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Article

Influence of Biological Maturation on Training Load and Physical Performance Adaptations After a Running-Based HIIT Program in Youth Football

by
Gonzalo Fernández-Jávega
,
Alejandro Javaloyes
,
Manuel Moya-Ramón
* and
Iván Peña-González
Sports Research Centre, Department of Sport Sciences, Miguel Hernández University of Elche, 03202 Alicante, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(13), 6974; https://doi.org/10.3390/app15136974
Submission received: 6 May 2025 / Revised: 16 June 2025 / Accepted: 18 June 2025 / Published: 20 June 2025
(This article belongs to the Special Issue Advances in Sports Science: Physical Performance Diagnostics and Enhancement)
Versions Notes

Abstract

Featured Application

This study highlights the importance of considering biological maturation when designing youth football training programmes. Players with advanced maturity (post-PHV) show superior baseline performance in the 5 m sprint, 30 m sprint, and vIFT, underscoring maturational influence on physical capacity. The individualised running-based HIIT protocol, based on vIFT percentages from the 30-15 IFT, effectively improved sprint and intermittent endurance performance. Notably, greater gains were observed in less mature players (pre-PHV), likely due to lower baseline fitness and higher internal load responses. Despite identical external loads, pre-PHV players experienced greater physiological stress, as indicated by higher RPE and internal load values. Therefore, coaches should monitor both internal and external load to adjust training individually, particularly for younger players. Although no significant “time × maturity group” interaction was found, both maturity groups benefited from HIIT. Coaches should set maturity-informed goals, acknowledging that post-PHV players may require longer or more intense interventions to achieve similar relative improvements.

Abstract

The aim of this study was to examine the influence of biological maturation on young football players adaptations and monitor and analyse variations in external (EL) and internal load (IL) during a running-based HIIT programme, according to the players’ maturity status. A total of 41 players (13.9 ± 0.7 years) participated, divided into an experimental group (EG) (n = 19) and a control group (CG) (n = 22). Acceleration (5 m and 30 m) and intermittent endurance (vIFT via the 30-15 IFT test) were assessed before and after eight weeks of intervention. EL and IL load variables and the rating of perceived exertion (RPE) were recorded. The results showed better initial performance in advanced maturity status players (post-PHV). Players from the EG had significant improvements in the 5 m and 30 m sprints and in the vIFT, whereas the CG only showed significant improvements in the 30 m sprint. Post-PHV players perceived less intensity (RPE) and thus, less IL than players with a delayed maturity status (pre-PHV). The pre-PHV group also exhibited significant improvements in the 30 m sprint and vIFT. However, no significant interaction effect (time × maturational group) was detected for any variable. These findings underscore the importance of considering maturity status in performance evaluation, training load prescription, and adaptations.

1. Introduction

The ability to perform high-intensity runs during football matches has been shown to be an important determinant in players performance [1]. Overlapping, supporting moves, recovery runs or covering actions are some of the crucial actions carried out through high-intensity runs [2]. Therefore, a developed running performance seems to be a requirement for young football players for reaching professional levels [3]. On-topic investigations reported that running ability discriminates between competitive levels in young football players, suggesting that, from a talent selection perspective, running performance may play a major role in players’ early performance [4].
The efficiency of aerobic and anaerobic capacity is the pathway for performing more and more intense high-intensity efforts [5]. In practice, coaches employ different training approaches to improve players’ running capacities. A substantial body of the scientific literature supports the use of high-intensity interval training (HIIT) for improving running performance in young football players [6,7]. The HIIT method is based on intense bouts of short-duration exercise (performed at intensities that typically range from near to above lactate threshold or critical speed/power), with recovery intervals at low intensity or complete rest [8].
In youth football, players are categorised by chronological age and grouped into 1-or 2-year cohorts, accordingly [9]. Players from the same age group may be in quite differing stages of maturational development. Maturation refers to the developmental process by which individuals transition into their adult state, while the concept of maturity status pertains to a distinct point within the progression of an individual’s maturation, expressed as age at peak height velocity (PHV) [10]. Evidence suggests that maturity status has a great influence on young players physical performance. Players with an advanced maturity status are generally taller and heavier and surpass less mature players in strength, jumping or change of direction performance [11,12]. The influence of biological maturation on running performance is not clearly defined yet. The latest literature evaluating young players running performance by intermittent running tests has established maturity development as a primary contributor to the outperforming running capacities of maturity-advanced players, although these associations may also be influenced by contextual factors such as playing position or category [4].
The maturation process brings with it physical and physiological adaptations such as fibre type composition, tendon size and stiffness, and hormonal or anthropometrical development (muscle mass and size increases) [13]. These maturity-related adaptations may influence the individual response to training stimulus. An extensive range of the literature has analysed the variation in adolescents adaptations to resistance training according to their maturity status, reporting differences in the adaptation pathways between maturity groups. Players advanced in maturation present accelerated gains in strength supported by gains in muscle mass due to rising concentrations of anabolic hormones, which enhances force production [14]. Adaptations for players less advanced in maturation may primarily come from neuromuscular adaptations and intra- and intermuscular coordination [15,16]. As mentioned, the literature concerning responses to endurance conditioning in youths, which encompass running performance capacity, presents inconsistent findings. There is not solid evidence for the existence of a window of opportunity for young athletes around their PHV, which has been advocated in the earlier literature [17]. Despite these concerns, it is reasonable to expect that growth-related changes in central and peripheral cardiovascular systems, neuromuscular function and metabolic capacities influence the adaptations of endurance and metabolic fitness throughout the maturity process [18].
The training process requires an understanding of the training response (commonly referred to as training load [TL]) associated with different training tasks to manage their impact on players’ development and performance [19]. This TL could be classified into external load (EL) and internal load (IL). The EL is a representation of the physical display of the player during training (e.g., total distance covered or number of accelerations, among others). On the other side, IL is the individual physiological response to the executed EL (e.g., rate of perceived exertion [RPE]) [20]. The relationship between these two measures is a widespread practical approach for monitoring how players are coping with the training process [21]. The maturity process, along with its associated adaptations, may potentially influence players’ external and internal responses during training tasks. Findings observed by Buchheit & Mendez-Villanueva [22] and Parr et al. [23] show that more mature players cover greater distances at higher speeds and reach higher maximum speeds than their less mature counterparts. As well, Teixeira et al. [24] reported that biological maturation significantly influenced RPE. Showing a possible difference in training load handling throughout the maturation development.
Due to the relevance of running capacities in young football players’ early performance and long-term prospects and the possible impact of maturity adaptations on physical performance, this study aimed to (1) examine the influence of the biological maturation on young football players’ adaptations to a running-based programme and (2) monitor and analyse variations in EL and IL during the running-based programme, according to the players’ maturity status. Based on the previous literature, we hypothesised that there will be differences in initial performance between maturity groups, as well as that training adaptations and training load will be influenced by biological maturation.

