Jump peak power assessment through power prediction equations in different samples

The aim of this study was to describe the characteristics of jump capacity in a group of secondary school students and to develop 2 specific equations-applied to boys and girls, respectively, to estimate the jump power of secondary school students. Four hundred and fifty-six boys (age, 14.1 ± 0.8 years; mass, 61.9 ± 15.7 kg; height, 1.64 ± 0.10 m) and 465 girls (age, 14.1 ± 0.9 years; mass, 55.1 ± 10.0 kg; height, 1.58 ± 0.07 m), all of them secondary school students, volunteered to participate in this study. They performed a vertical jump test (Abalakov) on a force platform, and jump height and peak power were measured. Most importantly, peak power was also estimated through a series of previously established power equations. For the purpose of establishing statistically significant differences, a p value ≤ 0.05 was fixed. The equations proposed by Canavan and Vesconvi, and Harman were the most precise with respect to actual power, reaching a percentage of 1.9-2.1 and 3.6-4.1%, respectively. The equations by Sayers and Lara showed a greater difference in percentage (9.9-12.4 and 22.4-24.2%, respectively) with that of actual power. Similar results were not obtained in other studies, which means that a specific equation will be required according to the characteristics of the assessed sample. Two equations specifically addressed to secondary school students will be established in this article: boys: ([61.8 jump height (cm)] + [37.1 body mass (kg)] - 1,941.6); girls: ([31 jump height (cm)] + [45 body mass (kg)] - 1,045.4). Crossvalidation tests that were done to prove the validity of said equations showed positive results. Practical applications: Those teachers who wish to estimate the jump power of their pupils can use these equations and thereby calculate jump power by the indirect method from jump height and body mass index, without any need to use any expensive tools.     

Power output estimation in vertical jump performed by young male soccer players

The purpose of the study was (a) to assess the accuracy of the regression equations available in the literature to estimate the actual peak power (PPac) of the countermovement jump (CMJ) executed by young male soccer players, (b) to develop new regression equations from this population, and (c) to verify whether regression equations obtained from age-based subgroups could increase the accuracy of the estimation (PPes) of PPac. In all, 117 young players (age: 13.6 ± 2.4 years) were enrolled in the study. Each subject performed 5 CMJs on a force platform. The new regression equations were obtained from the entire experimental sample (G1) and 3 age-based subsamples (G2 = prepubertal, G3 = peripubertal, G4 = postpubertal) using 2 different approaches: the best jump and the mean values achieved by each subject. All the equations in the literature underestimated the peak power (p < 0.00005) in all the groups. The approach based on the mean values was more accurate (adjusted R = 0.925, SEE = 302.9 W) than the one based on the best jump (adjusted R = 0.892; SEE = 360.8 W). Moreover, calculating the regression equations from the 3 age-based subsamples, SEE resulted improved (15.5% in G2, 5.6% in G3 and 0.9% in G4). Regression equations must be derived from homogeneous populations, in terms of gender, sports practice, and age. The approach based on the mean values for each subject was more accurate than the approach used in the literature up to now. In practical applications, regression equation estimates cannot be used to assess the performance of a single subject, because errors may exceed 50%, whereas they may be useful for group comparisons.     

Kettlebell swing training improves maximal and explosive strength

The aim of this study was to establish the effect that kettlebell swing (KB) training had on measures of maximum (half squat-HS-1 repetition maximum [1RM]) and explosive (vertical jump height-VJH) strength. To put these effects into context, they were compared with the effects of jump squat power training (JS-known to improve 1RM and VJH). Twenty-one healthy men (age = 18-27 years, body mass = 72.58 ± 12.87 kg) who could perform a proficient HS were tested for their HS 1RM and VJH pre- and post-training. Subjects were randomly assigned to either a KB or JS training group after HS 1RM testing and trained twice a week. The KB group performed 12-minute bouts of KB exercise (12 rounds of 30-second exercise, 30-second rest with 12 kg if <70 kg or 16 kg if >70 kg). The JS group performed at least 4 sets of 3 JS with the load that maximized peak power-Training volume was altered to accommodate different training loads and ranged from 4 sets of 3 with the heaviest load (60% 1RM) to 8 sets of 6 with the lightest load (0% 1RM). Maximum strength improved by 9.8% (HS 1RM: 165-181% body mass, p < 0.001) after the training intervention, and post hoc analysis revealed that there was no significant difference between the effect of KB and JS training (p = 0.56). Explosive strength improved by 19.8% (VJH: 20.6-24.3 cm) after the training intervention, and post hoc analysis revealed that the type of training did not significantly affect this either (p = 0.38). The results of this study clearly demonstrate that 6 weeks of biweekly KB training provides a stimulus that is sufficient to increase both maximum and explosive strength offering a useful alternative to strength and conditioning professionals seeking variety for their athletes.     

