Peak vertical jump power estimations in youths and young adults

Lower-body muscular power predicts performance on urban combat simulation

BACKGROUND

Military operations in urban environments requires faster movements and therefore may place greater demands on soldier strength and anaerobic ability.

OBJECTIVE

The aim was to study how physical fitness and body composition are associated with occupational test for urban combat soldiers before and after a 5-day military field exercise (MFE).

METHODS

Twenty-six conscripts (age = 20±1 yrs.) volunteered, of which thirteen completed the study. Occupational performance was determined by using the newly developed Urban Combat Simulation test (UCS); which included 50-m sprinting, moving a truck tire (56 kg) 2 meters with a sledgehammer, a 12-m kettlebell carry (2×20 kg) up the stairs with a 3-m ascent, 4-time sandbag lifts (20 kg) with obstacle crossing, and a 20-m mannequin (85 kg) drag. Aerobic and muscle fitness, as well as anaerobic capacity were measured, and, body composition was assessed with multifrequency bioimpedance analysis.

RESULTS

The UCS performance correlated significantly with standing long jump performance, as well as lower and upper body maximal strength before (r = -0.56 to -0.66) and after (r = -0.59 to -0.68) MFE, and, with body mass and FFM before (r = -0.81 to -0.83) and after (r = -0.86 to -0.91) MFE. In the regression analyses, fat free mass (R2 = 0.50, p = 0.01) and counter movement jump in combat load (R2 = 0.46, p = 0.009) most strongly explained the UCS performance.

CONCLUSION

This study demonstrated that muscle mass and lower body explosive force production together with maximal strength are key fitness components related to typical urban combat soldiers' military tasks. Physical training developing these components are recommended.

The relationship between lower limb muscle volume and peak vertical jump power in children

BACKGROUND: Vertical jump is an index representing leg power. It is important to determine factors that influence the vertical jump to help athletes improve their leg power. OBJECTIVE: This study aimed to determine the relationship between lower limbs muscle volume and peak vertical jump (VJ) power in children for both sexes. METHODS: Fourty children healthy boys (n= 20) and girls (n= 20) aged 10 to 12 years old, randomly performed three VJ modalities: squat jump (SJ), counter movement jump without (CMJ) and with arm swings (CMJarms). Lower limbs muscle volume (MV) estimated using a standard anthropometric method. Peak power (PP) calculated by Sayers equation. RESULTS: significant correlations between MV and Peak vertical jump power showed for both sexes. Likewise, significant correlations were found between MV and body mass for boys (r= 0.66; p= 0.001) and for girls (r= 0.59; p= 0.006). CONCLUSIONS: The correlation observed between peak vertical jump power and MV in both sexes can be considered as estimation tool of the lower limbs muscle power. Lower limb’s muscle volume are determining factor in muscle power for both sexes.

Potential Energy as an Alternative for Assessing Lower Limb Peak Power in Children: A Bayesian Hierarchical Analysis

The aim of this study was to analyze the use of potential energy (PE) as an alternative method to assess peak power of the lower limbs (PP) in children. 815 Spanish children (416 girls; 6–11 years old; Body Mass Index groups (n): underweight = 40, normal weight = 431, overweight = 216, obese = 128) were involved in this study. All participants performed a Countermovement Jump (CMJ) test. PP was calculated using Duncan (PP_DUNCAN), Gomez-Bruton (PP_GOMEZ) and PE_CMJ formulas. A model with PE_CMJ as the predictor variable showed a higher predictive accuracy with PP_DUNCAN and PP_GOMEZ than CMJ height (R² = 0.99 and 0.97, respectively; ELPD_diff = 1037.0 and 646.7, respectively). Moreover, PE_CMJ showed a higher linear association with PP_DUNCAN and PP_GOMEZ across BMI groups than CMJ height (β_PECMJ range from 0.67 to 0.77 predicting PP_DUNCAN; and from 0.90 to 1.13 predicting PP_GOMEZ). Our results provide further support for proposing PE_CMJ as an index to measure PP of the lower limbs, taking into account the children’s weight and not only the height of the jump. Therefore, we suggest the use of PE_CMJ in physical education classes as a valid method for estimating PP among children when laboratory methods are not feasible.

