Predicting anaerobic capabilities in 11-13-year-old boys

Anaerobic exercise is involved in many recreational and competitive sport activities. This study first established regression equations to predict maximal anaerobic power and then cross-validated these prediction equations. Using stepwise multiple regression analysis prediction equations for relative (watts per kilogram of body mass) and absolute (watts) mean and peak anaerobic power using the 30-second Wingate Test as the power measure were determined for 40 boys (age, 11-13 years). Percentage of body fat, free-fat weight, midthigh circumference, and 30-m dash were the independent predictive variables with the generated regression equations subsequently cross-validated using 20 different boys (age, 11-13 years). Significant correlations (Pearson r) were found for the cross-validation subjects between the measured power outputs and predicted power outputs for relative mean power (r = 0.48, p < 0.05), absolute mean power (r = 0.77, p < 0.01), and absolute peak power (r = 0.76, p < 0.01). Using paired t-tests, no significant mean differences (p > 0.05) were found for the same subjects between actual and predicted power outputs for relative mean power, absolute mean power, and absolute peak power. Prediction of maximal anaerobic power from selected anthropometric measurements and 30-m dash appears tenable in 11-13-year-old boys and can be accomplished in a simple cost- and time-effective manner.     

Validation of tetrapolar bioelectrical impedance method to assess human body composition

This study was conducted to validate the relationship between bioelectrical conductance (ht2/R) and densitometrically determined fat-free mass, and to compare the prediction errors of body fatness derived from the tetrapolar impedance method and skinfold thicknesses, relative to hydrodensitometry. One-hundred and fourteen male and female subjects, aged 18-50 yr, with a wide range of fat-free mass (34-96 kg) and percent body fat (4-41%), participated. For males, densitometrically determined fat-free mass was correlated highly (r = 0.979), with fat-free mass predicted from tetrapolar conductance measures using an equation developed for males in a previous study. For females, the correlation between measured fat-free mass and values predicted from the combined (previous and present male data) equation for men also was strong (r = 0.954). The regression coefficients in the male and female regression equations were not significantly different. Relative to hydrodensitometry, the impedance method had a lower predictive error or standard error of the estimates of estimating body fatness than did a standard anthropometric technique (2.7 vs. 3.9%). Therefore this study establishes the validity and reliability of the tetrapolar impedance method for use in assessment of body composition in healthy humans.     

Predicting in vivo soft tissue masses of the lower extremity using segment anthropometric measures and DXA

The purpose of this study was to derive and validate regression equations for the prediction of fat mass (FM), lean mass (LM), wobbling mass (WM), and bone mineral content (BMC) of the thigh, leg, and leg + foot segments of living people from easily measured segmental anthropometric measures. The segment masses of 68 university-age participants (26 M, 42 F) were obtained from full-body dual photon x-ray absorptiometry (DXA) scans, and were used as the criterion values against which predicted masses were compared. Comprehensive anthropometric measures (6 lengths, 6 circumferences, 8 breadths, 4 skinfolds) were taken bilaterally for the thigh and leg for each person. Stepwise multiple linear regression was used to derive a prediction equation for each mass type and segment. Prediction equations exhibited high adjusted R2 values in general (0.673 to 0.925), with higher correlations evident for the LM and WM equations than for FM and BMC. Predicted (equations) and measured (DXA) segment LM and WM were also found to be highly correlated (R2 = 0.85 to 0.96), and FM and BMC to a lesser extent (R2 = 0.49 to 0.78). Relative errors between predicted and measured masses ranged between 0.7% and -11.3% for all those in the validation sample (n = 16). These results on university-age men and women are encouraging and suggest that in vivo estimates of the soft tissue masses of the lower extremity can be made fairly accurately from simple segmental anthropometric measures.     

