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Comparison of different VO2max equations in the ability to discriminate the metabolic risk in Portuguese adolescents
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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 discriminate the 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 b
a Research Centre for Physical Activity, Health and Leisure, Faculty of Sport, University of Porto, Portugal 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
Abstract There is increasing evidence that cardiorespiratory fitness (CRF) is an important health marker already in youth. This study aimed to determine the ability of five VO2max equations to discriminate between low/high Metabolic Risk in 450 Portuguese adolescents aged 10–18. We measured waist and hip circumferences, fasting glucose, total cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides, and blood pressure. For each of these variables, a Z-score was computed. The HDL-cholesterol was multiplied by −1. A metabolic risk score was constructed by summing the Z scores of all individual risk factors. High risk was considered when the individual had ≥1 SD of this score. Cardiorespiratory fitness (CRF) was measured with the 20-m shuttle run test. We estimated VO2max from the CRF tests using five equations. ROC analyses showed a significant discriminatory accuracy for the Matsuzaka and Barnett(a) equations in identifying the low/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: AUC = 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, VO2max = 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 boys (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 other populations as well as in longitudinal studies; the identification of adolescents who have low CRF levels can help detect youth with an increased risk of metabolic disease. © 2010 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved. Keywords: Cardiorespiratory fitness; ROC analyses; Metabolic syndrome; Youth
1. Introduction The maximal rate of oxygen uptake (VO2max ) is considered the gold standard for measurement of CRF, which is a direct marker of physiological status and reflects the overall capacity of the cardiovascular and respiratory systems and the ability to carry out prolonged exercise.1 VO2max can be measured using direct (laboratory tests) and indirect (field-based tests) methods. The use of direct measures in school settings and in population based studies is limited due to their high cost, necessity of sophisticated instruments, qualified technicians and time constraints.2 Field-tests provide a practical alternative since they are ∗
Corresponding author. E-mail address: carla m [email protected] (C. Moreira).
time efficient, low in cost and equipment requirements, and can be easily administered to a large number of people simultaneously.2,3 One of the most common field-tests for assessing CRF among children and adolescents is the 20-m shuttle run test (20mSRT).3–5 The 20mSRT is a feasible fitness test, since a large number of subjects can be tested at the same time, it involves minimal equipment and low cost and it can be conducted indoors, outdoors, and on different surfaces in a relatively small area.6 This test is also valid and reliable for use in children and adolescents.3,7 However, as the 20mSRT is an indirect method, some error might always be present when estimations of CRF are done. Recent reports suggest that CRF is also an important health marker in young individuals.7,8 High CRF has been associated with a lower clustering of metabolic risk factors in young
1440-2440/$ – see front matter © 2010 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.jsams.2010.07.003
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Table 1 Equations 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
Barnett et al.14
27 boys, 28 girls, aged 12–17 years
(a) Gender, body weight, and speed
Boys and girls: VO2max = 31.025 + 3.238S −3.248 × A + 0.1536 × S × A where A is age; S is final speed (S = 8 + 0.5x last stage completed). Boys and girls: VO2peak = 25.8–6.6 × G − 0.2 × BM + 3.2 × S where G is gender (male = 0, female = 1); BM is body mass (kg); S is final speed Boys and girls: VO2peak = 24.4–5.0 × G − 0.8 × A + 3.4 × S where G is gender (male = 0, female = 1); A is age; S is final speed Boys and girls: VO2peak = 25.9 − 2.21 × G − 0.449 × A − 0.831 × BMI + 4.12 × S where G is gender (male = 0, female = 1); A is age; BMI is body mass index; S the maximal running speed Artificial neural network equation available: http://www.helenastudy.com/scientific.php
Matsuzaka et al.14
62 boys, 70 girls, aged 8–17 years
Ruiz et al.16
122 boys, 71 girls, aged 13–19 years
(b) Gender, age, and speed Gender, age, body mass index, and speed Gender, age, weight, height, and stage
Note that there are two predictive equations resulting from Barnett et al.14 : (a) with gender, body weight, and speed as outcome variables, and (b) with gender, age, and speed as outcome variables.
people,9–11 and results from longitudinal studies indicate that high CRF in childhood and adolescence is associated with a healthier cardiovascular profile disease later in life.12 Several equations have been developed to estimate VO2max from maximal speed attained during the 20mSRT (Table 1). The validity of these equations against a gold standard has been tested in several studies, with varying results. Most studies have used the Léger’s equation to estimate VO2max but, the equation reported by Léger does not seem to be the most valid one.6 To the best of our knowledge, it is undetermined whether the association between CRF and cardiovascular disease risk factors varies depending on the equation used to estimate VO2max . The aim of the present study was to determine the ability of five different VO2max equations (Léger,13 Barnett,14 Matsuzaka,15 and Ruiz16 ) to discriminate between low and high metabolic risk in Portuguese adolescents.
