Skip to main content
Sports Medicine - Open
Download PDF

Monitoring the Athlete Match Response: Can External Load Variables Predict Post-match Acute and Residual Fatigue in Soccer? A Systematic Review with Meta-analysis

Sports Medicine - Open volume 5, Article number: 48 (2019) Cite this article

Abstract

Background

Monitoring athletes’ external load during a soccer match may be useful to predict post-match acute and residual fatigue. This estimation would allow individual adjustments to training programs to minimize injury risk, improve well-being, and restore players’ physical performance and inform the recovery process.

Methods

Using a systematic review and meta-analysis of the literature, the aim is to determine which monitoring variables would be the strongest predictors of acute (immediately) and residual (up to 72 h) fatigue states in soccer. PubMed, SPORTDiscus, and Web of Science databases were searched (until September 2018). Studies concurrently examining soccer match-related external load metrics and subjective and/or objective measures were selected to determine pooled correlations (\( \overline{r} \)) with confidence intervals (CI). The quality and strength of the findings of each study were evaluated to identify overall levels of evidence.

Results

Eleven studies were included (n = 165 athletes). Acute (\( \overline{r} \) = 0.67; 95% CI = [0.40, 0.94]) and residual (24 h post-match, \( \overline{r} \) = 0.54; 95% CI = [0.35, 0.65]) changes in muscle damage markers and countermovement jump peak power output (CMJPPO) were, with moderate to strong evidence, largely correlated with running distance above 5.5 m s−1. No other external load metric was largely correlated with both biochemical and neuromuscular markers. For every 100-m run above 5.5 m·s−1, CK activity measured 24 h post-match increased by 30% and CMJPPO decreased by 0.5%. Conversely, the total distance covered did not present any evidence of a clear relationship with any fatigue-related marker at any time-point.

Conclusions

Running distance above 5.5 m·s−1 represents the most sensitive monitoring variable characterizing biochemical and neuromuscular responses, at least when assessed during the initial 24 h (not at 48 h/72 h) post-match recovery period. In addition, total distance covered is not sensitive enough to inform decision-making during the fatigue monitoring process.

Key Points

  • The running distance covered above 5.5 m·s−1 represents the most sensitive monitoring variable estimating post-match (up to + 24 h but not at 48–72 h) changes in biochemical and neuromuscular responses.

  • Total distance covered may represent the less sensitive variable to monitor.

  • For every 100-m run above 5.5 m·s−1 during match-play, creatine kinase activity measured 24 h post-match may increase by 30% and CMJ peak power output decrease by 0.5%.

Background

Soccer is considered a high-intensity intermittent sport with an unprecedented increase (up to 50%) in high-impulsive actions (e.g., number of high accelerations, sprint distance covered) occurring during match-play reported over the last decade [1]. Monitoring players’ responses (e.g., physiological and perceptual) to a soccer match is paramount to prescribe the optimal training dose at an individual level, minimize injuries, and restore physical performance for subsequent training and competition [2, 3]. During the past decade, there has been a substantial development of computer-aided tracking technology (e.g., multiple camera semi-automatic systems) for the examination of players’ external load [4] (i.e., activity performed such as total distance covered or the number of accelerations [5]) during training and match-play. These sophisticated systems are now capable of providing detailed analysis of external load demands, which allows individualized performance profiling of players to tailor training programs [4]. The emergence of manufacturer-specific algorithms has prompted the development of some recent parameters (e.g., player load), expressed in absolute or relative terms, yet with a lack of validity and reliability for many of them such as metabolic power and its derivatives [6, 7]. Despite substantial technological advancements, there is a lack of consensus for selecting the most appropriate parameters for quantifying the short-term dose-response relationship and precisely informing on the “stress” experience by each individual player in elite soccer [8].

Soccer match-play is a stressor for various physiological (e.g., musculoskeletal, immunological, metabolic) regulatory systems [9, 10]. This stress results in acute (i.e., less than 3 h post-match) and residual (still evident up to 72 h post-match) fatigue-induced impairments commonly characterized by neuro-mechanical alterations (e.g., decrease in maximal force production capacity) [10,11,12], perturbations in the biochemical milieu (e.g., increase in creatine kinase levels) [10, 13, 14] and in the psychometric state [10, 15]. While several factors (e.g., genotype and phenotype) [16] likely influence internal load experienced by each individual player, specific external load metrics (e.g., acceleration variables) may estimate match-related players’ fatigue status. It is thought that locomotor activities requiring repeated eccentric muscle contractions (e.g., acceleration and deceleration patterns, high-speed running distance) [17] can explain the aforementioned metabolic, physical, and psychometric disturbances [13, 15, 18, 19] and, in turn, the potential injury causation [19, 20]. However, the relationship between match external load metrics and markers of post-soccer-match fatigue remains unclear with conflicting results in the literature. For example, the extent of acute muscle damage was correlated with high-intensity (HI) running distance (> 4 m s−1) with either small (r = 0.24) [21] or very large magnitudes (r = 0.92 )[22]. Similar differences in magnitude exist between the extent of residual muscle damage (i.e., 48 h following the match) and high decelerations (r = 0.19 and 0.71) [12, 23]. Therefore, the lack of consensus regarding the effectiveness/preferred external load metrics to monitor players’ physiological and biochemical responses to a soccer match implies a need for a systematic review with meta-analysis. This may identify the most sensitive monitoring variables associated with post-match acute and residual fatigue-related markers. This information may allow informed decisions from a fatigue monitoring standpoint as well from a return to play and performance management perspective.

Therefore, using a systematic review and meta-analysis of the literature, the aim is to determine which external load metrics during a soccer match-play more effectively reflect the acute and residual changes in post-match muscle damage and neuromuscular and perceptual responses.

Methods

Literature Search Strategy

The systematic review with meta-analysis was conducted in accordance with the recommendations defined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) and the Population-Intervention-Comparators-Outcomes-Study design (PICOS) approach [24]. The literature search was computerized using PubMed, SPORTDiscus, and Web of Science databases, until the end of September 2018. The complete Boolean search strategy is presented in Table 1. No sex restriction was imposed during the search stage. The reference lists of all articles were examined to identify further eligible studies. Papers published in the epub ahead of print within the abovementioned time frame were also considered.

Table 1 Database search strategy
Full size table

The PICOS approach of this investigation can be detailed as follows: Population: soccer players. Intervention: official or friendly soccer match without extra time. Comparators: players’ soccer match-related external load metrics (e.g., players’ running distance above speed thresholds). Outcomes variables: the dependent variables are the acute (i.e., immediately) and residual (1, 2, and 3 days following the match) changes in biochemical, neuromuscular, and perceptual measures [12, 25, 26]. Study design: observational studies with a before-after intervention (i.e., a soccer match).