Validity of leg-to-leg bioelectrical impedance measurement in highly active women

The aim of this study was to compare the validity of the leg-to-leg bioelectrical impedance analysis (BIA) method with that of anthropometry using hydrostatic weighing (HW) as the criterion test. A secondary objective was to cross-validate previously developed anthropometric regression equations as well as to develop a new regression equation formula based on the anthropometric data collected in this study. Three methods for assessing body composition (HW, BIA, and anthropometric) were applied to 60 women university athletes. The means and standard deviations of age, weight, height, and body mass index (BMI) of athletes were as follows: age, 20.70 +/- 1.43; weight, 56.19 +/- 7.83 kg; height, 163.33 +/- 6.11 cm; BMI, 21.01 +/- 2.63 kg x m(-2). Leg-to-leg BIA (11.82 +/- 2.39) has shown no statistical difference between percentage body fat determined by HW (11.63 +/- 2.42%) in highly active women (p > 0.05). This result suggests that the leg-to-leg BIA and HW methods were somewhat interchangeable in highly active women (R = 0.667; standard error of estimate [SEE] = 1.81). As a result of all cross-validation analyses, anthropometric and BIA plus anthropometric results have generally produced lower regression coefficients and higher SEEs for highly active women between the ages of 18 and 25 years. The regression coefficients (0.903, 0.926) and SEE (1.08, 0.96) for the new regression formulas developed from this study were better than the all the other formulas used in this study.     

Predicting Total Body Fat from Anthropometry in Latino Children

Objective: To develop prediction equations for total body fat specific to Latino children, using demographic and anthropometric measures. Research Methods and Procedures: Ninety‐six Latino children (7 to 13 years old) were studied. Two‐thirds of the sample was randomized into the equation development group; the remainder served as the cross‐validation group. Total body fat was measured by DXA. Measures included weight, height, waist and hip circumferences, and skinfolds (suprailiac, triceps, abdomen, subscapula, thigh, and calf). Results: The previously published equation from Dezenberg et al. did not accurately predict total body fat in Latino children. However, newly developed equations with either body weight alone (intercept ± SE = 1.78 ± 1.53 kg, p > 0.05; slope ± SE = 0.90 ± 0.07, p > 0.05 against slope = 1.0; R² = 0.86), weight plus age and gender (intercept ± SE = 2.28 ± 1.20 kg, p > 0.05; slope ± SE = 0.91 ± 0.05, p > 0.05; against slope = 1.0; R² = 0.92), or weight plus height, gender, Tanner stage, and abdominal skinfold (intercept ± SE = 1.47 ± 1.01 kg, p > 0.05; slope ± SE = 0.93 ± 0.04, p > 0.05; against slope = 1.0, R² = 0.97) predicted total body fat without bias. Discussion: Unique prediction equations of total body fat may be needed for Latino children. Weight, as the single most significant predictor, can be used easily to estimate total body fat in the absence of any additional measures. Including age and gender with weight produces an equally stable prediction equation with increasing precision. Using a combination of demographic and anthropometric measures, we were able to capture 97% of the variance in measured total body fat.     

Estimation of human body composition by electrical impedance methods: a comparative study

This study 1) further validated the relationship between total body electrical conductivity (TOBEC) and densitometrically determined lean body mass (LBMd) and 2) compared with existing body composition techniques (densitometry, total body water, total body potassium, and anthropometry) two new electrical methods for the estimation of LBM: TOBEC, a uniform current induction method, and bioelectrical impedance analysis (BIA), a localized current injection method. In a sample of 75 male and female subjects ranging from 4.9 to 54.9% body fat the correlation between LBMd and LBM predicted from TOBEC by use of a previously developed regression equation was extremely strong (r = 0.962), thus confirming the validity of the TOBEC method. LBM predicted from BIA by use of prediction equations provided with the instrument also correlated with LBMd (r = 0.912) but overestimated LBM compared with LBMd in obese subjects. However, no such systematic error was apparent when new prediction equations derived from this heterogeneous sample of subjects were applied. Thus the TOBEC and BIA methods, which are based on the differing electrical properties of lean tissue and fat and which are convenient, rapid, and safe, correlate well with more cumbersome human body composition techniques.     

