Relationship between training load and injury risk in elite young male football players: A systematic review

Abstract

Objective

This study aimed to systematically review the relationship between training load and injury risk in elite football players and propose an evidence-based training load management strategy for promoting their sustainable development.

Methods

This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, with literature searches performed using the PubMed, Web of Science, SPORTDiscus, and MEDLINE databases. Study quality was assessed using the Newcastle–Ottawa Scale, levels of evidence were classified using the Oxford Centre for Evidence-Based Medicine (OCEBM) model, and the certainty of evidence was summarized using the Grading of Recommendations Assessment, Development and Evaluation framework.

Results

A total of nine studies met the inclusion criteria. For internal loads, psychosocial factors were not clearly associated with injury risk. Higher injury risk was observed in association with abrupt increases in session rating of perceived exertion (sRPE) and high monotony or strain, whereas stable moderate loads were associated with protective effects. For external loads, high or rapidly increasing volumes of distance, high-speed running, and accelerations were associated with an increased risk of injury, whereas lower or moderate loads were associated with protective effects. Acute–chronic workload ratio (ACWR) analysis indicated that very high ratios, particularly under low chronic loads, were associated with increased injury risk, whereas low ratios were associated with protective effects.

Conclusion

Abrupt increases in training load, rather than total load volume, were consistently associated with a higher incidence of injuries.

Keywords

Acute-chronic workload ratio high-speed running session rating of perceived exertion soccer

Introduction

Football is a globally popular sport with a consistently high injury incidence across all levels of play.^1–3 During the past decade, the high-intensity running and sprinting distances of players during a game have increased by 30%–35%.⁴ This increase in exercise load directly contributes to increased injury risk, with the risk increasing more substantially as the load intensity increases.⁵

Youth is a crucial developmental stage for professional football players. However, owing to differences in the development of sport skills and physical attributes, young and adult professional players exhibit different responses to training.⁶ Young male football players aged 8–19 years have a higher incidence rate of injury during training than adult professional football players.⁶ Moreover, once injured, the likelihood of reinjury significantly increases,^7–9 and players are more likely to be excluded from the team and less likely to progress to a professional career.¹⁰

As recommended by the International Olympic Committee (IOC) and previous studies,^11–13 regular monitoring of players’ workloads is considered essential for injury risk reduction. Accordingly, numerous studies have investigated the association between training load and injury risk using a range of internal and external load indicators, such as session rating of perceived exertion to reflect physiological and psychological stress, and total distance, sprint distance, and accelerations to quantify the objective work performed during training or competition.^11,14,15

While previous reviews have primarily focused on adult populations, evidence on load-injury relationships in elite youth football players remains limited and heterogeneous. Variations in review scope, study populations, monitoring approaches, and analytical focus have resulted in differing interpretations of how internal and external load metrics relate to injury risk. Importantly, many studies have reported injury risk in relation to workload accumulated across training periods, yet how different patterns and changes in internal and external loads across time relate to injury risk has not been consistently synthesized, which limits the practical application of current evidence in elite youth football contexts.

Therefore, the purpose of this systematic review is to clarify load-injury patterns in elite youth football players, with particular emphasis on the role of changes in internal and external loads over time, and to provide evidence-informed guidance for training load management in this population.

Methods

This systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 (PRISMA 2020) guidelines.¹⁶ The study protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) database (http://www.crd.york.ac.uk/PROSPERO/) (registration number: CRD420251023220).

Eligibility criteria

The following inclusion criteria were used to select studies.

Peer-reviewed articles written in English.

Studies that involved male elite young football players aged between 12 and 21 years,¹⁷ with elite players being defined as those who are part of an academy of an elite soccer club playing in the highest competition of their country,¹⁷ those playing in the national team, or highly skilled players (based on the league of their team) (P).¹⁸

Studies that reported internal and/or external load parameters (I) and compared different load intensities or patterns (C).

Studies in which soccer-related injuries were reported by medical staff or were self-reported (O).

Studies with an observational longitudinal cohort design (S).

Reviews, academic dissertations, meta-analyses, non-original research articles, opinion pieces, and reports with only an abstract were excluded.

Search strategy

We performed a comprehensive literature search in PubMed, Web of Science, SPORTDiscus (EBSCO), and MEDLINE until March 3, 2025. The references of the included articles were further examined to ensure all relevant articles were included. In each database, the search was performed using the following keywords: (“youth” OR “adolescent”) AND (“football” OR “soccer”) AND ((“workload” OR “training load” OR “External load” OR “Internal load” OR “load”)) AND ((“injury” OR “epidemiology” OR “risk”)). The detailed search strings used for each database are provided in Appendix A.

Two reviewers (TZ and LQ) independently selected studies. In cases of discrepancies, a third reviewer (QY) was consulted to resolve disagreements and achieve consensus. Inter-reviewer consistency was quantified using Cohen's kappa coefficient (k = 0.98). The EndNote (version 21) software (Clarivate Analytics, Philadelphia, PA, USA) was used to select studies and delete duplicates.

Data extraction

Data were extracted from the included studies by TZ, and uncertainties were addressed through consultation with QY. All data were recorded in an Excel spreadsheet (Microsoft Corporation, Washington, USA).

The data extraction sheet included the following: (1) general study descriptors (e.g., authors, publication year, follow-up duration, level of play, and study design); (2) study population details (e.g., age, sample size, and region); (3) workload and epidemiological data (e.g., external and internal loads and injury definition, location, severity, and type); and (4) study outcomes. For studies wherein original data were not provided,^14,19,20 numerical values were extracted from figures using WebPlotDigitizer (version 4.8),²¹ a tool with established reliability.²² To address missing, unclear, or graphically presented data, a structured verification procedure was applied:

Axis ranges were calibrated based on author-labeled scales.

Data points were selected manually (each figure was extracted three times to calculate mean values).

If graphical information was insufficient (e.g., unclear axes or indistinguishable data points), the corresponding variables were recorded as not available and were not interpreted further, thereby minimizing the risk of bias.

Consistency was independently verified by YX and BM, and inter-rater reliability was assessed using Cohen's kappa coefficient (k = 0.96).

