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thesis entitled

CORONARY HEART DISEASE RISK FACTORS AND
HEALTH-RELATED FITNESS 0F ADOLESCENTS
IN NITEROI, RIO DE JANEIRO, BRAZIL

presented by

Rosane Carla Rosendo da Silva

has been accepted towards fulfillment
of the requirements for

Ph.D. Kinesiology

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CORONARY HEART DISEASE RISK FACTORS AND HEALTH-RELATED
FITNESS OF ADOLESCENTS IN NITEROI, RIO DE JANEIRO, BRAZIL

By

Rosane Carla Rosendo da Silva

A DISSERTATION

Submitted to
Michigan State University
in partial fiilfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Kinesiology

1998

ABSTRACT

CORONARY HEART DISEASE RISK FACTORS AND HEALTH-RELATED
FITNESS or ADOLESCENTS IN NITEROI, RIO DE JANEIRO, BRAZIL
By

Rosane Carla Rosendo da Silva

The purpose of this study was to determine the status of selected risk factors for
coronary heart disease (CHD) and components of health-related physical fitness
(cardiorespiratory fitness and body fatness) among adolescents in NiterOi, Rio de Janeiro,
Brazil, taking into account the effects of race, socioeconomic status (SES) and sexual
maturation. The CHD risk factors included in the study were smoking, overweight,
elevated blood pressure, inactivity, relative fat distribution, and low fitness. The
relationship of cardiorespiratory fitness (CF) and physical activity (PA) with CHD risk
factors, and the clustering of risks were also investigated. A total of 329 subjects (126
males and 203 females) were included in the Study. The results indicated that race had no
relationship with CF, PA or the CHD risk factors. Non-Whites tended to Show a higher
prevalence of elevated BPS and overweight, and also more centrally distributed fat. Non-
White males were more likely to be smokers, while the reverse occurred among females.
SES did not explain any part of the variance in CF or in CHD risk factors, except that
lower middle class status explained 3% of the variance in PA. Lower SES individuals
were at higher risk for smoking, elevated BPS and inactivity. Middle class girls and lower

class boys were more likely to be overweight. CF had no relationship with BPS. BMI,

TER, or WHR. CF was inversely related to ZOSF, and a positive relationship was
observed with PA. Boys at risk for low fitness tended to have lower PA, higher BPS, and
significantly higher 26$F. Significantly lower PA, higher BMI, Z6SF and TER were
characteristic of females at risk. BPS also tended to be higher among these girls. PA did
not explain any part of the variance in 26SF, BMI, TER, WHR, or SBP. PA was
inversely related to DBP. Inactive boys tended to show higher BP values, while girls
tended to have lower CF and higher Z6SF, TER, WHR, and BPS. Risk factors clustered
within approximately 25% of individuals. Inactivity was present in all clusters. Few
individuals clustered 3 and 4 risk variables. Non-White females tended to cluster risk
factors more frequently than White females. The results demonstrated that CF and PA
had no or a low relationship with the selected risk factors measured in this study. They
also showed that Non-White and lower class subjects are more prone to have CHD risk

factors.

 

Copyright by
ROSANE CARLA ROSENDO DA SILVA

1998

DEDICATION

“ Because I experienced the future,
I need to be strong in the present,
I need to be restless,

I want a better country.”

Carta a Republica, Milton Nascimento

To Julia and all Brazilian youth.

 

ACKNOWLEDGEMENTS

This work is in many ways a result of the collaborative work, since we always
depend on many people. I would like to express my sincere thanks to all members of this
“team”. The list does not follow an order of importance and names are avoided as much
as possible so that I will not incur in the error of omitting names.

— To the Holy Father, for His blessings and for all the opportunities during this lifetime.

— To my parents, Farida and Aryldo (in memorium), for their love, support and the
example of hard work and dedication. A special thanks to Farida for being always
there for me and for taking care of all my business in Brazil while I am abroad,
allowing me to focus on the work.

- To Regina Celia, Edmir and Julia, my sister, brother-in-law, and niece, respectively,
for their love.

- To the Brazilian citizens, who allow the Government to invest in the education of
scholars through their taxes;

—— To Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior — CAPES,
Brazilian Ministry of Education, for the financial support for the program and
research;

- To the Departamento de Educacao Fisica and the Coordenacao de Capacitacao
Docente of Pro-Reitoria de Pesquisa e POs-Graduacao of Universidade Federal
Fluminense, for the four year leave to complete this program;

— To Dr. Robert M Malina, my mentor, for the wonderfiil learning experience;

- To Drs. Robert M Malina, James M Pivarnik, Martha E Ewing and Harry Perlstadt,

vi

the Committee members, for the guidance in the program and suggestions to this
work;

- To all teacher and professors in my academic life for providing me food for thought;

— To the State and Municipal Departments of Education in NiterOi, for giving me the
access to the schools;

— To the principals and physical education teachers who indirectly participated in the
study;

-— To all the subjects in the study, for their time and cooperation. Furthermore, for
giving me the opportunity to find answers to some of my questions;

— To Mr. Ralph L. L. N. Reys and Mr. Celio L. Baptista, undergraduate students in
Kinesiology, for assisting with the data collection, and doing more than the stipend
could cover;

— To all friends and family in Brazil and in the United States, for their prayers, letters,
e-mails, and phone calls expressing their love that helped me to be less homesick;

— To each and everybody who directly or indirectly supported me through this journey.

vii

TABLE OF CONTENTS

LIST OF TABLES ........................................................................................................ XI
LIST OF FIGURES ................................................................................................... XVII
CHAPTER I

INTRODUCTION .................................. . ........................................................................ 1
Research Questions ..................................................................................................... 4
Limitations .................................................................................................................. 5
Significance of the study .............................................................................................. 5

CHAPTER 2

REVIEW OF LITERATURE .......................................................................................... 6
Growth and sexual maturation of adolescents .............................................................. 8
Health-related physical fitness ....... _. ........................................................................... l9
Coronary heart disease risk factors ..................................... . ...................................... 24
Summary ................................................................................................................... 50

CHAPTER 3 .

METHODOLOGY ........................................................................................................ 52
The Brazilian context ................................................................................................ 53
Methodological aspects ............................................................................................. 69

CHAPTER 4

RESULTS ..................................................................................................................... 85
Descriptive Statistics ................................................................................................. 85
The effects of sociodemographic characteristics on health-related physical fitness

and on selected CHD risk factors. ...................................................................... 92
Effects of cardiopulmonary fitness on selected CHD risk factors ............................... 95
Effects of physical activity level on selected CHD risk factors ................................... 96
Clustering of risk factors ........................................................................................... 97

CHAPTER 5

DISCUSSION .............................................................................................................. 99
General characteristics of the sample ......................................................................... 99
The status of growth and sexual maturation of adolescents ...................................... 100

viii

The status of health-related physical fitness ............................................................. 107

The status of CHD risk factors ................................................................................. 1 10
CHAPTER 6
‘ SUMMARY AND CONCLUSIONS ........................................................................... 131
Summary ................................................................................................................. 131
Conclusions ............................................................................................................. l 33
Recommendations ................................................................................................... l 34
APPENDICES ............................................................................................................ 243
APPENDIX A
APPROVAL OF THE UNIVERSITY COMMITTEE ON RESEARCH INVOLVING
HUMAN SUBJECTS .............................................................................................. 244
APPENDIX B
MAP OF BRAZIL ....................................................................................................... 247
APPENDIX C
MAPS or NITEROI, RIO DE JANEIRO ................................................................... 249
APPENDIX D
LETTERS OF CONSENT FROM STATE AND MUNICIPAL DEPARTMENTS OF
EDUCATION IN NITEROI ............................................................................... 252
APPENDIX E
PARENTAL CONSENT ............................................................................................. 256
APPENDIX F
SUBJECT ASSENT .................................................................................................... 264
APPENDIX G
IDENTIFICATION FORM ......................................................................................... 267
APPENDIX H
CLASSIFICATION OF SOCIAL CLASS BY HEAD OF HOUSEHOLD
OCCUPATION ................................................................................................ 270
APPENDIX I
THE SELF-ASSESSMENT OF SEXUAL MATURITY - MALES ............................. 277

ix

APPENDIX J

THE SELF-ASSESSMENT OF SEXUAL MATURITY - FEMALES ....... . ................ 285
APPENDIXK
PHYSICAL ACTIVITY QUESTIONNAIRE .............................................................. 293

LIST OF TABLES

Table 1. Relative distribution (%) of stages of genital and breast development in Brazilian
males and females, respectively, by age group from 10 to 19 years. ........................... 137

Table 2. Relative distribution (%) of stages of pubic hair stages in Brazilian males and
females, respectively, by age group from 10 to 19 years ............................................. 139

Table 3. Correlations between self and physician assessments of secondary characteristics
in several Brazilian adolescents by sex. ...................................................................... 140

Table 4. Percentile values for high normal blood pressure and hypertension by age and
sex during adolescence. ............................................................................................. 140

Table 5. Relative composition (%) of the Brazilian population by race and geographic

‘ region ......................................................................................................................... 141
Table 6. Mean wages by occupational category and sex in Brazil, 1981-1990 .................... 141
Table 7. Work and study'status of Brazilian adolescents by household income. ................. 142

Table 8. Number and percentage of adolescents 10-19 years of age in the five
administrative regions of NiterOi. ............................................................................... 142

Table 9. Estimated population of 14 and 15 year old youth in NiterOi in 1997 .................... 143

Table 10. Number of schools and subjects according to the administrative areas in
NiterOi. ...................................................................................................................... 143

Table 11. Age- and sex-specific cut-off points of the body mass index (BMI) for the risk
of overweight and for overweight as recommended by Himes and Dietz (1994). ...... 144

Table 12. Inna-observer technical errors of measurement for anthropometric variables. 145
Table 13. Intra-observer measurement errors for blood pressures ....................................... 146

Table 14. Intraclass correlation coefficients for the anthropometric and blood pressure
variables. ................................................................................................................... 147

Table 15. Age distribution and descriptive statistics for age of the study sample by sex. 148
Table 16. Composition of the study sample by race within each sex. ................................. 148

Table 17. Distribution of socioeconomic Status categories in the study sample by age
group and sex. ............................................................................................................ 149

xi

 

Table 18. Heads of the household of the study sample by age group and sex. .................... 150

Table 19. Distribution of subjects by social class (socioeconomic status) by race by sex... 151

Table 20. Characteristics of the households of the study sample by age and sex ................. 152
Table 21. Working status of the subjects by age group and sex. ......................................... 152
Table 22. Descriptive statistics for anthropometric variables in males by age ..................... 153
Table 23. Descriptive statistics for anthropometric variables in females by age. ................. 154

Table 24. Descriptive statistics for age and anthropometric variables in males by race. age
groups combined. ...................................................................................................... 155

Table 25. Descriptive statistics for age and anthropometric variables in females by race,
age groups combined .................................................................................................. 156

Table 26. Distribution (%) of self-assessed stages of secondary sex characteristics in
males and females by age. .......................................................................................... 157

Table 27. Menarcheal status and descriptive statistics for age at menarche by age group
and in the total sample. .............................................................................................. 158

Table 28. Distribution (%) of self-assessed stages of secondary sex characteristics in
males and females by race, age groups combined. .' ..................................................... 159

Table 29. Menarcheal status and descriptive statistics for age at menarche by race, age
groups combined. ....................................................................................................... 160

Table 30. Distance covered in the lZ-minute run by age and sex, and by race and sex, age

groups combined. ................................................................ 161
Table 31. Quintile cutoff points of cardiorespiratory fitness by age group and sex. ............ 162
Table 32. Descriptive statistics for blood pressures (mm Hg) by age group and sex. .......... 163
Table 33. Prevalence of high blood pressure and hypertension by age and sex. .................. 164
Table 34. Cigarette smoking status by age group and sex ................................................... 165

Table 35. Physical activity scores and hours of television viewing by age group and sex.
and in the total sample by sex. .................................................................................... 167

Table 36. Physical activity status in the past 7 days by age group and sex. ......................... 169

xii

Table 37. Prevalence of the risk for inactivity by age group and sex. ................................. 169

Table 38. Relative frequency of different physical activities in 14 year old males. .......... 170
Table 39. Relative frequency of different physical activities in 15 year old males. ............. 171
Table 40. Relative frequency of different physical activities in 14 year old females. .......... 172
Table 41. Relative frequency of different physical activities in 15 year old females. .......... 173
Table 42. Prevalence of the risk for overweight and of obesity by age group and sex ......... 174
Table 43. Prevalence of undernutrition be age group and sex. ............................................ 175

Table 44. Regression analysis of cardiorespiratory fitness (12 minute run) on the
sociodemographic variables. ...................................................................................... 177

Table 45. Regression analysis of the body mass index (BMI) on the sociodemographic
variables............................. ...................................................................................... 178

Table 46.. Regression analysis of the sum of six skinfolds (Z6SF) on the
sociodemographic variables. ...................................................................................... 179

Table 47. Regression analysis of the waist-hip ratio on the sociodemographic variables. 180

Table 48. Regression analysis of the trunk-extremity skinfold ratio on the
sociodemographic variables. .- ..................................................................................... 181

Table 49. Regression analysis of systolic blood pressure on the sociodemographic
variables ..................................................................................................................... 182

Table 50. Regression analysis of diastolic blood pressure on the sociodemographic
variables ..................................................................................................................... 183

Table 5]. Regression analysis of physical activity score on the sociodemographic
variables ..................................................................................................................... 184