Predicting lower body power from vertical jump prediction equations for loaded jump squats at different intensities in men and women

The purpose of this study was to determine the efficacy of estimating peak lower body power from a maximal jump squat using 3 different vertical jump prediction equations. Sixty physically active college students (30 men, 30 women) performed jump squats with a weighted bar's applied load of 20, 40, and 60% of body mass across the shoulders. Each jump squat was simultaneously monitored using a force plate and a contact mat. Peak power (PP) was calculated using vertical ground reaction force from the force plate data. Commonly used equations requiring body mass and vertical jump height to estimate PP were applied such that the system mass (mass of body + applied load) was substituted for body mass. Jump height was determined from flight time as measured with a contact mat during a maximal jump squat. Estimations of PP (PP(est)) for each load and for each prediction equation were compared with criterion PP values from a force plate (PP(FP)). The PP(est) values had high test-retest reliability and were strongly correlated to PP(FP) in both men and women at all relative loads. However, only the Harman equation accurately predicted PP(FP) at all relative loads. It can therefore be concluded that the Harman equation may be used to estimate PP of a loaded jump squat knowing the system mass and peak jump height when more precise (and expensive) measurement equipment is unavailable. Further, high reliability and correlation with criterion values suggest that serial assessment of power production across training periods could be used for relative assessment of change by either of the prediction equations used in this study.     

Relations between force-velocity characteristics of the knee-hip extension movement and vertical jump performance

Relations between force-velocity characteristics of the multijoint movement of the lower limbs and vertical jump performance were investigated. A total of 67 untrained subjects (age: 19.54 +/- 2.38 years; height: 166.88 +/- 8.53 cm; body mass: 59.14 +/- 10.82 kg, mean +/- SD) performed isometric and isotonic knee-hip extension movements on a servo-controlled dynamometer, and the force-velocity relations were determined. Also, vertical jump (VJ) performance was measured with a jump gauge. The force-velocity relation was described with a linear function so that the maximum isometric force (Fmax) and the maximum unloaded velocity (Vmax) for the knee-hip extension movement were estimated by extrapolation. Maximum isometric force coincided with maximum isometric force, F(0) (F(0)/Fmax = 1.03 +/- 0.24). Maximum isometric force, Vmax, and maximum power output (Pmax) were positively correlated with VJ (r = 0.48, 0.68, and 0.76, respectively; p < 0.001). However, when Fmax, Vmax, and Pmax were normalized with body mass (BM), leg length (LL), and BM, respectively, no correlation was seen between Fmax/BM and VJ (r = 0.24, p > 0.05), and significant correlations were seen between Vmax/LL and VJ (r = 0.56, p < 0.001) and between Pmax/BM and VJ (r = 0.65, p < 0.001). On the other hand, Fmax and Vmax (r = 0.12, p > 0.05) and Fmax/BM and Vmax/LL (r = 0.05, p > 0.05) were not significantly correlated, indicating that Fmax and Vmax were independent variables. The present estimates of Fmax, Vmax, and Pmax can be useful for evaluating the actual performance of multijoint movement of the lower limbs. It is suggested that, although in untrained individuals the speed of movement might be a more important determinant of jump performance, jump performance ability has a potential to improve with increases in strength of the lower limb.     

