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Journal of Science and Medicine in Sport 14 (2011) 79–84

Original Research

Comparison of different VO2max equations in the ability to discriminatethe metabolic risk in Portuguese adolescents

Carla Moreira a,∗, Rute Santos a, Jonatan R. Ruiz b, Susana Vale a,Luísa Soares-Miranda a, Ana I. Marques a, Jorge Mota a

a Research Centre for Physical Activity, Health and Leisure, Faculty of Sport, University of Porto, Portugalb Unit for Preventive Nutrition, Department of Biosciences and Nutrition, NOVUM, Karolinska Institute, Sweden

Received 14 December 2009; received in revised form 24 June 2010; accepted 4 July 2010

bstract

There is increasing evidence that cardiorespiratory fitness (CRF) is an important health marker already in youth. This study aimedo determine the ability of five VO2max equations to discriminate between low/high Metabolic Risk in 450 Portuguese adolescents aged0–18. We measured waist and hip circumferences, fasting glucose, total cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides, andlood pressure. For each of these variables, a Z-score was computed. The HDL-cholesterol was multiplied by −1. A metabolic risk scoreas constructed by summing the Z scores of all individual risk factors. High risk was considered when the individual had ≥1 SD of

his score. Cardiorespiratory fitness (CRF) was measured with the 20-m shuttle run test. We estimated VO2max from the CRF tests usingve equations. ROC analyses showed a significant discriminatory accuracy for the Matsuzaka and Barnett(a) equations in identifying the

ow/high metabolic risk in both genders (Matsuzaka girls: AUC = 0.654, 95%CI: 0.591–0.713, p < 0.001, VO2max = 39.5 mL kg−1 min−1; boys:UC = 0.648, 95%CI: 0.576–0.716, p < 0.001, VO2max = 41.8 mL kg−1 min−1; Barnett(a) girls: AUC = 0.620, 95%CI: 0.557–0.681, p < 0.001,O2max = 46.4 mL kg−1 min−1; boys: AUC = 0.628, 95%CI: 0.555–0.697, p = 0.04, VO2max = 42.6 mL kg−1 min−1), and the Ruiz equation in

oys (AUC = 0.638, 95%CI: 0.565–0.706, p < 0.001, VO2max = 47.1 mL kg−1 min−1). The VO2max values found require further testing in otheropulations as well as in longitudinal studies; the identification of adolescents who have low CRF levels can help detect youth with an increasedisk of metabolic disease.

2010 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

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eywords: Cardiorespiratory fitness; ROC analyses; Metabolic syndrome;

. Introduction

The maximal rate of oxygen uptake (VO2max) is consid-red the gold standard for measurement of CRF, which is airect marker of physiological status and reflects the overallapacity of the cardiovascular and respiratory systems andhe ability to carry out prolonged exercise.1

VO2max can be measured using direct (laboratory tests)nd indirect (field-based tests) methods. The use of directeasures in school settings and in population based studies

s limited due to their high cost, necessity of sophisticatednstruments, qualified technicians and time constraints.2

ield-tests provide a practical alternative since they are

∗ Corresponding author.E-mail address: carla m moreira@sapo.pt (C. Moreira).

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440-2440/$ – see front matter © 2010 Sports Medicine Australia. Published by Eloi:10.1016/j.jsams.2010.07.003

ime efficient, low in cost and equipment requirements, andan be easily administered to a large number of peopleimultaneously.2,3

One of the most common field-tests for assessing CRFmong children and adolescents is the 20-m shuttle run test20mSRT).3–5 The 20mSRT is a feasible fitness test, sincelarge number of subjects can be tested at the same time,

t involves minimal equipment and low cost and it can beonducted indoors, outdoors, and on different surfaces in aelatively small area.6 This test is also valid and reliable forse in children and adolescents.3,7 However, as the 20mSRTs an indirect method, some error might always be present

hen estimations of CRF are done.Recent reports suggest that CRF is also an important health

arker in young individuals.7,8 High CRF has been associ-ted with a lower clustering of metabolic risk factors in young

sevier Ltd. All rights reserved.