Quality assessment

The quality of the included studies was assessed using the Newcastle–Ottawa Scale (NOS) for cohort studies.²³ This scale uses a 9-star system to evaluate studies across three broad categories: selection of study groups, comparability of the groups, and determination of either the exposure or outcome of interest in cohort studies. The higher the number of stars assigned to a study, the lower the risk of bias. The level of evidence of each included study was classified using the Oxford Centre for Evidence-Based Medicine (OCEBM) model.²⁴ The certainty of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework.²⁵

Data synthesis

Owing to substantial heterogeneity across the included studies, a quantitative meta-analysis was not performed. Specifically, marked differences were observed in study populations, training load metrics (internal and external load indicators), injury definitions, monitoring periods, and statistical approaches. Therefore, a qualitative synthesis was conducted to summarize and interpret the available evidence.

Results

Literature search and selection

Initially, 631 articles were retrieved from the literature search. After 270 duplicates were removed, 306 additional articles were excluded owing to irrelevance based on their titles and abstracts. The full texts of 56 articles were screened, of which 47 articles were excluded. Finally, 9 articles met the criteria for inclusion in this review (Figure 1).

Figure 1.

Flow diagram of the overall study protocol based on PRISMA guidelines.

Study characteristics

The publication dates of the included studies ranged from 2016 to 2023. All studies involved participants from Europe, with a total sample size of 394 elite young male football players. Of the nine included studies, five^{19,20,26–28} studies used internal load parameters to quantify load, two^29,30 studies used external load parameters, and two^14,15 studies used both. Injury was defined in the nine articles as follows: (1) injury that occurred during a scheduled training session or match that caused absence from the next training session or match,^15,26,27,30 (2) any physical complaint resulting from a soccer match or training that led to time loss (unable to take full part in future soccer activities)^19,20,28 or required medical attention (>1 day but still able to take full part in future soccer activities),¹⁴ and (3) overuse injuries diagnosed by a qualified physiotherapist.²⁹ A detailed description of study characteristics is provided in Table 1.

Table 1.

Characteristics of included studies

Study	Population	Follow-up	Injury definition	Quantification of training load
Bowen et al.³⁰ 2016 UK Prospective cohort	N = 32 Age 17.3 ± 0.9 English Premier League category 1 academy	2 seasons	A recordable injury was defined as one that caused any absence from future football participation.	Total distance (m): includes walking, jogging, fast running, and sprinting. High speed distance: distance covered (m) above > 20 km/h. Total load (AU): acceleration along x, y, and z axes. Global Positioning System (GPS) units: StatSports 10-Hz and 100-Hz accelerometer devices. Accelerations (no.): a change in GPS speed data for at least half a second with a maximum acceleration in the period of at least 0.5 m/s/s. Acute workload: 1-week load. Chronic workload: the 4-week rolling average acute workload. The acute: chronic workload ratio (ACWR) was calculated by dividing the acute workload by the chronic workload.
Delecroix et al. ²⁶ 2019 France Prospective cohort	N = 52, Age16.8 ± 0.9 N = 70, Age20.1 ± 0.3 Football club's academy plays in first French League	5 seasons	Any physical complaint sustained by a player that resulted from a football match or football training, that made the player unable to participate in future football training or a match.	Session rating of perceived exertion (sRPE): the intensity of all sessions and matches using the Borg 0-10 scale. Workload was calculated by multiplying the perceived intensity by the session or match duration. Absolute workload: sum of the load for the last 7/14/21/28 days. ACWR: 1-week workload divided by the average workload of the last 28/21/14 days. Week-to-week load changes: 1-week load divided by the accumulated load of the previous 7 days.
Raya-González et al.²⁷ 2019 Spain Prospective cohort	N = 22 Age 18.6 ± 0.6 Spanish First Division Under- 19 Championship	1 season	An injury that occurred during a scheduled training session or match that caused absence from the next training session or match.	sRPE: the intensity of each training session and match using Borg 0-10 scale. Weekly load: the sum of the load for all training sessions and matches for each week. ACWR: sum of the current week's sRPE training load (acute load) divided by the average weekly training load over the previous four weeks (chronic load).
Bacon and Mauger²⁹ 2017 UK Retrospective Cohort Study	N = 41 Age 17.8 ± 1.1 Barclays U21/U18	2 seasons	Overuse injuries (diagnosed by a qualified physiotherapist)	Total distance (m): weekly and whole season training/match averages. High speed running (m): weekly and whole season training/match averages. GPS units: not reported.
	Premier Leagues
Brink et al.¹⁴2011NetherlandsProspective cohort	N = 53Age16.5 ± 1.2Highest national competition level in The Netherlands	2 seasons	Any physical complaint sustained by a player that results from a soccer match or training, resulting in time loss (unable to take full part in future soccer activities) or medical attention (> 1 day but still able to take fullpart in future soccer activities).	sRPE: the intensity of each training session using the Borg 15-point scale.Weekly load: the sum of the load of all training sessions over a 1-week period.Monotony: the daily mean load divided by the standard deviation of the daily mean load over a 1-week period.Strain: the product of weekly load and monotony.Psychosocial Stress and Recovery: Recovery Stress Questionnaire for athletes (RESTQ- Sport).Duration: training sessions and matches in minutes.Training session load: the product of the sRPE and the duration of the training session.
Nilsson et al.¹⁵2023SwedenObservational cohort	N = 56Age 17-19Seweden's firstdivision club	4 seasons	When a player was absent from future football participation.	Average heart rate.sRPE: multiplying the rating of perceived exertion (RPE) value by the duration of each training session or game.Total distance: >0 km/hour.High-intensity running distance:15-19.79 km/hour.High speed distance:19.8-24.79 km/hour.Sprint distance: 24.8–29.79 km/hour.Maximal sprint distance: >29.8 km/hour.Accelerations: >0.50 m/s².Intense accelerations: >2.00 m/s² and very intense accelerations: >3.00 m/s².Decelerations: <−0.50 m/s².Intense decelerations: <−2.00 m/s² and very intense decelerations:<−3.00 m/s².GPS units and heart rate units: 10-Hz Polar Team Pro.
Mandorino et al. (2021a)¹⁹2021ItalyProspective cohort	N = 23Age 13.5 ± 0.3U14 sub-elite championship	1 season	An injury was reported if a player was unable to take full part in any future soccer activity participation.	sRPE: by multiplying the duration of each training or match by the individual RPE value assessed using the Borg CR-10 scale.The weekly load (WL) was calculated as the sum of the loads of all training sessions and matches over a period of one week.Cumulative loads: calculated for 2, 3, and 4 weeks (WL2, WL3, WL4).
				Monotony: by dividing the mean daily load by the standard deviation of the load over one week.Strain: by multiplying monotony by the weekly load.Chronic loads: the rolling averages for 2, 3, and 4 weeks.The ACWR was separately calculated for 2, 3, and 4 weeks.The relative week-to-week change: the percentage variation of the current WL for the previous WL (%WL).The players’ perceived recovery status was assessed through a modified 10-point total quality recovery (TQR) scale.
Mandorino et al. (2021b)²⁰2021ItalyProspective cohort	N = 23Age 13.5 ± 0.26U14 sub-elite championship	1 season	An injury was registered if the player was unable to take full part in future soccer training or match play.	sRPE: by multiplying the duration of each training or match for every single player by the RPE value quantified through the CR-10 Borg's scale.The WL: by adding the loads of all training sessions and matches over a week.Cumulative loads: calculated for 2, 3, and 4 weeks (WL2, WL3, WL4).Monotony: the mean daily load divided by the standard deviation of the load over one week.Strain: weekly load multiplied by monotony.The ACWR divides the weekly workload (acute load) by the average weekly workload over the previous 4 weeks (chronic load).Players’ perceived recovery status was estimated by adopting the modified 10-point TQR scale.
Mandorino et al. (2021c)²⁸2021ItalyProspective cohort	N = 22Age 13.5 ± 0.3U14 sub-elite championship	1 season	An injury was recorded if the player was unable to take full part in future soccer training or match.	sRPE: obtained by multiplying the RPE value, quantified through the CR-10 Borg's scale.Monotony: the mean daily load divided by the standard deviation of the load over one week.Strain: the sum of weekly load multiplied by monotony.WL: obtained by adding the training and matches loads for a week.Cumulative loads for a period of 2, 3, and 4 weeks (WL2, WL3, WL4) were calculated.The perceived recovery status of players was quantified using the 10-point TQR scale.

