players/Sessions
/Region
HR, heart rate; HSD, high-speed distance; lnRMSSD, natural logarithm of square root of the mean of the sum of the squares of differences between adjacent NN intervals; lnLF, natural logarithm of low-frequency; lnHF, natural logarithm of high-frequency; s-RPE, session-rating of perceived exertion; Yo-Yo IR1, Yo-Yo Intermittent Recovery – Level 1; Yo-Yo IR2, Yo-Yo Intermittent Recovery – Level 2; TD, total distance; TL, training load; TRIMP, training impulse; DOMS, delayed onset muscle soreness; SWA, SenseWear Mini Armbands; EL, external load; IL, internal load; MD, match-day; ACWR, acute-to-chronic workload rations; EWMA, exponentially weighted moving averages; Nr, not reported.
The mean methodological quality score for the 16 selected articles was 93.3%, with two articles achieving the maximum score of 100% ( Table 1 ). Among the nine selected studies, the quality score ranged between 86.7 and 100%. All articles achieved an overall rating score of >75% (excellent methodological quality). Potential limitations found were mainly related to the lack of explicit justification for the sample size (criterion 5) and the absence of clear acknowledgment of study limitations (criterion 16).
The “selection of the participant,” “exposure measurement,” “blinding outcome assessment” and “incomplete outcome data” were judged as low risk of selection of bias in 100% of the studies ( Figure 2 ). For most of the studies ( n = 14), the “confounding variables” domain was judged as low risk of selection of bias (87.5%), with two studies being judged as unclear, due to unclarity on the type (i.e., content) of training sessions practiced per week. For most of the studies ( n = 14) (87.5%) displayed unclear risk of bias to “selective outcome reporting” domain, because the studies did not clearly describe the exact number of players considered for the respective statistical analyses. No studies were judged with high risk of bias for each domain.
Risk of bias judgments for training load monitoring in highly trained and elite adult women’s soccer studies through RoBANS.
Regarding ETL during training sessions ( Table 2 ), eight studies have adopted speed-based intensity zones using arbitrary/fixed thresholds (between 12.2 and 18 km⋅h –1 ) ( Mara et al., 2015a , b ; Clemente et al., 2019 ; Costa et al., 2019a ; Douchet et al., 2021 ; Doyle et al., 2021 ; Romero-Moraleda et al., 2021 ; Xiao et al., 2021 ), while one study considered individual fitness level ( Scott and Lovell, 2018 ). Two studies reported that players covered greater total distance and high-speed distance (>12.2 km⋅h –1 ) during the pre-season compared to early competitive season, and then decreased late in the season ( Mara et al., 2015b ; Clemente et al., 2019 ). On the other hand, three studies reported that total distance and high-speed distance (>12.6 km⋅h –1 and >maximal aerobic speed [MAS]) were stable in training sessions during international tournaments, independently of the data reported for official matches ( Scott and Lovell, 2018 ; Costa et al., 2019a ; Doyle et al., 2021 ).
Training load data during training sessions in highly trained and elite adult women soccer players.
Reference | Training load variables | Values | Description |
TD (m) HSD > 14 km⋅h (m) Sprint distance > 20 km⋅h (m) | 3000 to 5000 200 to 400 1000 to 1500 | Mean of weekly load (lowest to highest load values) during 5 weeks of the pre-season. | |
HR (%HR ) TRIMP (AU) s-RPE (AU) | 70 ± 3 to 75 ± 4 72 ± 18 to 138 ± 29 193 ± 60 to 442 ± 159 | Mean ± standard deviations (lowest to highest load values) during 3 weeks of the competitive season. | |
HR (bpm) TRIMP (AU) s-RPE (AU) | 138 ± 13 to 149 ± 16.8 77 ± 36 to 110 ± 31 281 ± 117 to 369 ± 111 | Mean ± standard deviations (lowest to highest load values) during 1 week of the competitive season. | |