80 C. Moreira et al. / Journal of Science and Medicine in Sport 14 (2011) 79–84

Table 1Equations to estimate VO2max from the 20-m shuttle-run test in adolescents.

Study Sample Input variables Equation to estimate VO2max (mL kg−1 min−1)

Léger et al.13 188 boys and girls,aged 8–19 years

Speed and age Boys and girls:VO2max = 31.025 + 3.238S −3.248 × A + 0.1536 × S × A where A isage; S is final speed (S = 8 + 0.5x last stage completed).

Barnett et al.14 27 boys, 28 girls,aged 12–17 years

(a) Gender, bodyweight, and speed

Boys and girls: VO2peak = 25.8–6.6 × G − 0.2 × BM + 3.2 × Swhere G is gender (male = 0, female = 1); BM is body mass (kg); Sis final speed

(b) Gender, age,and speed

Boys and girls: VO2peak = 24.4–5.0 × G − 0.8 × A + 3.4 × S whereG is gender (male = 0, female = 1); A is age; S is final speed

Matsuzaka et al.14 62 boys, 70 girls,aged 8–17 years

Gender, age, bodymass index, andspeed

Boys and girls:VO2peak = 25.9 − 2.21 × G − 0.449 × A − 0.831 × BMI + 4.12 × Swhere G is gender (male = 0, female = 1); A is age; BMI is bodymass index; S the maximal running speed

Ruiz et al.16 122 boys, 71 girls,aged 13–19 years

Gender, age,weight, height,

Artificial neural network equation available:http://www.helenastudy.com/scientific.php

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ote that there are two predictive equations resulting from Barnett et al.14: (ge, and speed as outcome variables.

eople,9–11 and results from longitudinal studies indicate thatigh CRF in childhood and adolescence is associated with aealthier cardiovascular profile disease later in life.12

Several equations have been developed to estimateO2max from maximal speed attained during the 20mSRT

Table 1).The validity of these equations against a gold standard has

een tested in several studies, with varying results. Most stud-es have used the Léger’s equation to estimate VO2max but,he equation reported by Léger does not seem to be the mostalid one.6 To the best of our knowledge, it is undeterminedhether the association between CRF and cardiovascular dis-

ase risk factors varies depending on the equation used tostimate VO2max.

The aim of the present study was to determine the abil-ty of five different VO2max equations (Léger,13 Barnett,14

atsuzaka,15 and Ruiz16) to discriminate between low andigh metabolic risk in Portuguese adolescents.

. Methods

This was a cross-sectional assessment performed in twoecondary schools in the North of Portugal. The sample com-rised 450 adolescents (255 girls) apparently healthy andree of medical treatment, aged 10–18 years old. Adolescentsere evaluated during school physical education classes byhysical education teachers specially trained for this data col-ection. All participants were informed about the study’s aimnd parents provided written informed consent, along withhe adolescents’ verbal assent.

Body height was measured to the nearest millime-er in bare or stocking feet with the adolescent standing

pright against a stadiometer (Holtain Ltd., Crymmych, Pem-rokeshire, UK). Weight while lightly dressed was measuredo the nearest 0.10 kg using a portable electronic weight scaleTanita Inner Scan BC 532). Body mass index (BMI) was

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gender, body weight, and speed as outcome variables, and (b) with gender,

alculated as body weight (kg) divided by body height (m2).dolescents were categorized as non-overweight, overweight

nd obese, applying the cut-off points suggested by the Inter-ational Obesity Task Force.17 Waist and hip circumferenceeasurements were taken as described by Lohman.18 Theaist and hip circumferences were used to compute the waist-ip ratio (WHR). Skinfold thickness was measured on the leftide of the body to the nearest 0.1 mm with a skinfold caliperCaliper Holtain; Holtain Ltd., Walles, UK) at the followingites: triceps, subcapsular, and germinal. Each skinfold waseasured twice by a trained technician. The mean of the 2

rials was used in the analysis. The sum of the 3 skinfoldsas used as an indicator of total body fat.Capillary blood samples of participants were taken

rom the earlobe after at least 12 h of fasting. The bloodamples were drawn in capillary tubes (33 ml, Selzer)oated with lithium heparin and immediately assayed usingReflotron Analyser (Boehringer Mannheim, Indianapo-

is, IN). We measured plasma levels of total cholesterolTC), HDL-cholesterol (HDL-C), LDL-cholesterol (LDL-), triglycerides (TG) and glucose.

