Abstract
his study was to test the relationships between training workload (WL) parameters with variations in anaerobic power and change of direction (COD) in under-16 soccer players. Twenty-three elite players under 16 years were daily monitored for their WL across 20 weeks during the competition soccer season. Additionally, players were assessed three times for anthropometric, body composition, COD, and anaerobic power. A correlational analysis between the mean di erences between assessments and accumulated WL parameters were conducted. Moreover, a regression analysis was executed to explain the variations in the percentage of change in tness levels considering the accumulated WL parameters and peak height velocity. The accumulated daily loads during one week showed a large and a moderate correlation with peak power and COD at di erent periods of the season.
assessments and accumulated WL parameters were conducted. Moreover, a regression analysis was executed to explain the variations in the percentage of change in tness levels considering the accumulated WL parameters and peak height velocity. The accumulated daily loads during one week showed a large and a moderate correlation with peak power and COD at di erent periods of the season. Regression analysis showed no signi cant predictions for COD (F(12, 10)=1.2 ,p=0.41) prediction, acute load (F(12, 10)=0.63,p=0.78), or chronic load (F(12, 10)=0.59 ,p=0.81). In conclusion, it may be assumed that the values of the chronic workload and the accumulated training monotony can be used to better explain the physical capacities of young soccer players, suggesting the importance of psychophysiological instruments to identify the e ects of the training process in this population. Keywords: football; soccer; acceleration; deceleration; training monotony; training strain; starters; nonstarters; in-season; pre-season Int. J. Environ. Res. Public Health2020,17, 7934; doi:10.3390 /ijerph17217934 /journal/ijerph
Int. J. Environ. Res. Public Health2020,17, 7934 2 of 15 1. Introduction Soccer is a highly complex sport, which can be de ned as an intermittent activity, characterized by the use of aerobic and anaerobic metabolic pathways in order to provide energy during di erent technical and tactical situations during the game [1]. The aerobic metabolism is involved in low-intensity situations, whereas the anaerobic system is involved in the most signi cant part of the game, including the high intensity explosive e orts, which requires high levels of power [2,3]. The aerobic capacity is needed to maintain the energy providing during all the match, whereas the anaerobic capacity is fundamental in order to execute the main foundations of the game (explosive e orts, kicks, etc) [4]. So, it is safe to a rm that one of the most important variables for measuring the performance in soccer players is the physical conditioning, which involves both aerobic and anaerobic physical capacities [5] and the change of direction (COD) as well [6]. Despite the fact that aerobic and anaerobic power increases with age, which occurs due to biological development [7], the training process can improve the neural and muscular factors related to these physical capacities in young athletes, including the development of energy supply, hydrogen accumulation, and muscle activation [8]. In order to ensure the development of these qualities all season, there are many aspects that need to be controlled to optimize the gain and avoid the injuries, including: intensity, volume, density, mood states, recovery times [9], external and internal training WL (workload), and parameters obtained from this like the acute (AWL), chronic (CWL), acute: chronic workload ratio (ACWLR), training monotony (TM), and training strain (TS). These last ve parameters can be obtained from the ratings of perceived exertion (RPE) of the training session [10]. The point is, how much e ort is needed to change the physical capacities? To examine this, Bannister and colleagues [11] established a statistical model to explain how the athlete responds to a given training process. According to this model, there are two di erent training e ects:
from the ratings of perceived exertion (RPE) of the training session [10]. The point is, how much e ort is needed to change the physical capacities? To examine this, Bannister and colleagues [11] established a statistical model to explain how the athlete responds to a given training process. According to this model, there are two di erent training e ects: negative (fatigue) and positive ( tness), and the result of the training is the di erence between these two e ects. Some studies have been conducted analyzing the possible associations between WL and the changes in physical qualities. Brink and colleagues investigated the relation among training load, recovery, and performance in a monthly interval shuttle run test and did not nd any relationship among RPE and total quality recovery scores with the performance (more related to the duration and game play in the week before the test). Other trials were found in adult soccer players [12,13] and in other sports, like rugby [14]. Due to the popularity of soccer in adolescence and the creation of several young championships for this public, some studies have been published analyzing the relationships among di erent performance parameters. However, there is no found scienti c literature analyzing the relationship among the anaerobic power, the COD performance, and the total WL in under-16 soccer players, which justi es this research. Furthermore, it is important to highlight that understanding the process of these relationships can inform the tness coaches, providing for them important knowledge in order to organize the training program. Therefore, the aim of this study was to analyze the relationships between training WL parameters with variations in anaerobic power and COD in under-16 soccer players. 