Methodological quality

The median total NOS score was 6 (range, 4–7), with a median level of evidence of 3. According to the NOS, a score of ≥7 stars indicates a low risk of bias, that of 4–6 stars indicates a moderate risk, and that of ≤3 stars indicates a high risk.²³ In the OCEBM model, levels 1–2 indicate high-quality evidence, level 3 indicates moderate-quality evidence, and levels 4–5 indicate low- or very-low-quality evidence.²⁴ The scores of each study are presented in Table 2. The overall certainty ratings for the key outcome categories of the GRADE are presented in Table 3.

Table 2.

Quality of included studies

Study	NOS Score				Level of Evidence
Study	Selection	Comparability	Outcome	Total	Level of Evidence
Bowen et al.³⁰	3	1	2	6	3
Delecroix et al. ²⁶	3	1	2	6	3
Raya-González et al.²⁷	3	1	2	6	3
Bacon and Mauger²⁹	1	1	2	4	3
Brink et al.¹⁴	3	1	3	7	3
Nilsson et al.¹⁵	3	1	3	7	3
Mandorino et al. (2021a)¹⁹	3	1	3	7	3
Mandorino et al. (2021b)²⁰	3	1	3	7	3
Mandorino et al. (2021c)²⁸	3	1	3	7	3

Table 3.

Summary of the GRADE evaluation of the certainty of evidence

Outcome	Study design	Risk of bias	Indirectness	Inconsistency	Imprecision	Other	Certainty
sRPE abrupt increases / load spikes	Observational cohort (n = 7)	Serious	Serious	Not serious	Serious	None	Very low
Monotony and strain (internal load)	Observational cohort (n = 3)	Serious	Serious	Not serious	Not serious	None	Low
ACWR (internal load)	Observational cohort (n = 4)	Serious	Serious	Serious	Serious	None	Very low
Total distance (external load)	Observational cohort (n = 3)	Serious	Serious	Not serious	Not serious	None	Low
High-speed running distance	Observational cohort (n = 3)	Serious	Serious	Serious	Not serious	None	Low
Accelerations / decelerations	Observational cohort (n = 2)	Serious	Serious	Serious	Serious	None	Very low

Injury location, types, and severity

Injury locations and types were reported in seven studies, whereas injury severity was reported in four studies. Details regarding injury locations, types, and severity are visually summarized in Figures 2 –4. Injury severity was classified using different criteria across the included studies. Specifically, one study²⁹ classified injury severity as low (≤7 days missed; n = 34), medium (8–14 days missed; n = 23), and high (≥15 days missed; n = 28). In contrast, the remaining studies classified injuries as slight (no absence from training or match), minimal (1–3 days of football activity missed), mild (4–7 days of football activity missed), moderate (8–28 days of football activity missed), severe (>28 days of football activity missed),²⁸ and career-ending injuries.³¹

Figure 2.

Distribution of injury locations based on the number of injuries and injury incidence rates (per 1000 exposure hours).

Figure 3.

Distribution of injury types based on the number of injuries.

Figure 4.

Distribution of injury severity based on the number of injuries.

Internal load and injury risk

Psychosocial factors

Only one study investigated the relationship between psychological factors and injury risk in young athletes. The Dutch version of the Recovery-Stress Questionnaire (12 general and 7 sport-specific scales, rated on a 0–6 Likert scale) was used to evaluate this relationship.¹⁴ The findings revealed no significant association between stress recovery factors and injury risk.

sRPE

Seven studies used sRPE to quantify internal training load in young athletes, employing various scales and calculation methods.^{14,15,19,20,26–28} Four studies used the 0–10 Borg scale,^19,20,26,28 one study used the Borg 15-point scale,¹⁴ one study used the 0–10 Foster scale,²⁷ and one study used a modified category scale.¹⁵

Two studies reported no significant relationship between training load and non-contact injuries in under-19 (U19) players.^26,27 However, Delecroix et al.²⁶ found that cumulative sRPE over 3 (RR = 1.39) and 4 weeks (RR = 1.40) was associated with an increased risk of injury in under-21 (U21) players. Contrasting patterns emerged in three studies, wherein lower average sRPE during 30 days before injury followed by abrupt increases were correlated with a higher risk of injury.^15,19,28 In addition, non-contact injuries were associated with RPE increases and week-to-week load changes (but not weekly load, monotony, or strain).^19,28 Conversely, Brink et al.¹⁴ found that traumatic injuries were associated with increased monotony and strain.

