New studies on anthropometry 9781536195323, 1536195324

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HUMAN ANATOMY AND PHYSIOLOGY

NEW STUDIES ON ANTHROPOMETRY

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HUMAN ANATOMY AND PHYSIOLOGY

NEW STUDIES ON ANTHROPOMETRY

RICARDO J. FERNANDES ALEXANDRE IGOR ARARIPE MEDEIROS AND

RUI GARGANTA EDITORS

Copyright © 2021 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. We have partnered with Copyright Clearance Center to make it easy for you to obtain permissions to reuse content from this publication. Simply navigate to this publication’s page on Nova’s website and locate the “Get Permission” button below the title description. This button is linked directly to the title’s permission page on copyright.com. Alternatively, you can visit copyright.com and search by title, ISBN, or ISSN. For further questions about using the service on copyright.com, please contact: Copyright Clearance Center Phone: +1-(978) 750-8400 Fax: +1-(978) 750-4470 E-mail: [email protected]. NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works. Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the Publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book.

Library of Congress Cataloging-in-Publication Data ISBN: H%RRN

Published by Nova Science Publishers, Inc. † New York

In this recent book “New studies on Anthropometry” the editors have collected a diverse range of complimentary information applicable to a wide population related to both competitive sport, health and physical fitness, all written by experts in their area. This book will be of interest to those working in these fields as well as others both experienced and at career start, wanting to get a taste of the latest findings and points of interest in the area of study. Daniel Daly, PhD Professor Emeritus Faculty of Movement and Rehabilitation Sciences KU Leuven, Belgium

I highly recommend this book for anyone interested in the study of human movement. It is the product of several well-known scholars with an extensive history of studying human performance across a variety of subdisciplines. The importance of Anthropometrics in the analysis of human movement cannot be overstated, and this book provides a relevant and thorough review of several aspects of the field that will be of interest to both the novice and the expert. The topics range in scope from basic theory to highly applied research across such diverse concepts as aging, amputation, podiatry and dentistry. Each chapter is concise, well-written and accessible with several high-quality figures and tables. The combination of historical and recent data, together with perspectives on future trends, make the book an excellent resource for the movement scientist. Jeff A. Nessler, PhD Professor of Kinesiology California State University, San Marcos, USA

CONTENTS Preface

ix

Acknowledgments

xi

Chapter 1

Body Fat in Male Master Swimmers: Dual X-Ray Absorptiometry vs. Skinfold Thickness Equations Cássia Daniele Zaleski Trindade, Paulo Sehl, Cláudia Dornelles Schneider and Flávio Antonio de Souza Castro

1

Chapter 2

Bone Mineral Response to Physical Activity and Sport Practice Dalton M. Pessôa Filho, Danilo A. Massini, Anderson G. Macedo, Camila M. T. Vasconcelos, Thiago P. Oliveira and Luiz Gustavo Almeida dos Santos

19

Chapter 3

Foot Anthropometry Fernando Miguel Oliveira

39

Chapter 4

A Soccer Team Anthropometric Weighted Centroid Paulo Roriz and Henrique Martins

63

Chapter 5

Anthropometry of Rowing: An Update Ricardo Cardoso, Diogo Duarte Carvalho, Kirstin Morris, J. Arturo Abraldes and Ricardo J. Fernandes

93

Chapter 6

Use and Interpretation of Anthropometric Measures in Postmenopausal Women Maria Helena Rodrigues Moreira, José Aurélio Marques Faria and Ronaldo Eugénio Calçada Dias Gabriel

Chapter 7

Anthropometry in Dentistry – New Insights João C. Pinho, Francisco Maligno, Filipa Cardoso and Helena C. Silva

107

135

viii Chapter 8

Chapter 9

Chapter 10

Chapter 11

Chapter 12

Chapter 13

Chapter 14

Chapter 15

Contents Anthropometrics and Competitive Swimmers with a Disability: A Systematic Review Karla de Jesus, Kelly de Jesus, Flávio Antônio de Sousa Castro and Marcos Franken Effects of Body Mass Index on Segment Coordination and Joint Variability in Running Orlando J. Fernandes, Bruno Gonçalves, Joel Martin and Nelson Cortes