2. Materials and Methods

2.1. Study Design

A pre-post intervention design was developed to determine the effects of a HIIT programme on the young football players’ intermittent endurance. The HIIT training programme was integrated into the players’ usual football training programme for 8 weeks, and it was characterised by a short volume and progression in the intensity during the programme. To determine the effects of this programme, the players’ final velocity at the intermittent fitness test (vIFT) was assessed by means of the 30:15 IFT. Moreover, the time in a 5 m and 30 m sprint was also assessed. Players were initially assessed one week before they started the programme (assessing session 1 [AS1]) and this data was used as the baseline for the analyses and to calculate the training programme parameters. After the 8 weeks that the programme lasted, players were evaluated (AS2) to consider the effect of the training programme.

2.2. Participants

The sample used for this study consisted of two U14 category football teams and two U15 category teams from the same youth football academy, competing at the same competitive levels. Each team in each category was randomly assigned either to the experimental group (EG) or the control group (CG) after the AS1. Initially, the sample was composed of 48 participants (n[EG] = 22; n[CG] = 26), but ultimately the analyses were conducted with a sample of 41 participants (n[EG] = 19; n[CG] = 22). The reasons for the loss of 7 participants were (1) failure to complete the assessment sessions (due to injury, absence, or illness) (n = 5) and (2) failure to complete at least 80% of the training programme carried out with the EG (n = 2). The final sample of the study consisted of 19 participants in the EG (age: 14.09 ± 0.50 years; PHV: 0.37 ± 0.94 years; body height: 165.95 ± 8.76 cm; body weight: 54.18 ± 10.06 kg) and 22 participants in the CG (age: 13.79 ± 0.92 years; PHV: 0.12 ± 1.08 years; body height: 162.90 ± 10.50 cm; body weight: 53.04 ± 10.64 kg). The participants and their parents or guardians were informed of the aims and protocols of the study. Both the players and their parents/guardians signed an informed consent before their participation in the study. The protocol of this study was approved by the ethical guidelines of the hosting institution (reference number DPS.EC.01.17).

2.3. Maturity Assessment and Grouping

The most used indicator of the somatic maturity status in the field of sports is the “maturity offset” or the years from/to the peak height velocity (PHV). The PHV is considered a “benchmark” of the maximal rate of growth in height, and it occurs theoretically around fourteen years old in boys and around twelve years old in girls [10]. By the prediction of the years from/to the PHV [25,26], the investigator has accurate data of a young athlete’s maturity status, which is especially accurate in boys from 12 to 16 years old with an “on average” maturation [27]. For the analyses, players from the EG and CG were initially grouped into three maturity groups according to their years from/to PHV. These maturity groups were defined as pre-PHV (<−0.5 years), mid-PHV (from −0.5 to 0.5 years) and post-PHV (>0.5 years). However, for the comparison of the physical performance changes after the training programme according to the player’s maturity status, the sample was regrouped into two maturity groups (pre- and post-PHV) due to the small sample size when subdividing the EG sample. These two maturity groups were established with the cut-point on 0,0 years from/to the PHV (pre-PHV: n = 7; post-PHV: n = 12).

