Monitoramento das cargas de treinamento no ciclismo

Abstract

Cycling is a sport of high psychophysiological demand imposed to athletes, in which the optimal dose-response relationship between the applied training loads and performance is the key to reach positive adaptations to training. On other hand, if training loads are excessive or insufficient, the risk of injury and competition failure might be increased. Thus, it is fundamental to control training loads precisely, in which the integration of objective and subjective methods has been suggested as important to athlete monitoring. Furthermore, the performance assessment is important to evaluate the efficacy of the cyclist’s training program. Thus, the aims of the present study were: (1) to examine the mean power output reproducibility of cyclists in four 20-min uphill time trial (TT) and the reproducibility of pacing strategy employed; (2) to describe the relationship between TSS, Edward’s Trimp and session-RPE methods and evaluate the TSS in front of the other two methods to quantify the training loads in cycling. For the objective 1, the sample was composed by 18 moderately trained cyclists. The mean power output was not different (F = 0.150; p = 0.855; ƞp2 = 0.009) between TTs (287 ± 30, 287 ± 29, 286 ± 32 and 286 ± 34 W for TTs 1, 2, 3, and 4, respectively). High variability was shown on pacing strategy at the beginning and end of TTs (TE = 7.57%, 6.29% and 6.08% for the 0–2 min interval; 7.01%, 6.34% and 6.24% for the 18–20 min interval, between TTs 2–1, 3–2 e 4–3, respectively) and a main effect of time (F = 96.134; p < 0.001; ƞp2 = 0.850). Pairwise comparison revealed differences in normalized mean power output only between 0–2 and 2–4 min intervals (p < 0.001). The pacing strategy adopted by cyclists was not different between TTs (F = 1.970; p = 0.060; ƞp2 = 0.104) and performance groups (F = 1.052; p = 0.399; ƞp2 = 0.062), but differed between courses (F = 4.861; p = 0.006; ƞp2 = 0.233). The mean power output during the 20-min uphill TT is reproducible and cyclists appear to does not modify the pacing strategy following consecutive evaluations, what makes this protocol robust for field-based performance analysis. For the objective 2, the sample was composed by one amateur athlete. Seventy-six training data in a period of 23 weeks were evaluated. The mean (± standard deviation) of training load methods was 712.5 ± 438.3 A.U. for the session-RPE, 354.5 ± 20.6 A.U. for the Edward’s Trimp and 120.0 ± 59.7 A.U. for the TSS. The relationship between TSS and session-RPE, TSS and Edward’s Trimp and Edward’s Trimp and session-RPE was positive and very large (r = 0.83 to 0.90) for all comparisons. According to the values found, the TSS can be explained 81% by session-RPE and 75% by Edward’s Trimp; the Edward’s Trimp can be explained 69% by session-RPE. The Bland-Altman plots presented 95% limits of agreement of ± 53.20% between TSS and session-RPE, ± 58.20% between TSS and Edward’s Trimp and ± 69.0% between Edward’s Trimp and session-RPE. The training load methods analyzed showed good relationship, however with a tendency of less concordance between them following rises in training load. We can conclude that the evaluated methods showed good relationship, mainly between session-RPE and TSS, suggesting this subjective method as a good tool to be used in cycling training load monitoring. Considering the context of monitoring and evaluating training programs, the current study presented tools that can be used in cyclist’s routine.