Improved Prediction of Body Fat by Measuring Skinfold Thickness,Circumferences, and Bone Breadths

Objective: To develop improved predictive regression equations for body fat content derived from common anthropometric measurements. Research Methods and Procedures: 117 healthy German subjects, 46 men and 71 women, 26 to 67 years of age, from two different studies were assigned to a validation and a cross‐validation group. Common anthropometric measurements and body composition by DXA were obtained. Equations using anthropometric measurements predicting body fat mass (BFM) with DXA as a reference method were developed using regression models. Results: The final best predictive sex‐specific equations combining skinfold thicknesses (SF), circumferences, and bone breadth measurements were as follows: BFM_New (kg) for men = ?40.750 + [(0.397 × waist circumference) + [6.568 × (log triceps SF + log subscapular SF + log abdominal SF)]] and BFM_New (kg) for women = ?75.231 + [(0.512 × hip circumference) + [8.889 × (log chin SF + log triceps SF + log subscapular SF)] + (1.905 × knee breadth)]. The estimates of BFM from both validation and cross‐validation had an excellent correlation, showed excellent correspondence to the DXA estimates, and showed a negligible tendency to underestimate percent body fat in subjects with higher BFM compared with equations using a two‐compartment (Durnin and Womersley) or a four‐compartment (Peterson) model as the reference method. Discussion: Combining skinfold thicknesses with circumference and/or bone breadth measures provide a more precise prediction of percent body fat in comparison with established SF equations. Our equations are recommended for use in clinical or epidemiological settings in populations with similar ethnic background.     

Validation of submaximal prediction equations for the 1 repetition maximum bench press test on a group of collegiate football players

The purpose of the study was to determine the accuracy of 11 prediction equations in estimating the 1 repetition maximum (1 RM) bench press from repetitions completed by collegiate football players (N = 69) using 225 lb. The demographic variables race, age, height, weight, fat-free weight, and percent body fat were measured to determine whether these variables increased the accuracy of the prediction equations; race was the most frequently selected variable in the regression analyses. The validity of the prediction equations was dependent upon the number of repetitions performed, i.e., validity was higher when fewer repetitions were completed. Explained variability of 1 RM was slightly higher for all 11 equations when demographic variables were included. A new prediction equation was also developed using the number of repetitions performed and the demographic variables height and fat-free weight.     

The relative body fat and anthropometric prediction of body density of South Australian females aged 17-35 years

One hundred and thirty-five females were tested in order to: produce some normative percentage body fat (% BF) data on an Australian sample which represented a cross-section of physical activity patterns, cross-validate existing multiple regression equations which predict body density (BD) from anthropometric measurements, and if necessary develop population specific equations. Measurements were taken of 10 girths, 3 widths and 7 skinfolds. Body density was measured by underwater weighing with the residual volume (RV) being determined by helium dilution. The Siri equation was then used to convert BD to % BF. The % BF scores had an overall mean of 23.4 (range 10.8-49.2). The very active group (n = 45) had a significantly lower (p less than 0.05) relative body fat (X = 20.6% BF) than either the active (n = 45; 23.5% BF) or sedentary groups (n = 45; 26.2% BF). Previously published equations were found to have limited applicability to Australian subjects. A stepwise multiple regression was therefore used to develop the following equation (R = 0.893): BD(g X cm-3) = 1.16957-0.06447 (log10 sigma triceps, subscapular, supraspinale, front thigh, abdominal and calf skinfolds in mm)-0.00081 (gluteal girth in cm) + 0.0017 (forearm girth in cm) + 0.00606 (biepicondylar humerus breadth in cm). Only those predictors which resulted in a statistically significant increase in r (p less than or equal to 0.05) were included. The standard error of estimate of 0.00568 g X cm-3 was equivalent to 2.6% BF at the mean.     