TD (m) HSD > 12.6 km⋅h (m) s-RPE (AU) | 2201 to 4284 130 to 756 131 to 360 | Median (lowest to highest load values) during 9 days of international tournament. | |
HR (%HR ) TRIMP (AU) s-RPE (AU) | 74 ± 2 192 ± 21 326 ± 33 | Mean ± standard deviations values during 6 weeks of the competitive season. | |
HR (bpm) TRIMP (AU) %HR Time > 90 of %HR (min) s-RPE (AU) RPE (AU) | 139 ± 12 211 ± 81 73 ± 6 8 ± 7 338 ± 107 3 ± 2 | Mean ± standard deviations values during 6 weeks of the competitive season. | |
s-RPE (AU) | 604 ± 70 | Mean ± standard deviations value during 4 weeks of pre-season. | |
HR (bpm) TRIMP (AU) %HR s-RPE (AU) | 143 to 147 187 to 189 75 to 77 377 to 411 | Mean values during 14 days of the competitive season. | |
TD (m) HSD > 18 km⋅h (m) Number of sprints > 19.4 km⋅h (counts) Accelerations > 2 m⋅s (counts) Decelerations > −2 m⋅s (counts) HR (%) s-RPE (AU) RPE (AU) | 3870 ± 870; 5090 ± 620 124 ± 61; 155 ± 92 7 ± 3; 7 ± 3 28 ± 12; 56 ± 10 31 ± 12; 61 ± 14 63 ± 6; 67 ± 7 201 ± 47; 357 ± 50 3 ± 1; 5 ± 1 | Mean ± standard deviations values during 2 weeks: Low week load (1 week); heavy week load (1 week). | |
TD (m) HSD > 12.6 km⋅h (m) Number of sprints > 19.4 km⋅h (counts) Accelerations > 3 m⋅s (counts) Decelerations > −3 m⋅s (counts) s-RPE (AU) RPE (AU) | 3339 to 59335 58 to 389 2 to 14 28 to 56 21 to 46 203 to 721 3 to 7 | Median (lowest to highest load values) during 1 week of the competitive season. | |
RPE (AU) | 3 to 6 | Mean of weekly load (lowest to highest load values) during 10 weeks of the competitive season. | |
TD (m) HSD > 12.2 km⋅h (m) Sprint distance 19.4 km⋅h (m) Accelerations > 2 m⋅s (counts) Decelerations > −2 m⋅s (counts) | 6581 ± 847 880 ± 244 333 ± 107 49 ± 13 18 ± 9 | Mean ± standard deviations values during 1 week of the pre-season. | |
TD (m) HSD > 12.6 km⋅h (m) Number of sprints > 19.4 km⋅h (counts) Accelerations > 2 m⋅s (counts) Decelerations > −2 m⋅s (counts) | 6646 ± 111; 5437 ± 106 1415 ± 42; 1027 ± 40 27 ± 15; 24 ± 9 56 ± 19; 49 ± 14 22 ± 10; 20 ± 10 | Mean ± standard deviations values during 18 weeks: pre-season (6 weeks); competitive season (12 weeks). | |
TD (m) HSD > 15 km⋅h (m) Accelerations > 1 m⋅s (counts) Decelerations > −1 m⋅s (counts) s-RPE (AU) RPE (AU) | 2496 ± 1639 to 4975 ± 1319 170 ± 214 to 494 ± 248 70 ± 56 to 144 ± 39 17 ± 17 to 38 ± 16 167 ± 134 to 579 ± 139 | Mean ± standard deviations (lowest to highest load values) during 5 months of the competitive season. | |
HSD > 12.6 km⋅h (m) Minutes spent > 80 %HR á (min) TRIMP (AU) RPE (AU) | 250 to 2500 5 to 65 150 to 400 3 to 8 | Individual means (lowest to highest load values) during 21 days of the competitive season. | |
TD (m) HSD > 12.9 km⋅h (m) | 3662 to 18461 1173 to 4994 | Mean accumulated workloads over 4 weeks of the competitive season. |
HR, heart rate; HSD, high-speed distance; s-RPE, session-rating of perceived exertion; TD, total distance; TRIMP, training impulse.
Internal training load was quantified using HR- and RPE-based methods ( Table 2 ). Seven studies quantified ITL using HR ( Costa et al., 2018a , b , 2019b , 2019c , 2021b ; Scott and Lovell, 2018 ; Douchet et al., 2021 ). Seven studies individualized physiological responses to exercise relative to HR max obtained by an incremental protocol until exhaustion Costa et al., 2018a , b , 2019b , 2019c , 2021a , b ; Scott and Lovell, 2018 ). Six studies quantified ITL using RPE ( Scott and Lovell, 2018 ; Costa et al., 2019c ; Douchet et al., 2021 ; Doyle et al., 2021 ; Fernandes et al., 2021 ; Romero-Moraleda et al., 2021 ), while ten studies reported s-RPE ( Costa et al., 2018a , b , 2019a , 2019b , 2019c , 2021a , b ; Douchet et al., 2021 ; Doyle et al., 2021 ; Romero-Moraleda et al., 2021 ).