A new non-exercise-based Vo2max prediction equation for aerobically trained men

The purposes of the present study were to (a) modify previously published Vo(2)max equations using the constant error (CE = mean difference between actual and predicted Vo(2)max) values from Malek et al. (28); (b) cross-validate the modified equations to determine their accuracy for estimating Vo(2)max in aerobically trained men; (c) derive a new non- exercise-based equation for estimating Vo(2)max in aerobically trained men if the modified equations are not found to be accurate; and (d) cross-validate the new Vo(2)max equation using the predicted residual sum of squares (PRESS) statistic and an independent sample of aerobically trained men. One hundred and fifty-two aerobically trained men (Vo(2)max mean +/- SD = 4,154 +/- 629 ml.min(-1)) performed a maximal incremental test on a cycle ergometer to determine actual Vo(2)max. An aerobically trained man was defined as someone who had participated in continuous aerobic exercise 3 or more sessions per week for a minimum of 1 hour per session for at least the past 18 months. Nine previously published Vo(2)max equations were modified for use with aerobically trained men. The predicted Vo(2)max values from the 9 modified equations were compared to actual Vo(2)max by examining the CE, standard error of estimate (SEE), validity coefficient (r), and total error (TE). Cross-validation of the modified non-exercise-based equations on a random subsample of 50 subjects resulted in a %TE > or = 13% of the mean of actual Vo(2)max. Therefore, the following non-exercise-based Vo(2)max equation was derived from a random subsample of 112 subjects: Vo(2)max (ml.min(-1)) = 27.387(weight in kg) + 26.634(height in cm) - 27.572(age in years) + 26.161(h.wk(-1) of training) + 114.904(intensity of training using the Borg 6-20 scale) + 506.752(natural log of years of training) - 4,609.791 (R = 0.82, R(2) adjusted = 0.65, and SEE = 378 ml.min(-1)). Cross-validation of this equation on the remaining sample of 40 subjects resulted in a %TE of 10%. Therefore, the non-exercise-based equation derived in the present study is recommended for estimating Vo(2)max in aerobically trained men.     

Reliability and validity of a new repeated agility test as a measure of anaerobic and explosive power

The aim of this study was to evaluate the reliability and validity of a repeated modified agility test (RMAT) to assess anaerobic power and explosiveness. Twenty-seven subjects (age: 20.2 ± 0.9 years, body mass: 66.1 ± 6.0 kg, height: 176 ± 6 cm, and body fat: 11.4 ± 2.6%) participated in this study. After familiarization, subjects completed the RMAT consisting of 10 × 20-m maximal running performances (moving in forward, lateral, and backward) with ~25-second recovery between each run. Ten subjects performed the RMAT twice separated by at least 48 hours to evaluate relative and absolute reliability and usefulness of the test. The criterion validity of the RMAT was determined by examining the relationship between RMAT indices and the Wingate anaerobic test (WAT) performances and both vertical and horizontal jumps. Reliability of the total time (TT) and peak time (PT) of the RMAT was very good, with intraclass correlation coefficient > 0.90 and SEM < 5% and low bias. The usefulness of TT and PT of the RMAT was rated as "good" and "OK," respectively. The TT of the RMAT had significant correlations with the WAT (peak power: r = -0.44; mean power: r = -0.72), vertical jumps (squat jump: r = -0.50; countermovement jump: r = -0.61; drop jump (DJ): r = -0.55; DJ with dominant leg: r = -0.72; DJ with nondominant leg: r = -0.53) and 5 jump test (r = -0.56). These findings suggest that the RMAT is a reliable and valid test for assessing anaerobic power and explosiveness in multisprint sport athletes. Consequently, the RMAT is an easily applied, inexpensive field test and can provide coaches and strength and conditioning professionals with relevant information concerning the choice and the efficacy of training programs.     

Estimation of skeletal muscle mass by bioelectrical impedance analysis.

