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Fatigue and fitness modelled from the effects of training on performance

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The purpose of this study was to compare two ways of estimating both fatigue and fitness indicators from a systems model of the effects of training on performance. The model was applied to data concerning the training of a hammer thrower. The variations in performance were mathematically related to the successive amounts of training. The model equation was composed of negative (NF) and positive (PF) functions. The NF and PF were associated with the fatigue and fitness estimated in previous studies. Using another method, fatigue and fitness indicators were estimated from a combination of NF and PF. The influence of training on performance was negatively associated with fatigue (NI), and positively to fitness (PI). The changes in performance were well described by the model in the present study (r = 0.96, N = 19, P < 0.001). Significant correlations were observed between PF and PI (r = 0.90, P < 0.001) on the other. The absolute values and the time variations of PI and NI were closer to the change in performance than NF and PF. The NF and PF were accounted for mainly by the accumulation of amounts of training. On the other hand, NI and PI were accounted for rather by the impact of these amounts of training on performance.
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... c Attempting to manipulate large volumes of work, as can occur with simultaneous increases in several or all aspects of fitness, requires very careful fatigue management. Thus, very large volumes of work can (and often do) increase accumulated fatigue to a point that it becomes difficult to recover, as a result adaptation and performance suffer (18,19,49,102,200). c Simultaneous increases of noncompatible fitness factors during training for a few weeks or more can inhibit adaptation of one or more factors, including learning new skills (14). ...
... c Within the microcycle, programming wave-like loading from day to day or week to week seems to enhance training out comes (40,41,163,190,197). These findings encompass a variety of sports and resistance training (19,20,25,55,156,158). c For resistance training, and likely other forms of training (55), combination training (heavy plus light loading) creates a wave-like variation throughout a microcycle and makes a positive difference in performance adaptation, particularly RFD and power output. ...
... Training to failure entails a consistent relative maximum effort, resulting in poor fatigue management and inability to achieve a true loading spectrum (25,136). (19,20,25,156,158). Heavy and light resistance training days (24,158,159) typically consist of reduced loading (10-20%) and volume load on the light day and not training to failure as this would obviate the contrasting effects (failure always produces a relative maximum). ...
Article
Periodization can be defined as a logical sequential, phasic method of manipulating fitness and recovery phases to increase the potential for achieving specific performance goals while minimizing the potential for nonfunctional overreaching, overtraining, and injury. Periodization deals with the micromanagement of timelines and fitness phases and is cyclic in nature. On the other hand, programming deals with the micromanagement of the training process and deals with exercise selection, volume, intensity, etc. Evidence indicates that a periodized training process coupled with appropriate programming can produce superior athletic enhancement compared with nonperiodized process. There are 2 models of periodization, traditional and block. Traditional can take different forms (i.e., reverse). Block periodization has 2 subtypes, single goal or factor (individual sports) and multiple goals or factors (team sports). Both models have strengths and weaknesses but can be “tailored” through creative programming to produce excellent results for specific sports.
... Les travaux de Morton et al. (1996) en course à pied, Candau et al. (1992) en ski de fond ou encore Mujika et al. (1996) en natation, utilisent un facteur de pondération Y = e bx dont la valeur est liée à l'augmen- ayant utilisées cette forme de modèle dans l'optique de prédiction de la performance. A travers des activités comme la natation (Calvert et al., 1976;Chatard et Wilson, 2003), le marathon ou le triathlon (Millet et al., 2002;Morton et al., 1990;Wallace et al., 2014)) ou encore les sports de force comme l'haltérophilie ou le lancer du marteau (Busso et al., 1994(Busso et al., , 1991, les études relatent des niveaux de corrélations (r) compris entre 0.60 et 0.98, entre la performance prédite et la performance réalisée. Dans certains cas, les auteurs de ces études proposent des versions adaptées du modèle initial. ...
... TABLEAU 1.16 -Zones d'intensité en relation avec FC max (Edwards, 1993 (Busso et al., 1994(Busso et al., , 1991 (Saltin, 1964) ou encore l'inertie du système cardio-vasculaire qui entraine une sous estimation de FC pour les exercices intenses et inférieurs à 30 secondes (Bosquet et al., 2008). Ils caractérisent la perception qu'a un sportif de son bien-être, de sa fatigue, de la qualité de son sommeil, de son état psychologique (anxiété, dépression, motivation...). ...