Study Selection and Quality Assessment

A second phase consisted of applying selected inclusion criteria. These inclusion criteria were as follows:

  1. 1.

    The study was an original research, published in the English language in a peer-review journal.

  2. 2.

    The study population was soccer players.

  3. 3.

    The intervention was an on-field soccer match.

  4. 4.

    The time-motion analysis of locomotor-related activities was reported.

  5. 5.

    Measures of post-match muscular performance or markers of muscle damage or psychometric state were presented.

  6. 6.

    A correlation coefficient reflecting the relationship between one or more external load metrics and post-match fatigue-related markers was reported, or information needed to compute this coefficient was mentioned or available on a supplement file or was obtained from the author(s) of the study.

Attempts were made to contact the authors of the selected articles to request missing data. All authors were given 3 weeks to provide that data. After this period, the studies were assessed for risk of bias using an adapted version of a published scoring system [27]. Ten criteria were determined using the National Heart Blood Institute (NIH) guidelines for qualitative evaluation of observational cohort and cross-sectional studies and before-after (pre-post) studies with no control group. In addition, other versions of currently established scales used in sports sciences (e.g., Delphi and PEDro Scale, Newcastle – Ottawa quality assessment scale, Downs and Black) were considered. The quality assessment was based on the reporting of study methods and results with answer categories being “yes,” “partial,” and “no” (Table 2).

Table 2 Quality assessment criteria
Full size table

Summary scores (ranging from 0 to 1) were calculated as follows:

Summary score = [(number of ‘yes’ × 2) + (number of ‘partial’ × 1)] / (number of criteria × 2) (1)

Studies were then classified as high (≥ 0.75), moderate (0.50–0.75), or poor methodological quality (< 0.50) [27].

Independent Variables

The independent variables consisted of external load metrics related to soccer match-play. They are presently described by Gray’s classification [2] using three distinct levels:

  • Level 1: Typical distances covered in different running speeds

  • Level 2: All events related to changes in running speed: accelerations, decelerations, and changes of directions

  • Level 3: All events derived from the inertial sensors/accelerometers such as impacts above gravitational force thresholds

However, in each individual selected study, external load variables may have been presented with specific speed thresholds and, in turn, defined differently. Sprinting pattern, for example, has been defined as running speed above different thresholds: (i) 5 m s−1 [22], (ii) 5.5 m s−1 [12], (iii) 5.8 m s−1 [23], and (iv) 7 m s−1 [21]. Consequently, the independent variables were grouped by common zones based on thresholds used by practitioners on the field in elite soccer [8]:

  • High-intensity running (HIR): running speed greater than ~ 4 m s−1.

  • Very high-intensity running (VHIR): running speed greater than 5 to 5.5 m s−1

  • Sprint: running speed greater than 7 m s−1.

  • Moderate to high-intensity acceleration: acceleration greater or equal to + 2 m·s−2.

  • High-intensity acceleration: acceleration greater or equal to + 3 m s−2.

  • High-intensity deceleration: a deceleration lower or equal to − 3 m s−2.

  • Moderate- to high-intensity deceleration: a deceleration lower or equal to − 2 m s−2.

  • High-intensity impact: impact greater than 7 G (gravitational force). The impact is the instantaneous rate of acceleration and deceleration in the three axes, measured by the integrated accelerometer.

  • High change of direction: change of direction with a high-intensity deceleration.

Dependent Variables

The dependent variables extracted from the selected studies were systematically reviewed and grouped into three categories: biochemical, neuromuscular, and perceptual measures. Changes in the biochemical milieu were assessed through endocrine, immunological, and muscle damage markers (Table 3). Endocrine alterations consisted of changes in testosterone and cortisol concentrations. Changes in immunological markers were assessed by leucocytes counts. Changes in muscle damage markers consisted of measures of intracellular enzyme activity (creatine kinase and lactate dehydrogenase, CK and LDH, respectively) and in circulating concentrations of Myoglobin (Mb). The neuromuscular function was assessed by (i) maximal voluntary contractions using isokinetic dynamometers to measure several force-related variables (e.g., peak torque achieved by different lower-limbs muscle groups under maximal isometric; MVIC), concentric and eccentric contractions, (ii) vertical jump performance using force plates and/or optical timing systems (e.g., jump peak power output during a countermovement, CMJPPO). The perceptual responses were mainly based on lower limb delayed onset of muscle soreness (DOMS), rate of perceived exertion (RPE), the perceived recovery (TQR), and the brief assessment of mood (BAM+) measures. DOMS was assessed with visual analog scales in response or not to a “conditioning” stimulus (e.g., squatting). Other scales were used to quantify RPE, TQR, and BAM+.

Table 3 Characteristics of the selected studies
Full size table

Analysis and Interpretation of Results

To determine the relationship between match-related external load variables and post-match fatigue-related measurements, each relationship from an individual study was rated according to its direction (positive, negative, no relationship) and its magnitude as determined from reported correlations. The criteria adopted to categorize magnitudes of correlations (r) were as follows: ≤ 0.1, trivial; > 0.1–0.3, small; > 0.3–0.5, moderate; > 0.5–0.7, large; > 0.7–0.9, very large; and > 0.9–1.0, almost perfect [34]. Individual relationships were then summed and rated according to the predetermined levels of evidence adapted from Van Tulder et al. [35] recommendations:

➢ Strong evidence: consistently identified in two or more studies, which presented low heterogeneity (I2 < 30%) and including a minimum of two high-quality studies.

➢ Moderate evidence: consistently identified in two or more studies, which presented low heterogeneity (I2 < 30%) and including at least one high-quality study.

➢ Limited evidence: identified in one high-quality study, or multiple low- to moderate-quality studies that may not present low heterogeneity (I2 < 30%).

➢ Conflicting evidence: inconsistency in two or more studies where half of the studies are in agreement and the other half conflicting.

➢ No evidence: pooled results that are insignificant and derived from multiple moderately to substantially heterogeneous studies (I2 > 30%).

“Inconsistency” refers to a lack of similarity for correlation coefficients across studies. Study results are considered consistent when direction, magnitude, and statistical significance are sufficiently similar to lead to the same conclusions [36]. Consistency in direction is defined as 75% or more of the studies showing either a positive or negative correlation. Consistency in magnitude is defined as 75% or more of the studies showing an important or unimportant relationship [36]. Heterogeneity between studies was assessed using the I2 statistic, where an I2 of 30% or less is considered to indicate low heterogeneity and the cutoffs of 30% < I2 < 50% and I2 > 50% are indicative of moderate and substantial heterogeneity, respectively [37]. I2 describes the percentage of total variation across studies that is due to heterogeneity rather than chance and seeks to determine whether there are genuine differences underlying the results of the studies (heterogeneity), or whether the variation in findings is compatible with chance alone (homogeneity) [37]. Heterogeneity between studies was also assessed by the chi-squared test [36].