Blood pressure (BP) was measured using the Dinamapdult/pediatric vital signs monitors, model BP 8800Critikon, USA). Measurements were taken by a trained tech-ician and with all adolescents sitting after at least 5 min ofest. Two measurements were taken after 5 and 10 min ofest. The mean of these two measurements was considered.f the two measurements differed by 2 mm Hg or more, a thirdeasure was taken.CRF was measured using the 20mSRT as previously

escribed by Léger.13 This test requires participants to runack and forth between two lines set 20 m apart. Runningpeed started at 8.5 km/h and increased by 0.5 km/h each

inute, reaching 18.0 km/h at minute 20. Each level was

nnounced on the tape. The participants were told to keep upith the pacer until exhausted. The test was finished when thearticipant failed to reach the end lines concurrent with the

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udio signals on two consecutive occasions. Otherwise, theest ended when the subject stopped because of fatigue. Par-icipants were encouraged to keep running as long as possiblehroughout the course of the test. Number of shuttles per-ormed by each participant was recorded. Participants werehen classified according to the age and sex-specific cut-offoints of Fitnessgram 8.0 criteria, as belonging in, under,r above the health zone, respectively. The mean number ofaps performed by girls and boys was 28 and 49, respec-ively. Five different VO2max equations Léger,13 Barnett,14

atsuzaka,15 and Ruiz16 were used for estimating VO2maxrom the 20mSRT (Table 1).

We computed a continuous metabolic risk score (MRS)rom the following measurements: TC, HDL-C, LDL-C, TG,lucose, systolic blood pressure and the WHR. For each ofhese variables, a Z-score was computed. The HDL-C Z-scoreas multiplied by −1 to indicate higher cardiovascular riskith increasing value. Z scores by age and sex were com-uted for all risk factors. Then, a MRS was constructed byumming the Z scores of all individual risk factors. High riskas considered when the individual had ≥1 SD of this score.he score only applies to this study population. A similarpproach has been used before in adolescents.9

Descriptive data are presented as means and standard devi-tion unless otherwise stated. All variables were checked forormality and appropriately transformed if necessary. Sys-olic blood pressure, TC, TG and WHR were logarithmicallyransformed. Independent sample t tests with Bonferroni cor-ections were performed to compare sexes by continuous

ariables. Linear regression analyses were used to furthertudy the relationship between MRS and CRF resulting fromve different VO2max equations. Receiver operating char-cteristic (ROC) curve analyses were used to analyse the

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able 2haracteristics of adolescents with total body fat, BMI, and CRF.

Mean (SD)

Total (n = 450)

um of three skinfold (mm) 41.8 ± 15.8MI (kg/m2) 21.1 ± 3.1huttle run (no. of laps) 37 ± 20RF within body fat massUnder HZ 47.7 ± 16.0*

HZ or above 31.6 ± 9.6RF within BMIUnder HZ 21.6 ± 3.3HZ or above 20.2 ± 2.3

Number (%)MINon-overweight 336 (74.7)Overweight 97 (21.6)Obesity 17 (3.8)

RFUnder HZ 290 (64.4)HZ or above 160 (35.6)

D, standard deviation; BMI, body mass index; CRF, cardiorespiratory fitness; HZ* Student’s t-test, with Bonferroni corrections for differences between gender and

edicine in Sport 14 (2011) 79–84 81

otential diagnostic accuracy of the different VO2max equa-ions to discriminate between low and high MRS. The areander the curve (AUC) and 95% confidence interval (CI)ere calculated. The AUC represents the ability of the test to

orrectly classify adolescents having a low/high MRS. Thealues of AUC range between 1 (perfect test) to 0.5 (worthlessest). Data were analyzed with SPSS for Windows (version6.0) and Med Calc software. A p value under 0.05 denotedtatistical significance.

