Estimation of VO2max: a comparative analysis of five exercise tests

Thirty-eight female subjects (M +/ SD = 33 +/- 3.0 years) had VO2max measured on the cycle ergometer (M +/- SD = 37.3 +/- 6.4 ml.kg-1.min-1) and on the treadmill (M +/- SD = 41.3 +/- 6.6 ml.kg-1.min-1). VO2max was estimated for each subject from heart rate (HR) at submaximal workloads on the cycle ergometer using the Astrand-Rhyming nomogram (A/R) and the extrapolation method (XTP). VO2max was also estimated from three field tests: 1.5-mile run (RUN) (independent variable [IV] = time), mile walk (WALK) (IV = time, age, HR, gender, body weight), and the Queens College Step Test (ST) (IV = HR during 5-20 s recovery). Repeated measure ANOVA revealed significant mean differences between the criterion cycle ergometer VO2max versus A/R and XTP (20 and 12% overestimation). The WALK, RUN, and ST VO2max values were not significantly different from the criterion treadmill VO2max. The correlation between criterion VO2max estimated from the WALK and RUN were r = .73 (SEE = 4.57 ml,kg-1.min-1) and r = .79 (SEE = 4.13 ml.kg-1.min-1), respectively. The ST, A/R, and XTP had higher SEEs (13-13.5% of the mean) and lower r s (r = .55 to r = .66). These results suggest both the WALK and RUN tests are satisfactory predictors of VO2max in 30 to 39-year-old females.     

An accurate VO2max nonexercise regression model for 18-65-year-old adults

The purpose of this study was to develop a regression equation to predict maximal oxygen uptake (VO2max) based on nonexercise (N-EX) data. All participants (N = 100), ages 18-65 years, successfully completed a maximal graded exercise test (GXT) to assess VO2max (M = 39.96 mL x kg(-1) x min(-1), SD = 9.54). The N-EX data collected just before the maximal GXT included the participant's age; gender; body mass index (BMI); perceived functional ability (PFA) to walk, jog, or run given distances; and current physical activity (PA-R) level. Multiple linear regression generated the following N-EX prediction equation (R = .93, SEE = 3.45 mL x kg(-1) x min(-1), % SEE = 8.62): VO2max (mL x kg(-1) x min(-1)) = 48.0730 + (6.1779 x gender; women = 0, men = 1) - (0. 2463 x age) - (0.6186 x BMI) + (0.7115 x PFA) + (0.6709 x PA-R). Cross validation using PRESS (predicted residual sum of squares) statistics revealed minimal shrinkage (R(p) = .91 and SEE(p) = 3.63 mL x kg(-1) x min(-1)); thus, this model should yield acceptable accuracy when applied to an independent sample of adults (ages 18-65 years) with a similar cardiorespiratory fitness level. Based on standardized beta-weights, the PFA variable (0.41) was the most effective at predicting VO2max followed by age (-0.34), gender (0.33), BMI (-0.27), and PA-R (0.16). This study provides a N-EX regression model that yields relatively accurate results and is a convenient way to predict VO2max in adult men and women.     

Prediction of maximum oxygen consumption from walking, jogging, or running

The purpose of this study was to develop a submaximal, 1.5-mile endurance test for college-aged students using walking, jogging, or running exercise. College students (N = 101: 52 men, 47 women), ages 18-26years, successfully completed the 1.5-mile test twice, and a maximal graded exercise test. Participants were instructed to achieve a "somewhat hard" exercise intensity (rating of perceived exertion = 13) and maintain a steady pace throughout each 1.5-mile test. Multiple linear regression generated the following prediction equation: VO2 max = 65.404 + 7.707 x gender (1 = male; 0 =female) - 0.159 x body mass (kg) - 0.843 x elapsed exercise time (min; walking, jogging orrunning). This equation shows acceptable validity (R = .86, SEE = 3.37 ml x kg(-1) min(-1)) similar to the accuracy of comparable field tests, and reliability (ICC = .93) is also comparable to similar models. The statistical shrinkage is minimal (R(press) = 0.85, SEE(press) = 3.51 ml x kg(-) x min(-1)); hence, it should provide comparable results when applied to other similar samples. A regression model (R =.90, and SEE = 2.87 ml x kg(-1) min(-1)) including exercise heart rate was also developed: VO2 max = 100.162 +/- 7.301 x gender(1 = male; 0 =female) - 0.164 x body mass (kg) - 1.273 x elapsed exercise time -0.156 x exercise heart rate, for those who have access to electronic heart rate monitors. This submaximal 1.5-mile test accurately predicts maximal oxygen uptake (VO2max) without measuring heart rate and is similar to the 1.5-mile run in that it allowsfor mass testing and requires only a flat, measured distance and a stopwatch. Further, it can accommodate a wide range of fitness levels (from walkers to runners).     