The meta-analysis was processed in two consecutive phases. Some studies investigating the changes in muscle damage determined the activity of different serum skeletal muscle proteins (e.g., CK and LDH). As various markers are believed to provide a composite picture of muscle damage status, using more than one marker has been recommended [38]. Consequently, the correlation coefficients, associating two muscle damage markers (e.g., CK and LDH) with identical external load metric (e.g., HI running distance) were combined in this first step to obtain a single within-study correlation coefficient. The specific relationships (e.g., HI running distance vs. muscle damage markers) assessed in several selected studies were then meta-analyzed between studies during the second phase.

The meta-analytic procedure was conducted with StatsDirect software (v 2.8.0, StatsDirect, Cheshire, UK) package allowing to calculate pooled correlation coefficients (\( \overline{r} \)) with three different methods: (i) Hedges and Olkins random-effects method [39], (ii) Hedges and Olkins fixed-effects method and, (iii) Hunter and Schmidt random-effects method [40]. The latter method systematically presented equivalent or the lowest pooled correlation coefficient. Therefore, to minimize overestimation bias, it was decided to consider the results from Hunter and Schmidt method only (Tables 4 and 5). Moreover, for either a small number of studies (less than 30) or a heterogeneous set of studies, the least biased estimate of the true population correlation is believed to be provided by Hunter and Schmidt method [41]. Pooled correlation coefficients are presented with 95% of confidence limits/intervals (CL/CI). Finally, once the strongest predictor has been determined, the studies investigating this external variable were selected and the authors were contacted to obtain individual data. This allowed us to provide a relationship between this predictor and fatigue-related markers (percentage change in CMJPPO and CK, Fig. 2).

Table 4 Summary of findings from meta-analysis describing the relationship between external load metrics of level 1 [3] and fatigue-related markers
Full size table
Table 5 Summary of findings from meta-analysis describing the relationship between external load metrics (levels 2 and 3) [3] and fatigue-related markers
Full size table

Results

Study and Data Characteristics

The flow chart of the search and selection process is presented in Fig. 1. In summary, the searches identified 1456 relevant articles including all reference lists. After critically analyzing the titles and abstracts, the total number of relevant articles was reduced to 551. Once applying the selection criteria, eleven cohort studies were selected (Fig. 1) [12, 22, 23, 25, 36-42] and data were extracted for meta-analysis. The total number of players was 165 with 89% belonging to the elite level and only male soccer players were finally represented (Table 3). The average methodological quality of the included articles was 0.70 ± 0.12 (mean ± standard deviation) out of 1, ranging from 0.40 to 0.85, with 4 articles considered of a high (≥ 0.75) methodological quality (Table 3).

Fig. 1
figure 1

PRISMA flow chart

Full size image

Eight main external load variables with 10 additional derivatives were used in the selected studies as follows:

  • Level 1: Total distance (TD, 8 studies), HIR (7 studies), VHIR (7 studies), sprints (3 studies)

  • Level 2: Accelerations (5 studies) and decelerations (5 studies), high change of directions (1 study).

  • Level 3: High impacts (2 studies)

The most common group of dependent variables measured in the selected studies was the muscle damage biochemical markers (8 studies). The other dependent variables represented in at least two studies were CMJ peak power output (CMJPPO, 4 studies), MVIC (2 studies) and DOMS (2 studies)

These variables were measured within the first 3 h (Post; 6 studies), one (G + 24H 7 studies), two (G + 48H; 8 studies) and three days (G + 72H; 3 studies) post-match.

Total Distance

In relation to absolute TD, muscle damage markers and CMJPPO were assessed in at least two studies (n = 159 match observations, Table 4). Pooled data showed limited evidence of small (i.e., G + 48H) to moderate (Post and G + 24H) correlations between TD and post-match muscle damage markers (Table 4). The relationships between TD and CMJPPO were rated as trivial (i.e., G + 24H) to small (i.e., G + 48H). The correlation between relative TD (i.e., TD/min) and CMJPPO was rated as trivial, similarly to the correlation between relative TD and muscle damage markers (Table 4).

High-Intensity Running (> 4 m s−1):

In relation to HIR, muscle damage markers, DOMS and MVIC were assessed in at least two studies (n = 93 match observations). Pooled data showed strong evidence of a large correlation between HIR and muscle damage at Post (\( \overline{r} \) = 0.59; 95% CI = [0.35, 0.84]). At G + 24H and G + 48H, the magnitude of this relationship became moderate with limited evidence (Table 4). Between HIR and MVIC, there was limited evidence of a negative and large correlation at G + 24H and positively moderate correlations at G + 48H and G + 72H. The relationship between HIR and DOMS was rated as trivial at all time points (Table 4).

Very High-Intensity Running (> 5.5 m s−1):

In relation to absolute VHIR, muscle damage markers and CMJPPO were assessed in at least two studies. Pooled data (n = 146 match observations) showed moderate to strong evidence of large correlations between VHIR and muscle damage at Post (\( \overline{r} \) = 0.67; 95% CI = [0.40, 0.94]) and at G + 24H (\( \overline{r} \) = 0.54; 95% CI = [0.35, 0.65]). At G + 48H, the magnitude of this relationship became moderate with limited evidence. Between VHIR and CMJPPO, there was strong evidence of a negative and large pooled correlation (ṝ = − 0.52; 95% CI = [− 0.64, − 0.40]) at G + 24H. This relationship was rated as moderate at G + 48H with conflicted evidence (Table 4).

The correlations between relative VHIR (per unit of time) and muscle damage markers and CMJPPO output were rated as small, with limited evidence. Relative VHIR was moderately (G + 24H) to largely (G + 48H) correlated with the brief assessment of mood (1 study, n = 35), with limited evidence (Table 4).

Sprint Running

Muscle damage markers and MVIC were assessed with sprint running performance during the match. Data showed moderate to large correlations between sprint running distance and these two fatigue-related markers from G + 24H to G + 72H, with limited evidence (Table 4).

Acceleration Variables

Acceleration variables were related to muscle damage markers, CMJPPO, MVIC, and DOMS. Pooled data showed strong (i.e., at G + 24H) and limited (at Post and G + 48H) evidence of moderate correlations between high accelerations and muscle damage markers (Table 5).

