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The present study investigated effects of squat resistance training on intermuscular hypertrophy of quadriceps femoris muscles (i.e., rectus femoris, RF; vastus intermedius, VI; vastus medialis, VM; and vastus lateralis, VL). Eighteen university students (age: 24.1 ± 1.7 years, 9 females) underwent 7 weeks of parallel squat training (2 days/week) preceded by a 2-week familiarization period. Squat strength (1RM) and cross-sectional area (CSA) of four quadriceps muscles were assessed at baseline and at the end of the study. At posttest, 1RM and CSA of quadriceps muscles significantly increased (p < 0.01), with moderate-to-large effect (ES = 1.25–2.11) for 1RM (8.33 ± 6.64 kg), VM CSA (0.12 ± 0.08 cm2), and VL CSA (0.19 ± 0.09 cm2) and small effect (ES = 0.89–1.13) for RF CSA (0.17 ± 0.15 cm2) and VI CSA (0.16 ± 0.18 cm2). No significant differences were found in the changes of CSA between muscles (F = 0.638, p = 0.593). However, the squat 1RM gain was significantly associated only with the changes in CSA of the VL muscle (r = 0.717, p < 0.001). The parallel squat resulted in significant growth of all quadriceps muscles. However, the novelty of this study is that the increase in strength is associated only with hypertrophy of the VL muscle.

Background The impact that mechanical factors might have on gait reorganization was evaluated by the relationship between muscle mechanical capacity of isolated leg muscle groups and transition speed in previous studies. However, until now there are no studies that explored the relationship between muscle mechanical properties measured in cyclic multi-joint movements and gait transition speed. Research question What is the nature of the relationship between gait transition speed and muscle mechanical capacities measured in cyclic multi-joint movements? Methods The sample included 18 physically active male adults, stratified by anthropometric dimensions. Individual walk-to-run (WRT) and run-to-walk transition speed (RWT) were determined using the standard incremental protocol. Mechanical capacities of leg muscles were assessed by linear force-velocity models obtained during treadmill locomotion and on bicycle-ergometer. Results The results revealed inverse correlation between WRT and RWT and maximal force assessed on treadmill (F0; r = -0.57 and r = -0.54, respectively), as well with F0 (r = -0.65 and r = -0.58, respectively) and maximal power (Pmax; -0.66 and -0.65, respectively) collected on bicycle-ergometer. Significance This study confirmed that mechanical muscle capacities are important physical limitation factors of transition speed, explaining over 36 % of the variance. The findings showed that a novel approach, with high biomechanical similarities with natural locomotion, revealed different results (negative correlations) in comparison to previous studies.

Background The aim was to determine the relationship between the cross-sectional area of the quadriceps femoris and strength performance in the deep and parallel barbell squat. Methods The sample included 16 university students (seven female, 24.1 ± 1.7 years). Muscle strength was expressed as external load, including the one-repetition maximum and the body mass segments involved (calculated according to Dempster’s method). The cross-sectional area of the quadriceps femoris muscles was determined using ultrasound, while leg muscle mass was measured using the Bioelectrical Impedance method. Results The cross-sectional areas of the three vastii muscles and leg muscle mass showed moderate to strong correlation with external load in both squat types (r = 0.509–0.873). However, partial correlation (cross-sectional area of quadriceps femoris muscles were controlled) showed significant association only between leg muscle mass and deep squat (r = 0.64, p r = 0.67, p vs. r = 0.59, p R2 = 0.569; deep squat: R2 = 0.499, both p

The aim of the study was to compare the effects of two different training protocols, which differ in the duration of the eccentric phase, on the one-repetition maximum (1RM), thickness and contractile properties of elbow flexors. Twenty untrained college students were randomly divided into two experimental groups, based on the training tempo: FEG (Faster Eccentric Group: 1/0/1/0) and SEG (Slower Eccentric Group: 4/0/1/0). Training intervention was a biceps bending exercise, conducted twice a week for 7 weeks. The intensity (60–70% RM), sets (3–4) and rest intervals (120 s) were held constant, while repetitions were performed until it was not possible to maintain a set duration. In the initial and final measurements, 1RM, muscle thickness and tensiomyography parameters – contraction time (Tc) and radial deformation (Dm) – were evaluated. An ANCOVA model (using baseline outcomes as covariates) was applied to determine between-group differences at post-test, while Pearson’s product-moment correlation coefficient was used to investigate the relationship between absolute changes in muscle thickness and Dm. Muscle strength increase was greater for SEG than for FEG (6.0 ± 1.76 vs. 3.30 ± 2.26 kg, p < 0.01). In both groups muscle thickness increased equally (FEG: 3.24 ± 2.01 vs. SEG: 3.57 ± 1.17 mm, p < 0.01), while an overall reduction in Dm was observed (FEG: 1.99 ± 1.20 vs. SEG: 2.26 ± 1.03 mm, p < 0.01). Values of Tc remained unchanged. A significant negative relationship was observed between changes in muscle thickness and Dm (r = -0.763, Adj.R² = 0.560, p < 0.01). These results indicate that the duration of the eccentric phase has no effect on muscle hypertrophy in untrained subjects, but that slower eccentric movement significantly increases 1RM.