167

201

Body Fat Estimates: How Do They Relate to Each Other and to Cardiorespiratory Fitness? Julian D. Pillay, Firoza Haffejee and Tiago R. Pereira

215

Body Segment Parameters for Rigid Body Modelling in Biomechanical Analyses Tomohiro Gonjo and David Sims

253

Body Composition in Amputee Football Players: What do We Know? Mário A. M. Simim, Roberto A. Eneas, Bruno V. C. da Silva, Gustavo R. Mota, Alexandre I. A. Medeiros and Claudio O. Assumpção How Do the Anthropometric Variables Influence Volleyball and Beach Volleyball Performance? Antonio García de Alcaraz, Alexandre Medeiros, Geovani Messias da Silva, Francisco Oliveira Neto, Ricardo J. Fernandes, Karla de Jesus and Mário Simim The Runner Structure: Anthropometric Differences in Track Events Geovani Messias da Silva, Alexandre Medeiros, Cláudio Assumpção and Mário Simim Anthropometric Indicators and Health Status A Relationship from Infancy to Adulthood Thayse Natacha Gomes, Mabliny Thuany, Ana Carolina Reyes, Raquel Chaves, Michele Souza and Sara Pereira

281

297

323

337

About the Contributors

355

About the Editors

367

Index

369

PREFACE The process of reaching adulthood lasts approximately 20 years, being a period in which complex morphological, physiological and psychological development processes occur. Growth does not imply only an increase in height and weight, but also differentiation, organization, maturation and regression. Children and adolescents are not adults in miniature, since it is not only the small size, but mainly the evident differences in body proportions and the accentuated development of body features, that makes them different. Anthropometry is a biological science that focus on the study of human morphology involving the systematic measurement of the human body physical properties using quantitative variables. It is very useful for assessing the ups and downs of growing up, but also the changes that happens from adulthood, passing through the middle age, going to the old age. In fact, along these latter phases of human lifespan there is a gradual decline in body functioning, with evident physical modifications that should be assessed using updated anthropometric methods. This book consists of 15 chapters targeting high-quality research and review articles, covering current and future trends on Anthropometry. In fact, it includes exciting research topics provided by leading researchers in Sports and Exercise Science, but also on Podometry, Medical Dentistry and General Health. We hope that its parts will be well accepted regarding its conceptual, theoretical and practical approaches. As Nova Science Publishers Guest Editors we wish to extend a sincere thank you to the authors that contributed with their knowledge to this book, as well as to the reviewers for their thorough analysis and expert comments. We hope that the contents of this book might be useful for the Anthropometry related community, helping to consolidate existent knowledge and encouraging innovation and creativity. Ricardo J. Fernandes, Alexandre Medeiros and Rui Garganta

ACKNOWLEDGMENTS

We publicly acknowledge and show appreciation to the following reviewers that analyzed critically the book chapters: • • • • • •

• • • •

Ana Filipa Silva, N2i, Polytechnic Institute of Maia, Maia, Portugal; Research Centre in Sports Sciences, Health Sciences and Human Development, Portugal Arturo Abraldes, Department of Physical Activity and Sport, University of Murcia, Spain Carla McCabe, School of Sport, Ulster University, Northern Ireland Daniel Daly, Department of Movement Sciences, Catholic University of Leuven, Belgium David Pendergast, School of Medicine and Biomedical Sciences, University at Buffalo, United States of America João Pedro Duarte, Porto Biomechanics Laboratory, University of Porto, Porto, Portugal; Research Unity in Sport and Physical Activity, Faculty of Sport Sciences and Physical Education, University of Coimbra, Portugal João Ribeiro, Centre of Research, Education, Innovation and Intervention in Sport, Faculty of Sport, University of Porto, Portugal Manuel Paulo Cunha, Business Sciences and Sport Sciences Department of University Institute of Maia, Portugal Maria Paula Santos, Research Centre in Physical Activity, Health and Leisure, Faculty of Sport, University of Porto, Portugal Paulo Colaço, Centre of Research, Education, Innovation and Intervention in Sport, Faculty of Sport, University of Porto, Portugal

