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Research Quarterly for Exercise and Sport Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/urqe20

Physical Activity, Cardiovascular Fitness, and Adiposity in Children a

Wendell Taylor & Tom Baranowski

b

a

Center for Health Promotion Research and Development, School of Public Health , University of Texas Health Science Center , P. O. Box 20186, Houston , Texas , 77225 , USA b

Pediatrics, Georgia Prevention Institute , Medical College of Georgia , Augusta , Georgia , 30912-3710 , USA Published online: 26 Feb 2013.

To cite this article: Wendell Taylor & Tom Baranowski (1991) Physical Activity, Cardiovascular Fitness, and Adiposity in Children, Research Quarterly for Exercise and Sport, 62:2, 157-163, DOI: 10.1080/02701367.1991.10608706 To link to this article: http://dx.doi.org/10.1080/02701367.1991.10608706

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Research Quarterly for Exercise andSport © 1991 bythe American Alliancefor Health,

Physical Education, Recreation and Dance

Vol. 52, No.2, pp. 157-163

Physical Activity, Cardiovascular Fitness, and Adiposity in Children

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Wendell Taylor and Tom Baranowski With cardiovascularfitness (CW) as the dependent variable, relationships with habitual level ofphysical activity, age, gender, and body mass index (BMI) were investigated in a sample of 93 high adiposity and 93 low adiposity children, ages 8 to 13. A physical activity score (PAS) was computed for each child from a 2-day observation period. A physical working capacity index from cycleergometry (PWC 17t1 was the measure of CWo Low and high adiposity samples were classified by a median split (42.9 mm) on the sum of three skinfold measures (tricep, suprailiac, subscapula). For the high adiposity sample, PAS, age, BMI, and gender were significant and the overall model was significant (p < .001), accountingfor 38 % ofvariance in PWC17o• In the low adiposity sample, gender (p < .04) was significantly related to CW, but the overall model was not significant (p < .35). PAS, thus, was a significant predictor of CW among the high adiposity children, but not the low adiposity children. Mechanisms that may account for this difference include greater work for equal activity among the obese, a ceiling effect on CW among the low adiposity children, or differences in hormonal or metabolicfactors mediating the activity-CW relationship.

Key words: children, physical activity, physical fitness, adiposity

T

h e relationship between physical activity and cardiovascular fitness (CVF) has been documented among adults (Kohl, Blair, & Paffen barger, 1988). Physically active adults are generally more fit. Among children, however, the activity-fitness relationship is less clear, with genetic, maturational, and pubertal factors important contributors to fitness in children (Krahenbuhl, Skinner, & Kohrt, 1985). Some have contended heredity is the major contributor to fitness in children (Klissouras, Pirnay, & Petit, 1973), while others (Shephard, 1971) believe physical activity is a primary contributor. The literature on the activity-fitness relationship in children is inconsistent. Some studies have demonstrated such a relationship (Saris, Binkhorst, Cramwinchel, van Waesberghe, & van der Veen-Hezemans, 1980; Watson & O'Donovan, 1977; Weymans, Reybrouck, Stijns, &Knops, 1986), whereas others have not (Ilmarinen & Rutenfranz, 1980; Kobayashi et aI., 1978; Koch, 1980; Mirwald & Bailey, 1986; Mirwald, Bailey, Cameron, & Rasmussen, 1981). These studies have varied on a number of dimensions, which may account for these differences.

