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1.
Abstract

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.  相似文献   

2.
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.  相似文献   

3.
This study examined a method of predicting body density based on hydrostatic weighing without head submersion (HWwithoutHS). Donnelly and Sintek (1984) developed a method to predict body density based on hydrostatic weight without head submersion. This method predicts the difference (D) between HWwithoutHS and hydrostatic weight with head submersion (HWwithHS) from anthropometric variables (head length and head width), and then calculates body density using D as a correction factor. We developed several prediction equations to estimate D based on head anthropometry and differences between the sexes, and compared their prediction accuracy with Donnelly and Sintek's equation. Thirty-two males and 32 females aged 17-26 years participated in the study. Multiple linear regression analysis was performed to obtain the prediction equations, and the systematic errors of their predictions were assessed by Bland-Altman plots. The best prediction equations obtained were: Males: D(g) = -164.12X1 - 125.81X2 - 111.03X3 + 100.66X4 + 6488.63, where X1 = head length (cm), X2 = head circumference (cm), X3 = head breadth (cm), X4 = head thickness (cm) (R = 0.858, R2 = 0.737, adjusted R2 = 0.687, standard error of the estimate = 224.1); Females: D(g) = -156.03X1 - 14.03X2 - 38.45X3 - 8.87X4 + 7852.45, where X1 = head circumference (cm), X2 = body mass (g), X3 = head length (cm), X4 = height (cm) (R = 0.913, R2 = 0.833, adjusted R2 = 0.808, standard error of the estimate = 137.7). The effective predictors in these prediction equations differed from those of Donnelly and Sintek's equation, and head circumference and head length were included in both equations. The prediction accuracy was improved by statistically selecting effective predictors. Since we did not assess cross-validity, the equations cannot be used to generalize to other populations, and further investigation is required.  相似文献   

4.
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.  相似文献   

5.
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).  相似文献   

6.
The purpose of this study was to assess the accuracy of three recommended anthropometric equations for women and then develop an updated prediction equation using dual energy x-ray absorptiometry (DXA). The percentage of body fat (%BF) by anthropometry was significantly correlated (r = .896-.929; p < .01) with DXA, but each equation underestimated %BF (3.2-5.6 %BF; p < .01). The following DXA criterion (DC) equation was created: %BF= -6.40665 + 0.41946(S3SF) - 0.00126(S3SF)2 + 0.12515(hip) + 0.06473 (age); (S3SF = sum of triceps, suprailiac, thigh; hip = circumference in cm; age = years). The predicted residual sum of squares (PRESS) R2 was high (0.86), and the PRESS standard error of estimate (SEE) was low (2.5 %BF) for our sample of 150 women. The DC equation was further crosschecked on a separate sample of women (n = 25) and again showed excellent agreement. The DC equation appears to be a more accurate estimation of %BF in women.  相似文献   

7.
There have been few reports of advanced body composition profiles of elite fast bowlers in the sport of cricket. Therefore, the aim of the current study was to determine total, regional and unilateral body composition characteristics of elite English first-class cricket fast bowlers in comparison with matched controls, using dual-energy X-ray absorptiometry (DXA). Twelve male fast bowlers and 12 age-matched, non-athletic controls received one total-body DXA scan. Anthropometric data were obtained as well as left and right regional (arms, legs and trunk) fat mass, lean mass and bone mineral content. Fast bowlers were significantly taller and heavier than controls (P < 0.05). Relative to body mass, fast bowlers possessed greater lean mass in the trunk (80.9 ± 3.7 vs. 76.7 ± 5.9%; P = 0.047) and bone mineral content in the trunk (2.9 ± 0.3 vs. 2.6 ± 0.3%; P = 0.049) and legs (5.4 ± 0.5 vs. 4.6 ± 0.6%; P = 0.003). In the arm region, fast bowlers demonstrated significantly greater unilateral differences in bone mineral content (10.6 ± 6.6 vs. 4.5 ± 3.9%; P = 0.012). This study provides specific body composition values for elite-level fast bowlers and highlights the potential for muscle and bone imbalances that may be useful for conditioning professionals. Our findings also suggest beneficial adaptations in body composition and bone mass in fast bowlers compared with their non-athletic counterparts.  相似文献   

