Effects of a Forefoot-Oriented Exercise Intervention on Jumping Performance in Volleyball Players: a Randomized Controlled Intervention Study
Food Chemistry(2020)SCI 1区
Univ Antwerp | Bern Univ Appl Sci
Abstract
BACKGROUND: This study investigates the effects of a 12-week forefoot-oriented exercise intervention on jumping performance in male and female volleyball players. METHODS: A total of 93 (age 24.2 +/- 4.6 y) volleyball players with a similar training load were randomly assigned to an intervention group (IG; N.=42) performing a 15-min forefoot oriented intervention during their warm-up procedure for 12 weeks or a control group (CG; N.=51). Athletes were evaluated for jumping using squat jump (SJ) and countermovement jump (CMJ) tests before and after intervention. RESULTS: The CG showed improvements in SJ of 1.6 +/- 3.5 cm (7.4 +/- 14.7%) and CMJ of 0.6 +/- 3.5 cm (2.9 +/- 12.1%). The IG showed improvements in SJ of 1.1 +/- 3.8 cm (4.8 +/- 14.0%) and a decline in CMJ of -0.5 +/- 7.1 cm (1.1 +/- 20.2%). Twoway repeated measures analysis of variance (ANOVA) showed no significant interaction effects for SJ (P=0.535) and CMJ (P=0.297). Within subject tests indicated a significant time effect for SJ (P=0.001), but no significant group effect (P=0.560). In CMJ no significant main effects were found. CONCLUSIONS: Applying a forefoot-oriented exercise intervention over a period of 12 weeks showed no considerable effect on jumping performance in volleyball players.
MoreTranslated text
Key words
Running,Warm-up exercise,Volleyball,Athletes
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
2010
被引用1933 | 浏览
2008
被引用62 | 浏览
2007
被引用348 | 浏览
2007
被引用69413 | 浏览
2010
被引用134 | 浏览
2010
被引用263 | 浏览
2009
被引用308 | 浏览
1999
被引用1528 | 浏览
2008
被引用231 | 浏览
2012
被引用55 | 浏览
2013
被引用196 | 浏览
2015
被引用24 | 浏览
2014
被引用1 | 浏览
1999
被引用1272 | 浏览
2017
被引用124 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest