, 2005) using different types of hand dynamometers. Particularly, Espana-Romero et al. (2008) reported high reliability (ICC = 0.97 �C 0.98) of the handgrip strength test in 6�C12 year-old children, using the Takey dynamometer. selleck Perifosine Excellent test-retest reliability (r = 0.96 �C 0.98) of handgrip strength have been also showed in untrained adolescents (14�C17 years-old; Ruiz et al., 2006). In addition, Langerstrom et al. (1998) and Ruiz-Ruiz et al. (2002) found high reliability (r = 0.91 �C 0.97) of the handgrip strength test in healthy adults using the Grippit and Takei dynamometers, respectively. The results of this study are also, in accordance with those by Coelho e Silva et al. (2008; 2010) in young basketball players (14�C15.9 years-old and 12�C13.9 years-old, respectively) that reported high reliability (r = 0.

99) of handgrip strength using the Lafayette hand dynamometer. Table 3 Test-retest reliability of maximal handgrip strength in healthy children, adolescents and adults Our results support earlier findings that showed non-significant differences in handgrip strength between test and retest values (Espana-Romero et al., 2008; 2010a). In contrast, Clerke et al. (2005) found small but significant differences in handgrip strength between test and retest, in 13 to 17 year-old adolescents. The absence of warm-up or familiarization prior to testing in the above study may account for the differences in handgrip strength between test and retest measurements. Indeed, Svensson et al.

(2008), who also found differences in handgrip strength between test and retest suggested that children may learn over the trials a better technique or accomplish to squeeze harder. Therefore, the authors recommended a familiarization session and three maximal trials during the main testing. Reliability and age-effect Only a few studies addressed the issue of age-effect on reliability of handgrip strength in untrained participants (Table 4). The results of our study are in line with those of Espana-Romero et al. (2010a) who examined the reliability of the handgrip strength test in untrained children (6�C11 years-old) and adolescents (12�C18 years-old) using the Takey dynamometer and found high reliability in both age-groups. Moreover, Molenaar et al. (2008) compared the reliability of handgrip strength among three age-groups of untrained children (4�C6, 7�C9, and 10�C12 years old) using two different dynamometers (Lode dynamometer vs.

Martin vigorimeter), and reported no clear age-effect on reliability for both dynamometers. Entinostat Table 4 Test-retest reliability of maximal handgrip strength at different age-group. In contrast, Svensson et al. (2008) compared the reliability of the handgrip strength test among 6, 10 and 14 year old untrained children using the Grippit dynamometer, and showed greater reliability in 6 and 14 year old (ICC = 0.96) compared to 10 year old children (ICC = 0.78).

The average power with the full squat with 70kg also showed significant positive correlations with the sprint times. The CMJ height has been greatly used to access lower body power in soccer players (Wisloff, 1998; Helgerud, 2001; N��?ez, 2008; Ronnestad, 2008). Nevertheless, to our knowledge, only two previous studies selleck chemical Erlotinib (Gorostiaga, 2004; L��pez-Segovia, 2010) have used loaded countermovement jump (CMJL) exercise for testing lower limb power in this population. Unfortunately, these authors (Gorostiaga, 2004; L��pez-Segovia, 2010) did not include sprint evaluations in their studies. Different factors such as lower reliability of testing at very short distances, the static start position in the sprint test and the location of the first photoelectric cells (30 cm behind start in these two studies) could explain the lack relationship reported between CMJ and time at 10m.

Although, the relationship obtained between the vertical jump and 30m sprint time (present study: r= ?0.55; p<0.05 vs. r= ?0.60; p<0.01) was similar to the study of Wisloff (2004), the relationships observed between the vertical jump and last running meters are consistent with the results perceived with loaded jump, given a similarity of muscle action in both types of jumps. Significant association between peak power during loaded CMJ and later stages of the sprint (r=?0.544 to ?0.611; p��0.05) were obtained. The T10�C30 and T20�C30 were significantly related with peak power observed in the CMJL exercise with 20, 30, and 40kg external load.