Table 52. Mean and relative distribution (%) of sociodemographic variables by risk
status of cigarette smoking by sex. ............................................................................. 185

Table 53. Mean and relative distribution (%) of sociodemographic variables by risk
status of overweight by sex. ....................................................................................... 186

Table 54. Mean and relative distribution (%) of sociodemographic variables by risk
Status of systolic blood pressure by sex. ..................................................................... 187

xiii

Table 55. Mean and relative distribution (%) of sociodemographic variables by risk
status of diastolic blood pressure by sex. .................................................................... 188

Table 56. Logistic regression analysis of elevated diastolic blood pressure by
sociodemographic variables. ...................................................................................... 189

Table 57. Mean and relative distribution (%) of sociodemographic variables by risk
status of inactivity by sex. .......................................................................................... 190

Table 58. Mean and relative distribution (%) of sociodemographic variables by risk
status of low cardiorespiratory fitness by sex .............................................................. 191

Table 59. Regression analysis of the sum of six skinfolds on cardiorespiratory fitness,
adjusting for sex, chronological age, race and sexual maturation. ............................... 193

Table 60. Regression analysis of physical activity level on cardiorespiratory fitness,
adjusting for sex, chronological age, race and sexual maturation. ............................... 194

Table 6]. Regression analysis of the body mass index (BMI) on cardiorespiratory fitness,
adjusting for sex, chronological age, race, sexual maturation, the sum of six
skinfolds (Z6SF), and the mid—upper arm muscle area (UAMA) ..................... . ......... 195

Table 62. Regression analysis of the trunk-extremity ratio on cardiorespiratory fitness,
adjusting for sex, chronological age, sexual maturation, and the sum of six skinfolds
(Z6SF). ...................................................................................................................... 196

Table 63. Regression analysis of the waist-hip ratio on cardiorespiratory fitness.
adjusting for sex, chronological age, sexual maturation, and the body mass index
(BMI) ......................................................................................................................... 197

Table 64. Regression analysis of systolic blood pressure on cardiorespiratory fitness.
adjusting for sex, chronological age, sexual maturation, and body weight. ................. 198

Table 65. Regression analysis of diastolic blood pressure on cardiorespiratory fitness,
adjusting for sex, chronological age, sexual maturation, and body weight. ................. 199

Table 66. Comparisons of risk factors in males at risk and not at risk‘of low
cardiorespiratory fitness. ............................................................................................ 201

Table 67. Comparisons of risk factors in females at risk and not at risk of low
cardiorespiratory fitness. ............................................................................................ 202

Table 68. Regression analysis of the body mass index (BMI) on physical activity level.
adjusting for sex, chronological age, sexual maturation, the sum of six skinfolds
(26SF), and mid-upper arm muscle area (UAMA) ................................................... _..203

xiv

 

 

Table 69. Regression analysis of the sum of Six skinfolds (Z6SF) on physical activity
level, adjusting for sex, chronological age, and sexual maturation. ............................. 204

Table 70. Regression analysis of the trunk-extremity ratio on physical activity level,
adjusting for sex, chronological age, sexual maturation, and the sum of six skinfolds

Table 71. Regression analysis of the waist-hip ratio on physical activity level fitness.
adjusting for sex, chronological age, sexual maturation, and the body mass index
(BMI) ......................................................................................................................... 206

Table 72. Regression analysis of systolic blood pressure on physical activity level,
adjusting for sex, race, chronological age, sexual maturation, and body weight. ......... 207

Table 73. Regression analysis of diastolic blood pressure on physical activity level,
adjusting for sex, chronological age, sexual maturation, and body weight. ................. 208

Table 74. Regression analysis of cardiorespiratory fitness (12 minute run) on physical
activity level, adjusting for sex, chronological age, race, sexual maturation, the sum

of six skinfolds (E6SF), and mid-upper arm muscle area (UAMA). ........................... 209
Table 75. Comparisons of risk factors in males at risk and not at risk for inactivity ............ 21 1
Table 76. Comparisonsiof risk factors in females at risk and not at risk for inactivity ......... 212
Table 77. Clustering of risk factors by sex. ................................................ i ........................ 213
Table 78. Distribution of risk factors and clusters in males. ....................... . ....................... 214
Table'79. Distribution of risk factors and clusters in females. ............................................ 216

Table 80. Comparisons of selected variables in males grouped by number of clustered
risk factors. .................. . ............................................................................................. 21 8

Table 8]. Comparisons of selected variables by number of clustered risk factors in
females ...................................................................................................................... 219

Table 82. Comparision of Percentiles of Stature, weight and the BMI in Brazilian males
by age. ....................................................................................................................... 221

Table 83. Comparison of percentiles of stature, weight and BMI in Brazilian females by
age. ............................................................................................................................ 222

Table 84. Stature, components of stature, and the sitting height/stature ratio (SHSR) in
Brazilian adolescents by age and sex .......................................................................... 223

XV

 

Table 85. Means, standard deviations, and medians for skinfold thicknesses of Bazilian
adolescents by sex and age224

Table 86. Comparison of recommended cutoff points for undernutrition and overweight
for the body mass index (BMI) in Brazilian and US. adolescents by age and sex.......225

Table 87. Prevalence of undernutrition by sex and age according to different BMI
criteria ........................................................................................................................ 226

Table 88. Comparative data for body size and cardiorespiratory fitness ( 12 minute run) in
Brazilian adolescents. ................................................................................................ 227

Table 89. Prevalence of cigarette smoking by age group and sex in the present study and
in the Brazilian National Health and Nutrition Survey (PNSN). 229

Table 90. Comparison of blood pressures in Brazilian adolescents by age and sex. ............ 230

Table 91. Comparison of blood pressures of the study sample with adolescents in the US.
and Finland by age and sex. ....................................................................................... 231

Table 92. Blood pressure by weight status and sex in the study sample. ............................. 232
Table 93. Fat distribution and risk for elevated blood pressure by sex in the Study sample. 232
Table 94. Blood pressure by sex and race in the study sample ............................................ 233

Table 95. Prevalence of overweight by age and sex in the Study sample according to
Brazilian and US. criteria for the body mass index (BMI). ........................................ 233

Table 96. Prevalence of overweight by hours of TV viewing in each sex. .......................... 234

Table 97. Relative fat distribution in males and females of the study sample at risk and
not at risk for overweight. .......................................................................................... 235

Table 98. Skinfold thicknesses and the subscapular/triceps ratio by sex and age in this
study and a comparable sample of Brazilian adolescents. ........................................... 236

Table 99. Physical activity scores in the study sample by social class and sex, age groups
combined. .................................................................................................................. 237

Table 100. Physical activity score and self-activity rating in the study sample by sex, age

groups combined. ...................................................................................................... 238
Table 101. Physical activity scores by social class and sex. ............................ . .................. 239

xvi

LIST OF FIGURES

Figure 1. Proportionate mortality rates for several conditions in Brazil from 1977 through
1994 .......................................................................................................................... 241

Figure 2. Variation in proportionate mortality rates through the life span in Brazil ............. 241

Figure 3. Prevalence of overweight (BMI > 30 kg/mz) in adults 25-64 years (sexes

combined) bytsocioeconomic status ....................... 242
Figure 4. Relative distribution of reported ages at menarche in the present study and that

ofColli(1988)2-12
Figure 5. Map ofBrazil. .................................................................................................... 247
Figure 6. Map of the state of Rio de Janeiro, showing the location of NiterOi ..................... 249
Figure 7. Map oftheadministrative regions OfNiterOi. 250

xvii

CHAPTER 1
INTRODUCTION

Degenerative diseases, including coronary heart disease (CHD), are the major
causes of death of adults in developed countries and in some segments of the pOpulation
in developing countries, especially the more affluent. The high mortality/morbidity in the
last four decades is related to increases and changes in food consumption, decrease in
physical activity levels, increase in cigarette use, and also increase in stress levels. The
more affluent segments of the population in developing countries also experience these
changes in lifestyle, as they are more exposed to and can afford the amenities of the
developed world.

Mortality rates due to infectious diseases have declined in developing countries,
particularly in the second half of this century, while degenerative diseases are increasing
in all segments of the population. This shift of primary causes of death is defined as the
epidemiological; transition (Omran, 1971). Mortality due to cardiovascular diseases in
Brazil has increased from about 12% in 19303 to 33% in the 19805 (Briscoe, 1990). In the
most developed areas of the country (Southeast and South), proportionate mortality is
even higher, 35% and 38%.

Data on the major risk factors for CHD are presently limited for the Brazilian
population, especially at young ages. Hypertension has an estimated prevalence of 15%
(Ministry of Health, 1993), although studies from different parts of the country indicate

that the prevalence ranges from 6.6% to 55.8% (Rosendo da Silva, 1996). Smoking
prevalence is 13.9% and 10%, respectively, for males and females (Ministry of Health,

I

 

1993). The prevalence of overweight has increased in children 1 to 4 years of age as well
as in adults 25 to 64 years of age from 1974 to 1989, and it is higher in females than in
males (Monteiro et al., 1995b).

The prevalence of physical inactivity was estimated at 67% of the population in
1988 (Rodrigues, 1989) and recent data from the Survey on Standards of Life (IBGE on-
line, 1998) showed that 81% of the participants were inactive. Data from Rio Grande do
Sul showed that 47% of middle class and 25% of lower class adults exercise regularly in
their leisure time (Achutti et al., 1988 as cited by Briscoe, 1990). Unfortunately, the
definitions of activity level are not reported. The increase in CHD mortality in Brazil
appears to be partially explained by a corresponding increase in the prevalence of risk
factors in the population.

The atherosclerotic process begins in early life and manifests itself as CHD in
adulthood. However, Rowland (1991, 1996) has argued that there is no study to prove
that decreasing risk factors of CHD in early life will decrease disease occurrence later on.
Nonetheless, risk factors tend to track fiom adolescence to adulthood, independently or
clustered. For example, Raitakari et a1. (1995) reported that the risk factors of CHD
(hyperlipidemia, high blood pressure, smoking and physical inactivity) tend to cluster
from adolescence to young adulthood ( 12 to 24 years) in a Finnish sample.

The only risk factor more or less consistently related to exercise at young ages is
obesity (Rowland, 1991 ). Obesity is often related to physical inactivity and nutritional
lifestyle, and obesity in later childhood and adolescence are good predictors of adulthood
status (Vaccaro and Mahon, 1989). Obesity is thus an important risk factor in children.

especially when associated with hypertension and high serum lipids.

It is well reasonably established that regular physical activity can decrease the risk
of CHD in adults, thus diminishing the disease burden on society. School physical
education programs have adopted the health-related fitness model as a way to improve
physical fitness in childhood and adolescence and to promote an active lifestyle which
presumably carries over into adulthood. This model is based to a large extent on adult
morbidity and mortality, i.e., CHD (Bar-Or and Malina, 1995; Malina, 1995b; Rowland,
1996). It has been argued that regular exercise does not improve children's health per se,
and no data are currently available to support or refiJte the concept that regular physical
activity during childhood and adolescence prevents the development of risk factors
during growth and maturation. The extent to which exercise can slow the atherosclerotic
process is not known. Further. Després et a1...( 1.990) noted that data relating exercise to
decrease in CHD risk in children are not conclusive, especially for children with normal
ranges of risk factors. Exercise may play an impOrtant role for those at higher risk;
however, individual variation in the magnitude of response must be taken into account.

The debate on the role of school physical education in promoting active lifestyles
to be carried over into adulthood in the US. and Europe has influenced Brazilian scholars
to begin to discuss the role of physical education in Brazilian education in the middle of
the 19805. Several Brazilian scholars proposed a focus on health-related fitness in the
physical education curriculum (Nahas and Corbin, 1992a, 1992b; Guedes and Guedes.
1993a, 1993b, 1994). However, few studies of health-related physical fitness have been
done in Brazil (Barbanti, 1982; Bohme and Freitas, 1989; Guedes. 1994; Queiroz. 1992;
Silva, 1992) and these are presently limited to descriptive data on children and

adolescents from Itapira (S50 Paulo), Vicosa (Minas Gerais), Londrina (Parana). Rio

La)

Branco (Acre), and Manaus (Amazonas). No analyses of the data were provided on the
basis of race, socioeconomic status (SES), sexual maturation, or habitual physical
activity. Some authors (Guedes, 1994; Queiroz, 1992; Silva. 1992) related physical
fitness and cardiovascular health, but none investigated CHD risk factors. Few Studies on
habits of physical activity have been carried out in Brazilian children (Andrade et al..
1996, 1998; Matsudo et al., 1996, 1998).

The purpose of this study is to investigate the prevalence of selected risk factors
of CHD and components of health-related physical fitness in adolescents taking into

account race, SES, habitual physical activity, and sexual maturation.

' Research Questions

What is the status of selected risk factors for CHD and health-related physical
fitness of adolescents in NiterOi, Rio de 'Janeiro?

Sub-questions
I. To what extent do sociodemographic characteristics predict selected risk factors for

CHD and health-related physical fitness in adolescents?

.N

What is the relationship between cardiorespiratory fitness and selected risk factors for

CHD in adolescents?

U.)

. What is the relationship between level of physical activity and selected risk factors for
CHD in adolescents?

4. Do risk factors for CHD cluster within individuals?

\.5

‘\

b“

 

Limitations

The study is cross-sectional and permits only an indication of the status at the
time of the study. The Study was based on a convenience sample. It thus may not be
representative of the whole school population of the same age in NiterOi, and it may nor
represent adolescents of the same age that are out of the school system. Another
limitation is that students in the public school system are mainly from low SES families.
which represent the major economic status of the Brazilian population; however, the

characteristics of this group may differ from groups with higher SES background.

