ECSS Paris 2023: OP-AP32
INTRODUCTION: Individual load-velocity profiles (LVP), which is measured during multiple sets with incremental load repetitions performed at maximal velocity, have been proposed as an accurate method to assess muscular fitness and one-repetition maximum (1-RM). LVP can be assessed using several protocols consisting of different load increments patterns and reps to be performed. The commonly used strategies to assess the LVPs are to adjust the load increments using either the a) repetition mean propulsive velocity (MPV) performed at the different loads (standardized LVP [LVPSTND]) [1] or b) individuals’ %1-RM using multiple (LVPMULT) [2] or two (LVP2-POINT) [3] loads. Despite variations in sets and repetitions, no study has examined whether testing protocols affect 1-RM load and velocity (V1-RM). This study investigates the impact of different LVP methods on these measures. METHODS: Fifteen healthy males (25.9±3.9 yrs) visited the lab 4 times, separated by at least 48 h of rest. On day 1, participants 1-RMs (89.5±15.9 kg) were assessed and they familiarized with the Smith machine bench press exercise and trained to push the barbell as explosively as possible during the concentric phase. On days 2 to 4 (random order), participants’ LVPs were assessed using: LVPSTND (load increments: 10 kg for MPV>0.5 m/s, 1-5 kg for MPV<0.5 m/s; reps: 3 for MPV>1.0 m/s, 2 for MVP between 0.65-1.0 m/s, 1 for MPV<0.65 m/s), LVPMULT (20, 40, 60, 80, 90% of 1-RM), and LVP2-POINT (50 and 80% of 1-RM) methods, and 1-RM load and V1-RM were assessed. The execution velocity was measured using a linear position transducer (Vitruve, Madrid, Spain) attached to the barbell of a Smith machine. After assessing the normality of the distribution of the variables, 1-RM loads across methods were compared using a repeated-measure ANOVA followed by Bonferroni corrected pairwise post-hoc comparisons, while V1-RM were compared across methods using the Friedman test. RESULTS: The LVP testing method affected (F(2,28)=3.476, η²=0.199, p=0.045) 1-RM load. Bonferroni comparisons showed that solely the LVP2-POINT 1-RM (93.3±16.8 kg) was significantly higher (p=0.044) than the LVPSTND 1-RM (90.6±16.2 kg), whereas there were no differences between LVPMULT (92.3±16.2 kg) and LVP2-POINT (p=1.000) or LVPSTND (p=0.331). Conversely, V1-RM measured using LVPSTND (0.15±0.03 m/s), LVPMULT (0.14±0.04 m/s), and LVP2-POINT (0.16±0.04 m/s) were not different (χ²(2)=0.792, p=0.673). CONCLUSION: This study indicates that while the testing protocol used to assess LVP does not influence V1-RM it affects 1-RM load. In particular, the LVP2-POINT seems to allow an accurate estimation of 1-RM while minimizing fatigue. Therefore, LVP2-POINT could offer not only a higher, hence more accurate, 1-RM estimation, but also a more feasible and time-saving strategy to assess 1-RM due to the fewer sets needed. REFERNCES: [1] Sanchez-Medina et al., Int J Sports Med, 2010 [2] Banyard et al., J Strength Cond Res, 2017 [3] Garcia-Ramos et al., Strength Cond J, 2018
Read CV Tommaso GrossiECSS Paris 2023: OP-AP32
INTRODUCTION: The load-velocity relationship (LVR) is used to predict one-repetition maximum (1RM); however, it often yields inaccurate estimations. The minimum velocity threshold (i.e., the velocity at 1RM [MVT]) has been used to make predictions, whereby poor MVT reliability may contribute to erroneous predictions and limit application. To enhance predictions, optimal MVT-the velocity where the difference between actual and predicted 1RM from the same 1RM test is zero-has been proposed. Unlike some other prediction methods, optimal MVT needs to be established in a preliminary 1RM test and requires multiple loads to be performed within subsequent sessions. Currently, the predictive validity of optimal MVT across multiple testing sessions is unknown. Thus, the utility of various 1RM prediction models using optimal MVT in both the free-weight (FW) and Smith-machine (SM) back squat was investigated. METHODS: In this counterbalanced, randomised, crossover design, 14 trained males (FW 1RM=132.5±28.5kg; SM 1RM=163.9±30.4kg) performed a familiarisation session followed by three sessions each of FW and SM back squat 1RM testing separated by 72 hours and alternating between the exercises. Loads of 20, 40, 60, 80, 90, and 100% of 1RM were performed each visit with the load and concentric mean velocity recorded. The reliability of MVT was determined via intraclass correlation coefficient (ICC) and coefficient of variation (CV) with ICC>0.9 and