There was moderate evidence of a large and negative correlation (\( \overline{r} \) = − 0.61; 95% CI = [− 0.82, − 0.42]) at G + 24H between CMJPPO and high accelerations (Table 5). At G + 48H, this relationship was rated as moderate with limited evidence. There was limited evidence of moderate (i.e., G + 72H) to very large (G + 24H and G + 48H) correlations between high accelerations and MVIC of the dominant leg (Table 5). DOMS presented trivial to small correlations with high accelerations regardless of the time points.

Deceleration Variables

Deceleration variables were related to muscle damage markers, CMJPPO and DOMS. Pooled data (3 studies, n = 70 match observations) showed limited (at Post and G + 48H) and moderate (i.e., at G + 24H) evidence of positive and moderate correlations between high decelerations and muscle damage markers. There was limited evidence of small (G + 48H) to large (G + 24H) correlations between high decelerations and CMJPPO. DOMS presented trivial correlations with high decelerations regardless of the time points (Table 5).

High Impacts

In relation to the number of high impacts, muscle damage markers and CMJPPO were assessed. Pooled data (2 studies, n = 40 match observations) showed conflicted evidence of small correlations between high impact and muscle damage markers at G + 24H and G + 48H. There was also conflicted evidence for the moderate correlation between high impacts and CMJPPO at G + 24H (Table 5).

Regression Analysis

The relationship between VHIR and fatigue-related markers (percentage change in CMJPPO and CK, Fig. 2) could be represented by a linear function (3 studies, 2 of high quality, 11 matches, n = 87 player-match observations) (Fig. 2).

Fig. 2
figure 2

Relationship between match-related running distance above 5.5 m s−1 and post-match changes in CK (upper panel) and CMJ peak power output (lower panel). CK: creatine kinase; CMJ: countermovement jump

Full size image

Discussion

The aim of this systematic review with meta-analysis was to examine whether external load metrics during soccer match-play reflect acute and residual changes in post-match biochemical, neuromuscular, and perceptual responses. The main findings were as follows: (1) the match-related running distance above 5.5 m s−1 was identified as the only monitoring variable largely correlated with both biochemical and neuromuscular markers, (2) practically, at G + 24H, for each 100 m of VHI running distance covered, CK activity would increase by 30% and CMJPPO would decrease by 0.5%.

Among the biochemical variables systematically reviewed, there was strong evidence of two large pooled correlations (r = 0.54 to 0.67) between HIR or VHIR and changes in muscle damage markers (e.g., CK) at Post and G + 24H, respectively. These changes were also moderately correlated with high accelerations at G + 24H. The weaker correlation may be due to the restricted number of selected studies examining acceleration variables. Over fifty listed variables, high accelerations (rank four) and VHIR (rank two) were reported as the monitored variables that were the most used by elite soccer team practitioners to quantify training and match loads [8]. From an injury prevention standpoint, hamstring strain injuries predominantly occur during VHIR and high acceleration [20, 42, 43]. Additionally, injury to the hamstrings muscle group is the most commonly reported injury in male soccer players [42, 44, 45]. Therefore, in the selected studies, the MVIC evaluation was focused on the hamstrings muscles (i.e., at G + 24H and G + 48H) [21, 25]. Indeed, the injury mechanism may be due to the repeated and excessive lengthening demands placed on the hamstrings during the high eccentric force contractions involved during these specific efforts (e.g., VHIR) [43, 46]. Furthermore, as also supported by our results (Tables 4 and 5), exercise-induced muscle damage is more typically associated with the performance of fast eccentric muscle actions (e.g., high accelerations) than to the execution of lower velocity-based eccentric muscle actions (e.g., moderate accelerations) [47]. These fast eccentric muscle actions would exacerbate the mechanical stress characterized by cellular and subcellular structural disturbances, such as the focal disruption of the myofibers and cytoskeleton resulting in z-disk streaming [48, 49]. From our results, VHIR may represent the most sensitive external load metric to monitor changes in acute (immediately post-match) and residual (i.e., at G + 24H only) muscle damage status. Interestingly, while cumulative exposure or large week-to-week changes in VHIR may represent a substantial increase in injury risk [50,51,52], a high but gradual exposure to VHIR may confer additional protection to spikes in workload for soccer players [53].

While practitioners have reported the total distance covered as the most commonly tracked variable [8], our results show no evidence of any significant relationship with changes in muscle damage markers. In contrast to VHIR, low to moderate running intensities are thought to induce a lower magnitude of muscle damage [54], without significant perturbation in the membrane permeability [38, 55]. As a large proportion of the match-related total distance is covered at low intensity (e.g., walking, jogging) [56], this may explain an absence of a relationship with changes in muscle damage markers. Additionally, total distance has been largely correlated with salivary cortisol concentrations at G + 24H and G + 48H in only one study (Tables 4 and 5) [57]. These relationships with residual endocrine responses would need to be confirmed by future investigations.

Our results displayed a large correlation (strong evidence) between VHIR and change in CMJPPO, yet at G + 24H only. This change in CMJPPO was largely correlated with high accelerations at the same time-point but with moderate evidence due to the restricted number of studies considering this metric. These results highlight that the reduction in CMJPPO at G + 24H, in addition to the increase in muscle damage markers, may be related to the repetitive stress (i.e., mechanical) sustained by the neuromuscular system throughout the frequent VHIR and high acceleration actions [58]. The amount of eccentric-related actions likely characterizes this mechanical stress potentially inducing changes in joint sequencing (e.g., increase in eccentric phase duration), in a motor pattern used for performance (adjustment of neuromuscular recruitment strategies) [59, 60] and selective damage of type II muscle fibers [61]. Accordingly, it has been recently determined that an increase by 0.6 km in VHIR, as a specific match-related intense activity, may induce a decrement in CMJPPO by slightly more than the smallest worthwhile change (i.e., 1.0 W/kg) [62]. Consequently, the present meta-analytic impairment in CMJPPO may reflect the power-based load characterized by VHIR during soccer matches. Conversely, total distance covered, including a low proportion of high-intensity eccentric actions, was not related to post-match changes in CMJPPO.

