Greater training volume and/or intensity before the taper would allow higher performance gains, but would demand a greater reduction of the training load over a longer period. The results also pointed out the importance of training adaptations during the taper, in addition to fatigue dissipation.

The aim of this study was to assess responses to taper in elite athletes using computer simulations. Parameters of a non-linear model were derived from training and performance data over two seasons for eight elite swimmers. The fit between modelled and actual performances was statistically significant for each swimmer (r(2) = 0.56 +/- 0.06; P < 0.01). The simulations were used to estimate characteristics of step and progressive tapers that would maximize performance either (1) after regular training only or (2) after overload training of a 20% step increase in regular training for 28 days. The highest performance with a step taper was greater with than without prior overload training (101.4%, s = 1.6 vs. 101.1%, s = 1.4 of personal record; P < 0.01) but required a longer taper duration (22.4 days, s = 13.4 vs. 16.4 days, s = 10.3; P < 0.05). The optimal progressive taper led to a better performance only after the overload period (101.5%, s = 1.5; P < 0.001). Negative and positive influences of training were estimated as indicators of fatigue and adaptations to training respectively. During the optimal taper, the negative influence was completely removed, independently of the prior training, whereas the positive influence increased only after overload training. Our computer simulations show that the characteristics of an optimal training reduction in elite athletes depend on the training performed in the weeks prior to a taper.

This report aims to discuss the strengths and weaknesses of the application of systems modeling to analyze the effects of training on performance. The simplifications inherent to the modeling approach are outlined to question the relevance of the models to predict athletes’ responses to training. These simplifications include the selection of the variables assigned to the system’s input and output, the specification of model structure, the collection of data to estimate the model parameters, and the use of identified models and parameters to predict responses. Despite the gain in insight to understand the effects of an intensification or reduction of training, the existing models would not be accurate enough to make predictions for a particular athlete in order to monitor his or her training.

A nonlinear model of training responses was utilized to test whether a 2-phase taper could be more effective than a traditional linear taper. Simulations were conducted using model parameters previously determined in 6 nonathletes trained on a cycle ergometer (non-ATH) and 7 elite swimmers trained in sport-specific conditions (ATH). Linear and 2-phase tapers were compared after a 28-day overload period at 120% of normal training. The 2-phase taper was assumed to be identical to the optimal linear taper, except for the final 3 days during which the training load was varied to maximize the final performance. The optimal linear taper was characterized by a mean training reduction by 32 +/- 6% during 35 +/- 6 days in non-ATH and by 49 +/- 18% during 33 +/- 16 days in ATH. The last 3 days of the 2-phase taper were characterized by a significant increase in training load by 23 +/- 18% in non-ATH and 29 +/- 42% in ATH (p < 0.005). The optimal taper characteristics were not statistically different between non-ATH and ATH. The maximal performance reached with the 2-phase taper was higher by 0.04 +/- 0.02% in non-ATH and 0.01 +/- 0.01% in ATH than with the optimal linear taper (p < 0.001). Positive and negative influences of training on performance were estimated as indicators of adaptation and fatigue, respectively. The negative influence was completely removed during both tapers, whereas the positive influence was slightly further enhanced during the 2-phase pattern. In conclusion, simulations showed that a 20 to 30% increase in training at the end of the taper, as compared to a prolonged reduction in training, allowed additional adaptations without compromising the removal of fatigue.

The aim of the study is the modelling of training responses with a variable dose-response model in a sport discipline that requires highly complex coordination. We propose a method to optimise the training programme plan using the potential maximal performance gain associated with overload and tapering periods. Data from five female elite gymnasts were collected over a 3-month training period. The relationship between training amounts and performance was then assessed with a non-linear model. The optimal magnitude of training load reduction and its duration were investigated with and without an overload period using simulation procedures based on individual responses to training. The correlation between actual and modelled performances was significant (R² = 0.81 ± 0.02, P < 0.01). The standard error was 2.7%. Simulations revealed that taper preceded by an overload period allows a higher performance to be achieved compared to an absence of overload period (106.3 ± 0.3% vs. 105.1 ± 0.3%). With respect to the pre-taper load, the model predicts that optimal load reductions during taper were 48.4 ± 0.7% and 42.5 ± 1.0% for overloading and non-overloading strategies, respectively. Moreover, optimal durations of the taper period were 34 ± 0.5 days and 22 ± 0.5 days for overloading and non-overloading strategies, respectively. In conclusion, the study showed that the variable dose-response model describes precisely the training response in gymnasts.