The purpose of this study was to develop and cross-validate predictive equations for estimating skeletal muscle (SM) mass using bioelectrical impedance analysis (BIA). Whole body SM mass, determined by magnetic resonance imaging, was compared with BIA measurements in a multiethnic sample of 388 men and women, aged 18-86 yr, at two different laboratories. Within each laboratory, equations for predicting SM mass from BIA measurements were derived using the data of the Caucasian subjects. These equations were then applied to the Caucasian subjects from the other laboratory to cross-validate the BIA method. Because the equations cross-validated (i.e., were not different), the data from both laboratories were pooled to generate the final regression equation SM mass (kg) = [(Ht²/ R x 0.401) + (gender x 3.825) + (age x -0. 071)] + 5.102 where Ht is height in centimeters; R is BIA resistance in ohms; for gender, men = 1 and women = 0; and age is in years. The r(2) and SE of estimate of the regression equation were 0.86 and 2.7 kg (9%), respectively. The Caucasian-derived equation was applicable to Hispanics and African-Americans, but it underestimated SM mass in Asians. These results suggest that the BIA equation provides valid estimates of SM mass in healthy adults varying in age and adiposity.     

Blood serum concentration of lipids and lipoproteins and body composition

The purpose of this study is the analysis of the relationship of blood serum apolipoprotein E (apoE), total cholesterol (TC), triglycerides (TG) and high-density and low-density lipoproteins (HDL, LDL), with body mass index (BMI), relative body surface area (RBSA) and body muscle (BM) and body fat (BF). The subjects are males and females aged 14-16 (adolescent age group 1: n1M = 141, n1F = 151) and 18-25 (young adult group 2, n2M = 16, n2F = 46). Significant correlations of serum TG and HDL with somatometric indicators were not observed. In the female samples, TC content directly correlates (p < 0.05) with BF (r1 = 0.164; r2 = 0.418) and negatively correlates with BM (r1 = -0.165; r2 = -0.352). The blood serum concentration of apoE is significantly correlated with body composition in adolescent females (for BF r1 = -0.168; for BM r1 = 0.266; p < 0.05); in males 14-16 years old, the both correlations have a significance level p < 0.06. In young adult females TC and LDL content negatively correlates with RBSA (r2 = -0.386 and -0.377 respectively; p < 0.05) and positively correlates with BMI (r2 = 0.413 and 0.415 respectively; p < 0.05). Adolescent females and young adult females have opposite relationships between FC and apoE concentration. In females 14-15 years old apoE concentration decreases as FC increases. In females 16-17 the correlation disappears, and in older females apoE concentration and FC increase together.     

Predicting maximal aerobic capacity (VO2max) from the critical velocity test in female collegiate rowers

The objective of this study was to examine the relationship between the critical velocity (CV) test and maximal oxygen consumption (VO2max) and develop a regression equation to predict VO2max based on the CV test in female collegiate rowers. Thirty-five female (mean ± SD; age, 19.38 ± 1.3 years; height, 170.27 ± 6.07 cm; body mass, 69.58 ± 0.3 1 kg) collegiate rowers performed 2 incremental VO2max tests to volitional exhaustion on a Concept II Model D rowing ergometer to determine VO2max. After a 72-hour rest period, each rower completed 4 time trials at varying distances for the determination of CV and anaerobic rowing capacity (ARC). A positive correlation was observed between CV and absolute VO2max (r = 0.775, p < 0.001) and ARC and absolute VO2max (r = 0.414, p = 0.040). Based on the significant correlation analysis, a linear regression equation was developed to predict the absolute VO2max from CV and ARC (absolute VO2max = 1.579[CV] + 0.008[ARC] - 3.838; standard error of the estimate [SEE] = 0.192 L·min(-1)). Cross validation analyses were performed using an independent sample of 10 rowers. There was no significant difference between the mean predicted VO2max (3.02 L·min(-1)) and the observed VO2max (3.10 L·min(-1)). The constant error, SEE and validity coefficient (r) were 0.076 L·min(-1), 0.144 L·min(-1), and 0.72, respectively. The total error value was 0.155 L·min(-1). The positive relationship between CV, ARC, and VO2max suggests that the CV test may be a practical alternative to measuring the maximal oxygen uptake in the absence of a metabolic cart. Additional studies are needed to validate the regression equation using a larger sample size and different populations (junior- and senior-level female rowers) and to determine the accuracy of the equation in tracking changes after a training intervention.     