. Results

The 450 adolescents included 255 boys (43.3%) and95 girls (57.6%). Their mean age was 13.9 ± 1.9 years.he mean waist and hip circumferences were 74.6 ± 8.4nd 90.1 ± 8.4 cm, respectively. The mean WHR was.82 ± 0.06. Boys had lower levels of TC (144.8 vs50.1 mg/dl), HDL-C (42.8 vs 46.1 mg/dl), TG (53.7 vs8.9 mg/dl) and hip circumference (87.8 vs 91.8 cm) thanirls (p < 0.05), whereas girls had lower WHR (0.82 ratios 0.84 ratio, p < 0.05). Girls had lower plasma glucose val-es than boys (83.9 vs 86.1 mg/dl, p < 0.05). The prevalencef overweight and obesity were 18.8% (n = 48) and 5.5%n = 14) in girls, and 25.1% (n = 49) and 1.5% (n = 3) inoys, respectively (p > 0.05), according to the IOTF crite-ia. The prevalence of adolescents under the healthy zone,efined by the Fitnessgram 8.0 criteria was 86.3% (n = 220)or girls and 35.9% (n = 70) for boys (p < 0.05). The BMI

alues and the mean of sum of skinfold thickness was signif-cantly lower in boys and girls who were in the healthy zoner above compared with those who were under the healthyone (p < 0.05).

Girls (n = 255) Boys (n = 195)

47.1 ± 14.1 34.9 ± 15.2*

21.4 ± 3.2 20.7 ± 2.9*

28 ± 10.0 49 ± 23

48.8 ± 14.5* 44.1 ± 19.4*

37.1 ± 8.7 30.1 ± 9.3

21.7 ± 3.3* 21.4 ± 3.5*

19.6 ± 2.2 20.4 ± 2.3Number (%) Number (%)

193 (75.5) 143 (73.3)48 (18.8) 49 (25.1)14 (5.5) 3 (1.5)

220 (86.3) 70 (35.9)35 (13.7) 125 (64.1)

, healthy zone.CRF categories p < 0.05.

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The results also showed that girls had lower CFR thanoys, using the cut-offs for the five equations: Léger,13

arnett,14 Matsuzaka,15 and Ruiz,16 p < 0.05. Linear regres-ion analyses showed that the Matsuzaka (girls: −0.213,5%CI: −0.311, −0.116, p < 0.001; boys: −0.155, 95%CI:0.248, −0.062, p = 0.001), Barnett(a) (girls: −0.254,

5%CI: −0.389, −0.120, p < 0.001; boys: −0.170, 95%CI:0.285, −0.056, p = 0.004) and Ruiz (girls: −0.119, 95%CI:0.217, −0.021, p = 0.17; boys: −0.168, 95%CI: −0.293,

.044, p = 0.008) equations were negatively associated withRS in both genders.ROC analyses showed a significant discriminatory accu-

acy for the Matsuzaka and Barnett(a) equations in identify-ng the low/high MRS in both genders (Matsuzaka equationirls: AUC = 0.654, 95%CI: 0.591–0.713, p < 0.001; boys:UC = 0.648, 95%CI: 0.576–0.716, p < 0.001; Barnett(a)quation girls: AUC = 0.620, 95%CI: 0.557–0.681, p < 0.001;oys: AUC = 0.628, 95%CI: 0.555–0.697, p = 0.04). Theuiz equation also showed a significant accuracy in boys

AUC = 0.638, 95%CI: 0.565–0.706, p < 0.001) (Table 2).he CRF values at these points for the Matsuzaka equation

ere 39.5 and 41.8 mL kg−1 min−1 in girls and boys, respec-

ively; Barnett(a) values were 46.4 and 42.6 mL kg−1 min−1

n girls and boys, respectively; and for the Ruiz equation foroys, the value was 47.1 mL kg−1 min−1 (Table 3).

