Dataset Information

Passive Drag in Young Swimmers: Effects of Body Composition, Morphology and Gliding Position.

ABSTRACT: The passive drag (Dp) during swimming is affected by the swimmer's morphology, body density and body position. We evaluated the relative contribution of morphology, body composition, and body position adjustments in the prediction of a swimmer's Dp. This observational study examined a sample of 60 competitive swimmers (31 male and 29 female) with a mean (±SD) age of 15.4 ± 3.1 years. The swimmer's Dp was measured using an electro-mechanical towing device and the body composition was assessed using a bioelectrical impedance analyser. Body lengths and circumferences were measured in both the standing position and the simulated streamlined position. Partial correlation analysis with age as a control variable showed that Dp was largely correlated (p < 0.05) with body mass, biacromial- and bi-iliac-breadth, streamline chest circumference and breadth. Body mass, Body Mass Index, chest circumference and streamline chest circumference showed a significant and moderate to strong effect (?2 > 0.55) on Dp. Body mass was the best predictor of Dp explaining 69% of the variability. These results indicate that swimmers with lower Dp values were: (i) slimmer, with lower fat and fat-free mass, (ii) thinner, with lower shoulder breadth, chest circumference, and streamline trunk diameters (iii), shorter, with lower streamline height. These findings can be used for talent identification in swimming, with particular reference to the gliding performance.

SUBMITTER: Cortesi M

PROVIDER: S-EPMC7142561 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Passive Drag in Young Swimmers: Effects of Body Composition, Morphology and Gliding Position.

Cortesi Matteo M Gatta Giorgio G Michielon Giovanni G Di Michele Rocco R Bartolomei Sandro S Scurati Raffaele R

International journal of environmental research and public health 20200318 6

The passive drag (Dp) during swimming is affected by the swimmer's morphology, body density and body position. We evaluated the relative contribution of morphology, body composition, and body position adjustments in the prediction of a swimmer's Dp. This observational study examined a sample of 60 competitive swimmers (31 male and 29 female) with a mean (±SD) age of 15.4 ± 3.1 years. The swimmer's Dp was measured using an electro-mechanical towing device and the body composition was assessed usi ...[more]

PMID: 32197399

Similar Datasets

Project description:Understanding complex body-brain processes and the interplay between adipose tissue and brain health is important for understanding comorbidity between psychiatric and cardiometabolic disorders. We investigated associations between brain structure and anthropometric and body composition measures using brain magnetic resonance imaging (MRI; n = 24,728) and body MRI (n = 4973) of generally healthy participants in the UK Biobank. We derived regional and global measures of brain morphometry using FreeSurfer and tested their association with (i) anthropometric measures, and (ii) adipose and muscle tissue measured from body MRI. We identified several significant associations with small effect sizes. Anthropometric measures showed negative, nonlinear, associations with cerebellar/cortical gray matter, and brain stem structures, and positive associations with ventricular volumes. Subcortical structures exhibited mixed effect directionality, with strongest positive association for accumbens. Adipose tissue measures, including liver fat and muscle fat infiltration, were negatively associated with cortical/cerebellum structures, while total thigh muscle volume was positively associated with brain stem and accumbens. Regional investigations of cortical area, thickness, and volume indicated widespread and largely negative associations with anthropometric and adipose tissue measures, with an opposite pattern for thigh muscle volume. Self-reported diabetes, hypertension, or hypercholesterolemia were associated with brain structure. The findings provide new insight into physiological body-brain associations suggestive of shared mechanisms between cardiometabolic risk factors and brain health. Whereas the causality needs to be determined, the observed patterns of body-brain relationships provide a foundation for understanding the underlying mechanisms linking psychiatric disorders with obesity and cardiovascular disease, with potential for the development of new prevention strategies.

Project description:Total and regional body composition are strongly correlated with metabolic syndrome and have been estimated non-invasively from 3D optical scans using linear parameterizations of body shape and linear regression models. Prior works produced accurate and precise predictions on many, but not all, body composition targets relative to the reference dual X-Ray absorptiometry (DXA) measurement. Here, we report the effects of replacing linear models with nonlinear parameterization and regression models on the precision and accuracy of body composition estimation in a novel application of deep 3D convolutional graph networks to human body composition modeling. We assembled an ensemble dataset of 4286 topologically standardized 3D optical scans from four different human body shape databases, DFAUST, CAESAR, Shape Up! Adults, and Shape Up! Kids and trained a parameterized shape model using a graph convolutional 3D autoencoder (3DAE) in lieu of linear PCA. We trained a nonlinear Gaussian process regression (GPR) on the 3DAE parameter space to predict body composition via correlations to paired DXA reference measurements from the Shape Up! scan subset. We tested our model on a set of 424 randomly withheld test meshes and compared the effects of nonlinear computation against prior linear models. Nonlinear GPR produced up to 20% reduction in prediction error and up to 30% increase in precision over linear regression for both sexes in 10 tested body composition variables. Deep shape features produced 6-8% reduction in prediction error over linear PCA features for males only and a 4-14% reduction in precision error for both sexes. Our best performing nonlinear model predicting body composition from deep features outperformed prior work using linear methods on all tested body composition prediction metrics in both precision and accuracy. All coefficients of determination (R2) for all predicted variables were above 0.86. We show that GPR is a more precise and accurate method for modeling body composition mappings from body shape features than linear regression. Deep 3D features learned by a graph convolutional autoencoder only improved male body composition accuracy but improved precision in both sexes. Our work achieved lower estimation RMSEs than all previous work on 10 metrics of body composition.

