Effectiveness of individualized training based on force-velocity profiling on physical function in older men.

The study aimed to investigate the effectiveness of an individualized power training program based on force-velocity (FV) profiling on physical function, muscle morphology, and neuromuscular adaptations in older men. Forty-nine healthy men (68 ± 5 years) completed a 10-week training period to enhance muscular power. They were randomized to either a generic power training group (GPT) or an individualized power training group (IPT). Unlike generic training, individualized training was based on low- or high-resistance exercises, from an initial force-velocity profile. Lower-limb FV profile was measured in a pneumatic leg-press, and physical function was assessed as timed up-and-go time (TUG), sit-to-stand power, grip strength, and stair-climbing time (loaded [20kg] and unloaded). Vastus lateralis morphology was measured with ultrasonography. Rate of force development (RFD) and rate of myoelectric activity (RMA) were measured during an isometric knee extension. The GPT group improved loaded stair-climbing time (6.3 ± 3.8 vs. 2.3% ± 7.3%, p = 0.04) more than IPT. Both groups improved stair-climbing time, sit to stand, and leg press power, grip strength, muscle thickness, pennation angle, fascicle length, and RMA from baseline (p < 0.05). Only GPT increased loaded stair-climbing time and RFD (p < 0.05). An individualized power training program based on FV profiling did not improve physical function to a greater degree than generic power training. A generic power training approach combining both heavy and low loads might be advantageous through eliciting both force- and velocity-related neuromuscular adaptions with a concomitant increase in muscular power and physical function in older men.

Should we individualize training based on force-velocity profiling to improve physical performance in athletes?

The present study aimed to examine the effectiveness of an individualized training program based on force-velocity (FV) profiling on jumping, sprinting, strength, and power in athletes. Forty national level team sport athletes (20 ± 4years, 83 ± 13 kg) from ice-hockey, handball, and soccer completed a 10-week training intervention. A theoretical optimal squat jump (SJ)-FV-profile was calculated from SJ with five different loads (0, 20, 40, 60, and 80 kg). Based on their initial FV-profile, athletes were randomized to train toward, away, or irrespective (balanced training) of their initial theoretical optimal FV-profile. The training content was matched between groups in terms of set x repetitions but varied in relative loading to target the different aspects of the FV-profile. The athletes performed 10 and 30 m sprints, SJ and countermovement jump (CMJ), 1 repetition maximum (1RM) squat, and a leg-press power test before and after the intervention. There were no significant group differences for any of the performance measures. Trivial to small changes in 1RM squat (2.9%, 4.6%, and 6.5%), 10 m sprint time (1.0%, -0.9%, and -1.7%), 30 m sprint time (0.9%, -0.6%, and -0.4%), CMJ height (4.3%, 3.1%, and 5.7%), SJ height (4.8%, 3.7%, and 5.7%), and leg-press power (6.7%, 4.2%, and 2.9%) were observed in the groups training toward, away, or irrespective of their initial theoretical optimal FV-profile, respectively. Changes toward the optimal SJ-FV-profile were negatively correlated with changes in SJ height (r = -0.49, p < 0.001). Changes in SJ-power were positively related to changes in SJ-height (r = 0.88, p < 0.001) and CMJ-height (r = 0.32, p = 0.044), but unrelated to changes in 10 m (r = -0.02, p = 0.921) and 30 m sprint time (r = -0.01, p = 0.974). The results from this study do not support the efficacy of individualized training based on SJ-FV profiling.

The Battle of the Equations: A Systematic Review of Jump Height Calculations Using Force Platforms.

Vertical jump height measures our ability to oppose gravity and lower body neuromuscular function in athletes and various clinical populations. Vertical jump tests are principally simple, time-efficient, and extensively used for assessing athletes and generally in sport science research. Using the force platform for jump height estimates is increasingly popular owing to technological advancements and its relative ease of use in diverse settings. However, ground reaction force data can be analyzed in multiple ways to estimate jump height, leading to distinct outcome values from the same jump. In the literature, four equations have been commonly described for estimating jump height using the force platform, where jump height can vary by up to ∼ 15 cm when these equations are used on the same jump. There are advantages and disadvantages to each of the equations according to the intended use. Considerations of (i) the jump type, (ii) the reason for testing, and (iii) the definition of jump height should ideally determine which equation to apply. The different jump height equations can lead to confusion and inappropriate comparisons of jump heights. Considering the popularity of reporting jump height results, both in the literature and in practice, there is a significant need to understand how the different mathematical approaches influence jump height. This review aims to investigate how different equations affect the assessment of jump height using force platforms across various jump types, such as countermovement jumps, squat jumps, drop jumps, and loaded jumps.

