changes experienced in the later part of the second trimester, but plateauing, and even in some cases trending toward improvement before birth. 6 The goals of this current research were to determine how body anthropometry changes during pregnancy and to determine the relationship between anthropometric
Robert D. Catena, Nigel Campbell, Alexa L. Werner, and Kendall M. Iverson
Adam J. Zemski, Shelley E. Keating, Elizabeth M. Broad, and Gary J. Slater
not previously been explored. Anthropometry is the scientific procedure of acquiring surface anatomical dimensional measurements, including skinfolds, and is an easily accessible, inexpensive, mobile, and robust method of body composition assessment used in rugby union ( Ackland et al., 2012 ; Duthie
Adam J. Zemski, Elizabeth M. Broad, and Gary J. Slater
body composition are surface anthropometry and dual-energy X-ray absorptiometry (DXA) ( Ackland et al., 2012 ; Zemski et al., 2015 ). Surface anthropometry, which includes the indirect assessment of subcutaneous fat, is an easily accessible, inexpensive, mobile, and robust method of assessment. The
Zachary Merrill, Grace Bova, April Chambers, and Rakié Cham
specific segmentation method used, and these differences are challenging to control for as they depend on obesity, gender, and perhaps other body shapes. Although previous work has shown differences in anthropometry due to gender and obesity, 16 as well as differences between parameter calculation methods
Andrew A. Dingley, David B. Pyne, and Brendan Burkett
To characterize relationships between propulsion, anthropometry, and performance in Paralympic swimming.
A cross-sectional study of swimmers (13 male, 15 female) age 20.5 ± 4.4 y was conducted. Subject locomotor categorizations were no physical disability (n = 8, classes S13–S14) and low-severity (n = 11, classes S9–S10) or midseverity disability (n = 9, classes S6–S8). Full anthropometric profiles estimated muscle mass and body fat, a bilateral swim-bench ergometer quantified upper-body power production, and 100-m time trials quantified swimming performance.
Correlations between ergometer mean power and swimming performance increased with degree of physical disability (low-severity male r = .65, ±0.56, and female r = .68, ±0.64; midseverity, r = .87, ±0.41, and r = .79, ±0.75). The female midseverity group showed nearperfect (positive) relationships for taller swimmers’ (with a greater muscle mass and longer arm span) swimming faster, while for female no- and low-severity-disability groups, greater muscle mass was associated with slower velocity (r = .78, ±0.43, and r = .65, ±0.66). This was supported with lighter females (with less frontal surface area) in the low-severity group being faster (r = .94, ±0.24). In a gender contrast, low-severity males with less muscle mass (r = -.64, ±0.56), high skinfolds (r = .78, ±0.43), a longer arm span (r = .58, ±0.60) or smaller frontal surface area (r = -.93, ±0.19) were detrimental to swimming-velocity production.
Low-severity male and midseverity female Paralympic swimmers should be encouraged to develop muscle mass and upper-body power to enhance swimming performance. The generalized anthropometric measures appear to be a secondary consideration for coaches.
Herman van Werkhoven and Stephen J. Piazza
results of previous research. Similar methods have been employed by others. 13 , 21 It is important to note that our correlational findings of relationships between foot anthropometry and oxygen consumption does not necessarily imply the existence of mechanisms linking the two. More detailed studies
Ava Farley, Gary J. Slater, and Karen Hind
spectroscopy (BIS), and surface anthropometry (SA) ( Meyer et al., 2013 ). Despite differences in technology, resources, and technical expertise required, they are all susceptible to technical error and biological variation ( Ackland et al., 2012 ; Meyer et al., 2013 ), which significantly affects precision
Jim Dollman, Kevin Norton, and Graeme Tucker
The aim of this study was to compare urban and rural South Australian primary schoolchildren on measures of anthropometry, fitness, and environmental mediators of physical activity. The sample was comprised of 445 urban and 205 rural boys and 423 urban and 158 rural girls, all age 10–11 yrs at the time of testing. After controlling for socioeconomic status and ethnicity, rural girls and boys were faster over 1.6 k than their urban counterparts while rural girls were also faster over 50 m. Rural residence independently predicted participation in organized activity, increasing involvement in club sport, and decreasing involvement in school sport. Rural children reported a greater likelihood of participating in two or more physical education classes per week. It is evident that urban and rural South Australia differ in ways which impact on fitness and physical activity patterns of upper primary age children.
Paulo V. Mezzaroba and Fabiana A. Machado
This study aimed to determine the influence of age, anthropometry, and distance on stroke parameters of 10- to 17-y-old swimmers. Forty-six male swimmers were divided into 4 chronological age groups. Anthropometry and sexual maturity were assessed, and maximal efforts of 100, 200, and 400 m using front-crawl style were performed to determine stroke rate (SR), length (SL), and index (SI). Multiple linear regression, 1-way, and mixed ANOVA for repeated measures were used for statistical analyses. There was significant effect of distance for all stroke parameters (P < .001) and an age effect only for SL and SI (P < .001). Post hoc showed that the 10- to 17-year-old group significantly reduced SR with increasing distance (effect size –0.8 to –1.5 comparing 100, 200, and 400 m) but were not effective in offsetting this adaptation with increased SL, especially from 200- to 400-m distance, at which no group made both adjustments, highlighting the decreased efficiency with significant SI reduction (effect size –0.2 to –0.4 comparing 100, 200, and 400 m). Considering all stroke parameters, the performances were almost 100% explained, but SI itself could explain around 90% of the performance; furthermore, limb length contributed to explain all stroke parameter, and SI was the variable best predicted (around 75%) by anthropometrical (upper limbs and height) and descriptive variables (age and y of systematic training).Thus, distinct effects of distance and advancing age were found during childhood and adolescence on stroke parameters, and SI was highlighted as the best predictor of 100-, 200-, and 400-m maximal performances.
Thomas Korff and Jody L. Jensen
When performing skillful movement muscular and nonmuscular forces act in concert to produce a resultant force that complies with the goal of the task. Nonmuscular forces are directly dependent on the anthropometry of the performer. The purpose of this study was to determine the effect of age-related changes in relative anthropometric characteristics between 5 and 10 years of age on muscular power production during pedaling. A secondary purpose was to determine the dependence of this effect on movement speed. A torque-driven model of two-legged pedaling was used to track experimental kinematics and forces obtained from 6 experienced adult cyclists pedaling at 60 and 120 rpm. Relative anthropometric characteristics were modified to simulate pedaling for children of 5, 7.5, and 10 years of age. Analyses of variance revealed that age-related differences in anthropometry did not affect the muscular contribution to crank power (p > .05), while they had a significant effect on the muscular contribution to limb power (p < .05). Adjustments by the proximal muscle groups (muscles spanning the hip and knee joints) were necessary to account for anthropometry-driven changes in nonmuscular power. These effects were independent of movement speed. Our results provide researchers with useful information to interpret age-related differences in muscular power production more accurately.