SighifiCance of the study

In addition to descriptive data on growth, health-related physical fitness and
selected CHD risk factors, the study also provides information about the relationships of
SES and ethnicity with risk factors and health—related physical fitness. Such data may be
important to integrate physical education with other health programs targeted to this age

group.

CHAPTER 2

REVIEW OF LITERATURE

Socioeconomic changes that allow for improvements in living conditions,
nutrition and health care in developed countries also contribute to the shift from high
mortality due to infectious diseases to increased mortality due to degenerative diseases,
especially CHD (Omran, 1971). Since the increase in case fatality and prevalence of
these diseases were noted, studies have been. conducted to identify factors that enhance
the risk for CHD. Five major modifiable risk factors have been identified:
hyperlipidemia, cigarette smoking, hypertension, overweight/obesity, and physical
inactivity.

Physical activity is a relevant area of investigation and several agencies and
organizations such as the International Federation of Sports Medicine (1990); American
Heart Association (Fletcher et al., 1992), the World Health Organization/ International
Society and Federation of Cardiology (Bijnen et al., 1994), the Centers for Disease
Control and Prevention/American College of Sports Medicine (Pate et al., 1995), and the
Surgeon General US Public Health Service (CDC, 1996) have emphasized the
importance of the adoption of regular physical activity to enhance health status. Although
much is known about the effects of regular physical activity on disease prevention and
rehabilitation in the adult population, more research is needed on the determinants of
physical activity and on the effects of physical activity on children and adolescents.

Physical fitness is defined as “a set of attributes that people have or achieve, that

relates to the ability to perform physical activity” (Caspensen et al., 1985, p. 129).

6

 

no.
a

 

 

p.

 

 

Physical fitness has focused on two goals: health-related fitness and Skill-related fitness
( sports-related or performance-related fitness). In general, Skill-related fitness shows a
I i mited relationship to health, due to the high dependence on motor skills,
ca rdiorespiratory pOwer and capacity, muscular strength, power or endurance, body size,
be dy composition, motivation, and nutritional status (Bouchard and Shephard, 1994).
Health-related fitness includes components that are related to health status and
habitual physical activity, especially in adults. Recently, Bouchard and Shephard (1994)
p roposed several components of health-related physical fitness: morphological (body
(3 o rnposition, fat distribution, mass for height, joint flexibility, and bone density);
m uscular (power, Strength, and endurance); motor component (agility, balance,
3 oordination, and speed); cardiorespiratory (oxygen transport, function of heart and lung.
and blood pressure); and metabolic component (glucose tolerance, lipid and lipoprotein

m etabolism, and choice of metabolic substrate). Several of these components and specific

Pa Ctors are related to CHD.
The relationship between health-related physical fitness and CHD risk factors in

B 1 azilian adolescents must be studied in the context of growth and nutritional status

g 1 Vet) concern for nutritional conditiOns in parts of Brazil. A child’s current growth status

ects hrs/her nutritional history, and thrs rs especrally true in developing countries.
B Q . . . .
t 1") growth status and current nutritional status may Influence health-related physrcal
$85 as well as physrcal actrvrty. Sexual maturation IS an additional factor that

in :9. , ,
Llences variation in growth, health-related physrcal fitness and CHD risk factors

O] i z -
1 11g puberty. Therefore, this review is divided into three sections: Growth and

m Q
t Llration. health-related physical fitness, and CHD risk factors in adolescents,

respectively.
Growth and sexual maturation of adolescents

The growth status of children is an important parameter in evaluating the health
and nutritional status of a population (WHO, 1995). Grth refers to an increase in body
5 i ze; it is influenced by genetic and environmental factors in an interactive manner.

Al though the genetic factors are of predominant influence, environmental factors such as

n u trition have significant effect (Bouchard et al., 1997).

The most widely used method for assessing growth status is anthropometry.
S t ature and body weight are the major indicators because both measures are universally
a p plicable, inexpensive and non-invasive (WHO,‘1995). Three indices are commonly
u sed to evaluate growth status in children: height-for-age, weight-for—age, and weight-
2523 r—height. These indices are often expressed in terms of z-scores or percentiles relative to
1‘ e ference data to allow for classification of stunting, wasting, thinness, risk for
0' V’erweight, and obesity based on specific cut-off points (WHO, 1995). The United States
N at ional Center for Health Statistics (NCHS) data are recommended as the international

re f‘qarence, because these data fulfill most of the criteria of sampling and analysis required

fa. r— determining a reference population (WHO, 1983).
The major data set on the growth of Brazilian children is from the Santo Andre
St Ll (fly (Marcondes et al., 1982). Phase I of the study was carried out in 1968/69 on
db) i l dren from birth to 12 years of age. Children and adolescents from 10 to 19 years were
St K41 died in Phase II, which took place in 1978. The Santo André Study data have the
lid—1 i tation of non-generality to other groups within the country. At the time of the study.

t 0 Andre was one of the most industrialized crtres in Brazrl, wrth higher wages and
8

better living conditions than most cities in the rest of the country. Therefore, the sample
is from a more affluent segment of the Brazilian population. Nevertheless, the Santo
Andre data may be used as a reference for the country in the same manner that the NCHS
data are internationally used (Marcondes et al., 1982). However, when the Santo Andre
data are compared to the NCHS reference, the Brazilian population is shorter and lighter,
especially after 11 years of age (Marques et al., 1982).
Although several years have passed since the Santo Andre' survey, the data still
re present a better off sample compared to the other geographic regions of Brazil. A group
o f 400 students 10 to 14 years from Campina Grande (a municipality of Rio Grande do
N one, Northeast Brazil) was evaluated for stature and weight (Dinoa and Assis, 1990).

C ompared to the Santo Andre reference, the values from Campina Grande for height-for-

a g6 and weight-for-age are lower.

N u tritional status

lndices for the nutritional status among adolescents are the body mass index

(B MI, weight/heightz) and height-for-age (WHO, 1995), which are also ordinarily
CQ ITtipared to NCHS reference data. However, the use of height-for-age in an adolescent
55:. h) ple must be done cautiously due to the differential timing of growth spurts and the
in; fj L1 ence of sexual maturation on height and weight. Specific BMI percentiles are
raq Qt‘nmended to determine thinness (<5th percentile), risk for overweight (>85th
pQ x"‘Qeritile) and overweight (>95th percentile, Himes and Dietz, 1994; WHO, 1995).
Using data from the National Survey of Households, Anjos et al. (1998)

(1%
\J’Q l oped a BMI percentile reference for the Brazilian population aged 0 to 25 years.
9

Comparing this reference to the NCHS reference, mean BMI values are similar until 6
years of age and lower thereafter. Comparison of the 5th and the 95th percentiles between
the two populations shows that the differences increase after 5 years of age. The
d i fferences between populations are less than 5 BMI units for any age and sex.

Nutritional status is commonly evaluated using arm anthropometry. because
m Llscle mass is an indirect indicator of protein reserve (Gurney and Jelliffe, 1973). When
n utritional requirements are not met, the protein in the muscle mass is utilized to generate
energy. Malnourished individuals have smaller arm muscle areas because of lower
p rctein reserve. In many surveys, the upper arm muscle area is estimated from relaxed
art-n circumference and the triceps skinfold. Reference data are also derived from NC HS
(1 ata (Frisancho, 1990). Wasted individuals Show an upper arm muscle area < 5th
p ercentile for sex and age, while individuals with good nutrition have an upper arm
m u scle area > 95th percentile. Average values range from the 15th to the 85th percentiles.
S ex differences in estimated arm muscle area are significant after 12 years of age,

he fl ecting the male adolescent spurt in muscle mass.

1&1 a 1 nutrition

Chronic malnutrition affects normal growth. Individuals small for gestational age
ah E: Shorter in stature during childhood and also in adolescence (Frisancho et al., 1994),
sq %gesting that malnutrition during fetal life has a long term effect. Malnutrition has its
”1 6:3 Or lasting effects during early childhood, resulting in stunting that is often not

re \
eI‘sed.

Malnutrition in fetal life also appears to predispose individuals to cardiovascular

10

_I——-.«

 

 

disease in adulthood (Barker and Osmond, 1992). Sawaya et al. (1995) found that stunted
adolescents living in a shantytown in S50 Paulo were at higher risk for obesity (weight-
for-age > 120% NCHS reference data) than adolescents with normal height-for-age. The
prevalence of obesity was 10.8% for stunted boys and 7.8% for normal height-for-age
boys, and 35% and 13% for their female counterparts, respectively.
The major determinant for malnutrition is low SES. Low SES not only restricts
t h 6 amount and type of food to be prepared and served, but also influences living
conditions, including sanitation, water supply, and crowding. These are all related to
a ugmented risk for infectious diseases (Martorell, 1996). Low SES is also associated with
l e 55 access to health care (PAHO, 1994b). There is an overrepresentation of Blacks and

_P ardos in the lower SES in Brazil; hence, it is expected the prevalence of malnutrition to

b (3 higher in these racial groups.

Malnutrition also affects learning and intellectual performance, which influences

W’Ork qualification (Berg, 1973). The economic consequence of malnutrition is that the

I O Wer the qualification, the lower the income. Further, in a job that is more dependent on
p h 3’sical work output, output may be diminished in individuals exposed to malnutrition
C1 L—l I‘i ng childhood. A lower work output is in turn related to lower income, especially in
Lt h Skilled workers. Therefore, the malnutrition cycle includes lower work output, less
in Come generated, poor living conditions and more malnutrition (Martorell, 1996).
PI a 1 nutrition also decreases the number of working years, and increases accident rates at
M Q 1‘ k and absenteeism (Berg, 1973). In summary, malnutrition has negative effects on

i”‘4- . . .
Qome in two ways: directly by decreasing work capacity and indirectly by lowering

M.»—
3’ I‘I< qualification.

ll

To investigate the effects of malnutrition on work output comparisons of maximal
oxygen consumption (V 02 max) or physical work capacity at heart rate 170 beats per
minute (PWC 170) between well-nourished and undernourished individuals have been

made. Barac-Nieto et a1. (1984) studied Colombian boys 6 to 16 years and found that
u tidernourished children (either body weight or height <95th of Colombian reference
d ata) had lower absolute V02 max. However, when V02 max was expressed relative to
b Ody weight, undernourished children showed higher values than the well-nourished due
1: O the lower body weight of the former. When expressed in units of lean body mass
( LBM), V02 max was also higher in malnourished boys, which was explained by the
h i gher relative proportion of body weight as LBM. The higher V02 max/LBM was also
a C counted for by higher oxidative capacity per unit of LBM. Any task requiring a given
0 xygen (0;) consumption represents higher intensity for undernourished boys in this
g 1" Oup.
Differences in work output were also found in 12 year old boys of migrant
wOr-kers, who lived in Slums of Sao Paulo ‘( Desai et al., 1984). These children showed a
h i g her heart rate respOnse and higher VOzmax for each workload (ml/kg/min), lower
E) “C 170, and higher serum lactic acid levels one minute post-test, showing overall a
IQ Wer work capacity than higher SES boys. Lower values were also found in
‘7 r11: hropometric dimensions, and 57% of the variation in work capacity in the migrant
E) Q 3’s could be accounted for by weight and height. These data are consistent with
i) l""e‘vious findings ofDesai et al. (1981) in low and high SES boys and girls. Spurr and

§ i na (1986) also reported higher percentage of V0; max for the same workload in

h
'3 I nourished children.

12

 

Haas et a1. (1996) found that individuals exposed to a nutritional supplement from
gestation through the preschool years had a higher V02 max at 11 to 26 years than those
not exposed to the supplementation. The differences were greater in males than in
females. V02 max was inversely related to stunting (height at 3 years compared to
N CHS norms), but differences disappeared when adjusted for current body weight and

estimated LBM, indicating that LBM is a source of variation on later performance across

a I 1 levels of stunting. Barac-Nieto et a1. (1984) found that 80% of the variance of the low

V O; max of malnourished individuals is accounted for by differences in muscle mass.

B i ological Maturation

Maturation refers to the tempo and timing of progress toward the mature
b i ological state (Malina and Bouchard, 1991). Maturation varies with the biological
Sy Stem considered and also among individuals. Due to the variability in time and tempo
a. m orig individuals of same chronological age, biological maturity status needs to be

a. c C ounted for in analyses of growth, health related-fitness, and CHD risk factors of

i d Olescents.
Maturation can be assessed by several methods. All methods assess an event in a

(:Q’ ntinuous process. Skeletal maturation is the best method for assessing biological
it“! aturation because it can be used for the entire growth period. However, exposure to the
K \ 1"£135 is needed to determine Skeletal maturation, limiting the wide use of this method.
Somatic maturation includes the determination of age at peak height velocity
(I; ‘BI V), which requires longitudinal data that span adolescence. The initiation of the

(la
Q l escent growth spurt occurs at about 10 years in females and 12 years (Malina and

13

Bouchard, 1991). Before the growth spurt there are no significant differences in stature
among boys and girls. Because boys enter in the growth spurt later (longer period in a
relatively high growth velocity in preadolescence), and PHV is higher, sex differences in
stature emerge. Leg length is the major contributor to the sex difference in height. Due to
th e longer period in the prepubertal stage, when leg length growth is faster than trunk
1 e mgth, the sitting height/stature ratio is lower for males than for females, indicating
re latively longer legs.
Maturation also influences body composition. As the individual matures, the
C h emical composition of the fat-free mass changes. There is an increase in the protein.
f‘ at, and mineral content in the body and a decrease in water content. These changes are
re lated to the increases in muscle mass, fat mass and bone density. Body density
i n creases during growth due to increases in muscle mass and bone density (Malina and

B ouchard, 1991). However, as fatness accumulates in later adolescence, body density

d eclines.