Our results do not demonstrate evidence for significant relationships between any tracking variable and changes in post-match perceptual responses. While self-report, perceptual measures such as questionnaires are simple and efficient methods [63] to assess match-related load, only a few studies have investigated such a relationship [21, 25, 32]. Today, a large majority of elite soccer clubs collect self-report measures (e.g., perceived recovery such as TQR) daily to monitor players’ training-induced psychometric and wellbeing states [8]. However, they do not seem to use this monitoring tool as frequently for reflecting fatigue associated with performing home and away matches. This seems surprising since the match load represents the main determinant of a high weekly training load during the competitive season [64]. Particularly, subjective measures of mood disturbance, perceived stress and recovery may reflect acute and chronic loads with superior sensitivity and consistency in reference to more objective measures (e.g., muscle damage markers, CMJPPO) [65]. In our study, the lack of an association between subjective and objective measures provides support for the complementary inclusion of both measurements.

Practically, at G + 24H, for every 100 m of VHIR during a soccer match, CK activity would increase by 30% and CMJPPO would decrease by 0.5% (Fig. 2). Our meta-analytic results show, for the first time, that VHIR appears as the strongest predictor of alterations in muscle damage and peak power output since it was the only tracking variable largely related to these biochemical and neuromuscular fatigue-related makers. The systematic and meta-analysis of the current literature suggests that the running distance covered above 5.5 m s−1, may explain up to ~50% of the biochemical and neuromuscular post-match states. To date, other significant relationships between external load and post-match monitoring variables have yet to be determined for residual fatigue status especially at G + 48H and G + 72H.

The strongest correlations between external load metrics (i.e., VHIR) and post-match muscle damage or CMJPPO have been reported for acute fatigue (i.e., at Post) and at G + 24H only, when magnitude of changes in most of the fatigue-related markers are at their greatest (i.e., peak changes) [10, 13, 55]. There was no evidence of any significant relationships at G + 48H and G + 72H. The restricted number of studies (10 studies) examining the multi-factorial nature of fatigue incurred post-soccer match limits the strength of some of our conclusions. Furthermore, given the complexity of fatigue causing mechanisms and those responsible for its reversal, biochemical and neuromuscular changes induced by a match exhibit considerable variability [10, 55, 66]. Regarding muscle damage markers, and more specifically CK, part of the variability may be attributed to their rate of clearance from the circulation [67]. In addition, the players’ aerobic fitness level [21] and specific neuromuscular characteristics (e.g., lower body strength) [68,69,70,71] have been associated with match-related activity and fatigue development. Additionally, players’ degree of familiarization to eccentric training and actions (e.g., high decelerations) are believed to impact their recovery rate [72]. All these between-player discrepancies may explain weak correlations between some external load metrics (e.g., VHIR and/or acceleration patterns) and fatigue-related markers at G + 48H and G + 72H.

One limitation of this present review with meta-analysis is the between-study differences in definitions regarding the main players’ external load metrics. Indeed, while most of the selected studies tracked match-related sprinting efforts, only two of them applied a speed threshold above 25 km h−1, leading to a weaker strength of findings. As previously highlighted by others, gathering external load data from different tracking technologies and different products may induce some flaws (e.g., no agreed filtering methods, sampling rates, and data-processing algorithms across studies). As example, there can be substantial differences between products, particularly for threshold-based acceleration and deceleration variables [73, 74]. Finally, a relatively small number of studies was included in our analysis. Remarkably, all selected studies have been published over the last 7 years due to the recent technology development that allows collecting locomotor-related activities during competition. Additionally, these technologies are either mainly restricted to home matches (i.e., semi-automatic cameras or radio-frequency systems) or not allowed during official matches (i.e., GPS), while the situation is now evolving. Considering these limitations, further investigations would be needed to ascertain the strength of evidence regarding sprint (> 7 m s−1), acceleration and deceleration variables. Moreover, further studies should investigate the use of individualized external load thresholds (based on players’ physiological characteristics such as maximal aerobic speed and sprinting speed )[75, 76] can more efficiently reflect the acute and residual changes in post-match muscle damage, neuromuscular and perceptual responses.

Conclusions

While total distance is likely the most commonly monitored variable in elite soccer, it is not associated with changes in any post-game fatigue-related markers. A unique finding of our meta-analysis, however, was the observation of large correlations between match-related VHIR (above 5.5 m s−1) distance and both acute (Post) and residual (G + 24H but not G + 48H/G + 72H) changes in fatigue-related markers. Indeed, VHIR was identified as the only tracking variable that correlated largely with both biochemical and neuromuscular markers. Practically, at G + 24H, for every 100 m of VHI running distance covered, CK activity would increase by 30% and CMJPPO would decrease by 0.5%. VHIR, at least when assessed during the first 24 h of the recovery process, represents the most sensitive tracking variable to depict biochemical and neuromuscular loads resulting from soccer match-play.

Availability of Data and Materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Acc:

Acceleration

BAM+:

Brief assessment of the mood

CK:

Creatine kinase

CMJ:

Countermovement jump

CMJPPO :

Countermovement jump peak power output

conc:

Concentric

Decel:

Deceleration

DOMS:

Delayed onset muscle soreness

ecc:

Eccentric

GPS:

Global positioning system

HIR:

High-intensity running distance

KE:

Knee extension

KF:

Knee flexion

G + 24H:

Measured 24 h the match

G + 48H:

Measured 48 h after the match

G + 72H:

Measured 72 h after the match

Mod:

Moderate intensity running

MVIC:

Maximal voluntary isometric contraction

Post:

Measured after the match

PT:

Peak torque

TD:

Total distance covered

TQR:

Total quality of recovery

VHIR:

Very high-intensity running distance

References

  1. Barnes C, Archer DT, Hogg B, Bush M, Bradley PS. The evolution of physical and technical performance parameters in the english premier league. Int J Sports Med. 2014;35(13):1095–100.

    Article  CAS  PubMed  Google Scholar 

  2. Buchheit M, Simpson BM. Player Tracking Technology: Half-Full or Half-Empty Glass? Int J Sports Physiol Perform. 2016;14:1–23.

    Google Scholar 

  3. Scott D, Lovell R. Individualisation of speed thresholds does not enhance the dose-response determination in football training. J Sports Sci. 2018;36(13):1523–32.

    Article  PubMed  Google Scholar 

  4. Buchheit M, Allen A, Poon TK, Modonutti M, Gregson W, Di Salvo V. Integrating different tracking systems in football: multiple camera semi-automatic system, local position measurement and GPS technologies. J Sports Sci. 2014 Dec;32(20):1844–57.

    Article  PubMed  Google Scholar 

  5. Wallace LK, Slattery KM, Coutts AJ. A comparison of methods for quantifying training load: relationships between modelled and actual training responses. Eur J Appl Physiol. 2014 Jan;114(1):11–20.

    Article  CAS  PubMed  Google Scholar 

  6. Buchheit M, Manouvrier C, Cassirame J, Morin JB. Monitoring Locomotor Load in Soccer: Is Metabolic Power, Powerful? Int J Sports Med. 2015; Dec;36(14):1149–55.