Wendell Taylor is an assistantprofessorat the Center for Health Promotion ResearchandDevelopment, Schoolof Public Health, University of Texas Health Science Center, P. O. Box20186, Houston, Texas, 77225. Tom Baranowski is a professorin Pediatrics, Georgia Prevention Institute, Medical College of Georgia, Augusta, Georgia, 30912-3710. Submitted: April 3, 1990 Revision accepted:July 16, 1990

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These include sample size, sample selection, measures and assessments of fitness (e.g., V0 2max, ventilatory threshold, treadmill tests, cycle ergometer tests, mile run tests), various age ranges (before, during, and after puberty), research design (longitudinal, cross-sectional), method of physical activity assessment (self-report, parent recall, teacher recall), as well as type of physical activity assessed (lifestyle exercise, sports participation, conditioning programs). Anthropometric variables may moderate or confound the activity-fitness relationship in children (Watson & O'Donovan, 1977). Obesity and the activity-fitness relationship have not been thoroughly investigated. Nonobese children are more physically active (Johnson, Burke, & Mayer, 1956; Stefanik, Heald, & Mayer, 1959; Waxman & Stunkard, 1980) and more fit (Clark & Blair, 1988). Known hormonal (e.g., heightened insulin resistance) (Bjorntorp, 1987, 1990) and metabolic (e.g., lowered resting metabolic rate) (Reybrouck, Weymans, Vinckx, Stijns, & Vanderschueren-Lodeweyckx, 1987) differences between low and high adiposity groups may mitigate or enhance a relationship between physical activity and CVF. This study assessed whether physical activity was related to CVF among low and high adiposity children ages 8 to 13. Since earlier studies have shown age (Weymans etaI., 1986), gender (Sunnegardh & Bratteby, 1987), skinfold thickness (Clark & Blair, 1988), and body mass index (BMI) (Watson & O'Donovan, 1977) may confound or explain the activity-fitness relationship in children, these variables, as well as ethnic background (Anglo-, Black-, Mexican-American), were included in the analyses.

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Method Description of theSample

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The participants were 199 children residing in Galveston, Texas, recruited for Family Health Project IV (Baranowski et al., 1988). The age range of the subjects was 8 to 13 with school grades ranging from 3 through 6. Data were collected as part of a large methodologic (e.g., validity and reliability issues) study on techniques to enable children (third, fourth, fifth, and sixth grades) to accurately self-report diet and exercise habits. Written informed consent was obtained from each participant and the primary guardian.

Missing Cases There were 10 missing cases for PWC 170 values; all were females. The data were missing for the following reasons: five subjects were too short for the bicycle ergometer, three had medical exemptions (e.g., asthma), one was uncooperative, and one exceeded the heart rate ofl70 b-min" on the first minute of the second workload. There were 4 cases with missing skinfold thickness values because ofuncooperativeness. Since one child had missing values for both PWC I 70 and skinfolds, 13 cases were missing in all.

Measurement of the Variables Assessment of cardiovascularfitness-physical performance capacity. Physical performance capacity (PPC) was measured by the PWC I 70 method using a cycle ergometer with a graded exercise test (Durant, Dover, & Alpert, 1983). The test ended when the subject reached a 170 b-rnirr' heart rate, was exhausted, or was unable to pedal at the proper rate. The PPC was expressed as the cycle ergometer gradientatwhichaheartrateofl70 (PWC I 70 ) was reached. The PWC I 70 value controls for body weight of the subject by dividing the maximum workload (peak kg-min") achieved by kilograms of body weight (Durant et al., 1983). Observation of physical activity. Physical activity was assessed by all day observation for two days. The observation methods have been described in detail by Baranowski et al. (1984). The method used trained observers and a common observation instrument. Observers worked in pairs, alternating two-hour shifts to reduce fatigue and error. The mean percent agreement for interobserver reliability for activity has ranged from 82 to 90 (Baranowski et al., 1984; Baranowski, Hooks, Tsong, Cieslik, & Nader, 1987). Interobserver reliability was assessed as exact agreemen t on an in terval-by-interval basis for each of three different half-hour periods on each day of observation.

