SUBJECTS AND METHODS
Subjects
Adolescents were recruited from high schools in the area of Augusta, GA. Demographic information obtained from the school systems was used to select schools that enrolled both black and white students. In the Augusta area, relatively few students are from other racial or ethnic groups, and those few were excluded from the project. After receiving approval from the school principals, we distributed flyers to all students in the selected schools. For this report, we analyzed data from 421 subjects for whom we had complete data on all relevant measures. Subjects and their parents signed informed consent and assent documents in accordance with the Medical College of Georgia Human Assurance Committee.
Pubertal development
Subjects were placed in a private room alone and asked to read a prepared script and view a series of pictures showing different stages of pubertal development (5, 6). Male subjects self-determined gonadal and pubic development on a scale of 1 to 5. Female subjects self-determined breast and pubic development on a scale of 1 to 5. Female subjects answered 2 questions about menses: 1) whether they had had their first menstrual period, and 2), if so, when was the first day of their last menstrual period. Eighty-eight percent of the adolescents were in Tanner stages 4 and 5.
Anthropometry and body composition
Body weight (in shorts and tee-shirt) and height (without shoes) were measured with an electronic scale (model CN2OL; Cardinal Detecto, Webb City, MO) and wall-mounted stadiometer (Tanita Corporation of America, Arlington Heights, IL), respectively, and converted to body mass index (BMI; in kg/m2) to provide descriptive data. We used %BF rather than BMI as the primary index of fatness because BMI can misclassify as obese persons who have large amounts of fat-free mass, such as those who do large amounts of vigorous PA, and who in fact are overweight (7). It is fatness rather than weight that is associated with poor health (8).
%BF was measured with dual-energy X-ray absorptiometry (DXA; Hologic QDR-4500, Waltham, MA; software version 6.0). We previously found DXA to provide reliable %BF values in children (9). In this project, we performed repeat measurements on the new QDR-4500 machine with 219 adolescents and found the intraclass correlation for %BF to be 0.99 (10). For some subjects, DXA values were not available from the Hologic QDR-4500W model but were available only from the Hologic QDR-1000W model. For those persons, %BF values were derived from the prediction equations based on 284 adolescents who were assessed on both instruments, with the use of linear regression; race, sex, and QDR-1000W measurement were the predictor variables. The multiple R2 value for %BF was 0.99.
Cardiovascular fitness
CVF was measured with a multistage treadmill test. Heart rate (HR) was monitored with the use of a Polar Accurex Plus HR monitor (Port Washington, NY). Oxygen consumption (V̇O2) was measured with the use of a Sensormedics Vmax 229 cardiopulmonary system (Yorba Linda, CA). The treadmill protocol began with a 3-min warm-up at 0% grade and 2.0 mph. The speed was then increased 0.5 mph every 2 min until it reached 3.5 mph, at which time the grade increased 2% every minute until it reached 16% grade, after which the treadmill speed was increased by 0.5 mph every minute until voluntary exhaustion. Verbal cues were given to encourage a maximal effort. The subject was considered to have attained V̇O2max if he or she met 2 of the following 3 criteria: 1) an increase in HR <5 bpm between the final 2 workloads, 2) an increase in V̇O2 <100 mL between the final 2 workloads, and 3) a respiratory exchange ratio >1.00 (11).
Our primary index of CVF was submaximal in nature: the V̇O2 at an HR of 170 bpm per unit of body weight (V̇O2170, mL · kg−1 · min−1) (12). The HR at a given submaximal level of work or energy expenditure is a well-established submaximal and objective index of fitness in adolescents (13). Using all the treadmill workloads completed by each adolescent, we computed individual regression equations of V̇O2 on HR for each adolescent. The V̇O2 (in L/min) corresponding to an HR of 170 bpm was calculated for each adolescent and expressed per unit of body weight (mL · kg−1 · min−1). Adolescents who are more fit exhibit a higher V̇O2 (ie, a higher treadmill work rate) before their HRs reach 170 bpm. That is, a given metabolic load puts less strain on the cardiovascular system of adolescents who are more fit (and often relatively lean).
