject in the literature. In a study in which both aerobic and anaerobic fatigue protocol was implemented, and the balance level was determined with the Balance Error Scoring System (BESS), no difference was found between the balance performance and its time of recovery after both fatigue protocols. However, athletes returned to their initial balance performance values within 8–13 min after both fatigue protocols [58]. Steinberg et al. investigated the This study aimed to investigate the acute effects of aerobic and anaerobic exercises on dynamic balance skill and recovery time in female soccer players. In order to control the fatigue level, lactate concentrations of the subjects were also collected. According to the lactate test results, both fatigue protocols were found to be successful in creating fatigue. Although it was higher after the anaerobic exercise, the lactate level did not return to the initial level within 20 min after both fatigue conditions. Besides, as a result of strenuous aerobic or anaerobic exercise, female soccer players' ability to balance the support leg was affected negatively. After both aerobic and anaerobic loading, the recovery time of balance skill lasted about 10 min. The deterioration in the athletic single-leg stability test was observed to be higher after the aerobic fatigue protocols in all of the measurements. Furthermore, the difference was statically significant in Bpost (*p* < 0.01) and B5 (*p* < 0.05) values. In the literature, many studies suggest that aerobic and anaerobic fatigue negatively affect balance ability [41–43,56–61]. There are similar studies on this subject in the literature. In a study in which both aerobic and anaerobic fatigue protocol was implemented, and the balance level was determined with the Balance Error Scoring System (BESS), no difference was found between the balance performance and its time of recovery after both fatigue protocols. However, athletes returned to their initial balance performance values within 8–13 min after both fatigue protocols [58]. Steinberg et al. investigated the balance level after a Yo-Yo test with the Interactive Balance System (Tetrax) device and reported that balance skill returned to the initial level within 10 min after fatigue [62]. In the current study, balance skills returned to the initial level after approximately 10–15 min after both aerobic and anaerobic fatigue. Moreover, the present study found no difference between balance levels during the recovery time after aerobic or anaerobic fatigue. In a study conducted on a bicycle ergometer, participants performed two maximal Wingate tests lasting 30 s with a rest interval of 2 min. At the end of high-intensity activation, it was determined that balance skill was affected negatively and returned to the baseline level within 10 min [63]. Ishizuka et al. applied the functional fatigue protocol to 14 male and 9 female college-level soccer players and determined the balance with the Biodex Limit of Stability

balance level after a Yo-Yo test with the Interactive Balance System (Tetrax) device and reported that balance skill returned to the initial level within 10 min after fatigue [62]. In the current study, balance Test. As a result, the subjects were found to have returned their initial level within 10 min after the fatigue [59]. In a study by Matsuda et al., a functional fatigue protocol was applied to 100 recreationally active college students. After the functional fatigue protocol, it was reported in the measurements made with the balance error score system that the balance performance returned to its initial level within approximately 20 min [64]. Although a similar fatigue protocol was implemented, different balance performance recovery times were observed between the study mentioned above and our study. This difference is thought to be since soccer players have a better balance skill than other athletes and sedentary people. Contrary to the result of the current study, it is reported in some studies that the recovery time of balance ability lasts longer than 10 min, while in some studies, it is reported that balance ability is not affected after fatigue. In a study where the fatigue protocol involving sports activities was applied, it was reported that the participants' balance values returned to their initial values after 20 min as a result of the balance test conducted with the balance error score system [56]. In another study, in which balance measurements were made after a 25-min treadmill run, it took approximately 15 min for the athletes participating in the study to return their balance performance to its initial level [65]. In another study, where balance measurement was performed with the biodex balance system before and during a soccer match, it was determined that the dominant leg balance performance of the players was impaired while no change was observed in the support leg balance performance [66]. In contrast to this, soccer players' support leg balance skills were found to be negatively affected following the fatigue protocol in the present study. The reason for the difference is thought to be due to the degree of difficulty differences between the protocols of balance tests. In the literature, some studies did not observe any changes experienced in balance performance after fatigue. After soccer-specific fatigue [67] and after soccer training [68], balance measurements made with the Biodex Balance System have reported that the balance performance of soccer players is not affected by fatigue. Paillard reported that the return of post-fatigue balance ability to initial values depends on the duration, density, and intensity of the fatigue protocol performed [43]. In these studies, it is thought that the reason why there is no difference after fatigue is due to insufficient density and intensity of the fatigue protocol for impaired balance ability. In addition, longer times to return to initial values of balance performance were reported in these studies compared to the current study. The reason for these varied results may have been the difference between the branches of the athletes participating in the studies or the difference in balance measurement methods.

#### **5. Conclusions**

As a result of this study, it was clearly observed that balance performance is impaired in soccer players after both aerobic and anaerobic fatigue. The impairment of fatigue and balance performance are seen as significant risk factors. Although there is not enough data on the effects of fatigue on balance ability in soccer players, it is stated in several studies that fatigue increases the incidence of injury experienced [69] and that deterioration in balance performance may increase ankle injuries [70]. Many researchers also suggest that balance training should be performed to prevent injuries [1,71,72]. Therefore, trainers should give importance to balance training in order to prevent non-contact injuries caused by loss of balance. In future studies, it is suggested to investigate the effects of fatigue on the balance ability in athletes performing balance training.

The study was performed exclusively on healthy young adult female soccer players (who suffer from a higher prevalence of non-contact ACL injuries). A limitation of the study could be seen as a lack of control of the menstrual cycle. In addition, in this study, anaerobic fatigue protocol was performed with a bicycle ergometer and aerobic fatigue with a treadmill. The measurement of balance performance after a real soccer match is thought to provide a clearer picture of the effects of soccer-specific fatigue mechanisms on balance performance.