2. Methods This was a cross-sectional assessment performed in two secondary schools in the North of Portugal. The sample comprised 450 adolescents (255 girls) apparently healthy and free of medical treatment, aged 10–18 years old. Adolescents were evaluated during school physical education classes by physical education teachers specially trained for this data collection. All participants were informed about the study’s aim and parents provided written informed consent, along with the adolescents’ verbal assent. Body height was measured to the nearest millimeter in bare or stocking feet with the adolescent standing upright against a stadiometer (Holtain Ltd., Crymmych, Pembrokeshire, UK). Weight while lightly dressed was measured to the nearest 0.10 kg using a portable electronic weight scale (Tanita Inner Scan BC 532). Body mass index (BMI) was
calculated as body weight (kg) divided by body height (m2 ). Adolescents were categorized as non-overweight, overweight and obese, applying the cut-off points suggested by the International Obesity Task Force.17 Waist and hip circumference measurements were taken as described by Lohman.18 The waist and hip circumferences were used to compute the waisthip ratio (WHR). Skinfold thickness was measured on the left side of the body to the nearest 0.1 mm with a skinfold caliper (Caliper Holtain; Holtain Ltd., Walles, UK) at the following sites: triceps, subcapsular, and germinal. Each skinfold was measured twice by a trained technician. The mean of the 2 trials was used in the analysis. The sum of the 3 skinfolds was used as an indicator of total body fat. Capillary blood samples of participants were taken from the earlobe after at least 12 h of fasting. The blood samples were drawn in capillary tubes (33 ml, Selzer) coated with lithium heparin and immediately assayed using a Reflotron Analyser (Boehringer Mannheim, Indianapolis, IN). We measured plasma levels of total cholesterol (TC), HDL-cholesterol (HDL-C), LDL-cholesterol (LDLC), triglycerides (TG) and glucose. Blood pressure (BP) was measured using the Dinamap adult/pediatric vital signs monitors, model BP 8800 (Critikon, USA). Measurements were taken by a trained technician and with all adolescents sitting after at least 5 min of rest. Two measurements were taken after 5 and 10 min of rest. The mean of these two measurements was considered. If the two measurements differed by 2 mm Hg or more, a third measure was taken. CRF was measured using the 20mSRT as previously described by Léger.13 This test requires participants to run back and forth between two lines set 20 m apart. Running speed started at 8.5 km/h and increased by 0.5 km/h each minute, reaching 18.0 km/h at minute 20. Each level was announced on the tape. The participants were told to keep up with the pacer until exhausted. The test was finished when the participant failed to reach the end lines concurrent with the
C. Moreira et al. / Journal of Science and Medicine in Sport 14 (2011) 79–84
audio signals on two consecutive occasions. Otherwise, the test ended when the subject stopped because of fatigue. Participants were encouraged to keep running as long as possible throughout the course of the test. Number of shuttles performed by each participant was recorded. Participants were then classified according to the age and sex-specific cut-off points of Fitnessgram 8.0 criteria, as belonging in, under, or above the health zone, respectively. The mean number of laps performed by girls and boys was 28 and 49, respectively. Five different VO2max equations Léger,13 Barnett,14 Matsuzaka,15 and Ruiz16 were used for estimating VO2max from the 20mSRT (Table 1). We computed a continuous metabolic risk score (MRS) from the following measurements: TC, HDL-C, LDL-C, TG, glucose, systolic blood pressure and the WHR. For each of these variables, a Z-score was computed. The HDL-C Z-score was multiplied by −1 to indicate higher cardiovascular risk with increasing value. Z scores by age and sex were computed for all risk factors. Then, a MRS was constructed by summing the Z scores of all individual risk factors. High risk was considered when the individual had ≥1 SD of this score. The score only applies to this study population. A similar approach has been used before in adolescents.9 Descriptive data are presented as means and standard deviation unless otherwise stated. All variables were checked for normality and appropriately transformed if necessary. Systolic blood pressure, TC, TG and WHR were logarithmically transformed. Independent sample t tests with Bonferroni corrections were performed to compare sexes by continuous variables. Linear regression analyses were used to further study the relationship between MRS and CRF resulting from five different VO2max equations. Receiver operating characteristic (ROC) curve analyses were used to analyse the
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potential diagnostic accuracy of the different VO2max equations to discriminate between low and high MRS. The area under the curve (AUC) and 95% confidence interval (CI) were calculated. The AUC represents the ability of the test to correctly classify adolescents having a low/high MRS. The values of AUC range between 1 (perfect test) to 0.5 (worthless test). Data were analyzed with SPSS for Windows (version 16.0) and Med Calc software. A p value under 0.05 denoted statistical significance.
3. Results The 450 adolescents included 255 boys (43.3%) and 195 girls (57.6%). Their mean age was 13.9 ± 1.9 years. The mean waist and hip circumferences were 74.6 ± 8.4 and 90.1 ± 8.4 cm, respectively. The mean WHR was 0.82 ± 0.06. Boys had lower levels of TC (144.8 vs 150.1 mg/dl), HDL-C (42.8 vs 46.1 mg/dl), TG (53.7 vs 58.9 mg/dl) and hip circumference (87.8 vs 91.8 cm) than girls (p < 0.05), whereas girls had lower WHR (0.82 ratio vs 0.84 ratio, p < 0.05). Girls had lower plasma glucose values than boys (83.9 vs 86.1 mg/dl, p < 0.05). The prevalence of 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) in boys, respectively (p > 0.05), according to the IOTF criteria. The prevalence of adolescents under the healthy zone, defined by the Fitnessgram 8.0 criteria was 86.3% (n = 220) for girls and 35.9% (n = 70) for boys (p < 0.05). The BMI values and the mean of sum of skinfold thickness was significantly lower in boys and girls who were in the healthy zone or above compared with those who were under the healthy zone (p < 0.05).
Table 2 Characteristics of adolescents with total body fat, BMI, and CRF. Mean (SD)
Sum of three skinfold (mm) BMI (kg/m2 ) Shuttle run (no. of laps) CRF within body fat mass Under HZ HZ or above CRF within BMI Under HZ HZ or above BMI Non-overweight Overweight Obesity CRF Under HZ HZ or above
Total (n = 450)
Girls (n = 255)
Boys (n = 195)
41.8 ± 15.8 21.1 ± 3.1 37 ± 20
47.1 ± 14.1 21.4 ± 3.2 28 ± 10.0
34.9 ± 15.2* 20.7 ± 2.9* 49 ± 23
47.7 ± 16.0* 31.6 ± 9.6
48.8 ± 14.5* 37.1 ± 8.7
44.1 ± 19.4* 30.1 ± 9.3
21.6 ± 3.3 20.2 ± 2.3 Number (%)
21.7 ± 3.3* 19.6 ± 2.2 Number (%)
21.4 ± 3.5* 20.4 ± 2.3 Number (%)
336 (74.7) 97 (21.6) 17 (3.8)
193 (75.5) 48 (18.8) 14 (5.5)
143 (73.3) 49 (25.1) 3 (1.5)
290 (64.4) 160 (35.6)
220 (86.3) 35 (13.7)
70 (35.9) 125 (64.1)
SD, standard deviation; BMI, body mass index; CRF, cardiorespiratory fitness; HZ, healthy zone. * Student’s t-test, with Bonferroni corrections for differences between gender and CRF categories p < 0.05.
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The results also showed that girls had lower CFR than boys, using the cut-offs for the five equations: Léger,13 Barnett,14 Matsuzaka,15 and Ruiz,16 p < 0.05. Linear regression analyses showed that the Matsuzaka (girls: −0.213, 95%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, 95%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, 0.044, p = 0.008) equations were negatively associated with MRS in both genders. ROC analyses showed a significant discriminatory accuracy for the Matsuzaka and Barnett(a) equations in identifying the low/high MRS in both genders (Matsuzaka equation girls: AUC = 0.654, 95%CI: 0.591–0.713, p < 0.001; boys: AUC = 0.648, 95%CI: 0.576–0.716, p < 0.001; Barnett(a) equation girls: AUC = 0.620, 95%CI: 0.557–0.681, p < 0.001; boys: AUC = 0.628, 95%CI: 0.555–0.697, p = 0.04). The Ruiz equation also showed a significant accuracy in boys (AUC = 0.638, 95%CI: 0.565–0.706, p < 0.001) (Table 2). The CRF values at these points for the Matsuzaka equation were 39.5 and 41.8 mL kg−1 min−1 in girls and boys, respectively; Barnett(a) values were 46.4 and 42.6 mL kg−1 min−1 in girls and boys, respectively; and for the Ruiz equation for boys, the value was 47.1 mL kg−1 min−1 (Table 3).