Accelerometry
Free-living PA was measured with the use of MTI Actigraph monitors (model 7164; MTI Health Services, Fort Walton Beach, FL). The Actigraph uses a uniaxial accelerometer that measures vertical acceleration and deceleration in 1-min epochs. This accelerometer can be used to discriminate among light, moderate, and vigorous levels of PA (14). The monitors were initialized to begin recording when the subject left our laboratory after the first half day of testing. The monitors were affixed above the iliac crest of the right hip with an elastic belt in accordance with findings of Puyau et al (15), which suggests that this is the most efficacious placement. A research assistant instructed the subjects in the proper way to wear the monitor. The subjects were instructed to 1) wear the monitor for a period of 7 d, 2) remove it for sleep and any activity that may cause harm to either the monitor or another person (eg, during contact sports), and 3) bring the monitor back to us 1 wk later. These data were downloaded into a computer. Data from day 1 and day 7 were discarded because a full day of information was not available for those days. Because we could not always schedule the subjects for exactly 7 d after the first visit, we sometimes had less than a full week’s data; thus, we used in the analyses the 5 d immediately after the first visit. Movement counts were converted to average minutes per day spent in resting or light [<3 metabolic equivalents (METs)], moderate (3–6 METs), vigorous (6–9 METs), and very vigorous (9+ METs) PA (16). The minutes per day spent doing vigorous and very vigorous levels were combined into 1 variable (vigorous PA).
Controversy continues about the best way to express PA. If it is expressed as energy expended in movement, then heavier adolescents will appear to be engaging in relatively large amounts of PA because they use more energy than do lighter adolescents to move their bodies a given amount. The net result is that it will appear that heavier and lighter adolescents engage in similar amounts of PA (17). However, when PA is expressed as movement rather than energy expenditure (18), or if adjustment is made for body weight (17), then obese adolescents will appear to engage in less PA than do nonobese adolescents. For purposes of making exercise recommendations, the time spent in activities of various intensities seems more pertinent. Therefore, we expressed free-living PA as time spent (ie, min/d) in PA of a moderate or vigorous intensity; for some analyses, we used the total of both (MVPA).
Statistical analyses
All variables used in the study were checked for normality of distribution before the analyses, and appropriate transformations were applied when necessary. Group comparisons were made on unweighted means by using 2 × 2 analysis of covariance (sex × race) with adjustment for age. Pearson’s correlations were used to examine bivariate relations among the key variables. Hotelling’s t test was used to test the differences between correlated coefficients of correlation.
Standard multiple regression was used as the primary method to determine the degree to which variance in CVF and %BF was explained by PA after control for age, sex, race, and their interaction. When we used pubertal development rather than age in the analyses, the variance explained by the demographic variables was similar; thus, in the interests of parsimony, we used age in the statistical analyses.
Parallel analyses were done with either CVF or %BF as the outcome variable. We conducted analyses by using moderate PA and vigorous PA separately as well as the combined MVPA. For each of the outcome variables, we tested a series of models. Thus, model 1 addressed the influence of moderate PA and its interactions with the covariates, model 2 examined the influence of vigorous PA and its interactions with the covariates, and model 3 examined the influence of MVPA and its interactions with the covariates. In model 4, we tested two-factor interaction terms of PA with age, sex, and race and retained them in the models if they made significant contributions.
Statistical significance was set at P < 0.05 in all analyses. The statistical analyses were conducted with SPSS software (11.5; SPSS Inc, Chicago, IL).