Effects of compression garment on muscular efficacy, proprioception, and recovery after exercise-induced muscle fatigue onset for people who exercise regularly

Fatigue is a major cause of exercise-induced muscle damage (EIMD). Compression garments (CGs) can aid post-exercise recovery, therefore, this study explored the effects of CGs on muscular efficacy, proprioception, and recovery after exercise-induced muscle fatigue in people who exercise regularly. Twelve healthy participants who exercised regularly were enrolled in this study. Each participant completed an exercise-induced muscle fatigue test while wearing a randomly assigned lower-body CG or sports pants (SP); after at least 7 days, the participant repeated the test while wearing the other garment. The dependent variables were muscle efficacy, proprioception (displacements of center of pressure/COP, and absolute error), and fatigue recovery (muscle oxygen saturation/SmO2, deoxygenation and reoxygenation rate, and subjective muscle soreness). A two-way repeated measure analysis of variance was conducted to determine the effect of garment type. The results indicated that relative to SP use, CG use can promote muscle efficacy, proprioception in ML displacement of COP, and fatigue recovery. Higher deoxygenation and reoxygenation rates were observed with CG use than with SP use. For CG use, SmO2 quickly returned to baseline value after 10 min of rest and was maintained at a high level until after 1 h of rest, whereas for SP use, SmO2 increased with time after fatigue onset. ML displacement of COP quickly returned to baseline value after 10 min of rest and subsequently decreased until after 1 hour of rest. Relative to SP use, CG use was associated with a significantly lower ML displacement after 20 min of rest. In conclusion, proprioception and SmO2 recovery was achieved after 10 min of rest; however, at least 24 h may be required for recovery pertaining to muscle efficacy and soreness regardless of CG or SP use.

Determining jumping performance from a single body-worn accelerometer using machine learning

External peak power in the countermovement jump is frequently used to monitor athlete training. The gold standard method uses force platforms, but they are unsuitable for field-based testing. However, alternatives based on jump flight time or Newtonian methods applied to inertial sensor data have not been sufficiently accurate for athlete monitoring. Instead, we developed a machine learning model based on characteristic features (functional principal components) extracted from a single body-worn accelerometer. Data were collected from 69 male and female athletes at recreational, club or national levels, who performed 696 jumps in total. We considered vertical countermovement jumps (with and without arm swing), sensor anatomical locations, machine learning models and whether to use resultant or triaxial signals. Using a novel surrogate model optimisation procedure, we obtained the lowest errors with a support vector machine when using the resultant signal from a lower back sensor in jumps without arm swing. This model had a peak power RMSE of 2.3 W·kg^-1 (5.1% of the mean), estimated using nested cross validation and supported by an independent holdout test (2.0 W·kg^-1). This error is lower than in previous studies, although it is not yet sufficiently accurate for a field-based method. Our results demonstrate that functional data representations work well in machine learning by reducing model complexity in applications where signals are aligned in time. Our optimisation procedure also was shown to be robust can be used in wider applications with low-cost, noisy objective functions.

A new peak-power estimation equations in 12 to 14 years-old soccer players

The aim of this study was to develop an age and soccer-specific regression equation to estimate the peak power of children aged 12-14 from the height of their vertical jumps using a large sample (n = 188). This study included 188 male soccer players (age, 12.6 ± 0.55; height, 153.31 ± 8.38 cm; and body weight, 43.65 ± 7.58 kg). Their actual peak power values obtained from vertical jumps were recorded using a force platform. The body weights of the participants were measured using Tanita. A regression model was developed using body weight and vertical jump values. All data were analyzed with the IBM SPSS (version 21) statistical analysis program. A multiple linear regression model was used to generate the best estimation of peak power. In this regression model, Power = -1714,116 + [(47.788 ∗ body weight (kg)] + [(58,976 ∗ Countermovement jump height (cm)]. Actual peak power is highly predictable for 12-14-year-old football players. In line with the new model, the actual peak power values obtained in this study were close to the estimated peak power values obtained with the Tufano formula. This may be because of the larger sample size and the same branch used for both equation models.

The relationship between height of vertical jumps, functionality and fall episodes in patients with chronic obstructive pulmonary disease: A case-control study

This study aimed to compare the height of jumps and functional parameters in patients with chronic obstructive pulmonary disease (COPD) to those in healthy people, in addition to assessing the relationship among variables in patients with COPD. Twenty patients with COPD (forced expiratory volume [FEV1] % of predicted: 39.98 ± 11.69%; age: 62.95 ± 8.06 years) and 16 healthy people (FEV1% of predicted: 97.44 ± 14.45%; age: 59.94 ± 6.43 years) were evaluated, and all participants performed the Squat Jump (SJ) and Counter Movement Jump (CMJ) tests to assess rapid force considering the jumping height. Functional capacity was assessed using the self-selected walking speed tests, walking speed in 10 m, walking test in 6 min, balance on one leg, sitting and standing, timed up and go, and a stair-climbing test. In addition, the questionnaires on recall of falls, Falls Efficacy Scale-International (concern with falling), International Physical Activity Questionnaires, and Saint George Respiratory Questionnaire were administered. The height of the jumps showed no difference between the groups, but the COPD group performed worse in most functional tests and was more afraid of falling. The number of falls was correlated with height in the SJ (r = -0.51) and CMJ (r = -0.62) jumps (p < 0.05), and with the performance in different functional tests. We suggest that interventions targeting rapid force may bring improvements in functional mobility and physical fitness as well as reducing fall episodes in patients with COPD.