**Author Contributions:** Conceptualization, Ö.G.; Data curation, D.A. and F.A.; Formal analysis, Ö.G. and F.N.¸S.; Funding acquisition, G.L.; Investigation, D.A.; Methodology, Ö.G., F.A., and F.N.¸S.; Project administration, A.B. and F.N.¸S.; Supervision, G.L. and A.P.; Visualization, A.P.; Writing—original draft, D.A., F.A., and F.N.¸S.;

Writing—review & editing, A.B. and A.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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**1. Introduction**

The single objective global optimization problem involves finding a solution vector *x* = (*x1*, ... , *xD*) that minimizes the objective function *f*(*x*), where *D* is the dimension of the problem. The task of black box optimization is to solve the global optimization problem without clear objective function form or structure, that is, f is a "black box". This problem appears in many problems of engineering optimization, where complex simulations are used to calculate the objective function.

The differential evolution (DE) algorithm, proposed by Price and Storm in 1995, laid the foundation for a series of successful algorithms for continuous optimization. DE is a random black box search method, which was originally designed for numerical optimization problems [1], and it's also an evolutionary algorithm that ensures that every next generation has better solutions than the previous generation: a phenomenon known as elitism. The extensive study fields of DE are summarized lately in the references [2].

Studies on DE have yielded a number of improvements [3–17] to the classical DE algorithm, and the status of research on it can be easily obtained by noting the results of the Continuous Optimization Competition and the Evolutionary Computing Conference (CEC).

A popular variant of DE [18] is the algorithm proposed by Fukunaga and Tanabe called Success History-based Adaptive Differential Evolution (SHADE) [19]. In the optimization process, the scale factor *F* and the crossover rate *CR* of control parameters are adjusted to adapt to the given problem, and the "current to *p*best/1" mutation strategy and the external archive of poor quality solutions in JADE [20] are combined in SHADE. The SHADE algorithm ranked third in CEC2013. In the second year, the author proposed an improved scheme, adding a linear reduction to the population size called L-SHADE to improve the convergence rate of SHADE [21]. L-SHADE won the CEC2014 competition. The winners in the subsequent years were SPS-L-SHADE-EIG [22] (CEC2015), LSHADE-EpSin [23] (joint winner of CEC2016), and jSO [24] (CEC2017). These algorithms are all based on L-SHADE, which makes it one of the most effective variants of SHADE [25]. With the exception of the jSO, the other winners benefited from general enhancements in the area [26]. Consequently, this study applies an improved method to the SHADE, L-SHADE and jSO algorithms. LSHADE-ESP [27] came in second in CEC2018 and the jDE100 [28] won CEC2019. And the j2020 [29] algorithm, which was proposed on CEC2020 recently, is also within the reference range. Enhanced versions of these DE algorithms add new mechanisms or parameters for optimization, similar to those in other optimization algorithms [30], as described in substantive surveys of these areas [25,31–37]. Moreover, theoretical analysis supporting DE has also been provided, such as in [38–41].

The DE consists of three main steps: mutation, crossover, and selection. Many proposals [6,10,11,14, 24] have been made to improve the mutation process to improve optimization performance. For instance, four strategies of combining mutation and crossover was used in SHADE4 [6], SHADE44 [10] and L-SHADE44 [11] to create a new trial individual and realize an adaptive mechanism. A novel multi-chaotic framework was used in MC-SHADE [14] to generate random numbers for the parent selection process in mutation process. A new weighted mutation strategy with parameterization enhancement was used in jSO [24] to enhance adaptability. This paper also focuses on improving this process in the DE algorithm, especially SHADE-based algorithms.

The CEC2020 [42] single-objective boundary-constrained numerical optimization benchmark sets are designed to determine the improvement in performance obtained by increasing the number of the calculation of the fitness function of an optimization algorithm. There are thus two motivations for this study. First, we need to solve the problem of premature convergence of algorithms based on SHADE in high dimensional search spaces on CEC2020 benchmark sets, so that they can maintain a high population diversity and a longer exploration phase. Second, the improvement to the algorithm should be simple, should not excessively increase complexity, and should not render the proposed algorithm incomprehensible and less applicable, as discussed in [43]. We proposed a method using turning-based mutation, and apply it to the SHADE, L-SHADE, and jSO algorithms to yield good performance while using relatively simple algorithm structure. Through experimental analysis involving 10, 15, and 20 dimensions, the improved algorithms achieved better performance than the original algorithms as well as the advanced DISH [44] and jDE100 algorithms on CEC2020 benchmark sets, but were slightly worse than the j2020 algorithm. We also use population diversity measure and population clustering analysis to verify the effectiveness of the proposed method.

Section 2 describes the process of evolution from the DE algorithm to the SHADE, L-SHADE, and jSO algorithms, and turning-based mutation is introduced in Section 3. The experimental settings and results are described in Sections 4 and 5, respectively. Section 6 discusses the results, and the conclusion of this paper is given in Section 7.

#### **2. DE SHADE L-SHADE and jSO**


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**Toxic Effects of Cadmium on Crabs and Shrimps**

Xianjiang Kang1, Shumei Mu1, Wenyan Li2 and Na Zhao1 *1College of Life Sciences, Hebei University, 2College of Basic Medicine, Hebei University, China*


*Article* **The Influence of an Alpha Band Neurofeedback Training in Heart Rate Variability in Athletes**

**Christophe Domingos 1,\* , Carlos Marques da Silva 1 , André Antunes 2 , Pedro Prazeres 3 , Inês Esteves 4 and Agostinho C. Rosa 4**

**Abstract:** Neurofeedback training is a technique which has seen a widespread use in clinical applications, but has only given its first steps in the sport environment. Therefore, there is still little information about the effects that this technique might have on parameters, which are relevant for athletes' health and performance, such as heart rate variability, which has been linked to physiological recovery. In the sport domain, no studies have tried to understand the effects of neurofeedback training on heart rate variability, even though some studies have compared the effects of doing neurofeedback or heart rate biofeedback training on performance. The main goal of the present study was to understand if alpha-band neurofeedback training could lead to increases in heart rate variability. 30 male student-athletes, divided into two groups, (21.2 ± 2.62 year 2/week protocol and 22.6 ± 1.1 year 3/week protocol) participated in the study, of which three subjects were excluded. Both groups performed a pre-test, a trial session and 12 neurofeedback sessions, which consisted of 25 trials of 60 s of a neurofeedback task, with 5 s rest in-between trials. The total neurofeedback session time for each subject was 300 min in both groups. Throughout the experiment, electroencephalography and heart rate variability signals were recorded. Only the three sessions/week group revealed significant improvements in mean heart rate variability at the end of the 12 neurofeedback sessions (*p* = 0.05); however, significant interaction was not found when compared with both groups. It is possible to conclude that neurofeedback training of individual alpha band may induce changes in heart rate variability in physically active athletes.