    Article  CAS  PubMed  Google Scholar 

  7. Hader K, Mendez-Villanueva A, Palazzi D, Ahmaidi S, Buchheit M. Metabolic Power Requirement of Change of Direction Speed in Young Soccer Players: Not All Is What It Seems. PLoS One. 2016;11(3):e0149839.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Akenhead R, Nassis GP. Training Load and Player Monitoring in High-Level Football: Current Practice and Perceptions. Int J Sports Physiol and Perform. 2016;11(5):587–93.

    Article  Google Scholar 

  9. Reilly T, Drust B, Clarke N. Muscle fatigue during football match-play. Sports Med. 2008;38(5):357–67.

    Article  PubMed  Google Scholar 

  10. Silva JR, Rumpf MC, Hertzog M, Castagna C, Farooq A, Girard O, et al. Acute and Residual Soccer Match-Related Fatigue: A Systematic Review and Meta-analysis. Sports Med. 2018; Mar;48(3):539–83.

    Article  CAS  PubMed  Google Scholar 

  11. Hader K, Palazzi D, Buchheit M. Change of direction speed in soccer: How much brake is enough? Kineziologija. 2015;47(1):67–74.

    Google Scholar 

  12. Russell M, Sparkes W, Northeast J, Cook CJ, Bracken RM, Kilduff LP. Relationships between match activities and peak power output and Creatine Kinase responses to professional reserve team soccer match-play. Hum Mov Sci. 2016 Feb;45:96–101.

    Article  CAS  PubMed  Google Scholar 

  13. Silva JR, Ascensao A, Marques F, Seabra A, Rebelo A, Magalhaes J. Neuromuscular function, hormonal and redox status and muscle damage of professional soccer players after a high-level competitive match. Eur J Appl Physiol. 2013;113(9):2193–201.

    Article  CAS  PubMed  Google Scholar 

  14. Silva JR, Rebelo A, Marques F, Pereira L, Seabra A, Ascensao A, et al. Biochemical impact of soccer: an analysis of hormonal, muscle damage, and redox markers during the season. Appl Physiol Nutr Metab. 2014;39(4):432–8.

    Article  CAS  PubMed  Google Scholar 

  15. Hader K, Mendez-Villanueva A, Williams B, Ahmaidi S, Buchheit M. Changes of direction during high-intensity intermittent runs: Neuromuscular and metabolic responses. BMC Sports Sci Med Rehabil. 2014;6(1):2.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Impellizzeri FM, Rampinini E, Marcora SM. Physiological assessment of aerobic training in soccer. J Sports Sci. 2005 Jun;23(6):583–92.

    Article  PubMed  Google Scholar 

  17. Young WB, Hepner J, Robbins DW. Movement demands in Australian rules football as indicators of muscle damage. J Strength Cond Res. 2012;26(2):492–6.

    Article  PubMed  Google Scholar 

  18. Magalhaes J, Rebelo A, Oliveira E, Silva JR, Marques F, Ascensao A. Impact of Loughborough Intermittent Shuttle Test versus soccer match on physiological, biochemical and neuromuscular parameters. Eur J Appl Physiol. 2010;108(1):39–48.

    Article  PubMed  Google Scholar 

  19. Colby MJ, Dawson B, Heasman J, Rogalski B, Gabbett TJ. Accelerometer and GPS-derived running loads and injury risk in elite Australian footballers. J Strength Cond Res. 2014;28(8):2244–52.

    Article  PubMed  Google Scholar 

  20. Duhig S, Shield AJ, Opar D, Gabbett TJ, Ferguson C, Williams M. Effect of high-speed running on hamstring strain injury risk. Br J Sports Med. 2016;50(24):1536–40.

    Article  PubMed  Google Scholar 

  21. Draganidis D, Chatzinikolaou A, Avloniti A, Barbero-Alvarez JC, Mohr M, Malliou P, et al. Recovery kinetics of knee flexor and extensor strength after a football match. PloS One. 2015;10(6):e0128072.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Thorpe R, Sunderland C. Muscle damage, endocrine, and immune marker response to a soccer match. J Strength Cond Res. 2012;26(10):2783–90.

    Article  PubMed  Google Scholar 

  23. de Hoyo M, Cohen DD, Sanudo B, Carrasco L, Alvarez-Mesa A, Del Ojo JJ, et al. Influence of football match time-motion parameters on recovery time course of muscle damage and jump ability. J Sports Sci. 2016;34(14):1363–70.

    Article  PubMed  Google Scholar 

  24. Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Bmj. 2009;339:b2535.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Nedelec M, McCall A, Carling C, Legall F, Berthoin S, Dupont G. The influence of soccer playing actions on the recovery kinetics after a soccer match. J Strength Cond Res. 2014;28(6):1517–23.

    Article  PubMed  Google Scholar 

  26. Russell M, Northeast J, Atkinson G, Shearer DA, Sparkes W, Cook CJ, et al. Between-Match Variability of Peak Power Output and Creatine Kinase Responses to Soccer Match-Play. J Strength Cond Res. 2015;29(8):2079–85.

    Article  PubMed  Google Scholar 

  27. Kmet LM, Lee RC, Cook LS. Standard quality assessment criteria for evaluating primary research papers from a variety of fields; 2004.

    Google Scholar 

  28. Aquino RL, Goncalves L, Vieira LH, Oliveira LP, Alves GF, Santiago PR, et al. Biochemical, physical and tactical analysis of a simulated game in young soccer players. J Sports Med Phys Fitness. 2016;56(12):1554–61.

    PubMed  Google Scholar 

  29. Rampinini E, Bosio A, Ferraresi I, Petruolo A, Morelli A, Sassi A. Match-related fatigue in soccer players. Med Sci Sports Exerc. 2011;43(11):2161–70.

    Article  PubMed  Google Scholar 

  30. Romagnoli M, Sanchis-Gomar F, Alis R, Risso-Ballester J, Bosio A, Graziani RL, et al. Changes in muscle damage, inflammation, and fatigue-related parameters in young elite soccer players after a match. J Sports Med Phys Fitness. 2016; Oct;56(10):1198–205.

    PubMed  Google Scholar 

  31. Scott A, Malone J, Morgans R, Burgess D, Gregson W, Morton J, et al. The relationship between physical match performance an 48-h post-game creatine kinase concentrations in English Premier League soccer players. Int J Sports Sci Coaching. 2016;11(6):846–52.