xii

Acknowledgments •

•

• • •

Ricardo Rebelo-Gonçalves, Department of Human Kinetics, Polytechnic Institute of Leiria, Portugal; Research Unit for Sport and Physical Activity, University of Coimbra, Coimbra, Portugal Sara Pereira, Centre of Research, Education, Innovation and Intervention in Sport, Faculty of Sport, University of Porto, Portugal; Center of Research in Sport, Physical Education, Exercise and Health, Lusophone University, Lisbon, Portugal Tânia Amorim, FAME Laboratory, Department of Physical Education and Sport Science, University of Thessaly, Trikala, Greece Tânia Bastos, Centre of Research, Education, Innovation and Intervention in Sport, Faculty of Sport, University of Porto, Portugal Urbano Santana-Penín, Department of Stomatology, University of Santiago de Compostela, Spain

In: New Studies on Anthropometry Editors: Ricardo J. Fernandes et al.

ISBN: 978-1-53619-532-3 © 2021 Nova Science Publishers, Inc.

Chapter 1

BODY FAT IN MALE MASTER SWIMMERS: DUAL X-RAY ABSORPTIOMETRY VS. SKINFOLD THICKNESS EQUATIONS Cássia Daniele Zaleski Trindade1, Paulo Sehl2, Cláudia Dornelles Schneider3 and Flávio Antonio de Souza Castro1 1

Aquatic Sports Research Group, Universidade Federal do Rio Grande do Sul, Brasil 2 Medsize Institute, Brasil 3 Graduate Program in Rehabilitation Sciences, Universidade Federal de Ciências da Saúde de Porto Alegre, Brasil

ABSTRACT Body adiposity is influenced by age and may interfere with swimming performance. Thus, its estimation can be used to assess the effectiveness of an exercise or dietary intervention seeking to achieve a more competitive body or to monitor the health status of an athlete. There is a lack of equations to estimate adiposity in swimmers, specifically for master swimmers, or a validation of existing equations for these athletes to get optimal values to describe this important component of the body. Furthermore, considering the practical, cheaper and faster application of field tests, this chapter aimed to compare and verify the agreement between dual energy x-ray absorptiometry body fat results with those obtained from skinfold thickness using different predictive equations and to describe the body composition of male master swimmers. Twenty-two competitive male master swimmers were evaluated. In the first evaluation, they had the dual x-ray absorptiometry evaluation, which followed the standardized manufacturer protocol. In the second evaluation, skinfold measurement was performed following the International Society for the Advancement of Kinanthropometry protocol. Skinfold measurements were used twelve in body density predictive equations. Potential difference between equations and dual x-

2

Cássia Daniele Zaleski Trindade, Paulo Sehl et al. ray absorptiometry results were compared to the “null” with one-sample T test. Age-groups comparison were carried-out with one-way ANOVA. Bland-Altman and Person correlation coefficient analysis were applied. The effect sizes were calculated. All the statistics were performed in software IBM SPSS version 25, α = 0.05. Except for performance, age did not influence the body adiposity variables evaluated. For body adiposity agreement analysis, only the Durnin & Womersley equation, developed for 40 to 49 years old subjects, did not show a difference from the null; however, in the Bland-Altman analysis, the linear regression was significant (p = 0.049). The results demonstrated that age was not related to body adiposity but might influence body lean body mass. Moreover, no body composition variable influenced performance (no significant relationship between them was observed). Concerning the body adiposity, none of the equations accurately estimated body adiposity, so there is a need to consider acceptable surrogate measures. The use of raw values from skinfold thickness would help track body adiposity in master swimmers.

