50 > BMI

50 > BMI read FAQ > 24.99) according to WHO classification (WHO, 2004). Likewise, in case of weight/height indices, mean body fat percentage recorded in climbers was comparable to this observed in untrained students and amounted to 15.4%. However, when classified by Heath-Carter somatotype components, endomorphy component that reflects adiposity had the lowest contribution in climbers�� somatotype; the mean value being significantly (p<0.001) lower than that observed in untrained students (2.4 �� 0.79 vs. 3.6 �� 1.48, respectively). Regardless of comparable body height, climbers had significantly greater arm span and arm length (by about 6 and 2.5 cm, respectively) what was reflected in ape index and arm length index, the respective values being by about 1.5 (p<0.001) and 0.6 SD (p<0.

01) greater than observed in untrained students, respectively. Additionally, climbers exhibited significantly greater values in arm (32.7 �� 2.09 vs. 30.9 �� 2.52 cm) and forearm circumferences (28.3 �� 1.28 vs. 26.02 �� 1.80 cm) and in upper extremity girth index, while no differences were found for elbow width. On the other hand, climbers had by 1 SD (p<0.001) lesser knee width while no between-group differences were found for calf circumference. Moreover, climbers exhibited by about 1 SD less in pelvis-to-shoulder ratio comparing to untrained students. Likewise, for upper extremities climbers had significantly (p<0.05) longer lower limbs as expressed by the Manouvrier��s index. In order to reveal possible relationships between somatic indices and subjects�� climbing ability, Pearson��s correlation coefficients and partial correlations were calculated.

Apart from the obvious relations between the body fat and weight-to-height indices or between indices pertaining to the length of upper limb, significant negative correlations were found only for %FAT and ape index (?0.594; p<0,01) and for arm circumference index and BMI (r = ?0.497; p<0.05) or RI (r = ?0.587; p<0.01). Self-reported climbing ability significantly correlated with %FAT (r = ?0.614; p<0.01); besides that, no significant correlations with somatic indices were noted and none of the partial correlations proved significant. Only the ape index tended to correlate with the self-reported climbing ability (r = 0.397; p = 0.083). Discussion Despite the growing number of reports on rock climbing, those concerning anthropometric characteristics of climbers are rather scarce and inconsistent.

The results of this study do not support the view of Watts et al. (2003) that climbers are small in stature with low body mass as no differences between the climbers and untrained controls were found for basic Entinostat somatic features and body size-related indices. Body height and body mass of climbers were rather average and amounted to 180.0 cm and 70.7 kg, respectively, what was in line with the observations of Billat et al. (1995) and Grant et al.

2c) Four seconds after the initial MVC, PT was 62 6 �� 10 8 Nm,

2c). Four seconds after the initial MVC, PT was 62.6 �� 10.8 Nm, a 45 �� 13% increase compared to the pre-MVC value (Figure 2a). There was a sharp decline in PT in the following 60 s so that PT after 2 min was not kinase inhibitor KPT-330 significantly different (p>0.05) from the pre-MVC PT (Figure 2a). However, PT returned to baseline pre-MVC value only after 6 min. Figure 2 Time decay of PT (a), RTD & CT (b), and RR & ?RT (c) after a 5 s MVC in response to electrical stimulation reported as % change from unpotentiated values for study 1. * p< 0.05 for unpotentiated values. PT, peak twitch ... RTD and RR increased significantly (p<0.05) by 53 �� 13% and 50 �� 17%, respectively, immediately after the MVC whilst CT and ?RT were unchanged for the duration of the experiment (Figures 2b and and2c).2c).

RTD and RR returned to the pre-MVC values within 3 min after the initial MVC. The decay in PT was associated with a progressive fall in the RTD and in the RR (Figures 2b and and2c).2c). Correlation between PT vs RTD, PT vs RR and PT vs CT was r2 = 0.99 (p<0.001), 0.98 (p<0.001) and 0.56 (p<0.01), respectively, during the 10 min period after the MVC. EMD did not change at any time during this section of the experiment (data not shown). Study 2 Unpotentiated muscle: Torque response to repeated SS over 1 min SS torque response to the first 6 episodes of electrical stimulation (Figure 1c) delivered to the unpotentiated muscle in the min prior to the first MVC did not differ from each other (p>0.05) and the mean values did not differ from those of study 1. Mean values for PT, EMD, CT, ?RT, RTD and RR were respectively 43.

5 �� 12.9 Nm, 34.2 �� 3.1 ms, 85.9 �� 9.5 ms, 80.3 �� 10.5 ms, 0.52 �� 0.18 Nm/ms and 0.56 �� 0.21 Nm/ms (Table 2). Table 2 Responses of single stimulus at specific time points at rest for study 2 (n= 6) Potentiated muscle: Torque response to repeated SS after 10 MVCs PT immediately (4 s) after the first MVC (MVC 1) was increased by 56 �� 10% (Figure 3a) to 67.0 �� 17.7 Nm. PT immediately after MVCs 2�C10 was not different (p>0.05) from PT immediately after MVC 1 (Figure 3a). Figure 3 Time decay of PT (a), RTD & CT (b) and RR & ?RT (c) after a 5 s MVC in response to electrical stimulation reported as % change from unpotentiated values for study 2. * p< 0.05 from MVC 1. Other values were not different ... PT then decayed from 4�C45 s after each MVC so that at 16 s after MVC 1, PT fell significantly (p<0.

