Abstract
Background
The range values of different training and match intensity measures obtained to define benchmarks in female soccer players are needed. Usually, cohort studies analyse only one team with a relatively small sample size, which brings forth the need for a systematic review to generalise training and match intensity evidence.
Objectives
This review aimed to identify and summarise studies that have examined external and internal training or match intensity monitoring to provide range values for the main measures in female soccer players.
Methods
A systematic review of EBSCO, PubMed, Scielo, Scopus, SPORTDiscus and Web of Science databases was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
Results
From the 2853 studies searched, 44 were analysed in which the following range intervals were found for training: rated perceived exertion (RPE, 1–7 AU), session-RPE (s-RPE, 51–721 AU), total distance (2347–6646 m) and distance >19.4 km·h−1 (9–543 m). For matches, the range values were s-RPE (240–893 AU), total distance (5480–10581 m), distance ≥14 km·h−1 (543–2520 m), ≥ 18 km·h−1 (96–1680 m), number of accelerations (49–240) and deceleration (21–85) and player load (848–1096 AU).
Conclusions
This study provides range values of s-RPE, RPE, TRIMP, total distance and distance >19.4 km·h−1 regarding training; range intervals of s-RPE, heart rate average and maximum, total distance, distance ≥ 14 km·h−1, ≥ 18 km·h−1, ACC and DEC (> 2 ms−2) regarding matches for professional female players that can be used by coaches, practitioners or researchers to achieve similar training and competitive levels.
Introduction
Monitoring training/match intensity in soccer players is currently part of the daily process of sports scientists and strength and conditioning coaches. 1 Coaches and staff perceive intensity monitoring as worthwhile, regardless of the instruments and practices used. 2 One reason for this is that controlling intensity can help coaches and their staff individualise training stimuli, manage recovery strategies and mitigate fatigue and exposure to injury risk or dangerous situations.3,4
Usually, training/match intensity is referred as training/match load. However, a recent study suggested that the term ‘intensity’ would be more appropriate than load according to the ‘International System of Units’. Therefore, this systematic review will address this topic using intensity instead of load with the exception for specific measures such as player load. 5
Intensity is commonly organised into two main dimensions: external; and internal. 6 External intensity represents the mechanical intensity imposed on players by a training drill. 7 Tracking systems such as global positioning systems (GPSs) and local positioning systems or inertial measurement units (IMUs) are the most commonly used devices in practice and research for monitoring external intensity demands in soccer. 8 Typical outcomes obtained from these systems are: (i) distances covered at different velocity thresholds; (ii) changes-of-velocity measures such as accelerations (ACC), decelerations (DEC) and changes-of-direction performed at different intensities; and (iii) measures extracted from IMUs, which represent the overall external intensity. 9
Internal intensity represents the psychophysiological responses to the external intensity. 6 Usually, measurements related to internal intensity are based on heart rate (HR), biochemical factors, or rated perceived exertion (RPE), although HR and RPE are by far the most often used. 8 Although they are not perfectly correlated, internal and external intensity can be significantly correlated, depending on the measurements considered in an analysis. 10
Although training and match intensity monitoring are well-established research topics in sports sciences, 11 especially in soccer, 12 there is still a gap between genders. Most of the research involving intensity monitoring is focused on men, ranging from youth 13 to professional 14 players. However, over the past decade, there has been an exponential rise in the participation and professionalisation of female athletes. 15 This fact requires additional research in the field of external and internal intensity to provide useful information for coaches and identify the best practices for this population. 15
Aside from improving the technical and tactical skills, players must be prepared to tolerate higher physical demands and intensities at each competition level. Therefore, an understanding of match demands will aid coaches and practitioners in creating appropriate training plans. The proper application of available evidence from female soccer matches should improve players’ performance. In recent years, descriptive studies presenting some typical/normative values of intensity in different periods of the season (or based on playing positions) have been published.16,17
