Research and analysis of an exercise internal load system based on a big data context assessment
- DOI
- 10.2991/978-94-6463-264-4_48How to use a DOI?
- Keywords
- training load; match load; load monitoring; load quantification
- Abstract
- Objectives
To examine how Rating of Perceived Exertion (RPE) and session RPE (sRPE) are used in Olympic combat sports. Along with the quantification of training and competition load, the application effect is explored from the perspectives of reliability and validity. Methodsː Search databases in recent 10 years like Pubmed, Web of Science, and Google Scholar were consulted, and 28 categories of literature were finally included. Resultsː 1) Only judo competitions (ICC=0.84, 95%CI:0.11-1.00) demonstrate good reliability. 2) Overall, a good level of criteria validity was seen (r=0.44-0.92, 95%CI:0.15-0.95). The key variables determining validity were the sport, playing level, specific activities, cognitive level, training phase, and RPE collection time. 3) The criterion validity is positively correlated with both playing level and cognitive level. The validity for striking combat sports is superior to that for grappling combat sports, and the validity for the pre-competition phase is superior to that for the competition phase. RPE taken 30 minutes after training is more valid than RPE collected right away. Conclusion: The RPE and sRPE show good validity, although further research is required to assess its reliability. Combining internal and external load indicators to improve the systematicness of internal load monitoring in combat sports is necessary.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Chengjie Ye AU - Xinna Li AU - Jingwei Li AU - Jiachi Ye AU - Binghong Gao PY - 2023 DA - 2023/09/28 TI - Research and analysis of an exercise internal load system based on a big data context assessment BT - Proceedings of the 2023 3rd International Conference on Education, Information Management and Service Science (EIMSS 2023) PB - Atlantis Press SP - 427 EP - 435 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-264-4_48 DO - 10.2991/978-94-6463-264-4_48 ID - Ye2023 ER -