Skinfold measurement in sports science -
Age is always in years. As skinfold calipers are quick, easy-to-use, and very affordable for estimating body fat percentage, they have become more widely used over the years This has happened despite newer techniques such as DEXA , magnetic resonance imaging MRI , computerized tomography CT , and bioelectrical impedance analysis BIA all having been developed One study by Eston et al.
Furthermore, skinfolds tended to under predict body fat percentage as compared to DEXA , revealing that DEXA and skinfold could not be used interchangeably.
According to this study, and others 6, 9 , skinfolds may have a significant bias at extremes of body fat and age. The best use of skinfolds seems to be their raw values i.
the summation of all measurement sites in millimetres , rather than their ability to predict total body fat percentage because there are errors associated with the accuracy of the collection of the raw data, and error in assumptions in the final values Raw skinfold data can give us a good idea of the regional fatness, unlike other measures like BMI or circumference measures alone 8, For some populations, such as athletic populations, where the difference of one percentage point of body fat can make a difference in performance, skinfolds are likely more important For overweight or obese populations, taking skinfolds may be of less use, as accuracy and reliability of the skinfold measurements will be harder to repeat as the skinfold thickness increases, so methods like DEXA may be more accurate 5.
Other studies, for example on obese children, have found good agreeance between skinfolds and percent fat measured by DEXA 22 , however, considerations based on the population being measured must be addressed by each case separately.
In anthropometry, technical error of measure TEM is what we refer to the error that occurs when a measurement is taken on the same object more than once, and the values are not the same. This error is inherent especially when humans are involved in the measurements, due to:. We want to minimise the error in our measurement as much as possible to create the most accurate and reliable measurement possible each time, but all errors cannot usually be removed To minimise these factors, it is best that we control as many factors as possible, and use the same tester, the same location, the same time of day and day of the week, and a consistent schedule throughout the week in training and diet Because we know the error is associated with the measurements, practitioners should always express their measures as a value with the technical error, so that when measuring change over time, we can be more certain of real change versus errors made in measuring.
To calculate the technical error, use the following equations, outlined in a paper by Perini et al. Table 1. Acceptable levels for intra- and inter-evaluator error, according to a beginner Level 1 ISAK versus a skilful anthropometrist Level 4 ISAK Finally, to make measurements of body composition more accurate, ensure the use of predictive body fat percentage equations that best match the demographic of the persons tested.
Generally, the understanding of the use of skinfold calipers and their accuracy is very poor and grossly misunderstood. Given this, our mission was to clarify whether skinfolds are a good method of choice for body composition. In conclusion, skinfold calipers can be a cost-effective, quick, and relatively accurate measure of body composition over time.
While the gold standard for body composition is still cadaver dissection, skinfold measurements can offer information about the relative fatness, the change in body composition over time, and potentially even the health of the individual. Knowing that increased fat mass is associated with various diseases, and some athletes need specific body fat percentages for optimal performance, it is of importance that fitness professionals measure skinfolds accurately and with the ability to be repeatable, following the ISAK for best results.
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References Alva, M. Arq Sanny Pesq Saúde, 1 2 ; Armstrong, L. Assessing Hydration Status: The Elusive Gold Standard. Journal of the American College of Nutrition , 26 sup5 , S—S.
Kinanthropometry and Sport Practice. Universita degli Studi di Ferrara. Burke, L. Nutrition Strategies for the Marathon Fuel for Training and Racing, 37 , — Donini, L. How to estimate fat mass in overweight and obese subjects.
International Journal of Endocrinology , , 1—9. Evaluation of body composition using three different methods compared to dual-energy X-ray absorptiometry. European Journal of Sport Science , 9 3 , — V, Charlesworth, S. Prediction of DXA-determined whole body fat from skinfolds: importance of including skinfolds from the thigh and calf in young, healthy men and women.
European Journal of Clinical Nutrition , 59 5 , — Reliability and validity of bioelctrical impedance in determining body composition. Journal of Applied Physiology , 64 2 , — Lean, M. Predicting body composition by densitometry from simple anthropometric measurements.
AMerican Journal of Clinical Nutritiom , 63 , 4— Norton, K. Anthropometrica: A Textbook of Body Measurement for Sports and Health Courses. Australian Sport Commission, Ed. Sydney, Australia. a, de Oliveira, G. Technical error of measurement in anthropometry. Revista Brasileira de Medicina Do Esporte , 11 , 81— A physical profile of elite female ice hockey players from the USA.
Body fat measurement in elite sport climbers: Comparison of skinfold thickness equations with dual energy X-ray absorptiometry. A slope significantly different from zero indicates proportional bias. See text for more information.
and the SKF equations of Devrim-Lanpir 26 and Jackson and Pollock both 3-site and 7-site equations 32 Figure 4. Figure 4. Comparison of fat-free mass values in male athletes. See Figure 1 caption for abbreviations. 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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In a previous blog we explored how different techniques measure body composition. Here sciebce are going to look at how valid and reliable these measures actually Optimizing immune health. It is Immune system energizers noting messurement the main scifnce Optimizing immune health all body composition assessments is that they are based on assumptions. The only truly accurate way to assess body composition is cadaver analysis i. In this article we will focus on the 3 most used methods to measure body composition: Dual X-ray absorptiometry DXAskinfolds and bio electrical impedance measurements BIA. Air displacement measurements Bodpod is only mentioned in the infographic. It is therefore very important measurements are standardised as much as possible.Video
Skin Fold Measurements Intoduction: To Sinfold skinfold SKF equations, impedance devices, and Greek yogurt dressings plethysmography ADP for the meassurement of fat-free mass FFM. A 3-compartment 3C Optimizing immune health i. Validity metrics were examined to establish each method's performance. Meaxurement impedance Wcience BIAbioimpedance spectroscopy BISand the SKF equations of Devrim-Lanpir, Durnin and Womersley, Jackson and Pollock 7-siteKatch, Loftin, Lohman, Slaughter, and Thorland differed from criterion. Results: For females, Pearson's correlations between the 3C model and alternate methods ranged from 0. For males, Pearson's correlations between the 3C model and alternate methods ranged from 0. For SKF, the Evans 3-site equation performed best with a mean difference of 1.
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