Comparison of three models of actigraph accelerometers during free living and controlled laboratory conditions

Abstract

The aim of this study was to compare the outputs of three commonly used uniaxial Actigraph models (Actitrainer, 7164 and GT1M) under both free-living and controlled laboratory conditions. Ten adults (mean age = 24.7±1.1 years) wore the three Actigraph models simultaneously during one of day free-living and during a progressive exercise protocol on a treadmill at speeds between 1.5 and 5.5 miles per hour (mph). During free-living the three Actigraph models produced comparable outputs in moderate, vigorous and moderate-to-vigorous physical activity (MVPA) with effect sizes typically <0.2, but lower comparability was seen in sedentary and light categories, as well as in total step counts (effect sizes often >0.30). In controlled conditions, acceptable comparability between the three models was seen at all treadmill speeds, the exception being walking at 1.5 mph (mean effect size = 0.48). It is concluded that care should be taken if different Actigraph models are to be used to measure and compare light physical activity, step counts and walking at very low speeds. However, using any of these three different Actigraph models to measure and compare levels of MVPA in free-living adults seems appropriate.     

Moderate and vigorous physical activity intensity cut-points for the Actical accelerometer

Accelerometry is increasingly used as a physical activity surveillance device that can quantify the amount of time spent moving at a range of intensities. This study proposes physical activity intensity cut-points for the Actical accelerometer. Thirty-eight volunteers completed a multi-stage treadmill protocol at 3, 5, and 8 km · h?1 (2, 3.3, and 8 METs) while wearing Actical accelerometers initialized to collect data in 60-s epochs. Using a decision boundary analytical approach, moderate and vigorous physical activity intensity cut-points were derived for the Actical accelerometer. In adults (n = 26), the cut-point for moderate physical activity intensity occurred at 1535 counts per minute and the vigorous cut-point occurred at 3960 counts per minute. In children (n = 12), the cut-point for moderate physical activity intensity occurred at 1600 counts per minute and the vigorous cut-point occurred at 4760 counts per minute. Improved classification of physical activity intensity using the decision boundary cut-points was observed compared with using mean values for each protocol stage. The cut-points derived are recommended for use in adults. The cut-points derived for children confirm the findings of previous studies.     

The reliability and validity of the 20-meter shuttle test in American students 12 to 15 years old.

The purpose of this study was threefold: to determine (a) the test-retest reliability of the 20-m shuttle test (20 MST) (number of laps), (b) the concurrent validity of the 20 MST (number of laps), and (c) the validity of the prediction equation for VO2max developed by Léger, Mercier, Gadoury, and Lambert (1988) on Canadian children for use with American children 12-15 years old. An intraclass coefficient of .93 was obtained on 20 students (12 males; R = .91 and 8 females; R = .87) who completed the test twice, 1 week apart (MT1 = 47.80 +/- 20.29 vs. MT2 = 50.55 +/- 22.39 laps; p > or = .13). VO2peak was obtained by a treadmill test to volitional fatigue on 48 subjects. The number of laps run correlated significantly with VO2peak in males (n = 22; r = .65; F [1, 20] = 14.30 p < or = .001), females (n = 26; r = .51; F [1, 24] = 8.34; p < or = .01), and males and females = (r = .69; F [1, 46] = 42.54, p < or = .001). When the measured VO2peak (M = 49.97 +/- 7.59 ml.kg-1.min-1) was compared with the estimated VO2max (M = 48.72 +/- 5.72 ml.kg-1.min-1) predicted from age and maximal speed of the 20 MST (Léger et al., 1988) no significant difference was found, t (47) = -1.631; p > or = .11, between the means; the r was .72 and SEE was 5.26 ml.kg-1.min-1.(ABSTRACT TRUNCATED AT 250 WORDS)     

Predicting activity energy expenditure using the Actical activity monitor

This study developed algorithms for predicting activity energy expenditure (AEE) in children (n = 24) and adults (n = 24) from the Actical activity monitor. Each participant performed 10 activities (supine resting, three sitting, three house cleaning, and three locomotion) while wearing monitors on the ankle, hip, and wrist; AEE was computed from oxygen consumption. Regression analysis, used to create AEE prediction equations based on Actical output, varied considerably for both children (R2 = .45-.75; p < .001) and adults (R2 = .14-.85; p < .008). Most of the resulting algorithms accurately predicted accumulated AEE and time within light, moderate, and vigorous intensity categories (p > .05). The Actical monitor may be useful for predicting AEE and time variables at the ankle, hip, or wrist locations.     