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Reactivity to the presence of an observer was minimized by a family orientation session with the parents and child conducted the day before observation in their home to explain the observation procedures. After the initial 10 min of an observation day there were few detectable reactions to the presence of observer(s) (Baranowski et al., 1984). Each child was observed for 2 days (Monday and Tuesday or Wednesday and Thursday). Observers met the child at home at 7:00 am (or when the child awoke, iflater) and followed the child wherever he or she went (except school classrooms, restrooms, personal bedroom, inside a friend's home or motorized vehicles). School classrooms were not 0 bserved because observation could interfere with a class, restrooms because of modesty, personal bedrooms to allow a place to be alone, friend's homes because of difficulties in obtaining multiple permissions, and motorized vehicles because of insurance coverage. Except for friends' homes, moderate to intense physical activity was unlikely to occur in these locations. Each child was observed in the school locations where physical activity was likely (e.g., lunchroom, physical education class, and during recess). The end of each observation day occurred at the completion of dinner or 7:00 pm (whichever came first). The average hours of observation for Days 1 and 2 were 3.93 hours and 3.84, respectively. Each physical activity score (PAS), therefore, is based on an average of7. 77 hours ofdirect observation. The day was organized into consecutive 2-min intervals, and the characteristics of all physical activity were recorded for every 2-min time interval. Multiple physical activity levels were recorded per 2-min interval when changes in physical activity occurred during the interval. Computation of the physical adivity score. One physical activity score was computed for each day for each child. The greater the physical activity score, the more active the child. There were four possible levels ofphysical activity in this coding system. Based on the work of O'Hara, Baranowski, Simons-Morton, Wilson, and Parcel (1989), points were assigned to each activity as follows: stationary: 0; slow (team sport, walking, bicycle, other): 1; fast (team sport, walking, bicycle, other): 2; running: 3. The onepoint increments for intensity in physical activity have been shown to correlate well with heart rate measures (O'Hara et al., 1989), even after correcting for autocorrelation effects. The physical activity score for each child is the average activity points per minute, averaged across all 2-min intervals in the 2 observation days. A Pearson correlation of .56 between the physical activity score for Day 1 and Day 2 revealed moderate consistency in daily activity levels and justifies the consideration ofthe 2 days as an indicator ofhabitual activity. Classification of subjects. Height was measured by trained staff using a CDC anthropometer. Weight was measured by using a balance beam scale. An indicator of body size, BMI (weight [kg]/height [m]"), and an indi-

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Taylor andBaranowski

cator of adiposity (sum of triceps, subscapula, and suprailiac skinfold measures) (Lohman, Roche, & Martorell, 1988) were computed for each child. Subjects were classified as high or low adiposity based on a median split of the sum of skinfold thickness values. Subjects equal to or less than 42.9 mm on skinfold thickness were classified as low adiposity. A second set of analyses classified subjects based on gender-specific median splits; males with skinfold thickness values equal to or less than 38.9 and females with skinfold thickness values equal to or less than 49.6 were categorized as low adiposity. The pattern of results from the first and second set ofanalyses was the same for all groups (high, low, and combined). Therefore, only the results from the first set of analyses using the overall median split on skinfold are reported. Dataanalyticapproach. The independen tvariableswere age, gender (1 = male; 0 = female), BMI, PAS, sum ofthree skin fold thicknesses, and ethnic background. The dependentvariable was physical working capacity (PWC 170 ) . Ethnic background (a categorical variable with three levels) was represented by a vector of two dummy variables in a multiple regression analysis (Kleinbaum, Kupper, & Muller, 1988). A partial Ftest (a test of the significance of the R2 change, Kleinbaum et al., 1988) was applied to compare models with and without the ethnic background vectors. The objective was to select the most parsimonious model (asubsetofallindependentvariables) for regression analyses. Univariate, bivariate, and multivariate methods were used to analyze the best model. The univariate proced ures were frequencies, means, and standard deviations. The bivariate methods (Pearson correlations and one-way ANOVAs) showed the relationships between each independent variable and the dependent variable. Multiple linear regression investigated the linearrelationship between the dependent variable and the entire set of independent variables. All independent variables were entered simultaneously in a single step. Aaronson (1989) pointed out that variables should be entered simultaneously when direct effects, indirecteffects, and the magnitude ofthe associations between the independent variables and the dependent variable are not distinguishable theoretically. In the absence of a theoretical model, interpreting findings from empirical selection procedures (i.e., forward selection, backward elimination, and stepwise) isproblematic. Factors contributing to small differences and interrelationships among independent variables can produce misleading findings (Aaronson, 1989).