4. Discussion The results of this study indicate that a high number of adolescents did not meet the healthy zone criteria. Our data also showed that Matsuzaka and Barnett(a) equations seem to have the best trade-off between sensitivity and specificity for the VO2max equation to screen for MRS in both genders, and the Ruiz equation is the best-performing equation for boys. Linear regression analyses showed that VO2max estimated from these equations is negatively and significantly associated with MRS scores in both genders. Our results have important public health implications. The high percentage of adolescents who did not meet the healthy zone criteria (girls: 86.3%, n = 220; boys: 35.9%, n = 70) can help identify youth at increased risk of metabolic diseases and emphasize the importance of promoting healthy lifestyles at these ages. Indeed, high CFR during childhood and adolescence has been associated with a healthier cardiovascular profile during these years.19 Data from The European Youth Heart Study showed that high levels CRF and physical activity are associated with a favourable metabolic risk profile.9 CRF is influenced by several factors, including body fatness, age, sex, health status, and genetics, yet its principal modifiable determinant is habitual physical activity.20 Evidence
Table 3 Trade-off between sensitivity and specificity for the VO2 max equations to screen for MRS by gender. Girls Barnett(a) VO2max cut-offb Sensitivity Specificity AUC Barnett(b) VO2max cut-offb Sensitivity Specificity AUC Léger VO2max cut-offb Sensitivity Specificity AUC Matsuzaka VO2max cut-offb Sensitivity Specificity AUC Ruiz VO2max cut-offb Sensitivity Specificity AUC
pa
Boys
≤46.4 0.583 (0.408–0.745) 0.668 (0.600 − 0.731) 0.620 (0.557–0.681), p < 0.001
≤42.6 0.621 (0.423–0.793) 0.640 (0.550 − 0.714) 0.628 (0.555–0.697), p = 0.043
≤47.7 0.750 (0.578–0.879) 0.398 (0.332–0.468) 0.556 (0.492–0.619), p = 0.266
≤42.7 0.345 (0.180–0.543) 0.845 (0.779–0.897) 0.591 (0.517–0.661), p = 0.131
≤32.2 0.194 (0.82–0.36) 0.915 (0.869–0.949) 0.530 (0.465–0.593), p = 0.573
≤38.6 0.276 (0.128–0.472) 0.888 (0.829–0.932) 0.592 (0.518–0.662), p = 0.124
≤39.5 0.556 (0.381–0.721) 0.782 (0.720–0.836) 0.654 (0.591–0.713), p < 0.001
≤41.8 0.552 (0.357–0.735) 0.758 (0.684–0.822) 0.648 (0.576–0.716), p < 0.001
≤50.9 0.861 (0.705–0.953) 0.294 (0.233–0.360) 0.561 (0.497–0.624), p = 0.217
95% CI in parentheses; AUC, area under the curve. a Compares different AUC. b VO −1 min−1 . 2max expressed as mL kg 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).
c,d,e
≤47.1 0.793 (0.603–0.920) 0.478 (0.399–0.558) 0.638 (0.565–0.706), p < 0.001
pa
c
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suggests that sedentary behavior, low levels of physical activity and CRF in youth continue into adulthood 21,22 and may predispose young people to disease in later in life.23 Several equations have been developed to estimate VO2max from the maximal speed attained during the 20mSRT. One of the most widely used equation to estimate VO2max is the Léger equation.13 However, Ruiz 16 developed an artificial neural network equation to estimate VO2max from 20mSRT performance (stage), sex, age, weight, and height in adolescents and this new equation was shown to be more accurate than Léger equation. The equations developed to estimate VO2max use different variables such as age, gender, and anthropometric variables (skinfold thickness or body weight), and this leads us to different results. Furthermore, special attention should be paid when comparisons are made between studies. The effect of gender is not clear. Barnett14 found that gender was a significant predictor whereas Léger13 did not. It is clear that age is an important predictor because it helps take into account the improvement in running economy that occurs during growth and development.13,14 Recently, Ruiz12 suggested that the equation of Barnett(b) provides the closest agreement with directly measured values of VO2max (an index of cardiorespiratory fitness) in youths aged 13–19 years. In our study, the ROC analyses showed that Matsuzaka equation seems to have the best trade-off between sensitivity and specificity for the VO2max equation to screen for MRS by gender. However, the Barnett(a) equation has a significant discriminating accuracy to distinguish between low and high MRS in both genders and the Ruiz equation in boys. The Matsuzaka equation uses gender, age, BMI and the final speed attained in the 20mSRT for the prediction of VO2max . The CRF cut-offs obtained with ROC curve analyses indicate that boys have higher values than girls (39.5 mL kg−1 min−1 vs 41.8 mL kg−1 min−1 ). The decline in VO2max reported in girls during adolescence is usually attributed to the effect of increased body fat associated with sexual maturity.24 The CRF cut-offs in boys are somewhat similar to those proposed by The Cooper Institute25 (41.8 mL kg−1 min−1 vs 42 mL kg−1 min−1 ) whereas in girls the values are slightly higher (39.5 mL kg−1 min−1 vs 38 mL kg−1 min−1 ). The Cooper Institute cut-off values were extrapolated from the thresholds established for adults,26 while the cut-offs values suggested here have been mathematically calculated within the sample. Comparing ours results with those of the European Group of Pediatric Work Physiology, which considered a VO2max of ≥35 mL kg−1 min−1 for girls and ≥40 mL kg−1 min−1 for boys as a “Health Indicator”,27 based on expert judgment, our results are slightly higher in both genders. Data from CRF levels in the European Youth Heart Study (EYHS) sample of adolescents aged 9–10 years have been reported.28 In accordance with our results, the CRF levels in that study were almost identical with our results in boys (42 mL kg−1 min−1 vs 41.8 mL kg−1 min−1 ); however, girls showed lower CRF levels, in contrast to our results (37 mL kg−1 min−1 vs 39.5 mL kg−1 min−1 ).
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Recently, Lobelo29 has indicated CRF values, calculated via ROC curve analysis, of 44.1 and 40.3 mL kg−1 min−1 among 12–15 and 16–19 year-old boys and 36.0 and 35.5 mL kg−1 min−1 among 12–15 and 16–19 year-old girls, respectively. It should be noted that the approaches used to calculate the CRF thresholds were different in the previous studies and in our study, as well as the measured outcomes, age, and cultural and social factors of the adolescents studied. This study is limited because it consisted of a crosssectional design, which limits inferences about causality and its direction, as well as the convenient sample. Therefore, the results from this study should not be extended beyond this sample. Second, CRF was assessed indirectly. The fact of using the estimated VO2max in a shuttle run test as an indicator of CRF could lead to the error caused by the use of an estimating equation. Nevertheless, the shuttle-run test is currently administered in school settings and a large number of subjects can be tested at the same time which enhances participant motivation; its common use in large-scale studies makes it a valuable tool for studying CRF in youth. Schools, which commonly administer physical fitness tests, are great spaces for health surveillance and control systems for identifying high-risk adolescents. In this regard, the adolescents’ CRF information collected in schools as part of the Fitnessgram program can help detect youth at high MRF and has potentially large clinical and public health implications.29
5. Conclusion In conclusion, the present study indicates that Matsuzaka and Barnett(a) equations seem to have the best trade-off between sensitivity and specificity for the VO2max equation to screen for MRS in both genders, and the Ruiz equation is the best for boys. The VO2max values calculated via the ROC curve analyses for these equations are somewhat similar to those suggested by worldwide recognized organizations. However, the VO2max values found require further testing in other populations as well as in longitudinal studies.
