Within a population, coronary risk factors such as hypertension, hypercholesterolemia, dyslipidemia, cigarette smoking, diabetes mellitus, and family history of coronary heart disease (CHD) have been related to increased mortality and an earlier diminished quality of life(7,29). When a number of risk factors have accumulated, there may be as much as a 16-fold likelihood of acute myocardial infarction (15,30). Between cultures, however, absolute values of coronary risks, such as total cholesterol levels, may have different effects on the individual. For example, whereas American diets are high in cholesterol and fats (7), Japanese diets are low in saturated fat and high in antioxidants, and this appears to have beneficial effects on the oxidizability of low-density lipoprotein particles and thrombogenesis (10,26). This indicates that diet and cholesterol only comprise part of the CHD relationship and that other influences on atherogenicity require further study to determine contributing factors affecting health outcomes (3). Early detection of health risks and the establishment of positive health practices may be the link to lower morbidity and deterring rising health care costs(27).
Globally, landmark cases such as the Bogalusa Heart Study(21), Denmark Coronary Heart Disease Risk Factor, Physical Activity and Fitness Study (5), Framingham Heart Study (15), Lipid Research Clinics Program(27), Minnesota Heart Study (9), and the Northern Ireland Health and Activity Survey (16) contribute to the worldwide bank of knowledge that sedentary living and lifestyle behaviors have significant consequences on health risk factors. Identification of risk factors associated with CHD and methods of reducing these risks are strategies that may delay the onset of health problems affecting the individual and they may decrease national health care costs(25).
The evidence for the immediate and long-term impact of CHD risk factors in children appears to be less conclusive. Although it can be argued that these same CHD factors when present in adults are associated with increased risks from cardiovascular disease (18), the presence of similar factors in children may only lead to an early indication or a potential trend to the development of life-threatening diseases later in life. For example, atherosclerosis starts at an early age and progresses throughout the lifetime (5).
Increasing economic development in Southeast Asia has had a concomitant effect on lifestyles and health behaviors. In 1991, concern for rising health care costs prompted the government of Singapore to institute a National Health Policy committee to develop a decade-long framework for the “Healthy Family, Healthy Nation” campaign (19). This initiative addressed issues such as diet, exercise habits, smoking, and obesity in the Singapore population. One of the main concerns was the obesity level of the general population and, in particular, the obesity levels of Singaporean school children. This concern led to the establishment of the Trim and Fit program in schools with objectives to reduce the levels of obesity in school children through educational and physical activity programs.
At the XIth World Congress on Pediatric and Adolescent Gynecology held in Singapore, the director of School Health Services presented longitudinal data showing the growth rates of children from the three racial groups (i.e., Chinese, Malay, and Indian). This Ministry of Health report stated that hormonal and nutritional patterns differ between races. However, to date, little has been reported about body composition, cholesterol and lipoprotein levels, and other CHD risk factors of Asian groups. The task of monitoring changes in health risk requires constant updating because of the influence of changing dietary and lifestyle behaviors as well as growth patterns of the population.
Standard weight for height charts (20) and body stature measuring techniques continue to be used to determine status related to overweight and assignment into educationally sponsored Trim and Fit classes for those exceeding criterion-related standards. Other forms of body composition measurements such as bioelectrical impedance, skin-fold thickness, dual energy x-ray absorptiometry, and hydrodensitometry occur only in specialized clinics and fitness centers in Singapore.
The purpose of the present study was to conduct, in a field setting, coronary risk factor analysis comprising anthropometry, blood pressure, fasting plasma lipids and lipoproteins, glucose, and body composition and to document self-disclosed physical activity patterns for Singaporean school children.
A cross-sectional sample of 12 schools (one primary and one secondary school from each of the six geographical regions of Singapore) participated in the 1994 nationwide Youth Coronary Risk and Physical Activity Study conducted by the School of Physical Education. Children enter primary school at approximately 6 yr and enter secondary school at about 12 yr. To obtain a more evenly distributed age and gender sample, four additional schools (three primary and one secondary) joined the earlier screening program in 1995. The Ministry of Education, Ministry of Health, and individual school principals provided permission to conduct this study. All subjects (N = 1579) and parents of subjects provided written informed consent before testing. Experimental procedures and consent forms were subject to Nanyang Technological University, School of Physical Education human subject ethical review and approval.