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able 3rade-off between sensitivity and specificity for the VO2max equations to screen fo

Girls

arnett(a)VO2max cut-offb ≤46.4Sensitivity 0.583 (0.408–0.745)Specificity 0.668 (0.600 − 0.731)AUC 0.620 (0.557–0.681), p < 0.001

arnett(b)VO2max cut-offb ≤47.7Sensitivity 0.750 (0.578–0.879)Specificity 0.398 (0.332–0.468)AUC 0.556 (0.492–0.619), p = 0.266

égerVO2max cut-offb ≤32.2Sensitivity 0.194 (0.82–0.36)Specificity 0.915 (0.869–0.949)AUC 0.530 (0.465–0.593), p = 0.573atsuzakaVO2max cut-offb ≤39.5Sensitivity 0.556 (0.381–0.721)Specificity 0.782 (0.720–0.836)AUC 0.654 (0.591–0.713), p < 0.001

uizVO2max cut-offb ≤50.9Sensitivity 0.861 (0.705–0.953)Specificity 0.294 (0.233–0.360)AUC 0.561 (0.497–0.624), p = 0.217

5% CI in parentheses; AUC, area under the curve.a Compares different AUC.b VO2max expressed as mL kg−1 min−1.c AUC significantly different from Barnett(b) (p < 0.05).d AUC significantly different from Léger (p < 0.05).e AUC significantly different from Ruiz (p < 0.05).

edicine in Sport 14 (2011) 79–84

. Discussion

The results of this study indicate that a high numberf adolescents did not meet the healthy zone criteria. Ourata also showed that Matsuzaka and Barnett(a) equationseem to have the best trade-off between sensitivity andpecificity for the VO2max equation to screen for MRS inoth genders, and the Ruiz equation is the best-performingquation for boys. Linear regression analyses showed thatO2max estimated from these equations is negatively and

ignificantly associated with MRS scores in both gen-ers.

Our results have important public health implications. Theigh percentage of adolescents who did not meet the healthyone criteria (girls: 86.3%, n = 220; boys: 35.9%, n = 70) canelp identify youth at increased risk of metabolic diseasesnd emphasize the importance of promoting healthy lifestylest these ages. Indeed, high CFR during childhood and ado-escence has been associated with a healthier cardiovascularrofile during these years.19 Data from The European Youtheart Study showed that high levels CRF and physical activ-

ty are associated with a favourable metabolic risk profile.9

RF is influenced by several factors, including body fatness,ge, sex, health status, and genetics, yet its principal mod-fiable determinant is habitual physical activity.20 Evidence

r MRS by gender.

pa Boys pa

≤42.60.621 (0.423–0.793)0.640 (0.550 − 0.714)0.628 (0.555–0.697), p = 0.043

≤42.70.345 (0.180–0.543)0.845 (0.779–0.897)0.591 (0.517–0.661), p = 0.131

≤38.60.276 (0.128–0.472)0.888 (0.829–0.932)0.592 (0.518–0.662), p = 0.124

≤41.80.552 (0.357–0.735)0.758 (0.684–0.822) c

c,d,e 0.648 (0.576–0.716), p < 0.001

≤47.10.793 (0.603–0.920)0.478 (0.399–0.558)0.638 (0.565–0.706), p < 0.001

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uggests that sedentary behavior, low levels of physical activ-ty and CRF in youth continue into adulthood 21,22 and mayredispose young people to disease in later in life.23

Several equations have been developed to estimateO2max from the maximal speed attained during the 20mSRT.ne of the most widely used equation to estimate VO2max

s the Léger equation.13 However, Ruiz 16 developed anrtificial neural network equation to estimate VO2max from0mSRT performance (stage), sex, age, weight, and heightn adolescents and this new equation was shown to be moreccurate than Léger equation. The equations developed tostimate VO2max use different variables such as age, gen-er, and anthropometric variables (skinfold thickness or bodyeight), and this leads us to different results. Furthermore,

pecial attention should be paid when comparisons are madeetween studies. The effect of gender is not clear. Barnett14

ound that gender was a significant predictor whereas Léger13

id not. It is clear that age is an important predictor because itelps take into account the improvement in running economyhat occurs during growth and development.13,14 Recently,uiz12 suggested that the equation of Barnett(b) provides thelosest agreement with directly measured values of VO2maxan index of cardiorespiratory fitness) in youths aged 13–19ears.

