Abstract available in the 9th Annual Coaches and Sport Science College.

Average concentric velocity (ACV) can be used to objectively measure fatigue and intensity during resistance training and to assist in attempt selection during one-repetition maximum (1RM) testing. However, ACV may be different across individuals at similar intensities of 1RM, making it difficult for investigators to make objective load increases during 1RM testing. Further, it is unknown how strength levels are related to velocity at particular percentages of 1RM. Purpose: Therefore, the purpose of this study was to examine the relationship between absolute and relative strength in the back squat with ACV at various percentages of 1RM. Methods: Twenty-five college-aged, resistance trained men (Age: 23 ± 3 years, Body Mass: 87.70 ± 16.11 kg) performed a 1RM back squat followed by 2 single repetition sets at 30, 40, 50, 60, 70, 80, and 90% of the established 1RM. Three to 5 minutes of rest were administered between each single repetition set and the best velocity of the 2 sets at each intensity was used for analysis. ACV was calculated on each set using the Open Barbell System Version 2 (Seattle, WA, USA) linear position transducer. Wilks coefficient, a valid measure of relative strength, was also calculated from the 1RM squat. The difference between ACV at 90% of 1RM and ACV at 30% of 1RM was calculated and used as ACV decline. Pearson's product moment correlations were used to assess relationships between absolute and relative strength and ACV decline. Results: Mean squat 1RM was 167.0 ± 42.5, mean Wilks Coefficient was 109.75 ± 21.55 and mean ACV decline from 30 to 90%1RM was 0.65 ± 0.11m·s−1. There was a significant moderate correlation between 1RM and ACV decline from 30 to 90% 1RM (r = 0.48, p = 0.01). Additionally, there was a significant moderate correlation between relative back squat strength and ACV decline from 30 to 90% 1RM (r = 0.56, p= < 0.01). Conclusions: Our findings suggest that lifters with greater absolute and relative strength will experience a larger decrease in ACV between 30 and 90% of their 1RM. These findings may be due to stronger lifters possessing greater neuromuscular efficiency, resulting in a slower ACV at high percentages of 1RM; thus, displaying greater velocity decline from low to high intensities. Practical Applications: Therefore, if utilizing velocity based training at high intensities a stronger lifter might be prescribed a slower ACV than a weaker lifter.

The purpose of this study was to examine the effects of a recovery supplement compared with a placebo on muscle morphology in trained weightlifters. Vastus lateralis and muscle fiber cross sectional area of type I and type II fibers were compared between groups using a series of 2 × 2 (group × time) repeated measure ANOVAs. Both groups on average improved cross-sectional area of the vastus lateralis, type I and type II muscle fibers from pre-to-post but individual response varied within both groups. Greater magnitude of changes in type I and type II muscle fibers were observed for the placebo group but not for vastus lateralis cross sectional area. Additionally, subjects were divided into large and small fiber groups based on combined fiber size at the start of the investigation. These findings indicate that the recovery supplement utilized provided no greater effect compared with a placebo in a 12-week block periodization protocol in trained weightlifters. The primary determinate of fiber size changes in the study was determined to be the initial fiber size of muscle fibers with larger practical changes observed in the small fiber group compared with the large fiber group in type I, II, and ultrasound cross-sectional area (CSA).

PURPOSE: To examine differences in mean power output between high bar (HBS) and low bar back squats (LBS). METHODS: Six trained males (25.0 ± 3.1 years, 1.78 ± 0.04 m, 87.6 ± 7.5 kg) with previous squatting experience (experience: 7.5 ± 4.1 years, HBS 1RM: 157.0 ± 15.3 kg, squat/bodyweight: 1.8 ± 0.18) completed the study using a crossover design. Subjects completed a 4-week familiarization phase with both conditions. Mean power data was collected over 2 sessions using dual uniplanar force plates and 4 linear position transducers sampling at 1,000 Hz. Subjects were randomly assigned to the HBS or LBS for 1 set of 3 repetitions at 20, 30, 40, 50, 60, 70, 80, and 90% of their most recent HBS training 1RM with 3 to 5 minutes’ rest between sets and 2-7 days between testing conditions. A 2x8 repeated measures analysis of variance was used to determine interactions and main effects for condition and load with post-hoc tests conducted for statistical main effects. RESULTS: Analysis revealed significant main effects for load (p < 0.01) but not for condition. CONCLUSIONS: According to this pilot data, athletes seeking to increase power production ability should choose a squatting style in which they feel most proficient and comfortable. Furthermore, either the HBS or LBS can be used as the primary squatting movement, or as a secondary movement to provide variation. However, based on previous research it is likely that sport specific biomechanical parameters will influence the squatting style selection for the majority of athletes who participate in sports that involve jumping, sprinting, and change of direction.

No description available.

Abstract available in the Medicine & Science in Sports & Exercise.