Muscle mass increases during the growth spurt. Greater increases in muscle mass
D e Cur in males due to the influence of testosterone. On the other hand, fat mass increases
in fe males throughout puberty, as an influence of the estrogens. Subcutaneous fat
(I e creases in early puberty in males, especially in the extremities, and increases later in
I) u berry on both the trunk and extremities (Marshall and Tanner, 1986; Malina and
B‘ Q) uchard, 1991). This pattern of fat distribution in males, which results in the
3% elimination of proportionally more fat on the trunk, may increase the risk for
(1Q “enerative diseases. Puberty in males is also associated with a decrease in HDL

e
Ch Q . . . .
1 esterol and an increase in LDL cholesterol and tnglycerrdes.

l4

The assessment of sexual maturation is based on secondary sex characteristics:
breast development in females, genital development in boys, and pubic hair in both sexes.

Breast, genital, and pubic hair development are continuous processes. The most

commonly used criteria for assessing sexual maturation are those of Tanner (1962). Five
stages for each of the secondary sex characteristics are described, where stage 1 is the

i nfantiie state and stage 5 is the adult state. Stages 2, 3 and 4 represent sexual maturation
p bases. Pubic hair assessment may include a stage 6, showing a hair distribution beyond

1: he inverse triangle. This distribution was found in 80% of males and 10% of females by

I anner (1962); however, stage 6 was not included in the method because it is not usually

C empleted until the mid-twenties.
Data from the Santo Andre Study included the sexual maturation of Brazilian

m ales and females 10 to 19 years. The criteria of Tanner were applied by physicians. The

fi’equencies of the subjects in each stage of sexual maturation are shown in Tables 1 and

The results are consistent with data from other countries.

2 -

Assessment of secondary sex characteristics by direct observation raises issues of

p 1‘i vacy and consent. As a result, protocols for self-assessment have been developed. The

:2: Q l"lcordance of self-assessment and physician assessment of sexual maturation was

1 n V estigated in Brazilian youth (Saito, 1984; Carvalho, 1990; Matsudo and Matsudo,

I 994; Guimaraes and Passos, 1997).
Saito (1984) investigated the concordance of stages of sexual maturation in 78

i

<3 Olescents (42 females and 36 males), aged 11 to 18 years. She found Kappa
e

Q‘i‘—f‘i’rcients of 0.89 and 0.87 for girls and boys, respectively. Carvalho (1990) studied

1 Q
1 4 male students aged 10 to 17 years. For genital development, 47.5% of the Students

15

agreed with the physician assessment, while for pubic hair development the agreement
was 40.4%. Disagreement was found in 44.9% and 45.5% of the sample for one stage in
pubic hair and genital development, respectively. Disagreements of more than one stage
were low (5.6% for pubic hair and 6.9% for genital development). Disagreement was
higher at younger ages (11-12 years) than for older students.

Matsudo and Matsudo (1994) also investigated the concordance of self-
assessment and physician assessment of sexual maturation. A sample of public school
students (174 females and 178 males) ages 6-26 years was evaluated by two physicians
(one male and one female) at two different occasions. The subjects also assessed
thems elves twice. In both sexes, concordance for pubic hair was higher than concordance
0f breast and genital development. Concordance was higher at the extremes, stages 1 and

5' C0 Prelation coefficients between these assessments are shown in Table 3, and are

Slmn at to those in the literature.

Guimaraes and Passos (1997) studied the concordance between self-assessment
and physician assessment of stages of sexual maturation in a sample of 445 girls aged 8
to 1 8 years from 2 public schools and one private school. The Students self-assessed their
1) ubertal status according to the pictures of the stages described by Tanner (1962). Their
a Ssessment was followed by the physician’s. For breast development, concordance varied
fins ‘1') 65.4% to 73.5%, while for pubic hair the values ranged from 44.4% to 48.9%. The
int hors also found that concordance was greater in girls from public schools (lower SES).

l o o 0
I‘d suggested that the difference may be explained by a greater familiarity to the changes

1.
hat occurs in their bodies during puberty, allowing them to better perform the

8.
§
1 f~assessment

l6

 

Age at menarche is also used to assess sexual maturation in females, although
menarche is a late event in the maturation process. Three methods are used in the
detennination of age at menarche. The first method is Status quo, which provides a
sample estimate through probits analysis. With this method, the median age at menarche
in Brazilian girls is 12.6 years (Colli, 1988), which is not different from ages at menarche
of other Latin and North American countries. European females tend to have a later age
at menarche (Marshall and Tanner, 1986).

The second method for assessing age at menarche is the retrospective method,
whet e the female is asked to report at what age she started to menstruate. This method is
SUbiect to bias in recall. Colli (1988) found a mean age of menarche to be 12.5 years in

B r azi I i an girls, using the retrospective method.
The third method is the prospective method, which requires longitudinal data.
This is the most accurate method of determining the age at menarche in individual girls.
but representative samples are needed for a reliable estimate (Eveleth and Tanner, 1990).
Maturation is influenced by genetic and environmental factors. Age at menarche
has high heritability, in the order of 0.80. Malnutrition is one of the environmental factors
that C . . . . . . .
ontnbutes to later maturation and its influence is unequal in the different systems.
a*lt‘rutrition delays the age of menarche, but it does not appear to influence the height
gfii 1'1 during puberty (Marshall and Tanner, 1986). Menarche is also later in individuals

”’11

9 live in altitude and rural areas, and those from a high SES.

17

 

Maturity status

Maturation is a variable process among individuals. The time and tempo of the
process influence aspects of health-related fitness and risk factors for CHD. Age at PHV,
on average, is about 12.0 years in females and 14.0 years in males; menarche occurs, on
average, a bit more than a year after PHV (Malina and Bouchard, 1991).

Individuals advanced in maturation are taller than later maturers, although the
former catch-up by the end of adolescence. Advanced maturers are also heavier than later
maturers, but there is no catch-up in body weight. Conversely, Beunen et al. (1994) found
”0 difference in body weight between early and late maturing males at 30 years of age.

van Lenthe et al. (1996c) found a higher risk for obesity in early maturing males
and fe males of the Amsterdam Growth Study. The BMI and sum of skinfolds were higher
in ad\rranced boys and girls at 13 years and at 27 years“. These differences remained
Significant after adjusting for energy intake'and physical activity level.
Early maturation in males is associated with relatively more subcutaneous fat
deposition on the trunk compared to the extremities. Beunen et a1. (1994) found no
di Fferences in extremit k' f 1d 1 d l d ' o d l d
y s in o 5 among ear y an ate maturers tiring a o escence an
a t age 30, but found thicker trunk skinfolds in early maturers, indicating proportionately

QTe subcutaneous fat on the trunk.

Early maturing boys tend to have better performances than the late maturers,

é -
SIDecially in tasks that involve Strength and power. Differences in performance are also

5 Served in girls, where later maturers more often tend to have better performances.

13 .
al‘ly maturers of both sexes have higher absolute V02 max than late maturers;

*1 . .
QWever, these differences disappear when V02 is adjusted for body mass (Malina and

18

Bouchard, 1991).
Health-related physical fitness

Although there is a new classification of health-related physical fitness (Bouchard
and Shephard, 1994), this section will follow the traditional classification
(cardiorespiratory fitness and body composition). The components fat distribution and
blood pressure are included with the CHD risk factors since they are related to obesity
and hypertension.

A battery. of health-related fitness tests aimed initially for children and youth has
been operationalized to include cardiorespiratory endurance (walk/run test), muscular
endul‘a nce and strength (modified sit—up test), body composition (sum of triceps and calf
Skinfo lds, sum of triceps and subscapular or body mass index), and flexibility (sit-and-
reach ; AAHPERD, 1980). Cardiorespiratory fitness and body composition are considered
the two major components of health-related fitness due to their apparent relationship to
health in adults (Docherty, 1996). However, in measuring health-related items, only body

Cornposition has direct relationship with health status of children and youth. Overweight
and obese children and adolescents have increased risk for respiratory disorders,

0 Ft . . . . . . .
l‘IOpedlc problems, stigmatization and self-image problems, and cardiovascular risk.

1.
§ ‘ s abnormal glucose tolerance, hypertension, lipid profile abnormalities (Must, 1996),

Ln . .
q] poorer health and performance-related physrcal fitness (Malina et al., 1995a)

There is no scientific evidence that improving health-related physical fitness test

I

§ .
Slllts will improve the health status of children (Seefeldt and Vogel, 1989) and decrease

ri & 1,q . . . . .
later in life (Rowland, 1991, 1996). Although no evrdence is available, there IS an

19

 

emphasis on health-related physical fitness in school programs to prevent cardiovascular
diseases in later life, based on the establishment of an active lifestyle in childhood, which
will be carried throughout the lifespan. However, evidence for the tracking of physical

activity is equivocal (Malina, 1996b).

Cardiorespiratory fitness

Cardiorespiratory fitness is considered the key element of health-related fitness
and is ofien viewed in the context of aerobic power (V02 max) and the ability to perform
pr 010!) ged submaximal exercise (submaximal cardiorespiratory capacity). Aerobic power

is the rnost widely used assessment method of cardiorespiratory fitness.

Absolute V02 max (I/min) increases during growth and maturation; linear
i“creases are observed for boys, while V02 max levels off at 12—13 years in girls.
Incl‘ez—lses of the oxygen delivery chain and muscle mass are responsible for increases in
V 02 max with growth and maturation. For running activities, the increase in V02 max is

a1 30 related to a greater running economy due to an increase in stride length and a
ecl‘ease in stride frequency. There is no significant sex differences in V02 max in
ch ilclhood; however, as puberty approaches sex difference become more pronounced. The
d i Ibferences are usually explained by higher muscle mass and physical activity levels in

13
Q 3's compared to girls (Krahenbuhl et al., 1985). Relative V02 max (ml/kg/min)

b
Q‘hains constant for boys during growth, but decreases in females. The decrease is

1“3er associated with an increase in fat mass (Rowland, 1990).

Ethnic differences are also evident in V02 max. African American adolescent

20

females Show lower relative V02 max than non—Black American females (Pivarnik et al.,
1995) - The difference was explained by the higher body weight of the Black teenagers.
The ethnic difference also persists afier V02 max is regressed on fat-free mass,
indicating that a lower muscle mass and higher bone mass in African Americans may
explain part of the difference in V02 max.

Maturation status also influences V02 max. The more mature individual is taller
and heavier compared to the less mature. After controlling for chronological age, most of
the variation in absolute V02 max is explained by height and weight (Cunningham et al.,
I 984) . Early maturing boys have higher absolute V02 max values, largely due to greater

heart and muscle mass volumes and higher hemoglobin concentrations than late maturers.
the difference disappears in late adolescence. The differences remain signifiCant after
adj u St ing for body weight.

In? girls, the difference in absolute V02 max is smaller between early and late
matur-ersand also disappears in late adolescence. When adjusted for body weight, no
différences in V02 max are observed in girls of contrasting maturity status (Malina et al.,
1 99'7). Relative V02 max is higher in late maturers of both sexes due to their lower body

Weight (Malina and Bouchard, 1991).
Genetic effects on cardiovascular fitness are expressed by the interaction of the
D l‘enotypes associated with cardiac, vascular, and respiratory functions. Genetic
I r) fiUCDCCS appear to be lower for submaximal exercise capacity (< 10%) than for
b.) aximal exercise capacity (< 25%, Bouchard and Pérusse, 1994).
For testing large populations such as school children, a walk/run for either time or

I Stance has been used to indirectly assess aerobic power. Validity correlation

21

coefficients of the distance run compared to V02 max vary from 0.70 to 0.82 in school
age children (Docherty, 1996). Although the walk/run test has been structured to require
maximal effort, it is difficult to elicit maximal effort in children (Pate and Shephard,
1989).
Brazilian studies on health-related fitness have used the walk/run for time, 9 and

12 minutes, with children and adolescents, respectively (Barbanti, 1982; Guedes. 1994;
Queiroz, 1992; Silva, 1992). One exception is the longitudinal study of Nahas et al.
(1992), which used a walk/run for 1000 meters.

Cardiorespiratory fitness is also affected by regular physical activity. However, it
appears that most adolescents do not engage in exercise of sufficient intensity for
eXteI1Cled periods of time to allow for significant changes in cardiovascular fitness over

that e)ntpected with nOrrnal growth and maturation. Due to this low workload, or perhaps
to the poor validity of physical activity instruments in this age, the relationship between
habitual physical activity and cardiovascular fitness is only low to moderate. Physical
actiV’ity explains about 3% of the varianCe in V02 max in adolescents (Morrow and
reedson, 1994), and exercise training ordinarily results in increases in V02 max
( Kranonbuhl et al., 1985).
Low cardiorespiratory fitness is a risk factor for inactivity and mortality.
anison et a1. (1988) found that boys in the lowest quintile of the 600 yard (548.6 m)
mt} were almost twice as likely to be physically inactive in young adulthood than
L r) qividuals in the other quintiles; those in the other quintiles did not differ among
t 1‘ Qt‘riselves. Analyses of the run test results in elementary school (10-1 1 years) and high

Q l"1001 (15-18 years) boys yielded the same trend.