The ethnic groups in this study consisted of 1293 Chinese (81.9%), 137 Malay (8.7%), 84 Indian (5.3%), and 64 other (4.1%). This approximated the overall population of Singapore (76.5% Chinese, 14.8% Malay, 6.4% Indian, 2.3% other) (13). Because of the smaller Malay and Indian racial group numbers, all data were grouped together for analysis. The larger proportion of “others” was comprised of those of mixed or unknown heritage. To assure confidentiality, each subject's data were coded and stored on computer file. An individual feedback result form was given to each child with the results of their screening. School principals received their school-based summaries from the survey and coronary risk assessment.
Physical Activity and Exercise Questionnaire
Development of survey instrument. In a pilot study using the Sports Tester PE4000 (Polar, Electro Oy, Finland) heart rate monitors and a self-reported survey instrument, 24 students recorded daily activities and wore the monitors for 24 h on 3 d and at least 14 h on 2 other days of the week. Index scores for the survey form included five categories ranging from inactive through vigorous physical activities. The Pearson correlation between the self-reported data and the heart rate recordings was r = 0.67 (P= 0.01). Findings from this preliminary study indicated that school age children were moderately accurate in documenting their individual activities. These findings showed that when children self-disclosed higher activities, they also had higher recorded heart rates. This approach of recording children's heart rate scores assesses a similar dimension of comparison against the physical activity self-reports (31).
From this initial work, four multiple choice questions were selected to be used in a survey called “The Physical Activity and Exercise Questionnaire” similar in wording to the instrument reported by Aaron et al. (1) developed from the Youth Risk Behavior Survey produced by the Centers for Disease Control. Content validity was established through the Singapore Ministry of Health, who are familiar with the vocabulary and language levels of these students and who reviewed these questions for the applicability in the school context.
The final questionnaire comprised three sections. In the first section, the participants assessed their current level of physical activity using the following categorizations: 1) inactive, 2) relatively inactive, 3) light physical activity, 4) moderate physical activity, and 5) vigorous physical activity. The second section related to the participant's level of physical activity over the previous 14 d. Included in this section were questions relating to the time spent in “hard exercise”; “easy exercise”; weekly hours of television, video, or computer usage; and annual sports participation (noted on a scale of 1-5). To determine the reliability of the participants' responses in this second section, a third section required subjects to document the number of specific annual sports events. If the subject's answer to annual sports participation was not equal to the exact number of sport events listed, the subject's questionnaire was considered invalid and eliminated. A total of 63 (8.9%) were discarded because of improper compliance. The language of instruction in Singapore schools is English, and there was no need for individual dialect translations. Secondary school children provided independent self-reported answers to the questionnaire, whereas in primary schools, trained research assistants asked the questions verbally to the students and recorded their individual responses. It was determined that children under 9 yr were not accurate in recalling physical activity over 2 wk, and their data were not included in this analysis.
Validation of survey. To determine construct validity for the questionnaire in its final form, and as a trial for the procedures employed in the blood analysis, one school was selected as a pilot study for testing. These children (mean age ± SD = 14.3 ± 1.23 yr) were asked to document their previous 14 d of physical and leisure activities, to undergo all the body composition, and to participate in the blood-drawing procedures. All children (N = 745) in this school also participated in an annual National Physical Fitness Award test that included a 2.4-km walk/run of cardiovascular endurance. Indirect test validity of the questionnaire was made through a comparison between the students' responses regarding physical activity and results from the 2.4-km walk/run, BMI, and selected blood chemistry results.
Significant findings using Spearman rank correlations (rs) were found between physical activity and the 2.4-km walk/run(rs = 0.21, P = 0.01). One-way analysis of variance(ANOVA) showed significant differences (F (4,740) = 12.5,P = 0.001) between the five physical activity groups and the results of the endurance run. No significant interactions were found between BMI and physical activity groupings (F (4,724) = 1.23, P = 0.295). These findings verified a distinct relationship that the high physical fitness groups were differentiated by the self-disclosed physical activity survey categories.
