Participants
Twenty elite young players (mean ± standard deviation (SD); chronological age: 13.3 ± 0.5 years; height: 165.8± 11.7 cm; body mass: 50.7 ± 7.6 kg; peak height velocity: 13.3± 0.2 years; maturity off-set: -0.01± 0.56 years; VO2max, 48.2± 2.3 ml.kg−1.min−1), who regularly participated in football training, participated in this study's sample. These individuals competed in the U14 age bracket and, in accordance with the program established by the relevant federation, they first participated in the regional league and then progressed on to the national league. There were three attackers, four central defenders, four central midfielders, four wide defenders, five wide midfielders, and four central defenders on the team. The inclusion criteria were as follows: 1) at least three years of soccer experience; 2) active and regular participation in all phases of the study; 3) participants were not permitted to use any growth or maturation-affecting supplements; and 4) participants were not permitted to perform additional exercises. Exclusion criteria included: 1) not participating in 80 % of competitions (formal and informal) and training sessions during the season; 2) not attending one of the study's medical or physical examinations. This research was approved by the University of Mohaghegh Ardabili Ethical Committee. Similarly, we have done so with the Helsinki declaration (2013). All participants were informed of the risks and benefits of this study and have the option to withdraw at any time. The informed consent form was signed by the parent /legal guardian and players at the beginning of the study.
Study design
This investigation was carried out as a prospective study using an observational cohort design. It was carried out on a cross-sectional basis, and it produced practical results. The players were monitored by researchers during the whole season, and evaluations were carried out once the season containing the competitive matches had concluded. The current investigation was carried out over the course of 26 weeks. We divided the entire season into two halves that were equal in length (1st and 2nd halves). The season was divided into two halves: the first half (July to October, weeks 1 to 13 (8 matches and 50 training sessions (TS)) and the second halves (October to January, weeks 14 to26 weeks (11 matches and 40 TS)) (Figure 1).
Anthropometric parameters and body composition of the players were measured in one day. Then we calculated the maturity status of each player. Players reported their levels of DOMS and fatigue status using Hooper index questioners (~30 minutes before the sessions) [15]. In addition, the RPE that was monitored at the end of each training session (~ 30 minutes). One week in advance of the evaluation, there was a familiarization session that was planned. This "training load" was then calculated in conjunction with the total amount of training time to determine the total amount of accumulated effort for any given period.
Anthropometric measures and maturity offset
All anthropometric measurements, as well as measurements of body composition, were taken first thing in the morning. A skilled person used a stadiometer (Seca model 213, Germany) to measure the subject's height and sitting height with an accuracy of 5mm. The subject's weight was measured and recorded using a digital scale (Seca model 813, UK) with a precision of 0.1 per kg. The maturity off set and age at PHV were established by applying the Mirwald algorithm to the data acquired up top and basing the results on the collected information (16). Based on the information collected above and using the Mirwald formula, the maturity offset and age at PHV was determined [16]. The formula used is as follows: maturity offset = −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).
Internal training load
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 [17]. Then, WL was calculated considering s-RPE and training time for each training session. These data were used to obtain information and analyze weekly workload parameters (AW= the accumulated acute workload in the season; CW= 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) [18, 19]. In addition, workload, DOMS and fatigue parameters are shown with abbreviations such as AW1, AW2, CW1, CW2, ACWLR1, ACWLR2, TM1, TM2, TS1 and TS2 for the 1st and 2nd halves of the season. Besides all training load parameters are shown with abbreviations such as AW-total, CW- total, ACWLR- total, TM- total, TS- total.
Well-being status
In this study, we aimed to consider fatigue and DOMS. For that reason, Hopper Index questionnaire [15] used to gather data (scale of 1–7, in which 1 is very, very low and 7 is very, very high). This questionnaire was taken into consideration half an hour before the start of each session. Before beginning the study, the participants were given instructions on how to use the scale. The aforementioned data were obtained by adding up the values of each variable over the course of a week. It was decided to collect data independently so that the players wouldn't overhear the results of their teammates' competitions. Excel was the program of choice for developing the daily data register.
Statistical Analysis
Statistical analyses were performed using GraphPad Prism 8.0.1 (GraphPad Software Inc, San Diego, California, USA). The significance level was set at p < 0.05. Shapiro–Wilk was applied to check the normality of the data. Since the data did not show normal distribution, the variables were summarized as mean ± standard deviation (SD). It is worth noticing that when the performance variables were not normally distributed, non-parametric test were conducted through Kruskal-Wallis test to verify whether there were significant group differences. To address pairwise comparisons in variables, Mann Whitney U test was used. The Hopkins Statistical analyses were performed using GraphPad Prism 8.0.1 (GraphPad Software Inc, San Diego, California, USA). The significance level was set at p < 0.05. Shapiro–Wilk was applied to check the normality of the data. Since the data did not show normal distribution, the variables were summarized as mean ± standard deviation (SD). It is worth noticing that when the performance variables were not normally distributed, non-parametric test were conducted through Kruskal-Wallis test to verify whether there were significant group differences. To address pairwise comparisons in variables, Mann Whitney U test was used. The Hopkins threshold was used to quantify the effect size (ES) as follows: <0.2 = trivial, 0.2 to 0.6 = small, >0.6 to 1.2 = medium, >1.2 to 2.0 = large, >2.0 to 4.0 = very large, and >4.0, almost perfect [20]. Spearman correlation analysis was performed between training load parameters (AW1, AW2, CW1, CW2, ACWLR1, ACWLR2, TM1, TM2, TS1 and TS2) periods using PHV, Maturity, fatigue-1, fatigue-2, DOMS-1, DOMS-2 factors and training load parameters (AW-total, CW- total, ACWLR- total, TM- total, TS- total) periods using PHV, Maturity, fatigue, DOMS factors. The following ranges were considered for the correlation coefficient sizes: <0.1 = trivial; 0.1–0.3 = small; > 0.3–0.5 = moderate; > 0.5–0.7 = large; > 0.7–0.9 = very large; and >0.9 = nearly perfect [21]. The significance level was considered at p ≤ 0.05.