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

Relationship between methods of monitoring training load and physiological indicators changes during 4 weeks cross-country skiing altitude training

This study aimed to: (i) analyze the load characteristics of 4 weeks cross-country skiing altitude training; (ii) analyze the relationships between methods of monitoring training load and physiological indicators changes of elite male Chinese cross-country skiers during this period. Practitioners collected load data during 4 weeks of altitude training camp. Participants performed maximal oxygen uptake, lactate threshold, body composition, and skierg power test before and after the training camp to investigate the changes in physiological performance. Edwards TRIMP, Lucia TRIMP, and session rating of perceived exertion were collected as internal load. Training distance, time recorded by the Catapult module were collected as external load. The result revealed a " pyramid " pattern in the load characteristics during the altitude training camp. The correlation between luTRIMP and percent change in physiological indicators was highest. Percentage changes in lactate threshold velocity (r = .78 [95% CI -.01 to .98]), percentage changes in lactate threshold HR (r = .71 [95% CI .14- .99]), percentage changes in maximum HR (r = .83 [95% CI .19-1.00]), percentage changes in skierg power-to-weight ratio (r = .75 [95% CI -.28 to .98]) had very large relationships with luTRIMP. In cross-country skiing altitude training, training loads should be reasonably controlled to ensure that athletes do not become overly fatigued. Methods of training load monitoring that combine with athletes' physiological characteristics and program characteristics have the highest dose-response relationships, it is an important aspect of cross-country ski training load monitoring. The luTRIMP could be a good monitoring tool in cross-country skiing altitude training.

Training Load and Acute Performance Decrements Following Different Training Sessions

PURPOSE

To examine the differences in training load (TL) metrics when quantifying training sessions differing in intensity and duration. The relationship between the TL metrics and the acute performance decrement measured immediately after the sessions was also assessed.

METHODS

Eleven male recreational cyclists performed 4 training sessions in a random order, immediately followed by a 3-km time trial (TT). Before this period, participants performed the time TT in order to obtain a baseline performance. The difference in the average power output for the TTs following the training sessions was then expressed relative to the best baseline performance. The training sessions were quantified using 7 different TL metrics, 4 using heart rate as input, 2 using power output, and 1 using the rating of perceived exertion.

RESULTS

The load of the sessions was estimated differently depending on the TL metrics used. Also, within the metrics using the same input (heart rate and power), differences were found. TL using the rating of perceived exertion was the only metric showing a response that was consistent with the acute performance decrements found for the different training sessions. The Training Stress Score and the individualized training impulse demonstrated similar patterns but overexpressed the intensity of the training sessions. The total work done resulted in an overrepresentation of the duration of training.

CONCLUSION

TL metrics provide dissimilar results as to which training sessions have higher loads. The load based on TL using the rating of perceived exertion was the only one in line with the acute performance decrements found in this study.

The Fitness-Fatigue Model: What's in the Numbers?

PURPOSE

The purpose of this commentary is to outline some of the pitfalls when using the fitness-fatigue model to unravel the interaction between training load and performance. By doing so, we encourage sport scientists and coaches to interpret the parameters from the model with some extra caution.

CONCLUSIONS

Caution is needed when interpreting the fitness-fatigue model since the parameter values are influenced by the starting parameter values, the modeling technique, and the input of the model. Also, the use of general constants should be avoided since they do not account for interindividual differences and differences between training-load methods. Therefore, we advise sport scientists and coaches to use the model as a way to work more data-informed rather than working data-driven.

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

The emergence of the first Fitness-Fatigue impulse responses models (FFMs) have allowed the sport science community to investigate relationships between the effects of training and performance. In the models, athletic performance is described by first order transfer functions which represent Fitness and Fatigue antagonistic responses to training. On this basis, the mathematical structure allows for a precise determination of optimal sequence of training doses that would enhance the greatest athletic performance, at a given time point. Despite several improvement of FFMs and still being widely used nowadays, their efficiency for describing as well as for predicting a sport performance remains mitigated. The main causes may be attributed to a simplification of physiological processes involved by exercise which the model relies on, as well as a univariate consideration of factors responsible for an athletic performance. In this context, machine-learning perspectives appear to be valuable for sport performance modelling purposes. Weaknesses of FFMs may be surpassed by embedding physiological representation of training effects into non-linear and multivariate learning algorithms. Thus, ensemble learning methods may benefit from a combination of individual responses based on physiological knowledge within supervised machine-learning algorithms for a better prediction of athletic performance.In conclusion, the machine-learning approach is not an alternative to FFMs, but rather a way to take advantage of models based on physiological assumptions within powerful machine-learning models.