Stewart, Stewart equation For male athletes, the Pearson's correlations between the reference 3C model and alternate methods ranged from 0. Figure 5. Validity of fat-free mass estimates in male athletes.

Bland—Altman analysis indicated that proportional bias was present for the following methods: 3C Field, SFBIA Tanita , and the skinfold equations of Devrim-Lanpir 26 , Durnin and Womersley 38 , Evans 3-site and 7-site equations 1 , Forsyth 34 , Jackson and Pollock 3-site and 7-site equations 32 , 33 , Katch equation 35 , Lohman equation 16 , 36 , and Thorland equation 16 , 37 Figure 6.

Figure 6. Bland—Altman analysis of fat-free mass estimates in male athletes. As minimal wrestling weight is calculated using measures derived from FFM estimates, the MWW results see SDC1 for results regarding differences in MWW based upon skinfold prediction equation and impedance analysis device used are presented in Supplementary Materials only see SDC2 for Table S5 : Minimum Wrestling Weight Estimates for Male and Female Athletes and SDC3 Figure S7.

Comparison of Minimum Wrestling Weight Values in Female Athletes ; SDC4 Figure S8. Validity of Minimum Wrestling Weight Estimates in Female Athletes ; SDC5 Figure S9. Bland—Altman Analysis of Minimum Wrestling Weight Estimates in Female Athletes. Comparison of Minimum Wrestling Weight Values in Male Athletes.

Validity of Minimum Wrestling Weight Estimates in Male Athletes ; and SDC8 Figure S Bland—Altman Analysis of Minimum Wrestling Weight Estimates in Male Athletes. The current study had two primary aims: A to determine the most accurate skinfold prediction equations for young male and female athletes using a three-compartment model of body composition assessment; and B to examine the utility of alternative modes of body composition assessment compared to criterion measures.

This is the first study to examine the validity of skinfold prediction equations in young male and female athletes. The main findings indicate multiple discrepancies in FFM estimates for female and male athletes when compared to the 3C model.

In females, The Evans 3 and 7-site, Forsyth, and Jackson and Pollock 3-site SKF prediction equations performed best, while the Evans 3-site equation appeared to perform best when determining FFM in male athletes.

Additionally, the field 3C model can provide a suitable alternative measure of FFM for both male and female athletes when laboratory-grade criterion measures are not available.

In females, the SKF prediction equations of Devrim-Lanpir 26 , Durnin and Womersley 38 , Jackson and Pollock 7-site 33 , Katch 35 , Loftin 42 , Lohman 16 , 36 , Slaughter 43 , and Thorland 16 , 37 differed from the 3C model Figure 1. In the context of wrestling and MWW determination, this suggests the estimates of FFM and subsequently MWW are likely to fall within the limits of each weight class division often in 5.

However, this could impact wrestlers who are on the threshold of a certain MWW and weight class. There was evidence of proportional bias for the skinfold equations of Durnin and Womersley 38 , Evans 3-site and 7-site equations 1 , Jackson and Pollock 3-site and 7-site 32 , 33 , Katch 35 , Loftin 42 , Lohman 16 , 36 , Slaughter 43 , and Thorland 16 , 37 Figure 3.

Collectively, these findings indicate the Evans 7-site equation appears to perform best among SKF prediction equations for female athletes when determining FFM. This could potentially allow a female wrestler to compete in a lower weight class than what would be allowed if FFM was assessed more accurately.

Among the remaining body composition assessment modalities, no differences were observed between 3C Field, ADP [both Siri 44 and Brozek 45 equations], nor the UWW Brozek and Siri equations compared to the criterion 3C model when determining FFM for females.

The 3C Field resulted in a mean difference SEE of 0. However, there was proportional bias for the 3C Field, indicating that the model tended to overestimate FFM in those with low FFM levels but underestimate FFM in those with higher FFM. However, it should also be noted that the performance of the Field 3C model is dependent upon the field methods used to estimate D b and TBW, so alternate versions of this model may produce dissimilar results.