Physical activity was significantly correlated with total cholesterol(rs = 0.35, P = 0.019), high-density lipoprotein(rs = -0.38, P = 0.035), and triglycerides(rs = -0.27, P = 0.021). Additionally, hard exercise and easy exercise between groups of students were significantly correlated (rs = 0.25, P = 0.014). The survey instrument was considered valid and reliable, producing acceptable responses to self-reported physical activities and separating individual subjects into activity groups for further analysis of physical measurements.
In Singapore, the school week is comprised of 5 full days (Monday through Friday) and 1 half-day session (Saturday). In each of the schools included in this study, students assembled in a classroom during a regular Saturday morning session. During this time, students completed the Physical Activity and Exercise Questionnaire; underwent a physical assessment of blood pressure, height, and weight; were assessed for body composition using a skinfold caliper, tape measure, and bone vernier; then, had a blood sample taken for analysis of blood chemistry.
After the written completion of background information, participants were divided by gender and taken to a prepared classroom where relevant physical measures were taken by the researchers and/or trained research assistants of the same gender as the participant.
The order of the measurements was as follows: sitting blood pressure after a 5-min rest interval, height to the nearest centimeter (using a wall-mounted measuring tape), and weight to the nearest 0.2 kg (Model DSI-339, Detecto, USA). Anthropometry consisted of 8 skinfold (to the nearest 0.1 mm, skinfold caliper, Holtain, Dyfed, UK), 10 circumference (to the nearest 0.1 cm, Gulik tape, Creative Health Products, USA), and 2 bone-width (to the nearest 0.1 mm, sliding vernier, Holtain, Dyfed, UK) measurements (17). For consistency of procedures, all readings were taken in succession on the right side of the body, then repeated a second time. If skinfold or width readings exceeded 2 mm or circumference exceeded 2 cm, then an additional measurement was made. Calculation for intra- and intertester reliability was agreement ÷ (agreement + disagreement) · 100% (where agreement = measurement within specified range and disagreement = measurement outside defined range). Consistency between measurements was high (r ≥ 0.90,P < 0.05) for all reliability measures as determined in pilot and field assessments. For the purpose of this assessment, the Slaughter et al.(Janz et al.14) two measures of skinfold thickness are reported. Although not specific to Singapore, these formulas give an indication of percentage body fat according to the formulas: for boys%BF = 0.735 (triceps + calf) + 1.0 (SEE = 3.8) and for girls%BF = 0.610 (triceps + calf) + 5.1 (SEE = 3.8). The remaining measurements of anthropometry are currently under review for development of specific Asian body composition formulas.
For measurement of blood pressure, children sat in a chair with the forearm hanging loosely by their side. Blood pressure was taken using the indirect brachial artery auscultation method using a stethoscope and aneroid sphygmomanometer. Procedures were consistent with the techniques described by Alpert and Wilmore (2) and the ACSM(3). No subject had a systolic blood pressure greater than 160 mm Hg or a diastolic blood pressure greater than 95 mm Hg.
Fasting Plasma Profile
All subjects presented themselves in a 12-h fasting state and were advised to consume only water on the morning of the blood draw. Spot checking compliance consisted of questioning the participant's dietary behavior over the previous day. Those not in conformity with the established fasting protocols did not have their blood taken but were permitted to participate in other aspects of the assessment. Only five children were withdrawn because of lack of compliance.
The blood was drawn through a sterile needle lancet puncture of the finger of the nondominant hand. If the sample was difficult to obtain, further punctures were used to obtain 300 μL of whole blood. The blood was immediately centrifuged (Eastman Kodak Ektachem Microcentrifuge, Rochester, NY) for 45 s at 12,500 r·min-1, then placed upright on ice for later analysis. Dry-chemistry desk top analysis of blood (Ektachem DT-60 Analyzer, Rochester, NY) followed strict protocols as outlined by the National Cholesterol Education Program (4,28). A dry, four-layered colorimetric clinical chemistry slide (Kodak, Ektachem DT60) containing reagents for TCHOL, TRIG, and GLU, respectively, were inserted into the analyzer. By using a micropipette, 10 μL of plasma from the centrifuged blood sample were deposited onto the slide. For HDL-C, 50 μL of plasma was micropipetted into a special tube (Ektachem MicroHDL) containing HDL-C reagent. The contents were mixed for 30 s and stood for 5 min. The tube was then centrifuged, and 10 μL of supernate were transferred to the HDL-C slide. Total cholesterol (TCHOL), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and glucose (GLU) were directly determined through the analyzer while low-density lipoprotein cholesterol (LDL-C), very low-density lipoprotein cholesterol (VLDL-C), and the TCHOL/HDL-C ratio were derived by calculation according to the formulas: VLDL = Triglyceride ÷ 5 and TCHOL = HDL + LDL + VLDL.