2.4. Testing Procedures

Anthropometrics. The player’s body height and sitting height were measured using a fixed stadiometer (SECA Ltd., Hamburg, Germany ± 0.1 cm). The body weight was measured with a digital body composition monitor (Tanita Bc 601 Ltd., Tokyo, Japan ± 0.1 kg).
5 m and 30 m sprints. Players performed two 30 m sprints, and the time at 5 m and 30 m was registered by photoelectric cells (Witty System, Microgate, Bolzano, Italy). The best time for each distance was used for further analysis. There was a rest time of 3 min between repetitions. Players started the sprint at a standing position, 30 cm before the starting timing gate, and they were encouraged to perform each sprint at their maximal effort [28].
30-15 intermittent fitness test (IFT). The player’s intermittent endurance capability was measured using the 30-15 IFT [29]. This test consists of 30 s of running in a 40 m (with changes of direction) interspersed with 15 s of walking for recovery. The initial velocity of the test is 8 km·h−1 with 0.5 km·h−1 increments of velocity after each 30 s stage. The test finishes when the player is not able to maintain the running speed. The velocity of the last completed stage was registered as the final velocity in the 30-15 IFT (vIFT).
Prior to the testing procedures, players carried out a standardised warm-up which consisted of low-intensity running, dynamic stretching and high-intensity actions, including submaximal sprints and COD. Additionally, all players had prior experience with the sprint and 30-15 IFT tests as part of their regular performance monitoring within the academy. This ensured sufficient test familiarisation and minimised learning effects.

2.5. Training Programme

Both groups (EG and CG) performed three football training sessions per week. Given that all teams included in this study belonged to the same academy, and despite not performing the same tasks in each training session, the conditional contents of the tasks were planned by the responsible party of the academy and did not differ between teams, assuming that any effects of these trainings on players’ performance should be equal. Although the training content was matched, spontaneous variability in physical loads during football practice may have occurred. Goalkeepers were not included in the analysis. Additionally, an agreement was reached with the academy’s responsible and coaches that no analytical work focused on improving endurance could be carried out, although we acknowledge that the football training tasks themselves contain a cardiovascular component that was not controlled for in this study.
Additionally, to these football training sessions, players for the EG performed a HIIT before two of these training sessions. This HIIT-based protocol was performed in 40 m, with linear displacements and including changes of direction to continue running during the time that the HIIT lasted. The intensity of each HIIT was individualised to each player (%vIFT obtained in the AS1). All the HIIT parameters and the progression of the training programme are described in Table 1. During each week, players performed 1 LH (first training session) and 1 SH (second training session), and these HIITs were repeated the next week, as the players performed each HIIT twice [30]. Both the LH and SH were progressing in density (reducing the resting time) and in intensity (increasing the %vIFT). The HIIT protocol was developed following Buchheit & Laursen (2013) [30] recommendations and aligned with similar protocols successfully applied in youth athletes [31]. To complete the programme, players must attend (and perform) at least 80% (n = 13) of the HIIT training sessions. No injuries or adverse events were reported during the intervention, supporting its feasibility and tolerability in U14–U15 players.
Each team conducted their training sessions on the same field and at the same time throughout the study period. Prior to commencing the HIIT, a standardised warm-up was performed, which consisted of 5 min of low-intensity activities (e.g., steady-state running and joint mobility exercises), followed by 3 min of dynamic stretching. Additionally, one repetition of the scheduled HIIT protocol for that day was performed to prepare players for the specific demands of the session and to allow them to adjust to the task’s speed from their first repetition. IL (a.u.) and EL (a.u.) were calculated by multiplying session time x RPE (0–10) or % vIFT, respectively. Players were given sufficient time to recover and rehydrate before RPE was registered.

2.6. Statistical Analysis

Data were tested for normality using the Shapiro–Wilk test and for homogeneity of variances using Levene’s test. No major violations of assumptions were detected. Initial differences in physical performance variables across maturity groups were evaluated using a one-way analysis of variance (ANOVA) with a post hoc (Bonferroni) test to identify pairwise differences between maturity groups. A repeated-measures (RM) ANOVA was conducted, with post hoc (Bonferroni) analyses, to examine (1) the effect of the training programme by assessing differences in physical performance between assessment sessions 1 and 2 for each group (EG and CG) and (2) the interaction effect (“time × group”) to determine whether the changes resulting from the training programme differed between the EG and CG. The effect size (ES) for ANOVA comparisons was calculated using partial eta squared (ηp2). ηp2 is interpreted as follows: values of ηp2 = 0.01–0.05 indicate a small effect, values of η2 = 0.06–0.13 suggest a moderate effect, and values of η2 > 0.13 denote a large effect. ES at a 95% confidence interval (CI) for pairwise comparisons was calculated in Hedges g units, according to the recommendations of Lakens (2013) for small sample sizes and were interpreted as trivial (<0.24), small (0.25–0.49), moderate (0.50–0.99), and large (>1.00) [32].
Regarding the analyses with the EG, the relationship between the variables related to the EL and the IL was analysed using the Pearson’s correlation coefficient (r), and it was interpreted as trivial (≤0.09), small (0.10–0.29), moderate (0.30–0.49), high (0.50–0.69), very high (0.70–0.89) and almost perfect (≥0.90) [33]. The differences in EL and IL variables according to the kind of HIIT (LH and SH) were analysed by paired samples t-test. Another ANOVA (with a Bonferroni pairwise comparison) was used to analyse the differences in EL and IL variables between the maturity groups for each kind of HIIT separately and for the overall training sessions. For these analyses, each training session for every player was treated as an individual ‘observation’.
A further RM ANOVA (with Bonferroni post hoc testing) was conducted for each physical performance variable to examine the effect of the intervention (AS1 vs. AS2) across players with different maturity statuses (limited to two groups [Pre-PHV and Post-PHV]), as well as to assess the potential interaction effect of “time × maturational group”.
All calculations were performed using Microsoft Excel (Microsoft, Seattle, WA, USA) and JASP software (JASP Team, Version 0.17.3), and the level of significance was set at p < 0.05.