**Keywords:** electroencephalography; neurofeedback; biofeedback training and RMSSD

#### **1. Introduction**

Heart rate variability (HRV) is a measure of interest in the sports domain since it has been linked with autonomic nervous system (ANS) function and cardiovascular control. This system has been difficult to train/influence due to is nature in controlling the involuntary functions related to the organism [1]. It can be divided into two subsystems: sympathetic and parasympathetic. A greater activation of the former is observed in stressful situations, while the latter is more active in the recovery phases [2].

It has been reported that a decreased HRV is associated with worse clinical states [3,4], such as cardiovascular pathologies [5], panic disorder [6], depression [7] and anxiety [8] while an increased HRV has been positively associated with physiological health status [9], but only if a parasympathetic activity augmentation occurs and not a sympathetic activity augmentation [10,11]. Furthermore, relations have been established between HRV metrics and cognitive performance and emotional regulation [12,13], in which high values correspond to an increased ability of information processing and attentional focus maintenance,

**Citation:** Domingos, C.; Silva, C.M.d.; Antunes, A.; Prazeres, P.; Esteves, I.; Rosa, A.C. The Influence of an Alpha Band Neurofeedback Training in Heart Rate Variability in Athletes. *Int. J. Environ. Res. Public Health* **2021**, *18*, 12579. https:// doi.org/10.3390/ijerph182312579

Academic Editor: Mário António Cardoso Marques

Received: 25 October 2021 Accepted: 27 November 2021 Published: 29 November 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

suggested as important aspects to respond to changes in environment conditions [14,15]. Conversely, a lower HRV seems to be related to a greater sympathetic hyper-reactivity [16], which in turn is related to high stress [17], which has been appointed as a factor that can negatively impact sports performance [18].

From a technical perspective, HRV corresponds to the observed variation of the time interval between heart beats—RR intervals in the electrocardiogram signal [19], relevant in studying the cardiac behaviour in different contexts [20] and in gauging the state of the ANS [21]. The HRV temporal signal can be decomposed in a frequency power spectrum [22], with high frequency bands indicating prevalence of parasympathetic activity in the modulation of vegetative and cardiac activity. For short duration collections (≤5 min), the variable showing more consistency analysis is the root mean square of successive difference of the R–R intervals (RMSSD), which has been shown to have higher reproducibility [23].

An increase in psychological stress levels causes a decrease in the intervals measured in the high frequency bands of the heart beat interval and an increase in the low-frequency ones [24]. Studies using electroencephalography (EEG) have shown that changes in cerebral electrical activity, most specifically a reduction in the power of alpha band frequencies, are related to higher stress [24–28]. We can therefore consider that higher HRV and alpha band values are related to lower psychological stress.

Considering that the capacity to process and select situational [29] and emotional regulation information [30] is a relevant aspect in the clinical and sports spheres, new training methods and cognitive assessment have emerged, as is the case with Neurofeedback training (NFT).

This cognitive biofeedback technique, in which an individual can learn how to modify its cerebral electrical activity, has been mostly used in therapeutic situations, with positive results in several psychiatric situations [31]. Recently, there has been an increase of its use in improving sports performance [32]. It has been proved that NFT promotes improvement of cognitive ability, reaction time and visuospatial abilities [33,34], giving individuals a base to create self-regulating strategies [35]. This, in addition to being essential for stabilization and increase in performance [36], can be improved through the use of NFT and consequently can lead to a higher performance [35,37–39]. These data are in conformity with previous studies that connect increased values of HRV with ANS activity and better performance [40], in addition to a reduction in stress both in athletes and in cardiovascular and chronical pain patients [36].

Bearing in mind the importance that physiological parameters such as HRV may have on athletic performance, it is important to know to what extent NFT can positively contribute to increase HRV and alpha band power. Bazanova et al. (2013) reported an increase in HRV following 10 NFT sessions aiming to increase the alpha power in a non-athlete male population [41]. Regarding HRV and NFT in sport, there are only four studies carried out, all of which compare HRV training by biofeedback with neurofeedback training [9,37–39], but they all assessed the effects of NFT on the athlete's HRV.

Therefore, and considering that it is still unknown how NFT influences HRV in athletes, the current study's aim was to determine if an α-NFT can increase HRV.

#### **2. Materials and Methods**

#### *2.1. Subjects*

Participants were randomized into two groups: (a) 3 sessions/week intervention group and (b) a 2 sessions/week intervention group [42]. A total of 30 male studentathletes aged between 18 and 34 years (mean (M) ± standard deviation (SD): 21.20 ± 2.62 for the two-session protocol vs. 22.60 ± 1.12 for the three-session protocol, *p* = 0.464) participated in the experiment. Of these 30 participants, 3 were excluded from the study due to poor-quality of the collected HRV data (1 from the 3 sessions/week group and 2 from the 2 sessions/week group). All student-athletes were provided with details about the study's requirements before providing written informed consent to participate. Participants

had to be involved in federated sports or practice regular physical activity (minimum of 30 min of at least moderate intensity 5 times a week) [43] for over 5 years [44]. The inclusion criteria were as follows: (1) all the participants had no history of psychiatric or neurological disorders; (2) no psychotropic medications or addiction drugs; (3) normal or corrected-to-normal vision; (4) minimum age of 18 years and maximum age of 35 years; and (5) to have been practicing vigorous exercise regularly at least 5 times a week regardless of skill level. All student-athletes were provided with details about the study's requirements before providing written informed consent to participate. This study was carried out in accordance with the recommendations of local ethics guidelines and approved by the Ethics Committee of the Faculty of Human Kinetics, University of Lisbon (24/2017, approval date 26 June 2017) and in accordance with the standards for ethics in sport and exercise science research [45]. All participants gave written informed consent in accordance with the Declaration of Helsinki [46]. All data collected have been stored in a database with password protection to which only researchers related to the NFT project have access. Anonymity was guaranteed.