2. Materials and Methods 2.1. Participants Twenty-three elite soccer players U16 Iranian were evaluated. The subjects' maturity o set were 1.85 0.30 years; this means that they had passed the peak height velocity (PHV). The positions of the soccer players were defenders (n=9), mid elders (n=6), wingers (n=4), and forwards (n=4). Goalkeepers were not evaluated in the study due to physiological di erences in training and competition. Inclusion criteria
players U16 Iranian were evaluated. The subjects' maturity o set were 1.85 0.30 years; this means that they had passed the peak height velocity (PHV). The positions of the soccer players were defenders (n=9), mid elders (n=6), wingers (n=4), and forwards (n=4). Goalkeepers were not evaluated in the study due to physiological di erences in training and competition. Inclusion criteria for this study were as follows; (i) At least 90% of the in-season were
Int. J. Environ. Res. Public Health2020,17, 7934 3 of 15 trained in the study; (ii) players were not injured in the time frame in the study; (iii) players should not be cross-training within the time frame in the study; and (iv) the number of training sessions for players who did not participate in the weekly competition was adjusted with another session (i.e., high-intensity interval training or small side game). After receiving information about the study, all participants, together with the parents' consent, signed the consent form to participate in the study. This study started after the approval of the ethical code IR.UI.REC.1397.181 by the University of Isfahan, and in compliance with the declaration of Helsinki for human subjects. 2.2. Experimental Approach to the Problem This study includes; (1) studying the cohort along with monitoring the daily workload for 20 weeks in the competition season: early-season (EaS) weeks (w) W1 to W7; mid-season (MiS) W8 to W13; and end-season (EnS) W14 to W20 and (2) a semi-experimental study and 3 stages of evaluation; the rst stage of the evaluation took place in the last week of August (EaS=before league); the second stage of the evaluation took place in the third week of November (MiS=mid league); and the third stage of the evaluation was performed in the rst week of February (EnS=after league), Figure. The number of RPE with the training time session was used to calculated WL [15,16]. Then, AWL, CWL, ACWLR, TM, and TS were obtained from WL. The stages of the subjects' assessments were as follows: day 1, assessments of anthropometric and body composition (height, sitting height, body mass, body fat, and maturity); day 2, COD with the modi ed 505 test [17]; and day 3, the anaerobic power were assessments with Running-Based Anaerobic Sprint Test (RAST). All tests were performed at the same time and at the same temperature in the indoor track using a thermometer as the recommendations given for standard evaluations [18,19]. All the players were quite familiar with how to do the tests.Int. J. Environ. Res. Public Health 2020, 17, x 5 of 15 Int.
were assessments with Running-Based Anaerobic Sprint Test (RAST). All tests were performed at the same time and at the same temperature in the indoor track using a thermometer as the recommendations given for standard evaluations [18,19]. All the players were quite familiar with how to do the tests.Int. J. Environ. Res. Public Health 2020, 17, x 5 of 15 Int. J. Environ. Res. Public Health 2020, 17, x; doi: www.mdpi.com/journal/ijerph 3. Results Figure 1 shows weekly monitoring on training and matches load with the test timeline. Figure 1. Research outline of the weekly monitoring on training and match load and assessed sessions during the competition season. EaS (early-season = before league for first study and W1 to W7 for the second study); MiS (mid-season = mid league for fist study and W8 to W13 for the second study); and EnS (end-season = after league for fist study and W14 to W20 for the second study); wAL = weekly acute workload; W = Week; TS = Training sessions; ASS = Assessments, and A.U. =Arbitrary unit. Descriptive characteristics of players are presented in Table 1. Values are reported as mean ± SD. In the whole season, the accumulated AWL was 31859 ± 1121 Arbitrary unit (A.U)., accumulated CWL was 28806 ± 995.1 A.U., accumulated ACWRL was 17.53 ± 0.3 A.U., accumulated TM was 23 ± 0.4 A.U., and ultimately, accumulated TS was 27821 ± 1075 A.U. Table 1. Descriptive characteristics of 23 soccer player U16. Variables Mean ± SD Confidence Interval 95% Height (cm) 172.7 ± 4.2 171 to 174.4 Weight (kg) 61.3 ± 5.6 59 to 63.6 Sitting height (cm) 96.6 ± 2.1 91.8 to 93.4 Age at PHV (years) 13.6 ± 0.4 13.5 to 13.7 Maturity Offset (years) 1.9 ± 0.3 1.7 to 2 Age (years) 15.5 ± 0.2 15.3 to 15.5 Experience (years) 6.2 ± 1.6 5.6 to 6.9 VO 2max (ml.kg −1 .min −1 ) 48.4 ± 2.6 47.3 to 49.4 Body Fat (%) 8.3 ± 2.9 7.2 to 9.5 AWL (A.U.) 31,859 ± 1121 31,374 to 32,344 CWL (A.U.) 28,806 ± 995.1 28,375 to 29,236 ACWLR (A.U.)