Fosbury flop: predicting performance with a three-variable model

The goal of this study is to (a) find the most predictive anthropometric factors, (b) check the predictability of a new jumping motor test, and (c) predict Fosbury-flop (FFP) performance by using a multiregression analysis. The participants of this study were 49 girls (age 13.6 ± 0.48 years; height = 1.61 ± 0.07 m) and 68 boys (age 13.6 ± 0.47 years; height = 1.64 ± 0.10 m). We measured the height, the sitting height), the highest position touched by the hand in a standing position (HEIGHTARM), the highest position touched by the hand during a running 1-leg vertical jump with a semirestricted curved run-up (HMAX), and the best performance in the FFP. We then calculated the leg length (LEGLENGTH), the skelic index (ratio of legs length to the abdomen length, SKEL), the vertical performance (VP, difference between HMAX and HEIGHTARM). The ability level was deducted from the difference between (LEGLENGTH + VP) and FFP. Pearson correlation coefficients were calculated, and a multiple-regression analysis technique was applied to find the most predictive model (p < 0.05). The FFP was correlated with standing height (HEIGHT; r = 0.398; p < 0.05), HMAX (r = 0.707; p < 0.0005), ABILITY (r = 0.391; p < 0.005) but not with SKEL (r = 0.161; p = 0.01). The best multiple-regression model included HEIGHT, HMAX, and ABILITY with a high level of prediction (r2 = 0.94). In conclusion, the FFP performance can be predicted with equation: FFP = -0.618 HEIGHT + 0.898 HMAX + 0.669 ABILITY - 0.08. This equation is quite similar for both sexes, showing that 13-year-old girls and boys use the same method to jump high, which implies that the way to increase coordination or lower limb strength during training can be the same for junior boys and girls in high jump.     

The relationship between kinematic determinants of jump and sprint performance in division I women soccer players

The purpose of this study was to determine the relationship between measures of unilateral and bilateral jumping performance and 10- and 25-m sprint performance. Fifteen division I women soccer players (height 165 ± 2.44 cm, mass 61.65 ± 7.7 kg, age 20.19 ± 0.91 years) volunteered to participate in this study. The subjects completed a 10- and 25-m sprint test. The following jump kinematic variables were measured using accelerometry: sprint time, step length, step frequency, jump height and distance, contact time, concentric contact time, and flight time (Inform Sport Training Systems, Victoria, BC, Canada). The following jumps were completed in random order: bilateral countermovement vertical jump, bilateral countermovement horizontal jump, bilateral 40-cm drop vertical jump, bilateral 40-cm drop horizontal jump, unilateral countermovement vertical jump (UCV), unilateral countermovement horizontal jump, unilateral 20-cm drop vertical jump (UDV), and unilateral 20-cm drop horizontal jump (UDH). The trial with the best jump height or distance, reactive strength (jump height or distance/total contact time), and flight time to concentric contact time ratio (FT/CCT) was recorded to analyze the relationship between jump kinematics and sprint performance. None of the bilateral jump kinematics significantly correlated with 10- and 25-m sprint time, step length, or step frequency. Right-leg jump height (r = -0.71, p = 0.006, SEE = 0.152 seconds), FT/CCT (r = -0.58, p = 0.04, SEE = 0.176 seconds), and combined right and left-leg jump height (r = -0.61) were significantly correlated with the 25-m sprint time during the UCV. Right-leg FT/CCT was also significantly related to 25-m step length (r = 0.68, p = 0.03, SEE = 0.06 m) during the UDV. The combined right and left leg jump distance to standing height ratio during the UDH significantly correlated (r = -0.58) with 10-m sprint time. In comparison to bilateral jumps, unilateral jumps produced a stronger relationship with sprint performance.     

Effects of a 6-week plyometric training program on performances in pubescent swimmers