3. Results

Initial physical performance was different between players with different maturity statuses (Table 2). Players with advanced maturity status obtained significantly better results in the 5 m and 30 m sprints and in the vIFT, with large effect sizes.
Players from the EG had significant improvements in the 5 m sprint (t = −5.15, p < 0.001, ES(g) [95%CI] = −0.56 [−1.04; 0.08]), 30 m sprint (t = −8.38, p < 0.001, ES(g) [95%CI] = −0.22 [−0.67; 0.22]) and in the vIFT (t = 3.66, p < 0.001, ES(g) [95%CI] = 0.99 [0.40; 1.60]) after the training programme. The CG had significant improvements in the 30 m sprint (t = −4.19, p < 0.001, ES(g) [95%CI] = −0.30 [−0.79; 0.19]) but not in the 5 m sprint (t = −0.08, p = 0.469, ES(g) [95%CI] = −0.05 [−0.52; 0.43]) and vIFT (t = 0.77, p = 0.224, ES(g) [95%CI] = 0.08 [−0.36; 0.52]) between the AS1 and AS2. The RM ANOVA revealed that improvements in the 30 m sprint and in the player’s vIFT were significantly higher in the EG than in the CG (F = 5.77, p = 0.021, ηp2 = 0.13, and F = 4.35, p = 0.044, ηp2 = 0.10, respectively).
Pearson’s correlational analysis showed significant high correlations between the EL and IL, both for LH (r = 0.53) and SH (r = 0.62), as well as for the overall data (r = 0.59) (p < 0.001). Time (as a volume criterion) correlated to the EL (r = 0.98 in LH and SH) and to the IL (r = 0.49 and 0.61 in LH and SH, respectively) (p < 0.001). The RPE, as intensity criteria, correlated to the IL (r = 0.76 and 0.82 in LH and SH, respectively), while the %vIFT, as intensity criteria, correlated to the EL for LH (r = 0.31) and the overall training sessions (r = 0.42), but not for the SH (r = 0.15, p = 0.081).
Table 3 showed significant differences in the LH and SH programmed EL, with significant differences for the intensity (%vIFT) and total EL in favour of the SH, while the total time of sessions was the same. Table 3 also shows how the internal response of players was different for the different HIIT methods, with higher significant values in the player’s RPE and IL for the SH compared to the LH.
The comparison of EL and IL between the players with different maturity statuses revealed that, although the HIIT sessions were programmed with identical EL variables for all groups, the IL variables were statistically different between groups for both LH and SH. Players with advanced maturity status perceived less intensity (RPE) and thus, less IL than players with delayed maturity status (Table 4).
The RM ANOVA revealed an overall positive effect of the intervention on the 30 m sprint and vIFT (F = 10.81 and 11.60, respectively, p < 0.010). Specifically, the pre-PHV group exhibited significant improvements in the 30 m sprint (t = 2.91, p = 0.031, ES(g) = 0.41) and vIFT (t = 2.89, p = 0.048, ES(g) = 1.28) between AS1 and AS2. However, no significant changes were observed in the post-PHV group for any variable (t = 0.45–2.01, p > 0.050, ES(g) = 0.22–1.04). Likewise, no significant interaction effect (time × maturational group) was detected for any variable (F = 0.12–0.24, p = 0.632–0.732, ηp2 = 0.01).

4. Discussion

The present study examined the influence of biological maturation on the adaptations of young football players to a running-based training programme and analysed variations in EL and IL according to the players’ maturity status. Players with advanced maturity status displayed better initial performance in the 5 m sprint, 30 m sprint, and vIFT. The training programme proved effective in enhancing players’ physical performance, with greater improvements observed in the experimental group (EG) compared to the control group (CG), although no significant between-group differences were noted in the improvement of the 5 m sprint. Concurrently, differences in training load were observed, whereby players with advanced maturity status reported lower IL compared to their peers with delayed maturity status. Players with delayed maturity status significantly improved their 30 m sprint and vIFT performance, whereas no significant changes were observed in the post-PHV group; however, the RM ANOVA revealed no significant “time × maturity group” interaction effect.