Reference range tests performed daily identified low and high components of the premeasured controls. Intratest analysis consisted of multiple repeated samples using the same serum and interest analysis involved external validation by a nationally accredited hospital. Correlation coefficients between the two laboratories ranged from r = 0.563 (P = 0.001) for glucose to r = 0.980 (P = 0.001) for total cholesterol.
Data were analyzed using SPSS (Windows Version 7.5, Chicago, IL). Comparisons between genders were made using ANOVA for the appropriate variables. Self-reported physical activity to coronary heart disease risk factors was stratified by gender. Spearman rank order correlations were employed to examine the test-retest reproducibility of the questionnaire and to investigate the association between the estimates of physical activity with body mass index and blood chemistry. Analysis of subgroups of activity patterns (inactive through vigorous physical activity) were also performed using nonparametric tests due to the skewness of the data.
Statistical significance between physical activity groups (independent variable) and the coronary risk factors of TCHOL, blood pressure, and percent body fat (dependent variables) was determined using multivariate analysis of variance (MANOVA). Significant MANOVA effects were further analyzed with stepwise discriminant analysis and univariate analysis of covariance (ANCOVAR) with age and gender as the covariates. All significant findings were further examined using the Scheffé post-hoc test.
Demographic characteristics including height, weight, BMI, and blood pressure by age are reported in Table 1. Subjects ranged in age from 6 to 18 yr with a total of 730 boys (46.2%) and 849 girls (53.8%). Because of difficulty in assuring compliance with all testing variables, complete data sets were available for only 705 subjects. Blood pressure, blood samples, and physical activity patterns were not collected for children 8 yr or younger. Because fewer numbers of boys and girls were available in the sample population, the data for children 17 and 18 yr were combined.
From Table 1, height at each successive age division was greater than each preceding age group until 15 yr for boys and until 14 yr for girls. ANOVA showed significant differences between both genders at 9 yr(P = 0.009), 11 yr (P = 0.024), and beyond 12 yr(P < 0.01). Similarly, weight was heavier than each preceding age for boys until 16 yr and for girls until 14 yr, thereafter weight remained relatively constant. Significant differences were observed between the genders at 9 yr (P = 0.042) and beyond 12 yr (P < 0.05). BMI was greater for each age until 14 yr and then remained consistent after that age for both genders. There were no significant differences for BMI between the two genders at any age.
Percent body fat was calculated based on the sum of two skinfolds (triceps and calf) plus the corresponding Slaughter formulas(12,14). For boys, body fat percentage was higher at each age division from 6 to 9 yr, then consistent body fat was recorded during the pubertal years (up to age 14 yr) with a lower body fat recordings after 15 yr. For girls, body fat was about the same for each age from 6 until 12 yr with greater body fat recorded between 12 and 15 yr and about the same body fat readings, thereafter. Significant differences were found between boys' and girls' percent body fat at 6 and 7 yr and beyond 13 yr.
Blood pressure was different between the age groups for both genders, with higher systolic readings over most ages (with the exception of girls 12 yr and boys 14 yr). Significant differences were found between boys and girls(P = 0.001). There were eight children (0.47%) with systolic blood pressure greater than or equal to 140 mm Hg and 11 children (0.65%) with diastolic blood pressure greater than or equal to 90 mm Hg.