Non-exercise estimation of VO(2)max using the International Physical Activity Questionnaire

Non-exercise equations developed from self-reported physical activity can estimate maximal oxygen uptake (VO(2)max) as well as submaximal exercise testing. The International Physical Activity Questionnaire (IPAQ) is the most widely used and validated self-report measure of physical activity. This study aimed to develop and test a VO(2)max estimation equation derived from the IPAQ-Short Form (IPAQ-S). College-aged males and females (n = 80) completed the IPAQ-S and performed a maximal exercise test. The estimation equation was created with multivariate regression in a gender-balanced subsample of participants, equally representing five levels of fitness (n = 50) and validated in the remaining participants (n = 30). The resulting equation explained 43% of the variance in measured VO(2)max (SEE = 5.45 ml·kg(-1)·min(-1)). Estimated VO(2)max for 87% of individuals fell within acceptable limits of error observed with submaximal exercise testing (20% error). The IPAQ-S can be used to successfully estimate VO(2)max as well as submaximal exercise tests. Development of other population-specific estimation equations is warranted.     

The relationship between heart rate reserve and oxygen uptake reserve in children and adolescents

The purpose of the present study was to examine the relationship between oxygen uptake (VO2) and heart rate (HR) responses during rest and exercise in Chinese children and youth and to evaluate the relationships between maximal heart rate (%HRmax), heart rate reserve (%HRR), peak oxygen uptake (% VO2peak), and oxygen uptake reserve (% VO2R) in Chinese children and youth. Forty-nine Chinese children and youth were studied at rest and during a graded maximal exercise test on treadmill. Resting, submaximal and peak HR and VO2 were collected. Regression analyses were conducted to investigate the associations between the various forms of HR and VO2 measures. The equivalency between %HRR and % VO2R for adults was examined for children using data obtained in this study. Results indicated that all regression lines between HR measures and VO2 measures were significantly different from the line of identity (p < .05), except the regression line for %HRR versus %VO2 peak in boys. The equivalency between % VO2R and % HRR for adults was not demonstrated in children and adolescents in this study. In contrast, %HRR was more closely equivalent to % VO2 peak. Because a strong linear relationship was found between HR and VO2, HR measures, in terms of either %HRmax or %HRR, would still be a practical variable for prescribing appropriate exercise intensity for children and adolescents. Unlike results found for adults, a given % HRR in children and youth was not equivalent to its corresponding % VO2R.     

A Comparison of Accelerometers for Predicting Energy Expenditure and Vertical Ground Reaction Force in School-Age Children

In this pilot study of 16 children, we evaluated the reliability and validity of three accelerometers (Mini-Motionlogger [MML], Computer Science Applications, Inc. Actigraph [CSA], and BioTrainer) as indicators of energy expenditure and vertical ground reaction force. The children wore 2 of each type of monitor while they walked, ran, and performed 2 jumping tasks on a force plate and walked, jogged, and ran on a treadmill. Intrainstrument reliability of the monitors ranged from .64 to .98 across the treadmill tasks and from .69 to .98 across the force plate tasks, with the MML and CSA appearing more consistent than the BioTrainer. Analyses of variance were conducted to compare activity counts with criterion measures (oxygen utilization and force plate scores). All of the monitors generally differentiated among the treadmill tasks, mirroring the change in oxygen utilization. The CSA monitors corresponded more closely to the changes in force plate scores than the BioTrainer or the MML. Simple regression analyses indicated that count scores from all of the monitors were associated with oxygen utilization, with the MML and CSA exhibiting stronger relations (R values = .81 and .83, respectively) than the BioTrainer (R= .60). Similar analyses between the activity monitors and the force plate scores were also significant but the relations were not as strong as for oxygen utilization (R values = .46, .51, and .52, respectively). Based on backward elimination regression analyses, caloric expenditure on the treadmill tasks was predicted significantly with each of the MML (37 and 38% of variance) and CSA (39 and 42% of variance) units when body mass was included in the model. For the BioTrainer counts to provide the best prediction of caloric expenditure, both body mass and height were retained in the model, resulting in 20 and 25% explained variance. Future research to evaluate the utility of accelerometers should employ tasks that prevent the confounding of force and caloric expenditure.     

