Does maturity status explain training intensity and physical fitness variations of youth football players?

Abstract

Background: Different predictive maturity status methods are available for coaches to apply during the training process. However, understanding the potential use of such methods to explain both physical fitness and training intensity (TI) measures, can give coaches new insights to better adjust training according to each individual needs.

Objectives: The purpose of the present study was two-fold: (1) To analyse TI and physical fitness variations of youth football players after a full-season; and (2) to examine whether training intensity and physical fitness variations are explainable by estimated maturation status.

Methods: Twenty-seven youth elite Under-15 football players were daily monitored for TI measures during 38 weeks. At the beginning and at the end of the season, all players were assessed for physical fitness. Also, different methods of maturity status estimation were collected at the beginning of the season.

Results: Significant differences were found for all physical fitness and TI measures after the season. The 2-digit (2D) and 4-digit (4D) of left and right hands had negative moderate correlations with change of direction (COD) variations (r=-.39 to -.45 | p=.05 to .02). The right-fingers 2D:4D (RF2D:4D) had positive moderate-to-large correlations with all TI measures (r=-.40 to -.56 | p=.04 to .001). From the reported significant correlations, the RF2D:4D measure explained the greatest proportion of all TI variations (r=.40 to .62 | p=.04 to .001).

Results: The use of the 2D:4D method of maturity status estimation may be a potential predictor of TI variations. Furthermore, the maturity offset and 2D:4D methods may not be useful to predict physical fitness variations after an Under-15 football season. 

Background

The physiological and physical characteristics of academy football players are well described at different age-categories [1–3]. For instance, youth football players may present VO2max values as high as approximately 67 ml/kg/min and lactate concentrations that can be between 2 to 12 11.9 mmol·L^-1 during a football match [4]. During a youth football match, players spent a great proportion of playing time above 80% of their individual maximal heart rate (measured by a cardiorespiratory test until exhaustion), for all age-categories [5, 6]. Also, the anaerobic power values (measured by the Wingate Anaerobic Test (WAnT), can reach to approximately 11 W·kg^-1 in youth football players [7].

To cope with football match physiological and physical demands, youth players must gradually develop great physical fitness levels [8]. While a focus on the development of technical skills, agility and running speed seem to be more important in the U13 and U14 age-categories, the development of cardiorespiratory capacity can be crucial in U15 and U16 academy football players [9]. Also, at those age-categories (U15 and U16), the development and demonstration of strength, lower-limbs power and sprint performance assume imperative roles during match performance [10]. In fact, it was previously demonstrated that higher levels of lower-body strength showed strong associations with better sprint and jump performance in youth football players [10]. Furthermore, match running performance and the physical capacities described above seem to be related to each other, although its magnitude tends to be different when categorizing the players into the different on-field playing positions. Moreover, field tests aerobic performances such as during the Yo-Yo Intermittent Recovery (YYIR) test, are related with the capacity to spend more time executing high-intensity activities during a football match [12].

A football season is usually divided by three different moments (e.g., pre-season, in-season, and off-season), where it is expected that both youth and adult players present significant variations in terms of physical fitness during those different periods of the season [13]. However, it seems to persist some incongruences in literature regarding physical fitness changes throughout a football season [14, 15]. Despite that, it is well described that the pre-season is considered an important period where it is supposed to occur significant physical fitness improvements [16]. Those improvements can be even more pronounced if a training plan during the off-season period was not well prescribed for players of the same team [13]. Furthermore, during the in-season period slight improvements can be observed in some players, although there is a tendency for a maintenance of physical status during the in-season, followed by decreases during the final stages of the season [17]. The training-dose imposed to youth football players, and their accumulated perceived intensity of training, explain some magnitude of the observed physical fitness changes [18].

Furthermore, players’ maturational status might also influence youth players physical fitness changes throughout a full-season [19]. The physical development of males during the adolescence, usually starts peaking at approximately 14/15 of their chronological age [20]. However, this can vary significantly if it is considered the biological age of players. That is, players of the same chronological age can be at different levels of maturation [21]. Players that are more advanced in maturity status compared to their same-age colleagues, usually present greater physical performance mainly in strength manifestation as a consequence of greater increases in muscle mass [22]. On the other hand, more mature players in the U14 and U15 age-categories, can express the so-called “motor-awkwardness”, that may limit their performance in other tasks, such as technical skills [23].