The 3C Field, UWW [both Siri 44 and Brozek 45 equations], ADP [both Siri 44 and Brozek 45 equations] all demonstrated equivalence with the reference 3C model. However, there was also proportional bias for the F2FBIA Tanita , and BIS, which again indicates a tendency to overestimate measures of FFM in those with higher FFM.

In male athletes, the FFM values derived from the SKF equations of Devrim-Lanpir 26 and Jackson and Pollock both 3-site and 7-site equations 32 differed from the 3C model Figure 4 while proportional bias was present for the Devrim-Lanpir 26 , Durnin and Womersley 38 , Evans 3-site and 7-site 1 , Forsyth 34 , Jackson and Pollock 3-site and 7-site 32 , 33 , Katch 35 , Lohman 16 , 36 , and Thorland equations 16 , 37 Figure 6.

The current MWW certification process for high school boys wrestling in Wisconsin utilizes the Lohman equation, which comparatively, resulted in a mean difference SEE of 0. The Field 3C model resulted in a mean difference SEE of 1. However, proportional bias was present for the 3C Field, with a tendency to overestimate FFM in those with lower FFM but underestimate FFM in those with higher FFM.

Proportional bias was present for the F2FBIA Tanita indicating greater underestimation of FFM values in those with higher FFM. FFM was underestimated for most males by Tanita and became more pronounced as FFM increased as indicated by the negative slope of the Bland—Altman line Figure 6.

Previous research in college-age men 25 reported discrepancies in MWW values with SEEs of 3. Clark et al. However, the authors 58 reported large individual differences and systematic bias across the range of MWW values. Additionally, the BIA was able to predict MWW within 3.

Others reported no differences in MWW from UWW The UWW and SKF exhibited the highest degree of precision lowest SEE with SEE values of 1. In most high school settings, SKF is likely the modality of choice because of its low cost and ease of use. Conversely, Clark et al.

In high school wrestlers, the Lohman SKF equation was found to be a valid measure of FFM with a SEE of 2. Furthermore, impedance devices may have limitations with athletic populations, as previous research has indicated that generalized impedance-based equations underestimate body fluids in athletes, potentially influencing measures of FFM.

Future investigations in a large, mixed-sex group could provide new equations SKF and impedance for estimating FFM in youth athletes. Results from the current study indicate the Evans 7-site and 3-site SKF equations performed best for female and male athletes, respectively.

The current MWW certification process for girls' high school wrestling in Wisconsin does not appear to utilize the best SKF prediction equation available for this population. This could permit a female wrestler to compete in a lower weight class than what would be allowed if FFM was assessed more accurately.

For male wrestlers in Wisconsin, the Lohman equation is currently used, which provided an adequate estimate of FFM yet was not the best performing SKF prediction equation. The field 3C model can provide a suitable alternative measure of FFM for both male and female athletes when laboratory-grade criterion measures are not available.

The datasets associated with the current manuscript are not readily available as additional analysis is pending. Partial data may be available upon request. The studies involving humans were approved by University of Wisconsin—La Crosse. The studies were conducted in accordance with the local legislation and institutional requirements.

Conceptualization, AJ, GT, CD, JL, and JE; methodology, AJ, GT, CD, JL, and JE formal analysis, AJ, and GT; data collection: AJ, AA, CK, CD, MK writing—original draft preparation, AJ, GT, BM, AA, CK, CD, MC, JL, JE, JF, and MJ; writing—review and editing, AJ, GT, BM, AA, CK, CD, MC, JL, JE, JF, and MJ; project administration, AJ, CD, JL, and JE.

The authors declare that the results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. All authors contributed to the article and approved the submitted version.

This project was supported from an internal grant from Mayo Clinic Health System and the University of Wisconsin—La Crosse. GT has received support for his research laboratory, in the form of research grants or equipment loan or donation, from manufacturers of body composition assessment devices, including Size Stream LLC; Naked Labs Inc.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers.

Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Validity of the current NCAA minimum weight protocol: a brief review. Ann Nutr Metab. Lohman T. Advances in body composition assessment: current issues in exercise scienc e.