Practical implications • The identification of adolescents with low cardiorespiratory fitness levels can help detect youth with an increased risk of metabolic diseases. • The use of the FITNESSGRAM program in school settings is a very helpful tool to measure physical fitness. • Public health organizations, schools and parents should encourage the population to be more physically active.
Acknowledgement This study was supported by FCT (BD/44422/2008).
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References 1. Taylor HL, Buskirk E, Henschel A. Maximal oxygen intake as an objective measure of cardio-respiratory performance. J Appl Physiol 1955;8:73–80. 2. Welk GJ, Meredith MD. Fitnessgram/activitygram reference guide. 3rd ed. Dallas, TX: The Cooper Institute; 2008. p. 96–142. 3. Castro-Pinero J, Artero EG, Espana-Romero V, et al. Criterion-related validity of field-based fitness tests in youth: a systematic review. Br J Sports Med; doi:10.1136/bjsm.2009.058321, in press. 4. Léger LA, Lambert A, Goulet A, et al. Capacity aerobic des Quebecois de 6 a 17 ans—test Navette de 20 metres avec paliers de 1 min. Can J Appl Sport Sci 1984;9:64–9. 5. Tomkinson GR, Léger LA, Olds TS, et al. Secular trends in the performance of children and adolescents (1980–2000): an analysis of 55 studies of the 20 m shuttle run test in 11 countries. Sports Med 2003;33:285–300. 6. Ruiz JR, Silva G, Oliveira N, et al. Criterion-related validity of the 20-m shuttle run test in youths aged 13–19 years. J Sports Sci 2009;27:899–906. 7. Ortega FB, Artero EG, Ruiz JR, et al. Reliability of health-related physical fitness tests in European adolescents. The HELENA Study. Int J Obes (Lond) 2008;32(Suppl. 5):S49–57. 8. Castillo-Garzon M, Ruiz JR, Ortega FB, et al. A Mediterranean diet is not enough for health: physical fitness is an important additional contributor to health for the adults of tomorrow. World Rev Nutr Diet 2007;97:114–38. 9. Andersen LB, Harro M, Sardinha LB, et al. Physical activity and clustered cardiovascular risk in children: a cross-sectional study (The European Youth Heart Study). Lancet 2006;368:299–304. 10. Brage S, Wedderkopp N, Ekelund U, et al. Features of the metabolic syndrome are associated with objectively measured physical activity and fitness in Danish children: the European Youth Heart Study (EYHS). Diabetes Care 2004;27:2141–8. 11. Ruiz JR, Ortega FB, Meusel D, et al. Cardiorespiratory fitness is associated with features of metabolic risk factors in children. Should cardiorespiratory fitness be assessed in a European health monitoring system? The European Youth Heart Study. J Public Health 2006;14:94–102. 12. Ruiz JR, Castro-Pinero J, Artero EG, et al. Predictive validity of health-related fitness in youth: a systematic review. Br J Sports Med 2009;43:909–23. 13. Léger L, Mercier D, Gadoury C, et al. The multistage 20 metre shuttle run test for aerobic fitness. J Sports Sci 1988;6:93–101. 14. Barnett A, Chan L, Bruce I. A preliminary study of the 20-m multistage shuttle run as a predictor of peak V02 in Hong Kong Chinese students. Pediat Exerc Sci 1993;5:442–50.
15. Matsuzaka A, Takahashi Y, Yamazoe M, et al. Validity of the multistage 20-m shuttle-run test for Japanese children, adolescents, and adults. Pediat Exerc Sci 2004;16:113–25. 16. Ruiz JR, Ramirez-Lechuga J, Ortega F, et al. Artificial neural networkbased equation for estimating VO2max from the 20 m shuttle run test in adolescents. Artif Intell Med 2008;44:233–45. 17. Cole T, Bellizzi M, Flegal K, et al. Establishing a standard definition for child overweigth and obesity worldwide: internacional survey. BMJ 2000;320:1240–3. 18. Lohman T, Roche A, Martorell F. Anthropometric standardization reference manual. Champaign IL: Human Kinetics; 1991. p. 55–70. 19. Mesa JL, Ruiz JR, Ortega FB, et al. Aerobic physical fitness in relation to blood lipids and fasting glycaemia in adolescents: influence of weight status. Nutr Metab Cardiovasc Dis 2006;16:285–93. 20. Bouchard C, Perusse L. Heredity, activity level, fitness, and health. In: Bouchard C, Shephard RJ, Stephens T, editors. Physical activity, fitness, and health: international proceedings and consensus statement. Champaign, IL: Human Kinetics; 1994. p. 106–18. 21. Froberg K, Andersen LB. Mini review: physical activity and fitness and its relations to cardiovascular disease risk factors in children. Int J Obes 2005;29:S34–9. 22. Yang X, Telama R, Leskinen E, et al. Testing a model of physical activity and obesity tracking from youth to adulthood: the cardiovascular risk in young Finns study. Int J Obes (Lond) 2007;31:521–7. 23. Hasselstrom H, Hansen SE, Froberg K, et al. Physical fitness and physical activity during adolescence as predictors of cardiovascular disease risk in young adulthood. Danish Youth and Sports study. An eight-year follow-up study. Int J Sports Med 2002;23:S27–31. 24. Mota J, Guerra S, Leandro C, et al. Association of maturation, sex, and body fat in cardiorespiratory fitness. Am J Hum Biol 2002;14: 707–12. 25. The Cooper Institute for Aerobics Research. FITNESSGRAM test administration manual. 3rd ed. Champaign, IL: Human Kinetics; 2004. p. 38–39. 26. Blair SN, Kohl HW, Paffenbarger RS, et al. Physical-fitness and all-cause mortality—a prospective-study of healthy-men and women. JAMA 1989;262:2395–401. 27. Bell RD, Macek M, Rutenfranz J, et al. Health indicators and risk factors of cardiovascular diseases during childhood and adolescence. In: Rutenfranz J, Mocelin R, Klimt F, editors. Children and exercise XII. Champaign, IL: Human Kinetics; 1986. p. 19–27. 28. Ruiz JR, Ortega F, Rizzo N, et al. High cardiovascular fitness is associated with low metabolic risk score in children: the European Youth Heart Study. Pediatr Res 2007;61:350–5. 29. Lobelo F, Pate RR, Dowda M, et al. Validity of cardiorespiratory fitness criterion-referenced standards for adolescents. Med Sci Sports Exerc 2009;41:1222–9.