In this sense, a recent study conducted on 88 youth football players from different age-categories (U12, 13, 14 and 15), revealed that the accumulated training, maturity and initial physical fitness status explained only small and inconsistent proportions of the observed physical fitness changes after a full-season [24]. Another study conducted on 68 youth football players from different age-categories revealed that player’s maturation status have a moderate effect on match work rate [25]. Interestingly, a recent study revealed that the observed physical fitness changes after a full-season seemed to be influenced by both accumulated training intensity and maturation status [26].

Despite that, few studies focused on the influence that maturity status has on both training intensity physical fitness variations of U15 teams after a full-season [24, 26]. For those reasons, the present study aims to analyse TI and physical fitness variations of youth football players after a full-season and to examine whether training intensity and physical fitness variations are explainable by estimated maturation status.

Materials And Methods

Design and procedures

A prospective cohort study design was used in this study. An under-15 youth elite football team was analyzed throughout 33 weeks. Anthropometric and body composition measures were conducted. Also, the maturity status of each player was assessed using anthropometric data. Physical assessments were carried out in August 2021, and in April 2022. The participants were assessed during three days. Anthropometry, body compositions, flexibility and change of direction (COD) assessments were carried out in the first day. The assessment of anaerobic performance was made during the second day, using the Wingate test. In the third day of assessments, the 30-15 intermittent fitness test (30-15 IFT) was conducted to assess the participant’s aerobic performance. Only the COD and the 30-15 IFT assessments were conducted outdoors, on a synthetic turf soccer field. 

Participants

Twenty-seven youth elite football players (age: 15.0 ± 0.4 years old; height: 175 ± 0.6 cm; body mass: 62.1 ± 7.0 kg) from the same team participated in this study. The inclusion criteria were: (i) all players had to participate in at least 90% of training sessions throughout the season; (ii) for each week, the players had to participate in all training sessions; and (iii) not be injured during the observations and assessments. The goalkeepers were excluded from the sample. Before the beginning of this study, all participants and their parents or their legal representants signed a written informed consent form. Thus, all the advantages and disadvantages of the study procedures were well explained to all the involved. The present study followed the ethical recommendations for the study in humans as suggested by the Declaration of Helsinki (updated version from 2013). 

Internal intensity quantification

Internal intensity was collected using the rate of perceived exertion (RPE) based on the CR-10 Borg scale [27]. Thus, the RPE values were collected approximately 10–30 min after each training session, as recommended in previous research [28]. Based on the CR-10 scale, 1 means “very light activity” and 10 means “maximal exertion”. All players answered to the question “How intense was your session?”. Their responses were given in an individual way, and without the influence of their colleagues. Additionally, the duration of the training sessions, in minutes, was recorded. After obtaining each player RPE value, the session-rate of perceived exertion (s-RPE) was used [29]. To obtain the s-RPE values, the duration of each training session was recorded and multiplied by the RPE value attributed by each player, and was presented as an arbitrary unit (A.U.).

Moreover, from the s-RPE values, the weekly training intensity (wTI) (sum of the intensity of all sessions and match), mean training intensity (mTI) (mean of the intensity of all sessions and match), 5-day average (5d-AVG) (mean of the intensity of five training sessions without match), the training monotony (TM) (mean of training intensity of 7 days divided by the standard deviation), and the training strain (TS) (sum of the intensity of all sessions and match multiplied by training monotony per week) were calculated.  

