, 1999); 1090 W in young endurance athletes (Chamari et al., 1995), 813 W in subjects with recreational activities (Vandewalle et al., 1985); 879 W in untrained students (Linossier et al., 1996)). The measured with the F-v test rPmax for upper limbs is 4.7 W?kg?1, while other studies http://www.selleckchem.com/products/Vandetanib.html reveal higher values (10.7 W?kg?1 (Nikolaidis, 2006); 10.7 W?kg?1 in 44 year-olds and 12.3 W?kg?1 in physical education students (Adach et al., 1999); 10.7 W?kg?1 in swimmers (Mercier et al., 1993)). The corresponding value for lower limbs (12.2 W?kg?1) is lower than previous reports; 16.4 W?kg?1 (Nikolaidis, 2006); 13.0 W?kg?1 in untrained students (Linossier et al., 1996); 13.2 W?kg?1 in physical education students, 13.7 W?kg?1 in 44 year-olds (Adach et al., 1999). The ratio upper to lower limbs Pmax (0.

40) is lower than the 0.65 (Nikolaidis, 2006), 0.78 in 44 year-olds and the 0.93 in physical education students (Adach et al., 1999). Two possible explanations for the discrepancy of our results in comparison with previous data (lower values in all the F-v characteristics) might be the age of participants and the sport. All the characteristics measured by F-v test (force, velocity and power) correspond to age-dependent sport-related fitness parameters (muscular strength, speed and anaerobic power). Potential differences between arms and legs could be explained primarily due to muscle mass and muscle fibre type distribution. Muscle strength or force generating capacity is found closely related to muscle mass (Lanza et al., 2003; Metter et al., 2004) and muscle cross-sectional area (Maugha et al.

, 1984). It is proposed that upper limbs muscle mass is 22% (Abe et al., 2003) to 25% of lower limbs (Zatsiorsky, 2002). Our data additionally suggest that other factors, e.g. sport discipline in swimming, training, individualized technique and injuries, might also influence these differences. As shown in the Figure 2, there was a case of three female swimmers who had similar force in legs (120 N, 121 N and 122 N), but their corresponding force in arms differed (84 N, 66 N and 36 N) resulting in a wide range of ratio between upper and lower limbs (0.70, 0.54 and 0.30). A drawback of our study was the inherent limitation of laboratory methods to reproduce the real movements of swimming.

In addition, arms and legs�� power output was examined separately, which did not correspond to the complex movements of the sport that involve the coordination of upper and lower limbs. On the other hand, the laboratory methods provided valid and reliable measures of anaerobic power. Moreover, the distinction between arms and legs�� power came to terms Anacetrapib with the training practice, in which many exercises, either in pool or in the gym, focus on specific body parts. A remarkable observation from the present study was the variability of the ratios of mechanical characteristics between arms and legs in swimmers.

Statistical analysis After sphericity assumption was verified with the Mauchly test, a repeated measures analysis of variance was performed to detect the exercise and intensity effects in RPE and its interaction. Linear regressions were used to investigate the precision of EC prediction as a function of RPE. The standard error of the regression (Sy.x) was used a measure sellectchem of the goodness of the fit. Data analysis was performed with the SPSS 16.0 (SPSS Science, Chicago, USA) and the graphics designed with Sigma Plot 10.0 (SPSS Science, Chicago, USA). Data are presented as means and standard deviations. A minimum level of significance of P �� 0.05 was adopted. Results The loads that were used in each exercise and the duration of each bout are presented in Table 1.

When assessing the variations in RPE (see values also in Table 1) according to the four exercises and to the different loads, a general effect was identified for both independent variables. The RPE increased significantly with the exercise intensity (P=0,000; ��2=0.83) with an exception of the comparison between the first two bouts (12% vs. 16%). There were no significant differences between RPE in half squat and in bench press. The RPE during triceps extension was significantly higher compared to every other exercise and the RPE during Lat pull down was significantly lower when compared with every other exercise. Simple linear regressions were established to estimate the EC using RPE (Figure 2).Significant (p< 0,05) regression equations were noted for the bench press, triceps extension and lat pull down.

The linear regression that was obtained for the Half squat was not significant Figure 2 Simple regression analysis between energy cost (EC) and rate of perceived exertion (RPE): Lat Pull down (A), Bench Press (B) and Triceps Extension (C). Discussion The aim of the present study was to assess the accuracy of equations based on RPE obtained using the OMNI-RES to predict energy cost (EC) during low intensity resistance exercise (RE).The main finding of the present study was that EC can be accurately predicted from RPE during low intensity lat pull down, bench press and triceps extension in recreational body builders. Our results suggest that the accuracy of the prediction model based upon the half squat is not acceptable.

Generally, the RPE tended to be higher during triceps extension as compared with the remaining three exercises that were used in the present study. These results suggest that single-joint exercises result higher RPE than multiple joint exercises. This finding is consistent with Lagally et al. (2002b) who assessed RPE at intensities of 30 and 90% of 1RM in seven different exercises (both single-joint and multi-joint). Smolander et al. (1998), reported Dacomitinib similar differences in RPE in both young and old subjects performing single and multiple joint exercises. According to Hetzler et al.

55 m/s were excluded. So finally, the measurements were carried out on a sample of 27 women and check details 27 men. For each of the subjects we registered 20 gait cycles (40 steps). After hearing the signal the subject covered a distance of about 50 meters. From the collected data we were able to identify kinematic variables describing the temporal and phasic structure of locomotion, as well as the angular changes in the major joints of the lower limbs (ankle, knee and hip) in the sagittal plane. The values of these parameters were calculated separately for the left and right leg, which made it possible to determine the size of the differences and was the basis for assessing gait asymmetry. Body segments were defined by means of 39 reflective markers having a diameters of 25 mm attached to the head, trunk, pelvis, arms and legs.

Kinematic data were divided into individual gait cycles for each side of the body. A gait cycle was defined from heel strike to subsequent heel strike. Data for each cycle were normalized (0% GC �C 100% GC). For the purpose of analysis, the functional phases of gait were subdivided into (according to Perry, 1992) LR-loading response (10% GC), MST-mid stance (20% GC), TST-terminal stance (20% GC), PSW-pre swing (10% GC), ISW-initial swing (10% GC), MSW-mid swing (15% GC), and TSW-terminal swing (15% GC). To assess the normal distribution of acquired data we used the Shapiro-Wilk test. The student��s t test for independent groups was used to examine the statistical significance of differences between mean values of variables obtained during gait.

To determine the average level of diversification of the parameters in terms of gender in the characteristic phases of a standardized gait cycle, which is described below, we applied a two-way analysis of variance ANOVA with repeated measurements. To evaluate the level of gait asymmetry in the angular data, the authors employed a relative asymmetry index (RAI): RAI=X��Y100%,where: (1) – the average difference between the values noted for the right and left limbs in a given phase of the gait cycle (LR, MST, etc.) Y – total range of motion of the angular changes in the given phase (absolute value of the difference between the largest and the smallest angles for a given phase of the gait cycle).

The average difference () in successive phases of gait was calculated according to the following formula: X��=��i=li=n|Ri-Li|%GC,where: (2) R, L- instantaneous value of the angle of individual joints in the right and left lower limb, % GC – relative duration of the given phase in the gait cycle (number). Consistently, in accordance Batimastat with the adopted symbols and the way of their determination, the described equation for LR phase (10% GC) was as follows: X��LR=��i=li=10|Ri-Li|10. (3) Results Tables 2 and and33 show the values of selected kinematic parameters of gait, both in terms of gender and the side of the body.