CV<10% deemed reliable. Optimal MVT for both exercises was determined from the day one LVR and subsequently used to predict day two and three 1RMs from the submaximal loads in these sessions. Optimal MVT from linear regression was used in the linear multiple-point and two-point models, while polynomial multiple-point models used the polynomial regression-derived optimal MVT. Additionally, both individual and group mean optimal MVT was used for all models. The percentage difference between the actual and estimated 1RM on each day was used to determine model error (Ɛ) for each participant with Ɛ<2.5% considered excellent, 2.5-5% acceptable, and >5% unacceptable. RESULTS: Both FW (ICC=0.92, CV=6.62%) and SM (ICC=0.91, CV=5.38%) MVT were deemed reliable. From all prediction models, only the FW individual 40-90% multiple-point linear and SM individual 20&90% two-point models had all participants within the acceptable threshold across both days. None of the polynomial models for either exercise had all participants in the acceptable threshold and generally displayed greater Ɛ than the linear and two-point models. CONCLUSION: The predictive validity of all but two of the 1RM prediction models using optimal MVT from day one was unacceptable, despite the high MVT reliability within our trained population. Our findings indicate that using optimal MVT to estimate FW and SM 1RM may not be suitable, as it adds complexity without eliminating the need for a submaximal load near 1RM (i.e., 90% 1RM) to ensure acceptable predictive validity.
Read CV Kai HomerECSS Paris 2023: OP-AP32
INTRODUCTION: Greater sprint acceleration is achieved by producing large horizontal ground reaction forces (GRFs). Recently, our group found that higher maximal strength and power of the lower-limb, assessed by the force-velocity (F-V) profile in vertical jumps, are associated with greater horizontal GRF during the earlier and later steps of sprint acceleration, respectively. This study examined the effects of maximal strength training and power training on the F-V profile of the lower-limb and sprint acceleration kinetics. METHODS: Twenty-three healthy adults (7 females and 16 males, 23 ± 3 years) were assigned into the following three groups: jump squat group (JSQG, n = 7); back squat group (BSQG, n = 8); control group (CG, n = 8). JSQG and BSQG trained three times per week for 11 weeks, performing jump squats (0% and 20% 1RM, 3–5 sets × 4–6 reps at each load) and back squats (85% 1RM, 4 sets × 3–4 reps), respectively. CG was instructed to maintain their daily routines. Before and after the intervention, participants performed 15-m sprint accelerations to evaluate mean horizontal and resultant GRFs and mean angles of the GRF vector during the propulsive phase for the first and ninth steps. Jump squats were performed to evaluate the F-V profile of the lower-limb. Theoretical maximum force (F0), velocity (V0), and power (Pmax) were derived from the F-V profile. Generalized linear mixed-effects models were used to examine main and interaction effects (F-V profile: group × time; 15-m sprint acceleration: group × time × step). Between-group differences in percentage changes were compared using one-way analysis of variance or Kruskal-Wallis test. Cohen’s d effect sizes were calculated to interpret the magnitude of the between-group differences in percentage changes. RESULTS: Significant main effects of time were observed for F0, horizontal and resultant GRFs, and the angle of the GRF vector (p = 0.002–0.048), with no significant interaction effects. Between-group differences in percentage changes were not found for any variable of the F-V profile and sprint acceleration. For the F-V profile, JSQG and BSQG showed moderate changes in F0 compared to CG (d = 0.62 and 0.67). JSQG also showed small changes in Pmax compared to BSQG and CG (d = 0.43 and 0.30). For sprint acceleration, JSQG and BSQG showed small to moderate changes in the horizontal and resultant GRFs for the first step compared to CG (d = 0.38–0.99). JSQG also showed small to moderate changes in the horizontal and resultant GRFs (d = 0.25–0.66) and the angle of the GRF vector (d = −0.30 and −0.46) for the ninth step compared to BSQG and CG. CONCLUSION: The present results imply that in healthy adults, maximal strength training enhances F0, leading to an increased GRF in the earlier step, whereas power training enhances both F0 and Pmax, leading to increased GRFs in both the earlier and later steps during sprint acceleration.
Read CV Motoki KatsugeECSS Paris 2023: OP-AP32