0.3 1.7 to 2 Age (years) 15.5 ± 0.2 15.3 to 15.5 Experience (years) 6.2 ± 1.6 5.6 to 6.9 VO 2max (ml.kg −1 .min −1 ) 48.4 ± 2.6 47.3 to 49.4 Body Fat (%) 8.3 ± 2.9 7.2 to 9.5 AWL (A.U.) 31,859 ± 1121 31,374 to 32,344 CWL (A.U.) 28,806 ± 995.1 28,375 to 29,236 ACWLR (A.U.) 17.5 ± 0.3 17.4 to 17.7 TM (A.U.) 23 ± 0.4 22.8 to 23.2 TS (A.U.) 27,821 ± 1075 27,356 to 28,285 PHV = peak height velocity; VO2max = maximal oxygen consumption; AWL = the accumulated acute workload in the season; CWL = the accumulated chronic workload in the season; ACWLR = the accumulated acute: chronic workload ration in the season; TM = the accumulated training monotony in the season; TS = the accumulated training strain in the season, and A.U. =Arbitrary unit. Figure 1. Research outline of the weekly monitoring on training and match load and assessed sessions during the competition season. EaS (early-season=before league for rst study and W1 to W7 for the second study); MiS (mid-season=mid league for st study and W8 to W13 for the second study); and EnS (end-season=after league for st study and W14 to W20 for the second study); wAL=weekly acute workload; W=Week; TS=Training sessions; ASS=Assessments, and A.U.=Arbitrary unit. 2.3. Procedures 2.3.1. Anthropometric and Body Composition All anthropometric and body composition measurements were performed during the morning [20], by a skilled person with 5 years of experience. Measurements were performed according to the international society for the advancement of kinanthropometry (ISAK) guidelines [21]. In order to measure height, sitting height, and weight, the participants stood without shoes and with just shorts.
Int. J. Environ. Res. Public Health2020,17, 7934 4 of 15 For measurement height parameters, the Seca model 213, Germany with an accuracy of 5 mm and weight Seca model 813, UK with an accuracy of 0.1 per kg were used. Based on the information collected above and using the Mirwald formula, the maturity o set and age at PHV was determined [22]. The formula used is as follows: maturity o set= 9.236+0.0002708 (leg length sitting height) 0.001663 (age leg length)+0.007216 (age sitting height)+0.02292 (weight by height ratio), where R=0.94 , R2=0.891, and SEE=0.592) and for leg length=standing height (cm) - sitting height (cm) was used. To measure body fat percentage, seven subcutaneous fat thickness were used by the Jackson and Pollock method [22,23]. Data were collected by Lafayette Instrument Company (Lafayette, IN, USA) with an accuracy of 0.1 mm. All measurements were performed by one person on the right side of the body. The measured technical measurement error was considered according to the previous study [24]. 2.3.2. Monitoring Workloads Training Each player was asked individually: How did you feel about the intensity of the training? for each session on a Category-Ratio-10 Borg scale, half an hour after training. In this scale, number one refers to a very easy training session and number ten refers to a very high-intensity training session [25]. The WL was calculated by multiplying the training time (minutes) with session RPE [10]. These players were familiar with this method during the previous two years in the team. Other WL parameters were calculated as follows: a total load of daily training during the week was considered as weekly AWL; the uncoupled formula [16] was used to obtain the weekly CWL and ACWLR; weekly TM (weekly AWL SD of this week's AWL); and eventually weekly TS (weekly AWL weekly TM). This 20-week study was divided into 3 periods based on the competition schedule in the season then: EaS=W1 to W7; MiS=W8 to W13; and EnS=W14 to W20. 2.3.3. The Modi ed 505 Test The modi ed 505 test was used to assess COD [26]. This test was performed using