Champaign, IL: Human Kinetics Morrow JR, Fridye T, Monaghen SD. Generalizability of the AAHPERD health related skinfold test. Research Q Exerc Sport. Oppliger RA, Clark RR, Kuta JM. Efficacy of skinfold training clinics: a comparison between clinic trained and experienced testers.

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Arch Dis Child. Some sports have certain physique traits associated with them, such as gymnastics or diving.

In endurance sports such as marathons and cycling, lower body fat levels enhance efficiency and heat dissipation. In other sports with weight classes such as rowing or boxing, lower body fat levels will help enhance power to weight ratio compared to smaller, less muscular opponents.

In team sports such as rugby, different positions on the field have different roles requiring different body compositions to effectively be able to achieve their roles. Ongoing skinfold assessments are the most effective tool for monitoring body composition changes over time, and as an athlete, are just one of your tools that you can use to assess your progress in performance.

If you are interested in knowing more about skinfold assessments, having yourself assessed, or genrally interested in getting a strategic nutrition plan to support your training and lifestyle goals, contact our Sports Dietitian Kelsey Hutton, who will be happy to help.

Kelsey can be contact by email at: kelsey. h precisionathletica. au or you can book to see her for a one-on-one below. Skinfold Assessments — why we use them and you should too. Who should have their skinfolds done? BOOK NOW.

Air displacement plethysmography: validation in overweight and obese subjects. Obesity Research , 13 7 , pp. Peterson, M.

and Siervogel, R. Development and validation of skinfold-thickness prediction equations with a 4-compartment model. The American Journal of Clinical Nutrition , 77 5 , pp. Evans, E. and Arngrímsson, S. Skinfold prediction equation for athletes developed using a four-component model.

Medicine and Science in Sports and Exercise , 37 11 , pp. López-Taylor, J. and Torres-Naranjo, F. Accuracy of Anthropometric Equations for Estimating Body Fat in Professional Male Soccer Players Compared with DXA.

Journal of Sports Medicine , Silva, A. and Sardinha, L. Are skinfold-based models accurate and suitable for assessing changes in body composition in highly trained athletes?. Shakibaee, A. and Asgari, A. How accurate are the anthropometry equations in in Iranian military men in predicting body composition?.

Asian Journal of Sports Medicine, 6 4. and Falvey, E. Application of a sub-set of skinfold sites for ultrasound measurement of subcutaneous adiposity and percentage body fat estimation in athletes.

International Journal of Sports Medicine, 37 05 , pp. Application of a Sub-set of Skinfold Sites for Ultrasound Measurement of Subcutaneous Adiposity and Percentage Body Fat Estimation in Athletes.

Müller, W. and Ahammer, H. Body composition in sport: a comparison of a novel ultrasound imaging technique to measure subcutaneous fat tissue compared with skinfold measurement. British Journal of Sports Medicine, 47 16 , pp.

and Schwartz, S. A-mode and B-mode ultrasound measurement of fat thickness: a cadaver validation study. European Journal of Clinical Nutrition , p. Civar, S. and Ayceman, N. Validity of leg-to-leg bioelectrical impedance measurement in highly active males.

Biology of Sport, 20 3 , pp. Wilmore, J. and Behnke, A. An anthropometric estimation of body density and lean body weight in young men. Journal of Applied Physiology, 27 1 , pp.

Reilly, T. and Wallace, J. How well do skinfold equations predict percent body fat in elite soccer players?.

International Journal of Sports Medicine , 30 08 , pp. Withers, R. and Norton, K. Relative body fat and anthropometric prediction of body density of male athletes.

European Journal of Applied Physiology and Occupational Physiology , 56 2 , pp. Suarez-Arrones, L. and Méndez-Villanueva, A. Deurenberg, P.

and Seidell, J. Body mass index as a measure of body fatness: age-and sex-specific prediction formulas. British Journal of Nutrition , 65 2 , pp. Faulkner, J. Physiology of swimming. Research Quarterly. American Association for Health, Physical Education and Recreation , 37 1 , pp. Zemski, A.

and Slater, G. Pre-season body composition adaptations in elite Caucasian and Polynesian rugby union athletes. International Journal of Sport Nutrition and Exercise Metabolism , pp.