Results Model Selection A comparison (partial F test) was made between models with and without ethnic background variables.

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Ethnic background was not a significant factor after controlling for the other variables, F (2,178) = 2.96, p> .05, and, therefore, was not entered in subsequent regression analyses. Skinfold thickness was entered as a categorical variable (high-low adiposity) in two multiple regression models. The first model included all main effect terms (skinfolds, gender, age, BMI, PAS), while the second included the main effect terms and all possible two-way interactions with skinfolds (nine terms total). A significant partial F test, F (4,176) = 3.70, P < .01, showed the model with the interaction terms accounted for more variance than the main effects model, and three of the four interaction terms (skinfolds by age, PAS, and BMI) were statistically significant predictors. Given the complexity of this model and the fact that skinfold was the major contributor to this complexity, separate analyses were conducted by skinfold group: low adiposity (skinfolds ~ 42.9 min), high adiposity (skinfolds > 42.9 min), and combined.

Univariate Results Table 1 shows the characteristics for all three samples. In the combined group, girls represented 46% of the sample. Girls comprised 57% ofthe high adiposity sample and 35% of the low adiposity sample. As would be expected, the mean sum of seven skinfolds for the high adiposity group (M= 66.5± 19.3) was significan tlygreater (F [1,184] = 138.9, P < .001) than the mean in the low adiposity group (M = 31.9 ± 6.5). The PAS was a weighted average of Day 1 and Day 2 physical activity scores, with mean activity scores of .32 and .324 for Day 1 and 2, respectively. The Pearson correlation coefficient between the two scores was r (184) = .56, p« .001, revealing acceptable internal consistency between the two days of assessment.

Bivariate Results The means and standard deviations for age were similar across the samples. As expected, the skinfold thickness and BMI values varied by adiposity group. The high adiposity group was characterized by significantly lower physical activity scores, significantly lower CVF values, and significantly higher BMI values. The variances for PWC l 70 approached significance using a two-tailed test, F (91,92) = 1.53, P< .06. Figure 1 is a plot OfPWC l 70 and PAS in the combined sample; there was a low to moderate positive correlation between PWC l 70 and PAS, r(184) = .25, p< .001, as shown in Figure 1. Boys had significantlygreaterPWC 170 values than girls in each adiposity group (low adiposity, high adiposity, and combined samples). A correlation matrix shows none ofthe continuous variables was significantly related toPWC l 70in the low adiposity sample (see Table 2). In the

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high adiposity samples, skinfold thickness and BMI were negatively related to PWC I 70 , and PAS and age were positively related. PAS, however, accoun ted for only 11 % of the variance in PWC I 70 in the high adiposity group and 6% in the combined group.

Several variables were significant in the high adiposity sample regression analysis: gender, age, BMI, and PAS. Age and PAS were positively related to PWC I 70• BMI was negatively related to PWC I 70; boys (M = 11.89) had greater PWC I 70 values than girls (M= 10.19). The overall model was significant, F (4,88) = 15.26, P< .001.

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Regression Results Table 3 shows the adjusted R2 and the regression coefficients (raw and beta) for the variables in the three regression analyses. In the combined sample, skinfold thickness (P< .03), gender (P< .001), and BMI (P< .001) were significant in the regression analysis. Skinfold thickness and BMI were negatively related to PWC I 70 • Boys (M = 13.38) had greater PWC l 70 values than girls (M = 11.36). The overall model was significant, F (5,180) = 23.46, P< .001. Only gender was significant in the low adiposity sample. Boys (M = 14.37) were more fit than girls (M = 13.26). The model was not significant, F (4,88) = 1.13, P < .345. It accounted for less than 1 % of the variance. PHYSICAL WORKING CAPACITY INDEX 25

20

r----------------,

.