4.1. Influence of Maturation on Initial Performance

The results obtained indicate superior physical performance among players with an advanced maturity status, showing a greater performance of post-PHV players in running capacities such as acceleration (5 m sprint), linear velocity (30 m sprint), and intermittent running endurance (vIFT), which strengthens the evidence of the influence of maturity development on physical capacities. A higher physical performance among young football players with advanced maturity status has been widely reported by the scientific literature [22]. Recently, the research led by Carranza-García et al. [34] showed better performance in the post-PHV group concerning neuromuscular performance, acceleration, sprint, and intermittent running capacity, which aligns with the results obtained in this study [17]. Structural changes in muscles (e.g., fascicle length, muscle hypertrophy, tendon stiffness), neural improvements (e.g., fibre recruitment, increased preactivation, reduced agonist-antagonist co-contraction), and other changes (e.g., hormonal or metabolic) during the maturation process are responsible for the enhanced force application and speed in young athletes as they grow [13,17,35].
These growth-related changes enable players with greater maturity status to generate higher absolute and relative power output during runs compared to their less mature counterparts; consequently, each velocity achieved in the 30-15 IFT represents a smaller percentage of their maximum running speed. This factor will directly impact players running economy, increasing energy expenditure in pre-PHV players during the test. Running at a higher percentage of maximum speed requires greater metabolic effort, and, more importantly, it also involves a higher recruitment of type II muscle fibres, which are less efficient and underdeveloped in less mature players [36]. Additionally, it is necessary to consider biomechanical demands such as increased stride frequency or length in less mature players, which may further elevate total energy expenditure [31,37]. To be considered, during adolescence, a period of motor instability known as ‘adolescent awkwardness’ may arise, referred to as “adolescent awkwardness”, which may alter inter-muscular coordination, thus affecting running mechanics, which could further contribute to the increased energy cost and reduced efficiency observed in less mature players, ultimately impacting their running performance [38]. These statements highlight the significant impact of maturational development on running performance, emphasising the physiological, metabolic, and biomechanical advantages of more mature players.

4.2. Effects of the Running-Based HIIT Intervention on Physical Performance

Post-intervention analysis revealed significant improvements in the 5 m and 30 m sprints and in the vIFT performances in the EG; meanwhile, the CG only shows improvements in the 30 m sprint. The RM ANOVA showed higher significant improvements in the 30 m sprint and vIFT for EG compared to the CG. These differences suggest that the proposed HIIT training programme enhances players intermittent running capacities, which is aligned with the previous literature that has established HIIT training methodologies as an appropriate approach for developing young players key determinants of running performance, such as maximal oxygen uptake, maximal running performance, running economy or lactate threshold [7]. Drawing on the meta-analysis of Kunz et al. [6], running-based HIIT interventions, when compared to alternative training modalities (i.e., small-sided games, continuous running formats or soccer-specific drills), yielded moderate-to-large positive effects on running performance, particularly evident in the greater enhancement of peak oxygen uptake [31] and in the higher precision to regulate the specific training load prescribed to players [6]. In this regard, the post-intervention improvements in running performance shown in this study are consistent with the previous findings.
As shown in the specific literature, running-based HIIT enables the establishment of a determined individual acute physiological load (training individualisation) as well as the precise physiological target for stimulation [30,39]. The recent literature underpins the reliability of the 30-15 IFT regardless of the biological age of the player, ensuring a reliable measure for individualising running intensities [40]. Therefore, this HIIT intervention based on individual percentages of vIFT could potentially facilitate a more precise adjustment of training intensity to the individual capabilities of the players, optimising their physiological development and adaptations to training.

4.3. Variations in Training Load According to Maturation

The correlational analysis conducted in this study revealed a relationship between EL and IL for both LH and SH trainings, suggesting that the players’ perceptions of effort were aligned with the external loads proposed by the researchers. This finding is consistent with previous research exploring the relationship between internal and external load in football [41]. Conversely, the subjective task intensity, assessed via RPE, correlated with the actual proposed intensity (vIFT) only for the LH, but not for the SH. This discrepancy may be attributed to the possibility that, in short-duration efforts, the rating of perceived exertion (RPE) might lack sensitivity to detect subtle variations in intensity, as the time spent at high intensity appears to be an influencing factor in effort perception [42]. In addition to this, the paired samples t-test revealed differences in intensity and load between LH and SH, indicating the appropriate application of the methods.
However, the analysis of training load between players with different maturity statuses revealed that, despite the programmed EL being uniform for all groups, the individual responses (RPE and IL) to this stimulus differed significantly based on the players’ maturity statuses. Post-PHV players report an inferior IL than their less mature (pre-PHV) peers for the same EL (same work:rest times and same % of vIFT). Several studies have highlighted the importance of distinguishing between EL (e.g., distance covered, number of sprints, or accelerations) and IL (e.g., s-RPE, heart rate) to adequately interpret the body’s response to training [20,21]. This distinction enables coaches and strength and conditioning professionals to adjust and monitor loads to optimise adaptation and reduce injury risk [19]. In the present study, the strong correlation between EL and IL, as well as between time (regarded as a volume criterion) and IL, confirms the complex relationship between both types of loads.
During this intervention, less mature players perceived higher physiological stress under the same EL. These results align with the findings of Teixeira et al. [24], which expose differences in IL amongst maturation bands in a young football player sample [24]. The pre-PHV group of Teixeira’s intervention reported a higher average heart rate and percentage of maximum heart rate as well; RPE was higher in pre-PHV players. Previous research has also described that RPE seems to be more associated with training level, maturation, and stage of development than with training conditions or demands [43]. Biological maturation influences in IL have also been observed in accumulated training loads throughout the season; Nobari et al. [44] indicate that when maturity status and accumulated training load were included as covariates, the statistically significant differences in physical performance variables disappeared [45]. Attending to EL variations, the previous literature suggests superior high-intensity runs performance during training tasks and matches, covering greater distances at high speed [22,46].
These maturity-related differences in internal load perception may stem from both physiological and perceptual mechanisms. Less mature players may present lower running economy, reduced neuromuscular efficiency, and greater relative physiological strain, making the same external load feel more demanding. Furthermore, limited training experience, lower tolerance to discomfort, and developing self-regulation abilities may heighten the perceived exertion in pre-PHV players. These factors could explain the consistently higher IL and RPE values observed in less mature individuals exposed to standardised high-intensity stimuli.