    Article  Google Scholar 

  32. Shearer DA, Sparkes W, Northeast J, Cunningham DJ, Cook CJ, Kilduff LP. Measuring recovery: An adapted Brief Assessment of Mood (BAM+) compared to biochemical and power output alterations. J Sci Med Sport. 2017;20(5):512–7.

    Article  PubMed  Google Scholar 

  33. Varley I, Lewin R, Needham R, Thorpe R, Burbeary R. Association between match activity variables, measures of fatigue and neuromuscular performance capacity following elite competitive soccer matches. J Human Kin. 2018;60:93–9.

    Article  Google Scholar 

  34. Hopkins WG, Marshall SW, Batterham AM, Hanin J. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc. 2009 Jan;41(1):3–13.

    Article  PubMed  Google Scholar 

  35. van Tulder M, Furlan A, Bombardier C, Bouter L. Editorial Board of the Cochrane Collaboration Back Review G. Updated method guidelines for systematic reviews in the cochrane collaboration back review group. Spine. 2003;28(12):1290–9.

    PubMed  Google Scholar 

  36. Furlan AD, Pennick V, Bombardier C, van Tulder M, Editorial Board CBRG. 2009 updated method guidelines for systematic reviews in the Cochrane Back Review Group. Spine. 2009;34(18):1929–41.

    Article  PubMed  Google Scholar 

  37. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. Bmj. 2003;327(7414):557–60.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Brancaccio P, Lippi G, Maffulli N. Biochemical markers of muscular damage. Clin chem Lab Med : CCLM / FESCC. 2010;48(6):757-67.

  39. Hedges L, Olkin I. Statistical methods for meta-analysis. 1985 ed. London: American Press; 1985.

    Google Scholar 

  40. Hunter JE, Schmidt FL. Methods ofmeta-analysis. In: Correcting Error and Bias in Research Findings. Newbury Park: Sage Publications; 1990.

    Google Scholar 

  41. Field AP. Meta-analysis of correlation coefficients: a Monte Carlo comparison of fixed- and random-effects methods. Psychol Methods. 2001 Jun;6(2):161–80.

    Article  CAS  PubMed  Google Scholar 

  42. Woods C, Hawkins R, Hulse M, Hodson A. The Football Association Medical Research Programme: an audit of injuries in professional football-analysis of preseason injuries. Br J Sports Med. 2002;36(6):436–41 discussion 41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Opar DA, Williams MD, Timmins RG, Hickey J, Duhig SJ, Shield AJ. Eccentric hamstring strength and hamstring injury risk in Australian footballers. Med Sci Sports Exerc. 2015;47(4):857–65.

    Article  PubMed  Google Scholar 

  44. Woods C, Hawkins RD, Maltby S, Hulse M, Thomas A, Hodson A, et al. The Football Association Medical Research Programme: an audit of injuries in professional football--analysis of hamstring injuries. Br J Sports Med. 2004;38(1):36–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Ekstrand J, Walden M, Hagglund M. Hamstring injuries have increased by 4% annually in men's professional football, since 2001: a 13-year longitudinal analysis of the UEFA Elite Club injury study. Br J Sports Med. 2016 Jun;50(12):731–7.

    Article  PubMed  Google Scholar 

  46. Schache AG, Dorn TW, Blanch PD, Brown NA, Pandy MG. Mechanics of the human hamstring muscles during sprinting. Med Sci Sports Exerc. 2012 Apr;44(4):647–58.

    Article  PubMed  Google Scholar 

  47. Chapman D, Newton M, Sacco P, Nosaka K. Greater muscle damage induced by fast versus slow velocity eccentric exercise. Int J Sports Med. 2006;27(8):591–8.

    Article  CAS  PubMed  Google Scholar 

  48. Friden J, Lieber RL. Eccentric exercise-induced injuries to contractile and cytoskeletal muscle fibre components. Acta Physiol Scand. 2001 Mar;171(3):321–6.

    Article  CAS  PubMed  Google Scholar 

  49. Clarkson PM, Sayers SP. Etiology of exercise-induced muscle damage. Canadian J Appl Physiol. 1999;24(3):234–48.

    Article  CAS  Google Scholar 

  50. Bowen L, Gross A, Gimpel M, Li F. Accumulated workloads and the acute-chronic workload ratio relate to injury. Br J Sports Med. 2017;51(5):452–9.

    Article  PubMed  Google Scholar 

  51. Malone S, Owen A, Mendes B, Hughes B, Collins K, Gabbett TJ. High-speed running and sprinting as an injury risk factor in soccer: Can well-developed physical qualities reduce the risk? J Sci Med Sport. 2018;21(3):257–62.

    Article  PubMed  Google Scholar 

  52. Gabbett TJ. Debunking the myths about training load, injury and performance: empirical evidence, hot topics and recommendations for practitioners. Br J Sports Med. 2018;26.

  53. Gabbett TJ, Nassis GP, Oetter E, Pretorius J, Johnston N, Medina D, et al. The athlete monitoring cycle: a practical guide to interpreting and applying training monitoring data. Br J Sports Med. 2017;51(20):1451–2.

    Article  PubMed  Google Scholar 

  54. Nosaka K, Newton M. Difference in the magnitude of muscle damage between maximal and submaximal eccentric loading. J Strength Con Res. 2002;16(2):202–8.

    Google Scholar 

  55. Brancaccio P, Maffulli N, Limongelli FM. Creatine kinase monitoring in sport medicine. Br Med Bull. 2007;81-82:209-30.

    Article  PubMed  CAS  Google Scholar 

  56. Stolen T, Chamari K, Castagna C, Wisloff U. Physiology of soccer: an update. Sports Med. 2005;35(6):501–36.

    Article  PubMed  Google Scholar 

  57. Romagnoli M, Sanchis-Gomar F, Alis R, Risso-Ballester J, Bosio A, Graziani RL, et al. Changes in muscle damage, inflammation, and fatigue-related parameters in young elite soccer players after a match. J Sports Med Phy Fitness. 2016;56(10):1198–205.

    Google Scholar 

  58. Silva JR, Magalhaes JF, Ascensao AA, Oliveira EM, Seabra AF, Rebelo AN. Individual match playing time during the season affects fitness-related parameters of male professional soccer players. J Strength Cond Res. 2011;25(10):2729–39.

    Article  PubMed  Google Scholar 

  59. Kellis E, Kouvelioti V. Agonist versus antagonist muscle fatigue effects on thigh muscle activity and vertical ground reaction during drop landing. J Electromyogr Kinesio. 2009;19(1):55–64.

    Article  Google Scholar 

  60. Rodacki AL, Fowler NE, Bennett SJ. Vertical jump coordination: fatigue effects. Med Sci Sports Exerc. 2002;34(1):105–16.

