Exclusion criteria included pregnancy or breastfeeding, and currently being treated for or diagnosed with a cardiac, respiratory, circulatory, autoimmune, musculoskeletal, metabolic, hematological, neurological, or endocrine disorder or disease.

The study was conducted according to the Declaration of Helsinki guidelines, and procedures were approved by the University's Institutional Review Board for use of human subjects in research.

Body mass and height were initially assessed using a self-calibrating physician's scale and stadiometer to the nearest 0. Skinfold measures were conducted three times to the nearest 0. Skinfold technician test-retest reliability in the current study was ICC: 0.

Residual volume was determined in the UWW tank with subjects immersed at shoulder level using a closed-circuit oxygen dilution method Prior to each test, the system was calibrated, and the rebreathing bag was flushed out with oxygen and emptied with a vacuum pump.

An electronic nitrogen analyzer Med Science Nitralyzer, Needham Heights, MA was used to measure gas exchange while the subject was inspiring and expiring through the bag for multiple cycles. Next, the subject was instructed to place a nose clip on and to seal their lips tightly around the mouthpiece and breathe normally.

The subject was then instructed to forcefully expire as much air as possible. When the subject expired all their air, they signaled the technician, and then a valve was opened, which connected the subject to the rebreathing bag.

Once connected, the subject was instructed to deeply breathe in, followed by deep, rapid breaths in and out until an equilibrium was displayed on the electronic dashboard.

The residual volume was then calculated using the following equation from Wilmore 27 :. Electronic load cells suspended an underwater chair to assess the subject's weight underwater. An automated computer program converted the voltage measured at the load cell into weight in kilograms.

The computer used an average of readings per trial to determine a value that represented the subject's weight while submerged in the water. The UWW weighing chair was calibrated prior to each test. Following determination of residual volume, the subject stepped off the chair placed their back against the side of the tank with the water level at the neck.

With the subject off the chair, and motionless, the computer zeroed the UWW chair. After calibration, the weights were removed, and the subject assumed the position in the UWW chair. Once air bubbles stopped appearing, the computer recorded the weight and the technician tapped on the side of the tank, signaling to the subject to come up for air.

This procedure was repeated 5—10 times in order for the subject to produce a consistent UWW with an average of 2—4 trials within 0. Body composition variables i. Athletes were instructed to wear spandex or form-fitting clothing and wore a lycra swim cap.

All jewelry was removed prior to testing. Thoracic gas volume was predicted using manufacture settings. Whole body SFBIA measurements were assessed using a 50 kHz device Quantum IV, RJL systems, Clinton MI to determine resistance R , which was used to estimate body composition through select validated equations as later described.

Total body water TBW , extracellular water ECW and intracellular water ICW were assessed using BIS SFB7, ImpediMed, Carlsbad, CA with measurement frequencies to model the fluid content of the body by obtaining total body water estimates.

BIS utilizes Cole modeling 8 and mixture theories 9 to predict body fluids rather than regression equations used by BIA techniques. These SFBIA and BIS measurements were taken with the participant in the supine position prior to assessment using manufacturer-recommended hand-to-foot electrode arrangement.

Alcohol wipes were used prior to placement of the adhesive electrodes. Body composition was also assessed using a consumer-grade MFBIA device, the H20N scale InBody Inc.

Subjects completed two measurements on each device with an average of the two used for analysis. Data from UWW, ADP, and SKF were used in several body composition estimation equations.

Body composition estimates from F2FBIA Tanita , MFBIA InBody , and BIS ImpediMed were used, along with additional estimates obtained using SFBIA RJL raw bioimpedance in two FFM prediction equations Table 1. For the criterion 3C model, D b was taken from UWW, TBW was taken from BIS, and BM was taken from the calibrated scale.

A field-based 3C model, D b was taken from SKF, TBW was estimated from bioelectrical resistance from SFBIA RJL , and BM was taken from the calibrated scale. The TBW estimate from SFBIA RJL was obtained using the Matias et al.

equation 46 :. Two estimates were produced using the Siri 3-compartment model equation 47 :. This 3-compartment model has been previously used in college-aged men and women, with a total error of measurement value of 0.

MWW was estimated as:. Separate analyses were performed for males and females. The same statistical analysis procedures were performed for fat-free mass and minimal wrestling weight estimates.

To determine which methods differed from the criterion method 3C model with UWW D b and BIS TBW , a one-way analysis of variance ANOVA test with repeated measures was performed, with the body composition assessment method specified as a within-subjects factor.

Significant effects were followed up with pairwise t -tests, with the criterion 3C model specified as the reference group and using the Holm adjustment for multiple comparisons.

