scholarly journals Relationships between sleep, athletic and match performance, training load and injuries: A systematic review in soccer players

Author(s):  
Filipe Manuel Clemente ◽  
José Afonso ◽  
Júlio Costa ◽  
Rafael Oliveira ◽  
José Pino-Ortega ◽  
...  
Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 808
Author(s):  
Filipe Manuel Clemente ◽  
José Afonso ◽  
Júlio Costa ◽  
Rafael Oliveira ◽  
José Pino-Ortega ◽  
...  

The purpose of this systematic review was to summarize available evidence regarding the relationships between sleep and (i) athletic and match performance, (ii) training load, and (iii) injuries in soccer players. A systematic review of EBSCOhost (SPORTDiscus), PubMed, Cochrane Library, FECYT (Web of Sciences, CCC, DIIDW, KJD, MEDLINE, RSCI, and SCIELO) databases was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 297 titles were identified, of which 32 met the eligibility criteria. Results revealed that soccer players are no exception for sleep inadequacy. Although there was inconsistency in the findings, some studies suggested that sleep restrictions in soccer negatively affected athletic and match performance while also increasing the number and severity of musculoskeletal injuries. On the other hand, inconsistent results were found between sleep and athletic and match performance, and training load in soccer players. Physiological responses (and their intensity) during drill-based games were not influenced by changes in sleep. The available evidence is inconsistent; however, it appears to suggest that poor sleep affects soccer players’ performance and increases the risk of injury. However, it remains important to study this complex relationship further.


Author(s):  
Sullivan Coppalle ◽  
Guillaume Ravé ◽  
Jason Moran ◽  
Iyed Salhi ◽  
Abderraouf Ben Abderrahman ◽  
...  

This study aimed to compare the training load of a professional under-19 soccer team (U-19) to that of an elite adult team (EAT), from the same club, during the in-season period. Thirty-nine healthy soccer players were involved (EAT [n = 20]; U-19 [n = 19]) in the study which spanned four weeks. Training load (TL) was monitored as external TL, using a global positioning system (GPS), and internal TL, using a rating of perceived exertion (RPE). TL data were recorded after each training session. During soccer matches, players’ RPEs were recorded. The internal TL was quantified daily by means of the session rating of perceived exertion (session-RPE) using Borg’s 0–10 scale. For GPS data, the selected running speed intensities (over 0.5 s time intervals) were 12–15.9 km/h; 16–19.9 km/h; 20–24.9 km/h; >25 km/h (sprint). Distances covered between 16 and 19.9 km/h, > 20 km/h and >25 km/h were significantly higher in U-19 compared to EAT over the course of the study (p =0.023, d = 0.243, small; p = 0.016, d = 0.298, small; and p = 0.001, d = 0.564, small, respectively). EAT players performed significantly fewer sprints per week compared to U-19 players (p = 0.002, d = 0.526, small). RPE was significantly higher in U-19 compared to EAT (p =0.001, d = 0.188, trivial). The external and internal measures of TL were significantly higher in the U-19 group compared to the EAT soccer players. In conclusion, the results obtained show that the training load is greater in U19 compared to EAT.


Sports ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 53
Author(s):  
Maryam Abarghoueinejad ◽  
Adam D. G. Baxter-Jones ◽  
Thayse Natacha Gomes ◽  
Daniel Barreira ◽  
José Maia

The aim of this systematic review was to identify and synthesize the available information regarding longitudinal data addressing young soccer players’ motor performance changes. Following the Preferred Reporting Items for Systematic and Meta-analyses (PRISMA) statement, literature searches were performed in three databases: PubMed, ISI Web of Science and SCOPUS. The following descriptors were used: football, soccer, youth, young, player, athlete, physical performance, motor performance, longitudinal. The inclusion criteria were original articles in English with longitudinal data of young males (aged 10–18 years), with the aim to investigate motor performance serial changes. The initial search returned 211 records, and the final sample comprised 32 papers. These papers covered the European continent, and used mixed and pure longitudinal design with variation in sample size and age range. The reviewed studies tended to use different tests to assess the motor performance and aimed to identify changes in motor performance in several ways. In general, they indicated motor performance improvements with age, with a marked influence of biological maturity, body composition, and training stimuli. This review highlights the need for coaches and stakeholders to consider players’ motor performance over time whilst considering biological maturation, biological characteristics, and training stimuli.


Author(s):  
Caoimhe Tiernan ◽  
Thomas Comyns ◽  
Mark Lyons ◽  
Alan M Nevill ◽  
Giles Warrington

This study aimed to investigate the association between training load indices and Upper Respiratory Tract Infection (URTI) across different lag periods in elite soccer players. Internal training load was collected from 15 elite soccer players over one full season (40 weeks). Acute, chronic, Acute:Chronic Workload Ratio (ACWR), Exponentially Weighted Moving Averages (EWMA) ACWR, 2, 3 and 4-week cumulative load, training strain and training monotony were calculated on a rolling weekly basis. Players completed a daily illness log, documenting any signs and symptoms, to help determine an URTI. Multilevel logistic regression was used to analyze the associations between training load indices and URTIs across different lag periods (1 to 7-days). The results found a significant association between 2-week cumulative load and an increased likelihood of a player contracting an URTI 3 days later (Odds Ratio, 95% Confidence Interval: OR = 2.07, 95% CI = 0.026-1.431). Additionally, a significant association was found between 3-week cumulative load and a players’ increased risk of contracting an URTI 4 days later (OR = 1.66, 95% CI = 0.013–1.006). These results indicate that accumulated periods of high training load (2- and 3-week) associated with an increased risk of a player contracting an URTI, which may lead to performance decrements, missed training sessions or even competitions.


Sports ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 109
Author(s):  
Tom Douchet ◽  
Allex Humbertclaude ◽  
Carole Cometti ◽  
Christos Paizis ◽  
Nicolas Babault

Accelerations (ACC) and decelerations (DEC) are important and frequent actions in soccer. We aimed to investigate whether ACC and DEC were good indicators of the variation of training loads in elite women soccer players. Changes in the training load were monitored during two different selected weeks (considered a “low week” and a “heavy week”) during the in-season. Twelve elite soccer women playing in the French first division wore a 10-Hz Global Positioning System unit recording total distance, distance within speed ranges, sprint number, ACC, DEC, and a heart rate monitor during six soccer training sessions and rated their perceived exertion (RPE). They answered the Hooper questionnaire (sleep, stress, fatigue, DOMS) to get an insight of their subjective fitness level at the start (Hooper S) and at the end of each week (Hooper E). A countermovement jump (CMJ) was also performed once a week. During the heavy week, the training load was significantly greater than the low week when considering number of ACC >2 m·s−2 (28.2 ± 11.9 vs. 56.1 ± 10.1, p < 0.001) and number of DEC < −2 m·s−2 (31.5 ± 13.4 vs. 60.9 ± 14.4, p < 0.001). The mean heart rate percentage (HR%) (p < 0.05), RPE (p < 0.001), and Hooper E (p < 0.001) were significantly greater during the heavy week. ACC and DEC showed significant correlations with most outcomes: HR%, total distance, distance per min, sprint number, Hooper index of Hooper E, DOMS E, Fatigue E, RPE, and session RPE. We concluded that, for elite women soccer players, quantifying ACC and DEC alongside other indicators seemed to be essential for a more complete training load monitoring. Indeed, it could lead to a better understanding of the reasons why athletes get fatigued and give insight into neuromuscular, rather than only energetic, fatigue.


Author(s):  
David Sadigursky ◽  
Juliana Almeida Braid ◽  
Diogo Neiva Lemos De Lira ◽  
Bruno Almeida Barreto Machado ◽  
Rogério Jamil Fernandes Carneiro ◽  
...  

2021 ◽  
pp. 1-21
Author(s):  
William J.C. Allen ◽  
Kevin L. De Keijzer ◽  
Javier Raya-González ◽  
Daniel Castillo ◽  
Giuseppe Coratella ◽  
...  

2018 ◽  
Vol 19 (5) ◽  
pp. 576-584 ◽  
Author(s):  
Craig M. Whitworth-Turner ◽  
Rocco Di Michele ◽  
Ian Muir ◽  
Warren Gregson ◽  
Barry Drust

Sports ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 3
Author(s):  
Mauro Mandorino ◽  
António J. Figueiredo ◽  
Gianluca Cima ◽  
Antonio Tessitore

This study aimed to analyze different predictive analytic techniques to forecast the risk of muscle strain injuries (MSI) in youth soccer based on training load data. Twenty-two young soccer players (age: 13.5 ± 0.3 years) were recruited, and an injury surveillance system was applied to record all MSI during the season. Anthropometric data, predicted age at peak height velocity, and skeletal age were collected. The session-RPE method was daily employed to quantify internal training/match load, and monotony, strain, and cumulative load over the weeks were calculated. A countermovement jump (CMJ) test was submitted before and after each training/match to quantify players’ neuromuscular fatigue. All these data were used to predict the risk of MSI through different data mining models: Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM). Among them, SVM showed the best predictive ability (area under the curve = 0.84 ± 0.05). Then, Decision tree (DT) algorithm was employed to understand the interactions identified by the SVM model. The rules extracted by DT revealed how the risk of injury could change according to players’ maturity status, neuromuscular fatigue, anthropometric factors, higher workloads, and low recovery status. This approach allowed to identify MSI and the underlying risk factors.


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