Literature DB >> 33691278

The Influence of Different Training Load Quantification Methods on the Fitness-Fatigue Model.

Kobe M Vermeire, Freek Van de Casteele, Maxim Gosseries, Jan G Bourgois, Michael Ghijs, Jan Boone.   

Abstract

PURPOSE: Numerous methods exist to quantify training load (TL). However, the relationship with performance is not fully understood. Therefore the purpose of this study was to investigate the influence of the existing TL quantification methods on performance modeling and the outcome parameters of the fitness-fatigue model.
METHODS: During a period of 8 weeks, 9 subjects performed 3 interval training sessions per week. Performance was monitored weekly by means of a 3-km time trial on a cycle ergometer. After this training period, subjects stopped training for 3 weeks but still performed a weekly time trial. For all training sessions, Banister training impulse (TRIMP), Lucia TRIMP, Edwards TRIMP, training stress score, and session rating of perceived exertion were calculated. The fitness-fatigue model was fitted for all subjects and for all TL methods.
RESULTS: The error in relating TL to performance was similar for all methods (Banister TRIMP: 618 [422], Lucia TRIMP: 625 [436], Edwards TRIMP: 643 [465], training stress score: 639 [448], session rating of perceived exertion: 558 [395], and kilojoules: 596 [505]). However, the TL methods evolved differently over time, which was reflected in the differences between the methods in the calculation of the day before performance on which training has the biggest positive influence (range of 19.6 d).
CONCLUSIONS: The authors concluded that TL methods cannot be used interchangeably because they evolve differently.

Entities:  

Keywords:  impulse response model; influence curves; performance modeling; performance prediction; training monitoring

Mesh:

Year:  2021        PMID: 33691278     DOI: 10.1123/ijspp.2020-0662

Source DB:  PubMed          Journal:  Int J Sports Physiol Perform        ISSN: 1555-0265            Impact factor:   4.010


  1 in total

1.  The Use of Fitness-Fatigue Models for Sport Performance Modelling: Conceptual Issues and Contributions from Machine-Learning.

Authors:  Frank Imbach; Nicolas Sutton-Charani; Jacky Montmain; Robin Candau; Stéphane Perrey
Journal:  Sports Med Open       Date:  2022-03-03
  1 in total

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