Comparison of ultrasound and skinfold measurements in assessment of subcutaneous and total fatness

Ultrasound (A-scan mode) and skinfold methods were evaluated in the measurement of subcutaneous fat thickness and prediction of total fat weight (by whole body potassium counting). Based on intraobserver correlations on 39 men at 15 body sites, skinfold caliper measurements were more reproducible than ones obtained by ultrasound. Measurements made with the two techniques at the same site typically produced different mean estimates of fat thickness. However, scores were often highly correlated with each other, indicating similar relative rankings of subjects by each technique. Skinfolds were more highly correlated with total fat weight than were ultrasound measurements, but body weight and anthropometric measures had even higher correlations with total fat weight. Anthropometric measurements were highly correlated with fatness because of their association with body weight, and when this relationship was statistically controlled for, they typically lost their predictive effectiveness. Multiple regression analyses revealed that the best predictors of fat weight were body weight along with skinfold and ultrasound measurements. These results suggest that skinfolds are a more effective means of assessing subcutaneous fat than ultrasound, especially when the large difference in cost of equipment is considered.     

Magnetic resonance tomography measurement of the thigh adipose tissue volume in men

The volume of the thigh adipose tissue was estimated using magnetic resonance tomography (MRT) and anthropometric measurements. Eighty-seven physically well-developed men aged 18–45 years participated in the experiment. The MRT estimate of the thigh fat volume was 2206 ± 882 cm³. The results were used to derive two multiple linear regression equations for calculating the thigh adipose tissue volume from anthropometric parameters. The correlation coefficient between the thigh adipose tissue volumes calculated from the equation and measured by MRT was r = 0.97.     

Prediction of body volume by a stepwise linear regression technique

Body volume and 35 anthropometric measurements were obtained from 88 active soldiers using standard techniques. These anthropometric measurements were examined for their possible relationships to body volume using stepwise linear regression analysis. Four measurements (Body weight, anterior thigh skinfold thickness, subscapular skinfold thickness and suprailiac skinfold thickness) accounted for 99.7% of the variation in body volume and the introduction of each of these measurements in the equation was significant. The regression equation for predicting body volume from these 4 anthropometric measurements had a multiple correlation coefficient of 0.9987 (P less than 0.001). Body weight alone was correlated with body volume to the extent of 0.9966. An attempt has therefore been made to develop a multiple linear regression equation without incorporation of body weight in the regression analysis. Nine measurements were selected by stepwise linear regression analysis for predicting body volume. These nine measurements accounted for 97.1% of the variation in body volume. These equations have been validated on another small sample of 22 soldiers. The analysis has also revealed that a direct regression of body density from the anthropometric variables gives more accurate results than when estimated body volumes are utilized for calculating body density.     

Age and sex variation in measures of body composition among the elderly Bengalee Hindus of Calcutta, India

In the present cross-sectional study we examined 332 (171 men and 161 women) elderly (60 years and above) urban Bengalee Hindu resident in south Calcutta, India. Individuals were selected by random sampling procedure using local voter's registration list. Skin folds measures were used to compute body composition measures among them. There existed significant sex differences in various anthropometric body composition measures. Age had significant (p < 0.001) negative association with all anthropometric body composition measures namely percentage of body fat (PBF), fat mass (FM), arm muscle circumference (AMC), arm muscle area (AMA) and arm fat area (AFA) in both sexes. Fat free mass (FFM) in contrast had negative but not significant age impact. Regression analyses demonstrated that age had explained substantial amount of variance of PBF (men = 32%; women = 18.2%), FM (men = 18.2%; women = 12.8%), AMC (men = 23.4%; women = 19.2%), AMA (men = 22.2%; women = 10.2%) and AFA (men = 34%; women = 31%) in both sexes. Two-way ANOVA revealed age-sex interaction only had significant effect on FFM. The present investigation vindicated that there is a significant inverse age trends in anthropometric body composition measures among the Bengalee Hindus. Moreover, there existed sexual dimorphism in the effect of age on various anthropometric body composition measures.     