In our study, the ROC analyses showed that Matsuzakaquation seems to have the best trade-off between sensitivitynd specificity for the VO2max equation to screen for MRSy gender. However, the Barnett(a) equation has a signifi-ant discriminating accuracy to distinguish between low andigh MRS in both genders and the Ruiz equation in boys.he Matsuzaka equation uses gender, age, BMI and the finalpeed attained in the 20mSRT for the prediction of VO2max.he CRF cut-offs obtained with ROC curve analyses indicate

hat boys have higher values than girls (39.5 mL kg−1 min−1

s 41.8 mL kg−1 min−1). The decline in VO2max reported inirls during adolescence is usually attributed to the effect ofncreased body fat associated with sexual maturity.24 TheRF cut-offs in boys are somewhat similar to those pro-osed by The Cooper Institute25 (41.8 mL kg−1 min−1 vs2 mL kg−1 min−1) whereas in girls the values are slightlyigher (39.5 mL kg−1 min−1 vs 38 mL kg−1 min−1). Theooper Institute cut-off values were extrapolated from the

hresholds established for adults,26 while the cut-offs val-es suggested here have been mathematically calculatedithin the sample. Comparing ours results with those of

he European Group of Pediatric Work Physiology, whichonsidered a VO2max of ≥35 mL kg−1 min−1 for girls and40 mL kg−1 min−1 for boys as a “Health Indicator”,27

ased on expert judgment, our results are slightly highern both genders. Data from CRF levels in the Europeanouth Heart Study (EYHS) sample of adolescents aged 9–10ears have been reported.28 In accordance with our results,

he CRF levels in that study were almost identical with ouresults in boys (42 mL kg−1 min−1 vs 41.8 mL kg−1 min−1);owever, girls showed lower CRF levels, in contrast tour results (37 mL kg−1 min−1 vs 39.5 mL kg−1 min−1).

A

edicine in Sport 14 (2011) 79–84 83

ecently, Lobelo29 has indicated CRF values, calculatedia ROC curve analysis, of 44.1 and 40.3 mL kg−1 min−1

mong 12–15 and 16–19 year-old boys and 36.0 and5.5 mL kg−1 min−1 among 12–15 and 16–19 year-old girls,espectively. It should be noted that the approaches used toalculate the CRF thresholds were different in the previoustudies and in our study, as well as the measured outcomes,ge, and cultural and social factors of the adolescents studied.

This study is limited because it consisted of a cross-ectional design, which limits inferences about causality andts direction, as well as the convenient sample. Therefore, theesults from this study should not be extended beyond thisample. Second, CRF was assessed indirectly. The fact ofsing the estimated VO2max in a shuttle run test as an indi-ator of CRF could lead to the error caused by the use ofn estimating equation. Nevertheless, the shuttle-run test isurrently administered in school settings and a large numberf subjects can be tested at the same time which enhancesarticipant motivation; its common use in large-scale studiesakes it a valuable tool for studying CRF in youth. Schools,hich commonly administer physical fitness tests, are great

paces for health surveillance and control systems for iden-ifying high-risk adolescents. In this regard, the adolescents’RF information collected in schools as part of the Fitness-ram program can help detect youth at high MRF and hasotentially large clinical and public health implications.29

. Conclusion

In conclusion, the present study indicates that Matsuzakand Barnett(a) equations seem to have the best trade-offetween sensitivity and specificity for the VO2max equationo screen for MRS in both genders, and the Ruiz equations the best for boys. The VO2max values calculated via theOC curve analyses for these equations are somewhat similar

o those suggested by worldwide recognized organizations.owever, the VO2max values found require further testing inther populations as well as in longitudinal studies.

ractical implications

The identification of adolescents with low cardiorespira-tory fitness levels can help detect youth with an increasedrisk of metabolic diseases.The use of the FITNESSGRAM program in school settingsis a very helpful tool to measure physical fitness.Public health organizations, schools and parents shouldencourage the population to be more physically active.

cknowledgement

This study was supported by FCT (BD/44422/2008).

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