It is essential that these data are collected and interpreted correctly to inform decisions concerning training intensity management. 18 Although there is a consistent body of knowledge about training demands in male soccer players, there is a lack of similar knowledge in female players, as mentioned in a recent systematic review about locomotor demands monitoring in soccer. 19 In one such systematic review, less than 10% of the included studies about arbitrary speed zones involved females, while less than 5% addressed individualised speed zones. 19 A possible consequence of this lack of research is that practitioners had to apply evidence developed on male soccer players to female soccer players, which could be inappropriate. 20 It is critical, therefore, that data are collected from female soccer players and interpreted correctly to allow for effective decision-making related to intensity planning and periodisation. 15
The lack of evidence about intensity in female training sessions is not unique. Similarly, although more studies have focused on female match demands recently, there are still fewer studies on females than on males. A simple search on PubMed performed at January 29 of 2022 using the code line [(“soccer”)AND(”match demand*” OR “load*”)AND(”male*"OR"men*”)] and [(“soccer”)AND(”match demand*” OR “load*”)AND(”female*"OR"women*”)] yielded 130 publications on males and 56 on women published in the year 2021; overall, the search produced 771 publications on men and 280 on women. This means that both training and match demands have been researched less often in females than males. Despite such a bias, growing evidence can help provide range values that sports scientists and researchers can use to better define guidelines for practice or for research. In the case of research on females, although values have been reported in some cohorts,16,17 limitations related to sample size and the fact that most studies consider only one team restrict the generalisability of the evidence.
In addition to the importance of increasing the sample size and number of teams, there is a need to identify the typical values of different training and match intensity measures obtained per session or per week to define benchmarks or provide a range of predicted intensity scenarios in the season. One way to identify such values is to summarise the evidence from different studies conducted in women soccer regarding intensity monitoring. This may help to characterise ranges of expected values and help practitioners.
However, as far as we know, no systematic review has been conducted on this topic so far. Thus, this systematic review aims to identify and summarise studies that have examined external and internal training/match intensity monitoring in female soccer players and provide range values for the main measures. This summary may provide range values that will help coaches determine benchmarks for acceptable values of load and compare their players’ training and match demands with those of similar players from the same population.
Methods
The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines were followed to write this systematic review 21 and guidelines for performing systematic reviews in sport sciences. 22 The protocol of the systematic review was a priori registered in INPLASY (International Platform of Registered Systematic Review and Meta-Analysis Protocols) with the identification number INPLASY202170010 and the DOI 10.37766/inplasy2021.7.0010.
Eligibility criteria
The inclusion and exclusion criteria can be found in Table 1.
Eligibility criteria.
PICOS: (P) population; (I) intervention/exposure; (C) comparator; (O) outcomes; (S) study design.
The screening process related to analysis of the title, abstract and reference list of each article to locate potentially relevant studies was independently executed by two of the authors (AMV and MRG). Moreover, both authors also reviewed the full version of the included papers in detail to identify which article met the inclusion criteria. Additionally, a search within the reference lists of the included records was performed to add additional relevant studies. In the cases of discrepancies, a discussion was performed with the participation of a third author (RO). Possible errata for the included articles were considered.
Information sources
The following electronic databases were used to search for relevant publication on 31 of July 2021, after protocol registration: FECYT (MEDLINE, Scielo, and Web of Science), PubMed, and Scopus. A manual search was also conducted after search in electronic databases to retrieve additional studies that could fit our eligibility criteria.
Search strategy
Keywords and synonyms were entered in various combinations in the title, abstract or keywords: (“soccer” OR “football”) AND (“female” OR “women”) AND (“internal load” OR “external load” OR “workload” OR “training load” OR “training demands” OR “match” OR “matches” OR “game*” OR “load monitoring”). Search results were exported to EndNote 20.0.1 for Mac (Clarivate Analytics). No filters or limits were applied.
Data extraction
A specific spreadsheet was designed in Microsoft Excel (Microsoft Corporation, Readmon, WA, USA) to process the data extraction. The design followed the recommendations of the Cochrane Consumers and Communication Review Group's data extraction template. 23 In this spreadsheet, the information about inclusion and exclusion requirements and reasons was detailed. The selection of the articles was made independently by two authors (AMV and MRG). In the cases of discrepancies, a discussion was performed with the participation of a third author (RO).
