956 resultados para Critical power model


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The aim of the present study was to verify the applicability of anaerobic work capacity (AWC) determined from the critical power model in elite table tennis players. Eight male international level table tennis players participated in the study. The tests undertaken were: 1) A critical frequency test used to determinate the anaerobic work capacity; 2) Wingate tests were performed using leg and arm ergometers. AWC corresponded to 99.5 +/- 29.1 table tennis balls. AWC was not related to peak (r = -0.25), mean (r = -0.02), relative peak (r = -0.49) or relative mean power (r = 0.01), nor fatigue index (r = -0.52) (Wingate leg ergometer). Similar correlations for peak (r = -0.34), mean (r = -0.04), relative peak (r = -0.49), relative mean power (r = -0.14) and peak blood lactate concentration (r = -0.08) were determined in the Wingate arm ergometer test. Based on these results the AWC determined by a modified critical power test was not a good index for measurement of anaerobic capacity in table tennis players.

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The aim of this study was to test if the critical power model can be used to determine the critical rest interval (CRI) between vertical jumps. Ten males performed intermittent countermovement jumps on a force platform with different resting periods (4.1 +/- 0.3 s, 5.0 +/- 0.4 s, 5.9 +/- 0.6 s). Jump trials were interrupted when participants could no longer maintain 95% of their maximal jump height. After interruption, number of jumps, total exercise duration and total external work were computed. Time to exhaustion (s) and total external work (J) were used to solve the equation Work = a + b . time. The CRI (corresponding to the shortest resting interval that allowed jump height to be maintained for a long time without fatigue) was determined dividing the average external work needed to jump at a fixed height (J) by b parameter (J/s). in the final session, participants jumped at their calculated CRI. A high coefficient of determination (0.995 +/- 0.007) and the CRI (7.5 +/- 1.6 s) were obtained. In addition, the longer the resting period, the greater the number of jumps (44 13, 71 28, 105 30, 169 53 jumps; p<0.0001), time to exhaustion (179 +/- 50, 351 +/- 120, 610 +/- 141, 1,282 +/- 417 s; p<0.0001) and total external work (28.0 +/- 8.3, 45.0 +/- 16.6, 67.6 +/- 17.8, 111.9 +/- 34.6 kJ; p<0.0001). Therefore, the critical power model may be an alternative approach to determine the CRI during intermittent vertical jumps.

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PURPOSE: This study investigated the efficacy of an intermittent critical power model, termed the "work-balance" (W'BAL) model, during high-intensity exercise in hypoxia. METHODS: Eleven trained, male cyclists (mean ± SD; age 27 ± 6.6 yr, V[Combining Dot Above]O2peak 4.79 ± 0.56 L.min) completed a maximal ramp test and a 3 min "all-out" test to determine critical power (CP) and work performed above CP (W'). On another day an intermittent exercise test to task failure was performed. All procedures were performed in normoxia (NORM) and hypoxia (HYPO; FiO2 ≈ 0.155) in a single-blind, randomized and counter-balanced experimental design. The W'BAL model was used to calculate the minimum W' (W'BALmin) achieved during the intermittent test. W'BALmin in HYPO was also calculated using CP + W' derived in NORM (N+H). RESULTS: In HYPO there was an 18% decrease in V[Combining Dot Above]O2peak (4.79 ± 0.56 vs 3.93 ± 0.47 L.min ; P<0.001) and a 9% decrease in CP (347 ± 45 vs 316 ± 46 W; P<0.001). No significant change for W' occurred (13.4 ± 3.9 vs 13.7 ± 4.9 kJ; P=0.69; NORM vs HYPO). The change in V[Combining Dot Above]O2peak was significantly correlated with the change in CP (r = 0.72; P=0.01). There was no difference between NORM and HYPO for W'BALmin (1.1 ± 0.9 kJ vs 1.2 ± 0.6 kJ). The N+H analysis grossly overestimated W'BALmin (7.8 ± 3.4 kJ) compared with HYPO (P<0.001). CONCLUSION: The W'BAL model produced similar results in hypoxia and normoxia, but only when model parameters were determined under the same environmental conditions as the performance task. Application of the W'BAL model at altitude requires a modification of the model, or that CP and W' are measured at altitude.

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The characterization of the hyperbolic power-time (P-tlim) relationship using a two-parameter model implies that exercise tolerance above the asymptote (Critical Power; CP), i.e. within the severe intensity domain, is determined by the curvature (W') of the relationship. The purposes of this study were (1) to test whether the amount of work above CP (W>CP) remains constant for varied work rate experiments of high volatility change and (2) to ascertain whether W' determines exercise tolerance within the severe intensity domain. Following estimation of CP (208 ± 19 W) and W' (21.4 ± 4.2 kJ), 14 male participants (age: 26 ± 3; peak [Formula: see text]: 3708 ± 389 ml.min-1) performed two experimental trials where the work rate was initially set to exhaust 70% of W' in 3 ('THREE') or 10 minutes ('TEN') before being subsequently dropped to CP plus 10 W. W>CP for TEN (104 ± 22% W') and W' were not significantly different (P>0.05) but lower than W>CP for THREE (119 ± 17% W', P<0.05). For both THREE (r = 0.71, P<0.01) and TEN (r = 0.64, P<0.01), a significant bivariate correlation was found between W' and tlim. W>CP and tlim can be greater than predicted by the P-tlim relationship when a decrement in the work rate of high-volatility is applied. Exercise tolerance can be enhanced through a change in work rate within the severe intensity domain. W>CP is not constant.