Longitudinal changes in body composition assessed using DXA and surface anthropometry show good agreement in elite rugby union athletes. International Journal of Sport Nutrition and Exercise Metabolism , 20 XX , pp. Aandstad, A. and Anderssen, S.

Validity and reliability of bioelectrical impedance analysis and skinfold thickness in predicting body fat in military personnel. Military Medicine, 2 , pp. Nagy, E. and Moreno, L. Harmonization process and reliability assessment of anthropometric measurements in a multicenter study in adolescents.

International Journal of Obesity, 32 S5 , p. Lozano-Berges, G. and Vicente-Rodríguez, G. Assessing fat mass of adolescent swimmers using anthropometric equations: a DXA validation study.

Research Quarterly for Exercise and Sport, 88 2 , pp. Fonseca-Junior, S. and Pierucci, A. Validity of skinfold equations, against dual-energy x-ray absorptiometry, in predicting body composition in adolescent pentathletes.

However, ultrasound measures the subcutaneous fat tissue thickness in a decompressed state i. single layer , whereas skinfold assessment requires pinching of the skin and subsequent measurement of the same tissue in a compressed state i.

double layer. Using a prediction equation, US estimates the breakdown of 1 lean mass, and 2 fat mass, inside the body. López-Taylor recently investigated 31 different anthropometric equations against DXA in male soccer players of varying ethnicities [19].

Of these 31 equations, 14 and 17 were developed in athletic, and nonathletic populations, respectively. In general, the equations developed in athletes that had the highest agreements with DXA, with an equation by Civar et al. Ironically, an equation using a mere two skinfold sites abdomen and thigh developed in male nonathletes by Wilmore and Behnke [27] was more closely related with DXA, compared with the other equations developed in athletes.

The results of this study differ from those obtained from anthropometric comparisons in other male soccer players. In 45 professional male soccer players from the Premier League [28], a 7-site skinfold equation developed by Withers et al.

Recently, Suarez-Arrones et al. With the exception of one equation created by Deurenberg et al. in [31], and BIA via a Tanita device, body fat percentages derived from all skinfold equations had moderate or strong relationships with the body fat percentages derived via DXA [30].

However, the strength of the relationships differed among equations used, with an equation developed in by John Faulkner [32] having the strongest relationship with DXA [29]. The results from these studies demonstrate the lack of agreement between equations, and inconsistent outcomes when compared with more precise body composition assessment methods, such as DXA.

As demonstrated by Zemski et al. Substantial intra- and inter-observer variability exists [35, 36]. For example, varying the skinfold site by as little as 1 centimeter can produce significantly different results when experienced practitioners measure the same participant [7, 40].

The research regarding which skinfold equation s most accurately predict body fat percentage in athletes is inconsistent, at best. Factors including age, sport, race, gender, and others, appear to impact equation validity.

However, skinfold assessment can also be quite reliable and should be considered as a convenient, practical indicator of intra-individual regional and total body composition change over time. Although 3-site and 7-site skinfold equations are similar in accuracy, I lean towards collecting data on more sites.

In the case that a novel, highly accurate equation is developed, the practitioner will be better suited to apply the novel, more accurate equation with his or her data set.

Here are a few major advantages and disadvantages of skinfolds testing:. Skip to content Resources to Optimize Athletic Performance and Sports Sciences. Grey boxes are summary points Blue boxes give more detail about key terms or subjects How Skinfold Assessment Works Anthropometry involves the measurement of body dimensions, which can include height, weight, length, width, circumference, and skinfold thickness [1].

Ackland et al. Current status of body composition assessment in sport. Sports Medicine , 42 3 , pp. Where it All Began Given skinfold assessment simplicity and lack of required technology, it has been used to predict body density and total body fat for a long time. The New Age of Skinfold Equations and 3 vs.

An Ultrasound Teaser Despite the advancements in skinfold testing, new research using ultrasound US imaging techniques shows that any caliper-based skinfold assessment method lacks validity relative to its US-based counterpart [].