Moderate and vigorous physical activity intensity cut-points for the Actical accelerometer

Abstract

Accelerometry is increasingly used as a physical activity surveillance device that can quantify the amount of time spent moving at a range of intensities. This study proposes physical activity intensity cut-points for the Actical accelerometer. Thirty-eight volunteers completed a multi-stage treadmill protocol at 3, 5, and 8 km · h^?1 (2, 3.3, and 8 METs) while wearing Actical accelerometers initialized to collect data in 60-s epochs. Using a decision boundary analytical approach, moderate and vigorous physical activity intensity cut-points were derived for the Actical accelerometer. In adults (n = 26), the cut-point for moderate physical activity intensity occurred at 1535 counts per minute and the vigorous cut-point occurred at 3960 counts per minute. In children (n = 12), the cut-point for moderate physical activity intensity occurred at 1600 counts per minute and the vigorous cut-point occurred at 4760 counts per minute. Improved classification of physical activity intensity using the decision boundary cut-points was observed compared with using mean values for each protocol stage. The cut-points derived are recommended for use in adults. The cut-points derived for children confirm the findings of previous studies.     

The ecological validity of laboratory cycling: Does body size explain the difference between laboratory- and field-based cycling performance?

Previous researchers have identified significant differences between laboratory and road cycling performances. To establish the ecological validity of laboratory time-trial cycling performances, the causes of such differences should be understood. Hence, the purpose of the present study was to quantify differences between laboratory- and road-based time-trial cycling and to establish to what extent body size [mass (m) and height (h)] may help to explain such differences. Twenty-three male competitive, but non-elite, cyclists completed two 25 mile time-trials, one in the laboratory using an air-braked ergometer (Kingcycle) and the other outdoors on a local road course over relatively flat terrain. Although laboratory speed was a reasonably strong predictor of road speed (R2 = 69.3%), a significant 4% difference (P < 0.001) in cycling speed was identified (laboratory vs. road speed: 40.4 +/- 3.02 vs. 38.7 +/- 3.55 km x h(-1); mean +/- s). When linear regression was used to predict these differences (Diff) in cycling speeds, the following equation was obtained: Diff (km x h(-1)) = 24.9 - 0.0969 x m - 10.7 x h, R2 = 52.1% and the standard deviation of residuals about the fitted regression line = 1.428 (km . h-1). The difference between road and laboratory cycling speeds (km x h(-1)) was found to be minimal for small individuals (mass = 65 kg and height = 1.738 m) but larger riders would appear to benefit from the fixed resistance in the laboratory compared with the progressively increasing drag due to increased body size that would be experienced in the field. This difference was found to be proportional to the cyclists' body surface area that we speculate might be associated with the cyclists' frontal surface area.     

Validating the multimedia activity recall for children and adolescents in a large New Zealand sample

Abstract

The aim of the study was to validate the self-report Multimedia Activity Recall for Children and Adolescents (MARCA) against accelerometry for the assessment of physical activity in New Zealand children. Participants (n = 716, 10–18 years) recalled 3–4 days of activity using the MARCA and underwent a partially overlapping 7-day accelerometry protocol during a national survey. Spearman correlation coefficients (ρ) assessed the association between accelerometer-derived counts per minute and MARCA-derived physical activity level and time in locomotion. Both data sources estimated time spent in light and moderate–vigorous physical activity. Association and agreement between methods for light physical activity and moderate–vigorous physical activity was assessed using correlations and Bland–Altman plots respectively, and paired t-tests conducted. Accelerometer-derived activity counts were moderately correlated with both MARCA-derived physical activity level and locomotion (ρ = 0.38, P < 0.0001). The correlation between methods was –0.14 for light physical activity and 0.28 for moderate–vigorous physical activity (P < 0.0001). The MARCA overestimated moderate–vigorous physical activity compared with accelerometry (120 min, P < 0.0001), which increased as moderate–vigorous physical activity time increased. Some sex and ethnicity (Māori [indigenous] versus non-Māori) differences were observed. Overall, the MARCA indicated moderate validity for assessment of physical activity level, locomotion and moderate–vigorous physical activity and poor validity for assessment of light physical activity. This was comparable to other self-report tools. The MARCA has utility for future large-scale research.     