8.
Bilateral leg extension power and fat-free mass in young oarsmen   总被引：1，自引：0，他引：1  
We evaluated the impact of bilateral leg extension power and fat-free mass on 2000 m rowing ergometer performance in 332 young oarsmen (age 21+/-2 years, height 1.76+/-0.05 m, body mass 62+/-6 kg; mean+/-s). The 2000 m rowing performance time was correlated with height (1.62-1.93 m; R2=0.23, P<0.001), body mass (53-95 kg; R2=0.53, P<0.001), fat-free mass (47-82 kg; R2=0.58, P<0.001) and bilateral leg extension power (1202-3302 W; R2=0.38, P<0.001). Multiple regression analysis selected fat-free mass and bilateral leg extension power as regressor variables. Fat-free mass explained 58% of the variability in rowing performance and the inclusion of bilateral leg extension power improved the power of prediction by 5%. The results suggest that rowing involves almost every muscle in the body and that bilateral leg extension power is very important during this activity.  相似文献   

9.
The purpose of this study was to develop and cross-validate anthropometric body composition equations for the elderly (i.e., ≥ 65 years old). This was undertaken due to a lack of accurate and reliable body composition equations for the elderly. One-hundred fifty male (n = 75) and female (n = 75; mean age = 70 years, SD = 3.71 years) elderly were randomly assigned to either an equation development sample (n = 50) or an equation validation sample (n = 25), respectively. The male and female development and validation sample groups, respectively, were joined to make combined development (n = 100) and validation (n = 50) samples. Hydrodensitometry was used to determine participant body density, percent fat, fat-free mass, and fat weight for use as the criterion variables by which prediction equations could be developed and validated. The equations presented are for the prediction of body density [body density = 1.0554 + .0142 (gender) + .0267 (height) - .00022 (midaxillary) - .00086 (hip circumference)], percent fat [% fat = .1688 (body mass index) + .542 (hip circumference) -.1639 (weight) -5.7033 (gender) -7.9498], fat-free mass [fat-free mass = 30.3769 + 8.0108 (height) + .824 (weight) - .1355 (suprailiac) - .5419 (hip circumference)], and fat weight [fat weight = .2449 (weight) + .5218 (hip circumference) - .076 (thigh circumference) - 4.0299 (gender) - 37.8619]. The equations provided estimates that were not statistically different from the hydrostatically determined criterion variables but were statisfically different from estimates derived from other published "elderly" body composition equations.  相似文献   

10.
The aims of this study were to determine the validity of fat mass of the trunk as a predictor for visceral fat area at the umbilicus level and to develop equations to predict visceral fat mass at the umbilicus level using fat mass of the trunk measured by dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA). The participants were 121 normal Japanese adults (69 males, 52 females). Another 60 volunteer adults (34 males, 26 females) were recruited for examination of cross-validity. Altogether, 41 adults (15 males, 26 females) in the original group and 19 adults (7 males, 12 females) in the cross-validity group received BIA measurement. We measured fat mass by DXA and the BIA system, which was a single-frequency BIA with 8-point contact electrodes, and visceral fat area by computed tomography. We observed significant correlations for visceral fat area in waist circumference (0.56) and fat mass of the trunk measured by DXA (0.64). There was no significant difference in fat mass of the trunk between the DXA and BIA systems, but the BIA system tended to provide an underestimate compared with DXA. With combined fat mass of the trunk measured by DXA and waist circumference as predictors, visceral fat area was estimated by equation (1) (R = 0.87, R(2) = 0.76, standard error of the estimate = 20.9 cm(2)). When substituting fat mass of the trunk measured by BIA into equation (1), there was no significant difference in visceral fat area between the reference and predicted values. An equation using fat mass of the trunk measured by BIA (equation 2) was obtained (R = 0.89, R(2) = 0.78, standard error of the estimate = 20.7 cm(2)), but a systematic error was found for the males. There was cross-validity in both equations. In conclusion, fat mass of the trunk is an effective predictor for the visceral fat area at the umbilicus level. Fat mass of the trunk measured by BIA might be a valid method to predict visceral fat, although further studies with larger samples taking into account the extent and type of obesity are required.  相似文献   

11.
The primary purpose of this study was to investigate the accuracy of the DF50 (ImpediMed Ltd, Eight Mile Plains, Queensland, Australia) bioelectrical impedance analysis device using dual-energy x-ray absorptiometry as the criterion in two groups: endurance athletes and power athletes. The secondary purpose was to develop accurate body fat percentage prediction equations for each group based on bioelectrical impedance analysis data and/or the combination of bioelectrical impedance analysis and anthropometric data.