The Force-Velocity Profile for Jumping: What It Is and What It Is Not.

INTRODUCTION: Force-velocity profiling has been proposed in the literature as a method to identify the overall mechanical characteristics of lower extremities. A force-velocity profile is obtained by plotting for jumps at different loads the effective work as a function of the average push-off velocity, fitting a straight line to the results, and extrapolating this line to find the theoretical maximum isometric force and unloaded shortening velocity. Here we investigated whether the force-velocity profile and its characteristics can be related to the intrinsic force-velocity relationship. METHODS: We used simulation models of various complexity, ranging from a simple mass actuated by a linearly damped force to a planar musculoskeletal model comprising four segments and six muscle-tendon complexes. The intrinsic force-velocity relationship of each model was obtained by maximizing the effective work during isokinetic extension at different velocities. RESULTS: Several observations were made. First, at the same average velocity, less effective work can be done during jumping than during isokinetic lower extremity extension at this velocity. Second, the intrinsic relationship is curved; fitting a straight line and extrapolating it seem arbitrary. Third, the maximal isometric force and the maximal velocity corresponding to the profile are not independent. Fourth, they both vary with inertial properties of the system. CONCLUSIONS: For these reasons, we concluded that the force-velocity profile is specific for the task and is just what it is: the relationship between effective work and an arbitrary estimate of average velocity; it does not represent the intrinsic force-velocity relationship of the lower extremities.

Association Between Physical Performance Tests and External Load During Scrimmages in Highly Trained Youth Ice Hockey Players.

PURPOSE: To investigate the relationship between physical performance tests and on-ice external load from simulated games (scrimmages) in ice hockey. METHODS: A total of 14 players completed a physical performance test battery consisting of 30-m sprint test-run and 30-m sprint test-skate (including 10-m split times and maximum speed), countermovement jump, standing long jump, bench press, pull-ups, and trap bar deadlift and participated in 4 scrimmages. External load variables from scrimmages included total distance; peak speed; slow (< 11.0 km/h), moderate (11.0-16.9 km/h), high (17.0-23.9 km/h), and sprint (> 24.0 km/h) speed skating distance; number of sprints; PlayerLoad™; number of high-intensity events (> 2.5 m/s); accelerations; decelerations; and changes of direction. Bayesian pairwise correlation analyses were performed to assess the relationship between physical performance tests and external load performance variables. RESULTS: The results showed strong evidence (Bayes factor > 10) for associations between pull-ups and high-intensity events (τ = .61) and between maximum speed skate and peak speed (τ = .55). There was moderate evidence (Bayes factor >3 to <10) for 6 associations: both maximum speed skate (τ = .44) and countermovement jump (τ = .44) with sprint speed skating distance, countermovement jump with number of sprints (τ = .46), pull-ups with changes of direction (τ = .50), trap bar with peak speed (τ = .45), and body mass with total distance (τ = .49). CONCLUSION: This study found physical performance tests to be associated with some of the external load variables from scrimmages. Nevertheless, the majority of correlations did not display meaningful associations, possibly being influenced by the selection of physical performance tests.

The effects of being told you are in the intervention group on training results: a pilot study.

Little is known about the placebo effects when comparing training interventions. Consequently, we investigated whether subjects being told they are in the intervention group get better training results compared to subjects being told they are in a control group. Forty athletes (male: n = 31, female: n = 9) completed a 10-week training intervention (age: 22 ± 4 years, height: 183 ± 10 cm, and body mass: 84 ± 15 kg). After randomization, the participants were either told that the training program they got was individualized based on their force-velocity profile (Placebo), or that they were in the control group (Control). However, both groups were doing the same workouts. Measurements included countermovement jump (CMJ), 20-m sprint, one-repetition maximum (1RM) back-squat, a leg-press test, ultrasonography of muscle-thickness (m. rectus femoris), and a questionnaire (Stanford Expectations of Treatment Scale) (Younger et al. in Clin Trials 9(6):767-776, 2012). Placebo increased 1RM squat more than Control (5.7 ± 6.4% vs 0.9 ± 6.9%, [0.26 vs 0.02 Effect Size], Bayes Factor: 5.1 [BF10], p = 0.025). Placebo had slightly higher adherence compared to control (82 ± 18% vs 72 ± 13%, BF10: 2.0, p = 0.08). Importantly, the difference in the 1RM squat was significant after controlling for adherence (p = 0.013). No significant differences were observed in the other measurements. The results suggest that the placebo effect may be meaningful in sports and exercise training interventions. It is possible that ineffective training interventions will go unquestioned in the absence of placebo-controlled trials.

Associations between Power Training-Induced Changes in Body Composition and Physical Function in Older Men: A Pre-Test-Post-Test Experimental Study.

BACKGROUND: It is well-established that cross-sectional measurements of poor body composition are associated with impaired physical function and that power training effectively enhances total lean mass and physical function in older adults. However, it is unclear if power training-induced changes in body composition are associated with improved physical function in older adults. AIM: The present study investigated associations between body composition and physical function cross-sectionally and with power training-induced changes in older men. METHODS: Forty-nine older men (68 ± 5 yrs) completed a 10-week biweekly power training intervention. Body composition was measured using dual-energy X-ray absorptiometry. Physical function was assessed as a composite Z-score combining measures from Sit-to-stand power, Timed up-and-go time, and loaded and unloaded Stair-climbing time (15 steps). Linear and quadratic regression analyses were performed to assess associations between body composition and physical function. RESULTS: At baseline, total (R2 = 0.11, p < 0.05) and percentage body fat (R2 = 0.15, p < 0.05) showed a non-linear relationship with physical function. The apex of the quadratic regression for body composition was 21.5% body fat. Furthermore, there was a non-linear relationship between changes in body fat percentage and physical function from pre- to post-intervention (R2 = 0.15, p < 0.05). CONCLUSION: The present study's findings indicate that participants with a body composition of ~20% body fat displayed the highest level of physical function at baseline. Furthermore, despite small pre-post changes in body fat, the results indicate that those who either preserved their body fat percentage or experienced minor alterations observed the greatest improvements in physical function.

Strength and Power Testing of Athletes: Associations of Common Assessments Over Time.

PURPOSE: This study examined the associations among common assessments for measuring strength and power in the lower body of high-performing athletes, including both cross-sectional and longitudinal data. METHODS: A total of 100 participants, including both male (n = 83) and female (n = 17) athletes (21 [4] y, 182 [9] cm, 78 [12] kg), were recruited for the study using a multicenter approach. The participants underwent physical testing 4 times. The first 2 sessions (1 and 2) were separated by ∼1 week, followed by a period of 2 to 6 months, whereas the last 2 sessions (3 and 4) were also separated by ∼1 week. The test protocol consisted of squat jumps, countermovement jumps, jump and reach, 30-m sprint, 1-repetition-maximum squat, sprint cycling, and a leg-press test. RESULTS: There were generally acceptable correlations among all performance measures. Variables from the countermovement jumps and leg-press power correlated strongly with all performance assessments (r = .52-.79), while variables from sprint running and squat-jump power displayed more incoherent correlations (r = .21-.82). For changes over time, the correlations were mostly strong, albeit systematically weaker than for cross-sectional measures. CONCLUSIONS: The associations observed among the performance assessments seem to be consistent for both cross-sectional data and longitudinal change scores. The weaker correlations for change scores are most likely mainly caused by lower between-subjects variations in the change scores than for the cross-sectional data. The present study provides novel information, helping researchers and practitioners to better interpret the relationships across common performance assessment methods.

Strength and Power Testing of Athletes: A Multicenter Study of Test-Retest Reliability.