The relationship between TL and fatigue has been examined in eight studies ( Mara et al., 2015b ; Costa et al., 2018a , 2019a , 2019b , 2021b ; Scott and Lovell, 2018 ; Douchet et al., 2021 ; Fernandes et al., 2021 ). During a 9-day international tournament, no significant within-subject correlations were observed between post-training night sleep parameters (e.g., total sleep time and sleep efficiency) and ETL metrics (e.g., distance and high-speed distance) ( Costa et al., 2019a ). On the other hand, small to moderate ( r = −0.43 to −0.17) within-subject correlations were observed between ITL (s-RPE and training impulse [TRIMP]) and sleep parameters (sleep duration and efficiency) during a 14-day competitive period ( Costa et al., 2021b ). Moreover, significant differences in sleep patterns and autonomic nervous activity responses when night training sessions were compared to competitive day matches and rest days, suggesting that the time of day for soccer practice may disrupt sleep patterns and nocturnal autonomic activity ( Costa et al., 2018a , 2019b ). In addition, Douchet et al. (2021) showed that a week with more accelerations and decelerations were significantly associated ( r = 0.94) with increased fatigue as witnessed by the greater RPE and perceived well-being (i.e., Hooper index). Associations were also found between perceived well-being (i.e., stress and fatigue) and s-RPE ( r = 0.69) during a 10-week competitive period ( Fernandes et al., 2021 ).
Self-reported measures of fatigue have shown significant associations with ELT (e.g., high-speed distance) on the previous day during a tournament ( Scott and Lovell, 2018 ). Scott and Lovell (2018) described that self-reported fatigue and muscle soreness were negatively associated (small magnitude) with high-speed distance covered ( r = −0.20) using either fixed (>12.6 km/h –1 ) or individual thresholds during a 21-day training camp. Finally, Mara et al. (2015b) showed that self-reported fatigue and sleep times were not correlated with the total distance covered >12.6 km⋅h –1 , whereas muscle soreness was negatively correlated (moderate magnitude) with ETL parameters during the pre-season.
Information of the dose–response relationship between TL and training adaptations in highly trained and elite adult women soccer players is limited to one study ( Mara et al., 2015b ). Positive correlations were reported between changes in intermittent endurance capacity assessed through performance in the Yo-Yo Intermittent Recovery Test – level 2 after the pre-season ( r = 0.70), and accumulated ETL ( r = 0.71; r = 0.56, respectively), high-speed distance (>12.6 km⋅h –1 ) and accelerations (>2 m⋅s –2 ) during the pre-season ( Mara et al., 2015b ).
Only one study reported the relationship between TL and injuries (defined as an event that caused the player to miss at least 1 subsequent practice or match and lower extremity injuries) in women soccer players ( Xiao et al., 2021 ), revealing that players that sustained an injury had significantly higher 2-, 3-, and 4-week accumulated TL and total distance covered as compared with injury-free players during the same time frame.
In the current systematic review, we confirmed the limited information available about training outcomes in highly trained and elite adult women soccer players, especially in the relationship between TL, training adaptation and injuries ( Kirkendall and Krustrup, 2021 ). Additionally, current monitoring practices in highly trained and elite adult women soccer players are sparse, which underline the need for conducting studies or surveys based on that implemented in men’s soccer ( Akenhead and Nassis, 2016 ).