Physical Fitness Assessments

Anthropometry and body composition

Each participant height was measured using a stadiometer (Seca model 213, Germany) with an accuracy of ±5 mm. While, the participants weight was measured using a balance (Seca model 813, UK) with a precision of 0.1 per kilogram. All players were assessed without shoes and with their lower back as close to the stadiometer as possible. For measuring body composition, three-point skinfolds (chest, abdominal and thigh) were conducted to measure the the body fat percentage (BF%). All skinfold measures were assessed using a Lafayette caliper (Lafayette, IN, USA) with an accuracy of 0.1 mm. The skinfold measurements were applied twice on the right side of the athlete’s body, and the final score recorded were the mean of two measurements. If the measurement error was high (>5%), the measurements had to be performed again and the median of the three repetitions were used for analysis. All measurements were performed by an ISAK credited person. Thus, the calculations of body density and body fat% were calculated based on the Jackson and Pollock formula [30]. 

Sit and Reach test

For conducting the Sit and Reach test, all participants had to sit on the floor with their bare feet against the sit-and-reach equipment and with their middle fingers stacked on top of one another. Participants were informed to stretch as far as possible without bending their knees. The final outcome to be used was the distance between the tip of the middle fingers and the toe line, as previously recommended [31]. 

Modified 5-0-5 COD test

For measuring the participant's ability to change directions, the modified 505 test was used as in elsewhere [32]. Three cones were placed at 5-meters apart from each other, and a pair of photocells with a digital timer connected to it was placed at cone B. The photocell system used was the Newtest Power timer 300-series, that was adjusted to each player’s hip height. Each participant started the test 70 cm before the cone A (starting line). After a beep sound, each participant had to run as quickly as possible until reaching cone C, turn on the cone C line and return as quickly as possible through the photocells (cone B). Test time was measured to the nearest 0.01 s with the fastest value obtained from 2 maximal trials. After each trial, the players had a 3-minute recovery. 

Wingate test

For measuring the anaerobic performance of each participant, the Wingate Anaerobic Test (WAnT) was performed on a cycle ergometer (Monark model 894-E, Vansbro, Sweden). During 5 seconds, the participants had to pedal at maximum speed to determine the repetition per minute (RPM) in the ergometer monitor. After that, a braking force was determined by the product of body mass in kg by 0.075. The participants had to pedal at their maximum effort during 30 seconds with verbal encouragement from the coach and/or colleagues. The peak power (PP) and fatigue index (FI) measures were used for further analysis [33]. 

30-15 Intermittent Fitness test

For measuring the aerobic performance, the 30-15 Intermittent Fitness test (30-15 IFT) was applied. The test initial velocity was set at 8 km.h⁻¹ during the first run, and was increased by 0.5 km/h^-1 after each running sequence. All participants had to run back and forth within a 40-meter straight line. Each shuttle consists of 30-second runs interspersed with 15-seconds of walking. 3-meter zones were delineated in both extremities and at the middle of the test setup. Each participant had to complete as many stages as possible, and the test ended when the players could not maintain the running speed demanded, or could not reach the 3-meter zone before the beep during three times. As a final outcome to be analyzed, the velocity of intermittent fitness test (VIFT) score of each participant was recorded. The VIFT consists of the final velocity recorded during the last stage [34, 35]. Also, the Vo2max was estimated by the following equation for each player [36]: Estimated Vo2max = 28.3 −(2.15 × 1) −(0.741 × age) −(0.0357 × mass) + (0.0586 × age × VIFT) + (1.03 × VIFT). 

Maturity Status

Maturity Offset and Age at PHV

To determine the age at peak heigh velocity (PHV) of each player, the maturity offset was calculated using the chronological age, standing height, sitting height, leg length and body weight measures, according to the following equation [37]: Maturity Offset = −9.236 + 0.0002708 (leg length × sitting height) −0.001663 (chronological age × leg length) + 0.007216 (chronological age × sitting height) + 0.02292 (mass by height ratio). For measuring the sitting height, the athletes were asked to sit on the 50 cm height box, facing forwards. Then the height between the highest point of the head and the bottom of the box the player was sitting in, was measured. For measuring the leg length, the standing height minus the sitting height was calculated for each athlete. Finally, to obtain the age at PHV, the chronological age was subtracted by the maturity offset score of each player. 