4.4. Relationship Between Maturation and Training Adaptations

The training intervention yielded an overall positive effect on 30 m sprint and vIFT performances, yet significant improvements were confined to the pre-PHV group, with no significant changes observed in the post-PHV group. This finding suggests that players with a less advanced maturity status may adapt more effectively to the HIIT stimulus employed in this study. Such differential responses could be underpinned by variations in perceived intensity and IL between maturity groups, as evidenced by the higher IL reported by pre-PHV players despite identical EL prescriptions. A greater internal response to training load, potentially reflecting heightened physiological stress or effort perception, may facilitate more pronounced improvements in sprint and intermittent endurance capacities, a notion supported by prior research linking IL to training adaptations in youth athletes [41]. Alternatively, the lower baseline physical performance levels of the pre-PHV group could indicate a greater adaptive potential, allowing for more substantial relative gains following training. This aligns with the principle that individuals with lower initial fitness levels often exhibit larger improvements due to a wider scope for physiological adaptation [47].
The significant enhancements in 5 m and 30 m sprint times within the pre-PHV group are consistent with traditional perspectives in the literature, which suggest that players prior to PHV may benefit more from training stimuli targeting neural and plyometric adaptations [48]. Pre-pubertal athletes may exhibit pronounced gains in sprint performance due to neuromuscular adaptations, such as improved motor unit recruitment and intermuscular coordination, rather than structural changes more typical in post-PHV individuals [49]. These findings are corroborated by studies demonstrating that sprint-specific interventions [50,51] enhance sprint velocity in youth footballers, with effects potentially accentuated in less mature players. Long-term athlete development (LTAD) models, such as those proposed by Ford et al. [52], further posit windows of opportunity for speed development prior to PHV, attributing these gains to the natural maturation of neuromuscular systems, which may be amplified by targeted training programmes [52]. However, conflicting findings have also been reported in this regard. For instance, the systematic review by Ramirez-Campillo et al. [53] demonstrated similar improvements across maturity groups following plyometric training interventions, despite the traditional assumption that pre-PHV individuals exhibit greater adaptability to such training stimuli [53].
Conversely, LTAD frameworks typically position the optimal window for endurance development during or post-PHV, driven by maturation-related enhancements in aerobic capacity and metabolic efficiency [54]. This contrasts with our results, where pre-PHV players exhibited significant vIFT improvements, suggesting that the HIIT protocol may have elicited endurance gains independent of traditional endurance-focused windows. The literature presents conflicting evidence regarding such windows for endurance trainability. For example, Weber et al. [55] observed greater responsiveness to endurance training around PHV, whereas Rowland [56] identified substantial gains in pre-PHV. However, Baquet et al. [18] found that pre-, circum-, and post-pubertal children achieved comparable peak VO2 improvements following intermittent endurance training, indicating that endurance adaptations may not be strictly tied to a specific maturational stage [52]. This variability underscores the influence of training intensity, duration, and individual factors over maturational timing alone, challenging the existence of a definitive endurance trainability window. An additional consideration is that the observed improvements in the pre-PHV group may primarily reflect enhancements in sprint velocity rather than cardiorespiratory or metabolic capacity. The 30-15 IFT, while a valid measure of intermittent endurance [28], relies heavily on maximal running speed, particularly in its later stages. For pre-PHV players, a limiting factor in test performance could be their inability to sustain the required velocity due to lower baseline sprint capacity, rather than central fatigue or aerobic limitations. As for that, improvements in 30-15 IFT in pre-PHV players may come due to both improved sprint mechanics and a reduced percentage of maximum sprint speed required at each stage.
Notably, the RM ANOVA revealed no significant “time × maturity group” interaction effect, and both pre- and post-PHV groups exhibited similar ES in physical performance changes. This suggests that, although only the pre-PHV group showed significant pre-post intervention improvements in the 30 m sprint and vIFT, the overall training adaptations did not differ significantly between the pre- and post-PHV groups when considering the statistical interaction. This lack of interaction may be attributed to the study’s limited sample size (n = 19 in the EG). A post hoc power analysis using G*Power (version 3.1.9.7) (effect size f = 0.25, α = 0.05, total n = 19) indicated a statistical power of 0.55 to detect a time × maturity interaction. Although this value is moderate, it falls below the commonly accepted threshold of 0.80, suggesting a potential risk of Type II error. As such, non-significant interaction effects should be interpreted with caution, and future studies with larger sample sizes are recommended to confirm these findings.
As previously mentioned, there is ongoing debate regarding the existence of a specific window of opportunity for enhanced trainability and greater adaptations in speed and endurance [53]. As previously shown, findings of Ramirez-Campillo et al. [53] reported similar sprint improvements across pre-, mid-, and post-PHV groups following plyometric training interventions. Regarding endurance, discrepancies persist in the literature about whether a specific period of heightened trainability exists. Baquet et al. [18] observed similar peak VO2 improvements across maturational groups following intermittent endurance training [18], suggesting a relatively uniform adaptability throughout childhood and adolescence. Growth-related changes such as increased predominance of glycolytic metabolism, enhanced activation of mitochondrial biogenesis pathways, and gains in lean body mass undoubtedly influence endurance development [56,57,58,59], yet these adaptations may contribute linearly to performance gains across maturation rather than being confined to a critical window [60].
To further elucidate these findings, future research should explore the biological mechanisms underpinning these adaptations, such as changes in muscle fibre type, neuromuscular efficiency, or anaerobic power, which remain underexplored in youth footballers [18]. Larger sample sizes and longitudinal designs could also enhance the detection of maturational interactions, addressing the limitations of the current study. Future studies should consider stratified randomisation to minimise potential bias from maturity differences. Moreover, incorporating direct measures of cardiorespiratory fitness (e.g., VO2 max) alongside the 30-15 IFT could clarify whether the observed improvements stem from speed enhancements or broader metabolic adaptations, offering a more comprehensive understanding of maturity-related training responses in young football players. Coaches should consider monitoring IL more closely in pre-PHV players, as their higher physiological responses may require a particular load adjustment.