Three studies highlighted that injured athletes often endured higher load periods than uninjured peers,^19,20,28 which is consistent with the evidence of rapid load escalations.¹⁵ Threshold-specific findings reported by Mandorino et al. (2021b)²⁰ indicated heightened non-contact injury risk with TQR >8 AU alongside low three-week cumulative load (<2,532 AU) and high strain (>909.68 AU). High 3-week loads (>5,455 AU) reduced injury risk by 83.70%; however, this reduction was negated by intense sessions (RPE > 8 AU), which increased injury risk by 81.25%.²⁰

Acute-chronic workload ratio for internal load

Four studies assessed the sRPE-derived ACWR, with inconsistent outcomes.^19,20,26,27 Two studies found no associations between ACWR and the risk of contact or non-contact injuries in U19 and U21 cohorts,^26,27 whereas Mandorino et al. (2021a)¹⁹ found a significant association between 4-week ACWR and the risk of non-contact injuries. In addition, Mandorino et al. (2021b)²⁰ reported that an ACWR of <0.76 reduced the risk of non-contact injuries, whereas an ACWR of >0.76 increased the risk by 63.80%.

External load and injury risk

Total distance

Three studies analyzed the total distance.^15,29,30 Bowen et al.³⁰ found that 4-week distances of 112,244–143,918 m increased injury risk (RR = 1.64), whereas those of >143,918 m showed no effect on injury risk. Low 1-week distances (0–8,812 m) reduced the risk of overall (RR = 0.25) and non-contact injuries (RR = 0.30). Bacon and Mauger²⁹ found that weekly distances of ≥23,700 m were associated with a lower risk of overuse injuries compared to those of ≤19,404 m. Nilsson et al.¹⁵ reported a lower 30-day mean distance before injury (2,523.3 m), with significant daily increases (0.38 m/day).

High-speed running distance

Three studies analyzed the high-speed running distance (HSRD).^15,29,30 Bowen et al.³⁰ found that 4-week HSRD (3,502–5,123 m) increased the risk of non-contact injuries (RR = 2.14), and 1-week HSRD of 856–1,449 m was associated with increased injury risk (RR = 1.73). Conversely, lower 1-week HSRD (0–756 m) showed protective effects (RR = 0.30). Bacon and Mauger²⁹ found no association between HSRD and the risk of overuse injuries. Nilsson et al.¹⁵ found no significant differences in mean daily HSRD before injury but observed significant daily increases (1.26 m/day), with reduced sprint and maximal sprint distances.

Acceleration and deceleration

Two studies investigated acceleration and deceleration.^15,30 Bowen et al.³⁰ reported that completing 9,254 or more accelerations greater than 0.5 m/s² over 3 weeks increased the risk of overall (RR = 3.84) and non-contact injuries (RR = 5.11), while performing 744–2,861 accelerations reduced the injury risk (RR = 0.31). High weekly loads (474–648 AU) increased injury risk (RR = 1.65), very high loads (≥648 AU) increased the risk of contact injuries (RR = 4.84), and low loads (0–130 AU) reduced injury risk (RR = 0.27). Nilsson et al.¹⁵ found lower accelerations during 30 days before injury, with reductions in mean intense (>2.00 m/s²) and very-intense (>3.00 m/s²) accelerations. However, daily increases were observed. Similarly, pre-injury decelerations were lower, with significant increases in daily and intense decelerations.

Duration

Brink et al.¹⁴ examined the relationship between training/match duration and injury risk during two competitive seasons. They found that weekly durations were higher among players with traumatic injuries (7.27 ± 1.69 h) than among control players (6.70 ± 2.00 h), with a 14% increase in risk per unit (OR = 1.14). However, training/match duration was not associated with the risk of overuse injuries.

ACWR for external load

Bowen et al.³⁰ examined ACWR across external load metrics. For total distance, very high ratios (≥1.76) increased the risk of contact injuries (RR = 4.98), whereas low ratios (0–0.32) reduced the overall injury risk (RR = 0.28) under a low chronic load (<22,335 m). For HSRD, high ratios (1.41–1.96) increased the risk of non-contact injuries (RR = 2.55) under a low chronic load (<938 m), and moderate-to-high ratios (0.91–1.34) increased injury risk (RR = 2.09) under a high chronic load (>938 m). Conversely, low ratios (0–0.36) showed protective effects (RR = 0.47). For acceleration, very high ratios (>1.77) increased the risk of contact injuries (RR = 4.98), whereas low ratios (0–0.33) reduced injury risk (RR = 0.29) under a low chronic load (<1,856). For total load, moderate-to-high ratios (0.88–1.32) increased the risk of non-contact injuries (RR = 1.87), whereas low-to-moderate ratios (0.44–0.88) increased the risk of contact injuries (RR = 1.92). Forest plots illustrating the effect sizes for injury risk are provided, with Figure 5 presenting relative risks (RRs) and Figure 6 displaying odds ratios (ORs).

Figure 5.

Forest plot of relative risks (RRs) with 95% confidence intervals for the association between training load and injury risk.

Figure 6.

Forest plot of odds ratios (ORs) with 95% confidence intervals for the association between training load and injury risk.

Discussion

This systematic review aimed to elucidate the relationship between internal and external loads and injury risk in young football players and to develop a scientific evidence-based training load management strategy for the sustainable development of their professional football careers. In line with the gaps identified in previous literature, this review places particular emphasis on how changes and patterns in internal and external training loads over time, rather than isolated or cumulative load values, relate to injury risk in elite youth football players. Overall, the findings suggest a complex and multifaceted association between various load metrics and injury risk. Specifically, sudden increases in training load, rather than its overall volume, were consistently associated with a higher incidence of injuries. Internal load appears to reflect individual vulnerability, as indicated by physiological and perceptual responses, whereas external load is more closely associated with biomechanical stress and injury occurrence. This complexity highlights the need for integrated monitoring approaches to mitigate injury risk.

Although similar associations between rapid load increases and injury risk have been reported in adult professional football players,^32,33 developmental and maturity-related factors—such as ongoing growth, neuromuscular development, and load tolerance—may substantially modify these relationships in youth populations. Consequently, this review seeks to inform a personalized, evidence-based load management strategy specifically tailored to elite youth football players.

Effects on internal load on injury risk

Rapid increases in training intensity or volume have been consistently associated with non-contact and traumatic injuries,^15,19 which is consistent with the findings reported by studies on rugby,^34–37 cricket,³⁸ and Australian football.^39,40 Such rapid load escalations can push players into a training maladaptation phase. During this phase, the adaptive capacity of the body is exceeded,⁴¹ leading to physiological imbalances.⁴² In addition, increased sRPE increases perceptual fatigue, which impairs performance and increases injury risk during this phase. Sharp week-to-week increases^19,28 or cumulative increases over 3–4 weeks²⁶ were consistently observed prior to non-contact injuries.