001) from the 4 s value PT, but PT was still 29 �� 7% above the unpotentiated value after 45 s. Interestingly the following MVCs showed similar PT at 4 s after MVC, but PT was significantly (p<0.05) higher 30 and 45 s after MVC 2 and 8, 12, 16, 30 and 45 s after MVC 5 and 10 compared to MVC 1, indicating a slower decay Dacomitinib of PT (Figure 3a). In addition PT at 45 s after the first MVC was significantly lower (p<0.05) than were the values 45 s after any of the following MVCs (2�C10).

Statistical analysis After sphericity assumption was verified wit

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 selleckchem 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 Batimastat similar differences in RPE in both young and old subjects performing single and multiple joint exercises. According to Hetzler et al.

99 years) They were all right-handed and able to perform first s

99 years). They were all right-handed and able to perform first serves. None of the participants played tennis outside the timetable for data collection during the research. All the participants provided informed consent according to the Declaration of Helsinki. The Extremadura University Ethical Committee ref 1 approved the procedure. Measures Product variables analyzed were stroke accuracy, measured by radial error (Robins et al., 2006), variable error, which represents serve errors made in respect of deviation from the serve target area, and the ball speed. Process variables (Table 1) were measured over the trajectory of the hand holding the racket along the antero-posterior (X), the transverse (Y), and the longitudinal (Z) axes.

With respect to non-linear variables, these give information about the structure and characteristics of the variability present in the time series. These time series were derived from the position of the hand holding the racket during its trajectory, from the beginning of the movement until the moment the racket hit the ball. Table 1 Dependent variables analyzed in the research. In each instant kinematic variable the standard deviation (SD) and the variation coefficient (CV) was analyzed Tasks, material and measurements Each tennis player performed 20 first serves. They were instructed to hit the ball with as much power and accuracy as they could, and to avoid sending the balls into the area known in tennis slang as the ��T�� (the line intersection which divides both service boxes from their respective service lines).

The ball bounce on the tennis court surface was video recorded in every serve (Sony HDR- HC3E). The video camera was set at a height of 3 meters and was positioned at the back of the court. In order to measure accuracy, a Visual Basic 5.0 application was developed (Menayo, 2010). This facilitated the calculation of real-space Cartesian coordinates for the ball bounces through a digitization process from the video recording of the serves. Non-linear kinematic variables were analyzed by using a software application created with Visual Basic 5.0, from an algorithm for calculating Approximate Entropy (Pincus, 1991). To measure ball speed, a radar gun (Sports Radar SR3600) was used. This radar device, which records the speed of moving objects with an accuracy of +/? 1 km/h, was positioned behind the tennis player, facing the direction of the stroke (Figure 1).

An electromagnetic motion tracking system Polhemus Fastrak? was used to record and analyze kinematic variables and this was connected to a computer (Toshiba Satellite 1900). This tracking system has 6 Degree-of-Freedom motion tracking sensors, with an accuracy of 0.08 cm for position (X, Y and Z Cartesian space coordinates) and 0.15 degrees for angular orientation (azimuth, elevation, and roll), and records at a frequency Brefeldin_A of 120 Hz. Figure 1 Automated measurement system.

For reference, 180 deg indicated full knee extension and normal s

For reference, 180 deg indicated full knee extension and normal standing position, respectively. The ankle in a neutral position was equal to 90 deg (angles 0�C90 deg indicated dorsiflexion selleck catalog and angles 90�C180 deg indicated plantarflexion). The raw EMG data were low-pass filtered at 500 Hz and high-pass filtered at 10 Hz to eliminate movement artefacts, using a Butterworth fourth-order zero-lag filter. The onset/offset time selected from starting knee extension of the swinging leg to impact the ball. After removing the signal offset, the root mean square (RMS) was estimated from raw EMG signal data using a smoothing window. In each kick, we examined the (1) maximum RMS of RF, VM and VL muscles, (2) maximum knee angular velocity (KAV), (3) maximum ankle angular velocity (AAV), (4) maximum foot velocity (FV) and (4) maximum ball velocity (BV).

Foot velocity (Vfoot) was estimated as the velocity of the center of mass of the foot, which was calculated in each frame based on ankle and toe marker data. The mechanics of collision between the foot and ball were analyzed as suggested by Lees and Nolan (1998). Particularly, the resultant ball velocity (Vball) was calculated from V foot as follows: vball = 1.23 �� vfoot + 2.72 The Pre-stretching and Post-stretching values for each protocol were averaged across days and therefore for each participant there were four values: pre- and post- static stretching and pre- and post-dynamic stretching ones. Subsequently, in each variable, the percentage differences between pre- and post- stretching protocol were calculated and compared between protocols.

Statistical Analysis A one-way analysis of variance was used to compare relative changes in each dependent variable between static and dynamic stretching. The level of significance was set at p �� 0.05. When justified, paired sample t-tests were performed to confirm significant changes within each condition. Effect sizes (ES) were calculated and are also reported. The power was �� 0.94 and the test�Cretest reliability values for the testing order of tests ICCRs (intraclass correlation reliability) were �� 0.97. Results An example of EMG raw data of RF, VL, and VM activity after different acute stretching methods is illustrated in Figure 2. The descriptive results of raw EMG and KAV data are presented in Table 2 while mean group values are presented in Figure 3.

The ANOVA showed a statistically significant higher increase in RF EMG (Figure 3) after dynamic stretching (37.50% �� 9.37%) versus a non-significant (?8.33% �� 3.89%) decrease after static stretching (p = 0.015) (ES �� AV-951 0.94). Similarly, VL EMG increased after dynamic stretching (20% �� 10.21%) but it decreased (?6.60% �� 8.77%) after static stretching (p = 0.004) (ES �� 0.98). There was also a statistically significant increase in VM EMG after dynamic stretching (12.00% �� 6.29%) as opposed to a decrease (?12.00% �� 5.