Project description:BackgroundWe aimed to examine maternal metabolic correlates of neonate body composition, and the potential mediating effect of the placenta.MethodsData were collected throughout pregnancy and at delivery. An oral glucose tolerance test (OGTT) was conducted in order to diagnose or rule out gestational diabetes mellitus (GDM). Maternal weight and blood pressure were taken and hypertension and gestational weight gain (GWG) were defined. Gestational age, birth weight (BW) and weight to length ration (WLR) were recorded. Photographs were taken of the placenta, and the widths and lengths were measured digitally. Body composition was analysed via air displacement plethysmography or dual-energy x-ray absorptiometry. Mediation models were conducted to determine the mediation effect of the placental variables on the relationships between maternal health variables and neonate outcomes. Next, interaction terms were added to models to determine how maternal and placental variables interacted in their effect on neonate outcomes.ResultsA total of n = 280 women were included in the analysis. Majority were overweight or obese. Fourteen percent of women developed GDM during pregnancy, 5% had hypertension during pregnancy, 32% were HIV positive, and 32% had anemia. For the birth weight outcome, coefficients of BMI were attenuated by the addition of placental variables (Model 1 β=18.66 vs Model 2 β=16.40). Similar patterns were evident for GWG and hypertension, and for the WLR outcome. In all cases the addition of the placental variables attenuated associations between maternal exposures and neonatal outcomes, yet the level of significance did not change. Inclusion of interaction terms reversed the direction of the relationships between hypertension and BW and WLR, and between GWG and WLR.ConclusionThe placenta buffers some harmful effects of obesity, GWG, and hypertension on neonate size, and placental efficiency interacted with most maternal risk factors to either counteract, or attenuate relationships with neonate size at birth. However the placenta was unable to completely counteract the negative effect of excess nutrient supply on in utero growth.

Project description:IntroductionThe relation between socioeconomic position (SEP) and obesity measured by body mass index (BMI) has been extensively reviewed, but there is less research on the association between SEP and body composition. Fat distribution and muscle quality have been linked to adverse health outcomes such as cardiovascular disease, diabetes and poor physical capability. There is some evidence of secular changes in body composition with increasing fat-mass and reducing muscle quantity and strength, but it is unclear whether there have been secular changes in social inequalities in body composition. The aim is to perform a systematic review of the existing literature on the association between SEP and body composition and to explore any secular changes.MethodsThe systematic review will be carried out according to PRISMA guidelines. An electronic search of MEDLINE and Embase Classic + Embase will be conducted using OvidSP as the database interface, as well as SPORTDiscus using EBSCO. Two independently working reviewers will initially screen abstracts to exclude papers that are clearly ineligible, followed by a full-text screening to exclude papers not meeting all inclusion criteria. Any disagreements will be resolved through discussion. Data extraction and quality assessment of eligible papers will be carried out by 2 reviewers using a standardised form. The reference lists of identified papers will be searched for additional papers. Original studies in the English language, which examine the association between SEP at any age and body composition at the same or later age will be included if they use any recognised measures of SEP (e.g. income, occupation, over-crowding) and a recognised measure of body composition (total, proportional or location of fat mass and fat-free mass, using any appropriate methods, excluding anthropometry). Due to expected heterogeneity, a narrative synthesis is expected, with a descriptive summary to be provided in tables. If there is consistency in reporting of associations, a random-effects meta-analysis will be used to provide an overall summary estimate.DiscussionThe results of the review will summarise the existing evidence on social inequalities in body composition. Findings will identify gaps in knowledge and where further research is required.Systematic review registrationPROSPERO CRD42019119937.

Dataset Information

Passive Drag in Young Swimmers: Effects of Body Composition, Morphology and Gliding Position.

Publications

Passive Drag in Young Swimmers: Effects of Body Composition, Morphology and Gliding Position.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets