Recovery-stress balance and injury risk in team sports

2021 ◽  
pp. 87-97
Author(s):  
Michel Brink ◽  
Koen Lemmink
2020 ◽  
Vol 11 ◽  
Author(s):  
Daniel Boullosa ◽  
Arturo Casado ◽  
João Gustavo Claudino ◽  
Pedro Jiménez-Reyes ◽  
Guillaume Ravé ◽  
...  

Author(s):  
Talko Bernhard Dijkhuis ◽  
Ruby Otter ◽  
Marco Aiello ◽  
Hugo Velthuijsen ◽  
Koen Lemmink

AbstractInjuries of runners reduce the ability to train and hinder competing. Literature shows that the relation between potential risk factors and injuries are not definitive, limited, and inconsistent. In team sports, workload derivatives were identified as risk factors. However, there is an absence of literature in running on workload derivatives. This study used the workload derivatives acute workload, chronic workload, and acute: chronic workload ratios to investigate the relation between workload and injury risk in running. Twenty-three competitive runners kept a daily training log for 24 months. The runners reported training duration, training intensity and injuries. One-week (acute) and 4-week (chronic) workloads were calculated as the average of training duration multiplied by training intensity. The acute:chronic workload ratio was determined dividing the acute and chronic workloads. Results show that a fortnightly low increase of the acute:chronic workload ratio (0.10–0.78) led to an increased risk of sustaining an injury (p<0.001). Besides, a low increase of the acute:chronic workload ratio (0.05–0.62) between the second week and third week before an injury showed an association with increased injury risk (p=0.013). These findings demonstrate that the acute:chronic workload ratio relates to injury risk.


2020 ◽  
Vol 50 (9) ◽  
pp. 1613-1635 ◽  
Author(s):  
Renato Andrade ◽  
Eirik Halvorsen Wik ◽  
Alexandre Rebelo-Marques ◽  
Peter Blanch ◽  
Rodney Whiteley ◽  
...  

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
João Gustavo Claudino ◽  
Daniel de Oliveira Capanema ◽  
Thiago Vieira de Souza ◽  
Julio Cerca Serrão ◽  
Adriano C. Machado Pereira ◽  
...  

2021 ◽  
Vol 7 (2) ◽  
pp. e001091
Author(s):  
Alli Gokeler ◽  
Anne Benjaminse ◽  
Francesco Della Villa ◽  
Fillippo Tosarelli ◽  
Evert Verhagen ◽  
...  

Athletes in team sports have to quickly visually perceive actions of opponents and teammates while executing their own movements. These continuous actions are performed under time pressure and may contribute to a non-contact ACL injury. However, ACL injury screening and prevention programmes are primarily based on standardised movements in a predictable environment. The sports environment provides much greater cognitive demand because athletes must attend their attention to numerous external stimuli and inhibit impulsive actions. Any deficit or delay in attentional processing may contribute to an inability to correct potential errors in complex coordination, resulting in knee positions that increase the ACL injury risk. In this viewpoint, we advocate that ACL injury screening should include the sports specific neurocognitive demands.


2021 ◽  
Vol 13 (2) ◽  
pp. 34-37
Author(s):  
Panagiotis Poulios ◽  
Athanasios Serlis ◽  
Peter P Groumpos ◽  
Ioannis Gliatis

Artificial intelligence (AI) application opens an exciting perspective for predicting injury risk and team sports performance. A better understanding of the techniques of AI employed and of the sports that are using AI is warranted. The purpose of this study is to identify which AI approaches have been applied to investigate sports performance and injury risk


Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 324
Author(s):  
Sergio J. Ibáñez ◽  
Carlos D. Gómez-Carmona ◽  
David Mancha-Triguero

In previous studies found in the literature speed (SP), acceleration (ACC), deceleration (DEC), and impact (IMP) zones have been created according to arbitrary thresholds without considering the specific workload profile of the players (e.g., sex, competitive level, sport discipline). The use of statistical methods based on raw data could be considered as an alternative to be able to individualize these thresholds. The study purposes were to: (a) individualize SP, ACC, DEC, and IMP zones in two female professional basketball teams; (b) characterize the external workload profile of 5 vs. 5 during training sessions; and (c) compare the external workload according to the competitive level (first vs. second division). Two basketball teams were recorded during a 15-day preseason microcycle using inertial devices with ultra-wideband indoor tracking technology and microsensors. The zones of external workload variables (speed, acceleration, deceleration, impacts) were categorized through k-means clusters. Competitive level differences were analyzed with Mann–Whitney’s U test and with Cohen’s d effect size. Five zones were categorized in speed (<2.31, 2.31–5.33, 5.34–9.32, 9.33–13.12, 13.13–17.08 km/h), acceleration (<0.50, 0.50–1.60, 1.61–2.87, 2.88–4.25, 4.26–6.71 m/s2), deceleration (<0.37, 0.37–1.13, 1.14–2.07, 2.08–3.23, 3.24–4.77 m/s2), and impacts (<1, 1–2.99, 3–4.99, 5–6.99, 7–10 g). The women’s basketball players covered 60–51 m/min, performed 27–25 ACC-DEC/min, and experienced 134–120 IMP/min. Differences were found between the first and second division teams, with higher values in SP, ACC, DEC, and IMP in the first division team (p < 0.03; d = 0.21–0.56). In conclusion, k-means clustering can be considered as an optimal tool to categorize intensity zones in team sports. The individualization of external workload demands according to the competitive level is fundamental for designing training plans that optimize sports performance and reduce injury risk in sport.


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