PURPOSE: This study examined the test-retest reliability of common assessments for measuring strength and power of the lower body in high-performing athletes. METHODS: A total of 100 participants, including both male (n = 83) and female (n = 17) athletes (21 [4] y, 182 [9] cm, and 78 [12] kg), were recruited for this study, using a multicenter approach. The participants underwent physical testing 4 times. The first 2 sessions (1 and 2) were separated by ∼1 week, followed by a period of 2 to 6 months, whereas the last 2 sessions (3 and 4) were again separated by ∼1 week. The test protocol consisted of squat jumps, countermovement jumps, jump and reach, 30-m sprint, 1-repetition-maximum squat, sprint cycling, and a leg-press test. RESULTS: The typical error (%) ranged from 1.3% to 8.5% for all assessments. The change in means ranged from -1.5% to 2.5% for all assessments, whereas the interclass correlation coefficient ranged from .85 to .97. The smallest worthwhile change (0.2 of baseline SD) ranged from 1.2% to 5.0%. The ratio between the typical error (%) and the smallest worthwhile change (%) ranged from 0.5 to 1.2. When observing the reliability across testing centers, considerable differences in reliability were observed (typical error [%] ratio: 0.44-1.44). CONCLUSIONS: Most of the included assessments can be used with confidence by researchers and coaches to measure strength and power in athletes. Our results highlight the importance of controlling testing reliability at each testing center and not relying on data from others, despite having applied the same protocol.

Validity of Force-Velocity Profiling Assessed With a Pneumatic Leg Press Device.

PURPOSE: The aim of this study was to examine the concurrent validity of force-velocity (FV) variables assessed across 5 Keiser leg press devices. METHODS: A linear encoder and 2 independent force plates (MuscleLab devices) were mounted on each of the 5 leg press devices. A total of 997 leg press executions, covering a wide range of forces and velocities, were performed by 14 participants (29 [7] y, 181 [5] cm, 82 [8] kg) across the 5 devices. Average and peak force, velocity, and power values were collected simultaneously from the Keiser and MuscleLab devices for each repetition. Individual FV profiles were fitted to each participant from peak and average force and velocity measurements. Theoretical maximal force, velocity, and power were deduced from the FV relationship. RESULTS: Average and peak force and velocity had a coefficient of variation of 1.5% to 8.6%, near-perfect correlations (.994-.999), and a systematic bias of 0.7% to 7.1% when compared with reference measurements. Average and peak power showed larger coefficient of variations (11.6% and 17.2%), despite excellent correlations (.977 and .952), and trivial to small biases (3.9% and 8.4%). Extrapolated FV variables showed near-perfect correlations (.983-.997) with trivial to small biases (1.4%-11.2%) and a coefficient of variation of 1.4% to 5.9%. CONCLUSIONS: The Keiser leg press device can obtain valid measurements over a wide range of forces and velocities across different devices. To accurately measure power, theoretical maximal power calculated from the FV profile is recommended over average and peak power values from single repetitions, due to the lower random error observed for theoretical maximal power.

Force-velocity profiling in athletes: Reliability and agreement across methods.

The aim of the study was to examine the test-retest reliability and agreement across methods for assessing individual force-velocity (FV) profiles of the lower limbs in athletes. Using a multicenter approach, 27 male athletes completed all measurements for the main analysis, with up to 82 male and female athletes on some measurements. The athletes were tested twice before and twice after a 2- to 6-month period of regular training and sport participation. The double testing sessions were separated by ~1 week. Individual FV-profiles were acquired from incremental loading protocols in squat jump (SJ), countermovement jump (CMJ) and leg press. A force plate, linear encoder and a flight time calculation method were used for measuring force and velocity during SJ and CMJ. A linear regression was fitted to the average force and velocity values for each individual test to extrapolate the FV-variables: theoretical maximal force (F0), velocity (V0), power (Pmax), and the slope of the FV-profile (SFV). Despite strong linearity (R2>0.95) for individual FV-profiles, the SFV was unreliable for all measurement methods assessed during vertical jumping (coefficient of variation (CV): 14-30%, interclass correlation coefficient (ICC): 0.36-0.79). Only the leg press exercise, of the four FV-variables, showed acceptable reliability (CV:3.7-8.3%, ICC:0.82-0.98). The agreement across methods for F0 and Pmax ranged from (Pearson r): 0.56-0.95, standard error of estimate (SEE%): 5.8-18.8, and for V0 and SFV r: -0.39-0.78, SEE%: 12.2-37.2. With a typical error of 1.5 cm (5-10% CV) in jump height, SFV and V0 cannot be accurately obtained, regardless of the measurement method, using a loading range corresponding to 40-70% of F0. Efforts should be made to either reduce the variation in jumping performance or to assess loads closer to the FV-intercepts. Coaches and researchers should be aware of the poor reliability of the FV-variables obtained from vertical jumping, and of the differences across measurement methods.