5. Conclusions

This study demonstrates that biological maturation significantly influences initial running performance in young football players, with post-PHV players exhibiting superior baseline 5 m sprint, 30 m sprint, and vIFT capacities. The running-based HIIT intervention effectively enhanced these performance metrics in the experimental group, with significant improvements primarily observed in the pre-PHV group, suggesting greater adaptability among less mature players. However, the absence of a significant “time × maturity group” interaction suggests that training adaptations did not differ markedly between pre- and post-PHV groups, potentially due to the study’s limited sample size. This finding aligns with the previous literature, highlighting ongoing controversy regarding critical maturity points or ‘windows of opportunity’ for developing physical performance. Despite identical external load prescriptions, pre-PHV players reported higher internal loads, indicating a heightened physiological response that may drive their pronounced gains. These findings highlight the interplay between maturity status, training load perception, and performance outcomes, with implications for tailoring HIIT protocols to individual developmental stages in youth football.

Author Contributions

Conceptualisation, G.F.-J. and I.P.-G.; methodology, G.F.-J. and A.J.; software, G.F.-J.; validation, A.J., M.M.-R. and I.P.-G.; formal analysis, G.F.-J.; investigation, G.F.-J.; resources, I.P.-G.; data curation, G.F.-J.; writing—original draft preparation, G.F.-J.; writing—review and editing, A.J., M.M.-R. and I.P.-G.; visualisation, G.F.-J.; supervision, A.J., M.M.-R. and I.P.-G.; project administration, I.P.-G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Miguel Hernandez University (protocol code DES.MMR.GFJ.23; 18 April 2023).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The datasets generated and analysed during the current study are not publicly available but may be made available by the corresponding author upon reasonable re-quest. Data will be accessible for a period of three years following the publication of this article. Interested researchers must contact the corresponding author via email with a justified explanation of the intended use of the data. Access will be granted at the discretion of the corresponding author, provided that the request aligns with ethical standards and data protection regulations.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. HIIT programme progression for LHs and SHs.
Table 1. HIIT programme progression for LHs and SHs.
WeeksSession 1 (LH)Session 2 (SH)
SeriesWork:Rest% vIFTSeriesWork:Rest% vIFT
1–251′:1′80%1030″:30″90%
3–451′:30″80%1030″:15″90%
5–651′:1′85%1030″:30″95%
7–851′:30″85%1030″:15″95%
LH: long HIIT; SH: short HIIT; vIFT: final velocity at 30-15 intermittent fitness test.
Table 2. Descriptive data (M ± SD) and means comparison (ANOVA) of physical performance variables according to the players’ maturity groups in the AS1.
Table 2. Descriptive data (M ± SD) and means comparison (ANOVA) of physical performance variables according to the players’ maturity groups in the AS1.
Pre-PHVMid-PHVPost-PHVF pηp2
n121316
5 m sprint (s)1.21 ± 0.071.16 ± 0.091.08 ± 0.04 ab16.06<0.0010.458
30 m sprint (s)5.41 ± 0.554.94 ± 0.58 a4.48 ± 0.16 ab14.67<0.0010.436
vIFT (km·h−1)16.88 ± 2.4817.42 ± 2.1918.97 ± 0.94 ab4.650.0160.196
vIFT: final velocity in the 30-15 intermittent fitness test; a statistically different (p < 0.05) to pre-PHV; b statistically different (p < 0.05) to mid-PHV.
Table 3. Descriptive data (M ± SD) and means comparison (paired samples t-test) of the EL and IL variables according to the kind of HIIT.