This study examined in pubescent swimmers the effects on front crawl performances of a 6-week plyometric training (PT) in addition to the habitual swimming program. Swimmers were assigned to a control group (n = 11, age: 14.1 ± 0.2 years; G(CONT)) and a combined swimming and plyometric group (n = 12, age: 14.3 ± 0.2 years; GSP), both groups swimming 5.5 h · wk(-1) during a 6-week preseason training block. In the GSP, PT consisted of long, lateral high and depth jumps before swimming training 2 times per week. Pre and posttests were performed by jump tests (squat jump [SJ], countermovement jump [CMJ]) and swim tests: a gliding task, 400- and 50-m front crawl with a diving start (V400 and V50, m · s(-1)), and 2 tests with a water start without push-off on the wall (25 m in front crawl and 25 m only with kicks). Results showed improvement only for GSP for jump tests (Δ = 4.67 ± 3.49 cm; Δ = 3.24 ± 3.17 cm; for CMJ and SJ, respectively; p < 0.05) and front crawl tests (Δ = 0.04 ± 0.04 m · s(-1); Δ = 0.04 ± 0.05 m · s(-1); for V50 and V400, respectively; p < 0.05). Significant correlations were found for GSP between improvements in SJ and V50 (R = 0.73, p < 0.05). Results suggested a positive effect of PT on specific swimming tasks such as dive or turn but not in kicking propulsion. Because of the practical setup of the PT and the relevancy of successful starts and turns in swimming performances, it is strongly suggested to incorporate PT in pubescent swimmers' training and control it by jump performances.     

Relative net vertical impulse determines jumping performance

The purpose of this investigation was to determine the relationship between relative net vertical impulse and jump height in a countermovement jump and static jump performed to varying squat depths. Ten college-aged males with 2 years of jumping experience participated in this investigation (age: 23.3 ± 1.5 years; height: 176.7 ± 4.5 cm; body mass: 84.4 ± 10.1 kg). Subjects performed a series of static jumps and countermovement jumps in a randomized fashion to a depth of 0.15, 0.30, 0.45, 0.60, and 0.75 m and a self-selected depth (static jump depth = 0.38 ± 0.08 m, countermovement jump depth = 0.49 ± 0.06 m). During the concentric phase of each jump, peak force, peak velocity, peak power, jump height, and net vertical impulse were recorded and analyzed. Net vertical impulse was divided by body mass to produce relative net vertical impulse. Increasing squat depth corresponded to a decrease in peak force and an increase in jump height and relative net vertical impulse for both static jump and countermovement jump. Across all depths, relative net vertical impulse was statistically significantly correlated to jump height in the static jump (r = .9337, p < .0001, power = 1.000) and countermovement jump (r = .925, p < .0001, power = 1.000). Across all depths, peak force was negatively correlated to jump height in the static jump (r = -0.3947, p = .0018, power = 0.8831) and countermovement jump (r = -0.4080, p = .0012, power = 0.9050). These results indicate that relative net vertical impulse can be used to assess vertical jump performance, regardless of initial squat depth, and that peak force may not be the best measure to assess vertical jump performance.     

Development of a submaximal test to predict elliptical cross-trainer VO2max

The purpose of this study was to develop an equation to predict VO2max from a submaximal elliptical cross-trainer test. Fifty-four apparently healthy subjects (25 men and 29 women, mean +/- SD age: 29.5 +/- 7.1 years, height: 173.3 +/- 12.6 cm, weight: 72.3 +/- 7.9 kg, percent body fat: 17.3 +/- 5.0%, and elliptical cross-trainer VO2max: 43.9 +/- 7.2 ml x kg(-1) x min(-1)) participated in the study and were randomly assigned to an original sample group (n = 40) and a cross-validation group (n = 14). Each subject completed an elliptical cross-trainer submaximal (3 5-minute submaximal stages) and a VO2max test on the same day, with a 15-minute rest period in between. Stepwise multiple regression analyses were used to develop an equation for estimating elliptical cross-trainer VO2max from the data of the original sample group. The accuracy of the equation was tested by using data from the cross-validation group. Because there was no shrinkage in R2 between the original sample group and the cross-validation group, data were combined in the final prediction equation (R2 = 0.732, standard error of the estimate = 3.91 ml x kg(-1) x min(-1), p < 0.05): VO2max = 73.676 + 7.383(gender) - 0.317(weight) + 0.003957(age x cadence) - 0.006452(age x heart rate at stage 2). The correlation coefficient between the predicted and measured VO2max values was r = 0.86. Dependent t-tests resulted in no significant differences (p > 0.05) between predicted (43.8 ml x kg(-1) x min(-1)) and measured (43.9 ml x kg(-1) x min(-1)) VO2max measurements. Results indicate that the protocol and equation developed in the current study can be used by exercise professionals to provide acceptably accurate estimates of VO2max in non-laboratory-based settings.     