Children's Activity Rating Scale (CARS): description and calibration

A five-level Children's Activity Rating Scale (CARS) was designed to categorize the intensity of physical activities and discriminate between levels of energy expenditure in young children. The CARS was used by trained observers over a 12-month period to assess physical activities of 3-4 year-old children during field observations. Agreement among observers using the CARS was 84.1% for 389 paired observation periods. The energy expenditure for each level was assessed by measuring VO2s and heart rates of 5-6 year-old children (12 boys, 13 girls) while they performed eight activities representing the CARS levels. Mean VO2s for the eight activities in Levels 1-5 ranged from 7.1 to 37.5 ml.kg BW-1.min-1 (1 to 5.42 METS; 14.5% to 80.6% of VO2max). Mean heart rates ranged from 89 to 183 b.min-1 for activities in Levels 1-5. VO2s and heart rates at each level were significantly different from all other levels. These data demonstrate that the CARS encompasses a wide range of energy expenditures, discriminates between levels of energy expenditure, and can be used by trained observers to reliably evaluate physical activity and estimate energy expenditure of young children.     

Pedometer reliability, validity and daily activity targets among 10- to 15-year-old boys

The aims of this study were to: (1) determine whether the number of pedometer counts recorded by adolescents differs according to the adiposity of the participant or location on the body; (2) assess the accuracy and reliability of pedometers during field activity; and (3) set adolescent pedometer-based physical activity targets. Seventy-eight 11- to 15-year-old Boy Scouts completed three types of activity: walking, fast walking and running. Each type was performed twice. Participants wore three pedometers and one activity monitor during all activities. Participants were divided into groups of normal weight (BMI < 85th percentile) and at risk of being overweight (BMI > or = 85th percentile). Intra-class correlations across the three activities indicated reliability (r = 0.51 - 0.92, P < 0.001). This conclusion was supported by narrow limits of agreement that were within a pre-set range that was practically meaningful. Multivariate analysis of covariance indicated adiposity group differences, but this difference was a function of the increased stature among the larger participants (P < 0.001). Ordinary least-squares regression models and multi-level regression models showed positive associations between the number of pedometer and activity monitor counts recorded by the three groups of participants during all activities (all P < 0.001). The mean number of counts recorded for all participants during the fast walk was 127 counts per minute. In conclusion, the pedometers provided an accurate assessment of adolescent physical activity, and a conservative estimate of 8000 pedometer counts in 60 min is equivalent to 60 min of moderate to vigorous physical activity.     

Physiological characteristics of America's Cup sailors

The aim of this study was to assess the physiological profile of America's Cup grinders and mastmen, by measuring energy expenditure during sailing and assessing their aerobic and anaerobic fitness. The study focused on estimating the energy used during grinding activity, by measuring oxygen uptake (VO(2)) during sail setting in real sailing conditions. In the laboratory, using an arm-cranking ergometer, we measured VO(2peak) during an incremental maximal exercise test and total energy expended during the effort and recovery phases of an all-out test that simulated grinding activity, in six grinders and mastmen and ten sailors of the same crew. Total energy used during grinding corresponded to 45% (s = 9) and 51% (s = 5) of that used in the all-out test (234 kJ, s = 21.7) for tacks and gybes, respectively. In both grinding activity and the all-out test, VO(2) increased during and after the effort. The "VO(2) top value" was 53% (s = 8.6), 68% (s = 5.5), and 78% (s = 3.1) of VO(2peak) (4.7 l . min(-1), s = 0.43) in tacks, gybes, and the all-out test, respectively. During fast sequences of grinding activity, the "VO(2) top value" reached 65% (s = 7.1) VO(2peak) in tacks and 91% (s = 3.3) VO(2peak) in gybes. Our results suggest that grinders and mastmen are characterized by a high anaerobic capacity but their performance can be improved by powering aerobic fitness, to increase this energy contribution to all-out efforts and to guarantee fast recovery when grinding activity is repeated with short rest intervals.     

Accelerometer-measured daily physical activity related to aerobic fitness in children and adolescents

Maximum oxygen uptake (VO(2PEAK)) is generally considered to be the best single marker for aerobic fitness. While a positive relationship between daily physical activity and aerobic fitness has been established in adults, the relationship appears less clear in children and adolescents. The purpose of this paper is to summarise recently published data on the relationship between daily physical activity, as measured by accelerometers, and VO(2PEAK) in children and adolescents. A PubMed search was performed on 29 October 2010 to identify relevant articles. Studies were considered relevant if they included measurement of daily physical activity by accelerometry and related to a VO(2PEAK) either measured directly at a maximal exercise test or estimated from maximal power output. A total of nine studies were identified, with a total number of 6116 children and adolescents investigated. Most studies reported a low-to-moderate relationship (r = 0.10-0.45) between objectively measured daily physical activity and VO(2PEAK). No conclusive evidence exists that physical activity of higher intensities are more closely related to VO(2PEAK), than lower intensities.     

