Protocolos y herramientas para la valoración y programación del ciclistatiempos límite y tests de estimación del máximo estado estable

  1. Lillo Bevia, Jose Ramon
unter der Leitung von:
  1. Jesús García Pallarés Doktorvater/Doktormutter

Universität der Verteidigung: Universidad de Murcia

Fecha de defensa: 22 von Februar von 2019

Gericht:
  1. Javier Chavarren Cabrero Präsident/in
  2. Ernesto de la Cruz Sánchez Sekretär/in
  3. Cristóbal Sánchez Muñoz Vocal

Art: Dissertation

Zusammenfassung

The doctoral thesis presented in this document is structured in three different parts. The first part of the work is composed of studies I and II, where the validation work of two different workload cycling tools, "drive indoor trainer Cycleops Hammer" and "PowerTap P1 Pedals Power Meter ", is detailed. In both articles, randomized and counterbalanced incremental workload tests (100-500 W) were performed, at 70, 85 and 100 rev·min-1 cadence, with sitting and standing pedalling in 3 different Hammer unit cadences. Then, the results are compared against the values measured by a professional SRM crankset. In general terms, no significant differences were detected between the Hammer devices and the SRM, while strong intraclass correlation coefficients were observed (?0.996; p=0.001), with low bias (-5,5 a 3,8), and high values of absolute reproducibility (CV<1,2%, SEM<2,1). The PowerTap P1 pedals showed strong correlation coefficients in a seated position (rho ? 0.987). They underestimated the power output obtained in a directly proportional way to the cadence, with an average error of 1.2%, 2.7%, 3.5% for 70, 85 and 100 rev?min-1. However, they showed high absolute reproducibility values (150-500 W, CV = 2.3%, SEM <1.0W). These results prove that both are valid and reproducible devices to measure the power output in cycling, although caution should be exercised in the interpretation of the results due to the slight underestimation. The second part of the thesis is devoted to the study III, where the time to exhaustion (TTE) at the workloads related to the main events of the aerobic and anaerobic pathway in cycling were analysed in duplicate in a randomized and counterbalanced manner (Lactic anaerobic capacity (WAnTmean), the workload that elicit VO2max -MAP-, Second Ventilatory Threshold (VT2) and at Maximal Lactate Steady State (MLSS). TTE values were 00:28±00:07, 03:27±00:40, 11:03±04:45 and 76:35±12:27 mm:ss, respectively. Moderate between-subject reproducibility values were found (CV=22.2%,19.3%;43.1% and 16.3%), although low within-subject variability was found (CV=7.6%,6.9%;7.0% y 5.4%). According to these results, the %MAP where the physiological events were found seems to be a useful covariable to predict each TTE for training or competing purposes. Finally, in the third part of the work, the results of studies IV y V have been included. The validity of two different methods to estimate the cyclists' workload at MLSS was evaluated. The first method was a 20 min time trial test (20TT), while the second method was a one-day incremental protocol including 4 steps of 10 minutes (1day_MLSS). The 20TT test absolute reproducibility, performed in duplicate, was very high (CV = -0.3±2.2%, ICC = 0.966, bias = 0.7±6.3 W). 95% of the mean 20TT workload overestimated the MLSS (bias 12.3±6.1W). In contrast, 91% of 20TT showed an accurate prediction of MLSS (bias 1.2±6.1 W), although the regression equation "MLSS (W) = 0.7489 * 20TT (W) + 43.203" showed even a better MLSS estimates (bias 0.1±5.0 W). Related to the 1day_MLSS test, the physiological steady state was determined as the highest workload that could be maintained with a [Lact] rise lower than 1mmol·L-1. No significant differences were detected between the MLSS (247±22 W) and the main construct of the test (DIF_10to10) (245±23 W), where the difference of [Lact] between minute 10 of two consecutive steps were considered, with high correlations (ICC = 0.960), low bias (2.2W), as well as high within-subject reliability (ICC = 0.846, CV = 0.4%, Bias = 2.2±6.4W). Both methods were revealed as valid predictors of the MLSS, significantly reducing the requirements needed to individually determine this specific intensity.