Methodological assessment
The methodological quality was assessed using the methodological index for non-randomized studies (MINORS) by two independent authors (AMV and MRG). 24 The global ideal score being 16 for non-comparative studies. MINORS consists of 12 items, four of which are only applicable to comparative studies which was not the case of the included studies. Thus, only eight items were applied. Each item is rated as 0 when the criterion is not reported in the article, 1 if reported but not sufficiently fulfilled, or 2 when adequately met. Higher scores indicate good methodological quality of the article and low risk of bias. The highest possible score is 16 for non-comparative studies. MINORS has yielded acceptable inter- and intra-rater reliability, internal consistency, content validity and discriminative validity.24,25
Results
Study identification and selection
A total of 2853 original articles (FECYT: 1429; PubMed: 608; Scopus: 816) were initially retrieved, of which 1142 were duplicates. Thus, a total of 1711 original articles were found. After this, 1661 articles were excluded after their titles and abstracts were checked. Furthermore, six of the initially excluded articles were retrieved for further analysis, of which one was excluded. The full texts of the remaining 55 articles were checked, leading to the exclusion of another nine articles according to criterion #1 and seven more according to criterion #2. Additionally, five articles were included from additional sources. Ultimately, 44 articles met all the inclusion criteria and were included in the qualitative synthesis. All the steps followed for selecting articles are listed in Figure 1.
Preferred reporting item for systematic reviews and meta-analyses (PRISMA) flow diagram.
Methodological quality
The overall methodological quality of the cross-sectional studies can be found in Table 2.
Methodological assessment using MINORS checklist.
Note: *The MINORS checklist asks the following information (2 = High quality; 1 = Medium quality; 0 = Low quality):
1. Clearly defined objective.
2. Inclusion of patients consecutively.
3. Information collected retrospectively.
4. Assessments adjusted to objective.
5. Evaluations carried out in a neutral way.
6. Follow-up phase consistent with the objective.
7. Dropout rate during follow-up less than 5%.
8. Appropriate statistical analysis.
Results of the studies
Study characteristics
Table 3 presents the characteristics of the studies. From the 44 studies included, only three included young soccer players.53,63,66 Eight studies included amateur players,27,34,42,53,57,60,63,66 while the remaining 35 studies included professional soccer players.
Thirty studies analysed matches,26–28,30–32,36–38,40–43,46,49–52,55–63,65,67 eight studies analysed training sessions16,17,29,33,34,39,64,66 and five studies analysed both matches and training sessions.35,44,48,53,54
Six studies analysed internal measures,17,29,33,39,64,66 25 studies analysed external measures28,30–32,36–38,41,42,44,46,49,50,52–59,61–63,65 and 10 studies analysed both internal and external measures.16,26,27,35,40,43,48,51,60,67.
Study characteristics.
ND: non-described; HR: heart rate; NR: number; DC: distance covered; TL-HR/GPS: training intensity based on HR and GPS metrics; RPE: rated perceived exertion; s-RPE: session rated perceived exertion; GPS: global positioning system; HRavg: average heart rate; HRmax: heart rate maximum; bpm: beats per minute; HSR: high-speed running; VHSR: very high-speed running; ACC: acceleration; DEC: deceleration; HMLD: high metabolic intensity distance; AU: arbitrary units.
Results of internal and external training/match intensity
Table 4 presents the results for internal and external intensity. In the last rows of Table 4, we present the range intervals for the main measures used for internal and external measures.
Results for internal and external training intensity.
A: amateur; P: professional; HR: heart rate; TD: total distance; NR: number; NR.s: number per second; DC: distance covered; TL-HR/GPS: training intensity based on HR and GPS metrics; RPE: rated perceived exertion; s-RPE: session rated perceived exertion; AU: arbitrary units; GPS: global positioning system.
Table 5 presents the results of external and internal match intensity as averages ± standard deviation or range intervals (minimum and maximum). To avoid including more rows, Table 6 also includes a column designed as ‘overall team’ that contains information from studies that analysed playing positions. In the last line of Table 5, we present the range interval for the main measures used for internal and external measures.