Thesis
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... To date, most of the studies used linear time-invariant FFM for modelling athletic performance (Banister, Calvert, Savage, et al. 1975;Banister and Hamilton 1985;Banister and Fitz-Clarke 1993;Banister, Green, McDougall, et al. 1991;Busso, Candau, and Lacour 1994;Busso, Carasso, and Lacour 1991;Busso, Häkkinen, Pakarinen, et al. 1990, 1992Morton, Fitz-Clarke, and Banister 1990;Mujika, Busso, Lacoste, et al. 1996;Vermeire, Van de Casteele, Gosseries, et al. 2021). In these studies, authors considered that the model parameters (e.g. ...
... In addition, adding a fatigue component to the model did not significantly improve the model performances, except for one subject. That is in line with literature since the most complex models (assuming that complexity is related to the number of components included in the model) did not ensure the best performances (Busso, Candau, and Lacour 1994). Once again, one should consider the population studied in studies before generalising in so far as athletes may train with much higher workloads than those studied here, which might render a fatigue component valuable in such cases. ...
Thesis
Full-text available
The first models of training effects on athletic performance emerged with the work of Banister and Calvert through the so-called Fitness-Fatigue model (FFM). One major drawback of FFMs is that the features stem from a single source of data. That is not in line with the existing consensus about a multifactorial aspect of athletic performance. Hence, multivariate modelling approaches from statistics and machine-learning (ML) emerged. A research issue arises from the quantification of training Loads (TL) in resistance training (RT) which lack of physiological evidence. In the first study, we provided a new method of TL quantification in RT based on physiological observations. To achieve that, we initially modelled the torque-velocity profiles of fifteen participants during an isokinetic leg extension task and assessed a set of physiological responses to various resistance exercises intensities. Each session was volume-equated according to the formulation of volume load (i.e. the product of the number of repetitions and the relative intensity). Higher led to greater muscular fatigue described by neuromuscular impairments. Conversely, systemic and local pulmonary responses (measured through oxygen uptake) and metabolic changes (according to blood lactate concentrations) were more significant at low intensities, suggesting different contributions of metabolic pathways. From these results, we provided a new index of TL based on the neuromuscu- lar impairments observed at exercise. We showed that to exponentially weight TL by the average rate decay of force development rate yielded better correla- tions with any of the significant physiological responses to exercise. In addition, information compressed within a principal component could be a valuable TL index. In the second study, we provided a robust modelling methodology that relies on model generalisation. Using data from elite speed skaters, we compared a dose-response model to regularisation methods and machine-learning models. Regularisation procedures provided the greatest performances in both generalisa- tion and accuracy. Also, we highlighted the pertinence of computing one model over the group of athletes instead of a model per athlete in a context of a small sample size. Finally, ML approaches could be a way of improving FFMs through ensemble learning methods. In the third study, we modelled acceleration-velocity directly from global posi- tioning system (GPS) measurements and attempted to predict the coefficients of the relationship between acceleration and velocity. First, a baseline model was defined by time-series forecasting using game data only. Then, we proceeded to multivariate modelling using commercial features. A regularised linear regression and a long short term memory neural network were compared. Finally, we extracted features directly from raw GPS data and compared these features to the commercial ones for prediction purposes. The results showed only slight differences between model accuracy, and no models significantly outperformed the baseline in the prediction task. Given the multi- factorial nature of athletic performance, using only GPS data for predicting such athletic performance criterion provided an acceptable accuracy. Using time-domain and frequency-domain features extracted from raw data led to similar performances compared to the commercial ones, despite being evidence-based. It suggests that raw data should be considered for future athletic performance and injury occurrence analysis. Lastly, we developed an athlete management system for long-distance runners. This application provided an athlete monitoring module and a predictive module based on a physiological model of running performance. A second development was realised under the SAP analytics cloud solution. Team management and automated dashboards were provided herein, in close collaboration with a professional Rugby team.
... To begin to understand how the sapply function can be used in lieu of an explicit for-loop to implement FFMs, recall the general form of the FFM components (Busso, Candau and Lacour, 1994): ...
... = c("optimx"), .export = c("train_test", "vdrObjectiveLL", "simulateVDR2", "mape")) %dopar%{ train_test(dat, parmat, bounds, main = FALSE, splits = splits, initial = initial, currentSplit = i)} # By default results returned as a list stopCluster(cl) # Stop cluster names(fitted_splits) <-paste0("split_",1:nSplits) # Add names to the list for each split # Compute model performance across splits and add to the existing list object mape_train_across <-matrix(NA, nrow = nStarts, ncol = nSplits) mape_test_across <-matrix(NA, nrow = nStarts, ncol = nSplits) for (i in 1 ]$fittedModel$mape_test } fitted_splits$across_splits <-list("training_mape" = c("mean" = mean(mape_train_across), "sd" = sd(mape_train_across)), "testing_mape" = c("mean" = mean(mape_test_across), "sd" = sd(mape_test_across))) provides a simple way to repetitively fit a given model for multiple starting points, with the results of this process collected in a dataframe (lines [28][29][30][31][32][33][34][35][36][37]. This function train_test also accounts for the case when you wish to fit to the entire block 1 data and test predictions on block 2, as will be demonstrated shortly using the mock data developed earlier; facilitated via the function argument main = TRUE assuming object passed to argument dat is the full dataset of both block 1 and block 2 data. ...