Prediction equation for head volume of Japanese young adults

Accurate measurement of head volume is indispensable for precise assessments of body composition determined by hydrostatic weighing without head submersion. The purpose of this study was to establish a prediction equation for head volume measured by the immersion method from multiple regression analysis using head parameters (head circumference, head length, head breadth, neck girth and head thickness) as independent variables. The participants were 106 Japanese young adults (55 males and 51 females) aged 17-27 years. Intra-class correlation coefficients (ICCs) for each head parameter and head volume in males and females were very high (ICC = 0.993-0.999, 0.992-0.998). Head circumference was closely related to head volume measured by the immersion method (r = 0.719, 0.861, P < 0.05), and was the most important parameter for the prediction equation in both sexes. Head breadth was related poorly (r = 0.475, 0.500, P < 0.05) and showed a small individual difference. It was, therefore, excluded from the independent variables. The prediction equation for males was predicted head volume = 122.10X1 + 106.19X3 + 37.16X4 - 89.46X5 - 4754.93, R = 0.909, SEE = 121.75 ml, and that for females was predicted head volume = 213.83X1 + 45.24X3 + 36.85X4 - 74.34X5 - 8912.43, R = 0.913, SEE = 136.26 ml (where X1 = head circumference, X3 = head length, X4 = neck girth, X5 = head thickness, and SEE = standard error of the estimate). The limits of agreement for predicted and measured head volume were -234.5 to 234.1 ml for males, and -261.0 to 261.0 ml for females. In cross-validation groups of both sexes, there were no significant differences between measured head volume and predicted head volume. The correlation coefficients between measured head volume and predicted head volume in males and females were 0.894 and 0.908, respectively. The predicted head volume from prediction equations was considered to have high reliability and validity.     

运动对无氧阈、最大吸氧量的影响与心功能的关系

两年游泳系统训练后,男女运动员ＶＯ２ｍａｘ绝对值和相对值都无显著性变化,无氧阈明显提高。表明,ＡＴ的显著改善并不需要ＶＯ２ｍａｘ明显提高。训练后,ＡＴ和心泵血功能都有显著性提高,并且ＣＯ与ＡＴ的相关比ＣＯ与ＶＯ２ｍａｘ相关更为密切,提示,ＡＴ提高与心泵血功能变化有关,心功能可能是影响ＡＴ的重要因素。训练使心脏舒张功能得以改善,这是心脏对运动的又一适应。     

Prediction of segmental lean mass using anthropometric variables in young adults

The aim of the present study was to develop and cross-validate anthropometrical prediction equations for segmental lean tissue mass (SLM). One hundred and seventeen young healthy Caucasians (67 men and 50 women; mean age: 31.9 ± 10.0 years; Body Mass Index: 24.3 ± 3.2 kg · m(-2)) were included. Body mass (BM), stretch stature (SS), 14 circumferences (CC), 13 skinfolds (SF) and 4 bone breadths (BB) were used as anthropometric measurements. Segmental lean mass of both arms, trunk and both legs were measured by dual energy X-ray absorptiometry as the criterion method. Three prediction equations for SLM were developed as follows: arms = 40.394(BM) + 169.836(CCarm-tensed) + 399.162(CCwrist) - 85.414(SFtriceps) - 39.790(SFbiceps) - 7289.190, where Adj.R (2) = 0.97, P < 0.001, and standard error of estimate (SEE) = 355 g;trunk = 181.530(BM) + 155.037(SS) + 534.818(CCneck) + 175.638(CCchest) - 88.359(SFchest) - 147.232(SFsupraspinale) - 46522.165, where Adj.R(2) = 0.97, P < 0.001, and SEE = 1077g; and legs = 55.838(BM) + 88.356(SS) + 235.579(CCmid-thigh) + 278.595(CCcalf) + 288.984(CCankle) - 84.954(SFfront-thigh) - 53.009(SFmedial calf) - 28522.241, where Adj.R (2) = 0.96, P < 0.001, and SEE = 724 g. Cross-validation statistics showed no significant differences (P < 0.05) between observed and predicted SLM. Root mean squared errors were smallest for arms (362 g), followed by legs (820 g) and trunk (1477 g). These new prediction equations allow an accurate estimation of segmental lean mass in groups of young adults, but estimation errors of 8 to 14% can occur in certain individuals.