Results for internal and external match intensity.
A: amateur; P: professional; HR: heart rate; TD: total distance; NR: number; DC: distance covered; TL-HR/GPS: training intensity based on HR and GPS metrics; RPE: rated perceived exertion; s-RPE: session rated perceived exertion; GPS: global positioning system; HRavg: average heart rate; bpm: beats per minute; HSR: high-speed running; VHSR: very high-speed running; ACC: acceleration; DEC: deceleration; HMLD: high metabolic intensity distance; m/min: meters per minute.
Results for external training intensity, internal and external match intensity by players positions.
A: amateur; P: professional; HR: heart rate; NR: number; NR.s: number per second; TL-HR/GPS: training intensity based on HR and GPS metrics; RPE: rated perceived exertion; s-RPE: session rated perceived exertion; GPS: global positioning system; HRAvg: average heart rate; bpm: beats per minute; HSR: high-speed running; ACC: acceleration; DEC: deceleration; Draw 0-0: match with no score; Leading: match with a winning result; Trailing: running behind other team; Draw but not 0–0: match with a result of 1-1 or 2-2 etc.
Table 6 presents the results for external training and internal and external match intensity by playing position. To improve the clarity and interpretation of the table, we organised the table from training to match intensity according to playing positions as defined by each study, which resulted in different divisions.
Finally, an additional column for the overall team was added with the corresponding values reported by the studies or the range values between playing positions (excluding goalkeepers’ data). In Table 6, no range interval was provided since there were diverse contexts and different determinations of playing positions.
Discussion
This systematic review aimed to identify and summarise studies that had examined external and internal training or match intensity monitoring in female soccer players and provide range values for the main training and match measures. The scientific research and screening steps focused primarily on papers that have quantified external or internal measures with at least one training week or more than two matches. Contextual factors such as the relationship with injuries, type of training session or competition, period of the season, match period (first and second halves), match status and playing positions were obtained in the research.
This section, which addresses all the findings, was organised into the following subsections: training intensity (internal and external) by overall team and by playing position and match intensity (internal and external) by overall team and by playing position.
Training intensity
Training intensity, as mentioned above, is often described as either external or internal68,69 and can be manipulated to promote favourable adaptive responses to training. 6 Athlete monitoring allows practitioners to access information to determine whether athletes are responding appropriately to training and match demands. 70 In this way, coaches and practitioners can try to minimise the risk of excessive intensity and optimise athletic performance. 71
Internal intensity is often referred to as the psychophysiological stressors imposed on an athlete due to the prescription of external physical stimuli.6,70 Measurements of internal intensity can be subjective or objective. 6
According to the rationale described above, the present systematic review yielded five studies that allowed data extraction from the main findings regarding internal intensity measured by session rated perceived exertion (s-RPE),16,29,35,64,66 which presented a range interval of 51–721 arbitrary units (AU), a mean of 440 AU without injury and ≥ 517 AU with higher injury risk. 66 Even considering the mandatory confinement due to the COVID-19, a mean of 489 AU was reported. 64 The s-RPE values reported by Watson et al. 66 were related to an amateur squad team. Amateur teams usually have fewer training sessions per week than professional teams, which may influence the coach's intensity prescriptions and player perceptions.
Moreover, one study addressed player status and positions. 16 The authors found that on match-day minus two, starters reported significantly higher s-RPE, while playing position showed differences in all training sessions. 16
Previously, s-RPE showed significant correlations (p < 0.001) with all training activities in female soccer players and appears to be a robust measure to be considered in daily training. 72 An essential recommendation for practitioners and coaches to ensure reliability and validity of the s-RPE measure is to use standardised instructions and allow for an anchoring procedure to familiarise the athlete with the s-RPE scale. 73
When training duration was not considered, the RPE values reported by the players varied between 3 ± 1 (1–6) AU in the study by Costa et al. 29 which is similar to the values reported by Doyle et al. 16 study (mean 4 and range of 1–7 AU). Moreover, in a Chile women's national soccer team, they ranged from 6 ± 1 and 5 ± 0.1 (no range intervals were provided) during normal training and confinement training, respectively. 64 Possible differences could be attributed to the different training days number (3 days 29 vs. 5 days 64 vs. 7 days 16 ).