On the contrary, consistent moderate internal loads showed protective associations, as high 3-week cumulative sRPE (>5,455 AU) was associated with an 83.70% reduction in the risk of non-contact injuries.²⁰ However, this protection is context-dependent; intense sessions (RPE > 8 AU) can negate the benefits, increasing injury risk by 81.25%.²⁰ In this review, ACWR <0.76 was associated with lower injury incidence in youth football populations.²⁰ A similar directional pattern has been reported in earlier work involving adult athletes from cricket, Australian football, and rugby, where ACWR between approximately 0.8 and 1.3 was associated with lower injury incidence.⁴³ The differences in reported ACWR thresholds may be driven by population-specific factors, including age, training history, and sport-specific load demands. Consequently, ACWR interpretation should be population-specific, underscoring the need for youth-specific reference ranges.

Evidence on psychosocial factors was limited to one included study,¹⁴ which did not identify a significant association with injury risk. Nevertheless, psychosocial stress may influence perceived exertion, stress, fatigue, and emotional recovery.^44–50 For example, high stress levels can exacerbate sRPE responses to the same external demands, highlighting the potential value of incorporating psychosocial recovery into training programs to enhance the resilience of athletes to physical stress.

The relationship between maturity and injury risk remains controversial. Some studies have shown that early-maturing players have a higher risk of injury,^51–53 whereas others have reported unclear associations.^54–56 Our findings align with this inconsistency, as Delecroix et al.²⁶ found distinct associations between cumulative load and injury risk in U19 and U21 players.

To minimize injury risk and support the long-term development of young football players, it is necessary to adopt a gradual and controlled approach to internal load management. A 3-week cumulative sRPE >5,455 AU was associated with lower non-contact injury incidence. Similarly, ACWR <0.76 was associated with lower injury incidence. These thresholds can serve as reference points for structuring weekly and block-periodized loads. In addition, incorporating psychological recovery strategies into load management may be beneficial, as psychosocial stress can amplify perceived exertion and undermine physical resilience. Selecting training loads based on the maturity status and tolerance of each individual is essential, ensuring that players adapt positively while avoiding the maladaptation phase. This approach balances performance enhancement with long-term health maintenance and injury prevention.

Effects of external load on injury risk

Total distance showed an inverted U-shaped relationship with injury risk. Moderate-to-high weekly distances (≥23,700 m) were associated with a lower incidence of overuse injuries,²⁹ potentially reflecting cardiovascular and muscular adaptation capacities.^57,58 However, cumulative 4-week distances of 112,244–143,918 m were associated with higher overall injury incidence,³⁰ likely owing to the accumulation of microtrauma to joints and ligaments.⁵⁹ Conversely, very low distances (0–8,812 m weekly) were also associated with lower injury incidence, although this pattern may reflect underloading and potential deconditioning.³⁰ Low 30-day averages before injury with daily increases (0.38 m/day) suggested that the rate of load increase, rather than baseline values, characterized pre-injury patterns.¹⁵ These findings suggest that an optimal range of distance can balance performance improvement with injury prevention. Consequently, training programs should aim to maintain total distances within thresholds that promote fitness without overwhelming the musculoskeletal system of athletes.

HSRD and accelerations/decelerations increased injury risk through eccentric and shear forces.⁶⁰ Specifically, 4-week HSRD (3,502–5,123 m) were associated with a 2-fold increase in the risk of non-contact injuries,³⁰ as high-velocity efforts strain the hamstrings and quadriceps, particularly in young individuals with developing neuromuscular control.⁶¹ Daily HSRD increases suggested maladaptive loading patterns,¹⁵ whereas high acceleration volumes were associated with a substantially higher incidence of non-contact injuries³⁰ by inducing rapid force absorption that overwhelms immature tissues.⁶² Similarly, decelerations showed pre-injury decreases followed by increases.¹⁵ These findings highlight the need to monitor directional changes that contribute to anterior cruciate ligament strains.⁶² Furthermore, a prolonged duration of training/matches (7.27 ± 1.69 h) was associated with a higher incidence of traumatic injury,¹⁴ as prolonged exposure exacerbates fatigue without proportional recovery.^26,63,64

ACWR values of ≥1.76 for total distance are associated with a 4-fold increase in the risk of contact injuries, particularly under a low chronic load.³⁰ Conversely, ACWR values of 0–0.32 show protective effects, promoting recovery and reducing injury risk. The effects of ACWR are modulated by chronic load levels, with high chronic HSRD (>938 m) attenuating the impact of increased ratios.³⁰ These findings are consistent with those of this review, indicating that external load primarily contributes to injuries through biomechanical stress, influenced by internal factors such as perceived exertion. These findings highlight the need for individualized load management strategies to optimize performance and prevent injuries.

According to the findings of this review, evidence-based thresholds can guide external load management to reduce injury risk and foster the gradual development of young football players. Training plans should target these safe thresholds by structuring weekly and block-periodized loads that build tolerance progressively while avoiding abrupt increases. Specifically, weekly total distances of 23,700–40,000 m and cumulative 4-week accumulations of <112,000 m are the most beneficial factors. Very low weekly distances (0–8,812 m) show protective effects but risk deconditioning. HSRD should be maintained below 3,500 m over 4 weeks. Similarly, abrupt increases in accelerations and decelerations should be avoided. Training should be controlled to <7 h per week to reduce the risk of traumatic injuries. In addition, ACWR values of 0–0.32 show protective effects, whereas ratios of ≥1.76 for total distance increase injury risk up to 4-fold. Integrating GPS-derived external metrics with internal indicators can allow coaches to personalize loads according to the maturity, recovery capacity, and tolerance of players. This combined monitoring approach not only supports performance development but also minimizes the likelihood of injuries, promoting long-term health and sustainable growth in young football players.

Methodological quality considerations

The methodological quality of the included studies should be considered when interpreting the findings of this review. Overall, the evidence base comprises observational studies of moderate methodological quality, which limits the strength of causal inference. Consistent with this, the GRADE assessment indicated that the certainty of evidence across the main outcomes ranged from low to very low. Consequently, the findings should be interpreted as indicative of consistent directional associations rather than definitive estimates of effect magnitude. However, the consistency of results across studies with relatively stronger methodological characteristics supports the overall direction of the observed associations.

The included studies used different perceived exertion scales that vary in numerical range and category structure. As a result, absolute values obtained from these scales are not directly comparable across studies. Accordingly, internal load findings in this review were interpreted with an emphasis on the direction and consistency of associations, rather than direct comparison of scale-specific values.