Eighty male athletes (40 elite endurance athletes and 40 were power athletes), age 19–48 with body mass indexes ranging from 18.9 to 37.4 were recruited. Anthropometric measurements were taken. Body composition was assessed by dual-energy x-ray absorptiometry and bioelectrical impedance analysis. An athlete-specific bioelectrical impedance analysis prediction equation was developed by stepwise regression analysis using dual-energy x-ray absorptiometry as the criterion and bioelectrical impedance analysis data and anthropometric measurements as predictor variables.

The DF50 bioelectrical impedance analysis significantly overestimated body fat percentage by 6.4 ± 0.5 in the entire group (p < .001) and in both the endurance group (6.1 ± .6, p < .001) and the power group (6.7 ± 0.7, p < .001). The endurance and power group showed no significant difference in the error of estimation by bioelectrical impedance analysis (p = .554), indicating that bioelectrical impedance analysis has the same error in both groups. The final prediction equation incorporated both anthropometric variables as well as bioelectrical impedance analysis variables and produced an adjusted r2 of .982 and a standard error of the estimate (SEE) of 1.98 for the entire group. This prediction equation used bioelectrical impedance analysis measurements and anthropometric measurements, specifically trunk measurements, to account for trunk size, a common source of error in bioelectrical impedance analysis equations. Follow-up validation studies are necessary to further validate the equations produced.  相似文献   

12.
The ability of bioelectrical impedance analysis and anthropometry to predict fat mass and fat-free mass was compared in a sample of 82 male athletes from a wide variety of sports, using dual-energy X-ray absorptiometry (DXA) as the reference method. The percent fat measured by DXA was 10.9+/-4.9% (mean +/- s), and fat mass was predicted with a standard error of the estimate of 1.7 kg for skinfolds and 2.8 kg for bioelectrical impedance analysis (P < 0.001). Fat-free mass was predicted with a standard error of the estimate of 1.7 kg for anthropometry and 2.6 kg for bioelectrical impedance analysis (P < 0.001). Regression of various individual skinfolds and summed skinfolds, to examine the effect of skinfold selection combinations by stepwise regression, produced an optimal fat mass prediction using the thigh and abdominal skinfold sites, and an optimal fat-free mass prediction using the thigh, abdominal and supra-ilium sites. These results suggest that anthropometry offers a better way of assessing body composition in athletes than bioelectrical impedance analysis. Applying the derived equations to a separate sample of 24 athletes predicted fat and fat-free mass with a total error of 2.3 kg (2.9%) and 2.2 kg (2.7%), respectively. Combining the samples introduced more heterogeneity into the sample (n = 106), and the optimal prediction of fat mass used six skinfolds in producing a similar standard error of the estimate (1.7 kg), although this explained a further 4% of the variation in DXA-derived fat. Fat-free mass was predicted best from four skinfolds, although the standard error of the estimate and coefficient of determination were unchanged.  相似文献   

13.
In this study, we developed allometric exponents for scaling Wingate anaerobic test (WAnT) power data that are effective in controlling for body mass (BM) and lean body mass (LBM) and established a normative WAnT data set for college-age women. One hundred women completed a standard WAnT Allometric exponents and percentile ranks for peak (PP) and mean power (MP) were established. Allometric exponents were applied to WAnT scores for an independent sample (n=31) to assess external validity. PP and MP were 477.0 W (SD = 80.0) and 372.6 W (SD = 61.5), respectively. Allometrice exponents for PP and MP scaled for BM were b = 0.92 and b = 0.76, respectively, and for LBM they were b = 0.93 and b = 0.91, respectively. In the independent sample, these exponents produced correlations between allometrically scaled PP and MP and BM of r = -.02 and r = .02, respectively. Correlations between allometrically scaled PP and MP and LBM were r = .004 and r = -.02, respectively. The allometric exponents were effective in partialing out the effect of BM for PP and MP and demonstrated acceptable levels of external validity when applied to an independent sample. The allometric exponents and normative values provide a useful tool for comparing WAnT scores in college-age women without the confounding effects of BM or LBM.  相似文献   