Furthermore, HR measures can serve as objective markers of internal intensity and can enable practitioners to design training sessions that meet the demands of the game and provide appropriate aerobic stimuli.74,75 Measuring an individual athlete's HR can allow professionals to consider the principle of individuality in intensity monitoring. 76
However, there are some significant limitations to using HR for quantifying internal intensity, including the need for knowledge of technical proficiency and expertise in interpreting the results. HR is also a poor variable for measuring high-intensity activities such as resistance, speed and power-based training modalities. 76 For these reasons, HR is reliable for continuous efforts performed at intensities below the anaerobic threshold and without significant intensity variations. 76 This is probably why HR measures were not especially common in the studies included in the present systematic review, especially when absolute values were considered.
Meanwhile, different training impulse (TRIMP) methods for monitoring intensity based on HR have been suggested.77,78 A method proposed to facilitate calculating internal intensity is to use HR intensity across a session and multiply the obtained value by the session duration. This method provides a quantitative internal training- or match-based measure of physiological intensity. 12 TRIMP measures were used in two studies included in the present systematic review.29,33 They used Banister TRIMP to control internal intensity, reporting overall team values of 212 ± 81 (67–498) and 185 ± 43 AU. These values are lower than those reported by Brown et al., 79 who reported values of 918 ± 325 AU (a total of 750 min/week) concerning a short (2 weeks) and very high-volume training period (increased by 39%). Despite its relevancy, the limited number of studies obtained in the presented systematic review makes us suggest that more studies should analyse TRIMP to confirm previous results.
When measuring external intensity, it is essential to determine the speed and accelerometry thresholds using absolute and individualised methods.26,52 Absolute thresholds are easy to use, and they enable practitioners to compare physical characteristics between players in different positions. Considering that external intensity is related with several objective measures of training/competition (e.g. running distances, ACC and DEC), their quantification allows a better organisation of training plan prescription as well as a better training volume. 80
External intensity data from five studies were obtained primarily by total distance,16,35,44,45,53 and secondarily by distances covered at speeds higher than 19.4 km·h−1.16,35,44,45,53 These studies indicate a range value of 2347–6581 m in total distance during the in-season and ∼6646 m during the pre-season.
Nevertheless, total distance does not reflect the different intensities that occur during training sessions. Thus, the use of generic speed thresholds facilitates player monitoring. However, it might not reflect the true energetic demands of the athlete, potentially leading to the misinterpretation of external demands. 81 A possible solution to this problem is to apply individualised thresholds to quantify running distances. 82 The individualisation of speed thresholds can be expressed in relation to maximum aerobic speed or maximum sprint speed. 83 Practitioners can then use these thresholds to determine individualised values, which reflect both high- and very-high-intensity exercise modalities. Meanwhile, there is little evidence to suggest that using individual thresholds is better than using generic thresholds when monitoring elite female soccer players. 84 For instance, none of the studies included in the present systematic review analysed individual speed thresholds for intensity, which makes it mandatory to conduct more studies with such individuality.
Thereafter, for some teams, that kind of task is almost impossible to put into practice; thus, we recommend using general speed thresholds, which are better than nothing. In that sense, other measures, including distances covered at speeds higher than 19.4 km·h−1, showed a range interval of 9–543 m.16,44,45,53 However, only the study of Doyle et al. 16 presented such a high distance, while the remaining studies presented maximum values of 30 m, 35 333 m, 44 27 m 45 and 116–162 m. 53
Considering playing positions, only external intensity was addressed (see Table 6). 53 In general, central defenders covered the greatest total distance, central midfielders covered the greatest distance at 15–20 km·h−1 and wide midfielders covered the greatest distance at >20 km·h−1. Central midfielders and forwards displayed the most DEC, and central defenders displayed the most ACC.