Keywords: anthropometry, predictive equations, swimming, adiposity

INTRODUCTION Swimming is a highly technical sport as it demands overcoming drag and generating propulsion in the water, which requires different combinations of strength, power and endurance, depending on the swimming event duration [51]. To achieve success in competition, the swimmer needs systematic and intense training to improve technical and physiological conditioning [38]. Swimming for master athletes begins at the age of 25 years. The age categories are separated every five years and swimmers with different backgrounds participated: (i) those who were competitive swimmers since childhood and never stopped training and competing; (ii) those who swam when young and returned to training and competitions as adults; and (iii) those who never trained or competed in swimming and, after adults, started this process [26, 67]. Thus, a great heterogeneity among its participants is to be expected. Data from world master competitions revels that the number of competitors and their performances have improved over the years [26]. However, as age rises, physical performance decreases and swimming race time increases in an exponentially from 36 to 70 years old [58]. The aging process also impacts body composition [31]. Authors have shown a decline in muscle mass and change in muscle composition throughout the lifespan [60], and there is a concomitant rise and redistribution of body fatness (increasing trunk fat, mainly abdominal fat, and decreasing appendicular fat, mainly subcutaneous fat) that contributes to the major age-related diseases, promotes physical disability and impairs independence in the elderly [44]. However, the extent of the contribution of aging on muscle loss and body composition and whether the muscle loss and body composition changes are inevitable along the aging process with athletic training were some of the topics assessed in a systematic review [34]. These authors found that master strength/power athletes had greater strength compared to age-matched master endurance athletes and untrained

Body Fat in Male Master Swimmers

3

individuals and are comparable to young untrained individuals; also, the body fat percentage of all master athletes were like young untrained controls. The maintenance of structured exercise training into older age provides optimal aging, with better physical function and general health characteristics [14]. However, exercise benefits are related to the specific exercise mode performed [34]. Body composition can impact performance in sports [2]. Regarding swimming, comparing body components, improvements in lean mass over a season are the mainly responsible for improving performance [46] [48], since studies which assessed the influence of body fat did not agree [3, 37, 39, 52, 56]. Results obtained with young swimmers revealed that the linear measurements (like body height, hand length or upper limbs span) were those, among the anthropometric variables, which better helped to predict performance [18, 27] and upper limbs cycle kinematic variables [22, 35]. Still, fat free mass, together with linear measurements, could predict the personal best swim speed for 100 m [40]. Studies with master swimmers evaluated the relation of body fatness and anthropometric variables with performance in an ultra-endurance running event (26.4 k) and no relationship was found [24, 25], and data with pool races are still lacking regarding body composition. Forces in water can act differently depending on the specific body weight that changes during inspiration and exhalation, with its value also depending on specific weight of bone, muscle and subcutaneous fat tissue [41]. As fat is less dense than water, energy spent to stay at surface and maintain body position is lower when body fat is increased, so its relationship with buoyance can help explaining why body fat does not impair performance in swimming [29]. As seen with wetsuits, improvements in density are more beneficial for leaner than for fatter subjects [10]. However, fat can also increase the drag coefficient, so the extent of body fatness benefits would be the level when buoyancy gains in performance are lower than drag ones [29]. In a streamline position, body density and body surface area can explain up to 85% of the overall variability of torque [66], but the buoyancy in this position is not the same as in swimming due the upper and lower limbs dynamics. During front crawl, buoyancy generates a moment around the center of mass that raises the lower limbs and lowers the head, acting against the “leg-sinking moment” generated by the hands forces [64]. So, the relationship of body fat and swimming performance needs to be deeply investigated. Knowing that body adiposity changes are important to assess the effectiveness of an exercise or dietary intervention seeking to achieve a more competitive body or to monitor the health status of an athlete [59], research has been dedicated to assessing body composition and estimating its components. Many techniques exist for describing the constituent components of the body and these were extensively reviewed [2]. From lab to field tests, they can be chemical (molecular) and anatomical (tissue/system) and are categorized as follows: (i) direct (e.g., cadaver dissection); (ii) indirect, where a surrogate variable is measured to estimate tissue or molecular composition (e.g., dual x-ray