Wearable microtechnology incorporating GPS, local positioning systems or triaxial accelerometers have shown good ability to measure ETL based on distance, speed, and accelerations in team sports ( Scott et al., 2016 ; Torres-Ronda et al., 2022 ). In this context, one key aspect of training prescription is to understand how the individual athlete is coping with the imposed training demands. While the use of individualized intensity zones to quantify ITL (e.g., based on HR max or HR reserve ) is widely adopted among sports practitioners, especially in men’s soccer ( Dellal et al., 2012 ; Akenhead and Nassis, 2016 ), the use of individualized intensity zones for ETL quantification (based on speed and acceleration) is not fully established, especially in women’s soccer. Actually, the individualization of speed-based EL based on testing metrics (e.g., MAS; and maximal sprinting speed, MSS) has received increased attention in adult men ( Hunter et al., 2015 ; Rago et al., 2019c , 2020 ) and youth soccer ( Mendez-Villanueva et al., 2013 ; Abbott et al., 2018 ) players, but not in women players. Generally, match-analysis reports in women’s soccer described physical match data using two different sprint thresholds, based on either fixed (20 km⋅h –1 ) and individualized (90% mean speed obtained from a 20-m sprint test) speed zones ( Nakamura et al., 2017 ). Similar patterns were observed between halves, and playing positions, but fixed speed zones may have likely underestimated the mean duration, distance, and the number of sprint sequences ( Nakamura et al., 2017 ). Additionally, only one study employed individual speed zones based on MAS and MSS, showing that individualizing ETL metrics did not improve the relationship between training load and self-reported fatigue ( Scott and Lovell, 2018 ). In this study ( Scott and Lovell, 2018 ), HSD > 12.6 km⋅h –1 ranged between 250 to 2500 m during 21 days of the competitive season. However, the latter study employed MAS and MSS separately, adopting the following criteria: distance covered >80% MAS, MAS, >50% MSS and 65% MSS ( Scott and Lovell, 2018 ). Moreover, it is important to note the use of 50–65% MSS that could be close to the potential MAS (15–18 km⋅h –1 ) in elite athletes ( Rago et al., 2020 ), considering that women soccer players peak approximately at 30–32 km⋅h –1 during a match ( Datson et al., 2014 , 2017 ). This assumes a linear relationship between aerobic (MAS) and anaerobic ( Li et al., 2019 ) power that may consequently result in an erroneous interpretation of ETL. In this context, once MAS and MSS have been obtained from incremental and 40-m sprint tests, respectively, suggested by the assessment of anaerobic speed reserve (ASR) ( Bundle et al., 2003 ). The use of ASR rely on the fact that different players with the same MAS, but different sprinting capacity, require different training prescription when exercising at intensities above MAS ( Buchheit and Laursen, 2013 ). However, no information is available regarding ASR-based training load in women’s soccer. Similarly, no data are available on the individual training prescriptions based on maximal acceleration capacity in women soccer players. This might be relevant due to the frequent acceleration demands required during soccer training and match play, as well as the variations in acceleration capacity observed throughout different periods of the season ( Mara et al., 2015b ).
Internal training load is usually quantified using HR monitors, which generically provide information about the aerobic contribution during exercise ( Achten and Jeukendrup, 2003 ). A potential strength of HR-based methods is the information about aerobic contribution based on the strong relationship with oxygen consumption during exercise, when data are expressed as percentage of HR max or HR reserve ( Achten and Jeukendrup, 2003 ). On the other hand, a potential limitation of HR-based methods is the failure to detect anaerobic-oriented efforts such as sudden sprints or explosive bursts commonly observed during soccer training and match-play ( Achten and Jeukendrup, 2003 ; Dellal et al., 2012 ). Therefore, an integrated approach encompassing both ETL and ITL is imperative to provide a full picture the exercise demands placed on the athletes. Nonetheless, HR-based variables are sensitive in detecting day-to-day variations in TL in highly trained and elite adult women soccer players under different competitive conditions, such as a domestic league competitive period ( Costa et al., 2018a ) or an international tournament ( Scott and Lovell, 2018 ).
Beyond the usefulness of HR-based methods, and despite the development of women’s soccer, most women’s teams worldwide might still have weak budgets compared to that of men’s teams to acquire sophisticated equipment, which frequently results in adopting cost-free methods based on the post-training subjective RPE ( Costa et al., 2019c ). The use of RPE-based methods such as the s-RPE is deemed to be valid in women soccer players based on its large relationship with HR-based methods (e.g., training impulse, Edwards’ TL; Costa et al., 2019c ). The latter study ( Costa et al., 2019c ), found an large correlation between s-RPE with TRIMP ( r = 0.74–0.82), with TRIMP values ranging from 211 ± 81 AU and s-RPE values from 388 ± 107 AU. However, the connection between RPE-based and ETL parameters is unknown in women soccer players. This would be useful to discriminate between TL parameters, providing practitioners with evidence-based TL metrics to be adopted in their monitoring systems.
