Search

Abstract Purpose To compare linear and curvilinear models describing the force–velocity relationship obtained in lower-limb acyclic extensions, considering experimental data on an unprecedented range of velocity conditions. Methods Nine athletes performed lower-limb extensions on a leg-press ergometer, designed to provide a very broad range of force and velocity conditions. Previously inaccessible low inertial and resistive conditions were achieved by performing extensions horizontally and with assistance. Force and velocity were continuously measured over the push-off in six resistive conditions to assess individual force–velocity relationships. Goodness of fit of linear and curvilinear models (second-order polynomial function, Fenn and Marsh’s, and Hill’s equations) on force and velocity data were compared via the Akaike Information Criterion. Results Expressed relative to the theoretical maximal force and velocity obtained from the linear model, force and velocity data ranged from 26.6 ± 6.6 to 96.0 ± 3.6% (16–99%) and from 8.3 ± 1.9 to 76.6 ± 7.0% (5–86%), respectively. Curvilinear and linear models showed very high fit (adjusted r 2 = 0.951–0.999; SEE = 17-159N). Despite curvilinear models better fitting the data, there was a ~ 99–100% chance the linear model best described the data. Conclusion A combination between goodness of fit, degrees of freedom and common sense (e.g., rational physiologically values) indicated linear modelling is preferable for describing the force–velocity relationship during acyclic lower-limb extensions, compared to curvilinear models. Notably, linearity appears maintained in conditions approaching theoretical maximal velocity. Using horizontal and assisted lower-limb extension to more broadly explore resistive/assistive conditions could improve reliability and accuracy of the force–velocity relationship and associated parameters.

ContextStrength-endurance mainly depends on the power output, which is often expressed relative to the individual’s maximal power capability (Pmax). However, an individual can develop the same power, but in different combinations of force and velocity (force-velocity condition). Also, at matched power output, changing the force-velocity condition results in a change of the velocity-specific relative power (Pmaxv), associated with a change in the power reserve. So far, the effect of these changing conditions on strength-endurance remains unclear.PurposeWe aimed to test the effects of force-velocity condition and power output on strength-endurance.MethodsFourteen sportsmen performed (i) force- and power-velocity relationships evaluation in squat jumps and (ii) strength-endurance evaluations during repeated squat jump tests in 10 different force-velocity-power conditions, individualized based on the force- and power-velocity relationships. Each condition was characterized by different (i) relative power (%Pmax), (ii) velocity-specific relative power (%Pmaxv), and (iii) ratio between force and velocity (RFv). Strength-endurance was assessed by the maximum repetitions (SJRep), and the cumulated mechanical work (Wtot) performed until exhaustion during repeated squat jump tests. Intra and inter-day reliability of SJRep were tested in one of the 10 conditions. The effects of %Pmax, %Pmaxv, and RFv on SJRep and Wtot were tested via stepwise multiple linear regressions and two-way ANOVAs.ResultsSJRep exhibited almost perfect intra- and inter-day reliability (ICC=0.94 and 0.92, respectively). SJRep and Wtot were influenced by %Pmaxv and RFv (R2 = 0.975 and 0.971; RSME=0.243 and 0.234, respectively; both p < 0.001), with the effect of RFv increasing with decreasing %Pmaxv (interaction effect, p = 0.03). %Pmax was not considered as a significant predictor of strength-endurance by the multiple regressions analysis. SJRep and Wtot were higher at lower %Pmaxv and in low force-high velocity conditions (i.e., lower RFv).ConclusionStrength-endurance was almost fully dependent on the position of the exercise conditions relative to the individual force-velocity and power-velocity relationships (characterized by %Pmaxv and RFv). Thus, the standardization of the force-velocity condition and the velocity-specific relative power should not be overlooked for strength-endurance testing and training, but also when setting fatiguing protocols.

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. [ABSTRACT FROM AUTHOR]

Purpose: To compare linear and curvilinear models describing the force–velocity relationship obtained in lower-limb acyclic extensions, considering experimental data on an unprecedented range of velocity conditions. Methods: Nine athletes performed lower-limb extensions on a leg-press ergometer, designed to provide a very broad range of force and velocity conditions. Previously inaccessible low inertial and resistive conditions were achieved by performing extensions horizontally and with assistance. Force and velocity were continuously measured over the push-off in six resistive conditions to assess individual force–velocity relationships. Goodness of fit of linear and curvilinear models (second-order polynomial function, Fenn and Marsh's, and Hill's equations) on force and velocity data were compared via the Akaike Information Criterion. Results: Expressed relative to the theoretical maximal force and velocity obtained from the linear model, force and velocity data ranged from 26.6 ± 6.6 to 96.0 ± 3.6% (16–99%) and from 8.3 ± 1.9 to 76.6 ± 7.0% (5–86%), respectively. Curvilinear and linear models showed very high fit (adjusted r2 = 0.951–0.999; SEE = 17-159N). Despite curvilinear models better fitting the data, there was a ~ 99–100% chance the linear model best described the data. Conclusion: A combination between goodness of fit, degrees of freedom and common sense (e.g., rational physiologically values) indicated linear modelling is preferable for describing the force–velocity relationship during acyclic lower-limb extensions, compared to curvilinear models. Notably, linearity appears maintained in conditions approaching theoretical maximal velocity. Using horizontal and assisted lower-limb extension to more broadly explore resistive/assistive conditions could improve reliability and accuracy of the force–velocity relationship and associated parameters. [ABSTRACT FROM AUTHOR]

