A model to investigate adaptive processes by means of antagonistic dynamics has been developed by Perl (2001). The PerformancePotential-Model (PerPot) helps to simulate the relationship between training and performance by using a state-event-model with adaptive delays. Due to the fact that training influences the delay of physiological responses, the PerPot software allows the determination of model parameters by constant (global) or varying delay parameters (local). We intend to determine whether PerPot can be used to validly analyse adaptations to training in elite cyclists (preparation period). Two different variables for training and performance were tested. Beyond that we used local delays to identify periods of different adaptation chronology. Methods Two road cyclists (S1, professional, 30,000km/year; S2, elite, 22,000 km/year) and one mountain biker (S3, elite, 13hrs/wk) collaborated voluntarily in our 5-month study. The training stimulus (input) was quantified daily by heart rate (TRIMP, Millet et al., 2002) and power output (P, Watt) (SRM-System). Performance (output) was determined three times weekly by cycle ergometer tests: an incremental exercise test to exhaustion (IET, kJ/kg) and a 10sec-Wingate-Test (WT, Watt). Additional physiological parameters (urea, creatine kinase, haematocrit, HRV) were measured before each testing. Intraclass Correlation Coefficient (ICC) between modeled and real performances was calculated to estimate model validity (model fit). Results An acceptable model fit (ICC) with global delay parameters for input TRIMP as well as P could only be achieved for S1 (TRIMP to IET/WT: S1 = .93/.76, S2 = .58/.38, S3 = .36/.18; P to IET/WT: S1 = .89/.56, S2 = .42/.42, S3 = .53/.16). A better model fit was achieved for IET than for WT, whereas no evidence for the benefit of either input alternative was found. With local delay parameters, we identified 3 to 5 adaptation periods. Afterwards the PerPot was fitted by using the subdivided datasets, whereby an excellent model fit (ICC) was achieved for both TRIMP (M = .86, SD = .14, range = .61 - .97) and P (M = .84, SD = .13, range = .63 - .98). Discussion The results confirm that long-term adaptation is characterised by phases of different time delay, so that a time varying model is useful for analysing training effects (Busso et al., 1997). The progression of the physiological parameters provides indications of the course of delay variation. References Busso, T., Denis, C., Bonnefoy, R., Geyssant, A. & Lacour, J. R. (1997). Modeling of adaptations to physical training by using a recursive least squares algorithm. J Appl Physiol, 82 (5), 1685-1693. Millet, G. P., Candau, R. B., Barbier, B., Busso, T., Rouillon, J. D. & Chatard, J. C. (2002). Modelling the transfers of training effects on performance in elite triathletes. Int J Sports Med, 23 (1), 55-63. Perl, J. (2001). PerPot: A metamodel for simulation of load performance interaction. Europ J Sport Sci, 1 (2), 1-13.
© Copyright 2009 14th annual Congress of the European College of Sport Science, Oslo/Norway, June 24-27, 2009, Book of Abstracts. Veröffentlicht von The Norwegian School of Sport Sciences. Alle Rechte vorbehalten. / Übersetzt mit https://www.DeepL.com/Translator