... system output) results from training loads (i.e. system input) and studies have been conducted in both humans (Busso et al., 1994) and animals (Philippe et al., 2019(Philippe et al., , 2015. According to the model from Banister (Banister et al., 1975), the effects of training bouts on parameters related to fatigue and aptitude responses were constant over time. ...
... Until recently, models have mostly applied in endurance activities such as running cross country skiing (Candau et al., 1992), running (Banister & Hamilton, 1985), swimming (Mujika et al., 1995;Thomas et al., 2008Thomas et al., , 2009, cycling (Busso, 2003;Thomas & Busso, 2005), and triathlon (Banister et al., 1999;Millet et al., 2002Millet et al., , 2005, especially in humans. Furthermore, late studies investigated modelling in high-intensity sports requiring more complex motor skills such as hammer throw (Busso et al., 1994), artistic gymnastics (Sanchez et al., 2013), and more recently short-track speed skating (ST) (Méline et al., 2019). However, all these studies used impulse functions and did not consider that adaptations to training are not spontaneous but progressive (Philippe et al., 2019). ...
Article
Mathematical models are used to describe and predict the effects of training on performance. The initial models are structured by impulse-type transfer functions, however, cellular adaptations induced by exercise may exhibit exponential kinetics for their growth and subsequent dissipation. Accumulation of exercise bouts counteracts dissipation and progressively induces structural and functional changes leading to performance improvement. This study examined the suitability of a model with exponential terms (Exp-Model) in elite short-track speed (ST) skaters. Training loads and performance evolution from fifteen athletes (10 males, 5 females) were previously collected over a 3-month training period. Here, we computed the relationship between training loads and performance with Exp-Model and compared with previous results obtained with a variable dose-response model (Imp-Model). Exp-Model showed a higher correlation between actual and modelled performances (R2 = 0.83 ± 0.08 and 0.76 ± 0.07 for Exp-Model and Imp-Model, respectively). Concerning model parameters, a lower τ A 1 (time constant for growth) value was found (p = 0.0047; d = 1.4; 95% CI [0.4;1.9]) in males compared to females with Exp-model, suggesting that females have a faster adaptative response to training loads. Thus, according to this study, Exp-model may better describe training adaptations in elite ST athletes than Imp-Model.
... Each swimmer performed Semi-Tethered-Tests (3x20m freestyle, increasing resistance) twice a week, indicating the actual person's performance level. The data analysis was performed with two different versions of the FF model, each once with a preload value of zero and once with a preload computed over days to compare predictive accuracy and highlight the importance of past training effects: [FF1] Busso, Candau, & Lacour (1994); [FF2] Busso (2003). The models with preload over days are noted as FF1pr or FF2pr, respectively. ...
... Whilst these recommendations may provide productive approaches and avenues for future research, it is important to recognise that they fail to address some of the limitations inherent to the structure of basic FFMs. It has been known for an extended period that the standard FFM and subsequent first-order filter extensions suffer from several limitations that are at odds with the conceptual understanding of training response (Banister et al., 1975;Busso et al., 1991Busso et al., , 1994Calvert et al., 1976;Morton et al., 1990;Rasche & Pfeiffer, 2019). Three key limitations include: 1) the linearity assumption, where performance can be arbitrarily increased by simply increasing the training dose and for example doubling the training dose leads to double the improvement (Hellard et al., 2006;Rasche & Pfeiffer, 2019); 2) the independence assumption, where there is no interaction between training sessions and therefore training performed on a given day does not influence the response generated from another session; and 3) the deterministic assumption, where uncertainty in model parameters and observed performance are not modelled directly and are not updated based on incoming information (Kolossa et al., 2017). ...