Developing a new muscle power prediction equation through vertical jump power output in adolescent women

Explosive power is a performance determinant in many sports activities. Vertical jump tests for assessing power output are widely employed. Accurate and reliable methods are needed to predict human power output using the widely employed vertical jump height.To determine vertical jump capacity by using force platform in high school-level girls and to develop an equation that predict vertical jump muscle power (MP) (watts) through body composition and vertical jump height.An experimental group consisting of 87 high school-level young sedentary girls (mean; age; 16.49 ± 1.93, height;161.25 ± 6.21, weight; 55.59 ± 10.27) and a validation (control) group consisting of a similar population of 30 people (mean; age; 16.14 ± 1.31, height; 163.30 ± 6.28, weight; 56.65 ± 9.59), participated in this study. A stepwise linear regression model, including fat free body mass, vertical jump height and fat percentage as independent parameters was applied to develop a new muscle power (MP) estimation equation. Pearson product-moment correlation coefficients were calculated between actual and predicted MP.The new prediction equation obtained from regression analysis for muscle power (MP) could explain 74.5% (R) of the variation. A strong and high correlation was observed between the Pearson product-moment correlation coefficients of the actual and predicted MP (experimental; r = 0.863; P < .000) and (control; r = 0.898; P < .000).The direct measurements of muscle power (MP) require researchers to access costly and complex instruments. This need will be met by the MP estimation equations obtained from a simple vertical jump height and body composition measurement.

When Jump Height is not a Good Indicator of Lower Limb Maximal Power Output: Theoretical Demonstration, Experimental Evidence and Practical Solutions

Lower limb external maximal power output capacity is a key physical component of performance in many sports. During squat jump and countermovement jump tests, athletes produce high amounts of mechanical work over a short duration to displace their body mass (i.e. the dimension of mechanical power). Thus, jump height has been frequently used by the sports science and medicine communities as an indicator of the power output produced during the jump and by extension, of maximal power output capacity. However, in this article, we contend that squat jump and countermovement jump height are not systematically good indicators of power output produced during the jump and maximal power output capacity. To support our opinion, we first detail why, theoretically, jump height and maximal power output capacity are not fully related. Specifically, we demonstrate that individual body mass, push-off distance, optimal loading and the force-velocity profile confound the jump height-power relationship. We also discuss the relationship between squat jump or countermovement jump height and maximal power output capacity measured with a force plate based on data reported in the literature, which added to our own experimental evidence. Finally, we discuss the limitations of existing practical solutions (regression-based estimation equations and allometric scaling), and advocate using a valid, reliable and simple field-based procedure to compute individual power output produced during the jump and maximal power output capacity directly from jump height, body mass and push-off distance. The latter may allow researchers and practitioners to reduce bias in their assessment of lower limb mechanical power output by using jump height as an input with a simple yet accurate computation method, and not as the first/only variable of interest.

The effect of different stretching protocols on vertical jump measures in college age gymnasts

BACKGROUND

Gymnastics is a sport that requires rapid display of explosive power expressed through the vertical jump. Recent studies have shown that a static-stretching based warm-up is ineffective for explosive power development. The aim of this study was to compare different stretching protocols and their effect on vertical jump measures.

METHODS

Eleven gymnasts (9 males, 2 females; 23.18±2.52 yrs) participated in this randomized crossover study. Participants were measured on the countermovement jump (CMJ), squat jump (SQJ), and depth jump (DJ) at baseline (no warm-up). Participants were then randomly placed into one of four stretching protocols: Static (ST), dynamic (DY), static + dynamic (ST+DY), and dynamic + static (DY+ST) and tested on the CMJ, SQJ, and DJ. A photoelectric cell device was used to measure vertical jump height (VJH), flight time (FT), power output (PO), and Reactive Strength Index (RSI). The non-parametric Friedman test was used to test differences between stretching protocols.

RESULTS

The DY protocol showed significant improvements in VJH, FT, and PO in the CMJ. The ST, ST+DY, and DY+ST protocols did not show any significant improvements.

CONCLUSIONS

A warm-up consisting of dynamic movements that resemble those used in the sport of gymnastics can improve vertical jump measures, as reflected through the CMJ.

Estimation of Peak Muscle Power From a Countermovement Vertical Jump in Children and Adolescents

Gomez-Bruton, A, Gabel, L, Nettlefold, L, Macdonald, H, Race, D, and McKay, H. Estimation of peak muscle power from a countermovement vertical jump in children and adolescents. J Strength Cond Res 33(2): 390-398, 2019-Several equations to predict muscle power (MP) from vertical jump height (VJH) have been developed in adults. However, few have been derived in children. We therefore aimed to: (a) evaluate the validity of existing MP estimation equations from a vertical countermovement jump (CMJ) in children and adolescents and (b) develop and validate a new MP estimation equation for use in children and adolescents. We measured peak MP (in watts) and VJH (in centimeters) during a CMJ using a force platform in 249 children and adolescents (9-17 years; 119 boys and 130 girls). We compared actual (force platform) with predicted (12 existing prediction equations) MP using repeated-measures analysis of variance and estimated bias using modified Bland-Altman plots. We developed a new prediction equation using stepwise linear regression, assessed predictive error using leave-one-out and 10-fold cross-validation, and externally validated the equation in an independent sample (n = 100). All existing prediction equations demonstrated some degree of bias, either systematic bias (mean differences ranging 178-1,377 W; 8-64%) or bias at the extremes or interactions with sex. Our new prediction equation estimates MP from VJH and body mass: Power (W) = 54.2 × VJH (cm) + 34.4 × body mass (kg) - 1,520.4. With this new equation, there was no difference between actual and predicted MP (0%) and negligible differences (0.2-0.9%) in R and root mean square error between our observed and cross-validated sets. Actual and predicted MP were not different in our external validation (p = 0.12). The new equation demonstrates excellent validity and can be used to predict MP from a CMJ in children and adolescents.

Is Vertical Jump Height an Indicator of Athletes' Power Output in Different Sport Modalities?

Kons, RL, Ache-Dias, J, Detanico, D, Barth, J, and Dal Pupo, J. Is vertical jump height an indicator of athletes' power output in different sports modalities? J Strength Cond Res 32(3): 708-715, 2018-This study aimed to identify whether the ratio standard is adequate for the scaling of peak power output (PPO) for body mass (BM) in athletes of different sports and to verify classification agreement for athletes involved in different sports using PPO scaled for BM and jump height (JH). One hundred and twenty-four male athletes divided into 3 different groups-combat sports, team sports, and runners-participated in this study. Participants performed the countermovement jump on a force plate. Peak power output and JH were calculated from the vertical ground reaction force. We found different allometric exponents for each modality, allowing the use of the ratio standard for team sports. For combat sports and runners, the ratio standard was not considered adequate, and therefore, a specific allometric exponent for these 2 groups was found. Significant correlations between adjusted PPO for BM (PPOADJ) and JH were found for all modalities, but it was higher for runners (r = 0.81) than team and combat sports (r = 0.63 and 0.65, respectively). Moderate agreement generated by the PPOADJ and JH was verified in team sports (k = 0.47) and running (k = 0.55) and fair agreement in combat sports (k = 0.29). We conclude that the ratio standard seems to be suitable only for team sports; for runners and combat sports, an allometric model seems adequate. The use of JH as an indicator of power output may be considered reasonable only for runners.

Power Output Prediction From Jump Height and Body Mass Does Not Appropriately Categorize or Rank Athletes

The purposes of this study were (a) to verify the agreement of categorization and ranks based on the actual power output measured by a force plate (PPact) and the estimated power output (PPest) from jump height and body mass (BM), and (b) to verify whether the ratio standard is adequate to scale the PPact for BM. The countermovement jumps of 309 male athletes were analyzed. The athletes were first categorized into tertiles (superior, intermediate, and inferior) according to PPact and PPest. After that the athletes were ranked (highest to lowest power output) according to PPact and PPest. The PPest equation explained 81% of PPact variance (standard error of estimate = 277.4 W). The PPest (3,757.1 ± 579.8 W) displayed similar mean values compared with PPact (3,757.1 ± 642.3 W). However, the agreement between the categories generated by PPact and PPest was only moderate (k = 0.6; p < 0.01), and in the intermediate tertile, the categorization differs 38.8%. The agreement between the ranks analyzed from a Bland-Altman plot shows bias zero, but a wide limits of agreement (81 ranks; 26.2%). For the PPact scaling, the ratio standard may be considered as an adequate method for removing the BM effect, considering the lack of correlation between the scaled PPact (PPact/BM) and BM, and also the confirmation of Tanner's special circumstance. In conclusion, our findings indicate that the athlete's power output was not appropriately categorized or ranked when using PPest. Furthermore, the use of the scaled PPact is recommended to fairly compare athletes with different BMs.