#### *2.2. Experimental Design*

The 12 NFT sessions consisted of 25 trials of 60 s each with 5 s rest in-between, during which both EEG and HRV were recorded. The total NFT session time for each subject was 300 min in both intervention groups. Naturally, the participants who performed the most frequent protocol had more condensed NFT sessions than the subjects who per-formed the less frequent protocol.

Both the 2 sessions/week and the 3 sessions/week groups performed an instruction session and a pre-test before the 12 NFT sessions. At the end of completing all NFT sessions, a post-test was performed. The instruction session consisted in 5-min NFT practice trial, for participants to understand the training feedback, with instructions being given to clarify the purpose of these procedures in the context of the study. Both pre and post-tests were carried out on the same day of the first and last training sessions, respectively. In the beginning of each session, prior to the NFT, there was also a resting baseline recording which consisted of four epochs/periods of 30 s: two with the eyes open (EO) and two with the eyes closed (EC).

The participants were asked to be as relaxed as possible and to concentrate on a specific sport task.

#### *2.3. Electroencephalography (EEG)*

#### 2.3.1. Data Acquisition

During the experiment, the participants sat in a room with a controlled environment—silent room with no light. The EEG signals were recorded according to the international 10–20 system (Fp1, Fp2, F3, F4, F7, F8, C3, C4, T3, T4, P3, P4, T5, T6, O1, O2, Fz, Cz, and Pz), with a sampling frequency of 256 Hz. The Cz channel was chosen for feedback since this location is at the primary motor cortex and has been associated with sensory information processing over the sensorimotor area. Furthermore, it provides a measurement of the activity in both hemispheres and in the frontal lobe [47,48].

The ground was located at the forehead and the reference was the average of left and right mastoids. The signals were amplified by a 24-channel system (Vertex 823 from Meditron Electomedicina Ltd.a, São Paulo, Brazil) and were recorded by Somnium software platform (Cognitron, São Paulo, Brazil). The signals were filtered with an analog band-pass filter from 0.1 to 70 Hz in the amplifier and a digital band-pass filter from 4 to 30 Hz. Circuit impedance was kept below 10 kΩ for all electrodes before the sessions. Subjects were asked to sit comfortably and then to remain as still as possible and to avoid excessive blinking as well as abrupt movements.

#### 2.3.2. Individual Alpha Band (IAB)

Since a large interindividual difference in the alpha band has been reported, an individual alpha band (IAB) is often used instead of a standard fixed band based on a normative population [49].

EEG recordings of EO and EC periods during the resting baseline provide data for the calculation of alpha desynchronization and synchronization, respectively, enabling to determine frequency bands for each participant through amplitude band crossings [49]. The EEG signal for the channel Cz was notch-filtered at 50 Hz and lowpass-filtered at 30 Hz. The Welch's method was used to compute the power spectrum density for EO and EC, using an overlap of 10% and segments of 5s. The crossings between the two power spectra provide the transition frequencies to neighbouring bands: the lower frequency boundary (LB) of IAB and the upper frequency boundary (UB) of IAB. Thus, they define the IAB, which lays between the two crossings, as illustrated in [50]. The IAB information and their statistical comparisons between two NFT groups are summarized in Table 1.

**Table 1.** Descriptive differences between groups in relative IAB amplitude and HRV means.

M, mean; SD, standard deviation; IAB, individual alpha band; HRV, heart rate variability; RMSSD, root mean square of successive differences between normal heartbeats; S, session.a Differences between groups tested with Student's *t*-test.

#### *2.4. NFT Intervention Procotol*

Feedback is a determinant step for the protocol's success. Neural activity must be fed back by some parameter(s) and presented to the participant in a simple and direct representation of their value. In this study, the feedback parameter was the relative IAB amplitude in channel Cz, which uses the amplitude from 4 to 30 Hz as a normalization factor for IAB. This is calculated as in Equation (1), where the numerator indicates the averaged amplitude in IAB, denominator indicates the averaged amplitude in 4–30 Hz, the LB is the lower frequency boundary (LB) of IAB, UB is the upper frequency boundary (UB) of IAB, and X(k) is the frequency amplitude spectrum calculated by fast Fourier transformation (FFT) with a sliding window of 2 sec that shifted every 125 ms. The frequency resolution was 0.5 Hz. Using the amplitude spectrum instead of the power spectrum prevents excessive skewing, which results from squaring the amplitude, and thus increases statistical validity [51].

$$\text{Relative IAB amplitude} = \frac{\frac{\sum\_{\mathbf{k}=\mathbf{l}, \mathbf{B}}^{\text{UB}} \mathbf{X}(\mathbf{k})}{\mathbf{U}\mathbf{B} - \mathbf{L}\mathbf{B}}}{\frac{\sum\_{\mathbf{k}=\mathbf{l}}^{30} \mathbf{X}(\mathbf{k})}{30 - 4}}\tag{1}$$

The EEG training plugin included in the Somnium software was used to provide the visual feedback and is further detailed in [50]. The visual feedback display contains two objects: the first one in the centre and a second one in the lower left corner. These two objects change their shape and position, respectively, if the feedback parameter exceeds a certain predefined threshold (goal 1) and, in that case, if the participant is able to achieve goal 1 during a predefined amount of time (goal 2).

The central object is a small white prism with a rhombus base (four-sided) that changes. If goal 1 is being achieved, the number of sides of the base increases, progressively shaping and smoothing the white prism into a bigger purple sphere. If goal 1 stops being achieved,

the number of sides progressively decreases back to the initial rhombus shape, with its colour fading back to white and its size diminishing.