Overall team values for ACC and DEC were inconsistent because different thresholds were used in different studies,16,44,45,53 making comparisons difficult concerning the frequency or distance when performing these actions. Despite methodological differences, the evidence indicates that more ACC and DEC are performed at higher competition levels and needs to be considered when designing training plans.
The present systematic review presented only two studies that considered a match-day minus approach,16,44 reinforcing the need for intensity quantification following this approach in future studies. To clarify, match-day minus 5 (MD-5) means five days before the next match, MD-4 corresponds to four days before the next match, and so on. For instance, Mara et al. 44 presented energy expenditure and total distance outcomes using this approach while also using other GPS-derived measures. They recorded the highest energy expenditures and greatest total distances covered in the middle of the week. The same authors observed the lowest exergy expenditures and total distances covered in the first and last training sessions. 44 Doyle et al. 16 analysed several internal and external measures (see Table 4) and confirmed the pattern described in the previous study. 44 Despite only one study considered energy expenditure, 44 data from professional male soccer players seems to support this findings considering lower intensities in the day before and after the match while higher intensities were found in the middle days of the week. 14
Match intensity
Internal match intensity was commonly quantified using s-RPE and HR measures. Three studies used s-RPE,35,40,47 but data could be extracted from only two of these; the values ranged between 240 ± 79 and 892.50 ± 359.35,40 These values differ significantly probably because the lower values were obtained from a team from the third Collegiate division, whereas the highest values were obtained from a second Collegiate division team. It is likely that the level of competition significantly affects s-RPE. Thus, we speculate that teams from professional first divisions would present even higher values recorded in these two previous studies. Consequently, we suggest that future studies include s-RPE in match intensity analysis.
In the studies included in the present review, HR was the primary measure in six studies.26,27,40,48,60,67 However, it was measured using different metrics (intensity, average, exertion) and it was sometimes considered in combination with GPS measures. These differences make it difficult to provide a range value. The HR measures used to quantify internal match intensity were a load-HR/GPS-based metric, different thresholds of HR%, HRaverage, HRexertion and HR maximum (HRmax).26,27,39,40,48,60,67 HR average was the most often used, yielding a range interval of 162–173 bpm. However, this interval and measure did not correctly express intensity during the match, instead expressing only the range variety during several soccer actions. Therefore, we considered it worthwhile to apply such ranges in training sessions.
In elite female soccer players, the average HR during competitive matches ranged between 152 and 186 bpm, the equivalent of ∼80% and 90% of HRmax.26,43,85 HRmax was also used, although only one study provided a range interval of absolute values (181–194 bpm). In addition, in a semi-elite female soccer tournament, Strauss et al. 60 reported that female players spent most of the match in HR zones between 60 and 75 bpm and 75–85% of HRmax. Bozzini et al. 27 also showed that in out-of-conference matches, players spent ∼34 min at 80–89% of HRmax and ∼38 min at 90–100% of HRmax; meanwhile, during in-conference matches, they spent ∼42 min at 80–89% of HRmax and ∼34 min 90–100% of HRmax occurred. Future studies are needed to confirm such data.
Small-sided games are the activities most often used during soccer training sessions intended to mimic match demands. Mara et al. 75 demonstrated that small-sided games in soccer elicited a higher HR response (> 85% of HRmax) than medium and large-conditioned games. These results are in line with those reported by other studies26,43,60,85 and should be considered by practitioners and coaches when attempting to manage the intensity of training sessions.
Considering external match intensity, total distance was the most commonly used measure. The range interval obtained from professional soccer players (≥ 18 years) was 5480–10581 m.26,27,30,32,35–38,40,45,46,49,51–57,59,61,65,67 Amateur teams presented a lower range interval of 3994–8558 m.42,60,63 The match is the most demanding situation in soccer. Although the total distance covered can serve as a basic and auxiliary indicator of the demands of the match, placing too much emphasis on this indicator can undervalue other unique aspects of matches.12,26
Regardless of how much total distance female soccer players cover, high-intensity activities need to be considered to gain a more insightful overview of match demands. In elite female soccer, Krustrup et al. 43 showed that these activities made up 5% of the total match time.