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Active network scanning injects traffic into a network and observes responses to draw conclusions about the network. Passive network analysis works by looking at network meta data or by analyzing traffic as it traverses a fixed point on the network. It may be infeasible or inappropriate to scan critical infrastructure networks. Techniques exist to uniquely map assets without resorting to active scanning. In many cases, it is possible to characterize and identify network nodes by passively analyzing traffic flows. These techniques are considered in particular with respect to their application to power industry critical infrastructure.

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This paper develops some theoretical and methodological considerations for the development of a critical competence model (CCM). The model is defined as a set of skills and knowledge functionally organized allowing measurable results with positive consequences for the strategic business objectives. The theoretical approaches of classical model of competences, the contemporary model of competencies and human competencies model were revised for the proposal development. implementation of the model includes 5 steps: 1) conducting a job analysis considering which dimensions or facets are subject to revision, 2) identify people with the opposite performance (the higher performance and lower performance); 3) identify critical incidents most relevant to the job position, 4) develop behavioral expectation scales (bes) and 5) validate BES obtained for experts in the field. As a final consideration, is determined that the competence models require accurate measurement. Approaches considering excessive theoreticism may cause the issue of competence become a fashion business with low or minimal impact, affecting its validity, reliability and deployment in organizations.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Aim. - This study aimed to test if investigate whether the anaerobic work capacity is replenished while exercising at critical power intensity. Then, a known exercise duration, which demands high anaerobic energy contribution, was compared to intermittent exercise duration with passive and active (cycling at critical power intensity) rest periods.Methods. - Nine participants performed five sessions of testing. From the 1st to the 3rd sessions, individuals cycled continuously at different workloads (P-high, P-intermediate and P-low) in order to estimate the critical power and the anaerobic work capacity. The 4th and 5th sessions were performed in order to determine the influence of anaerobic work capacity replenishment oil exercise duration. They consisted of manipulating the resting type (passive or active) between two cycling efforts. The total exercise duration was determined by the sum of the two cycling efforts duration.Results. - The exercise duration under passive resting condition (408.0 +/- 42.0 s) was longer (p<0.05) than known exercise duration at P-intermediate (T-intermediate = 305.8 +/- 30.5 s) and than exercise duration performed under active resting conditions (T-active = 304.4 +/- 30.7s). However, there was no significant difference between T-intermediate and T-active.Conclusion. - These results demonstrated indirect evidence that the anaerobic work capacity is not replenished while exercising at critical power intensity. (C) 2008 Elsevier Masson SAS. All rights reserved.

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Purpose: The aim of this study was to verify whether there is an association between anaerobic running capacity (ARC) values, estimated from two-parameter models, and maximal accumulated oxygen deficit (MAOD) in army runners. Methods: Eleven, trained, middle distance runners who are members of the armed forces were recruited for the study (20 ± 1 years). They performed a critical velocity test (CV) for ARC estimation using three mathematical models and an MAOD test, both tests were applied on a motorized treadmill. Results: The MAOD was 61.6 ± 5.2 mL/kg (4.1 ± 0.3 L). The ARC values were 240.4 ± 18.6 m from the linear velocity-inverse time model, 254.0 ± 13.0 m from the linear distance-time model, and 275.2 ± 9.1 m from the hyperbolic time-velocity relationship (nonlinear 2-parameter model), whereas critical velocity values were 3.91 ± 0.07 m/s, 3.86 ± 0.08 m/s and 3.80 ± 0.09 m/s, respectively. There were differences (P < 0.05) for both the ARC and the CV values when compared between velocity-inverse time linear and nonlinear 2-parameter mathematical models. The different values of ARC did not significantly correlate with MAOD. Conclusion: In conclusion, estimated ARC did not correlate with MAOD, and should not be considered as an anaerobic measure of capacity for treadmill running. © 2013 Elsevier Masson SAS. All rights reserved.

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Few studies have focused on the metabolic responses to alternating high- and low-intensity exercise and, specifically, compared these responses to those seen during constant-load exercise performed at the same average power output. This study compared muscle metabolic responses between two patterns of exercise during which the intensity was either constant and just below critical power (CP) or that oscillated above and below CP. Six trained males (mean +/- SD age 23.6 +/- 2.6 y) completed two 30-minute bouts of cycling (alternating and constant) at an average intensity equal to 90% of CR The intensity during alternating exercise varied between 158% CP and 73% CP. Biopsy samples from the vastus lateralis muscle were taken before (PRE), at the midpoint and end (POST) of exercise and analysed for glycogen, lactate, PCr and pH. Although these metabolic variables in muscle changed significantly during both patterns of exercise, there were no significant differences (p > 0.05) between constant and alternating exercise for glycogen (PRE: 418.8 +/- 85 vs. 444.3 +/- 70; POST: 220.5 +/- 59 vs. 259.5 +/- 126mmol.kg(-1) dw), lactate (PRE: 8.5 +/- 7.7 vs. 8.5 +/- 8.3; POST: 49.9 +/- 19.0 vs. 42.6 +/- 26.6 mmol.kg(-1)dw), phosphocreatine (PRE: 77.9 +/- 11.6 vs. 75.7 +/- 16.9; POST: 65.8 +/- 12.1 vs. 61.2 +/- 12.7mmol.kg(-1)dw) or pH (PRE: 6.99 +/- 0.12 vs. 6.99 +/- 0.08; POST: 6.86 +/- 0.13 vs. 6.85 +/- 0.06), respectively. There were also no significant differences in blood lactate responses to the two patterns of exercise. These data suggest that, when the average power output is similar, large variations in exercise intensity exert no significant effect on muscle metabolism.