15

10

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HIGH ADIPOSl1Y SAllPLE

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:i"':al" IJ.

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a.. 'll. a :aa" .. a 00 :t~ ~ a .. .. ~'hclfi,a,&a a .. Aa

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LOW ADIPOSI1Y SAllPLE

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0

a a QI

'h

oo ' - - - - - L - -........- - - : : ' - : - - - J - - - - ' 0.2

0.4

0.6

0.8

PHYSICAL ACTIVITY SCORE - - TOTAL

Figure 1. Plot of physical working capacity index and physical activity score for all participants (N= 186).

Discussion Caveats Physical activitywas an importan t predictor ofCVF in the high, but not the low, adiposity or combined samples. Several threats to the internal validity of these findings must be considered. If the low adiposity children were all getting high levels of activity, there may not have been sufficient variation in physical activity or fitness to detect a relationship (a ceiling effect). Table 1 revealed virtually identical standard deviations for PAS in the adiposity groups. The low adiposity group alternatively had higher levels and a marginally significantly lower variance in fitness. The assessment of physical activity by a validated observation procedure (O'Hara et al., 1989) is a strong feature of this study. Most previous studies relied on questionnaire or interview responses, which some regard as limited by self-report and recall biases (Saris, 1985; Weymans et al., 1986). Several previous studies (e.g., Rutenfranz & Singer, 1980; Weymans et aI., 1986) categorized subjects by activity level. The continuous nature of the physical activity score used in this study provides more complete data about the children's physical activity patterns. There is also support for the validity of this study's fitness measure, PWC I 70• Saris et al. (1980) reported a

Table 1. Sample characteristics Samples Nominal Characteristics

Gender

HighAdiposity (n= 93)

Low Adiposity (n= 93)

Combined (N= 186)

40 53

60 33

100 86

Boys Girls

Continuous Characteristics Age (years) BodyMass Index (kg·m·2) Physical Activity Score PWC 170

M 10.09 21.47 .31 10.92

(SO)

(1.26) (3.88) (.11) (3.02)

M 10.11 16.53 .34 13.97

(SO)

(1.23) (1.59)** (0.12)* (2.44)**

M 10.10 19.00 .33 12.45

(SO)

(1.24) (3.86) (0.11) (3.14)

Note. A two-sample multivariate ANOVA for two independent samples was conducted on the four continuous variables in the lower half of the table. Hotelling's P was significant at .83, F(4,181) = 37.53, P < .001. Statistical tests in the table reflect the results of a one-wayANOVA betweenthe high and low adiposity samples for the variables age, BMI, PAS, and PWC170• "p « .05; **p< .001.

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PWC l 70value of12.4 (same as the combined sample mean value in this study) for the middle two quartiles of their 8-12 year-old group. However, bicycle ergometry may not be the best fitness measure because reduced mechanical efficiency is more typical ofobese children (Reybrouck et al., 1987). The representativeness of habitual physical activity by a 2-weekday observation period is limited. Although it appears unlikely that much activity occurs before 7:00 am or after 7:00 pm on weekdays, the weekends are missing. Lean children may get more physical activity than heavy children on weekends, so their level of activity may have been underrepresented. Although the correlation between Day 1 and Day 2 for the physical activity score was moderate, questionnaires often assess activity over more days, thereby providing a more reliable estimate of habitual physical activity. Future studies will have to assess whether more extensive observation periods are more closely related.