2D:4D Ratio

The digit two (2D) and digit four (4D) length fingers were measured [38]. Each player placed the right and left-hand palm on a scanner with the fingers kept 2 cm apart from each other. The image of player’s palms in scanner was transferred to a computer and the Kinovea software was used to analyse fingers’ length. The 2D and 4D fingers length were measured from the proximal phalanx to the distal phalanx. The ratio of both fingers was calculated as follows: 2D/4D. The model of the scanner was the Scanjet (5590 HP Scanjet, USA) with an accuracy of 0.01 cm measurement of second and fourth finger to the tip of the finger. The difference of right-fingers 2D:4D ratio (RF2D:4D) minus left-fingers 2D:4D ratio (LF2D:4D) was calculated [39]. The intra-observer reliability was assessed by the same observer two times a week apart. The intra-class correlation (ICC) for 2D:4D ratio was 0.93 and 0.95, respectively. 

Statistical Analysis

Tests of normal distribution and homogeneity (Kolmogorov–Smirnov and Levene’s, respectively) were conducted on all data before analysis. A repeated-measures ANOVA was used to analyse TI measures from W1 to W38. Effect size was indicated with partial eta squared for Fs. A paired sample t-test was used to determine differences as a repeated measures analysis in two conditions (Pre – Post) for physical fitness variables. Cohen d was used as the effect size indicator. To interpret the magnitude of the effect size, we adopted the following criteria [40]: d = 0.20, small; d = 0.50, medium; and d = 0.80, large. Posteriorly, the percentage of change of internal training intensity measures were calculated [100-(Pre*100)/Post] considering Pre as the mean values from W1 to W4 and Post as the mean values from W35 to W38. In addition, percentage of changes were calculated for physical fitness assessments [100-(Pre*100)/Post]. 

Also, the Pearson’s correlation coefficient r was used to examine the relationship between the maturity status [i) maturity-offset and age at PHV; and ii) 2D:4D finger length], and training intensity and physical fitness variations. To interpret the magnitude of these correlations, the following criteria was adopted [41]: r ≤ 0.1, trivial; 0.1 < r ≤ 0.3, small; 0.3 < r ≤ 0.5, moderate; 0.5 < r ≤ 0.7, large; 0.7 < r ≤ 0.9, very large; and r > 0.9, almost perfect. A regression analysis was used to model the prediction of percentage of change of maturity status assessment from the remaining variables with significant correlations. All data were analysed using the software Statistica (version 13.1; Statsoft, Inc., Tulsa, OK, USA) and the significance level was set at p<0.05.

Results

Descriptive statistics were calculated for each variable (Table 1). 

Table 1

Maturity status and physical fitness before (Pre) and after (Post) of seasons (mean ± SD).

 A paired measures t-test with participants’ physical fitness assessment revealed significant differences in Sit and Reach, 505 test, PP, FI and 30-15 IFT (p = 0.001, d = -0.32; p = 0.001, d = -0.47; p = 0.001, d =1.40; p = 0.001, d = -0.95 and p = 0.001, d = -2.02, respectively). For more information, see Figure 1. 

A repeated-measures ANOVA with mean data of TI measures revealed a significant main effect of TM: F (2.89) = 19.22, p = 0.001, η2_partial= 0.44; TS: F (6.99) = 9.67, p = 0.001, η2_partial = 0.29; mTI: F (6.43) = 15.24, p = 0.001, η2_partial = 0.39; wTI: F (7.24) = 9.39, p = 0.001, η2_partial = 0.28, and 5d-AVG, F (7.08) = 9.78, p = 0.001, η2 = 0.29. For more information, see Figure 2.

A correlation analysis was performed between the maturity status [i) maturity-offset and age at PHV, and ii) Left 2, Left 4, L F Ratio, Right 2, Right 4, R:F Ratio and Dif LR Ratio] and percentage of change of physical fitness assessment [Pre – Post (Sit and Reach test, 5-0-5, Peak Power, Fatigue Index and 30-15 IFT)] and the TI measures (TM, TS, mTI, wTI and 5d-AVG). Only a negative moderate correlation was found between percentage of change of 505 test and maturity offset (r =-.40, p= .04). See table 2. 