In this sense, several other running speed thresholds were used (commonly known as running, high-speed running (HSR), very HSR, and sprint distances). However, since different studies presented different intervals for each threshold, we opted to present the specific speed of distance covered.26–28,30–32,35–38,40–43,45,46,49–63,65
However, it has recently been suggested that employing male-related speed velocity zones thresholds in female team sports contexts could result in underestimations of external intensity. 86 Female-specific HSR velocity thresholds have been recommended in soccer due to physiological gender differences in physical fitness/capacity.63,87 Absolute thresholds for high-speed running distance and very HSR distance range between 16.0 and 19.0 km·h−1 and between 20.2 and 22.5 km·h−1, respectively. Furthermore, these thresholds have been recommended for elite female soccer players.86,87 Indeed, research on elite female soccer players advocated using > 19.8 km·h−1 for HSR distance and > 25.1 km·h−1 for very HSR or sprint distance as generic thresholds. 30 However, a recent study defined HSR as > 15 km·h−1 without including any other running or sprinting speeds. 88
The number and average distance of sprints by professional players range between 20–35 and ∼14–15 m per match, respectively, depending on the player's position.50,62 When acknowledging the physical demands of matches beyond overall running, it is important to understand the intense periods and actions that occur (i.e. sprints, repeated sprints, ACC and DEC). This is because these factors substantially influence the biomechanical and cardiometabolic demands experienced by female players.28,61 As mentioned before, these types of high-intensity efforts are critical components for practitioners and coaches to incorporate into their training strategies. For these reasons, we recommend that they be quantified.
Considering the previous information, we attempted to report the interval ranges for the most demanding actions in matches between all studies that included professional soccer female players. These ranges are as follows: The previous thresholds were defined to include more studies from the systematic review, which means that all data must be carefully interpreted. Thus, we suggest consulting all data in the tables to clarify the type of the teams and the specific thresholds used by each study.
Some studies considered player load.32,35,55,56,61,65 Despite some differences in the literature, this external intensity measure is generally related to the magnitude of changes in acceleration along the three-movement axis. 89 However, when analysing this measure, a problem related to the equipment used emerges; consequently, a problem emerges regarding the equations used to calculate player intensity, which is crucial for its reproducibility. 89 Considering this point, we found a range interval of 848–1096 AU in female professional soccer players.32,61,65
The previous range intervals must be carefully interpreted because, even among professional soccer players, there were several contextual factors that could have influenced the results, such as the use of international and domestic matches,26,37,49 friendly matches,45,65 different thresholds 52 and match results. 32
Moreover, other contextual factors, such as warm or hot environmental temperatures and the use of artificial turf, can also change the match demands by decreasing high-intensity running distances.61,90 However, such variables were not addressed by the studies included in the present systematic review. When implementing position-specific training plans to accommodate overall match's physical demands, coaches should also consider the contextual factors necessary for successful outcomes in female players’ physical performance and match preparation.
Finally, match intensity was also analysed in some studies by dividing matches into two halves. In these studies, higher values of external26,28,38,49,51,60,63,65 and internal measures26,51 were found in the first half than the second half. This information is very useful for coaches and their staff for a better preparation of their teams. For instance, it allows some simulations in training session with the specific intention of higher intensities in the first 45 min of training.