Cronin and Hansen (2005) observed similar results in professional rugby players between loaded (30kg) vertical jump height and 5m, 10m, and 15m sprint times. The higher relationships (R2= 41�C62%) observed in the present study were perceived with the longer distances rather than the initial run. As running velocity approaches maximum, those strength measures that require force to be produced at high velocities have been reported to be significantly related to sprint performance (Wilson, 1995; Young, 1995; Nesser, 1996). Wilson (1995) reported a significant relationship between force at 30 ms in a concentric squat jump and 30m sprint time (r= 0.62). Nesser (1996) claimed significant correlations between 40m sprint time and peak isokinetic torque at a velocity of 7.85 rad/s for the hip and knee extensors and knee flexors (r= 0.54 to 0.61).

We agree with the assertion that results show a slight tendency of increased relationships such as velocity and distance increased (Table 2). Moreover, data showed that power output during the vertical jump with 20kg best explained sprint performance. This parameter was also significantly correlated with all split speed measurements, including the first sprint stages. Although correlations do not signify causation, CMJ training with light loads could be important Dacomitinib to improve sprint performance in soccer player��s under-21.

Lozovina et al., 2009; Tan et al., 2009), in studies which developed and validated sport-specific tests (Mujika et al., 2006; Platanou, 2005), investigations which selleck chemicals Dovitinib focused on the intensity of the game (V. Lozovina, et al., 2003), or sport tactics and related statistics of the water polo game (Platanou, 2004). However, most of the studies mentioned so far sampled adult athletes (e.g. senior-age water polo players), while position specifics were mostly analyzed among three or four playing positions (i.e. goalkeepers were frequently not included in the analysis, and/or drivers and wings were observed as a single group �C field players). As far as we are aware both problems are understandable. Water polo is not one of the most popular sports in the world (like football or basketball for example) and it is therefore hard to find an appropriate sample of subjects (i.

e. adequate number of adequately trained athletes). This is chiefly the case with goalkeepers (one or two in each team). The second problem (e.g. studies not sampling young athletes) is also a logical consequence of the available number of subjects. Most particularly, if the study of adolescent athletes is intended then, due to the process of biological maturation, the subjects have to be near the end of puberty and homogenous in age (one or two years�� age difference at the most) and/or biological age must be controlled in the analysis (Faigenbaum, et al., 2009; Gurd and Klentrou, 2003; Latt, et al., 2009; Nindl et al., 1995). Since diversity in age is not a factor which can influence anthropometric status and/or motor achievements in adulthood (i.

e. senior-age athletes), it is logically more convenient to study adult athletes. The overall status of athletes in most sports can be observed during general and specific fitness tests. While general fitness tests (i.e. general motor and/or endurance capacities) are important indices of overall fitness status and allow a comparison of athletes from different sports (Frenkl et al., 2001), specific fitness tests allow a more precise insight into sport-specific capacities and therefore provide a basis for comparing athletes in the same sport (Bampouras and Marrin, 2009; Holloway et al., 2008; Hughes et al., 2003; Sattler et al., 2011).

However, Batimastat there is a clear lack of studies dealing with specific physical fitness profiles in water polo and, in particular, we found no study which has investigated this problem among high-quality junior water polo players. The aim of this study was to investigate the status and differences between five playing positions (Goalkeepers, Centers, Drivers, Wings and Points) in anthropometric measures and some specific physical fitness variables in high-level junior (17 to 18 years of age) water polo players. Material and Methods Participants The sample of subjects consisted of a total of 110 high-level water polo junior players.

The third marker proposed for EPC identification is VEGFR2, http://www.selleckchem.com/products/VX-770.html a protein predominantly expressed on the endothelial cell surface. Urbich and Dimmeler (2004) and Birn et al. (2005) claimed that EPCs were positive for CD34+, CD133 and VEGFR2 markers. CD34+ cells are multipotent progenitors that can engraft in several tissues (Krause et al., 2001), circulating CD34+ cells can be used to indirectly estimate hematopoiesis based on CD38, human leukocyte antigen (HLA) Dr, and CD33 markers. Patrick and Stephane (2003) found CD34+ stem cell from elite triathletes to be significantly lower than in healthy sedentary subjects. They stated that the low CD34+ counts and neutopenia as well as low lymphocyte counts could contribute to the increased upper respiratory tract infections observed in these athletes.