Table 2

 Correlations between maturity status (maturity-offset and age at PHV), and the variations of TI and physical fitness measures.  

In reference to correlation analysis performed between 2D:4D fingers. A negative moderate correlation was found between Right 4 and TM (r =-.40, p= .04) and 505 test (r =-.41, p= .04). In the same line, a negative moderate correlation between Right 2 and 505 test (r =-.45, p=.02), also between Right 4 and TM (r =-.40, p=.04).

On the other hand, positive moderate correlations between LF Ratio and TM, mTI, and wTI (r =.53, p=.01; r =.43, p=03. and r =.41, p= .04, respectively) were found. In the same direction, a positive moderate correlation between RF Ratio and TM and wTI (r =.40, p=.04, and r =.50, p=.01, respectively). Positive large correlations between RF ratio and TS, mTI, and 5d-AVG (r =.56, p=.001; r =.54, p=.01, and r =.51, p=01) were found. Lastly, a positive moderate correlation was found between Dif LR Ratio and TS and FI (r =.43, p=.03, and r =.40, p=05, respectively). See Table 3, for more information. 

Table 3

 Correlations between maturity status (2D:4D finger length) and the variations of TI and physical fitness measures.  

A multilinear regression analysis was performed to verify which variable of maturity status [i) maturity-offset and age at PHV, and ii) Left 2, Left 4, L F Ratio, Right 2, Right 4, R F Ratio and Dif LR Ratio] could be used to better explain the percentage of change of physical fitness [Pre – Post (Sit and Reach test, 5-0-5, Peak Power, Fatigue Index and 30-15 IFT)] and TI measures (TM, TS, mTI, wTI and 5d-AVG). For more information, see the table 4. 

Table 4

Values of regression analysis explaining percentage of change of the maturity status on the remaining variables.

Discussion

The aims of the present study were to analyse the training intensity and physical fitness variations of youth football players after a full-season, and to examine whether training intensity and physical fitness variations are explainable by different estimated maturation status methods. The main findings were that from the analysed maturity status estimations (maturity offset, age at PHV and 2D:4D), only the 2D:4D method revealed moderate to large associations with different TI measures and with COD variations. On the other hand, neither maturity offset nor age at PHV revealed significant relationships with both TI and physical fitness variations, except a moderate association between maturity offset and COD variations. The RF2D:4D measure explained the variations of all TI measures.

There are several studies reporting physical fitness seasonal variations of both juvenile and adult football players [15, 42]. The present study revealed significant improvements of all the analysed physical fitness measures, from the beginning to the end of the season. Indeed, Meckel et al. [15] also reported significant improvements in different physical fitness measures from the beginning to the end of a professional adult football season. The above-mentioned study [15] was conducted on adult elite population, and the physical fitness measures were not similar to the measures used in the present study. Given that, a direct comparison with our findings is not straightforward. However, a recent study [26] conducted on 23 under-16 football players, revealed that VO2max, PP and FI measures had significant variations between pre- and post-assessments, which is in concordance with the findings of the present study. Although some studies reported significant improvements in physical fitness after a football season, others revealed that these changes are not so straightforward as they may be dependent on baseline values [43, 44].

Regarding the different TI measures variations, our findings are congruent with some studies conducted on adult football players that showed significant between-week variations in different perceived TI measures throughout the season [45]. However, the overall literature has focused mainly on adult players and that there is a lack of studies conducted on youth [45]. From the available literature, a recent study conducted on under-16 football players analysed the between-week variations of perceived TI measures (wTI, TM and TS), revealed reduced between-week variability (CV < 6%) for all the analysed TI measures [46]. This is in contrast with our findings, which revealed significant main effects for all the analysed TI measures. These discrepancies in between-week TI variations in youth football may be related to different training methodologies and coach's ideologies, as this can influence team’s training and matches demands [47].