    Article  PubMed  Google Scholar 

  61. Clarkson PM, Tremblay I. Exercise-induced muscle damage, repair, and adaptation in humans. J Appl Physiol. 1988;65(1):1–6.

    Article  CAS  PubMed  Google Scholar 

  62. Morgans R, Di Michele R, Drust B. Soccer Match-Play Represents an Important Component of the Power Training Stimulus in Premier League Players. Int J Sports Physiol Perform. 2017;19:1–12.

    Google Scholar 

  63. Coutts AJ, Reaburn P. Monitoring changes in rugby league players' perceived stress and recovery during intensified training. Percept Mot Skills. 2008;106(3):904–16.

    Article  PubMed  Google Scholar 

  64. Los Arcos A, Mendez-Villanueva A, Yanci J, Martinez-Santos R. Respiratory and Muscular Perceived Exertion During Official Games in Professional Soccer Players. Int J Sports Physiol and Perform. 2016;11(3):301–4.

    Article  Google Scholar 

  65. Saw AE, Main LC, Gastin PB. Monitoring the athlete training response: subjective self-reported measures trump commonly used objective measures: a systematic review. Br J Sports Med. 2016;50(5):281–91.

    Article  PubMed  Google Scholar 

  66. Lac G, Maso F. Biological markers for the follow-up of athletes throughout the training season. Pathologie-biologie. 2004;52(1):43–9.

    Article  CAS  PubMed  Google Scholar 

  67. Warren GL, O'Farrell L, Rogers KR, Billings KM, Sayers SP, Clarkson PM. CK-MM autoantibodies: prevalence, immune complexes, and effect on CK clearance. Muscle Nerve. 2006;34(3):335–46.

    Article  CAS  PubMed  Google Scholar 

  68. Silva JR, Magalhaes J, Ascensao A, Seabra AF, Rebelo AN. Training status and match activity of professional soccer players throughout a season. J Strength Cond Res. 2013;27(1):20–30.

    Article  PubMed  Google Scholar 

  69. Owen A, Dunlop G, Rouissi M, Chtara M, Paul D, Zouhal H, et al. The relationship between lower-limb strength and match-related muscle damage in elite level professional European soccer players. J Sports Sci. 2015;33(20):2100–5.

    Article  PubMed  Google Scholar 

  70. Silva JR, Nassis GP, Rebelo A. Strength training in soccer with a specific focus on highly trained players. Sports Med - Open. 2015 2015/04/02;2(1):1-27.

  71. Tofari P, Kemp J, Cormack S. A Self-Paced Team Sport Match Simulation Results In Reductions In Voluntary Activation And Modifications To Biological, Perceptual And Performance Measures At Half-Time, And For Up To 96 Hours Post-Match. J Strength Cond Res. 2018;32(12):3552–63.

    PubMed  Google Scholar 

  72. Lavender AP, Nosaka K. A light load eccentric exercise confers protection against a subsequent bout of more demanding eccentric exercise. J Sci Med Sport. 2008;11(3):291–8.

    Article  PubMed  Google Scholar 

  73. Delaney JA, Cummins CJ, Thornton HR, Duthie GM. Importance, Reliability, and Usefulness of Acceleration Measures in Team Sports. J Strength Cond Res. 2018;32(12):3485–93.

    PubMed  Google Scholar 

  74. Thornton HR, Nelson AR, Delaney JA, Serpiello FR, Duthie GM. Interunit Reliability abd Effect of Data-Processing Methods of Global Positioning Systems. Int J Sports Physiol Perform. 2019;14(4):432–8.

    Article  PubMed  Google Scholar 

  75. Abt G, Lovell R. The use of individualized speed and intensity thresholds for determining the distance run at high-intensity in professional soccer. J Sports Sci. 2009;27(9):893–8.

    Article  PubMed  Google Scholar 

  76. Lovell R, Abt G. Individualization of time-motion analysis: a casecohort example. Int J Sports Physiol Perform. 2013;8:456–8.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

Open Access funding provided by the Qatar National Library.

Funding

No sources of funding were used to assist in the preparation of this article.

Author information

Authors and Affiliations

  1. National Sports Medicine Programme, Excellence in Football Project, Aspetar – Qatar Orthopaedic and Sports Medicine Hospital, P.O BOX 29222, Doha, Qatar

    Karim Hader, Maxime Hertzog & Joao R. Silva

  2. Performance Department, Paris Saint-Germain F.C., Saint Germain-en-Laye, France

    Karim Hader

  3. Auckland University of Technology, Sport Performance Research Institute New Zealand, Auckland, New Zealand

    Michael C. Rumpf

  4. Footballscience.net, Roedermark, Germany

    Michael C. Rumpf

  5. A-STEM, College of Engineering, Swansea University, Swansea, UK

    Liam P. Kilduff

  6. Welsh Institute of Performance Science, Sport Wales, Cardiff, UK

    Liam P. Kilduff

  7. Murdoch Applied Sport Science Laboratory, Murdoch University, Perth, Western Australia

    Olivier Girard

  8. Center of Research, Education, Innovation and Intervention in Sport (CIFI2D), Porto, Portugal

    Joao R. Silva

Authors
  1. Karim Hader
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael C. Rumpf
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maxime Hertzog
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liam P. Kilduff
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Olivier Girard
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Joao R. Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

KH and JRS designed the study; KH, JRS, MCR and MH gathered and organized the data; KH and JRS performed the quality assessment of the selected manuscripts and wrote the manuscript; KH analysed the data; KH, MCR, MH, LPK, OG AND JRS assisted in the interpretation of data; KH, MCR, MH, LPK, OG AND JRS critically revised the manuscript; KH, MCR, MH, LPK, OG and JRS—read and approved the final manuscript.

Corresponding author

Correspondence to Joao R. Silva.

Ethics declarations

Ethics Approval and Consent to Participate

Not Applicable.

Consent for Publication

Not applicable.

Competing Interests

The authors, Karim Hader, Michael Rumpf, Maxime Hertzog, Liam Kilduff, Olivier Girard, and Joao Silva, declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hader, K., Rumpf, M.C., Hertzog, M. et al. Monitoring the Athlete Match Response: Can External Load Variables Predict Post-match Acute and Residual Fatigue in Soccer? A Systematic Review with Meta-analysis. Sports Med - Open 5, 48 (2019). https://doi.org/10.1186/s40798-019-0219-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40798-019-0219-7

Keywords

  • External load
  • Time motion analysis
  • Monitoring, Fatigue
  • Recovery
  • Muscle damage
  • Perceptual responses
  • Neuromuscular performance