Physical Activity and Exercise Questionnaire
Five groupings of students were identified based on their self-assessment of personal current activity levels. Figure 1 shows physical activity patterns of school age children relating boys' and girls' hard exercise, easy exercise, television/video and computer usage, and annual number of sports played by each activity grouping. According to the questions asked, in the previous 14 d, boys self-disclosed that they were more vigorously active than girls (P = 0.001). Girls self-disclosed that they exercised at an easier pace than boys (P = 0.028). No differences were found between boys' and girls' groups in television and video viewing or computer game usage, but girls had lower multimedia use in the vigorous physical activity group (P = 0.001). Boys and girls were significantly different (P = 0.028) for hard exercise at the three most active groups and for easy exercise at the three middle-active groups. As expected, the number of annual sports played for both boys and girls was greater with each consecutive physical activity group.
Physical Activity by Age
Through self-disclosed documentation, boys were more physically active than girls at each age group from 10 to 17 yr (Fig. 2). There was relatively consistent activity between the light and moderate categories for the boys, with less active physical activities documented for girls 13 yr.
Boys revealed that they participated in about 3-5 d of hard exercise in the previous 2 wk, whereas girls stated they had lower hard exercise patterns with fewer days of hard exercise from 13 to 17 yr (Fig. 3a). On the other hand, girls participated in more easy exercises across the ages than boys with a peak of 6-8 d of easy exercise for the girls at 12 yr, but fewer girls participated in easy exercises after this age(Fig. 3b). Boys disclosed easy exercise to be 6-8 d per 14 d at 15 and 17 yr. Comparison between hard and easy exercises shows more days of easy than hard exercise at each age for girls and more days of easy exercise after 14 yr for boys.
Over the ages, no discernible pattern is noted for boys or girls in multimedia usage such as television or video viewing or computer game playing(Fig. 3c). There appears to be a consistent 2-4 h of daily multimedia usage across each age.
Regarding annual sports participation, about two to three sports are played by boys and girls from 10 to 12 yr (Fig. 3d). More girls documented they were active in annual number of sports at 13 yr, usually at the time when they enter secondary school, but then fewer were active in sports thereafter. Boys' annual participation in competitive sports remained consistent between two to three per year with fewer documenting sports participation from 15 yr onward.
Mean (± SD) total cholesterol, high- and low-density lipoprotein, triglyceride, and glucose blood plasma results are reported inTable 2. No differences between genders were noted, and no discernible pattern was prevalent between different ages for any of the blood plasma results. There were 109 children (6.4%) with TCHOL greater than or equal to 180 mg·dL-1, 42 children (2.5%) with LDL greater than or equal to 130 mg·dL-1 and 24 children (1.4%) with TRIG greater than or equal to 180 mg·dL-1. Boys and girls 17 yr had lower TCHOL and LDL and the girls at this age had higher HDL than other ages. Further testing would possibly indicate whether these subjects were fitter than the other subjects. Only three children (0.17%) had GLU greater than or equal to 160 mg·dL-1 with no one above 170 mg·dL-1.
Relationships between CRF and Physical Activity Patterns
Correctional analyses. A Spearman rank order correlation analysis(rs) was performed between the self-disclosed physical activity patterns and coronary risk factors for all subjects(Table 3). For boys, physical activity correlated with TCHOL (rs = -0.13, P = 0.018) and TRIG(rs = -0.18, P = 0.001). No similar patterns in blood chemistry were noted for girls. For girls, body fat correlated with physical activity (rs = -0.22, P = 0.001), hard exercise (rs = -0.17, P = 0.001), and sports played(rs = -0.27, P = 0.001). Body fat correlations for boys were significant with hard exercise, easy exercise, and sports played(rs = -0.13, P = 0.018). Blood pressure was significantly correlated with physical activity and hard exercise(rs = -0.12, P = 0.025) for girls and only systolic blood pressure was correlated with multimedia usage (rs = 0.12, P = 0.029) for boys.
Multivariate analyses. Significant main effects between physical activity (independent variable) and cholesterol, blood pressure, and body fat(dependent variables) were determined by MANOVA analysis. Wilk's criterion for determination of significance indicated a significant main effect across physical activity groups for all dependent variables (F (4,637) = 11.1, P = 0.0001). Scheffé post-hoc analysis(Table 4) indicated that statistical differences were most prevalent between physical activity (groups 2 and 5) and TCHOL(P = 0.018) and between physical activity (groups 1, 4, and 5; 2, 4, and 5; and 3 and 4) and body fat (P = 0.0001). Generally, this analysis indicated that the lower the physical activity, the higher the TCHOL and body fat and, conversely, the higher the physical activity group, the lower the TCHOL and body fat.