The second object is a cube whose position on screen is related to the period during which goal 1 kept being achieved continuously. If it happens for more than a predefined period of time (2 s), goal 2 is accomplished, and the cube moves upwards until goal 1 stops being achieved. If that happens, it will start moving downwards back to the initial position unless goal 2 is achieved again. Therefore, the participant's task is to move the cube upwards as much as possible [50].

The feedback threshold was set to 1.0 in the first session (i.e., the quotient between the mean IAB amplitude and the EEG total average amplitude 3–40 Hz had to be larger than 1, as shown in Equation (1)) Afterwards, it was adjusted according to the percentage of time during which the feedback parameter was above the threshold in each session. If this percentage exceeded 60%, the threshold would be increased by 0.1 in the next session. In contrast, if the percentage was below 20%, the threshold would be decreased by 0.1 in the next session [52].

Although inhibiting mental self-talk seems to be one of the best strategies to achieve self-regulation of EEG activity during NFT [49,53–56], participants were instructed only to concentrate on their sport activity as much as possible. If the feedback provided on screen was positive and the goals were being achieved, that would mean their strategy was working. If not, they were encouraged to find new strategies to achieve the goals.

#### *2.5. Heart Rate Variability (HRV)*

#### 2.5.1. Data Acquisition

For HRV analysis the RR interval data were gathered during the training session, in accordance with the methodological considerations proposed by the European Society of Cardiology (1996) and by Billman and associates (2015) [57,58]. The cardiac cycle duration and respective RR intervals were measured using a Polar H7 (Kempele Finland) heart rate monitor strapped around the participant's chest. The RR interval data were paired with the Elite HRV collection application, and processed and analysed using Kubios software (Kuopio, Finland).

#### 2.5.2. Root Mean Square of Successive Differences (RMSSD)

The HRV was analysed using a time-domain measure, RMSSD, which corresponds to the root mean square of the successive differences between adjacent RR intervals (RRI). Data collection started after a 30 s stabilization period, following which RR intervals were monitored throughout the duration of the task. RR interval data was recorded at a rate of 250 Hz following the equation:

$$\text{RRSSD} = \sqrt{\frac{\sum\_{i}^{\text{N}} (\text{RR}\_{i+1} - \text{RR}\_{i})^{2}}{\text{N} - 1}} \tag{2}$$

where (RRi+1 − RRi ) 2 corresponds to the square of the difference between RR interval time-length, N corresponds to the total number of intervals. RMSSD was chosen as the HRV variable of interest since it seems to be more related to vagal activity [57] and has shown greater reliability than other spectral variables indicators [23].

#### *2.6. Statistical Analysis*

The comparison of session means related to the relative amplitude of IAB and HRV were performed using Student's t-test for independent samples and Mann-Whitney U when normality was not verified. Generalized estimating equations, followed by Bonferroni post hoc test, were used to estimate between-group and within-group effects on IAB and HRV. Data were analysed using SPSS software for Windows version 25.0 (SPSS Inc., Chicago, IL, USA). Statistical significance was established as *p* < 0.05 in all tests. Computed by G\*Power software (version <IP_ADDRESS>) for a 0.05 significance level and a 0.95 power before experiment.

#### **3. Results**

Differences between relative IAB amplitude and HRV means during NFT training between both protocols are presented in Table 1.

Table 2 presents the results concerning IAB and HRV at baseline and after 12 sessions, as well as the effect of interaction using per-protocol analyses for each group (time—session 1 and session 12—vs. group).

**Table 2.** Individual alpha band and heart rate variability at session 1 and session 12: within and between protocol groups.

IAB, Individual alpha band; HRV, heart rate variability. Betas are presented as unstandardized coefficients between the interaction time\*group with the respective 95% confidence intervals. a Between-group changes significant at *p* < 0.05 b Within-group changes significant at *p* < 0.05.

> When considering the interaction time with the group, only the three-sessions protocol experienced changes in the IAB (β, 0.211; *p* = 0.018). Moreover, it was found significance between session 1 and session 12 in IAB (β, 0.237 *p* < 0.001) and in HRV (β, 15.909 *p* = 0.025).

#### **4. Discussion**

The aim of the study was to verify whether the NFT had direct implications for the increase in HRV. IAB was used to perform the NFT and two different groups to help clarify the extent to which this biofeedback training could impact on HRV.

Higher HRV values have been correlated with a greater information processing capacity and attention focus maintenance, suggested as important aspects in order to respond to changes in environment conditions [15]. According to Lehrer and colleagues (2000), HRV alterations due to biofeedback training in respiratory control are also associated with a higher capacity for regulating heart rate, which the authors suggested as being related to a more efficient action of baroreflexes [14]. Other interventions that led to an increase in HRV seem to be related to changes in important aspects of sports performance, such as a better technique and lower anxiety index [38].

According to data presented in Table 2, when the total training period (12 weeks) is considered, we verify that only the group that performed the most frequent training per week improved both relative IAB amplitude and HRV (RMSSD) means significantly. If an NFT is applied with a weekly frequency of three sessions, the results can clearly be achieved after 12 sessions, but the same does not happen with a lower weekly NFT frequency protocol in IAB. This lower frequency intervention will probably need more total sessions so that the effects can be observed. Even though HRV demonstrates significant changes, it is imperative to notice that had nothing to do with the group interaction. The group that performed the 3 weekly sessions had on average, a lower baseline value than the group that performed the 2 weekly sessions. However, Table 1 proves that the baseline values between groups are not significantly different, which further reinforces the effectiveness of a training in the IAB with a weekly load of three sessions.

As already mentioned, in addition to the fact that there are few studies in the sport/exercise research field that combine NFT and HRV, they only compare the effects of each training protocol without analysing how NFT can influence HRV [9,37–39]. Despite this, Rijken (2016) was careful to mention that although their study was not performed to understand the influence of NFT on HRV, he observed what seemed to be an association between the two variables [39]. These data are supported by a previous study that demonstrated the influence of an alpha band training (NFT) on HRV [59]. Recently, a case study was carried out on a patient with stroke and it was found that NFT had a positive effect on

increasing HRV [60]. Although this is only one case and belongs to a very different from that observed in this study, the results are in agreement with our findings.