Strengths, limitations, and future directions

This review synthesizes evidence on the association between training load and injury risk in elite youth football, integrating internal and external metrics to provide a more holistic view of load-injury dynamics. Methodological rigor was strengthened through dual quality appraisal (NOS and OCEBM), assessment of the certainty of evidence using the GRADE framework, inter-rater reliability assessment, and forest-plot visualization. Importantly, the review offers added applied value by consolidating temporal load-change patterns and threshold ranges across metrics, providing population-specific reference points and an individualized load-management framework that considers physiological factors and potential psychosocial influences to inform training and injury prevention.

However, this review has several limitations. First, data extracted from charts^14,19,20 may involve uncertainty due to image resolution and plotting accuracy. Second, comparisons are challenged by inconsistencies in injury definitions and sRPE scales across studies, which can affect load quantification and data standardization. Third, a quantitative meta-analysis was not performed owing to heterogeneity among the included studies; however, this qualitative review provides valuable directional evidence. Fourth, the level of evidence in a majority of included studies was moderate (level 3), which limits the strength of conclusions and should be considered when interpreting the findings. Finally, potential publication bias should be considered, as studies with significant or positive findings are more likely to be published.

Future studies should prioritize prospective cohort designs adopting standardized protocols, including uniform injury definitions, sRPE scales, and GPS measurement standards, to enhance data comparability and strengthen longitudinal evidence. Refining ACWR thresholds across different maturity stages and investigating the influence of psychosocial factors on internal load responses can further advance holistic load management. In addition, future reviews could consider meta-analytic modeling as data comparability improves. In practice, training programs should integrate individualized strategies that combine internal and external load metrics; consider players’ maturity, fitness, and load history; and take psychosocial recovery strategies into account where feasible.

Conclusion

This review suggests that abrupt increases in internal and external training loads, rather than overall load volume, are more consistently associated with injury incidence in youth football players. By synthesizing evidence with a specific focus on temporal changes in training load across both internal and external metrics, this review helps address inconsistencies in previous findings and provides a clearer interpretation of load–injury relationships in elite youth football. Consistent moderate loading and gradual progression appear to be linked with more favorable adaptation profiles, whereas sudden load spikes are associated with higher injury incidence. The integrated use of subjective (sRPE) and objective (GPS- and ACWR-derived) measures provides a robust basis for individualized load monitoring. Finally, the adoption of standardized protocols and further longitudinal research are required to translate these findings into effective and context-specific injury prevention strategies. However, these conclusions are supported by low to very low certainty evidence and should therefore be interpreted with caution.

Footnotes

Acknowledgments

The authors gratefully acknowledge the contributions of all collaborators involved in this study.

ORCID iDs

Tingyu Zhang

Ying Xu

Miguel Ángel Gómez Ruano

Qing Yi

Author contributions

Tingyu Zhang: Conceptualization, Methodology, Writing – original draft; Lihang Qian: Visualization, Writing – original draft; Bo Miao: Investigation, Writing – original draft; Ying Xu: Investigation, Writing – original draft; Qing Yi: Conceptualization, Methodology, Supervision, Writng – review & editing; Jorge: Conceptualization, Writing – review & editing; Miguel Ángel Gómez Ruano: Conceptualization, Writing – review & editing.

Ethical considerations

This article does not contain any studies with human or animal participants.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Funding

Q.Y. was supported by the Social Science Planning Funds of Liaoning Province, China under Grant No. L24CTY004 and LiaoNing Revitalization Talents Program under Grant No.XLYC2403132.

Declaration of conflicting interest

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Miguel Ángel Gómez Ruano is an Editorial Board member of the International Journal of Sports Science & Coaching.

Data availability

All data are available from the corresponding author on reasonable request.

Appendix A: Search strings for each database

PubMed (n = 149):

((“youth"[Title/Abstract] OR “adolescent"[Title/Abstract])

AND (“football"[Title/Abstract] OR “soccer"[Title/Abstract])

AND (“training load"[Title/Abstract] OR “workload"[Title/Abstract]

OR “internal load"[Title/Abstract] OR “external load"[Title/Abstract])

AND (“injury"[Title/Abstract] OR “risk"[Title/Abstract]

OR “epidemiology"[Title/Abstract]))

Web of Science (n = 276):

TS = ((“youth” OR “adolescent”)

AND (“football” OR “soccer”)

AND (“training load” OR “workload” OR “internal load” OR “external load”)

AND (“injury” OR “risk” OR “epidemiology”))

SPORTDiscus (n = 59):

(TI “youth” OR AB “youth” OR TI “adolescent” OR AB “adolescent”)

AND (TI “football” OR AB “football” OR TI “soccer” OR AB “soccer”)

AND (TI “training load” OR AB “training load” OR TI “workload” OR AB “workload” OR TI “internal load” OR AB “internal load” OR TI “external load” OR AB “external load”)

AND (TI “injury” OR AB “injury” OR TI “risk” OR AB “risk” OR TI “epidemiology” OR AB “epidemiology”)

MEDLINE (n = 147):

(adolescent.mp. OR youth.mp.)

AND (football.mp. OR soccer.mp.)

AND (training load.mp. OR workload.mp. OR internal load.mp. OR external load.mp.)

AND (injury.mp. OR risk.mp. OR epidemiology.mp.)

References

Lopez-Valenciano

Ruiz-Perez

Garcia-Gomez

, et al. Epidemiology of injuries in professional football: a systematic review and meta-analysis. Br J Sports Med 2020; 54: 711–718. doi:10.1136/bjsports-2018-099577

Ekstrand

Hagglund

Walden

. Injury incidence and injury patterns in professional football: the UEFA injury study. Br J Sports Med 2011; 45: 553–558. doi:10.1136/bjsm.2009.060582

Drawer

Fuller

. Evaluating the level of injury in English professional football using a risk based assessment process. Br J Sports Med 2002; 36: 446–451. doi:10.1136/bjsm.36.6.446

Barnes

Archer

Hogg

, et al. The Evolution of Physical and Technical Performance Parameters in the English Premier League. Int J Sports Med 2014; 35: 1095–1100. doi:10.1055/s-0034-1375695

Colby

Dawson

Heasman

, et al. Accelerometer and GPS-Derived Running Loads and Injury Risk in Elite Australian Footballers. J Strength Cond Res 2014; 28: 2244–2252. doi:10.1519/JSC.0000000000000362

Pfirrmann

Herbst

Ingelfinger

, et al. Analysis of Injury Incidences in Male Professional Adult and Elite Youth Soccer Players: A Systematic Review. J Athl Train 2016; 51: 410–424. doi:10.4085/1062-6050-51.6.03

Hägglund

Waldén

Ekstrand

. Previous injury as a risk factor for injury in elite football: a prospective study over two consecutive seasons. Br J Sports Med 2006; 40: 767–772. doi:10.1136/bjsm.2006.026609

Fulton

Wright

Kelly

, et al. Injury risk is altered by previous injury: a systematic review of the literature and presentation of causative neuromuscular factors. Int J Sports Phys Ther 2014; 9: 583–595.