RESULTS
Descriptive statistics
The group means and the race × sex comparisons for the key variables are shown in Table 1. Although detailed discussion of these differences is beyond the scope of this report, we include them to indicate the adjustments that were made in analyzing the effect of PA. Adolescent girls had higher %BF than did adolescent boys. For CVF (V̇O2-170), adolescent boys and whites had significantly higher values than did adolescent girls and blacks, respectively. The adolescent boys had significantly higher values than did the adolescent girls on moderate and vigorous PA.
Descriptive characteristics for subjects in the study1
| All subjects (n = 421) | White male (n = 102) | Black male (n = 94) | White female (n = 104) | Black female (n = 121) | Group comparison2 | |
| Age (y) | 16.2 ± 1.2 | 16.2 ± 1.3 | 15.9 ± 1.1 | 16.2 ± 1.1 | 16.3 ± 1.2 | |
| Weight (kg) | 65.4 ± 15.8 | 67.5 ± 14.6 | 68.9 ± 16.1 | 59.8 ± 13.4 | 65.8 ± 17.2 | M>F, B>W |
| BMI (kg/m2) | 23.1 ± 5.1 | 22.1 ± 4.3 | 23.0 ± 5.1 | 22.4 ± 4.5 | 24.7 ± 5.7 | B>W |
| Percentage body fat | 24.3 ± 10.0 | 18.8 ± 7.9 | 16.9 ± 9.3 | 29.8 ± 7.3 | 30.0 ± 7.8 | F>M |
| CVF:V̇O2170 (mL · kg−1 · min−1) | 27.6 ± 8.2 | 33.2 ± 6.8 | 32.0 ± 8.5 | 24.6 ± 5.8 | 21.9 ± 5.3 | M>F, W>B |
| V̇O2max (mL · kg−1 · min−1)3 | 38.7 ± 11.2 | 47.6 ± 8.8 | 43.7 ± 10.6 | 34.2 ± 7.0 | 29.6 ± 6.8 | M>F, W>B |
| Moderate PA (min/d)4 | 39.3 ± 24.9 | 46.2 ± 26.1 | 49.0 ± 28.2 | 31.3 ± 17.9 | 32.9 ± 22.5 | M>F |
| Vigorous PA (min/d)4 | 5.0 ± 7.8 | 6.4 ± 7.6 | 8.6 ± 12.0 | 2.9 ± 3.7 | 2.8 ± 4.3 | M>F |
| MVPA (min/d)4 | 44.3 ± 30.4 | 52.6 ± 31.9 | 57.6 ± 36.6 | 34.2 ± 19.8 | 35.7 ± 25.3 | M>F |
1
All values are x̄ ± SD. V̇O2170, oxygen consumption at heart rate 170 beats/min; V̇O2max, maximal oxygen consumption; PA, physical activity; MVPA, moderate-vigorous PA.2
Group comparisons were conducted by analyses of covariance (sex by race) after adjustment for age (P < 0.05). M, males; F, females; W, whites; B, blacks.3
V̇O2max was available in 253 subjects (72 white males, 53 black males, 57 white females, and 71 black females).4
Log-transformed values were used in the analysis, but the nontransformed values are presented in the table.Open in new tab
Correlational analyses
The intercorrelations among main variables across all subjects are shown in Table 2. The correlation between %BF and relative CVF was −0.69 (P > 0.001), which shows that %BF and CVF were highly and inversely correlated. %BF was inversely correlated with all the indexes of PA, whereas CVF was positively correlated with all PA measures. Vigorous PA was significantly more highly correlated with %BF and CVF than was moderate PA (P < 0.001). Vigorous PA also was significantly more highly correlated with CVF than was moderate PA (P < 0.001). Correlations among PA categories were all significant.
Bivariate correlations between percentage body fat (%BF), cardiovascular fitness (CVF), and physical activity (PA)
1
Log transformed.2
P < 0.001.3
P < 0.01.Open in new tab
Regression analyses
The statistics of regression models that used %BF or CVF as the outcome variable are shown in Table 3. Each model included the covariates (age, sex, race, and the sex × race interaction). Vigorous PA was the only significant predictor of %BF (model 2); neither moderate PA (model 1) nor MVPA (model 3) explained significant amounts of variance. None of the two-factor interaction terms of PA with age, sex, and race were significant (results not shown).
