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| Campo DC | Valor | Lengua/Idioma |
|---|---|---|
| dc.contributor.author | Moya-Ramon, Manuel | - |
| dc.contributor.author | Javaloyes, Alejandro | - |
| dc.contributor.author | Sarabia, José Manuel | - |
| dc.contributor.other | Departamentos de la UMH::Ciencias del Deporte | es_ES |
| dc.date.accessioned | 2026-09-23T16:23:38Z | - |
| dc.date.available | 2026-09-23T16:23:38Z | - |
| dc.date.created | 2018 | - |
| dc.identifier.citation | Journal of Science and Cycling, volumen 7, número 1, páginas 17–23, 2018 | es_ES |
| dc.identifier.issn | 2254-7053 | - |
| dc.identifier.uri | https://hdl.handle.net/11000/40775 | - |
| dc.description.abstract | Quantification models aim to accurately reflect the magnitude of the training stress imposed to the athlete, especially in sports with high training volumes, such as road cycling. The aim of this study was to explore the concurrent validity of a new whole-body bioenergetic TRIMP model (Hayes & Quinn, 2009) correlating the obtained training load scores in road cycling with other commonly used models (the Banister TRIMP, the sRPE model and Training Stress Score (TSS)). After three weeks of familiarization with procedures and the performed test to determine VO2max, critical power (Pcrit), anaerobic work capacity (AWC) and Maximal Power Output (Pmax), 12 well-trained road cyclists performed 8 weeks of individual training to record their individual training data (duration, heart rate, power output and rate of perceived effort (RPE)). Different Pearson’s correlation was performed to assess the relationship between models and the changes in fitness. A very large correlation was found between Hayes & Quinn’s TRIMP and RPE session (r = 0.90; p < 0.001) and TSS (r = 0.88; p < 0.01) and a moderate correlation was found with Banister’s TRIMP (r = 0.64; p < 0.05). No significant correlation was found between changes in fitness and Hayes & Quinn’s TRIMP. According to these findings, Hayes & Quinn´s TRIMP is a promising mathematical model based on an individual´s bioenergetic profile that could be used to quantify training load in road cycling. This research increases our knowledge about training load models that use power output to measure intensity. | es_ES |
| dc.format | application/pdf | es_ES |
| dc.format.extent | 7 | es_ES |
| dc.language.iso | eng | es_ES |
| dc.publisher | Cycling Research Center | es_ES |
| dc.rights | info:eu-repo/semantics/openAccess | es_ES |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.subject | cycling | es_ES |
| dc.subject | training impulse | es_ES |
| dc.subject | power output | es_ES |
| dc.subject | training load | es_ES |
| dc.subject | heart rate | es_ES |
| dc.subject.other | CDU::7 - Bellas artes::79 - Diversiones. Espectáculos. Cine. Teatro. Danza. Juegos.Deportes | es_ES |
| dc.title | Hayes & Quinn’s TRIMP Concurrent Validity for Cycling | es_ES |
| dc.type | info:eu-repo/semantics/article | es_ES |
| dc.relation.publisherversion | https://doi.org/10.28985/180130.jsc.04 | es_ES |
2018 Moya-Ramon - Hayes & Quinn’s TRIMP Concurrent Validity for Cycling.pdf
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