Movement profile influences systemic stress and biomechanical resilience to high training load exposure

2019 ◽  
Vol 22 (1) ◽  
pp. 35-41 ◽  
Author(s):  
Barnett S. Frank ◽  
Anthony C. Hackney ◽  
Claudio L. Battaglini ◽  
Troy Blackburn ◽  
Stephen W. Marshall ◽  
...  
2018 ◽  
Vol 36 (21) ◽  
pp. 2431-2437 ◽  
Author(s):  
Thomas Sawczuk ◽  
Ben Jones ◽  
Sean Scantlebury ◽  
Kevin Till

2019 ◽  
Vol 10 ◽  
Author(s):  
Ben E. W. Cousins ◽  
John G. Morris ◽  
Caroline Sunderland ◽  
Anthony M. Bennett ◽  
Golnaz Shahtahmassebi ◽  
...  

Author(s):  
Leonardo Cesanelli ◽  
Berta Ylaitė ◽  
Giuseppe Messina ◽  
Daniele Zangla ◽  
Stefania Cataldi ◽  
...  

High-level young athletes need to face a wide spectrum of stressors on their journey to élite categories. The aims of the present study are (i) to evaluate session rate of perceived exertion (sRPE) at different training impulse (TRIMP) categories and the correlations between these two variables and, (ii) evaluate the correlations between sRPE, fluid loss, and carbohydrate consumption during exercise. Data on Edward’s TRIMP, sRPE, body mass loss pre- and post- exercise (∆), and carbohydrate consumption (CHO/h) during exercise have been acquired from eight male junior cyclists during a competitive season. One-way ANOVA and correlation analysis with linear regression have been performed on acquired data. sRPE resulted in a significant difference in the three TRIMP categories (p < 0.001). sRPE resulted in being very largely positively associated with TRIMP values (p < 0.001; R = 0.71). ∆ as well as CHO/h was largely negatively related with sRPE in all TRIMP categories (p < 0.001). The results confirmed the role of fluid balance and carbohydrate consumption on the perception of fatigue and fatigue accumulation dynamics independently from the training load. Young athletes’ training load monitoring and nutritional-hydration support represent important aspects in athlete’s exercise-induced fatigue management.


2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Mikhail I. Stolbov ◽  
Maria A. Shchepeleva ◽  
Alexander M. Karminsky

AbstractThe study empirically assesses how macroprudential policy interacts with systemic risk, industrial production, and monetary intervention on a global level from January 2006 to December 2018. We adopt the aggregate proxies of these variables, capturing their global effects, and use a novel econometric technique, namely, smooth local projections. The study finds that global macroprudential policy leads the monetary policy, exhibiting a countercyclical pattern concerning industrial production. The latter has an inverse bidirectional linkage with systemic risk. Thus, an ex-ante tight macroprudential policy can indirectly mitigate global systemic risk through its pro-growth effect on industrial production, although no convincing evidence exists for the direct impact of a macroprudential intervention on systemic risk. The study results endure several extensions and a robustness check, which builds on alternative measures of global systemic stress and real economic activity, thereby legitimizing the increased importance attached to the macroprudential policy since the 2007–2009 global financial crisis.


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.


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