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

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.

The Fitness–Fatigue Model: What’s in the Numbers?

“…It is known, however, that an individual model fit is necessary when trying to relate TL and performance. 12 In literature, values are found ranging from 4 to 51, and from 4 to 74 for τ 1, and τ 2 , respectively. 2,4,[12][13][14][15] Although some coaching platforms enable coaches to adjust these values to the user's preference, it is futile to estimate what parameter values will best suit the individual athlete without fitting the model.…”

“…Moreover, studies have shown that different input variables (ie, TL calculations) can lead to substantially different results in the parameter values. The study of Mitchell et al 13 and the study of Vermeire et al 12 have both shown that the input of different TL quantification methods will lead to different τ values on the same data set. The parameter values of these studies differ considerably from the original papers, which gives rise to question the validity of the commonly used parameter values.…”

“…To demonstrate this, a result is shown from a global optimization (see below) of the model to data of one of the subjects in an earlier study in Figure 1. 12 As this optimization scanned a series of values for each combination of parameters in the fitness-fatigue model (a range of 0-3 for k and 0-60 for τ), a minimum absolute model error (residual sum of squares) could be plotted for each parameter value combination in Figure 1. From this figure, we can deduct that different subsets of parameter values can lead to similar model errors, which makes it hard to select the right parameter values.…”

“…However, research has shown that different TL quantification methods lead to different k and τ values. 12,13 In an ideal situation, the τ values would not be different, depending on the input, and only the k values would be influenced since this is the factor that makes it possible to convert the input to the required output. Again, this implies that the absolute parameter values are to be interpreted with caution.…”

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

“…Since FFMs are sensitive to the nature of the model input [ 25 ], a consistent training quantification method that is not biased by the type of training is required across training sessions.…”