The purpose of this study was to develop and validate a regression equation to estimate peak power (PP) using a large sample of athletic youths and young adults. Anthropometric and vertical jump ground reaction forces were collected from 460 male volunteers (age: 12-24 years). Of these 460 volunteers, a stratified random sample of 45 subjects representing 3 different age groups (12-15 years [n = 15], 16-18 years [n = 15], and 19-24 years [n = 15]) was selected as a validation sample. Data from the remaining 415 subjects were used to develop a new equation ("Novel") to estimate PP using age, body mass (BM), and vertical jump height (VJH) via backward stepwise regression. Independently, age (r = 0.57), BM (r = 0.83), and VJ (r = 0.65) were significantly (p < 0.05) correlated with PP. However, age did not significantly (p = 0.53) contribute to the final prediction equation (Novel): PP (watts) = 63.6 × VJH (centimeters) + 42.7 × BM (kilograms) - 1,846.5 (r = 0.96; standard error of the estimate = 250.7 W). For each age group, there were no differences between actual PP (overall group mean ± SD: 3,244 ± 991 W) and PP estimated using Novel (3,253 ± 1,037 W). Conversely, other previously published equations produced PP estimates that were significantly different than actual PP. The large sample size used in this study (n = 415) likely explains the greater accuracy of the reported Novel equation compared with previously developed equations (n = 17-161). Although this Novel equation can accurately estimate PP values for a group of subjects, between-subject comparisons estimating PP using Novel or any other previously published equations should be interpreted with caution because of large intersubject error (± >600 W) associated with predictions.

The purpose of this study was to examine physical and performance differences between grade levels and playing positions within High-School football players. Two thousand three hundred and twenty-seven athletes were tested for height, weight, 40-yd sprint time, proagility time, and vertical jump height. Mean scores across age groups and playing positions were compared using repeated-measures analysis of variance (ANOVA) and 1-way ANOVAs. The results indicate that defensive players in the 11th and 12th grades were significantly faster in the 40-yd sprint, quicker in the proagility, and generated more power than 9th and 10th grade defensive players across all positions (p < 0.05). Similarly, offensive players in the 11th and 12th grades were significantly faster, quicker, and jumped higher than did football players in lower grades (p < 0.05). Overall, these data suggest that there are distinct differences in the physical and performance characteristics of high-school football players. The greatest difference is observed between the sophomore and junior years. Older, more mature athletes are faster, quicker, and capable of generating more power than younger athletes. Practically, these data lend support to the common 3-tiered approach (i.e., Freshman, Junior Varsity, and Varsity) most high schools use for their football programs. This approach is likely indicated to allow for physical maturation of young players and to allow time for the development of strength, power, speed, and agility necessary to compete with older players.

Background. The purpose of this study was to develop and validate anthropometric body composition prediction equations for elderly (i.e., Ն 65 years old) men. This was necessary because of a lack of accurate and reliable predictive equations specifically developed for this population.

Relationships between sprinting speed, body mass, and vertical jump kinetics were assessed in 243 male soccer athletes ranging from 10–19 years. Participants ran a maximal 36.6 meter sprint; times at 9.1 (10 y) and 36.6 m (40 y) were determined using an electronic timing system. Body mass was measured by means of an electronic scale and body composition using a 3-site skinfold measurement completed by a skilled technician. Countermovement vertical jumps were performed on a force platform - from this test peak force was measured and peak power and vertical jump height were calculated. It was determined that age (r=−0.59; p<0.01), body mass (r=−0.52; p<0.01), lean mass (r=−0.61; p<0.01), vertical jump height (r=−0.67; p<0.01), peak power (r=−0.64; p<0.01), and peak force (r=−0.56; p<0.01) were correlated with time at 9.1 meters. Time-to-complete a 36.6 meter sprint was correlated with age (r=−0.71; p<0.01), body mass (r=−0.67; p<0.01), lean mass (r=−0.76; p<0.01), vertical jump height (r=−0.75; p<0.01), peak power (r=−0.78; p<0.01), and peak force (r=−0.69; p<0.01). These data indicate that soccer coaches desiring to improve speed in their athletes should devote substantive time to fitness programs that increase lean body mass and vertical force as well as power generating capabilities of their athletes. Additionally, vertical jump testing, with or without a force platform, may be a useful tool to screen soccer athletes for speed potential.

Historically, video games required little physical exertion, but new systems utilize handheld accelerometers that require upper-body movement. It is not fully understood if the metabolic workload while playing these games is sufficient to replace routine physical activity. The purpose of this study was to quantify metabolic workloads and estimate caloric expenditure while playing upper-body accelerometer-controlled and classic seated video games. Nineteen adults completed a peak oxygen consumption treadmill test followed by an experimental session where exercising metabolism and ventilation were measured while playing 3 video games: control (CON), low activity (LOW) and high activity (HI). Resting metabolic measures (REST) were also acquired. Caloric expenditure was estimated using the Weir equation. Mean oxygen consumption normalized to body weight for HI condition was greater than LOW, CON, and REST. Mean oxygen consumption normalized to body weight for LOW condition was also greater than CON and REST. Mean exercise intensities of oxygen consumption reserve for HI, LOW, and CON were 25.8% ± 5.1%, 6.4% ± 4.8%, and 0.8% ± 2.4%, respectively. Estimated caloric expenditure during the HI was significantly related to aerobic fitness, but not during other conditions. An active video game significantly elevated oxygen consumption and heart rate, but the increase was dependent on the type of game. The mean oxygen consumption reserve during the HI video game was below recommended international standards for moderate and vigorous activity. Although upper-body accelerometer-controlled video games provided a greater exercising stimulus than classic seated video games, these data suggest they should not replace routine moderate or vigorous exercise.