Table 3. Descriptive data (M ± SD) and means comparison (paired samples t-test) of the EL and IL variables according to the kind of HIIT.
LHSHt pHedge’s g (95% CI)
time (min)8.62 ± 1.258.66 ± 1.250.210.8320.03 (−0.22; 0.27)
vIFT (%)82.67 ± 2.5092.35 ± 2.5031.33<0.0013.85 (3.44; 4.26)
EL (a.u.)713.37 ± 108.15799.35 ± 116.856.19<0.0010.76 (0.51; 1.01)
RPE (1–10)6.40 ± 1.256.90 ± 1.383.030.0030.37 (0.13; 0.62)
IL (a.u.)54.94 ± 12.3159.80 ± 15.422.820.0050.13 (0.10; 0.59)
vIFT (%): percentage of the final velocity in the 30-15 intermittent fitness test; EL: external load; RPE: rate of perceived exertion; IL: internal load; LH: long HIIT; SH: short HIIT; CI: confidence interval.
Table 4. Descriptive data (M ± SD) and means comparison (ANOVA) of EL and IL variables according to the players’ maturity groups.
Table 4. Descriptive data (M ± SD) and means comparison (ANOVA) of EL and IL variables according to the players’ maturity groups.
Pre-PHVMid-PHVPost-PHVF pηp2
LHn284754
time (min)8.57 ± 1.268.67 ± 1.268.61 ± 1.250.060.943<0.001
vIFT (%)82.56 ± 2.5282.66 ± 2.5282.59 ± 2.520.100.9020.002
EL (u.a.)710.71 ± 110.23717.29 ± 110.72711.34 ± 106.770.050.953<0.001
RPE (1–10)7.07 ± 0.726.68 ± 1.165.82 ± 1.30 ab13.20<0.0010.173
IL (u.a.)60.27 ± 8.4857.45 ± 11.2850.00 ± 13.16 ab8.94<0.0010.124
SHn305054
time (min)8.83 ± 1.278.65 ± 1.268.57 ± 1.250.440.6440.007
vIFT (%)92.50 ± 2.5492.50 ± 2.5392.13 ± 2.500.350.7060.005
EL (u.a.)817.08 ± 119.51800.25 ± 119.38788.66 ± 113.930.570.5670.009
RPE (1–10)7.93 ± 0.947.02 ± 1.24 a6.20 ± 1.32 ab19.94<0.0010.233
IL (u.a.)70.58 ± 15.6260.45 ± 13.10 a53.19 ± 13.92 ab14.92<0.0010.186
overalln5897108
time (min)8.70 ± 1.268.66 ± 1.258.59 ± 1.250.190.8280.001
vIFT (%)87.85 ±5.4787.73 ± 5.5487.36 ± 5.400.190.8280.001
EL (u.a.)765.73 ± 126.08760.05 ± 122.00750.00 ± 116.550.360.6950.003
RPE (1–10)7.52 ± 0.946.86 ± 1.21 a6.01 ± 1.32 ab31.71<0.0010.196
IL (u.a.)65.60 ± 13.6159.00 ± 12.28 a51.60 ± 13.58 ab22.62<0.0010.148
vIFT (%): percentage of the final velocity in the 30-15 intermittent fitness test; EL: external load; RPE: rate of perceived exertion; IL: internal load; LH: long HIIT; SH: short HIIT. a statistically different (p < 0.05) from pre-PHV; b statistically different (p < 0.05) from mid-PHV. n = total number of observations (sessions × players).
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Fernández-Jávega, G.; Javaloyes, A.; Moya-Ramón, M.; Peña-González, I. Influence of Biological Maturation on Training Load and Physical Performance Adaptations After a Running-Based HIIT Program in Youth Football. Appl. Sci. 2025, 15, 6974. https://doi.org/10.3390/app15136974

AMA Style

Fernández-Jávega G, Javaloyes A, Moya-Ramón M, Peña-González I. Influence of Biological Maturation on Training Load and Physical Performance Adaptations After a Running-Based HIIT Program in Youth Football. Applied Sciences. 2025; 15(13):6974. https://doi.org/10.3390/app15136974

Chicago/Turabian Style

Fernández-Jávega, Gonzalo, Alejandro Javaloyes, Manuel Moya-Ramón, and Iván Peña-González. 2025. "Influence of Biological Maturation on Training Load and Physical Performance Adaptations After a Running-Based HIIT Program in Youth Football" Applied Sciences 15, no. 13: 6974. https://doi.org/10.3390/app15136974

APA Style

Fernández-Jávega, G., Javaloyes, A., Moya-Ramón, M., & Peña-González, I. (2025). Influence of Biological Maturation on Training Load and Physical Performance Adaptations After a Running-Based HIIT Program in Youth Football. Applied Sciences, 15(13), 6974. https://doi.org/10.3390/app15136974

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