The purpose of our study was to assess data reproducibility from 2 consecutive front squat workouts, spaced 1 week apart, performed by American college football players (n = 18) as they prepared for their competitive season. For each workout, our methods entailed the performance of 3-6 front squat repetitions per set at 55, 65, and 75% of subject's 1 repetition maximum (1RM) load. In addition, a fourth set was done at a heavier load, with a resistance equal to 80 and 83% of their 1RM values, for the first and second workouts, respectively. A triple-axis accelerometer was affixed to a barbell to quantify exercise performance. Per load, the accelerometer measures peak values for the following indices: force, velocity, and power. To assess data reproducibility, inter-workout comparisons were made for 12 performance indices with 4 statistical test-retest measures: intraclass correlation coefficients, coefficients of variation (CVs), and the SEM expressed in both absolute and relative terms. Current results show that the majority of performance indices exceeded intraclass correlation (0.75-0.80) and CV (10-15%) values previously deemed as acceptable levels of data reproducibility. The 2 indices with the greatest variability were power and velocity values obtained at 55% of the 1RM load; thus, it was concluded that higher movement rates at the lightest load were the most difficult aspect of front squat performance to repeat successfully over time. Our practical applications imply lighter loads, with inherently higher rates of barbell movement, yield lower data reproducibility values.
A Vertec jump measurement and training system measures vertical jump heights but not additional variables that would reveal how the performance was achieved. Technology advances to equipment now include additional variables that elucidate how jump performance is achieved. However, acceptance of new jump-related equipment is predicated on the reliability of the vertical heights it measures in relation to those assessed by the Vertec. Thus, our study compared vertical jump height reliability data from a newly created instrumented platform to those concurrently derived from the Vertec. Methods required subjects (n = 105) to perform 2 jump trials separated by at least 2 days of rest. Trials began with a warm-up, followed by 3 to 5 maximal-effort jumps. The Vertec was placed directly over the platform so, as jumps occurred, subjects took off and landed on the instrumented device. At the jump apex subjects contacted the highest Vertec slapstick possible to assess maximum height attained. Four height measurements were derived from each jump: 3 platform-based calculations (from subject's take-off, hang time, and landing) and 1 Vertec. The platform-based calculations were compared to Vertec data to assess the reliability of the instrumented device. Intraclass correlation coefficient (0.90), coefficient of variation (17.3%), standard error of measurement (0.9 cm), and smallest real difference (3.7 cm) results showed heights calculated from platform take-offs were most reliable to Vertec values. It was concluded take-off from the platform yielded jump heights that are a viable alternative to those derived from the Vertec. Practical applications suggest coaches may use the platform to derive reliable vertical jump data in addition to other variables to better understand the performance of their athletes.
The current study purpose examined the vertical height-anthropometry relationship with jump data obtained from an instrumented platform. Our methods required college-aged (n = 177) subjects to make 3 visits to our laboratory to measure the following anthropometric variables: height, body mass, upper arm length (UAL), lower arm length, upper leg length, and lower leg length. Per jump, maximum height was measured in 3 ways: from the subjects' takeoff, hang times, and as they landed on the platform. Standard multivariate regression assessed how well anthropometry predicted the criterion variance per gender (men, women, pooled) and jump height method (takeoff, hang time, landing) combination. Z-scores indicated that small amounts of the total data were outliers. The results showed that the majority of outliers were from jump heights calculated as women landed on the platform. With the genders pooled, anthropometry predicted a significant (p < 0.05) amount of variance from jump heights calculated from both takeoff and hang time. The anthropometry-vertical jump relationship was not significant from heights calculated as subjects landed on the platform, likely due to the female outliers. Yet anthropometric data of men did predict a significant amount of variance from heights calculated when they landed on the platform; univariate correlations of men's data revealed that UAL was the best predictor. It was concluded that the large sample of men's data led to greater data heterogeneity and a higher univariate correlation. Because of our sample size and data heterogeneity, practical applications suggest that coaches may find our results best predict performance for a variety of college-aged athletes and vertical jump enthusiasts.
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