Unstandardized regression coefficients (β), SEs, semipartial correlations (sr) and adjusted model R2 examining the association of percentage body fat (%BF) and cardiovascular fitness (CVF) with physical activity (PA) after adjustment for sex, race, sex × race interaction, and age from multiple regression1
| %BF | CVF | ||||||||||
| Model | Predictor variable | β | SE | P | sr | R2 | β | SE | P | sr | R2 |
| 1 | Moderate PA2 | −0.64 | 1.51 | 0.67 | −0.02 | 0.36 | 4.80 | 1.23 | 0.001 | 0.15 | 0.37 |
| 2 | Vigorous PA2 | −4.19 | 1.02 | 0.001 | −0.16 | 0.38 | 6.17 | 0.82 | 0.001 | 0.29 | 0.42 |
| 3 | Moderate-vigorous PA2 | −0.157 | 1.43 | 0.27 | −0.04 | 0.36 | 5.63 | 1.15 | 0.001 | 0.19 | 0.38 |
1
Model intercept was 13.91 (SE = 8.73, P > 0.11), 22.32 (SE = 7.24, P < 0.002), and 17.38 (SE = 8.64, P < 0.045) for Models 1, 2, and 3 for %BF, respectively, and was 25.57 (SE = 7.05, P < 0.001), 26.85 (SE = 5.68, P < 0.001), and 22.05 (SE = 6.92, P < 0.002) for Models 1, 2, and 3 for CVF, respectively. All estimates were adjusted for sex, race, sex × race interaction, and age.2
Measures were log transformed.Open in new tab
Variation in CVF was significantly explained by moderate PA (model 1), vigorous PA (model 2), and MVPA (model 3). The semipartial correlation for vigorous PA (0.29) was substantially higher than the semipartial correlation for moderate PA (0.15). No two-factor interaction term was retained in model 4 (results not shown).
DISCUSSION
The primary finding of this study is that, after adjustment for demographic factors, adolescents who engaged in relatively large amounts of vigorous PA tended to have a better CVF and a lower %BF than those who did not. For %BF, participation in moderate PA did not explain a significant proportion of the variance. For CVF, moderate PA did explain a significant but smaller proportion of the variance in CVF. The relation of vigorous PA to lower levels of body fatness is consistent with the results of other recent studies in San Diego adolescents (19) and European 9–10-y-olds (20). An intervention study from our institute found that obese adolescents who maintained a higher intensity of exercise during physical training sessions tended to be those who improved most in CVF and %BF; however, some obese adolescents found it difficult to maintain high-intensity PA (12).
Some observational studies have failed to show differences between obese and nonobese adolescents in free-living PA, especially when PA was expressed as energy expenditure (derived from doubly labeled water), rather than as movement. One limitation of such studies is that it is difficult to know how to express energy expenditure during PA to account for differences in body composition; ie, heavier adolescents use more energy than do lighter adolescents to move their bodies for a given amount of PA (17). Another limitation is that such studies cannot distinguish moderate PA from vigorous PA (21). Therefore, studies that use energy expenditure to measure PA have not always found that fatter adolescents were less active than leaner adolescents, even though the former did show differences when PA was expressed as movement rather than energy expenditure (18, 22, 23).