Journal of Science and Medicine in Sport 14 (2011) 79–84
Original Research
Comparison of different VO2max equations in the ability to discriminate the 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 b
a Research Centre for Physical Activity, Health and Leisure, Faculty of Sport, University of Porto, Portugal 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
Abstract There is increasing evidence that cardiorespiratory fitness (CRF) is an important health marker already in youth. This study aimed to determine the ability of five VO2max equations to discriminate between low/high Metabolic Risk in 450 Portuguese adolescents aged 10–18. We measured waist and hip circumferences, fasting glucose, total cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides, and blood pressure. For each of these variables, a Z-score was computed. The HDL-cholesterol was multiplied by −1. A metabolic risk score was constructed by summing the Z scores of all individual risk factors. High risk was considered when the individual had ≥1 SD of this score. Cardiorespiratory fitness (CRF) was measured with the 20-m shuttle run test. We estimated VO2max from the CRF tests using five equations. ROC analyses showed a significant discriminatory accuracy for the Matsuzaka and Barnett(a) equations in identifying the low/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: AUC = 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, VO2max = 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 boys (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 other populations as well as in longitudinal studies; the identification of adolescents who have low CRF levels can help detect youth with an increased risk of metabolic disease. © 2010 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved. Keywords: Cardiorespiratory fitness; ROC analyses; Metabolic syndrome; Youth
1. Introduction The maximal rate of oxygen uptake (VO2max ) is considered the gold standard for measurement of CRF, which is a direct marker of physiological status and reflects the overall capacity of the cardiovascular and respiratory systems and the ability to carry out prolonged exercise.1 VO2max can be measured using direct (laboratory tests) and indirect (field-based tests) methods. The use of direct measures in school settings and in population based studies is limited due to their high cost, necessity of sophisticated instruments, qualified technicians and time constraints.2 Field-tests provide a practical alternative since they are ∗
Corresponding author. E-mail address: carla m [email protected] (C. Moreira).
time efficient, low in cost and equipment requirements, and can be easily administered to a large number of people simultaneously.2,3 One of the most common field-tests for assessing CRF among children and adolescents is the 20-m shuttle run test (20mSRT).3–5 The 20mSRT is a feasible fitness test, since a large number of subjects can be tested at the same time, it involves minimal equipment and low cost and it can be conducted indoors, outdoors, and on different surfaces in a relatively small area.6 This test is also valid and reliable for use in children and adolescents.3,7 However, as the 20mSRT is an indirect method, some error might always be present when estimations of CRF are done. Recent reports suggest that CRF is also an important health marker in young individuals.7,8 High CRF has been associated with a lower clustering of metabolic risk factors in young
1440-2440/$ – see front matter © 2010 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.jsams.2010.07.003
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Table 1 Equations 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
Barnett et al.14
27 boys, 28 girls, aged 12–17 years
(a) Gender, body weight, and speed
Boys and girls: VO2max = 31.025 + 3.238S −3.248 × A + 0.1536 × S × A where A is age; S is final speed (S = 8 + 0.5x last stage completed). Boys and girls: VO2peak = 25.8–6.6 × G − 0.2 × BM + 3.2 × S where G is gender (male = 0, female = 1); BM is body mass (kg); S is final speed Boys and girls: VO2peak = 24.4–5.0 × G − 0.8 × A + 3.4 × S where G is gender (male = 0, female = 1); A is age; S is final speed Boys and girls: VO2peak = 25.9 − 2.21 × G − 0.449 × A − 0.831 × BMI + 4.12 × S where G is gender (male = 0, female = 1); A is age; BMI is body mass index; S the maximal running speed Artificial neural network equation available: http://www.helenastudy.com/scientific.php
Matsuzaka et al.14
62 boys, 70 girls, aged 8–17 years
Ruiz et al.16
122 boys, 71 girls, aged 13–19 years
(b) Gender, age, and speed Gender, age, body mass index, and speed Gender, age, weight, height, and stage
Note that there are two predictive equations resulting from Barnett et al.14 : (a) with gender, body weight, and speed as outcome variables, and (b) with gender, age, and speed as outcome variables.
people,9–11 and results from longitudinal studies indicate that high CRF in childhood and adolescence is associated with a healthier cardiovascular profile disease later in life.12 Several equations have been developed to estimate VO2max from maximal speed attained during the 20mSRT (Table 1). The validity of these equations against a gold standard has been tested in several studies, with varying results. Most studies have used the Léger’s equation to estimate VO2max but, the equation reported by Léger does not seem to be the most valid one.6 To the best of our knowledge, it is undetermined whether the association between CRF and cardiovascular disease risk factors varies depending on the equation used to estimate VO2max . The aim of the present study was to determine the ability of five different VO2max equations (Léger,13 Barnett,14 Matsuzaka,15 and Ruiz16 ) to discriminate between low and high metabolic risk in Portuguese adolescents.