The goal of the study was to assess the relationship between rookie player’s Pre-Draft Combine physical abilities and basketball performance in the first NBA season. In strictly homogenized sample of players (N = 58) who matched the inclusion criterion of average playing time and number games in the period 2012-2015, the results indicate that Pre-Draft Combine testing procedures show low to moderate correlations with only few observed basketball performance variables in the first NBA season. The highest correlation was found between upper body strength and number of rebounds (r = .403, p = .002) and blocked shots (r = .333, p = .011). Regression model of Combine performance explained 24.7% of basketball performance with three physical performance tests. Practical application might suggest that some parts of the Combine might be restructured in order to include some other tests more informative tests for the future player performance and player selection. The paper is a part of the project III47015, funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia – Scientific Projects 2011 – 2019.

Walking and running represent the two basic, fundamental patterns of human locomotion. The lowest speed at which a man spontaneously switches from walking to running is called preferred transition speed (PTS). There are a considerable number of inconsistencies regarding the importance and influence of body size and muscle factors in the phenomenon of gait transition speed, in the previous research findings. The objectives of this study were to determine: (1) the relationship between longitudinal, transversal and circular dimensions of the body and PTS; (2) the relationship between body proportions and PTS; (3) the relationship between body composition variables and PTS; (4) the predictive power of human allometry in determination of PTS. The sample comprised 59 male subjects (age 21.76 ± 1.93 years) heterogeneous in physical dimensions. We measured 15 anthropometric variables, body composition, and determined individual PTS using the standard increment protocol. Statistical analysis included Pearson correlation and Multivariate linear regression. The value of PTS in this study was 7.96 ± 0.38 km h-1. the highest correlations with PTS were recorded for variable lower leg length (r = -0.488), foot length (r = 0.418) and leg length (r = -0.410, p < 0.01). Body proportions showed higher correlations with the PTS in compare to individual anthropometric variables, and the highest correlation was observed between the proportion of the thigh length/lower leg length and PTS (r = 0.521, p < 0.01). The amount of body fat and percentage of body fat had low inverse correlation with PTS (r = -0.250, p < 0.05). Linear regression showed that 31% of the PTS variance can be explained by the variables lower leg length and foot length. Results of hierarchical regression showed that the 50.4% of the PTS variance can be explained through four body proportions. The main results of this experiment indicate that the proportions of the body are better PTS predictors in compare to the individual anthropometric variables. This means that body constitution, and especially the proportions between the leg segments and percent of the leg muscle mass, are more important PTS determinants than length of individual body segments.

The goal of the present study was to explore the relationship between maximum strength, the vertical jump, acceleration and change of direction performance in healthy young male students. The sample of variables included the following variables: body mass (BM), one repetition maximum on the half-squat test (Squat 1RM), one repetition maximum normalized for body mass (Squat 1RM_rel), peak power during the concentric phase of countermovement jump (Ppeak CMJ), vertical jump height during CMJ (CMJ_H), time for the 20m sprint (20m Srint) and time for the agility T-Test (Agility T-Test). The relationship was tested with the Pearson Coefficient of linear correlation (r). The results showed significant correlation between body mass with Squat 1RM_rel and peak concentric power during CMJ (r=-.424, and r=.377, respectively). Peak concentric power during CMJ additionally has a significant correlation with the change of direction abilities, 20m sprint, and vertical jump height, (r=-.401; r=-.467; r=.656; p

The purpose of the present study was to explore the relationship between mechanical characteristics of hip, knee and ankle extensor and flexor muscle groups and gait transition speed. The sample included 29 physically active male adults homogenized regarding their anthropometric dimensions. Isokinetic and isometric leg muscle mechanical characteristics were assessed by an isokinetic dynamometer, while individual walk-to-run (WRT) and run-to-walk transition speeds (RWT) were determined using the standard increment protocol. The relationship between transition speeds and mechanical variables scaled to body size was determined using Pearson correlation and stepwise linear regression. The highest correlations were found for isokinetic power of ankle dorsal flexors and WRT (r=.468, p

The first goal of the study was to examine the relationship between maximum muscle strength and optimal drop height (DHopt), while the second goal was to examine the relationship between regression models for the prediction of DHopt and DHopt determined by variable H. A total of 30 respondents, students of the Faculty of Sport and Physical Education participated in the experiment. During the experiment, eight altitudes were randomized in the range of 0.12 to 0.82 m. The instruction was to achieve a higher jump, with a shorter duration of rebound. A positive statistically significant correlation between DHopt determined by prediction method with 1 RM / BW 0.67 and MDS (p

The purpose of this study was to explore the relationships between preferred transition speed (PTS) and anthropometric characteristics, body composition and different human body proportions in males. In a sample of 59 male students, we collected anthropometric and body composition data and determined individual PTS using increment protocol. The relationships between PTS and other variables were determined using Pearson correlation, stepwise linear and hierarchical regression. Body ratios were formed as quotient of two variables whereby at least one significantly correlated to PTS. Circular and transversal (except bitrochanteric diameter) body dimensions did not correlate with PTS. Moderate correlations were found between longitudinal leg dimensions (foot, leg and thigh length) and PTS, while the highest correlation was found for lower leg length (r=.488, p.01). Two parameters related to body composition showed weak correlation with PTS: body fat mass (r=-.250, p.05) and amount of lean leg mass scaled to body weight (r=.309, p.05). Segmental body proportions correlated more significantly with PTS, where thigh/lower leg length ratio showed the highest correlation (r=.521, p.01). Prediction model with individual variables (lower leg and foot length) have explained just 31% of PTS variability, while model with body proportions showed almost 20% better prediction (R(2)=.504). These results suggests that longitudinal leg dimensions have moderate influence on PTS and that segmental body proportions significantly more explain PTS than single anthropometric variables.