4

Cássia Daniele Zaleski Trindade, Paulo Sehl et al.

absorptiometry, underwater weighing); or (iii) doubly indirect, where an indirect measure is used to predict another indirect measure (regression models) [2]. Mostly, data regarding body adiposity and the validation of new and old methods are indirect [2]. Laboratory techniques are less available and can be time-consuming and expensive, which makes the accuracy of measures more difficult to access [2, 4]. Some studies that provide estimations by skinfold equations analyzed swimmers in their sample [13, 20] but did not provide a sport-specific equation. Specific equations developed or validated for swimmers were also explored [36, 63]. Adolescent swimmers fat mass was assessed using anthropometric equations, mostly for young people [30]. The results obtained with the Durnin & Rahaman equation [11] were not significantly different from dual x-ray absorptiometry results. A fat mass equation for elite swimmers that accurately predicted dual x-ray absorptiometry derived fat mass from body mass and seven skinfolds was also developed [36]. Comparisons were performed between a skinfold equation developed for children that uses just two sites with dual x-ray absorptiometry and K-40 procedure in competitive swimmers and, for a single comparison, no differences were found [5]. So, there are few studies carried out to systematize a field method for swimmers and, to our knowledge, there has been no investigation of master swimmers. There is a lack of equations to estimate adiposity in master swimmers or a validation of existing equations for these athletes to get optimal values to describe this important component of the body. Furthermore, considering the practical, cheaper and faster application of field tests, the aim of this chapter was as following: (i) to compare and verify the agreement between dual x-ray absorptiometry body fat results with those obtained from skinfold thickness and different predictive equations (ii) to describe male master swimmers body composition; and (iii) to verify the relations of dual x-ray absorptiometry body fat with skinfolds results (whole body and segmented), swimming performance, lean mass and the training level. With this chapter, we intend to contribute to the evolution of the anthropometric assessment of master swimmers, especially in relation to aspects of body composition.

DEVELOPMENT Twenty-two male master swimmers who were actively training (swimming at least three times per week and 2000 m a day) and competing regularly in the last two years took part in this study. They were divided into three groups according to age: equal or less than 39 years, 40 to 49 years and ≥ than 50 years. Participants that abstained from training longer than four weeks or presented injury or sickness that could impair the data collection were excluded. Swimmers were questioned about their best time in competition during the last year and training experience. To compare the swimming competition levels, performance data were transformed according to the Fédération Internationale de Natation (FINA)

Body Fat in Male Master Swimmers

5

points table into points. The data presented in this chapter are part of a larger study previously approved by the local ethics committee and conducted according to the Declaration of Helsinki (1964). Anthropometric measurements were taken in accordance with the International Society for the Advancement of Kinanthropometry (ISAK) protocol [57] by the same researcher (level 1 accredited anthropometrist) to minimize differences in data collection. Body mass was measured with subjects who were shoeless and using swimming suits over a digital standing scale (balance G-Tech Glass 200®, 150 kg capacity and 0.1 kg accuracy). Height (stretched stature) was measured in standing position, feet side-by-side, heels supported and head in the Frankfort plane (portable stadiometer Cescorf®, 3 m capacity and 0.1 cm accuracy). Body mass index was subsequently calculated in kg/m2. Skinfold thickness measurements were made using a calibrated caliper (Cescorf®, 0.1 mm) in eight different sites: triceps, subscapular, biceps, supraspinal, iliac crest, abdominal, front thigh and medial calf. Waist girth was measured with a flexible steel tape (Cescorf®, 0.1 cm accuracy) at the narrowest point between the 10th rib and the top of the iliac crest, perpendicular to the long axis of the trunk. Measurements were taken in duplicate, in a non-consecutive way, and the mean of these values were taken for analysis, unless the difference between values exceeded 1% for height, body mass and girth, and 5% for skinfolds, in which case a third measure was taken, and the median value was used for analysis. The technical error of measurement for each skinfold measurement was calculated from twenty participants of this study and expressed as percentage values: triceps (2.1%), subscapular (2.7%), biceps (3.1%), supraspinal (2.6%), iliac crest (2.2%), abdominal (2.4%), front thigh (2.7%) and medial calf (3.2%). Body composition was assessed through the dual-energy X-ray absorptiometry (dual x-ray absorptiometry; Hologic Discovery W, EUA), using the manufacturer software. Participants were instructed to avoid moderate or intense exercise 24 h before, alcohol 72 h before the test and products with caffeine or calcium-based medications 24 h before the test without previous communication to the research team. In addition, athletes were asked to perform 4 h of fasting before the dual x-ray absorptiometry test but were allowed water consumption, wear light clothes and remove any metallic material (e.g., earrings, bracelets, piercing). During the test, participants were positioned in the supine position, aligned with their upper limbs along the body and centralized on the examination table with hips and shoulders extended to start scanning by X-rays, thus enabling the correct scanning of body composition. Twelve equations were selected and are described in Table 1 Equations inclusion criteria were: matched for age, developed for adult athletes, or being previously validated for adult swimmers. For each body density result, body fat estimation (that corresponds to chemically defined fat/lipids) was assessed using the same equation of the original study [6, 53]. The equation of Withers et al. [63] by the sum of seven skinfolds (7) was derived from the 1987 dataset, but it was not described in the original study. Body fat mass in