This analysis was performed using the rstatix package for R v. Additionally, equivalence testing was performed to determine which methods were statistically equivalent to the criterion method.

The mean difference between the criterion and alternate methods was also calculated. Correlations between the criterion method and alternate methods were established using Pearson's r and Lin's concordance correlation coefficient CCC 53 , The standard error of the estimate SEE was estimated via regression procedures.

All analyses were performed in R v. equation 56 ], BIS, and the SKF 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 Figure 1.

Figure 1. Comparison of fat-free mass values in female athletes. Estimates were compared using one-way analysis of variance with repeated measures. The significant effect of method was followed up with pairwise t -tests, using the 3C model as the reference group.

The Holm adjustment was performed to correct for multiple comparisons. See footnote on Table 1 for abbreviations. For female athletes, the Pearson's correlations between the reference 3C model and alternate methods ranged from 0. Figure 2. Validity of fat-free mass estimates in female athletes.

Each specified method was compared to the reference 3-compartment 3C model. The Pearson's correlation r , Lin's concordance correlation coefficient CCC , and standard error of the estimate SEE are displayed.

Bland—Altman analysis indicated that proportional bias was present i. Figure 3. Bland—Altman analysis of fat-free mass estimates in female athletes.

The diagonal line indicates the linear relationship between the difference between methods y and the average of the methods x. 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.

However, in order to minimize such errors, we adopted the following strategies: use of good quality skinfold caliper, all anthropometrics were extensively trained in volunteer older adults, measures were taken three times and we provided previous orientation about hydration status.

BIA and SF equation showed a strong level of concordance to estimate body fat percentage in all participants and among women when compared to our standard reference i. A strong level of concordance was observed between DXA and the anthropometric equation developed by Durnin and Womersley in men, while BIA had a moderate concordance in this group.

Future research in older adults should consider various methods, different ethnic groups and the development of new SF equations. NCD Risk Factor Collaboration NCD-RisC L, Abdeen ZA, Hamid ZA, Abu-Rmeileh NM, Acosta-Cazares B, Acuin C, et al.

Worldwide trends in body-mass index, underweight, overweight, and obesity from to a pooled analysis of population-based measurement studies in ·9 million children, adolescents, and adults.

Lancet London, Engl. Elsevier; ; — da Silveira EA, Vieira LL, Jardim TV, de Souza JD, da Silveira EA, Vieira LL, et al. Obesity and its association with food consumption, diabetes mellitus, and acute myocardial infarction in the elderly.

Arq Bras Cardiol. Article PubMed PubMed Central Google Scholar. Echouffo-Tcheugui JB, Short MI, Xanthakis V, Field P, Sponholtz TR, Larson MG, et al. Natural history of obesity sub-phenotypes: dynamic changes over two decades and prognosis in the Framingham heart study.

J Clin Endocrinol Metab. Hubert HB, Feinleib M, McNamara PM, Castelli WP. Obesity as an independent risk factor for cardiovascular disease: a year follow-up of participants in the Framingham heart study.

Article CAS Google Scholar. Reilly JJ, El-Hamdouchi A, Diouf A, Monyeki A, Somda SA. Determining the worldwide prevalence of obesity. Article PubMed Google Scholar. Okorodudu DO, Jumean MF, Montori VM, Romero-Corral A, Somers VK, Erwin PJ, et al.

Diagnostic performance of body mass index to identify obesity as defined by body adiposity: a systematic review and meta-analysis. Int J Obes. Padwal R, Leslie WD, Lix LM, Majumdar SR.

Relationship among body fat percentage, body mass index, and all-cause mortality. Ann Intern Med. Tyrovolas S, Haro J-M, Mariolis A, Piscopo S, Valacchi G, Bountziouka V, et al. Skeletal muscle mass and body fat in relation to successful ageing of older adults: the multi-national MEDIS study.

Arch Gerontol Geriatr. González-Ruíz K, Medrano M, Correa-Bautista J, García-Hermoso A, Prieto-Benavides D, Tordecilla-Sanders A, et al. Comparison of bioelectrical impedance analysis, slaughter skinfold-thickness equations, and dual-energy X-ray absorptiometry for estimating body fat percentage in Colombian children and adolescents with excess of adiposity.

Article CAS PubMed Central Google Scholar. Hillier SE, Beck L, Petropoulou A, Clegg ME. A comparison of body composition measurement techniques. J Hum Nutr Diet. Article CAS PubMed Google Scholar. Chambers AJ, Parise E, Mccrory JL, Cham R.

