The findings of this study indicate that for training doses above an optimal level, recovery phases would be required to maximize gain in performance. The question that arises from these data is whether the model proposed in this study would be adequate to describe the responses during the peaking period observed in athletes (25). To address this issue, the model was driven with the average-fit parameters obtained in this study to compare output with recently reported experimental data (15). In that previous report, trained subjects had doubled the amount of their habitual training over 2 wk before reducing training to about half of their habitual training. Maximal aerobic power close to the performance criterion used in the present study decreased significantly during overload and returned to its initial level over the two weeks of recovery. The model proposed in this study was used to simulate performance under similar conditions. The simulations began at steady state with three training sessions per week corresponding to 450 t.u. The additive term was set at 240 W to give an initial level of performance close to 340 W. During intensified training, daily training (400 t.u. for each session) was maintained over 2 wk before reducing training to three sessions per week (200 t.u. for each). These training amounts were chosen according our study and would be lower than actual training reported in ref. 15. The resulting model simulations are depicted in Figure 4; agreement was good with the experimental data of Halson et al. (15) that showed a decrease in performance from 340 to 320 W with intensified training and recovery of initial level over 2 wk of reduced training. It is noteworthy that performance exceeded its initial level over the third week of reduced training.
The amounts of training in this study are, however, difficult to compare with other data. The method to quantify training is only adapted to this study. Because only work intensity could vary between training sessions, the computation allowed us to take any change in training amount into account. The arbitrary unit used in this study could be compared with training impulse (Trimp) calculating training quantity from duration and intensity of each phase of exercise (3). The 400–450 t.u. for each training session would represent around 100 Trimp. Previous application of Model 2-Comp to data of two subjects who trained 28 consecutive days with 100–150 Trimp each day yielded 8 and 11 d for tn (24). These data are in line with those obtained with the model proposed in this study. Nevertheless, training amounts in these experiments appeared to be lower than in endurance athletes (2,4). Average training over 280 d in elite triathletes was 217 ± 34 Trimp per day (21). The value for optimal training found in this study should be greatly lower than usual training in athletes. Long-term adaptation could improve the tolerance to exercise repetition yielding to an increase in optimal training.
Other limits in the conclusions should be addressed according shortcomings inherent to model or arising from the design of the experiment. The great simplifications in training quantification and transfer function make hazardous extrapolation from modeled data to situations different than studied. Moreover, the fitness improvement and the training intensity could have an impact on the results of this study. The performance fit could be also improved by dividing the experimental period in two distinct phases in accordance with training. This is an additional issue to show that Model 2-Comp could be imperfect to describe response to training with various regimens. Nevertheless, the initial question that arose from the results was whether the difference in fitness between the two training phases could have flawed the outcomes of this study. The higher fitness during the second training period did not appear to yield to a diminished positive response to training doses. The gain terms for adaptation and fatigue were both slightly greater for the second period of training. The difference between training phases did not reach, however, the limits of statistical significance. With Model 2-Comp, when performance reached a steady state with constant training, effect of each training dose should remained unchanged to allow adaptations to be maintained. The assumptions underlying the development of the proposed model was also based on data indicating that the fatigue influence of exercise bouts should be greater for greater training amounts (8). Moreover, the data of this study were analyzed using a model with parameters free to vary over time without any assumption on these variations (5). This previous report showed that gain term for adaptation did not appear to decrease as fitness increased or when training was steeply increased. The better fit of performance with fatigue factor varying with training could not be thus attributed to a decrease in positive effect of training due to higher fitness.
Another concern involves the type and the number of performance trials. The precision and frequency of performance measurement was necessary to accurately model the responses to training. The total number of measurements also increased the power of the statistical analysis to test the confidence of the decrease in residual variation with increasing model complexity. The frequency of performance testing was the same for the two phases of training and the phases without training. Three tests per week to measure Plim5′ could cause, however, a high-intensity-orientated training program. Although the amount of high-intensity work could appear unusual, the subjects tolerated well this work regimen because performance increased regularly during the first sequence of training. In our previous report (5), we indicated that O2max increased by 20.5 ± 7.0% after the first phase of training (P < 0.001). Data of this study could, however, be dependent on this particular feature of the experiment. Further studies using lower intensity for exercise are needed to determine how the model proposed in this study would describe response to training using other combination of work volume and intensity.
In conclusion, the nonlinear model proposed in this study appeared to describe the responses to training more precisely than previous models. The present data suggest an inverted-U-shape relationship between daily amounts of training and performance. Furthermore, these data would be helpful to extrapolate response to training using more intensified work or varied regimens. Nevertheless, model shortcomings would limit prediction to training situation close to our experiment, i.e., a step increase in training over a short period. Difference in training strategy or long-term adaptation could affect the responses in a manner different than model prediction.
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