The main strength of the study and what makes it so important is that it is the first study, of our knowledge, that tries to understand the influence of an α-NFT in the increase of HRV in athletes, demonstrating that performing thee neurofeedback sessions per week can lead to improvements in HRV. The training individualization was also considered (IAB was used instead of the fixed alpha band) [49].

There were some limitations that should be considered. First, the mental strategies were not recorded. Future work should include a questionnaire or scale to better understand what strategies athletes/individuals are using during NFT and which mental strategies are helpful to enhance NFT learning of training frequency band activity [38]. The present study should therefore be considered exploratory. Additionally, no cognitive laboratory tests or stress and anxiety scales were performed to notice physiological and psychological behavioural changes, thus it is imperative to not generalize the results. Finally, it would be important to include a sham group.

#### **5. Conclusions**

Ultimately, the 3 sessions/week group showed effective increments in relative IAB amplitude and HRV (RMSSD) mean values in student athletes. However, no interaction between groups and HRV time were found. suggesting that NFT can indeed be an effective tool to consider in the sports domain, in order to induce changes in HRV and cognitive parameters, but has nothing to do with the weekly training frequency. Future research should replicate the three sessions/week protocol based on a pre-test and post-test associated to anxiety and stress scales and in agreement with specific sport performance to better understand how the increased alpha and consequently higher HRV contributes to a better sporting performance. Additionally, stronger conclusions could be drawn in future studies with sham control conditions.

**Author Contributions:** C.D., A.A. conceived and designed the study and performed the analysis and interpretation of the data. C.M.d.S., I.E., P.P. and A.C.R. drafted, revised, and approved the final version. All authors have read and agreed to the published version of the manuscript.

**Funding:** This project was supported by the Portuguese Foundation for Science and Technology, I.P., Grant/Award Number UID/CED/04748/2020.

**Institutional Review Board Statement:** All students were informed about the possible risks of the investigation before providing written informed consent to participate. This study was carried out in accordance with the recommendations of local ethics guidelines and approved by the Ethics Committee of the Faculty of Human Kinetics, University of Lisbon (24/2017, approval date 26 June 2017) and in accordance with the standards for ethics in sport and exercise science research [39].

**Informed Consent Statement:** Informed consent was obtained from all individual participants included in the study. All data collected has been stored in an encrypted database where only researchers related to the NFT project have access. Anonymity was guaranteed.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Acknowledgments:** The authors gratefully acknowledge the support provided by the Portuguese Olympic Committee.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Histopathological Change Following Cox-Maze IV Procedure for Atrial Fibrillation Procedure for Atrial Fibrillation**

**Histopathological Change Following Cox-Maze IV**

DOI: 10.5772/intechopen.72786

Takashi Murashita Takashi Murashita Additional information is available at the end of the chapter

[15] Kirchhof P, Benussi S, Kotecha D, et al. ESC guidelines for the management of atrial fibrillation developed in collaboration with EACTS. European Heart Journal.

[16] Gaynor SL, Schuessler RB, Bailey MS, et al. Surgical treatment of atrial fibrillation: Predictors of late recurrence. The Journal of Thoracic and Cardiovascular Surgery.

[17] Ad N, Cheng DCH, Martin J, et al. Surgical ablation for atrial fibrillation in cardiac surgery: A consensus statement of the International Society of Minimally Invasive

[18] Ad N, Henry L, Hunt S, et al. Do we increase the operative risk by adding the coxmaze III procedure to aortic valve replacement and coronary artery bypass surgery? The

[19] Barnett SD, Ad N. Surgical ablation as treatment for the elimination of atrial fibrillation: A meta-analysis. The Journal of Thoracic and Cardiovascular Surgery. 2006;**131**:1029-1035

[20] Kanderian AS, Gillinov AM, Patterson GB, et al. Success of surgical left atrial appendage closure (assessment by transesophageal echocardiography). Journal of the American

[21] Gelsomino S, La Meir M, Lucà F, et al. Treatment of lone atrial fibrillation: A look at the past, a view of the present and a glance at the future. European Journal of Cardio-

[22] Syed F, Oral H. Electrophysiological perspectives on hybrid ablation of atrial fibrillation.

[23] Pak HN, Hwang C, Lim HE, et al. Hybrid epicardial and endocardial ablation of persistent or permanent atrial fibrillation: A new approach for difficult cases. Journal of

Cardiothoracic Surgery (ISMIC) 2009. Innovations. 2010;**5**:74-83

Journal of Thoracic and Cardiovascular Surgery. 2012;**143**:936-944

College of Cardiology. 2008;**52**:924-929

Thoracic Surgery. 2012;**41**:1284-1294

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Cardiovascular Electrophysiology. 2007;**18**:917-923

2016;**37**:2893-2962

62 Cardiac Arrhythmias

2005;**129**:104-111

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.72786

#### **Abstract**

The prevalence of atrial fibrillation and the likelihood of undergoing concomitant surgical ablation at the time of open heart surgery are increasing. Currently, the conventional cutand-sew Maze procedure has been predominantly replaced by Cox-Maze IV procedure, in which new energy sources such as radiofrequency energy and/or cryoablation are applied. Cox-Maze IV procedure has been associated with lower rate of complications than a cutand-sew procedure. However, some previous studies reported the lower success rate of Cox-Maze IV procedure, possibly because radiofrequency ablation or cryoablation cannot always achieve transmurality. For the success of surgical ablation, achieving transmurality, defined as complete atrial wall thickness of fibrotic changes, is of paramount importance. A review of previous articles regarding histopathological changes of the atrial tissue following surgical ablation is performed. The effectiveness of new energy sources such as radiofrequency and cryoablation in terms of histological transmurality is discussed.

**Keywords:** atrial fibrillation, Maze procedure, radiofrequency, cryoablation

#### **1. Introduction**

Surgical ablation for atrial fibrillation (AF) has been under continuous development for over two decades. The most recent guidelines for the surgical treatment of AF reported by the Society of Thoracic Surgeons (STS) state that surgical ablation for persistent AF can be performed without adding operative risk and is recommended at the time of concomitant mitral valve operations, isolated aortic valve operations, isolated coronary artery bypass grafting, and combined aortic valve and coronary artery bypass surgery (class I strength of recommendation) [1]. Surgical ablation is also recommended to symptomatic AF refractory to medical or catheter-based therapy in the absence of structural heart disease (class II strength of recommendation) [1].

Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons

around the orifices of the pulmonary veins and the remaining are located in other sites such as right atrium, left atrium, crista terminalis, and left atrial appendage. The findings of their paper, which reported that the triggers were successfully treated with radiofrequency ablation, led to an explosion of efforts by a number of cardiologists and cardiac surgeons to treat AF with catheter ablation and surgical techniques, respectively. The concept of treating AF was originally focused on isolating the pulmonary veins, either by catheters or surgical devices.

Histopathological Change Following Cox-Maze IV Procedure for Atrial Fibrillation

http://dx.doi.org/10.5772/intechopen.72786

65

However, AF produces unfavourable changes in atrial function and structure, which is called remodeling [21]. After many years of paroxysmal AF, the macroreentrant circuits of AF can become self-perpetuating. At this point, paroxysmal AF can become long-standing or persistent AF, and the underlying electrophysiologic culprit is no longer the focal triggers, but rather the macroreentrant circuits themselves. Therefore, for long-standing or persistent AF, simple isolation of pulmonary veins is not an effective treatment because the focal triggers do not account for onset of AF for this type of AF. In these patients, it is necessary to interrupt the macroreentrant circuits by placing additional linear lesions in the atria. This concept has led

The era of cryosurgery began with the development of automated cryosurgical equipment in the 1960s. Cooper et al. described cryosurgical resection of parenchymal organs using liquid nitrogen-refrigerated clamp in 1966 [22]. Cryosurgery has been an integral part of the surgical treatment of cardiac arrhythmias since the 1970s. With the recent technological development of cryoablation devices, the use of cryothermy in the treatment of cardiac arrhythmias

Cryoablation is effective in producing electrical silent ablation lines, and can be used judiciously safely without injuring surrounding structures such as coronary arteries and valve

Gage et al. described the mechanisms of tissue injury in cryosurgery [23]. The adverse effect of low temperature on cells begins as temperature falls into the hypothermic range. The function and structure of cells are stressed, and cell metabolism progressively fails. As the temperature goes further down and falls into the freezing range, water is crystallized, which causes more serious consequences than the earlier cooling. Ice crystal formation first occurs in the extracellular spaces, and with further cooling, it occurs within the cell. Intracellular ice formation requires temperatures colder than −40°C. Once intracellular ice is formed, it disrupts organ-

The progress to a stable lesion can be divided into three phases: (1) freeze/thaw phase, (2)

haemorrhagic and inflammatory phase, and (3) replacement fibrosis phase [24].

to a surgical ablation technique called Cox-Maze procedure.

**3. Cryoablation**

is increasing.

tissue.

**3.1. Introduction of cryoablation**

**3.2. Mechanisms of tissue injury in cryoablation**

elles and cell membranes, and cell death is practically certain.

**Figure 1.** Scheme of the bi-atrial Cox-Maze procedure IV utilizing radiofrequency (RF) or cryoablation energy sources.

The first clinical surgical ablation for AF was introduced by Dr. James Cox in 1987, and was termed the Cox-Maze I. The successful 22 cases were reported in 1991 [2]. Over the subsequent years, the operation evolved into the Cox-Maze III or the cut-and-sew Maze [3], which has been applied extensively in clinical practice [4]. In the meantime, the introduction of ablation technology has significantly changed the attitude. Damiano et al. employed a combination of radiofrequency energy and cryoablation to replace several of the Cox-Maze III cut and sew lesions and termed this procedure as the Cox-Maze IV [5, 6]. Finally, the lesion sets of the Cox-Maze IV have evolved to its current form [7, 8] (**Figure 1**).

Khargi et al. reported that conventional cut-and-sew Cox-Maze III procedure is getting less frequently performed, and alternative sources of energy were predominantly used in all surgical ablation cases (92.0%), and almost always (98.4%) in concomitant procedures [9]. Cryoablation is employed as an alternative source of energy [10–12]. As compared to radiofrequency and cryoablation, other energy sources such as microwave, laser, and high-frequency ultrasound have proven less effective, and are not commercially available now [13–18].


**2.3 General or only high-risk pregnant women testing?**

Presently available information that supports the hypothesis that an inappropriate first trimester surge in maternal FT4, whatever the circulating TSH, would interfere with the

Thyroid in Pregnancy 47

Glinoer (Glinoer et all., 1995) also documented somewhat higher TSH levels among the subpopulation of women with elevated antibody levels and these findings are confirmed in the

Serum concentrations of FT4 were lower in TPO Ab positive as compared to TPO Ab

Fig. 9. Serum concentrations of FT4 in TPO Ab positive and TPO Ab negative women

An answer to the question of screening cost-effectiveness of thyroid function in pregnancy was already presented by Dosiou (Dosiou et all., 2008). In this study is not defined costefficiency, but it is unquestioned fact that early diagnosis of thyroid disorder is cost-effective

The importance of maternal thyroxine for the development of the fetus brain early in pregnancy has received increasing acceptance. It has more recently become evident that maternal hypothyroxinemia results in the birth of children with decreased mental and

This project proved the usefulness of universal screening of thyroid disease in pregnancy. The occurrence of pathological results in laboratory tests was 679/3577. Determination of the specific reference intervals for TSH, FT4, and TPO Ab in pregnancy is one of the basic requirements when implementing the general examination. Cooperation with gynaecologists differed, the main stumbling block was the willingness of gynaecologists to

**2.5 Cost effectiveness of the thyroid failure screening**

and beneficial for both mother and child.

inform pregnant women about the project.

