The purpose of our study was to examine the ability of anthropometric variables (body mass, total arm length, biacromial width) to predict bench press performance at both maximal and submaximal loads. Our methods required 36 men to visit our laboratory and submit to anthropometric measurements, followed by lifting as much weight as possible in good form one time (1 repetition maximum, 1RM) in the exercise. They made 3 more visits in which they performed 4 sets of bench presses to volitional failure at 1 of 3 (40, 55, or 75% 1RM) submaximal loads. An accelerometer (Myotest Inc., Royal Oak MI) measured peak force, velocity, and power after each submaximal load set. With stepwise multivariate regression, our 3 anthropometric variables attempted to explain significant amounts of variance for 13 bench press performance indices. For criterion measures that reached significance, separate Pearson product moment correlation coefficients further assessed if the strength of association each anthropometric variable had with the criterion was also significant. Our analyses showed that anthropometry explained significant amounts (p < 0.05) of variance for 8 criterion measures. It was concluded that body mass had strong univariate correlations with 1RM and force-related measures, total arm length was moderately associated with 1RM and criterion variables at the lightest load, whereas biacromial width had an inverse relationship with the peak number of repetitions performed per set at the 2 lighter loads. Practical applications suggest results may help coaches and practitioners identify anthropometric features that may best predict various measures of bench press prowess in athletes.

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.

No abstract

No abstract