A proposed method for determining peak power in the jump squat exercise

In recent years a great deal of research has been published using peak power (PP) in the jump squat (JS) exercise as a measure of athletic performance. However, no standardized method for the determination of PP exists at this time to accurately evaluate this variable. Our proposed method (PM) for determining PP (PPPM) in the JS uses the product of vertical ground reaction forces and velocity of the center of mass of both the subject and the external resistance of a loaded Olympic bar. Fifteen male subjects with a mean age of 27 +/- 3 years, weight of 78 +/- 17 kg, and height of 175 +/- 10 cm participated in this study. PP was measured in the JS at five different testing loads (30%, 35%, 40%, 45%, and 50% body weight) based on methods commonly discussed in the literature to compare PP results of previous methods to those obtained using the PM. Paired t-tests at different load levels were used for statistical analysis with an overall alpha = 0.05. The average PP among five testing loads, measured by the PM, was 3782 +/- 906 W. PP derived from the product of force and velocity of the bar alone was 72% lower than PPPM at 1057 +/- 243 W (P < 0.0001). The PP estimated by the product of bar velocity and vertical ground reaction forces of the bar plus the subject was 8% higher than PPPM at 4100 +/- 844 W (P = 0.0001). Our results indicate that using the methods traditionally reported in the literature may cause an overestimation of PP during athletic performance. Using the PM in future research will facilitate test validity and enable the generalization of results outside the scope of specific research projects.     

Acute effects of whole-body vibration on jump force and jump rate of force development: a comparative study of different devices

The goal of this study was to compare the acute effects of whole-body vibration (WBV) delivered by 3 devices with different mechanical behavior on jump force (JF) and jump rate of force development (JRFD). Twelve healthy persons (4 women and 8 men; age 30.5 ± 8.8 years; height 178.6 ± 7.3 cm; body mass 74.8 ± 9.7 kg) were exposed to WBV for 15 and 40 seconds using 2 professional devices (power plate [PP; vertical vibration] and Galileo 2000 [GA; oscillatory motion around the horizontal axis in addition to vertical vibration]) and a home-use device [Power Maxx, PM; horizontal vibration]). The JF and JRFD were evaluated before, immediately after, and 5 minutes after WBV. The JF measured immediately after 40 seconds of vibration by the GA device was reduced (3%, p = 0.05), and JRFD measured after 5 minutes of rest after 40 seconds of vibration by the PM device was reduced (12%, p < 0.05) compared with the baseline value. The acute effects of WBV (15 or 40 seconds) on JF and JRFD were not significantly different among the 3 devices. In conclusion, our hypothesis that WBV devices with different mechanical behaviors would result in different acute effects on muscle performance was not confirmed.     

Kinetic and training comparisons between assisted, resisted, and free countermovement jumps

Elastic band assisted and resisted jump training may be a novel way to develop lower-body power. The purpose of this investigation was to (a) determine the kinetic differences between assisted, free, and resisted countermovement jumps and (b), investigate the effects of contrast training using either assisted, free, or resisted countermovement jump training on vertical jump performance in well-trained athletes. In part 1, 8 recreationally trained men were assessed for force output, relative peak power (PP·kg(-1)) and peak velocity during the 3 types of jump. The highest peak force was achieved in the resisted jump method, while PP·kg(-1) and peak velocity were greatest in the assisted jump. Each type of jump produced a different pattern of maximal values of the variables measured, which may have implications for developing separate components of muscular power. In part 2, 28 professional rugby players were assessed for vertical jump height before and after 4 weeks of either assisted (n = 9), resisted (n = 11), or free (n = 8) countermovement jump training. Relative to changes in the control group (1.3 ± 9.2%, mean ± SD), there were clear small improvements in jump height in the assisted (6.7 ± 9.6%) and the resisted jump training group (4.0 ± 8.8%). Elastic band assisted and resisted jump training are both effective methods for improving jump height and can be easily implemented into current training programs via contrast training methods or as a part of plyometric training sessions. Assisted and resisted jump training is recommended for athletes in whom explosive lower-body movements such as jumping and sprinting are performed as part of competition.