6

Cássia Daniele Zaleski Trindade, Paulo Sehl et al.

kilograms resulting from the Mitchell et al. [36] equation was converted to percent body fat using the equation: BF% = (BFM x BM)/100, where BF% refers to percent body fat, BFM refers to absolute body fat mass and BM refers to total body mass (kg). Similarly, adipose body mass in kilograms resulting from the Kerr & Ross [23] equation was converted to percent body adipose. The adipose value corresponds to anatomically defined tissue that contains a lipid fraction and fat free components (water, protein and electrolytes) [32]. Table 1. Skinfolds equations used to estimate body density, body-fat percentage or body fat mass/and adipose body mass Criterion Method Specific Equations Forsyth & UWW Sinning, 1973 [16] Withers et al. UWW 1987 [63] Generalized Equations Durnin & UWW Rahaman, 1967 [11] Durnin e UWW Womersley, 1974 [12]

Petroski, 1995 [42]

UWW

Body fat/or adipose equations Evans et al., 2005 UWW [13] Kerr & Ross, CDA 1988 [23]

Mitchell et al., 2020 [36] Reilly et al., 2009 [47]

DXA DXA

Population

Equations

athletes 19 – 22 y

BD = 1.103 – 0.00168 x (subsc) – 0.00127 x (abdo)

elite athletes mean 17 – 31 y

BD = 1.0988 –0.0004 x (tric + subsc + supraesp + bicp + abdo + thigh + calf)

18 – 34 y

BD = 1.161 – 0.0632 x Log10 (tric + subsc + iliac c. + bicp)

17 – 72 y 30 – 39 y 40 – 49 y 50+ y 18 – 61 y

BD = 1.1765 – 0.0744 x Log10 (tric + subsc + iliac c. + bicp) BD = 1.1422 – (0.0544 x Log (tric + subsc + iliac c. + bicp)) BD = 1.162 – (0.07 x Log (tric + subsc + iliac c. + bicp)) BD = 1.1715 – (0.0779 x Log (tric + subsc + iliac c. + bicp)) BD = 1.10726863 – 0.00081201 (subsc + tric + iliac c.+ calf) + 0.00000212 (subsc + tric + iliac c.+ calf)2 – 0.00041761 (age)

college athletes 18 – 23 y 6 – 77 y

BF = 8.997 + 0.24658 x (abdo + thigh + tric) – 6.343 x (sex) – 1.998 x (race) ABM = (1) Z ADIP= [(tric + subsc + supraesp + abdo + thigh + calf) x (170.18/heigh) – 116.41]/34.79 (2) M ADIP (kg) = [(Z ADIP x 5.85) + 25.6] / (170.18/height)3 BFM = 0.16 x (bd mass) + 8.78 x Loge (tric + subsc + bicp + supraesp + abdo + thigh + calf) – 1.83 x (sex) – 32.77 BF = 5.174 + (0.124 x thigh) + (0.147 x abdo) + (0.196 x tric) + (0.13 x calf)