Fatigue has been defined as the inability to complete a task that was once achievable within a recent time frame ( Pyne and Martin, 2011 ). Acute (immediately after) and residual (up to 72 h) fatigue may temporarily impair players’ readiness to train and compete ( Silva et al., 2018 ). In this context, monitoring TL may be useful to infer about acute and residual fatigue, allowing individual adjustments to training programs, improve well-being, restore physical capacity, and inform about the recovery process ( Hader et al., 2019 ). Generally, women soccer players may need up to 72 h to achieve full neuromuscular recovery after a competitive match ( Andersson et al., 2008 ; Krustrup et al., 2010 ; Sjokvist et al., 2011 ). Specifically, sprint performance, countermovement jump (CMJ), and peak torque in knee extension and flexion are reduced after a match ( Andersson et al., 2008 ; Krustrup et al., 2010 ; Sjokvist et al., 2011 ). However, changes in neuromuscular function following a match and throughout the recovery period need further elucidation in women’s soccer.
Notably, self-reported measures of fatigue are widely accepted among practitioners due to their ease to use and low-cost ( Hooper et al., 1995 ). Indeed, subjective measures have shown acceptable sensitivity and consistency in athletes ( Saw et al., 2016 ). For instance, self-reported measures of fatigue have shown significant associations with GPS-based TL on the previous day during a tournament ( Scott and Lovell, 2018 ). In this study ( Scott and Lovell, 2018 ), irrespective of the quantification method adopted for HSD > 12.6 km⋅h 1 and very HSD (fixed or individualized speed zones), negative small correlations were observed with fatigue and soreness ( r = −0.25 to −0.14). Actually, it seems that self-reported outcomes might be dependent on the training and competitive context, underlining the need to consider studies with more extensive periods (e.g., full or multiple seasons) and over a wide range of fatigue and recovery indicators; specially because there is very limited information about acute and residual fatigue in relation to TL in highly trained and elite adult women soccer players. Findings in men’s soccer have shown significant correlations between various indicators of acute and residual fatigue and TL ( Thorpe et al., 2017a ; Hader et al., 2019 ). Specifically, non-invasive measures of fatigue such as sitting HR, submaximal HR, CMJ and self-reported questionnaires have shown responsiveness to daily and acute changes in TL over time ( Thorpe et al., 2015 , 2017b ). These measures can be routinely applied to a number of athletes to monitor changes in training status ( Buchheit, 2014 ). Moreover, subjective measures of fatigue can be easily incorporated into the monitoring systems, with the advantage of being cost-free and showing responsiveness to TL ( Saw et al., 2016 ).
Information regarding HR measures during recovery after training sessions or matches in highly trained and elite adult women’s soccer has been predominantly conducted during sleep time ( Costa et al., 2018a , b , 2019a , 2019b , 2021b ). Most players from the same team presented fluctuations in nocturnal cardiac autonomic activity (i.e., coefficient of variation ranging from 2.8 to 9.0%) ( Costa et al., 2019a ). However, no within-subject associations over time were observed between TL (e.g., s-RPE, TRIMP and distance > 12 km⋅h –1 ) and HR parameters during sleep ( Costa et al., 2019a , 2021b ). The authors ( Costa et al., 2019a , 2021b ) suggested that the amount of training and match demands (s-RPE ranging between 348 to 690 AU and TRIMP between 191 to 247 AU) prescribed to the players was not high enough to cause meaningful changes in cardiac sympathetic and parasympathetic activities during sleep. Additionally, no evidence is available about resting and submaximal HR in women soccer players, as previously described in men’s soccer ( Naranjo et al., 2015 ). However, women soccer players showed significant differences in sleep patterns and autonomic nervous activity responses when night training sessions were compared to competitive day matches and rest days, suggesting that the time of day for soccer practice may disrupt sleep patterns and nocturnal autonomic activity ( Costa et al., 2018a , 2019b ). For example, during a 9-day international tournament, no significant within-subject correlations were observed between post-training night sleep parameters (sleep time, sleep efficiency and heart rate variability during sleep) and both ETL (i.e., HSD > 12.6 km⋅h 1 ranging between 130 to 756 m) and s-RPE (i.e., ranging between 131 to 360 AU) ( Costa et al., 2019a ). On the other hand, small within-subject correlations were observed between ITL (s-RPE [ranging between 377 to 411 AU] and TRIMP [ranging between 187 to 189 AU]) and sleep parameters (sleep duration and efficiency) during a 14-day competitive period ( Costa et al., 2021b ). Thus, even under stress imposed by tournament scheduling and training and match loads, the players maintained relatively good consistency in sleep habits to recover from the training sessions and matches.