Suarez-Arrones et al. Body fat assessment in elite soccer players: cross-validation of different field methods. Science and Medicine in Football , pp. Summary The research regarding which skinfold equation s most accurately predict body fat percentage in athletes is inconsistent, at best.

Here are a few major advantages and disadvantages of skinfolds testing: Advantages Disadvantages High reliability if the tester is experienced and consistent Low validity, and very low validity in larger subjects Low cost Tester expertise required Quick to execute High inter-tester variability i.

reliability can be poor when the tester does not remain the same Minimal equipment and subject participation required Most skinfold calipers have an upper limit of 45—60 mm, limiting their use to moderately overweight subjects No technology necessary Prediction equations may only be valid in the population in which they are derived Allows for regional body fatness assessment Some subjects may feel uncomfortable stripping down to bare skin in front of the tester References Fosbøl, M.

and Zerahn, B. Contemporary methods of body composition measurement. Clinical Physiology and Functional Imaging , 35 2 , pp.

Wagner, D. and Heyward, V. Techniques of body composition assessment: a review of laboratory and field methods. Research Quarterly for Exercise and Sport, 70 2 , pp. Meyer, N. and Müller, W. Body composition for health and performance: a survey of body composition assessment practice carried out by the Ad Hoc Research Working Group on Body Composition, Health and Performance under the auspices of the IOC Medical Commission.

British Journal of Sports Medicine , pp. Harrison, G. and Wilmore, J. Skinfold thicknesses and measurement technique. Anthropometric Standardization Reference Manual, , pp. Heyward, V. Evaluation of body composition. Sports Medicine, 22 3 , pp. Olds, T. and Marfell-Jones, M.

International standards for anthropometric assessment. Potchefstroom ZA : International Society for Advancement of Kinanthropometry. Ackland, T. Wang, J. and Pierson, R. Anthropometry in body composition: an overview. Annals of the New York Academy of Sciences , 1 , pp. Edwards, D. Observations on the distribution of subcutaneous fat.

Clinical Science , 9 , pp. Keys, A. and Brozek, J. Body fat in adult man. Physiological Reviews , 33 3 , pp. Jackson, A. and Pollock, M. Generalized equations for predicting body density of men.

British Journal of Nutrition , 40 3 , pp. and Ward, A. Generalized equations for predicting body density of women. Medicine and Science in Sports and Exercise, 12 3 , pp.

Durnin, J. and Womersley, J. Body fat assessed from total body density and its estimation from skinfold thickness: measurements on men and women aged from 16 to 72 years.

British Journal of Nutrition , 32 1 , pp. Biaggi, R. and Chen, K. Comparison of air-displacement plethysmography with hydrostatic weighing and bioelectrical impedance analysis for the assessment of body composition in healthy adults—.

The American Journal of Clinical Nutrition , 69 5 , pp. Gately, P. and Wright, A. Comparison of body composition methods in overweight and obese children. Journal of Applied Physiology , 95 5 , pp. Ginde, S. and Heymsfield, S.

Air displacement plethysmography: validation in overweight and obese subjects. Obesity Research , 13 7 , pp. Peterson, M. and Siervogel, R. Development and validation of skinfold-thickness prediction equations with a 4-compartment model.

The American Journal of Clinical Nutrition , 77 5 , pp. Evans, E. and Arngrímsson, S. Skinfold prediction equation for athletes developed using a four-component model. Medicine and Science in Sports and Exercise , 37 11 , pp.

López-Taylor, J. and Torres-Naranjo, F. Accuracy of Anthropometric Equations for Estimating Body Fat in Professional Male Soccer Players Compared with DXA. Journal of Sports Medicine , Silva, A.

and Sardinha, L. Are skinfold-based models accurate and suitable for assessing changes in body composition in highly trained athletes?. Shakibaee, A. and Asgari, A. How accurate are the anthropometry equations in in Iranian military men in predicting body composition?.