Personal best marathon time and longest training run, not anthropometry, predict performance in recreational 24-hour ultrarunners

In recent studies, a relationship between both low body fat and low thicknesses of selected skinfolds has been demonstrated for running performance of distances from 100 m to the marathon but not in ultramarathon. We investigated the association of anthropometric and training characteristics with race performance in 63 male recreational ultrarunners in a 24-hour run using bi and multivariate analysis. The athletes achieved an average distance of 146.1 (43.1) km. In the bivariate analysis, body mass (r = -0.25), the sum of 9 skinfolds (r = -0.32), the sum of upper body skinfolds (r = -0.34), body fat percentage (r = -0.32), weekly kilometers ran (r = 0.31), longest training session before the 24-hour run (r = 0.56), and personal best marathon time (r = -0.58) were related to race performance. Stepwise multiple regression showed that both the longest training session before the 24-hour run (p = 0.0013) and the personal best marathon time (p = 0.0015) had the best correlation with race performance. Performance in these 24-hour runners may be predicted (r2 = 0.46) by the following equation: Performance in a 24-hour run, km) = 234.7 + 0.481 (longest training session before the 24-hour run, km) - 0.594 (personal best marathon time, minutes). For practical applications, training variables such as volume and intensity were associated with performance but not anthropometric variables. To achieve maximum kilometers in a 24-hour run, recreational ultrarunners should have a personal best marathon time of ～3 hours 20 minutes and complete a long training run of ～60 km before the race, whereas anthropometric characteristics such as low body fat or low skinfold thicknesses showed no association with performance.     

Standards and predicted values of anaerobic threshold

Mean values for body size, body composition and endurance indices have been obtained from a homogeneous group of 125 physically active men to find predicted values of AT (age 23.4 +/- 4.3 years; height 175.9 +/- 6.5 cm; weight 72.2 +/- 8.9 kg; body fat 17.9 +/- 4.7% body weight, muscularity index 19.0 +/- 1.5 kg fat-free mass/cm2 X 10(-4) height; forced vital lung capacity 5667 +/- 815 cm3; VO2max 48.5 +/- 6.0 cm3 X kg-1 X min-1; anaerobic threshold 61.0 +/- 7.8% VO2max). Endurance performance and fitness indices were a little higher than average, but about 10% lower than in endurance-trained athletes. The authors suggest that standards of anaerobic threshold (AT) for ergonomics and endurance training should be about 55-65% VO2max, but not lower than 1800 cm3 O2 X min-1. The coefficients of correlation of AT relating to VO2max, PFO2 and submaximal load were significant at the 0.01 level. Using regression analysis, predicted values of AT were developed. A predicted value of AT can be obtained from the regression line of AT on Lsubmax used as a nomogram, during a simple PWC170 exercise test without blood or gas analysis.     

Relationship of push-ups and sit-ups tests to selected anthropometric variables and performance results: a multiple regression study

The purpose of this study was to explore whether selected anthropometric measures such as specific skinfold sites, along with weight, height, body mass index (BMI), waist and hip circumferences, and waist/hip ratio (WHR) were associated with sit-ups (SU) and push-ups (PU) performance, and to build a regression model for SU and PU tests. One hundred apparently healthy adults (40 men and 60 women) served as the subjects for test validation. The subjects performed 60-second SU and PU tests. The variables analyzed via multiple regression included weight, height, BMI, hip and waist circumferences, WHR, skinfolds at the abdomen (SFAB), thigh (SFTH), and subscapularis (SFSS), and sex. An additional cohort of 40 subjects (17 men and 23 women) was used to cross-validate the regression models. Validity was confirmed by correlation and paired t-tests. The regression analysis yielded a four-variable (PU, height, SFAB, and SFTH) multiple regression equation for estimating SU (R2 = 0.64, SEE = 7.5 repetitions). For PU, only SU was loaded into the regression equation (R2 = 0.43, SEE = 9.4 repetitions). Thus, the variables in the regression models accounted for 64% and 43% of the variation in SU and PU, respectively. The cross-validation sample elicited a high correlation for SU (r = 0.87) and PU (r = 0.79) scores. Moreover, paired-samples t-tests revealed that there were no significant differences between actual and predicted SU and PU scores. Therefore, this study shows that there are a number of selected, health-related anthropometric variables that account significantly for, and are predictive of, SU and PU tests.     