2. Methods This was a cross-sectional assessment performed in two secondary schools in the North of Portugal. The sample comprised 450 adolescents (255 girls) apparently healthy and free of medical treatment, aged 10–18 years old. Adolescents were evaluated during school physical education classes by physical education teachers specially trained for this data collection. All participants were informed about the study’s aim and parents provided written informed consent, along with the adolescents’ verbal assent. Body height was measured to the nearest millimeter in bare or stocking feet with the adolescent standing upright against a stadiometer (Holtain Ltd., Crymmych, Pembrokeshire, UK). Weight while lightly dressed was measured to the nearest 0.10 kg using a portable electronic weight scale (Tanita Inner Scan BC 532). Body mass index (BMI) was
calculated as body weight (kg) divided by body height (m2 ). Adolescents were categorized as non-overweight, overweight and obese, applying the cut-off points suggested by the International Obesity Task Force.17 Waist and hip circumference measurements were taken as described by Lohman.18 The waist and hip circumferences were used to compute the waisthip ratio (WHR). Skinfold thickness was measured on the left side of the body to the nearest 0.1 mm with a skinfold caliper (Caliper Holtain; Holtain Ltd., Walles, UK) at the following sites: triceps, subcapsular, and germinal. Each skinfold was measured twice by a trained technician. The mean of the 2 trials was used in the analysis. The sum of the 3 skinfolds was used as an indicator of total body fat. Capillary blood samples of participants were taken from the earlobe after at least 12 h of fasting. The blood samples were drawn in capillary tubes (33 ml, Selzer) coated with lithium heparin and immediately assayed using a Reflotron Analyser (Boehringer Mannheim, Indianapolis, IN). We measured plasma levels of total cholesterol (TC), HDL-cholesterol (HDL-C), LDL-cholesterol (LDLC), triglycerides (TG) and glucose. Blood pressure (BP) was measured using the Dinamap adult/pediatric vital signs monitors, model BP 8800 (Critikon, USA). Measurements were taken by a trained technician and with all adolescents sitting after at least 5 min of rest. Two measurements were taken after 5 and 10 min of rest. The mean of these two measurements was considered. If the two measurements differed by 2 mm Hg or more, a third measure was taken. CRF was measured using the 20mSRT as previously described by Léger.13 This test requires participants to run back and forth between two lines set 20 m apart. Running speed started at 8.5 km/h and increased by 0.5 km/h each minute, reaching 18.0 km/h at minute 20. Each level was announced on the tape. The participants were told to keep up with the pacer until exhausted. The test was finished when the participant failed to reach the end lines concurrent with the
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audio signals on two consecutive occasions. Otherwise, the test ended when the subject stopped because of fatigue. Participants were encouraged to keep running as long as possible throughout the course of the test. Number of shuttles performed by each participant was recorded. Participants were then classified according to the age and sex-specific cut-off points of Fitnessgram 8.0 criteria, as belonging in, under, or above the health zone, respectively. The mean number of laps performed by girls and boys was 28 and 49, respectively. Five different VO2max equations Léger,13 Barnett,14 Matsuzaka,15 and Ruiz16 were used for estimating VO2max from the 20mSRT (Table 1). We computed a continuous metabolic risk score (MRS) from the following measurements: TC, HDL-C, LDL-C, TG, glucose, systolic blood pressure and the WHR. For each of these variables, a Z-score was computed. The HDL-C Z-score was multiplied by −1 to indicate higher cardiovascular risk with increasing value. Z scores by age and sex were computed for all risk factors. Then, a MRS was constructed by summing the Z scores of all individual risk factors. High risk was considered when the individual had ≥1 SD of this score. The score only applies to this study population. A similar approach has been used before in adolescents.9 Descriptive data are presented as means and standard deviation unless otherwise stated. All variables were checked for normality and appropriately transformed if necessary. Systolic blood pressure, TC, TG and WHR were logarithmically transformed. Independent sample t tests with Bonferroni corrections were performed to compare sexes by continuous variables. Linear regression analyses were used to further study the relationship between MRS and CRF resulting from five different VO2max equations. Receiver operating characteristic (ROC) curve analyses were used to analyse the
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potential diagnostic accuracy of the different VO2max equations to discriminate between low and high MRS. The area under the curve (AUC) and 95% confidence interval (CI) were calculated. The AUC represents the ability of the test to correctly classify adolescents having a low/high MRS. The values of AUC range between 1 (perfect test) to 0.5 (worthless test). Data were analyzed with SPSS for Windows (version 16.0) and Med Calc software. A p value under 0.05 denoted statistical significance.
3. Results The 450 adolescents included 255 boys (43.3%) and 195 girls (57.6%). Their mean age was 13.9 ± 1.9 years. The mean waist and hip circumferences were 74.6 ± 8.4 and 90.1 ± 8.4 cm, respectively. The mean WHR was 0.82 ± 0.06. Boys had lower levels of TC (144.8 vs 150.1 mg/dl), HDL-C (42.8 vs 46.1 mg/dl), TG (53.7 vs 58.9 mg/dl) and hip circumference (87.8 vs 91.8 cm) than girls (p < 0.05), whereas girls had lower WHR (0.82 ratio vs 0.84 ratio, p < 0.05). Girls had lower plasma glucose values than boys (83.9 vs 86.1 mg/dl, p < 0.05). The prevalence of 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) in boys, respectively (p > 0.05), according to the IOTF criteria. The prevalence of adolescents under the healthy zone, defined by the Fitnessgram 8.0 criteria was 86.3% (n = 220) for girls and 35.9% (n = 70) for boys (p < 0.05). The BMI values and the mean of sum of skinfold thickness was significantly lower in boys and girls who were in the healthy zone or above compared with those who were under the healthy zone (p < 0.05).
Table 2 Characteristics of adolescents with total body fat, BMI, and CRF. Mean (SD)
Sum of three skinfold (mm) BMI (kg/m2 ) Shuttle run (no. of laps) CRF within body fat mass Under HZ HZ or above CRF within BMI Under HZ HZ or above BMI Non-overweight Overweight Obesity CRF Under HZ HZ or above
Total (n = 450)
Girls (n = 255)
Boys (n = 195)
41.8 ± 15.8 21.1 ± 3.1 37 ± 20
47.1 ± 14.1 21.4 ± 3.2 28 ± 10.0
34.9 ± 15.2* 20.7 ± 2.9* 49 ± 23
47.7 ± 16.0* 31.6 ± 9.6
48.8 ± 14.5* 37.1 ± 8.7
44.1 ± 19.4* 30.1 ± 9.3
21.6 ± 3.3 20.2 ± 2.3 Number (%)
21.7 ± 3.3* 19.6 ± 2.2 Number (%)
21.4 ± 3.5* 20.4 ± 2.3 Number (%)
336 (74.7) 97 (21.6) 17 (3.8)
193 (75.5) 48 (18.8) 14 (5.5)
143 (73.3) 49 (25.1) 3 (1.5)
290 (64.4) 160 (35.6)
220 (86.3) 35 (13.7)
70 (35.9) 125 (64.1)
SD, standard deviation; BMI, body mass index; CRF, cardiorespiratory fitness; HZ, healthy zone. * Student’s t-test, with Bonferroni corrections for differences between gender and CRF categories p < 0.05.
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The results also showed that girls had lower CFR than boys, using the cut-offs for the five equations: Léger,13 Barnett,14 Matsuzaka,15 and Ruiz,16 p < 0.05. Linear regression analyses showed that the Matsuzaka (girls: −0.213, 95%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, 95%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, 0.044, p = 0.008) equations were negatively associated with MRS in both genders. ROC analyses showed a significant discriminatory accuracy for the Matsuzaka and Barnett(a) equations in identifying the low/high MRS in both genders (Matsuzaka equation girls: AUC = 0.654, 95%CI: 0.591–0.713, p < 0.001; boys: AUC = 0.648, 95%CI: 0.576–0.716, p < 0.001; Barnett(a) equation girls: AUC = 0.620, 95%CI: 0.557–0.681, p < 0.001; boys: AUC = 0.628, 95%CI: 0.555–0.697, p = 0.04). The Ruiz equation also showed a significant accuracy in boys (AUC = 0.638, 95%CI: 0.565–0.706, p < 0.001) (Table 2). The CRF values at these points for the Matsuzaka equation were 39.5 and 41.8 mL kg−1 min−1 in girls and boys, respectively; Barnett(a) values were 46.4 and 42.6 mL kg−1 min−1 in girls and boys, respectively; and for the Ruiz equation for boys, the value was 47.1 mL kg−1 min−1 (Table 3).