Preprint
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The standard fitness-fatigue model (FFM) is known to include several limitations described by the linearity assumption, the independence assumption, and the deterministic assumption. These limitations ensure that the modelled response to chronic training does not match the complexity observed in practice. The purpose of part II of this review series was to describe previous extensions to the standard FFM to address these limitations, providing key mathematical insights and resources to both explain technical elements and enable researchers and practitioners to fit these extended models to their own data. To address the linearity assumption of the standard FFM and the associated limitation that doubling the training load predicts twice the performance improvement, two distinct extensions are reviewed including the addition of a non-linear transform to training inputs and inclusion of non-linear terms within the system of differential equations. To address the independence assumption where the response to a training session is unaffected by previous sessions, a popular extension where fatigue is updated as an exponentially weighted moving average of previous training loads is reviewed. Finally, the review introduces the concept of state-space models where uncertainty in the estimates of fitness, fatigue and performance measurement can be directly modelled eliminating the unsuited deterministic assumption of the standard FFM. The review also highlights how state-space models can be further expanded to include features such as the Kalman filter where parameter estimates can be updated with incoming performance measurements to better predict and manipulate training to optimise performance. Collectively, the range of topics covered in this review series and the resources provided should enable researchers and practitioners to better investigate the extensive area of FFMs and determine in what contexts models can assist with training monitoring and prescription.
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This study aims to provide a transferable methodology in the context of sport performance modelling, with a special focus to the generalisation of models. Data were collected from seven elite Short track speed skaters over a three months training period. In order to account for training load accumulation over sessions, cumulative responses to training were modelled by impulse, serial and bi-exponential responses functions. The variable dose-response (DR) model was compared to elastic net (ENET), principal component regression (PCR) and random forest (RF) models, while using cross-validation within a time-series framework. ENET, PCR and RF models were fitted either individually (M_I) or on the whole group of athletes (M_G). Root mean square error criterion was used to assess performances of models. ENET and PCR models provided a significant greater generalisation ability than the DR model ( p = 0.018, p < 0.001, p = 0.004 and p < 0.001 for ENET_I , ENET_G, PCR_I and PCR_G, respectively). Only ENET_G and RF_G were significantly more accurate in prediction than DR (p < 0.001 and p < 0.012). In conclusion, ENET achieved greater generalisation and predictive accuracy performances. Thus, building and evaluating models within a generalisation enhancing procedure is a prerequisite for any predictive modelling.
Article
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.
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A systems model of training effects on performance was applied to eight initially untrained subjects who were volunteers for an endurance training program for the purpose of verifying the statistical adequacy of the systems structure. In the model initially proposed by T. W. Calvert, E. W. Banister, M. V. Savage, and T. Bach (IEEE Trans. Syst. Man Cybern. 6: 94-102, 1976), the performance changes were related to the successive training loads by three first-order transfer functions. In the present study, the number of first-order components was statistically tested. A model including only one component, which had a positive effect on the performance, provided a significant fit with the performances in every subject. A second component significantly improved the fit in only two subjects. This further component, which had a negative effect on performance, was identified as fatigue. Nevertheless, a two-antagonistic component model is proposed to provide a good representation of the training responses. However, the low level of exercise demands and the inaccuracy of the fit could have impaired the evidencing of a fatiguing effect during the presently studied training protocol.
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Recent application of modeling techniques to physical training has opened the possibility for prediction from training. Solution of the inverse problem, determining a training program to produce a desired performance at a specific time, is also possible and may yield strategies for achieving better training and tapering (complete or relative rest for a period before competition) regimens for competitive athletes. A mathematical technique derived from model theory is described in this paper that allows the design of an optimal strategy of physical preparation for an individual to do well in a single future competitive event or cluster of events. Simulation results, using default parameters of a training model, suggest that presently accepted forms of taper for competition may remain too rigorous and short in duration to achieve the best result possible from the training undertaken.
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A low hemoglobin level or even frank anemia is common among female endurance runners; controversy presently exists on the effectiveness of iron supplementation. In the past inadequate descriptions of training and too infrequent measurement of iron indices over a sufficiently long period, have made it difficult to establish any relationship between iron status and training upon which to base a rational iron therapy. In this study 5 young women distance runners age 18-25 years have been studied for 300 days. A numerical index was used to quantify the extent of an individual's daily training effort and a conceptual model of the effect of training allowed definition of the extent of consequent fatigue, to be calculated. Red blood cell number and hemoglobin concentration were measured regularly throughout, and during the last 200 days serum iron, ferritin, total iron binding capacity and percent transferrin saturation were also measured. It has been shown in most subjects that serum iron and transferrin saturation varied in phase with training and the fatigue index, throughout the period while serum ferritin varied out of phase. It is suggested that supplementing iron intake may be of little use during heavy training and concomitant high fatigue because transferrin saturation is also very high at this time and ineffective in promoting absorption of dietary iron.