this week's AWL); and eventually weekly TS (weekly AWL weekly TM). This 20-week study was divided into 3 periods based on the competition schedule in the season then: EaS=W1 to W7; MiS=W8 to W13; and EnS=W14 to W20. 2.3.3. The Modi ed 505 Test The modi ed 505 test was used to assess COD [26]. This test was performed using a Newtest Power timer 300-series. The photocells of this device were adjusted based on each player's hip height. After the warm-up, they stood at a distance of 70 cm before the start line. Immediately after hearing the sound of the starter, the player started to run inside the designated route, then passed through two photocells 5 m from the start line (midline). The time from here was recorded by the device. At this stage, the athlete crossed the nish line, which was 5 m away from the midline. It should be touched with one foot. Finally, the player quickly returned to the same route to cross the midline again. The photo- nish system was recorded at a time of complete (2 5 m). All subjects performed 2 trials test with a 3-min recovery. The best time of these two repetitions was considered as the record of each player. The intra-class correlation coe cient (ICC) was 0.94 for this test. 2.3.4. Anaerobic Power Test The RAST test was used to assess anaerobic power [27]. The settings of the photo- nish system were done with the players and how to start according to the modi ed 505 test. Each player performed 6 repetitions of 35 m between the photocells at maximum speed. There were only 10 s of rest between each repetition. Body mass was measured before the experiment. Then, based on the recorded times (each player in 6 repeats) after the test, the following formulas were used with Excel to obtain the results of anaerobic power variables; RAST of peak power (RPP)=the highest value; RAST of minimum power (RMP)=the lowest value; RAST of average power [28]=sum of all six values divided by 6; and RAST of fatigue index (RFI)=(RPP RMP)/total time
the recorded times (each player in 6 repeats) after the test, the following formulas were used with Excel to obtain the results of anaerobic power variables; RAST of peak power (RPP)=the highest value; RAST of minimum power (RMP)=the lowest value; RAST of average power [28]=sum of all six values divided by 6; and RAST of fatigue index (RFI)=(RPP RMP)/total time to covert the 6 sprints. The test retest ICC was 0.91 for this test. 2.4. Statistical Analysis Statistical analyses were performed using GraphPad Prism 8.0.1 (GraphPad Software Inc, San Diego, California, USA). The signi cance level was set atp<0.05. Data are presented as mean
Int. J. Environ. Res. Public Health2020,17, 7934 5 of 15 and SD. ShapiroWilk was applied to check the normality of the data. Pearson correlation analysis was performed between the WL parameters (except ACWLR) and RAP with PHV. While Spearman correlations were used for physical tness tests (except RAP) and ACWLR, due to non-normality, this section has been done based on the mean di erences between the steps. The e ect size of the correlations was determined by considering the following thresholds [29,30]:<0.1=trivial; 0.10.3=small ;>0.30.5=moderate;>0.50.7=large;>0.70.9=very large; and>0.9=nearly perfect. Then, multiple linear regression analysis between training WL parameters, with variations in anaerobic power, COD, and maturity variables, were performed. The intended regression type was least-squares. The reliability for assessments, ICC were applied. 3. Results Figure Descriptive characteristics of players are presented in Table. Values are reported as mean SD. In the whole season, the accumulated AWL was 31859 1121 Arbitrary unit (A.U)., accumulated CWL was 28806 995.1 A.U., accumulated ACWRL was 17.53 0.3 A.U., accumulated TM was23 0.4 A.U., and ultimately, accumulated TS was 27821 1075 A.U. Table 1.Descriptive characteristics of 23 soccer player U16. Variables Mean SD Con dence Interval 95% Height (cm) 172.7 4.2 171 to 174.4 Weight (kg) 61.3 5.6 59 to 63.6 Sitting height (cm) 96.6 2.1 91.8 to 93.4 Age at PHV (years) 13.6 0.4 13.5 to 13.7 Maturity O set (years) 1.9 0.3 1.7 to 2 Age (years) 15.5 0.2 15.3 to 15.5 Experience (years) 6.2 1.6 5.6 to 6.9 VO 2max(mL.kg 1 .min 1 ) 48.4 2.6 47.3 to 49.4 Body Fat (%) 8.3 2.9 7.2 to 9.5 AWL (A.U.) 31,859 1121 31,374 to 32,344 CWL (A.U.) 28,806 995.1 28,375 to 29,236 ACWLR (A.U.) 17.5 0.3 17.4 to 17.7 TM (A.U.) 23 0.4 22.8 to 23.2 TS (A.U.) 27,821 1075 27,356 to 28,285 PHV=peak height velocity; VO2max=maximal oxygen consumption; AWL=the accumulated acute workload in the season; CWL=the accumulated chronic workload in the season; ACWLR=the accumulated acute: chronic workload ration in the season; TM=the accumulated training monotony in the season; TS=the accumulated training strain in the season, and A.U.=Arbitrary unit. Figure cient between PHV with mean
Description
This study analyzes the impact of training workload on anaerobic power and change of direction in youth soccer players.