4. Discussion The results of this study indicate that a high number of adolescents did not meet the healthy zone criteria. Our data also showed that Matsuzaka and Barnett(a) equations seem to have the best trade-off between sensitivity and specificity for the VO2max equation to screen for MRS in both genders, and the Ruiz equation is the best-performing equation for boys. Linear regression analyses showed that VO2max estimated from these equations is negatively and significantly associated with MRS scores in both genders. Our results have important public health implications. The high percentage of adolescents who did not meet the healthy zone criteria (girls: 86.3%, n = 220; boys: 35.9%, n = 70) can help identify youth at increased risk of metabolic diseases and emphasize the importance of promoting healthy lifestyles at these ages. Indeed, high CFR during childhood and adolescence has been associated with a healthier cardiovascular profile during these years.19 Data from The European Youth Heart Study showed that high levels CRF and physical activity are associated with a favourable metabolic risk profile.9 CRF is influenced by several factors, including body fatness, age, sex, health status, and genetics, yet its principal modifiable determinant is habitual physical activity.20 Evidence
Table 3 Trade-off between sensitivity and specificity for the VO2 max equations to screen for MRS by gender. Girls Barnett(a) VO2max cut-offb Sensitivity Specificity AUC Barnett(b) VO2max cut-offb Sensitivity Specificity AUC Léger VO2max cut-offb Sensitivity Specificity AUC Matsuzaka VO2max cut-offb Sensitivity Specificity AUC Ruiz VO2max cut-offb Sensitivity Specificity AUC
pa
Boys
≤46.4 0.583 (0.408–0.745) 0.668 (0.600 − 0.731) 0.620 (0.557–0.681), p < 0.001
≤42.6 0.621 (0.423–0.793) 0.640 (0.550 − 0.714) 0.628 (0.555–0.697), p = 0.043
≤47.7 0.750 (0.578–0.879) 0.398 (0.332–0.468) 0.556 (0.492–0.619), p = 0.266
≤42.7 0.345 (0.180–0.543) 0.845 (0.779–0.897) 0.591 (0.517–0.661), p = 0.131
≤32.2 0.194 (0.82–0.36) 0.915 (0.869–0.949) 0.530 (0.465–0.593), p = 0.573
≤38.6 0.276 (0.128–0.472) 0.888 (0.829–0.932) 0.592 (0.518–0.662), p = 0.124
≤39.5 0.556 (0.381–0.721) 0.782 (0.720–0.836) 0.654 (0.591–0.713), p < 0.001
≤41.8 0.552 (0.357–0.735) 0.758 (0.684–0.822) 0.648 (0.576–0.716), p < 0.001
≤50.9 0.861 (0.705–0.953) 0.294 (0.233–0.360) 0.561 (0.497–0.624), p = 0.217
95% CI in parentheses; AUC, area under the curve. a Compares different AUC. b VO −1 min−1 . 2max expressed as mL kg 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).
c,d,e
≤47.1 0.793 (0.603–0.920) 0.478 (0.399–0.558) 0.638 (0.565–0.706), p < 0.001
pa
c
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suggests that sedentary behavior, low levels of physical activity and CRF in youth continue into adulthood 21,22 and may predispose young people to disease in later in life.23 Several equations have been developed to estimate VO2max from the maximal speed attained during the 20mSRT. One of the most widely used equation to estimate VO2max is the Léger equation.13 However, Ruiz 16 developed an artificial neural network equation to estimate VO2max from 20mSRT performance (stage), sex, age, weight, and height in adolescents and this new equation was shown to be more accurate than Léger equation. The equations developed to estimate VO2max use different variables such as age, gender, and anthropometric variables (skinfold thickness or body weight), and this leads us to different results. Furthermore, special attention should be paid when comparisons are made between studies. The effect of gender is not clear. Barnett14 found that gender was a significant predictor whereas Léger13 did not. It is clear that age is an important predictor because it helps take into account the improvement in running economy that occurs during growth and development.13,14 Recently, Ruiz12 suggested that the equation of Barnett(b) provides the closest agreement with directly measured values of VO2max (an index of cardiorespiratory fitness) in youths aged 13–19 years. In our study, the ROC analyses showed that Matsuzaka equation seems to have the best trade-off between sensitivity and specificity for the VO2max equation to screen for MRS by gender. However, the Barnett(a) equation has a significant discriminating accuracy to distinguish between low and high MRS in both genders and the Ruiz equation in boys. The Matsuzaka equation uses gender, age, BMI and the final speed attained in the 20mSRT for the prediction of VO2max . The CRF cut-offs obtained with ROC curve analyses indicate that boys have higher values than girls (39.5 mL kg−1 min−1 vs 41.8 mL kg−1 min−1 ). The decline in VO2max reported in girls during adolescence is usually attributed to the effect of increased body fat associated with sexual maturity.24 The CRF cut-offs in boys are somewhat similar to those proposed by The Cooper Institute25 (41.8 mL kg−1 min−1 vs 42 mL kg−1 min−1 ) whereas in girls the values are slightly higher (39.5 mL kg−1 min−1 vs 38 mL kg−1 min−1 ). The Cooper Institute cut-off values were extrapolated from the thresholds established for adults,26 while the cut-offs values suggested here have been mathematically calculated within the sample. Comparing ours results with those of the European Group of Pediatric Work Physiology, which considered a VO2max of ≥35 mL kg−1 min−1 for girls and ≥40 mL kg−1 min−1 for boys as a “Health Indicator”,27 based on expert judgment, our results are slightly higher in both genders. Data from CRF levels in the European Youth Heart Study (EYHS) sample of adolescents aged 9–10 years have been reported.28 In accordance with our results, the CRF levels in that study were almost identical with our results in boys (42 mL kg−1 min−1 vs 41.8 mL kg−1 min−1 ); however, girls showed lower CRF levels, in contrast to our results (37 mL kg−1 min−1 vs 39.5 mL kg−1 min−1 ).
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Recently, Lobelo29 has indicated CRF values, calculated via ROC curve analysis, of 44.1 and 40.3 mL kg−1 min−1 among 12–15 and 16–19 year-old boys and 36.0 and 35.5 mL kg−1 min−1 among 12–15 and 16–19 year-old girls, respectively. It should be noted that the approaches used to calculate the CRF thresholds were different in the previous studies and in our study, as well as the measured outcomes, age, and cultural and social factors of the adolescents studied. This study is limited because it consisted of a crosssectional design, which limits inferences about causality and its direction, as well as the convenient sample. Therefore, the results from this study should not be extended beyond this sample. Second, CRF was assessed indirectly. The fact of using the estimated VO2max in a shuttle run test as an indicator of CRF could lead to the error caused by the use of an estimating equation. Nevertheless, the shuttle-run test is currently administered in school settings and a large number of subjects can be tested at the same time which enhances participant motivation; its common use in large-scale studies makes it a valuable tool for studying CRF in youth. Schools, which commonly administer physical fitness tests, are great spaces for health surveillance and control systems for identifying high-risk adolescents. In this regard, the adolescents’ CRF information collected in schools as part of the Fitnessgram program can help detect youth at high MRF and has potentially large clinical and public health implications.29
5. Conclusion In conclusion, the present study indicates that Matsuzaka and Barnett(a) equations seem to have the best trade-off between sensitivity and specificity for the VO2max equation to screen for MRS in both genders, and the Ruiz equation is the best for boys. The VO2max values calculated via the ROC curve analyses for these equations are somewhat similar to those suggested by worldwide recognized organizations. However, the VO2max values found require further testing in other populations as well as in longitudinal studies.
