48 resultados para MAXIMAL SUBGROUPS
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Purpose. Isokinetic tests are often applied to assess muscular strength and EMG activity, however the specific ranges of motion used in testing (fully flexed or extended positions) might be constrictive and/or be painful for patients with injuries or under-going rehabilitation. The aim of this study was to examine the effects of different ranges of motion (RoM) when determining maximal EMG during isokinetic knee flexion and extension with different types of contractions and velocities. Methods. Eighteen males had EMG activity recorded on the vastus lateralis, vastus medialis, semitendinosus and biceps femoris muscles during five maximal isokinetic concentric and eccentric contractions for the knee flexors and extensors at 60° • s -1 and 180° • s -1. The root mean square of EMG was calculated at three different ranges of motion: (1) a full range of motion (90°-20° [0° = full knee extension]); (2) a range of motion of 20° (between 60°-80° and 40°-60° for knee extension and flexion, respectively) and (3) at a 10° interval around the angle where peak torque is produced. EMG measurements were statistically analyzed (ANOVA) to test for the range of motion, contraction velocity and contraction speed effects. Coefficients of variation and Pearson's correlation coefficients were also calculated among the ranges of motion. Results. Predominantly similar (p > 0.05) and well-correlated EMG results (r > 0.7, p ≤ 0.001) were found among the ranges of motion. However, a lower coefficient of variation was found for the full range of motion, while the 10° interval around peak torque at 180° • s -1 had the highest coefficient, regardless of the type of contraction. Conclusions. Shorter ranges of motion at around the peak torque angle provides a reliable indicator when recording EMG activity during maximal isokinetic parameters. It may provide a safer alternative when testing patients with injuries or undergoing rehabilitation.
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Objective. The aim of this study was to verify the possibility of lactate minimum (LM) determination during a walking test and the validity of such LM protocol on predicting the maximal lactate steady-state (MLSS) intensity. Design. Eleven healthy subjects (24.2 ± 4.5 yr; 74.3 ± 7.7 kg; 176.9 ± 4.1 cm) performed LM tests on a treadmill, consisting of walking at 5.5 km h -1 and with 20-22% of inclination until voluntary exhaustion to induce metabolic acidosis. After 7 minutes of recovery the participants performed an incremental test starting at 7% incline with increments of 2% at each 3 minutes until exhaustion. A polynomial modeling approach (LMp) and a visual inspection (LMv) were used to identify the LM as the exercise intensity associated to the lowest [bLac] during the test. Participants also underwent to 24 constant intensity tests of 30 minutes to determine the MLSS intensity. Results. There were no differences among LMv (12.6 ± 1.7 %), LMp (13.1 ± 1.5 %), and MLSS (13.6 ± 2.1 %) and the Bland and Altman plots evidenced acceptable agreement between them. Conclusion. It was possible to identify the LM during walking tests with intensity imposed by treadmill inclination, and it seemed to be valid on identifying the exercise intensity associated to the MLSS. Copyright © 2012 Guilherme Morais Puga et al.
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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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AIM: To compare five different protocols for estimating the lactate minimum speed (LMS) with that for estimating the maximal lactate steady state (MLSS) in Arabian horses, in order to obtain a more rapid method for monitoring aerobic capacity and prescribing training schedules. METHODS: Eight purebred Arabian horses were conditioned to exercise on a treadmill for 12 days then submitted to three to five exercise sessions to determine the MLSS. Blood samples were collected from a jugular catheter at specific intervals for measurement of lactate concentrations. The MLSS was the velocity maintained during the last 20 minutes of constant submaximal exercise, at which the concentration of lactate increased by no more than 1.0 mmol/L. The LMS test protocols (P1 - P5) included a warm-up period followed by a high-intensity gallop. The speed was then reduced to 4 m/s, and the incremental portion of the test was initiated. In P1, P2, and P3, the velocity increment was 0.5 m/s, and the duration of each incremental stage was three, five and seven minutes, respectively. In P4 and P5, the velocity increments were 1.0 and 1.5 m/s, respectively, and the duration of the stages was fixed at five minutes each. A second-degree polynomial function was fitted to the lactate-velocity curve, and the velocity corresponding to the lowest concentration of lactate was the LMS. RESULTS: Only the mean LMS determined by P1 and P2 did not differ from the velocity determined by the MLSS test (p > 0.1). There was a strong correlation (r >0.6) between P1 and the MLSS velocity. A limits of agreement plot revealed that the best agreement occurred between the MLSS test and P1 (mean bias = 0.14 m/s), followed by P2 (bias = -0.22 m/s). The lactate concentrations associated with the various LMS protocols did not differ. CONCLUSIONS: This study shows the variation between protocols of the LMS test for determining the onset of blood lactate accumulation but also reveals that, at least for Arabian horses, the P1 protocol of the LMS has good agreement with the MLSS. © 2013 Copyright New Zealand Veterinary Association.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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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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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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The aim of this study was to establish the validity of the anaerobic threshold (AT) determined on the soccer-specific Hoff circuit (AT(Hoff)) to predict the maximal lactate steady-state exercise intensity (MLSSHoff) with the ball. Sixteen soccer players (age: 16.0 +/- 0.5 years; body mass: 63.7 +/- 9.0 kg; and height: 169.4 +/- 5.3 cm) were submitted to 5 progressive efforts (7.0-11.0 km.h(-1)) with ball dribbling. Thereafter, 11 players were submitted to 3 efforts of 30 minutes at 100, 105, and 110% of AT(Hoff). The AT(Hoff) corresponded to the speed relative to 3.5 mmol.L-1 lactate concentration. The speed relative to 4.0 mmol.L-1 was assumed to be AT(Hoff4.0), and the AT(HoffBI) was determined through bisegmented adjustment. For comparisons, Student's t-test, intraclass correlation coefficient (ICC), and Bland and Altman analyses were used. For reproducibility, ICC, typical error, and coefficient of variation were used. No significant difference was found between AT test and retest determined using different methods. A positive correlation was observed between AT(Hoff) and AT(Hoff4.0). The MLSSHoff (10.6 +/- 1.3 km.h(-1)) was significantly different compared with AT(Hoff) (10.2 +/- 1.2 km.h(-1)) and AT(HoffBI) (9.5 +/- 0.4 km.h(-1)) but did not show any difference from LAn(Hoff4.0) (10.7 +/- 1.4 km.h(-1)). The MLSSHoff presented high ICCs with AT(Hoff) and AT(Hoff4.0) (ICC = 0.94; and ICC = 0.89; p <= 0.05, respectively), without significant correlation with AT(HoffBI). The results suggest that AT determined on the Hoff circuit is reproducible and capable of predicting MLSS. The AT(Hoff4.0) was the method that presented a better approximation to MLSS. Therefore, it is possible to assess submaximal physiological variables through a specific circuit performed with the ball in young soccer players.