Hägglund

Waldén

Ekstrand

. Injury recurrence is lower at the highest professional football level than at national and amateur levels: does sports medicine and sports physiotherapy deliver? Br J Sports Med 2016; 50: 751–758. doi:10.1136/bjsports-2015-095951

10.

Bangert

Jaber

Trefzer

, et al. The Impact of Injury on Career Progression in Elite Youth Football—Findings at 10 Years. J Clin Med 2024; 13: 1915. doi:10.3390/jcm13071915

11.

Bourdon

Cardinale

Murray

, et al. Monitoring Athlete Training Loads: Consensus Statement. Int J Sports Physiol Perform 2017; 12: S2-161–S2-170. doi:10.1123/IJSPP.2017-0208

12.

Halson

. Monitoring training load to understand fatigue in athletes. Sports Med 2014; 44: 139–147. doi:10.1007/s40279-014-0253-z

13.

Schwellnus

Adami

Bougault

, et al. International Olympic Committee (IOC) consensus statement on acute respiratory illness in athletes part 1: acute respiratory infections. Br J Sports Med 2022; 56: 1066–1088. doi:10.1136/bjsports-2022-105759

14.

Brink

Visscher

Arends

, et al. Monitoring stress and recovery: new insights for the prevention of injuries and illnesses in elite youth soccer players. Br J Sports Med 2010; 44: 809–815. doi:10.1136/bjsm.2009.069476

15.

Nilsson

Börjesson

Lundblad

, et al. Injury incidence in male elite youth football players is associated with preceding levels and changes in training load. BMJ Open Sport & Exercise Medicine 2023; 9: e001638. doi:10.1136/bmjsem-2023-001638

16.

Page

McKenzie

Bossuyt

, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Br Med J 2021; 372: n71. doi:10.1136/bmj.n71

17.

Verstappen

van Rijn

Cost

, et al. The Association Between Training Load and Injury Risk in Elite Youth Soccer Players: a Systematic Review and Best Evidence Synthesis. Sports Med Open 2021; 7: 6. doi:10.1186/s40798-020-00296-1

18.

Jones

Almousa

Gibb

, et al. Injury Incidence, Prevalence and Severity in High-Level Male Youth Football: A Systematic Review. Sports Med 2019; 49: 1879–1899. doi:10.1007/s40279-019-01169-8

19.

Mandorino

Figueiredo

Condello

, et al. The influence of maturity on recovery and perceived exertion, and its relationship with illnesses and non-contact injuries in young soccer players. Biol Sport 2022; 39: 839–848. doi:10.5114/biolsport.2022.109953

20.

Mandorino

Figueiredo

Cima

, et al. A Data Mining Approach to Predict Non-Contact Injuries in Young Soccer Players. Int J Comput Sci Sport 2021; 20: 147–163. doi:10.2478/ijcss-2021-0009

21.

Higgins

JPT TJ

Chandler

, et al. Cochrane Handbook for Systematic Reviews of Interventions. 2nd ed. UK: Chichester, 2024.

22.

Drevon

Fursa

Malcolm

. Intercoder Reliability and Validity of WebPlotDigitizer in Extracting Graphed Data. Behav Modif 2017; 41: 323–339. doi:10.1177/0145445516673998

23.

GA Wells

O'Connell

Peterson

, V, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses (2000).

24.

Oxford Centre for Evidence-Based Medicine, https://www.cebm.ox.ac.uk/resources/levels-of-evidence/ocebm-levels-of-evidence.

25.

Zhang

Coello

Guyatt

, et al. GRADE guidelines: 20. Assessing the certainty of evidence in the importance of outcomes or values and preferences-inconsistency, imprecision, and other domains. J Clin Epidemiol 2019; 111: 83–93. doi:10.1016/j.jclinepi.2018.05.011

26.

Delecroix

Delaval

Dawson

, et al. Workload and injury incidence in elite football academy players. J Sports Sci 2019; 37: 2768–2773. doi:10.1080/02640414.2019.1584954

27.

Raya-González

Nakamura

Castillo

, et al. Determining the Relationship Between Internal Load Markers and Noncontact Injuries in Young Elite Soccer Players. Int J Sports Physiol Perform 2019; 14: 421–425. doi:10.1123/ijspp.2018-0466

28.

Mandorino

Figueiredo

Cima

, et al. Predictive Analytic Techniques to Identify Hidden Relationships between Training Load, Fatigue and Muscle Strains in Young Soccer Players. Sports (Basel) 2021: 10.

29.

Bacon

Mauger

. Prediction of Overuse Injuries in Professional U18-U21 Footballers Using Metrics of Training Distance and Intensity. J Strength Cond Res 2017; 31: 3067–3076. doi:10.1519/JSC.0000000000001744

30.

Bowen

Gross

Gimpel

, et al. Accumulated workloads and the acute:chronic workload ratio relate to injury risk in elite youth football players. Br J Sports Med 2017; 51: 452–459. doi:10.1136/bjsports-2015-095820

31.

Fuller

Ekstrand

Junge

, et al. Consensus statement on injury definitions and data collection procedures in studies of football (soccer) injuries. Br J Sports Med 2006; 40: 193–201. doi:10.1136/bjsm.2005.025270

32.

Bowen

Gross

Gimpel

, et al. Spikes in acute:chronic workload ratio (ACWR) associated with a 5-7 times greater injury rate in English Premier League football players: a comprehensive 3-year study. Br J Sports Med 2020; 54: 731–738. doi:10.1136/bjsports-2018-099422

33.

Malone

Owen

Newton

, et al. The acute:chonic workload ratio in relation to injury risk in professional soccer. J Sci Med Sport 2017; 20: 561–565. doi:10.1016/j.jsams.2016.10.014

34.

Gabbett

. The Development and Application of an Injury Prediction Model for Noncontact, Soft-Tissue Injuries in Elite Collision Sport Athletes. J Strength Cond Res 2010; 24: 2593–2603. doi:10.1519/JSC.0b013e3181f19da4

35.

Cross

Williams

Trewartha

, et al. The Influence of In-Season Training Loads on Injury Risk in Professional Rugby Union. Int J Sports Physiol Perform 2016; 11: 350–355. doi:10.1123/ijspp.2015-0187

36.