Title: The Analysis of the Effect of Weightlifting Shoes and Bare Feet Lifting on the Performance Measures of Back Squat. Objectives: The main goal of this study was to compare speed, power, and depth of the back squat performed both barefoot and in weightlifting shoes using the training protocol 5 × 5 (5 sets with 5 reps) at 70 % one-repetition maximum (1RM). Methods: Ten elite, male participants (27 ± 3.54 years old, 93 ± 10.23 kg of body weight, 179.28 ± 8.54 cm of height) were involved and assessed for the purposes of the study. All participants' 1RM back squat was not lower than 1.5 times of their body weight and they all had many years of experience using weightlifting as a part of their athletic development. The assessment was carried out at the training adaptation lab of the Department of Physiology and Biochemistry, the Faculty of Physical Education and Sport, Charles University in Prague (UK FTVS). Each participant followed the same training protocol during two sessions not less than 48 hours apart; the first one performed barefoot and the second one wearing the weightlifting shoes. Participants were asked to perform three repetitions, first with 20 % of their estimated 1RM, then with 40 % and 60 %, followed by two repetitions with 70 % and 80 % of their estimated 1RM to determine their...

Introduction: Monitoring an athlete’s stress and recovery state across sequential training bouts can be used to gauge fitness and fatigue levels (i.e., preparedness). Previous studies have used jumping performance, biochemical markers, and questionnaires to estimate preparedness. However, self-report questionnaires are the most common due to economical and practical means. The Short Recovery and Stress Scale (SRSS) is an 8-item questionnaire ideal for monitoring; however, convergent validity of the SRSS with physiological and performance measures needs to be investigated. Purpose: Thus, the purpose of this study was to determine whether changes in collegiate weightlifter’s training volume-load, biochemical markers, and jumping performance correlate to changes in the SRSS. Methods: 12 collegiate weightlifters (8 males, 4 females) with >1yr of competition experience trained for 4 weeks and were tested at the beginning of each week (T1-T4). Training volume-load with displacement (VLd) was monitored weekly for all exercises. Testing was conducted following an overnight fast and included hydration, SRSS (0-6 scale with 6 indicating highest recovery and stress), and blood draws (resting testosterone (T), cortisol (C), T:C, creatine kinase (CK)) followed by unloaded (0kg) and loaded (20kg) squat jumps (SJ) on force platforms. Pearson correlation coefficients were calculated between the change in SRSS scores and all other variables from T1-T2, T1-T3, and T1-T4. Alpha level was set at p< 0.05. Results: Inverse relationships were observed between changes in recovery items and C (r= -0.61 to -0.72, p< 0.05), and unloaded and loaded SJ height and relative peak power (r= -0.59 to -0.64, p< 0.05) from T1 to T2, and T1 to T3. Similarly, positive relationships were observed between changes in stress items and C (r=0.61 to 0.72, p< 0.05), and unloaded and loaded SJ height and relative peak power (r=0.58 to 0.84, p< 0.05) across all time points. No significant relationships were observed between changes in SRSS items and VLd or T, T:C, CK. Conclusion: Relationships between changes in some SRSS items and C agree with previous findings highlighting C as an indicator of training stress. Nonetheless, the non-significant relationships between changes in SRSS items, VLd, and other biochemical markers disagrees with previous findings. This may partly be explained by the smaller undulations in VLd in the current study, which is characteristic of actual training. Further, relationships between changes in some SRSS items and jumping performance were opposite of what was expected indicating athlete’s perception of their stress and recovery state does not always correspond with their ability to perform. Practical Application: These results provide some evidence for the convergent validity of the SRSS. Nonetheless, weightlifting coaches should be cautious in using results from a single test to estimate an athlete’s preparedness. Thus, we recommend the SRSS be included as part of a multi-dimensional monitoring program for weightlifters.

Excerpt:The purpose of this article is to present and discuss the phases of the pull that precede the power position, as well as present some suggestions for how to coach these positions...

PURPOSE: To examine the influence of three distinct training phases on isometric mid-thigh pull (IMTP) measures in well-trained weightlifters. METHODS: Pre- and post-block IMTP data from 11 collegiate weightlifters was used for analysis. The mean of the best two attempts from each athlete for measures of PF and RFD from 0-50ms, 0-100ms, 0-150ms, 0-200ms, and 0-250ms were used for comparison. In total, results from five timepoints for each of the 11 athletes were examined in order to compare the effects of the three training phases. RESULTS: A repeated measures ANOVA revealed no statistically significant (p ≥ 0.05) effects of training on any of the variables measured. When comparing post block values from each phase to pre-training cycle values, the largest increase in RFD200 (d = 0.22) and RFD250 (d=0.22) occurred post strength-power (SP) phase, while the peak in RFD50 (d = 0.32), RFD100 (d = 0.31), and RFD150 (d = 0.22) occurred after the peak/taper (PT) phase. CONCLUSIONS: Based on the results of the study, it is possible that changes in IMTP RFD may reflect the expected adaptations of block periodization. Rather than examining RFD changes at only one time-band, it may be valuable to monitor RFD across multiple time bands as changes in early and late RFD may not occur proportionally during a peak/taper phase.