Velocity strength training requires exercise modalities that allow athletes to reach very high movement velocity, which is limited during vertical movements involving body weight. Purpose: To quantify the mechanical outputs developed during horizontal squat jumps (HSJs), notably the movement velocity, in comparison with vertical squat jumps (SJs) with and without loads. Methods: Thirteen healthy male athletes performed SJs without additional loads (SJ0) and with a load of ∼60% of body mass (SJ60), and during HSJs performed lying on a roller device with (assisted HSJ [AHSJ]) and without (HSJ) rubber-band assistance. Instantaneous lower-limb extension velocity, force, and power output were measured and averaged over the push-off phase. Results: The force was significantly higher during SJ60 than during SJ0, which was higher than during HSJ and AHSJ. Extension velocity was significantly different across all conditions, with 0.86 (0.07), 1.29 (0.10), 1.59 (0.19), and 1.83 (0.19) m·s−1 for SJ60, SJ0, HSJ, and AHSJ conditions, respectively. Differences in force and velocity values between SJ0 and the other conditions were large to extremely large. Differences were observed in power values only between SJ60 and SJ0, SJ60 and AHSJ, and SJ0 and HSJ. Conclusions: HSJ modalities allow athletes to reach very to extremely largely greater lower-limb extension velocities (HSJ +24.0% [16%], AHSJ +42.8% [17.4%]) compared to those achieved during SJ0. HSJ and AHSJ modalities are inexpensive and practical modalities to train limb-extension velocity capabilities, that is, the ability of the neuromuscular system to produce force at high contraction velocities. [ABSTRACT FROM AUTHOR]

In jumping, countermovement increases net propulsive force and improves performance. We aimed to test whether this countermovement effect is velocity specific and examine the degree to which this varies between athletes, sports or performance levels. Force-velocity profiles were compiled in high-level skiers (N= 23) and sprinters (N= 30), with their performance represented in their overall world ranking and season-best 100 m time, respectively. Different ratios between force-velocity variables were computed from squat and countermovement jumps (smaller = less effect): jump height (CRh), maximum power (CRP), force (CRF), and velocity (CRv). Countermovement effect differed per velocity (inverse relationship between CRF and CRv, rs = −0.74, p<.001), and variation force-velocity profiles with countermovement. Skiers exhibited smaller CRF (rrb = −0.675, p<.001), sprinters smaller CRv (rrb = 0.426, p=.008), and "moderate" velocity conditions did not differentiate groups (CRP or CRh, p>.05). 33% of the variance in skiers' performance level was explained by greater maximum force and a lower CRF (i.e., high explosiveness at low-velocities without countermovement), without an association for sprinters. Countermovement effect appears specific to movement velocity, sport and athlete level. Consequently, we advise sports-specific assessment, and potentially training to reduce the countermovement effect per the relevant velocity. [ABSTRACT FROM AUTHOR]

Specific force capacities might be a limiting factor for alpine skiing performance, yet there is little consensus on the capabilities in question, and whether they differ between disciplines. We aimed to test discipline (speed and technical) and performance (event‐specific world standing) effects on lower limb force‐production qualities. National‐level skiers (N = 31) performed loaded squat jumps and isometric mid‐thigh pulls to detect dynamic force output at extremely low and high velocities and maximum isometric force and rate of force development, respectively. Discipline differences were assessed via a general linear model including performance and allowing for interaction effects, with performance associations further characterized via distinct Pearson's correlations. Jump height did not differentiate disciplines, with absolute power slightly higher in speed athletes (F(1,27) = 4.42, P =.045, ω2 = 0.10), and neither variables were related to performance. Speed athletes possessed greater dynamic force at low velocities (F0; F(1,27) = 13.8, P <.001, ω2 = 0.17), and greater relative and absolute maximum isometric force (F(1,25) = 11.19‐20.70, ω2 = 0.16‐0.22, P <.003). Overall, higher ranked athletes possessed more force‐dominant profiles (F(1,27) = 16.28, ω2 = 0.34; r = 0.60 to 0.67, P <.001) and increased rate of force development characteristics (average and maximum, r = −0.50 to −0.82, P <.048). Very robust associations existed between maximum isometric force and speed performance (r = −0.88, P <.001), but only a trend for higher absolute isometric force in technical athletes (r = −0.49, P =.052). Alpine skiers display a preponderance for dynamic force output at low velocities, and isometric force for speed athletes, which highlights the interest in specific assessment and conditioning practices for ski athletes. [ABSTRACT FROM AUTHOR]