Stepwise discriminant analysis revealed that percent body fat and TCHOL were the dependent variables responsible for the significant main effects of physical activity level for the CHD risk factors. ANCOVAR, using age and gender as the covariates, was employed to determine significant differences for the dependent variables on physical activity levels. In this analysis with age as the covariate, significant differences were noted between physical activity groups for blood pressure and body fat, but not for TCHOL. The same was found with sex as the covariate.
This study sought to establish specific coronary risk factor values and to document the self-disclosed activity patterns of school-age children in Singapore. These baseline data are valuable as a means of comparing similar information between countries, cultures, and age divisions. The self-disclosed physical activity patterns can also be related to the coronary risk factors measured in this study. This information can potentially be useful in the early detection of coronary heart disease risk.
Population-specific body composition formulas for Asian children of various ethnic groups are currently under review in Singapore. The subjects of this study were generally shorter and lighter than Caucasian and black children reported in previous studies (17,18,21). This study similarly shows that BMI was greater at each age for both boys and girls, remaining consistent for boys at 20 kg·m-2 around 14 yr and at 19 kg·m-2 for girls around 13 yr. Using skinfold formulas for children from western countries, Slaughter(12,14) reported higher percentage body fat for boys and girls at each age. Skinfold equations form the two-component model dividing the body into fat mass and fat free mass (FFM). For children, these equations differ between individuals based on average water and mineral fractions of the FFM, thereby increasing the likelihood of error in predicting body fat percentages. No specific studies have attempted to define water and mineral fractions of FFM between sport participants, genders, or races(14).
Major risk factors associated with CHD are monitored by many agencies in Singapore including the ministries of defense, education, finance, and health(13,19,20,24). With a concern that smoking is an independent risk factor for CHD, antismoking regulations for school children have been implemented and are vigilantly enforced in an attempt to curtail one of the influences on CHD.
Concerning hypertension, another CHD risk factor, no child in this study had a blood pressure exceeding 160/95 mm Hg, and this is generally consistent with other findings (22). The Second Task Force on Blood Pressure Control in Children (2) defined hypertension as the average systolic and/or diastolic blood pressures greater than or equal to the 95th percentile for age and gender with measurements obtained on at least three occasions. With only seven children with blood pressure readings greater than or equal to 140/90 mm Hg, evidence is consistent that hypertension in this population was relatively uncommon. Alpert et al.(2) reported similarly that essential hypertension during childhood is not usually a major health problem. The fact that only a few children in this study had elevated blood pressure may indicate a limitation in the random subject sampling methodology employed in this study or the fact that these Asian children are less prone to hypertension than other populations.
Using self-disclosed physical activity as a measure of coronary risk showed some relationship between TCHOL, TRIG, body fat, and systolic blood pressure for boys and body fat, BMI, and blood pressure in girls. Other reports(8,15,27,30) describe a stronger relationship between coronary risk factors and physical fitness than between coronary risk factors and physical activity. Further study of these interactions in the Asian population are needed. Comprehensive international studies have been reported (23) showing the great urgency for standardization of assessment techniques in both health-related physical activity and fitness for descriptive epidemiology. Perhaps the first step is cross-cultural comparisons. The National Heart, Lung, and Blood Institute Workshop on physical activity (26) recommended better methods of physical activity assessment; better definitions of physical activity, fitness, and health; standardization methods of data collection; and longitudinal studies as well as cardiovascular and metabolic studies of responses to acute exercise in youth. The present study provides another link to the cross-cultural relationship of physical activity to coronary risk factors. Previous work (11) in Singapore supports the efforts to reverse the low physical activity trend exhibited by many school children.