Following an acute fatigue phase, it is expected that chronic exposure to TL contributes to benefits in players’ fitness levels, resulting in positive health or performance adaptations ( Mara et al., 2015b ). Thus, understanding changes in physiological and functional capacities of women soccer players is of upmost importance given its meaningful connection with physical performance during a match ( Krustrup et al., 2005 ). Currently, the effectiveness of various training interventions (e.g., interval, resisted sprint, and plyometric training) in women’s soccer is well-documented ( Datson et al., 2014 ). However, it is important to note that some coaches could prescribe training programs based on their professional and educational background, with less attention to published training interventions ( Rago, 2020 ). This could be due to the fact that evidence-based analytic drills do not always fit within the technical staff philosophy. In this sense, observational studies considering the exercise prescribed by coaches and the associated training outcomes could aid in understanding the effectiveness of training programs without the need to design intervention studies. For example, employing field performance tests at different seasonal points while simultaneously quantifying TL allows the computation of the relationship between TL and changes in performance ( Jaspers et al., 2017 ). To date, only one study has adopted an observational design based on fitness testing at different seasonal points (i.e., 6 weeks of pre-season and 12 weeks of the competitive season) concerning TL; positive correlations were reported between changes in intermittent endurance capacity and ELT ( Mara et al., 2015b ). However, the latter study did not consider the individual capacity to adjust TL and only included maximal tests (e.g., jump, sprint, and time-to exhaustion). In this context, non-invasive measures of resting or submaximal HR may aid in detecting training adaptations without having the athletes to perform until exhaustion ( Buchheit, 2014 ; Naranjo et al., 2015 ; Rago et al., 2019d ). Also, different force-time and force-velocity components during the different phases of a jump might be sensitive in detecting training-induced changes ( Gathercole et al., 2015 ). In summary, information regarding the dose–response relationship between TL and training adaptations in highly trained and elite adult women soccer players is limited to one study ( Mara et al., 2015b ), which did not consider the individual capacity to quantify TL. Thus, further studies are warranted to explore the dose–response relationship that may elicit the desired long-term performance outcomes.
It has been reported that players might sustain illnesses or time-loss injuries during the season ( Fuller et al., 2006 ). Injury incidence in women’s soccer ranged between 1.2–7.0 injuries per 1,000 training hours, and 12.6–24.0 per 1,000 match hours ( Giza et al., 2005 ; Jacobson and Tegner, 2007 ; Tegnander et al., 2008 ; Alahmad et al., 2020 ). The latter studies were based on descriptive epidemiological information, without inferences computed on injury incidence or risk in relation to training outcomes. Only one study reported information about the relationship between TL and injuries. Xiao et al. (2021) found that higher accumulated player load and total distance covered (values ranging between 3662 to 18461 m) were associated with injury in women soccer players during the same time frame. Thus, it is currently not possible to provide an explanation about training-related factors associated to injury or medical assistance. Alternatively, registering the occurrence of medical assistance (instead of time-loss injuries), as previously described in other team sports, could be of interest ( Martinez-Riaza et al., 2017 ). Indeed, erroneous training progressions could result in delayed muscle soreness ( Thorpe et al., 2017a ) with an associated search for medical assistance. In general, the connection between training outcomes and injury risk has yet to be explored in women soccer players.
Information about TL in women soccer players is very sparse. Thus, it is very difficult for practitioners to consider evidence-based practices for training sessions beyond solid information available from match-analysis studies. For example, further studies on training contents, loads and adaptations are warranted to design match-like practice sessions and drills for women’s soccer. Moreover, the dose–response relationships between TL, fatigue, training adaptions and injuries need to be clarified to understand the optimal training stimulus to enhance performance while preserving players’ health. Also, future studies should encompass extensive periods with different seasonal phases (e.g., off-season, pre-season and in-season) and fixtures (e.g., ordinary microcycles, congested periods, national team breaks) with special emphasis on how TL affects training outcomes (e.g., acute fatigue, training adaptations, and injury risk) as previously examined in men’s soccer ( Jaspers et al., 2017 ). In addition, as future research, would be imperative to understand the importance of training load prescription and adaptation within youth women’s soccer players from different competitive levels, which may help to preventing decrements in performance, or enhancing recovery in women’s soccer population.