swimmers 16 – 29 y athletes mean 24 y

Note: y = years; BD = total body density; BF% = body-fat percentage; ABM = adipose body mass; BFM = body fat mass; tric = triceps skinfold; subsc = subescapular skinfold; bicp = biceps skinfold; supraesp = supraespinale skinfold; iliac c. = iliac crest skinfold; abdo = abdominal skinfold; thigh = front thigh skinfold; calf = median calf skinfold. Age in years; sex: male = 1; race = 0; UWW = underwater weighing; DXA = dual-energy X-ray absorptiometry; CDA = cadaver dissection analysis.

The normality of the data distribution of all variables was assessed using the Shapiro– Wilk test. Descriptive statistics are presented in mean ± standard deviation and 95% confidence intervals for all measured variables. ANOVA was applied for age group comparisons. The effect size was obtained from η2 and classified as small (η2 ≥ 0.01),

Body Fat in Male Master Swimmers

7

medium (η2 ≥ 0.06) or large (η2 ≥ 0.14) [8]. A simple sample t-test was used to compare the differences between dual x-ray absorptiometry and skinfolds equations means with the null value; if data showed similarity, then a Bland-Altman analysis was conducted with a linear regression analysis between the average and difference values. Correlation analysis was conducted using the Person correlation coefficient. The IBM SPSS 25.0 statistical package was used and the level of significance was set at 5%.

RESULTS Table 2 shows the anthropometric, training and performance data of Brazilian master swimmers. As age did not have a large effect on body fat, comparisons between dual x-ray absorptiometry and skinfold measurements and equations were made with the whole sample. Except for performance, no differences were found in the group analysis for anthropometric and training variables. However, body mass, BMI, body fat, sum of skinfolds and waist girth had a medium effect size for age. Lean mass and performance had 25 and 55% of their variance explained by age, respectively. Performance was not impacted for lean body mass and body fat (p > 0.05). More than half (54%, n = 12) of the swimmers were engaged in complementary exercise (e.g., running, strength, cycling and tennis) and this was associated with lower body fat percentage (r = - 0.55, p < 0.05). Table 2. Anthropometric, training and performance data of Brazilian master swimmers (n = 22) Group 1 (n = 8) 34.0  3.1 years 80.3  7.4 74.1 – 86.4

Group 2 (n = 7) 45.9  1.8 years 80.1  6.6 73.1 – 87.1

Group 3 (n = 7) 60.3  6.2 years 77.4  5.4 71.7 – 83.1

Height (cm)

181.0  5.2 176.7 – 185.3

175.6  8.1 167.1 – 184.1

176.0  3.7 172.1 – 179.8

BMI (kg/m2)

24.5  2.0 22.8 – 24.1

26.1  2.8 23.1 – 29.1

25.0  1.9 23.0 – 27.1

Body fat DXA (%)

22.3  4.8 18.3 – 26.3

25.5  4.4 20.9 – 30.2

25.4  3.9 21.3 – 29.5

Body lean mass DXA (kg)

59.9  5.7 55.1 – 64.7

58.1  4.6 53.2 – 62.9

55.1  3.1 51.9 – 58.3

6 skinfolds

75.2  21.6 57.2 – 93.3

92.0  15.5 75.7 – 108.3

86.8  19.0 66.8 – 106.7

7 skinfolds

79.7  22.4 61.0 – 98.5

97.4  16.4 80.1 – 114.7

92.0  19.4 71.6 – 112.3

Body mass (kg)