A few interesting facts emerge from the analysis of activity patterns across various ages. Boys tended to be more physically active than girls with fewer fluctuations between 10 and 17 yr. Girls, on the other hand, reported fewer activities from about 13 yr onward. The greater number of hard and easy exercises for boys after 16 yr may be due to an awareness and effort by the boys in preparation for their 2.5-yr national service physical requirements. Fewer annual sports participants at each successive year beyond 10 yr could be attributed to a concern for the high stress placed on students to do well in their academic school-leaving examinations. Rajan (24) contends that the high regard that parents and the education system place on scholastic achievement has led to a more sedentary lifestyle, adding further to the notion that children are becoming more obese with less concern for positive exercise behaviors.
Not surprising, in this study, boys reported more physical activity than girls and boys exercised harder than girls. A fairly equal distribution of boys and girls reported watching television, playing video games, or using the computer. Another finding was that the more active the child regardless of gender, the more likely he or she was to participate in a greater number of sports. Myers et al. (21) indicated that sedentary activity was reported more by girls and by black children than by white or Hispanic children. Although racial issues related to physical activity are probably factors of the environment, it was noted that black children played more at basketball, dance, and rope skipping than white children who participated more in football and gymnastics. The choice of activities in the present study probably is related to environmental factors such as the high heat and humidity in the tropics. Further investigations of climate and physical activity are called for (21), but these studies must also account for socioeconomic status. As Singapore becomes more economically developed, the incidence of preventable health disease is on the rise (20). Isolating individual coronary risk factors is only part of the overall effort needed to combat sedentary living, obesity, and lifestyle diseases.
Again, issues of standardization are addressed with the constraints of comparison of cross-sectional population body composition data in children(6). Increasing evidence of obesity in young people is a current concern in Singapore and worldwide (24). Baseline information provides the foundation by which researchers and practitioners can assess small or large groups as well as plan intervention strategies consistent with national health policies.
Knowledge of blood lipids, lipoproteins, blood pressure, and physical activity patterns provides information about coronary risks in an Asian youth population. Although very few children in this study were at risk for heart disease, longitudinal analysis will be beneficial for tracking this population as the further development of Singapore provides changing personal lifestyle behaviors and global influences on a newly developed country.
This research was funded by the Academic Research Fund of the National Institute of Education, Nanyang Technological University, Singapore.
Address for correspondence: Gordon J. Schmidt, Ph.D., School of Physical Education, National Institute of Education, Nanyang Technological University, 469 Bukit Timah Road, Singapore 259756. E-mail:firstname.lastname@example.org.
1. Aaron, D. J., A. M. Kriska, S. R. Dearwater, et al. The epidemiology of leisure physical activity in an adolescent population.Med. Sci. Sports Exerc.
2. Alpert, B. S., and J. H. Wilmore. Physical activity and blood pressure in adolescents. Pediatr. Exerc. Sci.
3. American College of Sports Medicine. ACSM's Guidelines for Exercise Testing and Prescription
, 5th Ed. Philadelphia: Williams & Wilkins, 1995, pp. 1-150.
4. American Heart Association. National Cholesterol Education Program: Physician's Cholesterol Education Handbook
. Dallas: AHA, 1988, pp. 1-24.
5. Anderson, L. B. and J. Haraldsdottir. Coronary heart disease risk factors, physical activity and fitness in young Danes.Med. Sci. Sports Exerc.
6. Bar-Or, O. and T. Baranowski. Physical activity, adiposity and obesity among adolescents. Pediatr. Exerc. Sci.
7. Casperson, C. J. and R. K. Merrit. Physical activity trends among 26 states, 1986-1990. Med. Sci. Sports Exerc.
8. Eaton, C. B., K. L. Lapane, C. E. Garber, et al. Physical activity, physical fitness and coronary heart disease risk factors.Med. Sci. Sports Exerc.
9. Folsom, A. R., C. J. Casperson, H. L. Taylor, et al Leisure time physical activity and its relationship to coronary risk factors in a population-based sample. Am. J. Epidemiol.
10. Freedman, D. S., G. L. L. Burke, D. W. Harsha, et al. Relationship of changes in obesity to serum lipid and lipoprotein changes in childhood and adolescence. Childhood Obesity and Lipoproteins
11. Gilbey, H. and M. Gilbey. The physical activity of Singapore primary school children as estimated by heart rate monitoring.Pediatr. Exerc. Sci.