22

Blair et al. (1989) related physical fitness (measured by a maximal treadmill
exercise test) to risk for all-cause and cause-specific mortality in adults. Both males and
females in the lowest quintile of physical fitness had a higher risk for mortality due to all
causes, cardiovascular disease and cancer than those in the other four quintiles. Those in

the other quintiles did not differ in risk.
Body composition

Body composition is the only component of health-related fitness that is directly
related to health in youth. This component is assessed by skinfolds, stature, and weight.
Skin 1“ Old values are used alone or in combination. The commonly used skinfolds are the

tr 1"3311) s, subscapular, suprailiac, and medial calf. The sum of the triceps and calf, and the
sum Of the subscapular and triceps skinfolds are also used. The medial calf and triceps
Skinfolds are used in children age 6 to 9 years, and the subscapular and triceps skinfolds
are used in children 10 to 18 years. Other skinfolds and sums of skinfolds can also be
Used in the assessment of body composition.
Health-related physical fitness is ordinarily estimated relative to population based
percentiles or criterion-referenced standards. The best method of classification has yet to
Q defined. Percentile norms for skinfolds are available from the National Children and
Quth Fitness Studies I and II (NCYFS, Ross et al., 1987; Safrit. 1995). Criterion-
PQFQI-enced standards were developed for the recent version of the AAHPERD health-
Pfi l ated physical fitness battery, Physical Best (AAHPERD, 1989). Percentiles for
g k i hfolds in Brazilian children are only available for non-nationally representative
88‘ l”‘I‘lples (Barbanti, 1982, 1983; Bohme and Freitas, 1989; Goldberg et al., 1986; Guedes.

23

..’ .
4s.\

b".

 

.‘x'

IV~
8‘“
is

o
i‘.
I’ l
I I
..

1994; Silva, 1992; Queiroz, 1992). On average, skinfolds are lower in Brazilian than in

American children, especially at the higher percentiles.

Height and weight are used to calculate the BMI, weight/stature? Age and sex—
specific BMI criterion-referenced standards are recommended for the Physical Best.
NHANES percentiles are also used, and cut-off points for the risk for obesity and obesity
have been defined as the 85th and the 95th percentiles, respectively (Himes and Dietz,
1994). The criteria for risk for obesity and obesity can also be applied to the triceps

skinfold using NHANES norms (Must eta1., 1991b).

Coronary heart disease risk factors

Although hyperlipidemia, hypertension, cigarette smoking, overweight/obesity,
and D hysical inactivity are the major cardiovascular risk factors, the present study will

only assess non-invasive risk factors for CHD. Therefore, blood lipids will not be

c ~ . . .
Ot‘tsidered in this revrew.

igarette smoking

Cigarette smoking is correlated with CHD and there is a dose-response
1‘ § 1 . . . . . . . .
ationshtp between smoking and CHD. The relative risk of CHD mortality 15 1.5 in
S 1}) . . . .
Okers versus non smokers. Smoking interacts With other risk factors for CHD; other
l'lfoundmg variables such as obesrty, dietary habits and actrvrty level, may influence the

l i h
k between smoking and CHD. The thrombogenic and atherogenic effects of smoking

E. .
I‘Q the major mechanisms for CHD in smokers (Hays et al., 1996). When smoking

24

cessation occurs there is a quick decrease in the thrombolytic effects and the progression
of the atherosclerotic process is slowed.

Smoking prevalence has decreased since late 19605 for males and late 19705 for
females. However, the reductions have been less significant in women, teenagers. and
individuals in lower income and education groups (Townsend et al., 1994). Black
individuals initiate smoking later than Whites. Although Black adults are more likely to
be smokers than Whites, they are more likely to be light smokers (< 20 cigarettes, Rogers
et al., 1995),

SmOking that begins in adolescence is predictive of smoking during adulthood
(1:083, E1 Ster, 1994). Data from the Youth Risk Behavior Surveillance (YRBS) in 1995
(CDC. 199 6) show that 34.8% of high school students (9th to 12th grades) currently
Smoke (2 1 cigarette/day in the last 30 days). White and Hispanic individuals are more
prone to be smokers than Black students in both sexes. Almost one-half of these students
(191% Of the total sample) smoked 2 20 cigarettes per day. Compared with data from

I the YRBS in 1993 (30.5% for .>. 1 cigarette/day and 13.8% for 2 ’70 cigarettes/day), the
prevalence of smoking has increased, especially for Black teenagers (Kann et al., 1995).
The Bogalusa Heart Study1 shows similar racial differences in cigarette smoking,

although a decrease in cigarette use among adolescents was recently observed (Greenlund

et al., 1996),

‘——\

l
Blogalusat Louisiana, is a bi-racial community-based mixed longitudinal study of the
ear y natural history of cardiovascular disease that started in 1976.

25

Blood Pressure and Hypertension

_ Blood pressure (BP) varies with age. As individuals grow older, BP values tend to
increase. Systolic blood pressure (SBP) increases rapidly in the first week of postnatal
life, and slower up to the second month of life. From 2 to 12 months it remains stable.
and thereafter SBP shows a steady increase until late adolescence. The increase in SBP
seems to only occur with a simultaneous increase in body weight and heightl(Brandao et
al,1989)

Diastolic blood pressure (DBP) shows a different pattern. It increases in the first
week of post natal life, decreases through the second or third months, and then starts to
slowly increase for the rest of the first year, remaining lower than the first week values.
Small changes are noticed up to 6 years and then DBP increases are parallel to SBP
(Sinaiko, 199 5).,

Using the data fromNHES Cycles II and III, Szklo (1979) showed that from 6 to
12 years SBP was higher in girls. At 12 years there was a crossover and boys presented
higher values from then on. No consistent patterns were observed for DBP. A similar
trend was shown in the F els Longitudinal Study (Malina and Roche, 1983).This
phenomenon suggests an effect of sexual maturation, as girls enter puberty at earlier ages.

In adulthood, increases in BP are sex-specific. There is a steeper increase in males
during early adulthood which continues into middle age. In females, the steeper increase
occurs in the middle-age years. The rate of increase, however, varies among individuals
(Ward, 1995).

Blood pressure is also influenced by race, where Black individuals show higher

Values. Although the difference may not always be significant, it is observed in early

26

childhood and continues throughout the lifespan. Alpert and F ox ( 1 993), using meta-
analysis found that significant race effects on BP were present in boys from birth to 12
years for DBP and in girls from birth to 18 years for SBP and 19-25 years for DBP.
Kozinetz (199 1) showed that Black girls 7 to 18 years had higher BP values (both SBP
and DBP); however, only in the 10-12 years group the differences were significant. The
difference persisted after controlling for body weight, height and sexual maturation.

Data from the Fels Longitudinal Study show that BP status during childhood
tracks into adulthood (13 to 40 years of age), with correlation coefficients ranging from
0.39 to 0.24 for SBP and from 0.37 to 0.20 for DBP. Although the correlations are low,
the tracking shows that adolescents with high normal DBP had a higher relative risk of
adult hypertension, 1.9 for males and 2.6 for females (Beckett et al., 1992).

BP is affected by body weight, where heavier individuals show, on average,
higher values - The rate of increase of BP during growth seems to be faster in obese
individualS. Law and Shiell (1996), in a meta-analytic review, found that BP is inversely
related to birth weight in pre-adolescent children and adults, positively related in

neonates, and inconsistent in adolescents. They also reported that the magnitude of this
relationship increases with age. Adolescent-onset adult overweight increases the
prevalence 0f hypertension by 8.5 times compared to a lean cohort (Srinivasan et al.,
1996).
39111 SBP and DBP aggregate in families. Genetic and cultural transmissible
effects contl‘ibute to the phenotypic variance. Familial aggregation studies show that
genetic faCt01‘s account for 60-70% of the variation in BP, while environmental factors

explain the remaining variation (Ward, 1995). Brandao et al. (1992) studied the familial

27

aggregation of blood pressure in a Brazilian sample of adolescents 10 to 15 years. It was
estimated that 82% and 64% of the phenotypic variance of SBP and DBP, respectively, is
explained by genetic factors.

BP is maintained by the integration of fiinctions of the cardiovascular system and
vasoactive hormones. Failure inthe integration of these fiinctions results in hypertension.
Similarities or differences in patterns across families and populations may occur because
of different pressure-maintaining mechanisms (James and Baker, 1995). In children, the
common household appears to have an important influence in BP. As expected, natural
siblings have higher correlation coefficients than adopted children, but coefficients are
greater between siblings than between parent and natural child and parent and adopted
child. In adult siblings, the correlation coefficients remain constant even when the adults
were exposed to different environmental factors (Ward, 1995). It seems that once
adulthood is reached, environmental factors are less influential than genetic factors.

Elevated BP or hypertension is defined as persistently high arterial BP that may
have n0 knoWn cause (essential, idiopathic, or primary) or may be associated with other
diseases (secondary, Novak, 1995). Truly hypertensive patients tend to havehigh BP
throuShout the day (T. Pickering, 1995). Hypertension is also defined as “an upward
resetting of the set point of blood pressure” (G. Pickering, 1995). These definitions imply
a CUt‘Off 130th between normal and. high BP. However, definitions are arbitrary,
65139913")? because the data relating BP to mortality are largely from clinical settings (T.
Pickering, 1995)_

Readings in the clinical setting may be higher than normal due to anxiety (also

known as White coat hypertension). It yields the possibility of biased interpretations.

, Ambulatory measures tend to give a better estimate of the true BP than clinical
measurement, and it may be also due to a greater number of readings (the phenomenon of
regression toward the mean). It is important to ensure that the subject is comfortable,
rested, quiet and at ease, so that the measurement is more likely to be repeatable.
Hypertension is to be confirmed by multiple measures at least in 2 subsequent visits one
or several weeks apart.

The measurement of BP for diagnosis of hypertension has been standardized by
the Joint National Committee on Detection, Evaluation, and Treatment of High Blood
Pressure (IN C -V, 1993). During the measurement the arm should be supported and kept
at the heart level, an appropriate cuff size and a recently calibrated sphygmomanometer
should be used, and the two or more readings should be within 5 mm Hg, taken at least 2
minutes apart - No caffeine intake or cigarette smoking should occur at least 30 minutes
before the measure, and the individual should rest for 5 minutes prior to the measure. In
adults and children, DBP is recorded based on the 5th Korotkoff sound (D5, National
High BIOOd Pressure Education Program, 1996). The average of two measures is used in

classifying Children. The classification of hypertension for children differs from that for
adults, in Which percentile values are used instead of cut-off values. High normal BP is a
SBP and/ or DBP > 90th percentile for age, and hypertension when BP is 2 95th
percentile (Table 4, Task Force on Blood Pressure Control in Children, 1987).
The Prevalence of hypertension increases with age, is greater for Blacks than
Whites, and it is greater for less educated individuals independent of race (CDC, 1994;
Sorel et 31-4 1 991, 1992). Individuals in low SES and low education groups have a higher

prevalence 0f hypertension than those in higher groups. In early adulthood and early

29

middle age, hypertension is more prevalent in males than in females, but at older ages the
situation is reversed. -

The prevalence of hypertension in children and adolescents is low, ranging from
0.26% to 1.9°/o - In children, a major cause for hypertension is renal disease while in
adolescents it is diagnosed as early mild hypertension (Sinaiko, 1995). Mild
hypertension in adolescence provokes similar cardiac ventricular and hemodynamic
changes observed in hypertensive adults (National High Blood Pressure Education
Program, 1996). For Brazilian children 10 to 15 years, the prevalence of hypertension
(>95th percentile) is 2.7% (Brandao et al., 1992).

Dress] er et al. (1986) investigated the extent to which the effect of ethnicity on BP
may be modified by the presence (or absence) of psychosocial resources. Psychological
resources were measured using an index that combines measures of social support
(number of extended kin in the community and perceived support available in
problematic situations), and a Scale of active coping (active, defensive or passive attitude

in given stressful situations)
Regardless of ethnicity, individuals with high psychological resources had lower
SBP and DBP than those with low resources, but the difference was more pronounced in
mixed-Blacks and Blacks. In an ethnically unequal society, a person in a disadvantaged
ethnic status may be at a higher psychosocial stress due to ethnicity, and those individuals
‘Wlth high reS<>urces cope better with this stress. Lifestyle stress, as a resultant of the
i ndividual’s attempt to maintain a higher status style of life which is discordant with
hiS/ her true Place in the social class hierarchy, offers a higher risk for diastolic

hypertension in developing societies (Dressler et al., 1987).

Dressler et al. (in press) also investigated the effects of socioeconomic variables
and skin color on blood pressure among Brazilian adults. SES was measured by cultural
consonance in lifestyle, defined as the degree to which individuals adhere to a cultural
model of appropriate lifestyle, and calculated by the number of the important items
owned. They found that darker-skinned Brazilians have higher blood pressure only if
they have low cultural consonance in lifestyle, which shows a significant interaction

effect between cultural consonance and skin color in relation to blood pressure.

Overweight and obesity

Major changes in dietary patterns have occurred over the last century in the now
developed countries. Popkin (1993) identified five patterns of dietary change: collecting
food, famine, receding famine, degenerative disease. and behavioral change. From the
collecting food pattern to the degenerative disease pattern, the main observed changes in

consumption are an increase in fat content (from low to high total fat and cholesterol),
decrease in fiber and polyunsaturated fatty acids, and increase in sugar and refined
carbohydrates These changes are partially responsible for the increase in overweight and
obesity in the population of developed countries, a public health concern. A concomitant
decrease in physical activity also contributes to the increase in overweight and obesity
(Popkin, 1994)_

AS the underdeveloped countries become more industrialized, the changes in diet
pattern and increase in the prevalence of overweight and obesity are observed. Some
segments of the population are moving towards the behavioral modification pattern,

where reduction in the consumption of fat and refined sugars and increase in fibers and

31

complex sugars are emphasized. In both developed and developing countries, well
educated and more affluent segments of the population are more likely to make dietary
changes.