Practical implications • The identification of adolescents with low cardiorespiratory fitness levels can help detect youth with an increased risk of metabolic diseases. • The use of the FITNESSGRAM program in school settings is a very helpful tool to measure physical fitness. • Public health organizations, schools and parents should encourage the population to be more physically active.
Acknowledgement This study was supported by FCT (BD/44422/2008).
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References 1. Taylor HL, Buskirk E, Henschel A. Maximal oxygen intake as an objective measure of cardio-respiratory performance. J Appl Physiol 1955;8:73–80. 2. Welk GJ, Meredith MD. Fitnessgram/activitygram reference guide. 3rd ed. Dallas, TX: The Cooper Institute; 2008. p. 96–142. 3. Castro-Pinero J, Artero EG, Espana-Romero V, et al. Criterion-related validity of field-based fitness tests in youth: a systematic review. Br J Sports Med; doi:10.1136/bjsm.2009.058321, in press. 4. Léger LA, Lambert A, Goulet A, et al. Capacity aerobic des Quebecois de 6 a 17 ans—test Navette de 20 metres avec paliers de 1 min. Can J Appl Sport Sci 1984;9:64–9. 5. Tomkinson GR, Léger LA, Olds TS, et al. Secular trends in the performance of children and adolescents (1980–2000): an analysis of 55 studies of the 20 m shuttle run test in 11 countries. Sports Med 2003;33:285–300. 6. Ruiz JR, Silva G, Oliveira N, et al. Criterion-related validity of the 20-m shuttle run test in youths aged 13–19 years. J Sports Sci 2009;27:899–906. 7. Ortega FB, Artero EG, Ruiz JR, et al. Reliability of health-related physical fitness tests in European adolescents. The HELENA Study. Int J Obes (Lond) 2008;32(Suppl. 5):S49–57. 8. Castillo-Garzon M, Ruiz JR, Ortega FB, et al. A Mediterranean diet is not enough for health: physical fitness is an important additional contributor to health for the adults of tomorrow. World Rev Nutr Diet 2007;97:114–38. 9. Andersen LB, Harro M, Sardinha LB, et al. Physical activity and clustered cardiovascular risk in children: a cross-sectional study (The European Youth Heart Study). Lancet 2006;368:299–304. 10. Brage S, Wedderkopp N, Ekelund U, et al. Features of the metabolic syndrome are associated with objectively measured physical activity and fitness in Danish children: the European Youth Heart Study (EYHS). Diabetes Care 2004;27:2141–8. 11. Ruiz JR, Ortega FB, Meusel D, et al. Cardiorespiratory fitness is associated with features of metabolic risk factors in children. Should cardiorespiratory fitness be assessed in a European health monitoring system? The European Youth Heart Study. J Public Health 2006;14:94–102. 12. Ruiz JR, Castro-Pinero J, Artero EG, et al. Predictive validity of health-related fitness in youth: a systematic review. Br J Sports Med 2009;43:909–23. 13. Léger L, Mercier D, Gadoury C, et al. The multistage 20 metre shuttle run test for aerobic fitness. J Sports Sci 1988;6:93–101. 14. Barnett A, Chan L, Bruce I. A preliminary study of the 20-m multistage shuttle run as a predictor of peak V02 in Hong Kong Chinese students. Pediat Exerc Sci 1993;5:442–50.
15. Matsuzaka A, Takahashi Y, Yamazoe M, et al. Validity of the multistage 20-m shuttle-run test for Japanese children, adolescents, and adults. Pediat Exerc Sci 2004;16:113–25. 16. Ruiz JR, Ramirez-Lechuga J, Ortega F, et al. Artificial neural networkbased equation for estimating VO2max from the 20 m shuttle run test in adolescents. Artif Intell Med 2008;44:233–45. 17. Cole T, Bellizzi M, Flegal K, et al. Establishing a standard definition for child overweigth and obesity worldwide: internacional survey. BMJ 2000;320:1240–3. 18. Lohman T, Roche A, Martorell F. Anthropometric standardization reference manual. Champaign IL: Human Kinetics; 1991. p. 55–70. 19. Mesa JL, Ruiz JR, Ortega FB, et al. Aerobic physical fitness in relation to blood lipids and fasting glycaemia in adolescents: influence of weight status. Nutr Metab Cardiovasc Dis 2006;16:285–93. 20. Bouchard C, Perusse L. Heredity, activity level, fitness, and health. In: Bouchard C, Shephard RJ, Stephens T, editors. Physical activity, fitness, and health: international proceedings and consensus statement. Champaign, IL: Human Kinetics; 1994. p. 106–18. 21. Froberg K, Andersen LB. Mini review: physical activity and fitness and its relations to cardiovascular disease risk factors in children. Int J Obes 2005;29:S34–9. 22. Yang X, Telama R, Leskinen E, et al. Testing a model of physical activity and obesity tracking from youth to adulthood: the cardiovascular risk in young Finns study. Int J Obes (Lond) 2007;31:521–7. 23. Hasselstrom H, Hansen SE, Froberg K, et al. Physical fitness and physical activity during adolescence as predictors of cardiovascular disease risk in young adulthood. Danish Youth and Sports study. An eight-year follow-up study. Int J Sports Med 2002;23:S27–31. 24. Mota J, Guerra S, Leandro C, et al. Association of maturation, sex, and body fat in cardiorespiratory fitness. Am J Hum Biol 2002;14: 707–12. 25. The Cooper Institute for Aerobics Research. FITNESSGRAM test administration manual. 3rd ed. Champaign, IL: Human Kinetics; 2004. p. 38–39. 26. Blair SN, Kohl HW, Paffenbarger RS, et al. Physical-fitness and all-cause mortality—a prospective-study of healthy-men and women. JAMA 1989;262:2395–401. 27. Bell RD, Macek M, Rutenfranz J, et al. Health indicators and risk factors of cardiovascular diseases during childhood and adolescence. In: Rutenfranz J, Mocelin R, Klimt F, editors. Children and exercise XII. Champaign, IL: Human Kinetics; 1986. p. 19–27. 28. Ruiz JR, Ortega F, Rizzo N, et al. High cardiovascular fitness is associated with low metabolic risk score in children: the European Youth Heart Study. Pediatr Res 2007;61:350–5. 29. Lobelo F, Pate RR, Dowda M, et al. Validity of cardiorespiratory fitness criterion-referenced standards for adolescents. Med Sci Sports Exerc 2009;41:1222–9.