12. Houtkooper, L. B. and S. B. Going. Body composition: how should it be measured? Does it affect sport performance? Sports Sci. Exchange
7(Suppl. 5):1-5, 1994.
13. Hughes, K. The Epidemiology of Cardiovascular Diseases in the Ethnic Groups of Singapore
. Tokyo: Southeast Asian Medical Information Center, 1993, pp. 1-55.
14. Janz, K. F., D. H. Nielsen, S. L. Cassady, J. S. Cook, Y. T. Wu, and J. R. Hansen. Cross-validation of the Slaughter skinfold equations for children and adolescents. Med. Sci. Sports Exerc.
15. Lerner, D. J. and W. B. Kanner. Patterns of coronary heart disease morbidity and mortality in the sexes: a 26-year follow up of the Framingham population. Am. Heart J.
16. MacAuley, D., E. E. McCrum, G. Stott, et al. Physical activity, lipids and apolipoproteins and Lp(a) in Northern Ireland Health and Activity Survey. Med. Sci. Sports Exerc.
17. MacDougall, J. D., H. A. Wenger, and H. J. Green.Physiological Testing of the High-Performance Athlete
. Champaign, IL: Human Kinetics, 1991, pp. 224-251.
18. Malina, R. M. and C. Bouchard. Subcutaneous fat distribution during growth. In: Fat Distribution during Growth and Later Health Outcomes
, C. Bouchard and F.E. Johnston (Eds.). New York: Plenum, 1988, pp. 63-84.
19. Ministry of Health. Report of the Review Committee on National Health Policies: Healthy Family, Healthy Nation
. Singapore: Singapore National Printers, 1991, pp. 1-24.
20. Ministry of Health. The Health of Singaporeans
. Singapore: Research and Evaluation Department, Ministry of Health, 1993, p.1-15.
21. Myers, L, P. K. Strikmiller, L. S. Webber, and G. S. Berenson. Physical and sedentary activity in school children grades 5-8: the Bogalusa Heart Study. Med. Sci. Sports Exerc.
22. Newman, W. P., D. S. Freedman, A. W. Voors, et al. Relation of serum lipoprotein levels and systolic blood pressure to early atherosclerosis. N. Engl. J. Med.
23. Oja, P. Descriptive epidemiology of health-related physical activity and fitness. Res. Q. Exerc. Sport
24. Rajan, U. Obesity: is it a problem in the Asia-Pacific region? Singaporean perspective. Proc. Malaysian Soc. Study of Obesity
25. Shephard, R. J. Worksite health and productivity. In:Worksite Health Promotion Economics Consensus and Analysis
, R. L. Kaman (Ed.). Champaign, IL: Human Kinetics, 1995, pp. 147-174.
26. Sopko, G., E. Obarzanek, and E. Stone. Overview of the National Heart, Lung and Blood Institute Workshop on physical activity and cardiovascular health. Med. Sci. Sports Exerc.
27. Tamir, I., G. Heiss, C. J. Glueck, B. Christensen, P. K. Kwiterovich, and B. M. Rifkind. Lipid and lipoprotein distributions in white children ages 6-19 yrs: the Lipid Research Clinics Program Prevalence Study. J. Chronic Dis.
28. U. S. Department of Health and Human Services.Report of the Expert Panel on Blood Cholesterol Levels in Children and Adolescents
(NIH Publication No. 91-2732). Washington, DC: U.S. Department of Health and Human Services, 1991.
29. Verschuren, W. M. M., D. R. Jacobs, B. P. M. Bloemberg, et al. Serum total cholesterol and long-term coronary heart disease mortality in different cultures. J. Am. Med. Assoc. (Southeast Asia)
30. Whitmer, R. W. Health care cost. In: Worksite Health Promotion Economics Consensus and Analysis
, R. L. Kaman (Ed.). Champaign, IL: Human Kinetics, 1995, pp. 79-96.
31. Young, D. R. and M. A. Steinhardt. The importance of physical fitness versus physical activity for coronary artery disease risk factors: a cross-sectional analysis. Res. Q. Exerc. Sport