Predictive equations for body fat and abdominal fat with DXA and MRI as reference in Asian Indians

Objective: To develop accurate and reliable equations from simple anthropometric parameters that would predict percentage of total body fat (%BF), total abdominal fat (TAF), subcutaneous abdominal adipose tissue (SCAT), and intra‐abdominal adipose tissue (IAAT) with a fair degree of accuracy. Methods and Procedures: Anthropometry, %BF by dual‐energy X‐ray absorptiometry (DXA) in 171 healthy subjects (95 men and 76 women) and TAF, IAAT, and SCAT by single slice magnetic resonance imaging (MRI) at L3–4 intervertebral level in 100 healthy subjects were measured. Mean age and BMI were 32.2 years and 22.9 kg/m², respectively. Multiple regression analysis was used on the training data set (70%) to develop equations, by taking anthropometric and demographic variables as potential predictors. Predicted equations were applied on validation data set (30%). Results: Multiple regression analysis revealed the best equation for predicting %BF to be: %BF = 42.42 + 0.003 × age (years) + 7.04 × gender (M = 1, F = 2) + 0.42 × triceps skinfold (mm) + 0.29 × waist circumference (cm) ? 0.22 × weight (kg) ? 0.42 × height (cm) (R ² = 86.4%). The most precise predictive equation for estimating IAAT was: IAAT (mm²) = ?238.7 + 16.9 × age (years) + 934.18 × gender (M = 1, F = 2) + 578.09 × BMI (kg/m²) ? 441.06 × hip circumference (cm) + 434.2 × waist circumference (cm) (R ² = 52.1%). SCAT was best predicted by: SCAT (mm²) = ?49,376.4 ? 17.15 × age (years) + 1,016.5 × gender (M = 1, F = 2) +783.3 × BMI (kg/m²) + 466 × hip circumference (cm) (R ² = 67.1). Discussion: We present predictive equations to quantify body fat and abdominal adipose tissue sub‐compartments in healthy Asian Indians. These equations could be used for clinical and research purposes.     

Muscularity and the density of the fat-free mass in athletes.

The purpose of this study was to use estimates of body composition from a four-component model to determine whether the density of the fat-free mass (D(FFM)) is affected by muscularity or musculoskeletal development in a heterogenous group of athletes and nonathletes. Measures of body density by hydrostatic weighing, body water by deuterium dilution, bone mineral by whole body dual-energy X-ray absorptiometry (DXA), total body skeletal muscle estimated from DXA, and musculoskeletal development as measured by the mesomorphy rating from the Heath-Carter anthropometric somatotype were obtained in 111 collegiate athletes (67 men and 44 women) and 61 nonathletes (24 men and 37 women). In the entire group, D(FFM) varied from 1.075 to 1.127 g/cm3 and was strongly related to the water and protein fractions of the fat-free mass (FFM; r = -0.96 and 0.89) and moderately related to the mineral fraction of the FFM (r = 0.65). Skeletal muscle (%FFM) varied from 40 to 68%, and mesomorphy varied from 1.6 to 9.6, but neither was significantly related to D(FFM) (r = 0.11 and -0.14) or to the difference between percent fat estimated from the four-component model and from densitometry (r = 0.09 and -0.16). We conclude that, in a heterogeneous group of young adult athletes and nonathletes, D(FFM) and the accuracy of estimates of body composition from body density using the Siri equation are not related to muscularity or musculoskeletal development. Athletes in selected sports may have systematic deviations in D(FFM) from the value of 1.1 g/cm3 assumed in the Siri equation, resulting in group mean errors in estimation of percent fat from densitometry of 2-5% body mass, but the cause of these deviations is complex and not simply a reflection of differences in muscularity or musculoskeletal development.     