Body fat depots are dependent on the balance between energy expenditure and
energy intake- An excess of energy intake, or a decrease in energy expenditure, or bOth.
results in increase in fat depots. The response to dietary caloric content is also influenced
in part by genetics. Gene-diet interaction appears to determine changes in body fat as
well as the pattern of distribution of these changes (Bouchard and Pérusse, 1994).
Hormonal changes during sexual maturation also contribute to the increase in fat depots.
especially in females (Malina and Bouehard, 1991).

There are several methods to estimate body composition. Most of the methods are
based on the two component model, where body mass is divided into fat-free mass and
fat mass. The gold standard method is de‘nsitometry or underwater weighing.
Anthropometric studies of body composition include lengths, body weight, breadths,
circumferences, and skinfold thicknesses. Skinfold thicknesses, which measure

subcutaneous adipose tissue, are the most commonly used for assessing body
composition due to the high correlation with percentage body fat and to the relative
SimPIiCitY 0f the method. Several skinfold sites are used and the values are either applied
10 an ecluation to predict body density (and percentage of body fat) or used in comparison
10 reference data (individual skinfolds or sum of skinfolds). The NCHS reference data for
The triceps Skinfold are presented in sex and age specific percentiles, allowing for

classification of overweight and obesity by the 85th and 95th percentiles, respectively

(Mustet al.. 1991b).

Overweight is also defined by the BMI. The BMI is dependent on body weight
and independent of height. The correlation between the BMI and percentage body fat
ranges from 0.40 to 0.60 (Roche, 1996). For adolescents, sex- and age-specific reference
data have been reported and values 285th percentile are classified as at risk for
overweight and those 2 95th percentiles as overweight (Himes and Dietz. 1994). The
BMI should be carefully used in adolescent males due to potential risk for
misclassification of muscular individuals as overweight.

Overweight not only contributes to a decrease in life expectancy, but is also a risk
factor for C I-ID, hypertension, diabetes, cardiovascular disease and chronic respiratory
disorders (P AHO, 1994b). The increase in the risk for disease occurs in young as well as
in older individuals. Must (1996) reviewed the consequences of obesity in early age.
Some of the short-term consequences are respiratory disorders, orthopedic problems.
srigmatization and self-image problems, and cardiovascular risk (abnormal glucose
tolerance, hypertension, and lipid profile abnormalities). The long term effect of obesity

is that young subjects in the'upper end of the normal distribution have a higher
probability for maintaining their ‘conditiOn from childhood into adulthood (Kannel et al.,
1995). Persistent obesity contributes in turn to the excess all-cause and CHD mortality,
3519991311)’ among males.

The Bogalusa Heart Study includes mixed-longitudinal observations of pre-school
and SChOOI Children, and of young adults. Data on individuals who participated in
different SUIVeys allow the study of tracking of overweight and obesity. The association
between adolescent and adult overweight was investigated in 783 individuals 13 to 17

years in 1976-1977 and who were also examined in 1988-1991 (Srinivasan et al., 1996).

(J)
Lu

The individuals were classified as overweight or lean according to age-, race-, and sex-
specific BMI percentiles of the Bogalusa population (BMI > 75th percentile and BMI
>25th and < 50th percentiles, respectively). Fifiy-eight percent of the overweight
adolescents remained overweight as adults, 41% of the lean adolescents remained lean.
while 10% became overweight. The predictive value of overweight was highest for Black
females (62%) and lowest for Black males (52%).

Prediction of obesity in adulthood from childhood measures was studied by
Rolland-Cachera et al. (1989). The BMI and four skinfolds (biceps, triceps, subscapular,
and suprailiac) were collected every 6 months from‘l to 17 years. The subjects were
measured again between 18 and 25 years of age. Correlation analyses compared measures
for every year from 1 to 16 years and the adult measure. For all comparisons, correlation
coefficients for the BMI were higher than those for skinfolds, thus leading to the
conclusion that BMI in childhood is more predictive than skinfolds for the prediction of
adu It values.

Tracking of overweight was investigated from early adolescence to young
adu lthood in the Amsterdam Growth Study which longitudinally evaluated children 13 to
1 6 years and then 'at 21- and 27 years (Twisk et al., 1995). Eighty four males and 98
fem ales were used in this analysis. Body fat was estimated by the sum of 4 skinfolds:
biceps, triceps, subscapular, and suprailiac. Interperiod correlation coefficients for age 13
to Subsequent ages were calculated. The coefficients ranged from 0.87 to 0.22 for males
from 14 to 27 years, and from 0.89 to 0.51 for females.
The predictive value of the childhood BMI for overweight at age 35 years

i . . . . ., ,
hereases wrth age. The predictive value rs moderate for ages < 13 years, good at 13

years, and excellent at 18 years (Guo et al., 1994). Individuals at either the 75th or 95th
percentiles of the BMI have higher risk for overweight at age 35 years than those at 50th
percentile. The odds ratio for overweight in individuals 8-18 years at the 75th percentile
of BMI is more than double the odds ratio for individuals at the 50th percentile. There
was no sex difference in this sample. The odds ratio doubles at 1 year of life for those at
t he 95th percentile and continues to increase during childhood and adolescence. Larger
values are observed in females (Guo et al., 1994). In summary, these studies show that
overweight at a younger age is a risk for overweight in adulthood.

Using the data from the Bogalusa Heart Study, Webber et al. (1995) showed
ethnic differences in BMI, triceps and subscapular skinfolds. They found no systematic
ethnic difference in the median BMI valuesfor males up to 20 years of age. After 20

yr ears, values for the median and upper percentiles were higher for White males. The BMI
values were consistently higher for Black females at most ages and at all percentiles.

The triceps skinfold tended to be thicker in White males, as well as the

SUb scapular skinfold (only at young adult ages). For girls, the triceps skinfold was greater
in Whites until 22 years of age, when skinfolds in Black women increased abruptly. For
the subscapular skinfold, no differences were observed in early childhood between White
and Black females; however, beginning in adolescence Black girls showed a steeper

in cI‘ease in the skinfold values.

, There is an ethnic variation in the prevalence of obesity and this variation is
dependent on the criteria used to define overweight and obesity. The most used criteria
are I triceps skinfold (T), BMI, and both triceps and BMI (T+BMI), which also includes

the criteria triceps only (Tong) and BMI only. Using data from several studies. Malina

(, 1 993a) reported a higher prevalence of obesity by the T criterion than for the BMI in
girls. By the BMI criterion, more White and Mexican American older girls (12-17 years)
were classified as obese, as well as when T+ BMI were used. More Blacks and Asians
girls were defined as obese by the To"... criterion. For younger girls (6-12 years). the T
criterion classified more White as obese, while for the BMI criterion more Black girls
Vvere obese. For T+ BMI and Toni,- criteria, the prevalences were similar among the ethnic
groups.

Whiteyounger boys showed a higher prevalence of obesity for the T criteria and

f or both triceps and BMI. By the BMI criterion, American Indians, followed by White

boys had the highest prevalences. In older boys, low prevalences and no major

differences among ethnic groups were observed for the T criterion, except for the Indian
boys who also showed high prevalence for T+ BMI.

Malina and'Katzmarzyk (in press) analyzed the validity of the BMI as an

i ndicator of the risk of overweight and overweight in adolescents by comparing the
Classifications of individuals using the following criteria: triceps skinfold, percentage of
body fat, and BMI. They found that ‘BMI has a high specificity (correctly assign
il'ld i viduals at no risk or not overweight); however, the sensitivities (correctly assigning
Sub j ects at risk or overweight) were low and variable among ethnic groups, increasing the
Charices for misclassification. Nevertheless, they concluded that BMI is an acceptable and

Val i d indicator, and should be used as a screening tool for individuals at risk of

0verweight or overweight.

F at distribution

Fat distribution has become an important issue in epidemiology of degenerative
diseases. A central distribution of body fat is related to higher levels of lipids, BP,
i Ilsulin, and other metabolic indicators, and is predictive of ischemic heart disease. Fat
distribution is a better predictor of CHD than BMI (Larsson, 1988). Central obesity was
found to be predictive of ischemic heart disease independent of BMI, cigarette smoking.
and age. The association between central obesity and CHD was similar between Black
and White men while it was stronger among White females (Freedman et al., 1995).
Obesity and regional fat distribution have heritability values ranging from 5% to 25° 0,
xvhlle the cultural transmission effect. is about 30% (Bouchard and Pérusse, 1994).
Although computed tomography has a higher reproducibility, fat distribution is
commonly based on a ratio of trunk (central) and extremity (peripheral) skinfold
thicknesses and/or the ratio of waist-to-hip circumferences, due to lower cost and easy
measurement in field-studies. Higher ratio values indicate more central fat distribution.
Seidell (1996) statedjthat it is still premature to define cut-off points of fat
disr ribution to classify high and low risk individuals, mainly because of different
met hods, cut-off points and small sample sizes that have been used in research.
For the ratio of trunk and extremity skinfolds, sex differences are small during
Chi I dhood. The ratio of upper to lower trunk skinfolds declines with age in both sexes
duri ng infancy and during childhood (Malina and Bouchard, 1988). Sex differences
incTease during adolescence, when males show higher values than females, thus showing
a greater fat accumulation in the trunk in males. In both sexes, Blacks and Mexican

Am eri cans have a more central distribution of fat than White Americans. Data on

37

waist-hip ratio also show that males have higher ratios than female adults (Malina,
1996a). In adolescent groups, Asian girls have more fat (TER) in the trunk than Mexican.
White and Black American girls, especially Whites and Blacks. Black boys and girls tend
to have higher TER values than Whites from childhood through adulthood. Fat
distribution using WHR as an indicator does not consistently show ethnic variation in
either boys or girls (Malina et al., 1995a).

Hammer et al. (1991) studied the impact of sexual maturation on fat distribution
among 10-15 year old females of different ethnicity (White, Asian and Mexican
American). In all ethnic groups, increase in sexual maturation stages was related to an
increase in hip and waisr circumferences and a decrease in the waist to hip ratio.
However, significant effects were only found for hip circumference and WHR.

van Lenthe et al. '(1996a) studied the influence of timing of maturation on fat
distribution in boys and girls aged 13 to 27 years. Early, normal and late maturation was
determined by skeletal age, peak height velocity (PHV, boys) and age at menarche (girls).
They found no difference in fat distribution between early and late maturers based on
skeletal age and PHV. Early menarcheal girls tended to have more centrally distributed
fat compared to late menarcheal girls.

Sexual maturation influences the relationship of central fat distribution and
cardiovascular risk factors. For sexually immature adolescents (based on a combination
of stages of breast/genital and pubic hair), the relationship was not significant but it
increased later in maturation. It is suggested that the relationship between fat distribution
and CHD risk increases with age and becomes significant from late adolescence on

(Sangi and Mueller, 1991). '

The tracking of fat distribution was investigated by van Lenthe et al. (1996b)
using data fiom the Amsterdam Growth and Health Study subjects aged 13 to 29 years.
Interage correlation and generalized estimating equations (GEE) were used in the
analyses. The correlation coefficients for TER varied from 0.43 to 0.53 for males and
from 0.53 to 0.58 for females, depending on the combination of skinfolds
(subscapular(SS)/triceps(T), SS/SS+T, SS+suprailiacfbiceps+T+SS+suprailiac). The
GEE coefficients varied form 0.56 to 0.67 in boys and from 0.57 to 0.70 in girls.

Katzmarzyk et al. (in press) also investigated the tracking of fat distribution in 7-
69 year old individuals. TER adjusted for overall fatness and waist circumference
adjusted for the BMI were the fat distribution indicators. Tracking was evaluated by both
interage correlation and permanence in the same quintile during the two surveys, which
were seven years apart. For TERM], correlation coefficients ranged from 0.23 to 0.63. The
tracking pattern of waistadj was more variable than TEde and varied from 0.21 to 0.77.
For both variables, interage correlation coefficients were consistently higher in females.

The tracking was higher in the extremes quintiles.

Television viewing

Television viewing has become a public health concern due to its relationship
with obesity, smoking, hostility, and aggression (Hoffman, 1996). The American
Academy of Pediatrics has recommended that children should spend no more than 2
hours a day of TV watching (American Academy of Pediatrics, 1986, cited by Gortmaker
et al., 1996); however, the reported average time of TV viewing in children and

adolescents is 3-4 hours (Stransburger, 1992, cited by Strong and Kelder, 1996). The link
39

between TV watching and obesity is twofold: low physical activity levels are observed
during TV watching and the chance of consumption of food, especially nutritionally
weak foods, is increased. The relationship between TV watching and obesity appears to
become stronger with age.

Dietz et al. (1985) demonstrated a causal relationship between TV viewing and
obesity in children aged 6 to 11 years. Cross-sectional and longitudinal data from the
NHES (Cycles 11 and III) were used to investigate the causal relationship. Individuals
who daily watched TV for longer periods were more obese, indicating a dose-response
effect. This effect remained after controlling for the effects of past history of obesity and
SES characteristics of the family. Children with more TV viewing in cycle 11 were more
likely to be obese at cycle III.