Possible Substantive Explanations A possible substantive explanation of the findings is that the high adiposity group required more work and exertion for any given level of physical activity (i.e., a Table 2. Pearson correlations between continuous variables and PWC'70 within adiposity groups Samples

Continuous Variables Age (years) Skinfold Thickness (mm) Body Mass Index(kg·m· 2) Physical Activity Score

High Adiposity

Low Adiposity

(n= 93)

(n= 93)

.30* -.40** -.41 ** .33*

Combined (N= 186)

greater work effort in terms of transporting more weight or less efficient movement produced greater fitness gains in the high adiposity sample). Reybrouck et al. (1987), for example, reported obese subjects performed with greater relative intensity and at a higher percentage ofV0 2max for a given exercise compared to controls. An alternative explanation is that because the low adiposity group was more fit than the high adiposity group, the level of physical activity in the low adiposity group was not vigorous enough to further enhance fitness (a ceiling effect). From a third perspective, metabolic and hormonal processes may operate differently in the two samples. Rolland-Cachera etal. (1984) noted obese children grow and develop at a faster rate than nonobese children. Perhaps latent metabolic differences predispose high adiposity children to have greater fitness from physical activity. Bjorntorp (1990) and Oscai and Palmer (1990) reviewed the literature on adipose tissue adaptations (e.g., lipid mobilization) to exercise and reported less energy is required per work load when the total mass of adipose tissue is small. Triglycerides are mobilized more efficiently for exercise in the physically well-trained individual who is characterized by small adipocytes in a small total mass of adipose tissue. Also, the exercisetrained individual has an increased capacity to release free fatty acids (FFA) from adipocytes and thus relies more on FFA oxidation during submaximal exercise with a sparing ofcarbohydrate (Oscai & Palmer, 1990). Differences in hormonal and metabolic processes by adiposity and fitness levels may, thus, account for the findings.

Implications andFuture Research

-.03 -.04 .03 .04

.13 -.54** -.50** .24**

"p « .01;**p< .001.

There is little consistency in the activity-fitness relationship literature about children. Earlier studies did not stratify by adiposity. Our findings suggest that physical activity may be more important for CVF in high adiposity

Table 3. Regression coefficients (raw and beta) for bestfitting models predicting PWC170

Variables in Model Gender (l=male; o=female) Age (years) Skinfold Thickness' (mm) Body Mass Index(kg·m·2) Physical Activity Score Constant Adjusted PercentVaria nce Accountedfor by Model

High Adiposity

LowAdiposity

(n= 93)

(n= 93)

b

beta

b 1.87 .55

.31*** .23**

1.13 -.06

-.33 7.22 9.41

-.42*** .26**

-.01 -.17 14.04

.38***

beta

.22* -.03 -.01 -.01

.01

Combined (N= 186) b beta

.26*** .09 -.17* -.38*** .11

1.61 .23 -.55 -.31 2.93 14.06 .38***

• Since skinfold thicknesswas the variable on which the adiposity split was made, it was not entered intothe regression analyses. * p < .05;**p < .01;***p « .001.

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children than average adiposity children. Classification by adiposity may account for some of the previously inconsistent results. Adiposity merits careful consideration in future research in this area.

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Authors' Note Preparation of this manuscript was supported by grants from the National Heart, Lung and Blood Institute, 5T32HL07555,5UOI HL39927,5RI8HL30682,and5ROI HL35131.

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Waxman, M., & Stunkard, A. J. (1980). Caloric intake and expenditure ofobese boys.JournalofPediatrics, 96,187-193. Weymans, M. L., Reybrouck, T. M., Stijns, H. J., & Knops, J. (1986). Influence of habitual levels of physical activity on the cardiorespiratory endurance capacity ofchildren. In J. Rutenfranz, R. Mocellin, & F. Klint (Eds.), Children and exercise XII (pp. 149-156). Champaign,IL: Human Kinetics.

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Physical activity, cardiovascular fitness, and adiposity in children.

With cardiovascular fitness (CVF) as the dependent variable, relationships with habitual level of physical activity, age, gender, and body mass index ...
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