They hypothesized three explanations (1) aerobic training could induce deleterious effect on BM by inhibition of central CD34+ SC growth; (2) intense training could depress the mobilization of CD34+ SC; (3) due to aerology of the damage / repair process. They concluded that CD34+ SC quantification in elite athletes should be helpful for both basic science research and sport clinicians. The aim of this study was to reveal the role of aerobic and anaerobic training programs on CD34+ stem cells and chosen physiological variables. Material and Methods Participants Twenty healthy male athletes aged 18�C24 years with a training history of 4�C9 years were recruited for this study. Athletes had to engage in regular exercise at least 3 days/week.

Healthy low active male and BMI matched participants (n=10) aged 20�C22 years were recruited as controls. Control subjects could not have a recent history of regular exercise. Participants were screened and asked to fill out a health and physical activity history questionnaire. All participants were nonsmokers, non-diabetic and free of cardiovascular, lung and liver diseases. Participants did not take any medications that affect the EPCs number or function. These include statins, angiotensin 11 receptor antagonists, ACE inhibitors, peroxisome proliferators activated receptor (PPAR��) agonists and EPO. Testing procedures Written informed consent was obtained from all participants and the study was approved by the University of Suez Canal Institutional Review Board.

All participants engaged in a preliminary screening visit to evaluate resting blood Batimastat pressure and fasting blood chemistry profile, to rule out the presence of cardiovascular disease and to obtain samples of blood for analyses and BMI testing. All subjects were given a weight data log and instructed to weight themselves in the morning and evening and record their body mass in the log. All participants refrained from caffeine and vitamins 48 hours prior to the test. Participants were instructed to record their intake of foods for the three days before the test on a provided log.

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

The first cluster was high quality teams (HIGH) (with rankings ranged selleck chemical from 1st to 7th positions), the second cluster was intermediate quality teams (INT) (with ranking positions ranged from 8th to 14th positions), and the third cluster was low quality teams (LOW) (rankings lower than 15th place). In order to analyse the influence of quality of opposition (Marcelino et al., 2011) the sample was divided into three groups of game contexts ��HIGH vs. HIGH�� (n= 729 ball possessions), ��HIGH vs. LOW�� (n= 194 ball possessions), and ��LOW vs. LOW�� (n= 527 ball possessions). Table 1 International FloorbalL Federation (IFF) rankings based on the two previous World Floorball Championships (retrieved from www.floorball.org; accessed on 01.21.2012).

Statistical analysis Binomial Logistic Regression was used to estimate regression weights and odds ratios of the relation between performance indicators and covariates according to ball possessions effectiveness (Bar-Eli et al., 2006; Marcelino et al., 2011). In this non-linear model of regression, the estimated regression coefficients represent the estimated change in the log-odds, corresponding to a unit change in the corresponding explanatory variable conditional on the other explanatory variables remaining constant (Landau and Everitt, 2004). In the first stage, the performance indicators were tested individually and, in a second stage, the adjusted model was performed with all variables that showed a relation to ball possession effectiveness in the previous stage (Landau and Everitt, 2004).

Odds ratios (OR) and their 95% confidence intervals (CI) were calculated and adjusted for ball possession effectiveness. The statistical analyses were performed using SPSS for Windows, version 17.0 (SPSS Inc., Chicago IL), and statistical significance was set at p<0.05. Results The distribution of relative frequencies from the studied variables across quality of opposition contexts is shown in Table 2. Table 2 Distribution of relative frequencies from the studied variables across the three game contexts (HIGH vs. HIGH; HIGH vs. LOW; and LOW vs. LOW) in men��s floorball teams. In the first stage, the models of the Binomial Logistic Regression were computed with one variable at each step (Table 3), the results showed that during the three game contexts there were no significant interactions with the covariate game period (p>0.

05). The relationships found reflected the importance in ball possession effectiveness of some tactical variables Anacetrapib in each game context that were fitted in the second stage of the model. Table 3 Model and fit information for the frequency of technical and tactical indicators performed by the teams during the three game contexts according to ball possessions effectiveness in men��s floorball teams The adjusted model (Table 3) fitted the three game contexts (HIGH vs. HIGH: LRT=154.7, p<0.0001; HIGH vs. LOW: LRT=104.5, p<0.