Gortmaker et al. (1996) also suggested a dose-response relationship between
obesity and TV viewing hours among children 10-15 years. The odds of being
overweight were 4.6 times for those watching more than 5 hours a day compared with
children who watched 0 to 2 hours. The attributable risk for the effect of TV watching on
obesity was 62%. Sidney et al. (1996) found odds ratios for obesity ranging from 1.5 to
2.3 in young White and Black adults (23 to 35 years) of both sexes for individuals
watching TV more than 4 hours a day. Conversely, DuRant et al. (1994) found no
relationship between observed TV time and activity levels in children 3-4 years.

suggesting that the relationship starts afier 4 years of age.

40

Physical activity

Due to the effects of regular exercise on the health of adults, the promotion of
physically active lifestyles has been emphasized in children and adolescents to foster
adherence to physical activity through the lifespan. The CDC/ACSM (Pate et al., 1995)
have issued a position statement emphasizing regular physical activity. The study of
physical activity habits is important to understand the determinants of exercise in children
and adults and to design programs supporting active lifestyles. Habitual physical activity
is influenced by both genetic (29%, due only to biologic transmissible variance) and by
environmental factors (71%, Pe'russe et. al., 1989).

There are a variety of methods for assessing physical activity in populations (Cale
and Almond, 1992; Hensley et al., 1993; LaPorte et al., 1985; Montoye and Taylor, 1984;
Saris, 1986). The methods are classified as direct and indirect (Paffenberger et al., 1993).
The direct methods include questionnaires, diary annotation, and mechanical and
electronic monitoring. Indirect methods .consist'of the assessment of diet, body
composition and, physiological fitness assessment; sports and recreational participation;
and occupational classification.

Questionnaires are the method of choice in epidemiological studies of large
populations because they permit the investigator to collect different variables from one
source in a relatively quick, easy, and inexpensive manner (Baranowski, 1988).
Questionnaires are also unobtrusive and non-reactive, and generally do not require much
effort to complete (Hensley et al., 1993). However, questionnaires have limitations
related to validity and reliability; other potential problems include recall limitations and

subjectivity in response.

41

One limitation of assessing physical activity through a questionnaire is that
physical activity is a behavior, a multidimensional experience. The assessment may
measure a single aspect of physical activity, isolating this aspect from the sociocultural
context in which the physical activity occurs. Children may relate physical activity to
exertion, competition, play, social interaction or skill development (Brustad, 1991);
therefore, they may perceive physical activity differently from how they are probed for
reporting the activity behavior. This difference between perception and report affects the
validity and reliability of self and interviewed administrated questionnaires.

The accuracy of physical activity reports immediately afier a one-hour exercise
bout was investigated in 20 normal weight and 24 obese young subjects, 21.9 i 6.5 years
(Klesges et al., 1990). Subjects were interviewed using the seven-day physical activity
recall questionnaire (7PAR) at three different occasions over a one week period. In one of
the interviews the subjects were asked to perform one hour of exercise. At the end of the
exercise, they self reported physical activity time in four categories that express intensity
levels: no movement, limb movement but stationary, slow trunk movement, and rapid
trunk movement (aerobic activity). Without being noticed, an observer rated the
intensities of the exercise in the same manner the subjects reported. Comparing the
observer to the subject data, 68% of the subjects overestimated, 18% estimated accurately
and 13% underestimated aerobic activity time. The data on 7PAR for the three
assessments showed no significant differences. The findings suggest that physical activity
recall is overestimated, which would Suggest even lower levels of habitual activity in the
population.

The subjectivity of responses also offers limitations to physical activity

42

questionnaires. Children have a shorter attention span and lower time estimation than
adults, which would influence reports of total time spent on a specific type of activity. A
shorter attention span may interfere with encoding information in memory. Recalling
physical activities is limited in children, especially in those under 10 years of age. School
children (3rd to 6th grades) reported difficulty in making accurate time estimates for self-
reported aerobic activity (Baranowski et al., 1984). Time overestimation in vigorous
activities and underestimation in regular activities are usual in children (Saris, 1986). The
use of a segmented day (such as before, during and afier school) appears to increase
accuracy in self report of aerobic activity by children (Baranowski et al., 1984). Accuracy
also increases when theinterval between activity and report is shortened (Sallis, 1991).

Maximal physical work capacity has been used as a physiological marker for
questionnaire validation, but an acceptable validation standard is still a problem
(Paffenberger et al., 1993). Significantrelationships between maximal oxygen .
consumption and physical activity questionnaires have been reported in adolescents, but
correlation coefficients range from -0.39 to 0.59, indicating low to moderate relationships
(Morrow and Freedson, 1994). In summary, the validity of self-reported questionnaires
shows low and many insignificant relationships (Baranowski, 1988).

The physical activity questionnaire for older children (PAQ-C) was develOped to
assess levels of moderate to vigorous physical activity in children older than grade 4
during the school year (Kowalski et al., 1997). The PAQ-C is a seven day recall
questionnaire that uses nine statements assessing common sports, leisure time activities
and games, and activity in physical education classes, recess, lunch, afier school. and in

the weekends. These statements are used in the calculation of a score, which is a

composite mean of the nine statements. Each of the statements ranges from 1 to 5, with
higher scores indicating higher levels of physical activity. PAQ-C also includes an
activity rating (how active the subject is compared to other of same sex and age). the
frequency of physical activity participation, and illness occurrence that may have
prevented physical activity in the week being recalled.

PAQ-C was validated against an activity rating, teacher’s rating of children‘s
physical activity, moderate to vigorous physical activity recall, 7PAR, a Caltrac motion
sensor, leisure time exercise questionnaire, and the Canadian home fitness step test. All
correlation coefficients were significant and low to moderate, consistent with other
validation efforts. Construct validity was supported by a moderate positive relationship
between PAQ-C and Harter’s athletic competence. Divergent validity was provided by a
non significant relationship with Harter’s behavioral conduct scale (Kowalski et al.,
1997). Crocker et al. (1997) reported that PAQ-C has acceptable measurement properties.
such as average internal consistency, test-retest reliability and reliability of 2 and 3 PAQ-
C scores over a year.

Although there are limitations in the use of PAQ-C, it is easy to administrate and
has low subject burden (the estimated time to completion is 20 minutes). The main
limitations are no discrimination between moderate and vigorous activities and no

estimation of energy expenditure (Crocker et al., 1997).

Tracking of physical activity

The tracking of physical activity during childhood and adolescence, and into

adulthood is also low to moderate. Both activity and inactivity appear to track. It is

44

suggested that the more experienced individuals in sport and physical activity tend to be
more active in early adulthood, especially males (Malina, 1993b). Dennison et al. (1988)
also found that the risk for inactivity in young adulthood (23-25 years) increased as the
number of low scores on a youth fitness test taken either in childhood or adolescence
increased.

Low interage coefficients for habitual physical activity were observed from 13 to
17 years of age (Mechelen and Kemper, 1995). Raitakari et al. (1994) noted significant
correlation coefficients ranging from 0.17 to 0.54 when analyzing physical activity habits
from adolescence to young adulthood (12 to 24 yrs) in both males and females. Tracking

of physical activity in adulthood has also shown low to moderate correlation coefficients

(Malina. l996b).

Physical activity epidemiology

Generally one-quarter to one-third of the population in developed countries is
physically inactive (Stephens and Caspersen, 1994). Because low physical activity levels
are related to higher risk for degenerative diseases, engagement in more active lifestyles
has been promoted. Physical activity levels vary with age, sex, SES, and educational
levels. Physical activity levels decrease with age, and the steepest decrease in activity
occurs during adolescence.

The steep reduction in physical activity during adolescence is independent of
method of assessment (Kemper, 1994). Data from the NCYFS also show decrease in
physical activity defined as engagement in activities outside of school physical education

(Ross et al.. 1985). The results of the YRBS -1995 (CDC, 1996), show the same trend of
45

decreased participation in all types of exercise (vigorous and moderate, stretching, and
strengthening) as well as in organized sports and physical education programs. The
decline of physical activity patterns seems to continue through older ages. However, there
is evidence that older individuals increase activity patterns more than younger adults
(Stephens and Caspersen, 1994).

For all ages, males are more likely to be active than females, and the sex
difference is more pronounced in vigorous activities than in moderate activities. The
difference is about 15% to 25% in school-age children and tends to increase because the
rates for decreasing in physical activity patterns are higher in females than in males
(Sallis, 1993). Aaron et al. (1993) estimated median values for leisure time activity at
22.5 hours/week in male adolescents and 6.6 hours/week in females. Data from the
YRBS -l995 also show sex differences in participation in stretching and strengthening
activities, school physical education, and organized sports. More males than females
achieved the Healthy People 2000 objectives for physical activity2 (Aaron et al., 1993).

There may be ethnic differences in physical activity. For all ages, White
individuals are more prone to be active than non-Whites (Aaron et al., 1993; CDC.
1993b; CDC, 1995). The same trend is observed for SES (income and education, CDC ,
1993b; Stephens and Caspersen, 1994). The fact that non-Whites are overrepresented in
lower SES groups may explain the ethnic difference in physical activity. Ford et al.
(1991) found a SES difference in overall time spent in physical activity in adult females

but not in males. Cauley et al. (1991) reported no differences between low and high SES

 

2 The following criteria were used: light to moderate activity- at least 30 minutes/day (3.5
hr/wk); vigorous activity- 3 or more days/week, at least 20 minutes/day (CDC, 1993a).

46

adult males and females for several physical activity variables: average kilocalories per
week, light activities, flights of stairs per day, and blocks walked per day. Significant
differences were observed in the vigorous and moderate activities only in males.

Spurr and Reina (1990) investigated the daily pattern of physical activity
(expressed as % V02 max) in normal and undernourished Colombian children 6 to 16
years. Children performed a maximal treadmill test, where a direct measure of V0; max
and heart rate were collected. During a week day, the subjects wore a heart rate monitor
for approximately 10 hours. Data from the heart rate monitor were averaged into 30
minute units, allowing for the estimation of % V02 max achieved during the day.
Typical activities included attendance to school, after-school play, homework preparation
and TV watching. The assessment of nutritional status was done by using weight- and
height-for-age for upper SES Colombian children. Marginal malnutrition was defined as
weight-for-age and height-for—age values <95% of the reference. As expected, V02 max
was significantly lower in the malnourished boys and girls. No differences were found for
average % V02 max during school or free time between nutritional status groups or for
time spent in three ranges of % V02 max ( <30%, 30-50%, and >50%). The average %
V03 / 12 hours was also not significant. In the same sample, Spurr (1990) found that
undernourished boys had lower daily energy expenditure (assessed by 24 hour heart rate
monitoring) and lower energy expended in activity than normal nourished boys. No

differences were observed in females.

47

Physical activity, cardiorespiratory fitness, and risk factors for CHD

The effects of regular physical activity on CHD risk factors are established for the
adult population, where changes in body composition, blood pressure and blood lipids are
observed in association with activity programs. However, these changes are not well
established in children and adolescents. The low prevalence of risk factors in the young
population may explain the apparent lack of chronic effect of habitual exercise on healthy
adolescents. On the other hand, individuals at higher risk appear to benefit from physical
activity interventions.

Hypertension is affected by physical activity and physical fitness level in
adolescents. Cross-sectional data show a relationship between physical activity (or
physical fitness) and BP. However, when the data are adjusted for a measure of body size
or fatness, the relationship tends to be reduced or lose statistical significance (Alpert and
Wilmore, 1994).

Comparing fitness levels of boys age 8 to 15 years, Kwee and Wilmore (1990)
found differences in BP among the four fitness groups. The boys in the highest fitness
group showed significantly lower SBP and DBP. The differences remained unchanged
after controlling for fatness, but not after adjusting for age. Tell and Vellar (1988),
,however, found that the relationship between fitness and BP was independent of the BMI
in children 10 to 14 years.

The relationship between cardiorespiratory fitness and BP was investigated in
Italian boys and girls 10 to 17 years (Bazzano et al., 1992). Cardiorespiratory fitness was
assessed by the 1-mile run, and the Physical Best criterion-referenced standards were

used to classify the children as passing or failing the test. No difference was found for

48

SBP in boys; however, DBP was lower in those who passed the cardiovascular fitness
test. No differences were observed for SBP or DBP between the two fitness groups of
girls. Sallis et al. (1988) also found a significant relationship between fitness and DBP in
boys, 11.9: 1.7 years, but not in girls of the same age.

Physical activity and physical fitness are related to body fatness. Individuals with
higher body fat tend to be less active and to have lower motor- and health-related fitness
scores, as shown by the data from the Leuven Growth Study of Belgian Boys (Beunen et
a1, 1983) and of Flemish Girls (Malina et al., 1995a). Fatness had a negative effect on
cardiovascular recovery from a step test, and accounted for 3% to 5% of the variance in
girls 7 to 17 years. For the PWC 170 , fatness explained 0% to 16% of the variance
depending on age (Malina et al., 1995a).

Tell and Vellar (1988) also found a negative relationship between the BMI and
aerobic capacity among boys and girls. BMI and triceps skinfolds were significantly
lower in children in the higher quartiles of V0; max. The difference in body fatness held
true for more active males, but not for females. Kemper (1986) found small differences in
percentage body fat in the most active male teenagers compared with the least active. In
females, the activity level had no effect on body fat.