An important limitation to this study is that it was cross-sectional in nature, thereby precluding the conclusion that high levels of vigorous PA cause adolescents to be more fit and less fat. Because there is a substantial hereditary component to both fitness and fatness (24), an adolescent who inherits a predisposition to be unfit or obese may tend to be less likely to engage in vigorous PA. The most likely scenario is that these relations are cyclic, with the result that a change in one is likely to cause a change in the other. That the cycle can be driven in a positive direction is supported by experimental studies in obese adolescents that showed controlled physical training to improve fitness and reduce fatness (25). However, the picture is less clear for nonobese adolescents, in whom several studies failed to show that such physical training improved fitness and body composition (26, 27). The limited information available suggests that, for nonobese adolescents, the interventions should be high in both intensity and volume (>80 min/d) (28, 29). Taken together, these data suggest that general exercise recommendations for adolescents should encourage vigorous PA. However, for 2 reasons, it is reasonable to recommend moderate PA for obese and unfit adolescents until higher intensities can gradually be attained. First, PA that is classified by time-motion analysis as moderate, such as brisk walking, may actually be quite strenuous for some unfit and obese adolescents. Second, vigorous PA that is especially tiring may lead adolescents to do less PA on the following day (30), thereby being counterproductive in the long term. Therefore, it seems sensible to encourage unfit and obese adolescents to engage in moderate PA and appropriate dieting, gradually progressing to higher intensities as they become more fit and less fat.
FOOTNOTES
2
Supported by the National Institutes of Health (HL64157).
We appreciate the assistance of Elizabeth Stewart in data management, Mark Litaker in statistical consultation, and several research assistants in data collection; we also appreciate the schools that cooperated in subject recruitment and the adolescents who served as subjects.
BG and PB participated in the design of the study and preparation of the manuscript. ZY performed the statistical analyses and participated in the preparation of the manuscript. MCH played an important role in the implementation of the study and data collection and participated in the preparation of the manuscript. None of the authors had any conflict of interest.
REFERENCES
1.Ogden CL, Flegal KM, Carroll MD, Johnson CL. Prevalence and trends in overweight among US children and adolescents, 1999–2000. JAMA 2002;288:1728–32.
2.Lee CD, Blair SN, Jackson AS. Cardiorespiratory fitness, body composition, and all-cause and cardiovascular disease mortality in men. Am J Clin Nutr 1999;69:373–80.
3.Koppes LLJ, Twisk JW, Kemper HC. Longitudinal trends, stability and error of biological and lifestyle characteristics. In: Kemper HC. ed. Amsterdam Growth and Health Longitudinal Study (AGAHLS): a 23-year follow-up from teenager to adult about lifestyle and health. Basel, Switzerland: Karger, 2004:44–63.
4.Miller J, Pratley R, Goldberg A, et al. Strength training increases insulin action in healthy 50- to 65-yr-old men. J Appl Physiol 1994;77:1122–7.
5.Brooks-Gunn J, Warren MP, Rosso J, Gargiulo J. Validity of self-report measures of girls’ pubertal status. Child Dev 1987;58:829–41.
6.Duke PM, Litt IF, Gross RT. Adolescents’ self-assessment of sexual maturation. Pediatrics 1980;66:918–20.
7.Kawabe H, Murata K, Shibata H, et al. Participation in school sports clubs and related effects on cardiovascular risk factors in young males. Hypertens Res 2000;23:227–32.
8.Allison DB, Zhu SK, Plankey M, Faith MS, Heo M. Differential associations of body mass index and adiposity with all-cause mortality among men in the first and second National Health and Nutrition Examination Surveys (NHANES I and NHANES II) follow-up studies. Int J Obes Relat Metab Disord 2002;26:410–6.
9.Gutin B, Litaker M, Islam S, Manos T, Smith C, Treiber F. Body-composition measurement in 9–11-y-old children by dual-energy X-ray absorptiometry, skinfold-thickness measurements, and bioimpedance analysis. Am J Clin Nutr 1996;63:287–92.
10.Litaker MS, Barbeau P, Humphries MC, Gutin B. Comparison of Hologic QDR-1000/W and 4500W DXA scanners in 13- to 18-year olds. Obes Res 2003;11:1545–52.
11.Armstrong N, Van Mechelen W. Paediatric exercise science and medicine. New York: Oxford University Press, 2000.