Asian Journal of Sports Medicine, 6 4. and Falvey, E. Application of a sub-set of skinfold sites for ultrasound measurement of subcutaneous adiposity and percentage body fat estimation in athletes. International Journal of Sports Medicine, 37 05 , pp. Application of a Sub-set of Skinfold Sites for Ultrasound Measurement of Subcutaneous Adiposity and Percentage Body Fat Estimation in Athletes.

Müller, W. This method relies on the measurer remaining consistent over time, which due to human error can be difficult. However, it is achievable, if they are well trained and experienced.

ISAK The International Society of the Advancement of Kinanthropometry has developed international standards for measuring anthropometry and is a reliable qualification [CG2] [LT3] to look out for.

This makes sure that the same sites and techniques are used every time. This is crucial, as even measuring just 1 cm out from the first site gives significant differences in the body composition calculations 5. This could be due to the skinfold measures themselves or the variance in the calculations used, or a combination of the two.

This is why practitioner training is crucial for accurate results. A bod pod is an egg-shaped device that you sit inside of. It uses air displacement plethysmograph to measure FM and FFM.

Underwater weight is measured while fully submerged in water and expelling all air inside of the lungs. As bone and muscle have a greater density than water and fat has a lower density than water someone with larger amounts of FFM will weigh more while in water. Underwater weight, out of water weight and the density of the water and residual volume of the lungs is used to calculate body density and estimate FM and FFM.

This technique may be the most accurate, however, it is very unpractical with few places offering the service. Researchers are still comparing these methods to determine which is the most accurate. It is hard to say as each comes with its pros and cons, and many factors that could be influencing the accuracy.

Short answer, No. So now to explain what body fat percentage actually is. When using skinfolds, the sum of up to 8 skinfold measurements is used in specific equations to give the best estimate of your current composition in mm. BIA and DXA scans, also use equations based on the data from the scans.

To start, body fat plays an important role in keeping our bodies functioning and well. Women, in particular, need more body fat. Body fat is also a key source of energy and important hormones. As mentioned earlier it is important to keep in mind that these calculations are estimates only.

Therefore, your body fat percentage is probably not that accurate. It can be daunting reading into how much body fat you have or being classified into a certain body type. There is too much pressure being put on athletes and now the general population to be a certain skin fold or body fat percentage.

Although this is drastically changing in professional sport, it, unfortunately, is lagging with age group athletes and in the general population. This is why I urge you not to worry about what the numbers say.

When discussing body fat percentage, it is important that is not only 1, probably inaccurate, but also 2, it could trigger or lead to other psychological issues in individuals that may already be feeling insecure.

Getting skinfolds or a scan done, can help to track changes made throughout training. These measurements can be good for creating a discussion , and possibly determine if any weight change is predominately muscle gain or if any weight lost is predominately fat loss. Many coaches and health professionals working with athletes may want to measure body comp for an indication of how their support is working for their athlete.

Then, some of you just like having the numbers. That is great, but this is definitely not the only way to measure your progress. It is important to focus on how you and your body are feeling during your training first. Have you progressed with your training? Are you hitting your performance targets?

Are you getting sick or injured often? Is your menstrual cycle regular? These questions are far more important than any number.

However, if you are getting body composition measured, remember the following:. There is some error with whatever method of measuring you decide to use. Your results are a good guide, but they should not be taken as a be all and end-all.

The time of day, what you have had to eat and drink can affect your results. It is best to measure at the same time on every occasion and consider eating similar foods prior to being measured.

For those measuring body composition, it is important to know what the person's perception of their body is. If they have body image issues, we advise caution with the language you use.

Changing your diet to alter your body composition may harm your health and performance. This will probably improve your performance beyond any number. Gibson, C. Body image among elite rugby union players. Gomes AC, et al. Body composition assessment in athletes: Comparison of a novel ultrasound technique to traditional skinfold measures and criterion DXA measure.

Journal of Science and Medicine in Sport. Comparisons of accuracy of estimating percent body fat by four bioelectrical impedance devices with different frequency and induction system of electrical current. The Journal of Sports Medicine and Physical Fitness ;55 Moon J.

Body composition in athletes and sports nutrition: an examination of the bioimpedance analysis technique. European Journal of Clinical Nutrition. Hume, P.