14.
This study aimed to introduce a technique using computer-assisted image analysis for measuring body segmental angles during a static strength element on parallel bars. Criterion validity and intra-rater reliability of measurements were evaluated using digital photography, skin markers and a gravity-reference goniometer. Twenty male former gymnasts participated in this study. They performed a strength hold element on parallel bars (V-sit) and they were photographed with legs extended and stabilized at the highest possible level. The leg to horizontal, trunk to vertical and arm to vertical angles were calculated and examined for reliability using image-pro software. The leg angle was also examined for its validity, by simultaneously using a Myrin goniometer. The two goniometric techniques indicated high leg angle measurements agreement (R = 0.997, p < 0.001). However, Bland-Altman analysis showed that there was a slight leg angle measurement overrating using image-pro software, especially at smaller angles. The Intra-class Correlation Coefficient (ICC) values were high for leg angle (R = 0.971), trunk angle (R = 0.957) and arm angle (R = 0.945), showing an excellent test-retest agreement. It was ascertained that the measurement of segmental angles during V-sit on parallel bars using digital photography and computer-assisted image analysis can be highly reliable when taken by the same experienced examiner.  相似文献   

15.
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 = 122.10X 1 + 106.19X 3 + 37.16X 4 - 89.46X 5 - 4754.93, R = 0.909, SEE = 121.75?ml, and that for females was predicted head volume = 213.83X 1 + 45.24X 3 + 36.85X 4 - 74.34X 5 - 8912.43, R = 0.913, SEE = 136.26?ml (where X 1 = head circumference, X 3 = head length, X 4 = neck girth, X 5 = 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.  相似文献   

16.
In 19 elite schoolboy rowers, the relationships between anthropometric characteristics, metabolic parameters, strength variables and 2000-m rowing ergometer performance time were analysed to test the hypothesis that a combination of these variables would predict performance better than either individual variables or one category of variables. Anthropometric characteristics, maximal oxygen uptake (V O 2m ax ), accumulated oxygen deficit, net efficiency, leg strength and 2000-m rowing ergometer time were measured. Body mass, V O 2max and knee extension correlated with 2000-m performance time (r = -0.41, -0.43 and-0.40, respectively; P 0.05), while net efficiency and accumulated oxygen deficit did not. Multiple-regression analyses indicated that the prediction model using anthropometric variables alone best predicts performance (R = 0.82), followed by the equation comprising body mass, V O 2max and skinfolds (R = 0.80). Although the regression equations increased the predictive power from that obtained using single variables, the hypothesis that a prediction model consisting of variables from different physiological categories would predict performance better than variables from one physiological category was not supported.  相似文献   

17.
The ability of bioelectrical impedance analysis and anthropometry to predict fat mass and fat-free mass was compared in a sample of 82 male athletes from a wide variety of sports, using dual-energy X-ray absorptiometry (DXA) as the reference method. The percent fat measured by DXA was 10.9 - 4.9% (mean - s ), and fat mass was predicted with a standard error of the estimate of 1.7 kg for skinfolds and 2.8 kg for bioelectrical impedance analysis (P?0.001). Fat-free mass was predicted with a standard error of the estimate of 1.7 kg for anthropometry and 2.6 kg for bioelectrical impedance analysis (P?0.001). Regression of various individual skinfolds and summed skinfolds, to examine the eff ect of skinfold selection combinations by stepwise regression, produced an optimal fat mass prediction using the thigh and abdominal skinfold sites, and an optimal fat-free mass prediction using the thigh, abdominal and supra-ilium sites. These results suggest that anthropometry off ers a better way of assessing body composition in athletes than bioelectrical impedance analysis. Applying the derived equations to a separate sample of 24 athletes predicted fat and fat-free mass with a total error of 2.3 kg (2.9%) and 2.2 kg (2.7%), respectively. Combining the samples introduced more heterogeneity into the sample (n=106), and the optimal prediction of fat mass used six skinfolds in producing a similar standard error of the estimate (1.7 kg), although this explained a further 4% of the variation in DXA-derived fat. Fat-free mass was predicted best from four skinfolds, although the standard error of the estimate and coefficient of determination were unchanged.  相似文献   