12.Gutin B, Barbeau P, Owens S, et al. Effects of exercise intensity on cardiovascular fitness, total body composition, and visceral adiposity of obese adolescents. Am J Clin Nutr 2002;75:818–26.
13.ACSM’s guidelines for exercise testing and prescription. 6th ed. Baltimore. Lippincott, Williams & Williams, 2000.
14.Puyau MR, Adolph AL, Vohra FA, Zakeri I, Butte NF. Prediction of activity energy expenditure using accelerometers in children. Med Sci Sports Exerc 2004;36:1625–31.
15.Puyau MR, Adolph AL, Vohra FA, Butte NF. Validation and calibration of physical activity monitors in children. Obes Res 2002;10:150–7.
16.Trost SG, Pate RR, Freedson PS, Sallis JF, Taylor WC. Using objective physical activity measures with youth: how many days of monitoring are needed? Med Sci Sports Exerc 2000;32:426–31.
17.DeLany JP, Bray GA, Harsha DW, Volaufova J. Energy expenditure in African American and white boys and girls in a 2-y follow-up of the Baton Rouge Children’s Study. Am J Clin Nutr 2004;79:268–73.
18.Ekelund U, Aman J, Yngve A, Renman C, Westerterp K, Sjostrom M. Physical activity but not energy expenditure is reduced in obese adolescents: a case-control study. Am J Clin Nutr 2002;76:935–41.
19.Patrick K, Norman GJ, Calfas KJ, et al. Diet, physical activity, and sedentary behaviors as risk factors for overweight in adolescence. Arch Pediatr Adolesc Med 2004;158:385–90.
20.Ekelund U, Sardinha LB, Anderssen SA, et al. Associations between objectively assessed physical activity and indicators of body fatness in 9- to 10-y-old European children: a population-based study from 4 distinct regions in Europe (the European Youth Heart Study). Am J Clin Nutr 2004;80:584–90.
21.Goran MI, Treuth MS. Energy expenditure, physical activity, and obesity in children. Pediatr Clin North Am 2001;48:931–53.
22.Goran MI, Hunter G, Nagy TR, Johnson R. Physical activity related energy expenditure and fat mass in young children. Int J Obes Relat Metab Disord 1997;21:171–8.
23.Moore LL, Gao D, Bradlee ML, et al. Does early physical activity predict body fat change throughout childhood? Prev Med 2003;37:10–7.
24.Bouchard C, Malina R, Pérusse L.Genetics of fitness and physical performance. Champaign, IL: Human Kinetics, 1997.
25.LeMura LM, Maziekas MT. Factors that alter body fat, body mass, and fat-free mass in pediatric obesity. Med Sci Sports Exerc 2002;34:487–96.
26.Tolfrey K, Jones AM, Campbell IG. Lipid-lipoproteins in children: an exercise dose-response study. Med Sci Sports Exerc 2004;36:418–27.
27.Tolfrey K, Campbell IG, Batterham AM. Exercise training induced alterations in prepubertal children’s lipid-lipoprotein profile. Med Sci Sports Exerc 1998;30:1684–92.
28.Eliakim A, Makowski GS, Brasel JA, Cooper DM. Adiposity, lipid levels, and brief endurance training in nonobese adolescent males. Int J Sports Med 2000;21:332–7.
29.Barbeau P, Litaker MS, Howe CA, Gutin B. Changes in body composition after a 10-mo physical activity intervention in 8–12 y old black girls in the MCG APEX study. Can J Appl Physiol 2002;27(suppl):S3 (abstr).
30.Kriemler S, Hebestreit H, Mikami S, Bar-Or T, Ayub BV, Bar-Or O. Impact of a single exercise bout on energy expenditure and spontaneous physical activity of obese boys. Pediatr Res 1999;46:40–4.
Google ScholarCrossrefPubMed © 2005 American Society for Clinical Nutrition