Purpose: Lower limbs' neuromuscular force capabilities can only be determined during single sprints if the test provides a good fit of the data in the torque-velocity (T–V) and power-velocity (P–V) relationships. This study compared the goodness of fit of single sprints performed against traditional (7.5% of the body mass) vs. optimal load (calculated based on the force production capacity and ergometer specificities), and examined if reducing the load in fatigued state enhances T–V and P–V relationship goodness of fit. Methods: Thirteen individuals performed sprints before (PRE) and after (POST) a fatiguing task against different loads: (1) TRAD: traditional, (2) OPT: optimal, and (3) LOW-OPT: optimal load reduced according to fatigue levels. Results: At PRE, OPT sprints presented a higher R2 of the T–V relationship (0.92 ± 0.06) and lower time to reach maximal power (Pmax) (48 ± 9%) when compared with TRAD sprints (0.89 ± 0.06 and 66 ± 22%, respectively, p < 0.01). At POST, the range of velocity spectrum was greater in the LOW-OPT (33 ± 4%) vs. TRAD (24 ± 3%) and OPT (26 ± 8%, p < 0.007). Similarly, the time to reach Pmax was lower in the LOW-OPT (46 ± 12%) vs. TRAD (76 ± 24%) and OPT (70 ± 24%, p < 0.006). Conclusion: Sprints performed against an OPT load and reducing the OPT load after fatigue improve the fit of data in the T–V and P–V curves. Sprints load assignment should consider force production capacities rather than body mass. [ABSTRACT FROM AUTHOR]

Context: Strength-endurance mainly depends on the power output, which is often expressed relative to the individual's maximal power capability (P max). However, an individual can develop the same power, but in different combinations of force and velocity (force-velocity condition). Also, at matched power output, changing the force-velocity condition results in a change of the velocity-specific relative power (P max v), associated with a change in the power reserve. So far, the effect of these changing conditions on strength-endurance remains unclear. Purpose: We aimed to test the effects of force-velocity condition and power output on strength-endurance. Methods: Fourteen sportsmen performed (i) force- and power-velocity relationships evaluation in squat jumps and (ii) strength-endurance evaluations during repeated squat jump tests in 10 different force-velocity-power conditions, individualized based on the force- and power-velocity relationships. Each condition was characterized by different (i) relative power (% P max), (ii) velocity-specific relative power (% P max v), and (iii) ratio between force and velocity (R Fv). Strength-endurance was assessed by the maximum repetitions (SJ Rep), and the cumulated mechanical work (W tot) performed until exhaustion during repeated squat jump tests. Intra and inter-day reliability of SJ Rep were tested in one of the 10 conditions. The effects of % P max, % P max v , and R Fv on SJ Rep and W tot were tested via stepwise multiple linear regressions and two-way ANOVAs. Results: SJ Rep exhibited almost perfect intra- and inter-day reliability (ICC=0.94 and 0.92, respectively). SJ Rep and W tot were influenced by % P max v and R Fv (R 2 = 0.975 and 0.971; RSME=0.243 and 0.234, respectively; both p < 0.001), with the effect of R Fv increasing with decreasing % P max v (interaction effect, p = 0.03). % P max was not considered as a significant predictor of strength-endurance by the multiple regressions analysis. SJ Rep and W tot were higher at lower % P max v and in low force-high velocity conditions (i.e., lower R Fv). Conclusion: Strength-endurance was almost fully dependent on the position of the exercise conditions relative to the individual force-velocity and power-velocity relationships (characterized by % P max v and R Fv). Thus, the standardization of the force-velocity condition and the velocity-specific relative power should not be overlooked for strength-endurance testing and training, but also when setting fatiguing protocols. [ABSTRACT FROM AUTHOR]

The aim was to determine the position of the one-repetition maximum (1RM) squat point on the force-velocity (F-V) relationship obtained during squat jump (SJ). Ten healthy athletes performed a 1RM squat during which ground reaction force and lower-limb extension velocity were measured, and six loaded SJs to determine individual F-V relationship. The goodness of fit of the linear F-V relationship with or without the 1RM point was tested. The vertical and horizontal coordinates were determined relative to the theoretical maximal force (F0) and the highest loaded SJ (load of 44.5 ± 4.6 % 1RM). The goodness of fit of the individual F-V relationship did not differ with or without the 1RM condition, even if the 1RM point was slightly below the curve (- 5 ± 5 %, P = 0.018). The 1RM point can be considered as a point of the F-V relationship. The velocity (0.22 ± 0.05 m.s-1) of the 1RM point corresponded to ~30 % of the velocity reached during the highest loaded SJ. The force developed in the 1RM condition was ~16 % higher than during the highest loaded SJ and ~11 % lower than F0. This finding underlines the difference between F0 and the 1RM condition. [ABSTRACT FROM AUTHOR]