Match intensity by playing position
The position of a player influences the distance covered during soccer matches. In the present systematic review, some studies analysed playing positions regarding external match intensity (see Table 6).26,28,30–32,38,40,46,50,51,53–57,59,60,62,63 However, Table 6 presents several divisions for playing positions, which makes it difficult to compare studies and establish range intervals. Thus, a general trend was found that midfielders typically cover the greatest total distance (8243–10985 m for professional players), followed by forwards (7483–10262 m for professional players) and defenders (7522–10229 m for professional players), whereas forwards and fullbacks (or wide midfielders) generally performed more high-intensity running thresholds and covered greater sprinting distances.28,30,31,40,46,53,56,63
Additionally, when playing positions were divided into defenders, midfielders and forwards, it was found that midfielders covered greater total and higher-intensity distances than other players.26,38,54 However, three studies reported that forwards presented higher values of high-intensity measures.57,62,63 Furthermore, if fullbacks were considered in addition to defenders, midfielders and forwards, the results showed that the most high-intensity actions were performed by fullbacks.32,55,56,61
Regarding internal match intensity, only two studies analysed playing positions.40,51 One study showed that s-RPE and HR average were higher in central midfielders, followed by flank players. The other study 51 showed the highest HR average value for wide midfielders, which is in line with Mara et al. 40 This study also presented the highest HRmax values for forwards and wide midfielders, whereas other HR measures were identical for all playing positions. 51
Considering external and internal match intensity quantification, studies that analysed goalkeepers reported that goalkeepers showed lower values than all other players for all measures. This is understandable due to the specific role of the position.40,51,59 Finally, it should be reinforced that the previous information was consensual regardless of the type of soccer team, age of players and competition level (professional or amateur).
Study limitations, future directions and practical applications
This study presents some limitations that should be acknowledged and future directions that should be addressed. The small number of studies investigating intensity indicates that much more research is needed in female training and match intensity quantification. Additionally, more studies are needed considering player positions analysis in external (only one study included 53 ) and internal (no studies included) training, as well as internal match (only two studies included40,51) intensity. In the same line, more studies considering the match-day minus approach (or simply considering each day of the microcycle) should be considered in future studies since only two studies16,44 were found in the present systematic review.
Moreover, few studies analysed contextual variables such as player status 16 or match result, 32 and none of the included studies analysed match location or opponent quality, which can affect the results. In addition, few studies encompassed full seasons,27,30,31,35,37,45,52,57,58,61,67 thus compromising data collection and consequently all generalisations.
Furthermore, the reviewed studies involved players of different competition levels from different countries with a wide range interval for age (15–31 years), which constitutes significant differences in context. This lack of uniformity in classifying running speed and acceleration thresholds limits comparisons between studies, thereby making it difficult to generalise their results. Finally, the fact that few studies analysed young soccer players or amateur teams using different measures did not allow us to obtain reference values.
Although the results suggested that the menstrual cycle phase does not significantly influence the training or match physical performance of female soccer players, 41 monitoring the menstrual cycle phases during training is recommended in future studies. 91
Despite the information presented in the previous paragraphs, the present study constitutes a relevant tool for the training and match intensity quantification of female players (professional and amateur). This tool can be used by coaches, their staff, and practitioners as a reference for future studies. For instance, the range values presented in this study can be replicated by other coaches, staffs or researchers. Such information will allow a better training intensity application for female soccer players.
Conclusions
This study provided range values for the main and most often used measures of internal and external training and match intensity (in absolute values) obtained from overall professional teams. Specifically, range intervals of s-RPE, RPE, TRIMP, total distance and distance >19.4 km·h−1 were provided regarding training; range intervals of s-RPE, hear rate average and maximum, total distance, distance ≥14 km·h−1, ≥18 km·h−1, number of ACC and DEC (>2 ms−2) and player intensity were provided regarding matches. This work provides range values coaches, their staff and practitioners can use to help female soccer players achieve desirable competitive levels. Coaches can attempt to replicate such values or even increase them, especially during training sessions.
Nonetheless, the intervals provided were retrieved from specific scenarios; when analysed, the following contextual variables must be taken into account: age, the skill level of players, level of competition, as well as other contextual factors, such as match results, match location, quality of opponents, playing positions and player status. Future research should consider attempting to better understand the methodology used to quantify training and explore how practical implications for real-training scenarios can be applied based on the collected measures.
Finally, GPS-based thresholds of running distances and accelerometry-based variables of intensity vary widely between studies, making it difficult for all possible comparisons to be made. Thus, we could not suggest specific thresholds for all variables.
Footnotes
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by this research was funded by the Portuguese Foundation for Science and Technology, I.P., Grant/Award Number UIDP/04748/2020. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
