Connolly, DR, Stolp, S, Gualtieri, A, Ferrari Bravo, D, Sassi, R, Rampinini, E, and Coutts, AJ. How do young soccer players train? A 5-year analysis of weekly training load and its variability between age groups in an elite youth academy. J Strength Cond Res 38(8): e423-e429, 2024-The aim of this study was to quantify the session rating of perceived exertion (sRPE), duration, and training load accrued across typical training weeks undertaken by youth soccer players. Differences between starters, nonstarters, and variations in training load variables were also investigated. Data were collected from 230 elite youth players in 4 age groups (U15, U16, U17, and U19) during 5 competitive seasons. Mixed models were used to describe variation between age groups and compare starters with nonstarters, with season as a fixed covariate effect. Week-to-week variation in training load was expressed as the percentage coefficient of variation. The main findings may be used to highlight a significant effect of age and playing status on training intensity, duration, and internal training load. Weekly training load increased progressively from the U15 to U17, with significant differences between each age group (p < 0.03). Lower mean weekly perceived intensity (sRPE) was noted in U15 when compared with the older age groups (4.2 vs. 4.6-4.9 arbitrary unit for U16 to U19, p < 0.001). Low weekly training load variation was observed across the different phases of the season in each age group, with the preseason exhibiting the greatest variance (3.6-6.2%). Differences in the training load are likely more attributable to changes in training duration rather than sRPE. Control of session duration seems to play an important role when aiming to control load in the academy environment, and practitioners should closely monitor the differences in duration and load being recorded between starters and nonstarters.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1519/JSC.0000000000004813 | DOI Listing |
mSphere
December 2024
Antibiotics Research and Re-evaluation Key Laboratory of Sichuan Province, School of Pharmacy, Chengdu University, Chengdu, China.
is a prominent Gram-negative and encapsulated opportunistic pathogen that causes a multitude of infections such as severe respiratory and healthcare-associated infections. Despite the widespread anti-microbial resistance and the high mortality rate, currently, no clinically vaccine is approved for battling . To date, messenger RNA (mRNA) vaccine is one of the most advancing technologies and are extensively investigated for viral infection, while infrequently applied for prevention of bacterial infections.
View Article and Find Full Text PDFJ Biomater Sci Polym Ed
January 2025
Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER)-Ahmedabad, Gandhinagar, India.
Zein, a plant-based protein obtained from the endosperm of corn ( L.) received colossal attention in recent years due to its promising features like being economical, mucoadhesive, gastro-resistant, biocompatible and aids to load hydrophilic and hydrophobic therapeutic agents. It can be employed for the fabrication of various drug delivery systems such as nanoparticles, micelles, hydrogels, nanofibers and films.
View Article and Find Full Text PDFFront Physiol
December 2024
Department of Health Sciences, Public University of Navarre (UPNA), Pamplona, Spain.
In this quasi-experimental before-after trial, we investigated the effects of a high-intensity, low-repetition inspiratory muscle training (HI-LRMT) protocol on respiratory muscle strength in instrumental musicians. In addition, was to estimate the prevalence of "non-responders" (NRs) in terms of muscle force after intervention. Healthy musicians ( = 48) were divided into 2 groups: HI-LRMT ( = 33) and a control group that did not train (CG, = 15).
View Article and Find Full Text PDFNeural Netw
December 2024
The school of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China. Electronic address:
Emotion recognition via electroencephalogram (EEG) signals holds significant promise across various domains, including the detection of emotions in patients with consciousness disorders, assisting in the diagnosis of depression, and assessing cognitive load. This process is critically important in the development and research of brain-computer interfaces, where precise and efficient recognition of emotions is paramount. In this work, we introduce a novel approach for emotion recognition employing multi-scale EEG features, denominated as the Dynamic Spatial-Spectral-Temporal Network (DSSTNet).
View Article and Find Full Text PDFInt J Prev Med
December 2024
Ph.D of Health Education, Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Background: Depression literacy has notable advantages in the early identification and treatment of depression. The current study was conducted with the aim of translating and investigating the validity and reliability of the Depression Literacy Questionnaire (D-Lit) in Iranian young adults.
Methods: The current study entailed a descriptive-analytical study in which the translation, validation, and preparation of the Persian version of D-Lit were conducted.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!