Ventilatory Threshold and Maximal Oxygen Uptake in Present Triathletes

Hue, Olivier; Gallais, Daniel Le; Chollet, Didier; Préfaut, Christian

doi:10.1139/h00-007

Cited by 53 publications

(57 citation statements)

References 27 publications

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“…Our group demonstrated that incremental cycling and running _ V V O 2max were similar in young triathletes without prior athletic specialization (Hue et al 2000b), which was the case of the present subjects. The cycling _ V V O 2max value was thus used to express both the running and cycling intensities in terms of % of _ V V O 2max during trials 1 and 2.…”

Section: Testing Protocolsupporting

confidence: 80%

Effects of the order of running and cycling of similar intensity and duration on pulmonary diffusing capacity in triathletes

Galy

Hue²,

Boussana³

et al. 2003

Eur J Appl Physiol

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To study the pathophysiological mechanisms involved in the decrease of post-triathlon diffusing capacity (DLco), blood rheologic properties (blood viscosity: eta(b); changes in plasma volume: deltaPV) and atrial natriuretic factor (ANF) were assessed in ten triathletes during cycle-run (CR) and run-cycle (RC) trials at a metabolic intensity of 75% of maximal oxygen consumption ( VO(2max)). The DLco was measured before and 10 min after trials. ANF and deltaPV were measured at rest, after the cycle and run of CR and RC trials, and at the end of and 10 min after exercise. RC led to a greater deltaDLco decrease, a lower ANF concentration and a lower deltaPV than did CR, whereas for both CR and RC eta(b) was increased throughout exercise and 10 min after. In addition, after CR the deltaDLco decrease was inversely correlated ( r=-0.764; P<0.01) with deltaPV. The association of decreased plasma volume, increased eta(b), and lower ANF concentrations after RC suggested that lower blood pulmonary volume may have caused the greater decrease in Dlco as compared with CR. The inverse correlation between deltaPV and deltaDLco reinforces the hypothesis that fluid shifts limit the post-exercise DLco decrease after the CR succession in triathletes. Lastly, cycling in the crouched position might increase intra-thoracic pressure, decrease thorax volume due to the forearm position on the handlebars, and weaken peripheral muscular pump efficacy, all of which would limit venous return to the heart, and thus result in low pulmonary blood volume. Compared with cycling, running appeared to induce the opposite effects.

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Section: Testing Protocolsupporting

confidence: 80%

Effects of the order of running and cycling of similar intensity and duration on pulmonary diffusing capacity in triathletes

Galy

Hue²,

Boussana³

et al. 2003

Eur J Appl Physiol

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“…] as gross criterion of endurance performance was slightly lower than that reported by Hue, Le Gallais, Chollet, and Préfaut (2000) and Schabort et al (2000), and similar to that reported by Hue (2003). Lactate values could not be compared because of various specifications such as defined bounds, running paces, or power outputs while cycling.…”

Section: Assessment Of Parameterssupporting

confidence: 47%

Predicting Elite Triathlon Performance: A Comparison of Multiple Regressions and Artificial Neural Networks

Hoffmann

Moeller

Seidel

et al. 2017

International Journal of Computer Science in Sport

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Two different computational approaches were used to predict Olympic distance triathlon race time of German male elite triathletes. Anthropometric measurements and two treadmill running tests to collect physiological variables were repeatedly conducted on eleven male elite triathletes between 2008 and 2012. After race time normalization, exploratory factor analysis (EFA), as a mathematical preselection method, followed by multiple linear regression (MLR) and dominance paired comparison (DPC), as a preselection method considering professional expertise, followed by nonlinear artificial neural network (ANN) were conducted to predict overall race time. Both computational approaches yielded two prediction models. MLR provided R² = 0.41 in case of anthropometric variables (predictive: pelvis width and shoulder width) and R² = 0.67 in case of physiological variables (predictive: maximum respiratory rate, running pace at 3-mmol·L -1 blood lactate and maximum blood lactate). ANNs using the five most important variables after DPC yielded R² = 0.43 in case of anthropometric variables and R² = 0.86 in case of physiological variables. The advantage of ANNs over MLRs was the possibility to take non-linear relationships into account. Overall, race time of male elite triathletes could be well predicted without interfering with individual training programs and season calendars.

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“…For future performance prediction by means of motor tests, studies usually suggest laboratory tests for the sake of objectivity of data gained (OʼToole, Douglas, & Hiller, 1989;Neumann, 1993;Hue, Le Gallais, Chollet, Boussana, & Préfaut, 2000;Bentley, Millet, Vleck, & McNaugthon, 2002). The most commonly used is a treadmill or cycling ergometer test up to vita maximum, further speed tests at AT, less on aerobic threshold (Schabort, Killian, St Clair Gibson, Hawley, & Noakes, 2000;Hue, 2003;Van Schuylenbergh, Vanden Eynde, & Hespel, 2004).…”

Section: Methodology Of Talent Selection For Short Triathlonmentioning

confidence: 99%

Verification of the model of predisposition in triathlon - structural model of confirmative factor analysis

Kovářová¹,

Kovář²

2012

Acta Gymnica

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BACKGROUND:The triathlon is a combination of three diff erent types of sport -swimming, cycling, and running. Each of these requires diff erent top level predispositions and complex approach to talent selection is a rather diffi cult process. Attempts to identify assumptions in the triathlon have so far been specifi c and focused only on some groups of predispositions (physiology, motor tests, and psychology). The latest studies missed the structural approach and were based on determinants of sport performance, theory of sports training and expert assessment.OBJECTIVE: The aim of our study was to verify the model of predisposition in the short triathlon for talent assessment of young male athletes age 17-20 years.METHODS: The research sample consisted of 55 top level triathletes -men, who were included in the Government supported sports talent programme in the Czech Republic at the age of 17-20 years. We used a confi rmative factor analysis (FA) and Path diagram to verify the model, which allow us to explain mutual relationships among observed variables. For statistical data processing we used a structure equating modeling (SEM) by software Lisrel L88. RESULTS:The study confi rms best structural model for talent selection in triathlon at the age of 17-20 years old men, which composed seventeen indicators (tests) and explained 91% of all cross-correlations (Goodness of Fit Index /GFI/ 0.91, Root Mean Square Residual /RMSR/ 0.13). Tests for predispositions in triathlons were grouped into fi ve items, three motor predispositions (swimming, cycling and running skills), aerobic and psychological predispositions. Aerobic predispositions showed the highest importance to the assumptions to the general factor (1.00; 0). Running predispositions were measured as a very signifi cant factor (-0.85; 0.28) which confi rms importance of this critical stage of the race. Lower factor weight showed clusters of swimming (-0.61; 0.63) and cycling (0.53; 0.72) predispositions. The psychological predispositions were found in the lowest eff ect (0.36; 0.87).CONCLUSIONS: We confi rm predispositions and tests for measurement of its level for top level male triathletes in the junior category. The model is in practice today in the Czech Republic, where we used the test standards for dividing Government support of athletic talent.

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Ventilatory Threshold and Maximal Oxygen Uptake in Present Triathletes

Cited by 53 publications

References 27 publications

Effects of the order of running and cycling of similar intensity and duration on pulmonary diffusing capacity in triathletes

Effects of the order of running and cycling of similar intensity and duration on pulmonary diffusing capacity in triathletes

Predicting Elite Triathlon Performance: A Comparison of Multiple Regressions and Artificial Neural Networks

Verification of the model of predisposition in triathlon - structural model of confirmative factor analysis

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