The available research regarding the biological maturity status of young athletes and its associations with physical fitness and TI measures lacks consistency. To the best of the authors knowledge, there are only few studies focusing on the relationships between estimated maturity status, physical performance and TI [24–26]. While, only one study focused on the influence of maturity on physical fitness and TI variations [42]. Furthermore, while some studies used the Mirwald’s biological maturity predictive equation [24, 25], other studies used the Khamis-Roche method [48] and the 2D:4D finger length method [42]. In the present study, the 2D:4D finger length method and the Mirwald’s maturity offset and age at PHV were used as predictor variables of both physical fitness and TI measures variations throughout a football season. However, the methodological discrepancies between the above-mentioned studies and the present study, make comparisons more difficult to generalize findings.

The Mirwald’s maturity offset and predicted adult height methods are the most non-invasive methods to predict the maturity status of young athletes [49]. However, the 2D:4D method was previously reported to be a potential predictor of physical fitness variations [42]. Indeed, the 2D:4D ratio of the left and right hands of 24 under-17 football players, had negative large correlations with VO2max variations (r = -0.55, p = 0.005; r = -0.50, p = 0.013) [42], which contrast with our findings. In fact, in the present study, the 2D and 4D of the left and right hands had negative moderate correlations with COD variations, while the maturity offset method presented a negative moderate correlation only with COD variations. Another study determined that the predictors used (initial fitness, maturity offset and training time) to explain the observed physical fitness variations were impacted by other factors that were not measured in their model [24]. However, the study of King et al. [24] used only the maturity offset method as a predictor variable. Although it seems evident that exists inconsistencies regarding the use of maturity offset method as a predictor of physical fitness changes, the use of 2D:4D method seems to be more promising, as showed by our model.

The present study had some limitations. The main limitation refers to the small sample size used. The fact that only one male team was included in the sample is another main limitation. However, in professional youth football competitions, the use of more than one team is a major concern for both coaches and practitioners. Future studies should use larger sample sizes and examine the associations between different predictive maturity status and objective internal TI variations, such as heart rate-based measures.

Conclusions

The overall physical fitness and TI measures revealed significant changes from the beginning to the end of the season. The 2D:4D method showed to be a promising predictor of subjective internal TI variations throughout the season. Specifically, the use of RF2D:4D measure explained the variations of all the analysed TI measures. Despite that, maturity offset, age at PHV and 2D:4D methods explain only a small proportion physical fitness measures, namely, COD performance. Coaches and practitioners can benefit from the use of the 2D:4D method in young football players, to better monitor and adjust weekly TI and ensure greater biological individualization and talent selection.

Abbreviations

COD: change-of-direction; 30-15 IFT: 30-15 intermittent fitness test; s-RPE: session-rate of perceived exertion; TM: training monotony; TS: training strain; wTI: weekly training intensity; mTI: mean training intensity; 5d-AVG: 5-day average; PP: peak power; FI: fatigue index; PHV: peak height velocity; D: digit; RF2D:4D: right finger 2-digit:4-digit; LF2D:4D: left finger 2-digit:4-digit Dif L:R-ratio: difference lef:right-ratio; 

Declarations

Ethics approval and consent to participate

Parents/guardians informed consents were obtained and signed regarding all players involved in this study. The study was conducted according to the Declaration of Helsinki (2013), and was approved by the institute’s research ethics committee (the Scientific Council of Polytechnic Institute of Viana do Castelo), with the code CTC-ESDL-CE005-2021.

 Consent for publication

No individual or indemnifiable data is being published as part of this manuscript. 

Competing interests

The authors declare no competing interests. 

Authors’ contributions

Conceptualization, R.S., F.M.C., and J.M.C.C.; methodology, R.S. and F.M.C.; formal analysis, F.T.G.F.; writing—original draft preparation, R.S., F.M.C., and J.M.C.C.; writing—review and editing, R.S., F.M.C., F.T.G.F., H.N., H.H., and J.M.C.C.; supervision, F.M.C. and J.M.C.C. All authors have read and agreed to the published version of the manuscript. All authors contributed equally to the manuscript and read and approved the final version of the manuscript. 

Funding

This research received no external funding. 

Availability of data and materials

The datasets generated and analysed during the current study are not publicly available due to ethical restrictions, however are available from the corresponding author on reasonable request.

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