Hulin

Gabbett

Lawson

, et al. The acute:chronic workload ratio predicts injury: high chronic workload may decrease injury risk in elite rugby league players. Br J Sports Med 2016; 50: 231–236. doi:10.1136/bjsports-2015-094817

37.

Gabbett

Jenkins

. Relationship between training load and injury in professional rugby league players. J Sci Med Sport 2011; 14: 204–209. doi:10.1016/j.jsams.2010.12.002

38.

Hulin

Gabbett

Blanch

, et al. Spikes in acute workload are associated with increased injury risk in elite cricket fast bowlers. Br J Sports Med 2014; 48: 708–712. doi:10.1136/bjsports-2013-092524

39.

Andrew

Gabbe

Cook

, et al.

Could Targeted Exercise Programmes Prevent Lower Limb Injury in Community Australian Football?

Sports Med 2013; 43: 751–763. doi:10.1007/s40279-013-0056-7

40.

Rogalski

Dawson

Heasman

, et al. Training and game loads and injury risk in elite Australian footballers. J Sci Med Sport 2013; 16: 499–503. doi:10.1016/j.jsams.2012.12.004

41.

Windt

Gabbett

. The workload—injury aetiology model. Br J Sports Med 2017; 51: 1559. doi:10.1136/bjsports-2016-096653

42.

Flockhart

Nilsson

Ekblom

, et al. A Simple Model for Diagnosis of Maladaptations to Exercise Training. Sports Med Open 2022; 8: 136. doi:10.1186/s40798-022-00523-x

43.

Gabbett

. The training—injury prevention paradox: should athletes be training smarterandharder? Br J Sports Med 2016; 50: 273–280. doi:10.1136/bjsports-2015-095788

44.

Shcherbak

Popovych

Kariyev

, et al. Psychological causes of fatigue in football players. J Phys Educ Sport 2023; 23: 2193–2202.

45.

Shcherbak

Lukashov

Koval

, et al. Compensatory phase of fatigue among football players in the organization of motivational structure. J Phys Educ Sport 2024; 24: 1528–1541.

46.

DCd

Afonso

Augusto

, et al. Influence of pre-induced mental fatigue on tactical behaviour and performance among young elite football players. Int J Sport Exerc Psychol 2023; 21: 917–929. doi:10.1080/1612197X.2022.2084765

47.

Noor

McCall

Jones

, et al. Perceived load, fatigue and recovery responses during congested and non-congested micro-cycles in international football tournaments. J Sci Med Sport 2021; 24: 1278–1283. doi:10.1016/j.jsams.2021.07.001

48.

Delaval

Abaïdia

A-E

Delecroix

, et al. Recovery during a congested schedule and injury in professional football. Int J Sports Physiol Perform 2022; 17: 1399–1406. doi:10.1123/ijspp.2021-0504

49.

Erikstad

Haugen

Høigaard

. Positive environments in youth football: Perceived justice and coach feedback as predictors of athletes’ needs satisfaction. German Journal of Exercise and Sport Research 2018; 48: 263–270. doi:10.1007/s12662-018-0516-1

50.

Kasai

Parfitt

Tarca

, et al. The use of ratings of perceived exertion in children and adolescents: a scoping review. Sports Med 2021; 51: 33–50. doi:10.1007/s40279-020-01374-w

51.

Monasterio

Bidaurrazaga-Letona

Larruskain

, et al. Relative skeletal maturity status affects injury burden in U14 elite academy football players. Scand J Med Sci Sports 2022; 32: 1400–1409. doi:10.1111/sms.14204

52.

Johnson

Williams

Bradley

, et al. Growing pains: Maturity associated variation in injury risk in academy football. Eur J Sport Sci 2020; 20: 544–552. doi:10.1080/17461391.2019.1633416

53.

McNaughton

Materne

. Skeletal Maturation is Associated with Injury-Risk in Youth Elite 1 Soccer Players: A 4-Season Prospective Study with Survival 2 Analysis. Orthop J Sports Med 2021: 9.

54.

Le Gall

Carling

Reilly

. Biological maturity and injury in elite youth football. Scand J Med Sci Sports 2007; 17: 564–572. doi:10.1111/j.1600-0838.2006.00594.x

55.

Johnson

Doherty

Freemont

. Investigation of growth, development, and factors associated with injury in elite schoolboy footballers: prospective study. Br Med J 2009; 338: b490. doi:10.1136/bmj.b490

56.

Wik

Chamari

Tabben

, et al. Exploring growth, maturity, and age as injury risk factors in high-level youth football. Sports Med Int Open 2024: 8.

57.

Myer

GDFAD

. Exercise is sports medicine in youth: Integrative neuromuscular training to optimize motor development and reduce risk of sports related injury. Kronos 2011; 10: 01–02. doi:10.64197/Kronos.10.01-02.675

58.

Lauersen

Andersen

. Strength training as superior, dose-dependent and safe prevention of acute and overuse sports injuries: a systematic review, qualitative analysis and meta-analysis. Br J Sports Med 2018; 52: 1557–1563. doi:10.1136/bjsports-2018-099078

59.

Blaker

Zaki

Little

, et al. Long-term Effect of a Single Subcritical Knee Injury: Increasing the Risk of Anterior Cruciate Ligament Rupture and Osteoarthritis. Am J Sports Med 2021; 49: 391–403. doi:10.1177/0363546520977505

60.

Ortega-Prados

Gonzalez-Sanchez

Galan-Mercant

. Innovative Rehabilitation of an Anterior Cruciate Ligament Tear in a Football Player: Muscle Chain Approach-A Case Study. J Clin Med 2025: 14.

61.

McBurnie

Dos’ Santos

. Multidirectional speed in youth soccer players: theoretical underpinnings. Strength & Conditioning Journal 2022; 44: 15–33. doi:10.1519/SSC.0000000000000658

62.

Stanczak

Bialy

Hagner-Derengowska

. Ligament Cell Biology: Effect of Mechanical Loading. Cell Physiol Biochem 2025; 59: 252–295. doi:10.33594/000000773

63.

Nedelec

McCall

Carling

, et al. Recovery in soccer: part I - post-match fatigue and time course of recovery. Sports Med 2012; 42: 997–1015.

64.

Dupont

Nedelec

McCall

, et al. Effect of 2 soccer matches in a week on physical performance and injury rate. Am J Sports Med 2010; 38: 1752–1758. doi:10.1177/0363546510361236