F(gl); p; η2 F(2.18) = 1.20; p = 0.324; η2 = 0.11 F(2.18) = 2.28; p = 0.129; η2 = 0.19 F(2.19) = 0.87; p = 0.435; η2 = 0.08 F(2.19) = 1.26; p = 0.306; η2 = 0.12 F(2.19) = 3.12; p = 0.067; η2 = 0.25 F(2.19) = 1.20; p = 0.324; η2 = 0.11 F(2.19) = 1.50; p = 0.253; η2 = 0.13

8

Cássia Daniele Zaleski Trindade, Paulo Sehl et al. Table 2. (Continued)

Waist girth (cm)

Training experience (y)

Performance (FINA points)

Group 1 (n = 8) 34.0  3.1 years 82.9  3.1 80.4 – 85.5

Group 2 (n = 7) 45.9  1.8 years 87.5  5.7 81.5 – 93.4

Group 3 (n = 7) 60.3  6.2 years 85.7  7.1 78.3 – 93.2

16.2  12.3 5.9 – 26.5

7.3  3.3 3.9 – 10.8

19.8  16.11 2.9 – 36.

366.0  62.2a 314.0 – 418.0

248.5 103.3b 140.1  357.0

191.2  50.8b 137.9 – 244.4[

F(gl); p; η2 F(2.19) = 0.83; p = 0.450; η2 = 0.08 F(2.17) = 1.77; p = 0.200; η2 = 0.17 F(2.20) = 11.33; p = 0.001; η2 = 0.55

Note: DXA = dual x-ray absorptiometry; 6 skinfold = triceps, subscapular, supraspinale, abdominal, front thigh and medial calf; 7 skinfold = triceps, subscapular, biceps, supraspinale, abdominal, front thigh and medial calf; BMI = body mass index. Different letters indicate groups difference, p < 0.05.

Figure 1 shows body fat results, according to skinfold and dual x-ray absorptiometry. There was a positive correlation between the sum of skinfold thickness and body fat per segment: abdominal, supraspinal and iliac crest with trunk (r = 0.61) android (r = 0.62) and gynoid (r = 0.51), front thigh and medial calf with legs (r = 0.60) and biceps and triceps with arms (r = 0.47). Also, the sum of six and seven skinfold thickness was correlated with total body fat (r = 0.65 and 0.64, respectively; all p < 0.05). Figure 2 shows athletes swimming experience. More than half of the master swimmers presented ≤ 10 years of swimming experience. The average age at which they started in the sport was 30.8  15.7 years, 50% (n = 11) started swimming with ≥ 30 years and ~64% (n = 14) with ≥ 25 years.

Figure 1. Body fat thickness results according skinfold and dual x-ray absorptiometry.

Body Fat in Male Master Swimmers

9

Figure 2. Distribution of training experience (n = 21) and performance (n = 22) between ages.

Regarding the skinfold equations agreement with dual x-ray absorptiometry, only the differences between the dual x-ray absorptiometry and the Durnin & Womersley [12] equation (developed for men aged 40 to 49 years) results for body fat were similar to the null (Table 3). The Bland Altman plot analysis is presented in Figure 3, with all results between the limits of the confidence intervals. However, the linear regression analysis between the average and the difference between the methods was significant (p = 0.049). Table 3. Body adiposity differences between methods (dual x-ray absorptiometry and skinfold equations) Equation Specific Equations Forsyth & Sinning, 1973 [16], Brozek Withers et al. 1987 [63], Siri Generalized Equations Durnin & Rahaman, 1967 [11], Siri Durnin & Womersley, 1974 [12], Siri Durnin & Womersley, 1974 (30-39) [12], Siri Durnin & Womersley, 1974 (40-49) [12], Siri Durnin & Womersley, 1974 (50+) [12], Siri Petroski, 1995 [42], Siri Body fat/or adipose equations Evans et al., 2005 [13] Kerr & Ross, 1988 [23](*) Mitchell et al., 2020 [36] Reilly et al., 2009 [47]

* = adipose body mass equation [6, 53]

Adiposity %

Mean difference between methods

p

20.7  5.2 15.5  3.4

3.5  4.2 -8.7  3.4

0.001