Measurement Agreement Between Samozino's Method and Force Plate Force-Velocity Profiles During Barbell and Hexbar Countermovement Jumps

Is the Force-Velocity Profile for Free Jumping a Sound Basis for Individualized Jump Training Prescriptions?

INTRODUCTION

Formulating individualized optimized jump training prescriptions based on the force-velocity profile has become popular, but its effectiveness has been contested. Such training programs have opposite effects on "maximal average force" and "maximal average shortening velocity," and we set out to investigate which training-induced changes in the neuromuscular system could cause such effects.

METHODS

We used a musculoskeletal simulation model with four body segments and six muscle-tendon actuators to simulate vertical squat jumps with different loads. Independent input was muscle stimulation over time, which was optimized for maximal jump height. We determined the force-velocity profile for a reference model and investigated how it changed when we modified muscle properties and initial postures.

RESULTS

We could not reproduce the reported training effects by realistically improving muscle properties (maximal force, shortening velocity, and rate of force development) or modifying initial postures of the model. However, the profile was very sensitive to gains in jump height at low loads but not high loads, or vice versa. Reaching maximal height in force-velocity profile jumps requires skill. We argued that submaximal performance in low-load or high-load jumps caused by lack of skill could be responsible for large imbalances in profiles before training. Differential skill training promoted by the individualized optimized approach could explain quick changes toward a balanced profile.

CONCLUSIONS

If the success of individualized optimized training studies is explained by selective skill improvements, training effects are unlikely to transfer to other tasks, and individualized optimized training will not be superior to other types of training.

Estimation of maximum lower limb muscle strength from vertical jumps

Determining the one-repetition maximum (1RM) is crucial for organizing training loads, but it also is time-consuming, physically demanding, and poses a risk of injury. Vertical jumps are a less demanding and well-established method to test the ability of the lower limbs to generate great forces over a short time, which may allow for the estimation of 1RM in squatting. The purpose of this study was to develop a model for estimating 1RM back squat from ground reaction forces during vertical jumps. Thirteen healthy participants completed a 1RM back squat test, countermovement jumps, and squat jumps. Five kinematic and kinetic variables (e.g., peak and mean power, relative net impulse, jump height, and peak kinetic energy during various phases) were derived from ground reaction forces collected via a Kistler force plate (1000 Hz). Five out of 5 variables correlated with 1RM in countermovement jump and squat jump (ICC = .96-.98, r = .88-.95, p < .001 and ICC = .97-.99, r = .76-.90, p < .05, respectively). The most accurate stepwise regression model (adjusted R2 = .90, SEE =  13.24 kg, mean error =  7.4% of mean 1RMm, p < .001) estimated 1RM back squat based on peak kinetic energy during countermovement jumps. Estimation errors ranged from 7.4% to 10.7% of mean measured 1RM, with no differences between estimated and measured values (d <  0.01, p = .96-1.00). Estimating 1RM via jump tests may offer a practical alternative to traditional methods, reducing injury risks, testing intervals, and effort. Our study proposes a new possible approach for estimating 1RM back squat from jump forces, providing coaches and sports professionals with a more efficient tool to monitor and adjust training loads.

Force-Velocity Profiling in Club-Based Field Hockey Players: Analyzing the Relationships between Mechanical Characteristics, Sex, and Positional Demands

The purpose of this study was to investigate differences between sex and positional demands in club-based field hockey players by analyzing vertical force-velocity characteristics. Thirty-three club-based field hockey athletes (16 males - age: 24.8 ± 7.3yrs, body mass: 76.8 ± 8.2kg, height: 1.79 ± 0.05m; 17 females - age: 22.3 ± 4.2yrs, body mass: 65.2 ± 7.6kg, height: 1.66 ± 0.05m) were classified into two key positional groups (attacker or defender) based on dominant field position during gameplay. Force-velocity (F-v) profiles were established by performing countermovement jumps (CMJ) using a three-point loading protocol ranging from body mass (i.e., zero external mass, 0%) to loads corresponding to 25% and 50% of their own body mass. Across all loads, between-trial reliability of F-v and CMJ variables was determined by intraclass correlation coefficients (ICCs) and coefficient of variation (CV) and deemed to be acceptable (ICC: 0.87-0.95, CV% 2.8-8.2). Analysis by sex identified male athletes had significantly greater differences in all F-v variables (12.81-40.58%, p ≤ 0.001, ES = 1.10-3.19), a more enhanced F-v profile (i.e., greater theoretical maximal force, velocity, and power values), plus overall stronger correlations between relative maximal power (P_MAX) and jump height (r = 0.67, p ≤ 0.06) when compared to female athletes (-0.71≤ r ≥ 0.60, p = 0.08). Male attackers demonstrated a more 'velocity-oriented' F-v profile compared to defenders due to significant mean differences in theoretical maximal velocity (v₀) (6.64%, p ≤ 0.05, ES: 1.11), however differences in absolute and relative theoretical force (F₀) (15.43%, p ≤ 0.01, ES = 1.39) led to female attackers displaying a more 'force-oriented' profile in comparison to defenders. The observed mechanical differences identify the underpinning characteristics of position specific expression of P_MAX should be reflected in training programmes. Therefore, our findings suggest F-v profiling is acceptable to differentiate between sex and positional demands in club-based field hockey players. Furthermore, it is recommended field hockey players explore a range of loads and exercises across the F-v continuum through on-field and gym-based field hockey strength and conditioning practices to account for sex and positional mechanical differences.

Is the Concept, Method, or Measurement to Blame for Testing Error? An Illustration Using the Force-Velocity-Power Profile

When poor reliability of “output” variables is reported, it can be difficult to discern whether blame lies with the measurement (ie, the inputs) or the overarching concept. This commentary addresses this issue, using the force-velocity-power (FvP) profile in jumping to illustrate the interplay between concept, method, and measurement reliability. While FvP testing has risen in popularity and accessibility, some studies have challenged the reliability and subsequent utility of the concept itself without clearly considering the potential for imprecise procedures to impact reliability measures. To this end, simulations based on virtual athletes confirmed that push-off distance and jump-height variability should be <4% to 5% to guarantee well-fitted force–velocity relationships and acceptable typical error (<10%) in FvP outputs, which was in line with previous experimental findings. Thus, while arguably acceptable in isolation, the 5% to 10% variability in push-off distance or jump height reported in the critiquing studies suggests that their methods were not reliable enough (lack of familiarization, inaccurate procedures, or submaximal efforts) to infer underpinning force-production capacities. Instead of challenging only the concept of FvP relationship testing, an alternative conclusion should have considered the context in which the results were observed: If procedures’ and/or tasks’ execution is too variable, FvP outputs will be unreliable. As for some other neuromuscular or physiological testing, the FvP relationship, which magnifies measurement errors, is unreliable when the input measurements or testing procedures are inaccurate independently from the method or concept used. Field “simple” methods require the same methodological rigor as “lab” methods to obtain reliable output data.