We have attempted to summarize current TL monitoring during training sessions in women’s soccer, which may help to inform the practitioners working with highly trained and elite players, but also identify knowledge gaps and make suggestions for future research. More specifically, from a physical and physiological perspective, future research should use monitoring technology to determine more accurately the physical and physiological demands of training in women soccer players.
Author contributions.
JC, VR, and JB contributed to the conceptualization and methodology. JC, VR, PB, EA, and JB contributed to the formal analysis and investigation. JC, VR, PB, and EA contributed to the data curation. VR, PF, AS, EA, and JB contributed to the writing—review and editing. JC and JB contributed to the writing—original draft preparation and visualization. JC contributed to the software. JB contributed to the validation, resources, supervision, and project administration. All authors read and agreed to the published version of the manuscript.
We would like to acknowledge the collaboration of the corresponding author of the selected articles for sending the requested missing information.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2022.943857/full#supplementary-material
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offset and delay fatigue are paramount. Over the last 50 years, several investigations have been reported on aspects of soccer be they nutrition-focused or those concerning the demands of the sport. Emanating from these scientific papers, observations have been made on the likely factors which result in the fatigue during match-play.
To Download RSK papers: Click on the papers! SPECIAL ADVANCE FIXTURES . RIGHT ON FIXTURES . BOB MORTON . CAPITAL INTERNATIONAL . Related: Week 35 Pool Fixtures for Sat 5 Mar 2022 - UK 2021/2022 . SOCCER 'X' RESEARCH . SOCCER PERCENTAGE . WINSTAR . BIGWIN SOCCER . POOLS TELEGRAPH . BIGWIN SOCCER PAPER. Week 36 Bigwin Soccer and Pool ...
Studies were considered as follows: (a) highly trained and elite adult (>18 years) women's soccer players; (b) continuous (minimum 1-week duration) TL monitoring in the context of the team routine; (c) TL collected from entire training session. Methodological qualitative assessments and risk of bias criteria were used for judging the studies.
Research has shown that over 30% of concussions in soccer are caused by heading the ball or by attempting to head the ball and colliding with a player, object, or the ground. 2 3 11% of children who suffer a concussion still have symptoms three months later. 4 Persistent post-concussion
This longitudinal multi-study investigation, spanning 2009 to 2021, analysed the career paths of Spanish academy soccer players. It consisted of three studies investigating players' transition ...
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Rsk Papers Week 11 2021. Week 11 rsk papers 2021: Welcome to Fortune Soccer here we provide you with RSK papers (Bob Morton, Capital International, Soccer 'X' Research) and papers from other other publishers such as WinStar, Bigwin Soccer, Special Advance Fixtures, Right On Fixtures, Weekly Pools Telegraph, Pools Telegraph, Temple of Draws ...
Rsk Papers Week 31 2021. Welcome to Fortune Soccer we are provide you with football pools papers from RSK and other publishers such as Bob Morton, Capital International, Soccer 'X' Research and WinStar, Bigwin Soccer, Special Advance Fixtures, Right On Fixtures, Weekly Pools Telegraph, Pools Telegraph, Temple of Draws, Dream International ...
Rsk Papers Week 33 2021. Welcome to Fortune Soccer we are provide you with football pools papers from RSK and other publishers such as Bob Morton, Capital International, Soccer 'X' Research and WinStar, Bigwin Soccer, Special Advance Fixtures, Right On Fixtures, Weekly Pools Telegraph, Pools Telegraph, Temple of Draws, Dream International ...
Week 50 Pool RSK papers page. Here, we furnish you with weekly and current pool RSK papers for your forecast and winning pleasure. Click on the images to view them more clearly. Enjoy. RSK PAPERS: Soccer X Research, Bob Morton, Capital International Special Advance. Right On Fixtures. Bob Morton. Temple Of Draws. Bigwin Winstar Capital ...
Week 52 RSK Pool Papers 2024: Soccer 'X' Research, Bob Morton, Capital International, Winstar, BigWin Week 52 rsk papers 2024: Welcome to fortune soccer. Home; Advertise Here; Banker Room. ... Week 50 Pool Result for Sat 15 Jun 2024 - Aussie 2024; Week 49 Pool Result for Sat 8 Jun 2024 - Aussie 2024; Week 48 Pool Result for Sat 1 Jun 2024 ...