Taper is a period of reduced training load before a competition. The aim of this final period is to recover from prior training in order to maximally profit from the positive adaptations to training (Mujika 2003). However, a load reduction in a too large extent and/or during a too long duration may cause detraining. On the contrary, an insufficient reduction and/or a too short taper duration may lead to an overtraining state. In this context, mathematical modelling of the training effects on performance appeared an useful and objective tool to determine the right adjustment between the extent of the load reduction and the taper duration. Recently, Busso (2003) statistically validated a new model based on model proposed by Banister et al. (1975). This new model formulation took into account the possibility of the level of fatigue induced by a training dose to vary with training. The results yielded to an inverted-U relationship between daily amounts of training and performance with an optimal daily training load (ODT). Consequently, we attempted to take advantage of this new model to examine the factors which could interfere on characteristics of optimal taper. Methods The results were obtained from simulations using the model proposed by Busso (2003). The model is defined by a transfer function where the impulse response is . The gain term for the negative component (k21t/2t/1.ek.ekôô...2) varies with training doses according to an impulse response which is .3t/3.ekô. The set of model parameters, i.e. 2 gain terms k1 and k3 and 3 time constants ô1, ô2 and ô3 were fixed to the mean values obtained by Busso (2003) from responses to training in 6 subjects. Arbitrary units, TU and PU, were respectively defined to quantify training and performance, so that the ODT load corresponded to 100 TU and allowed to maintain performance at 100 PU. First, the simulated training was assumed to be at a steady state with ODT. Then, taper was produced by a step reduction in training load which was maintained to a steady level thereafter. This situation was compared to simulations with an overloaded period (OT) featured by a 20% step increase in training over 28 days before taper. All the computations ere done using Scilab software. Results With or without OT, a complete stop of training (100% training load reduction) leaded to a performance drop (Fig. 1). Conversely, maintaining a sufficient training during taper allowed performance to exceed its level with ODT. The larger was the load reduction, the shorter was the taper duration to achieve the highest performance. The results showed that the performance gain was maximal for an optimal rate of training reduction which was higher with OT (38%) than without OT (30%). Likewise, the optimal taper duration was longer with OT (27 days) than without OT (19 days). The greatest performance was obtained with OT (101.4 PU against 101.1 PU without OT). Discussion/Conclusion The results of this theoretical study were in line with data of the literature (Mujika 2003). A minimal load would be required to make taper efficient. This points out the importance of training adaptations during taper which should not act only through fatigue dissipation. The characteristics of optimal taper would depend on the training done before taper. Greater solicitations before taper would allow to attain an higher performance, but would demand a greater reduction of training over a longer period. Nevertheless, this study derived from data obtained in beginners who volunteered for a training study (Busso 2003) and their responses may differ from athletes with greater training loads. Gathering data in more athletic subjects would be necessary to address more precisely this issue.
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