Comparison of Physical Capacities Between Nonselected and Selected Elite Male Competitive Surfers for the National Junior Team

Purpose:

To determine whether a previously validated performance-testing protocol for competitive surfers is able to differentiate between Australian elite junior surfers selected (S) to the national team and those not selected (NS).

Methods:

Thirty-two elite male competitive junior surfers were divided into 2 groups (S = 16, NS = 16). Their age, height, body mass, sum of 7 skinfolds, and lean-body-mass ratio (mean ± SD) were 16.17 ± 1.26 y, 173.40 ± 5.30 cm, 62.35 ± 7.40 kg, 41.74 ± 10.82 mm, 1.54 ± 0.35 for the S athletes and 16.13 ± 1.02 y, 170.56 ± 6.6 cm, 61.46 ± 10.10 kg, 49.25 ± 13.04 mm, 1.31 ± 0.30 for the NS athletes. Power (countermovement jump [CMJ]), strength (isometric midthigh pull), 15-m sprint paddling, and 400-m endurance paddling were measured.

Results:

There were significant (P ≤ .05) differences between the S and NS athletes for relative vertical-jump peak force (P = .01, d = 0.9); CMJ height (P = .01, d = 0.9); time to 5-, 10-, and 15-m sprint paddle; sprint paddle peak velocity (P = .03, d = 0.8; PV); time to 400 m (P = .04, d = 0.7); and endurance paddling velocity (P = .05, d = 0.7).

Conclusions:

All performance variables, particularly CMJ height; time to 5-, 10-, and 15-m sprint paddle; sprint paddle PV; time to 400 m; and endurance paddling velocity, can effectively discriminate between S and NS competitive surfers, and this may be important for athlete profiling and training-program design.

Physical determinants of interval sprint times in youth soccer players

Relationships between sprinting speed, body mass, and vertical jump kinetics were assessed in 243 male soccer athletes ranging from 10–19 years. Participants ran a maximal 36.6 meter sprint; times at 9.1 (10 y) and 36.6 m (40 y) were determined using an electronic timing system. Body mass was measured by means of an electronic scale and body composition using a 3-site skinfold measurement completed by a skilled technician. Countermovement vertical jumps were performed on a force platform - from this test peak force was measured and peak power and vertical jump height were calculated. It was determined that age (r=−0.59; p<0.01), body mass (r=−0.52; p<0.01), lean mass (r=−0.61; p<0.01), vertical jump height (r=−0.67; p<0.01), peak power (r=−0.64; p<0.01), and peak force (r=−0.56; p<0.01) were correlated with time at 9.1 meters. Time-to-complete a 36.6 meter sprint was correlated with age (r=−0.71; p<0.01), body mass (r=−0.67; p<0.01), lean mass (r=−0.76; p<0.01), vertical jump height (r=−0.75; p<0.01), peak power (r=−0.78; p<0.01), and peak force (r=−0.69; p<0.01). These data indicate that soccer coaches desiring to improve speed in their athletes should devote substantive time to fitness programs that increase lean body mass and vertical force as well as power generating capabilities of their athletes. Additionally, vertical jump testing, with or without a force platform, may be a useful tool to screen soccer athletes for speed potential.

Comparison and analysis of three different methods to evaluate vertical jump height

The purpose of this study was to compare three methods to assess vertical jump height, to determine their limitations and to propose solutions to mitigate their effects. The chosen methods were the contact mat, the optical system and the Sargent jump. The testing environment was designed such that all three systems simultaneously measured the vertical jump height. A total of 41 kinesiology students (18 women, 23 men, mean age 23·2 ± 4·5 years) participated in this study. Data show that the contact mat and the optical system essentially provide similar results (P = 0·912) and that the correlation coefficient between the two systems was 0·972 (r(2)  = 0·944). However, it was found that the Sargent jump has a tendency to overestimate the height, providing a measurement that is significantly different from the other two methods as the jumps are higher than 30·64 cm (P = 0·044). Through the design of the experiment, several sources of errors were identified and mathematically modelled. These sources include optical sensor placement, flat-footed landing and hip/knee bend. Whenever possible, the errors were quantified and solutions were proposed.