**3. Conclusion**

psychomotor development.

present study.

negative women as is showed on Fig. 9.

development of the cerebral cortex, even if maternal euthyroidism is maintained by normal circulating T3. There is at present increasing consensus (Morreale et all., 2004) that maternal hypothyroidism, both clinical and subclinical, requires early detection and prompt treatment, because of its important negative effects for the woman, the pregnancy and the child. There also exists a positive association between the presence of thyroid antibodies and pregnancy loss with postpartum thyroiditis.

The most practical approach is to screen all pregnant women for hypothyroidism as early in pregnancy as possible (or before conception). In the case of the mother, screening would reset in early diagnosis and treatment of subclinical hypothyroidism. Unfortunately, pregnant women with subclinical hypothyroidism seem to escape early clinical detection. In Mitchel study (Mitchel & Klein, 2004), 58% of the hypothyroid women were unaware of their disorder, and it took a median of 5 years from the time of the pregnancy for the clinical diagnosis to be made.

Vaidya study shows that targeted thyroid function testing of only high-risk pregnant women would miss nearly one-third of women with overt/subclinical hypothyroidism during early pregnancy (Vaidya et all., 2007). In Czech Republic, case finding screening is able to disclose less than 20% of asymptomatic mild or deep hypothyroidism or women with positive TPO Ab in pregnancy (Springer et all., 2009).

#### **2.4 Relationship between TPO Ab and TSH, resp. FT4**

The relationship between TPO Ab and TSH is not definite, despite it being known that women with high level of TSH more frequently have positive TPO Ab. In study group, divided by serum TSH concentration, were 44.1% TPO Ab positive (in part), with TSH >3.67 mU/l and 10.1% or 9.15% in the group with TSH < 0.06 mU/l or TSH in the reference interval, as is showed on Fig. 8.

Fig. 8. TPO Ab in groups with different TSH level

development of the cerebral cortex, even if maternal euthyroidism is maintained by normal circulating T3. There is at present increasing consensus (Morreale et all., 2004) that maternal hypothyroidism, both clinical and subclinical, requires early detection and prompt treatment, because of its important negative effects for the woman, the pregnancy and the child. There also exists a positive association between the presence of thyroid antibodies and

The most practical approach is to screen all pregnant women for hypothyroidism as early in pregnancy as possible (or before conception). In the case of the mother, screening would reset in early diagnosis and treatment of subclinical hypothyroidism. Unfortunately, pregnant women with subclinical hypothyroidism seem to escape early clinical detection. In Mitchel study (Mitchel & Klein, 2004), 58% of the hypothyroid women were unaware of their disorder, and it took a median of 5 years from the time of the pregnancy for the clinical

Vaidya study shows that targeted thyroid function testing of only high-risk pregnant women would miss nearly one-third of women with overt/subclinical hypothyroidism during early pregnancy (Vaidya et all., 2007). In Czech Republic, case finding screening is able to disclose less than 20% of asymptomatic mild or deep hypothyroidism or women

The relationship between TPO Ab and TSH is not definite, despite it being known that women with high level of TSH more frequently have positive TPO Ab. In study group, divided by serum TSH concentration, were 44.1% TPO Ab positive (in part), with TSH >3.67 mU/l and 10.1% or 9.15% in the group with TSH < 0.06 mU/l or TSH in the reference

pregnancy loss with postpartum thyroiditis.

with positive TPO Ab in pregnancy (Springer et all., 2009).

**2.4 Relationship between TPO Ab and TSH, resp. FT4**

Fig. 8. TPO Ab in groups with different TSH level

diagnosis to be made.

interval, as is showed on Fig. 8.

Glinoer (Glinoer et all., 1995) also documented somewhat higher TSH levels among the subpopulation of women with elevated antibody levels and these findings are confirmed in the present study.

Serum concentrations of FT4 were lower in TPO Ab positive as compared to TPO Ab negative women as is showed on Fig. 9.

Fig. 9. Serum concentrations of FT4 in TPO Ab positive and TPO Ab negative women


**2. Epidemiology**

CD is endemic in 21 countries in Latin America, from Mexico to the south of Argentina and Chile. Nevertheless, due to migrations and climate change, the disease has spread alarmingly to other parts of the world [6]. The World Health Organization (WHO) classifies this parasitic disease as a VBD (Vector-Borne Disease) that is among the unattended tropical diseases associated to extreme poverty in rural areas where the vectors are distributed, which favors their transmission route [7]. VBDs can be caused by bacteria, virus, and parasites, are usually transmitted by bloodsucker arthropods, represent 17% of all infectious diseases, and are prevalent in tropical and subtropical regions [8]. During the past three decades, the epidemiological overview of this disease has experienced important changes due to the implementation of vector control measures such as the use of pesticides and housing improvements [9]. In this respect, there has been a clear descent in the number of people infected by *T. cruzi*, going from 30 million in 1990 to 6–7 million currently infected. The incidence has also decreased from 700,000 estimated cases in 1990 to 30,000 cases per year in 2018. Furthermore, the mortality has gone down from 45,000 to 12,000 deaths in the same years [10, 11]. Despite all these efforts, CD continues to be an important health problem. Migration of asymptomatic people in chronic stage, who are unaware of their infection, has led to the spread of the disease to urban areas and nonendemic regions, increasing the frequency of cases in countries such as the United States, Japan, Australia, Spain, Italy, the United Kingdom, and other European countries where it is considered an emerging disease [6, 12]. In Latin America, Argentina (1,535,235), Brazil (1, 156, 821), and Mexico (876, 458) are the countries with the highest number of cases, followed by Bolivia with 607, 186 cases, making it the

*Modulation of Host Cell Apoptosis by* Trypanosoma cruzi*: Repercussions in the Development… DOI: http://dx.doi.org/10.5772/intechopen.103740*

country with the highest prevalence, with an estimate between 6.8 and 18% of the population seropositive for *T. cruzi* [13]. Recent estimations suggest that there could be 300,000 infected people with *T. cruzi* in the United States [14]. A study performed in Los Angeles reported a seroprevalence of 5.2% among Latin American migrants who also presented cardiac abnormalities [15]. Outside of America, Spain is the most affected country. It is estimated that only in this country there could be more than 50,000 infected individuals, most of them South American immigrants [16].