A comparison of prediction equations for the estimation of body fat percentage in non-obese and obese older Caucasian adults in the United States.

J Nutr Health Aging. Article CAS PubMed PubMed Central Google Scholar. Bacchi E, Cavedon V, Zancanaro C, Moghetti P, Milanese C. Sci Rep Nat Publ Group. Perissinotto E, Pisent C, Sergi G, Grigoletto F. ILSA working group Italian longitudinal study on ageing. Anthropometric measurements in the elderly: age and gender differences.

Br J Nutr. Silveira EA, Kac G, Barbosa LS. Obesity prevalence and associated factors in the elderly in Pelotas, Rio Grande do Sul state, Brazil: obesity classifi cation according to two cutoff points for body mass index.

Cad Saude Publica. Gupta N, Balasekaran G, Victor Govindaswamy V, Hwa CY, Shun LM. Comparison of body composition with bioelectric impedance BIA and dual energy X-ray absorptiometry DEXA among Singapore Chinese.

J Sci Med Sport. Henche SA, Torres RR, Pellico LG. An evaluation of patterns of change in total and regional body fat mass in healthy Spanish subjects using dual-energy X-ray absorptiometry DXA.

Eur J Clin Nutr. Ling CHY, de Craen AJM, Slagboom PE, Gunn DA, Stokkel MPM, Westendorp RGJ, et al. Accuracy of direct segmental multi-frequency bioimpedance analysis in the assessment of total body and segmental body composition in middle-aged adult population.

Clin Nutr. Silveira EA, Ferreira CCDC, Pagotto V, Santos ASEADC, Velasquez-Melendez G. Total and central obesity in elderly associated with a marker of undernutrition in early life - sitting height-to-stature ratio: A nutritional paradox. Am J Hum Biol. Article Google Scholar.

Forte R, Pesce C, de Vito G, Boreham CAG. The body fat-cognition relationship in healthy older individuals: does gynoid vs android distribution matter? Thiebaud RS, Abe T, Loenneke JP, Fujita E, Akamine T. Body fat percentage assessment by ultrasound subcutaneous fat thickness measurements in middle-aged and older adults.

Clin Nutr Elsevier; ;0. Clasey JL, Kanaley JA, Wideman L, Heymsfield SB, Teates CD, Gutgesell ME, et al. Regarding the validity of methods of body composition assessment in young and older men and women multiple letters.

J Appl Physiol. Gallagher D, Heymsfield SB, Heo M, Jebb SA, Murgatroyd PR, Sakamoto Y. Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr. Goran MI, Toth MJ, Poehlman ET. Assessment of research-based body composition techniques in healthy elderly men and women using the 4-compartment model as a criterion method.

Ravaglia G, Forti P, Maioli F, Boschi F, Cicognani A, Gasbarrini G. Measurement of body fat in healthy elderly men: a comparison of methods. J Gerontol Ser A Biol Sci Med Sci. Pagotto V, Silveira EA. Applicability and agreement of different diagnostic criteria for sarcopenia estimation in the elderly.

Silveira EA, Pagotto V, Barbosa LS, De OC, Pena GDG, Velasquez-Melendez G. Accuracy of BMI and waist circumference cut-off points to predict obesity in older adults. Cien Saude Colet. Silveira EA, Vieira LL, de Souza JD. High prevalence of abdominal obesity among the elderly and its association with diabetes, hypertension and respiratory diseases.

Bland JM, Altman DG. Comparing methods of measurement: why plotting difference against standard method is misleading. Lohman TG, Roche AF MR. Anthropometrics standardization reference manual. Habicht J-P. Estandarizacion de métodos epidemiológicos cuantitativos sobre el terreno. Bol Sanit Panam.

CAS Google Scholar. Durnin JVGA, 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.

Siri WE. Body composition from fluid spaces and density: analysis of methods. Google Scholar. Lin LI-K. A Concordance Correlation Coefficient to Evaluate Reproducibility. da Silveira EA, Araújo CL, Gigante DP, Barros AJD, de Lima MS.

Any comments, suggestions, or corrections? Please let us know. Search This Site. Testing Extra We have over fitness tests listed, so it's not easy to choose the best one to use. Latest FIFA Results A Chef for Athletes Pickleball Training Highest Attendance Figures Current Super Bowl African Games Euro 24 Major Events Calendar Popular Pages Super Bowl Winners Ballon d'Or Winners World Cup Winners World's Largest Stadiums Beep Test Latest Sports Added E-Bike Racing Hobby Horsing.

PAGES home search sitemap store. SOCIAL MEDIA newsletter facebook X twitter. SECURITY privacy policy disclaimer copyright.