Maximal oxygen uptake in Chilean workers of normal nutritional status

Maximum oxygen uptake (VO2max) was measured directly and predicted from cardiac frequency measurements in 54 healthy Chilean industrial workers aged 20 to 55 years, together with assessment of their dietary intake, body composition and blood chemistry. Measurement of VO2 was performed on a motor-driven treadmill. The predicted VO2max was obtained using a cycle ergometer by two methods: 1) the Astrand-Ryhming nomogram and 2) the linear relationship between "steady state" heart rate (HR) and submaximum work, with subsequent extrapolation to "maximum" heart rate. Extrapolation of the HR/load regression line to 170 bpm permitted determination of the physical working capacity at 170 bpm (W170). VO2max for the 20-29 year group (Group I) averaged 3624 ml.min-1 and decreased to 3066 ml.min-1 in the 50-55 year group (Group IV). Lower values were obtained using the Astrand-Ryhming nomogram and HR/load regression (-15% and -9% respectively). W170 was also affected by age (Group I: 190.6 W and Group IV: 158.5 W). No significant correlation were found between VO2max and plasma variables, with the exception of cholesterol (r = 0.59). On the contrary, anthropometric variables showed significant correlations with VO2max, which permitted the prediction of VO2max using multiple regression equations. The two best correlations were: 1. VO2max = 0.800 - 0.0225.(A) +0.0189.(W)+1.26.(H) (r = 0.87; p less than 0.001) 2. VO2max = 0.996 - 0.0176.(A) + 0.025.(W) + 0.838.(H) + 0.0255.(LBM) (r = 0.88; p less than 0.001) where A = years of age; W = body weight in kg; H = height in m and LBM = lean body mass in kg.     

Prediction of Percentage Body Fat in Rural Thai Population Using Simple Anthropometric Measurements

Objective: To develop and validate sex‐specific equations for predicting percentage body fat (%BF) in rural Thai population, based on BMI and anthropometric measurements. Research Methods and Procedures: %BF (DXA; GE Lunar Corp., Madison, WI) was measured in 181 men and 255 women who were healthy and between 20 and 84 years old. Anthropometric measures such as weight (kilograms), height (centimeters), BMI (kilograms per meter squared), waist circumference (centimeters), hip circumference (centimeters), thickness at triceps skinfold (millimeters), biceps skinfold (millimeters), subscapular skinfold (millimeters), and suprailiac skinfold (millimeters) were also measured. The sample was randomly divided into a development group (98 men and 125 women) and a validation group (83 men and 130 women). Regression equations of %BF derived from the development group were then evaluated for accuracy in the validation group. Results: The equation for estimating %BF in men was: %BF(men) = 0.42 × subscapular skinfold + 0.62 × BMI ? 0.28 × biceps skinfold + 0.17 × waist circumference ? 18.47, and in women: %BF(women) = 0.42 × hip circumference + 0.17 × suprailiac skinfold + 0.46 × BMI ? 23.75. The coefficient of determination (R²) for both equations was 0.68. Without anthropometric variables, the predictive equation using BMI, age, and sex was: %BF = 1.65 × BMI + 0.06 × age ? 15.3 × sex ? 10.67 (where sex = 1 for men and sex = 0 for women), with R² = 0.83. When these equations were applied to the validation sample, the difference between measured and predicted %BF ranged between ±9%, and the positive predictive values were above 0.9. Discussion: These results suggest that simple, noninvasive, and inexpensive anthropometric variables may provide an accurate estimate of %BF and could potentially aid the diagnosis of obesity in rural Thais.