18.
In 219 recreational male runners, we investigated changes in body mass, total body water, haematocrit, plasma sodium concentration ([Na(+)]), and urine specific gravity as well as fluid intake during a 100-km ultra-marathon. The athletes lost 1.9 kg (s = 1.4) of body mass, equal to 2.5% (s = 1.8) of body mass (P < 0.001), 0.7 kg (s = 1.0) of predicted skeletal muscle mass (P < 0.001), 0.2 kg (s = 1.3) of predicted fat mass (P < 0.05), and 0.9 L (s = 1.6) of predicted total body water (P < 0.001). Haematocrit decreased (P < 0.001), urine specific gravity (P < 0.001), plasma volume (P < 0.05), and plasma [Na(+)] (P < 0.05) all increased. Change in body mass was related to running speed (r = -0.16, P < 0.05), change in plasma volume was associated with change in plasma [Na(+)] (r = -0.28, P < 0.0001), and change in body mass was related to both change in plasma [Na(+)] (r = -0.36) and change in plasma volume (r = 0.31) (P < 0.0001). The athletes consumed 0.65 L (s = 0.27) fluid per hour. Fluid intake was related to both running speed (r = 0.42, P < 0.0001) and change in body mass (r = 0.23, P = 0.0006), but not post-race plasma [Na(+)] or change in plasma [Na(+)] (P > 0.05). In conclusion, faster runners lost more body mass, runners lost more body mass when they drank less fluid, and faster runners drank more fluid than slower runners.  相似文献   

19.
Our purpose was to compare the validity of the Computer Science and Applications, (CSA) Inc., accelerometer in laboratory and field settings and establish CSA count ranges for light, moderate, and vigorous physical activity. Validity was determined in 60 adults during treadmill exercise, using oxygen consumption (VO2) as the criterion measure, while 30 adults walked and jogged outdoors on a 400-m track. The relationship between CSA counts and VO2 was linear (R2 = .89 SEE = 3.72 ml.kg-1.min-1), as was the relationship between velocity and counts in the field (R2 = .89, SEE = 0.89 mi.hr-1). However, significant differences were found (p < .05) between laboratory and field measures of CSA counts for light and vigorous intensity. We conclude that the CSA can be used to quantify walking and jogging outdoors on level ground; however, laboratory equations may not be appropriate for use in field settings, particularly for light and vigorous activity.  相似文献   

20.
ABSTRACT

Selecting effective dietary strategies for professional football players requires comprehensive information on their energy expenditure (EE) and dietary intake. This observational study aimed to assess EE and dietary intake over a 14-day period in a representative group (n = 41) of professional football players playing in the Dutch Premier League (Eredivisie). Daily EE, as assessed by doubly labelled water, was 13.8 ± 1.5 MJ/day, representing a physical activity level (PAL) of 1.75 ± 0.13. Weighted mean energy intake (EI), as assessed by three face-to-face 24-h recalls, was 11.1 ± 2.9 MJ/day, indicating 18 ± 15% underreporting of EI. Daily EI was higher on match days (13.1 ± 4.1 MJ) compared with training (11.1 ± 3.4 MJ; P < 0.01) and rest days (10.5 ± 3.1 MJ; P < 0.001). Daily carbohydrate intake was significantly higher during match days (5.1 ± 1.7 g/kg body mass (BM)) compared with training (3.9 ± 1.5 g/kg BM; P < 0.001) and rest days (3.7 ± 1.4 g/kg BM; P < 0.001). Weighted mean protein intake was 1.7 ± 0.5 g/kg BM. Daytime distribution of protein intake was skewed, with lowest intakes at breakfast and highest at dinner. In conclusion, daily EE and PAL of professional football players are modest. Daily carbohydrate intake should be increased to maximize performance and recovery. Daily protein intake seems more than adequate, but could be distributed more evenly throughout the day.  相似文献   

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