Obese individuals appear to be less active than their leaner counterparts, although
the obese can expend an equal or even higher total amount of energy, due to their larger
body size (Bar-Or and Baranowski, 1994). However, obese boys were as active as non-
obese siblings in a playground, but were less active inside and outside the home

(Waxman and Stunkard, 1980).

Physical activity has been included in multidisciplinary interventions for the

49

obese, due to its effects on body fatness as well as on BP and blood lipids. Physical
activity for the obese children and adolescents is especially important because these

individuals usually have clusters of CHD risk factors that tend to track into adulthood.

Summary

The main points of this review can be summarized as follows:

1. Effects of nutritional status
- Malnourished children show lower cardiorespiratory fitness as well as lower
work output;
- No differences were observed in physical activity levels between normal and
marginally nourished children.

2. Effects of biological maturation
- Greater increase in fat mass in females and muscle mass in males;
- Males have proportionally higher subcutaneous fat accumulation in the trunk;
- Early maturers have increase risk for obesity as well as an increase in centrally
distributed fat;
- Early maturers also have higher V03 max, however, the difference disappears
in late adolescence;
- Females show higher BP values until 12 years of age, then males have higher
values due to the earlier beginning of sexual maturation in females.

3. Racial/ethnic differences

- Black females have lower V02 max which is independent of body weight and

fat-free mass;

50

- White boys and girls are more likely to smoke than Blacks, although an increase
in the prevalence is only observed in Black adolescents;
- Black individuals are more likely to be less active than their White counterparts.
4. SES differences
— Low SES groups are more likely to have higher BP and to be less active.
5. Tracking of risk factors
- Overweight, obesity, fat distribution, high normal blood pressure, and physical

activity tracks from adolescence into adulthood with low to moderate coefficients.

51

CHAPTER 3

METHODOLOGY

The purpose of this study was to investigate selected risk factors for CHD and
health-related physical fitness of 8th grade students fi'om the public school system in
Niteroi, Rio de Janeiro. Eighth grade students, 14-15 years of age, were selected because
the 8th grade is the last year of mandatory education. Some graduates do not continue
their education, while others enroll in night school so that they can work in the day. In
both cases, the 8th grade may be the last time for formal physical education programs and
an environment where skills for lifetime physical activity may be pursued. Physical
education classes are optional in night school.

The majority of students in the 8th grade are 14 -15 years of age. There are few
older students in this grade, but including them in the sample would create small groups
at the older ages and problems of low statistical power in the analyses.

The study was approved by the University Committee for Research Involving
Human Subjects (UCRIHS, Appendix A) at Michigan State University and followed the
guidelines for research involving human subjects of the Brazilian National Council of
Health (Ministry of Health, 1996). The data were collected fi'om mid-September to mid-
November 1997 and from mid-March to mid-May 1998.

Before describing the methodology, an overview of the social context of Brazil

and of Niteroi in particular will be presented to establish a background in the peculiarities

of the country.

52

The Brazilian context

Brazil has an area of more than 3 million square miles and is divided into five
geographical regions: North, Northeast, Southeast, South and Central West (Appendix
B). Diversity among these regions is noticeable from climate (trOpical in the North and
humid subtropical in the South) to ethnic groups, culture and degree of development.

The 1991 Brazilian Census estimated a population of 156.5 million inhabitants
(IBGE, 1993). As a characteristic of developing countries, the Brazilian population is
young; 35% is below 15 years of age. However, due to the decrease in mortality rates,
especially at young ages aswell as a decrease in fertility rates and increase in life
expectancy, the population is aging. In the last 3 decades, the population over 65 years of
age has almost doubled, from 3% in the 19605 to 5% in the 19905 (IBGE, 1993). The
population between 15 and 65 years increased from 54% to 60% in the same period. The
aging process poses new demands on the society: in the job market, in the educational
system and also in the health care system, especially due to increased prevalence of
degenerative diseases.

The population is primarily composed of native Brazilians, and those of European
and African ancestry. Brazil was colonized by Portugal, which led to a great immigration
of Portuguese to the “new land”. The native Brazilian population at the time of discovery
was estimated around 3 million people. Decrease in this population occurred due to the

different epidemics, smallpox and respiratory diseases, to which the natives were not
previously exposed and had no immunity (Crosby, 1972) as well as due to the battles for
the: ownership of the land. Africans were brought to Brazil as slaves to work in

agriculture. In the late 18005. after abolition of slavery, there was a large immigration of

53

Europeans, mainly to work in coffee plantations in Sao Paulo. Approximately one-third
of these immigrants were from Italy; the remainder were from Portugal, Spain, and
Germany. The immigration of Asians occurred in the beginning of this century and was
also related to the agricultural work force (Teixeira, 1993).

In the 1991 Census, 55.3% of the population was White. 4.9% was Black, 39.3%
was Pardo, and 0.5% was Asian. Pardos are individuals born to interracial relationship.
including the admixture between Blacks and Whites, Whites and Natives and Natives and
Blacks. The composition of the population varies by geographical region (Table 5).
Although a high percentage of Pardos is observed in the North, those Pardos are mainly
born to White and Native Brazilian parents.

The assessment of race is based on self-report, which poses limitations to
classification. In skin color conscious societies, persons who are phenotypically of one
race may define themselves as belonging to another (Dressler, 1991). Even though the
country is well-known for racial democracy, racial inequalities do exist and are found in
education and income (IBGE, 1993). Blacks and Pardos have a higher rate of illiteracy
and a lower educational level. The mean income for all types of jobs among Blacks and
Pardos is about one-half of the income of the Whites; however, the opportunity for
upward social mobility of Pardos is greater than that for Blacks (Skidmore and Smith,
1997).

Seventy-one percent of the population lives in urban areas. The economic growth

of Brazil was accelerated in the 19705 due to external money from institutions such as the
World Bank and the International Monetary Fund. The money was applied to sustain

industrial growth, mainly in the South and Southeast regions. This industrial growth

54

resulted in migration to these areas as individuals looked for job opportunities and better
living conditions. However, in the 19805 external debt obligations increased and export
earnings decreased. resulting in almost a decade of negative economic growth (Skidmore
and Smith, 1997). The economy started to grow again at a rate of 3.5% per year in the
l990s

Due to the economic crisis, the income distribution became even more unequal.
Brazil has one of the worst income distributions in the world, where the highest 10% of
the population share 48% of the income, while the lowest 50% share only 12.1% (IBGE,
1993). The Gross National Product per capita is equivalent to US 8 2,930, which
classifies the Brazilian economy as upper middle income (World Bank, 1995). On the
other hand, due to the skewed income distribution, 30% of the population is living below
the poverty line (IPEA, 1996). The percentage of the population in poverty varies among
the five geographical regions, where the Southeast, the South, and the Central West
regions have values between 20% and 25% and the North and the Northeast regions have
values between 43% and 45%. The mean income of workers older than 15 years in
metropolitan areas is 5.73 monthly minimal wages (MlVIW)3. Sz‘io Paulo is the only
metropolitan area with a mean income above the national median (714 MMW, IBGE on-
line, 1997). Table 6 presents the mean wages for males and females by occupational
category for the period of 1981-1990 (Oliveira, 1993).

Migration has increased problems in the urban areas: air pollution, shortage of

housing, haphazard growth of marginal populations in unsanitary areas such as slums,

3 The MMW in November, 1998 is approximately US$110 (O Globo On, 1998).

55

underemployment and unemployment, increase in the informal economy, increase in
violence (e.g., homicides) and drug addiction, and emergence of human ghettos (PAHO,
1994b). The burden on living conditions in urban areas posed by migration is even
great er because there is a lack of resources to maintain the structures needed to sustain
development. During the economic crisis, a deterioration in public services, such as
education and health, was observed due to low Governmental investments in those areas.
The most vulnerable urban group is children who tend to live in hostile situations,
such as poor housing, to have low quality and insufficient amounts of food (thus
contributing to undernutrition at young ages), to lack of parental supervision and even
abandonment, and to early entry into the labor force to increase family income.
Opportunities for education, recreation, and health care are decreased in this group of
children (WHO, 1991); Due to poverty, but not exclusively, the number of street children
has increased in Brazil in the last two decades. There are approximately 7 million street
Children in the whole country, most of whom are working children who return to home at
night (Swift, 1991). Children living in the streets are a minority; nevertheless, homeless
Children are a problem that remains to be solved
Poverty is related to children in the work force. Seventeen percent of 10-14 year
Old Children and 50% of 15-17 year old adolescents in Brazil were in the work force in
1990 (IPEA, 1996). Twenty-three percent of these children and 54% of these adolescents
came from families with household incomes less than 0.5 MMW. The majority of these
youngsters earns up to l MMW (Oliveira, 1993).
Table 7 shows the distribution of children and adolescents according to working

and St udying status and household income. As household income increases. more

individuals engage exclusively in studying. Low income limits the access of youth to
better education either in terms of having to quit school to work or having less time and
resources to study when they work and study. Because of this limitation, these
individuals are at a disadvantage in the job market due to their lower qualification
compared to individuals who only study; therefore, they have limited social mobility.
Social class determines health and disease status of a population because it
determines, to a large extent, lifestyle and exposure to different environments (Marmot et
al., 1987). Low SES is associated with high all-cause mortality as well as with other
health outcomes. Link and Phelan (1995) indicated that high SES individuals have higher
resources, such as easy access to medical care and information, that can be transformed in
health-related resources, and thus maintain an advantageous health status. However. these
resources are not readily available. to .low SES groups, creating differences in health

outcomes between high and low SES groups.

Education in Brazil

The educational system is divided into two levels: basic (composed by pre-school.
elementary and secondary education) and higher education (policy 9394/96, Brazilian
CO“gl‘ess, 1997). Elementary education includes the first eight years of education and it is
mandatory from 7 to 14 years of age; however, 14% of children in this age were not
enrolled in school in 1991. Only 18% of the population graduated from basic education
(IPEA, 1996). In 1990, almost 56% of the graduates of elementary education were aged

1410 1 5 years, while 35% were older than 15 years (IBGE. 1993)-

Secondary education encompasses three years and it is equivalent to the American

57

high school. It can also be designed to prepare individuals to the job market. Only 17% of
the population 15 to 19 years are enrolled; in secondary school (IPEA, 1996). Recent data
show that 27% of adolescents 15-17 years of age are in secondary school (Jornal da
Ciéncia-Email, personal communication, September 4, 1998). Higher education includes
both undergraduate and graduate programs, and an estimated 8% of the population has
completed a college degree (IPEA, 1996).

Illiteracy characterizes 19% of the population (World Bank, 1995), and 61% of
the heads of the households in Brazil in 1989—1990 had no more than four years of
education (IBGE, 1993). The mean number of years of education in the country is 5.1 for
males and 4.9 for females, and again the more developed areas have higher averages.
Mean education also differs among racial groups, where Asians and Whites have higher
level 5 (8.6 years and 5.9 years, respectively) than Blacks and Pardos (3.3 years and 3.6
years, IPEA, 1996). Over the last three decades, mean education has increased in the
POPUIation, although the difference between sexes and racial groups has persisted.

School period varies from 4.5 to 5 hours a day, 5 days a week in elementary
education and 6 days in secondary. The policy 9394/96 (Brazilian Congress, 1997)

1310130 ses that public schools become full time in the next few years.

Phys ical education

Concern about people’s health and fitness in the middle of the 18005 influenced
the adoption of health and physical education in school systems in the United States
(Swanson and Spears, 1995), United Kingdom (Kirk, 1992), Australia (Colquhoun and

Kirk, 1987) as well as in Brazil, where it became mandatory in 1855 (Betti. 1991;

58

Ghiraldelli, 1989).

In the early 19905, the inclusion of health-related physical fitness in the
curriculum was suggested, as the international literature on school physical education
pointed out the need for fostering an active lifestyle throughout the lifespan. As the
emphasis of physical education programs has been sports activities, the participation of
school physical education programs in health education/promotion seems to have been
minimal, especially because those who are not skilled tend not to be engaged in activity.
A survey of teachers and students of public and private basic education schools in Rio de
Janeiro showed that only 3% of students and 4.5% of teachers considered physical
activity as a health-related behavior (Borucihovitch et al., 1991).

A new policy (Ministry of Education,1997a) establishes that the purpose of
Physical education is to enhance participation in physical activities as a way to social

integration and leisure, and to learn how to exercise according to individual abilities and
limitations. The policy also gives physical education the task of contributing to a more
aCtiVe society through the teaching of the benefits of regular exercise. Schools may play
an in”lportant role in health promotion and physical education for children and
adOlescents; however, alternative programs in the community should be considered as
they extend the access to health and physical education to those who are out of the school
system.

Many schools are adapting their programs to the new policy. The lack of an
infra Structure for physical education classes (equipment and physical area) and shortage
Ofprofessionals appears to be the major limitations for shifiing to this new policy in both

the Public school system and in rural areas of the country.

59

Health status of the Brazilian population

Brazil combines mortality patterns of both developed and develOping countries.
Infectious diseases are the major cause of death from 0 to 4 years of age. Infant mortality
is 51 - 6/ 1,000 live births, although rates vary by geographical region and socioeconomic
status - Infant mortality has decreased by 40% in the last decades following economic
growth and improvements in living conditions. Life expectancy has increased as well; it
was 65.5 years in 1990, although the median age at death was 57 years (IBGE, 1993).

Both prevalence and proportionate mortality rates of degenerative diseases have
increased since 19605 while‘infectious disease mortality has decreased, as the country
became more developed. This is consistent with the epidemiological transition proposed
by Omran (1971). Figure 1 presents proportionate mortality rates for infectious diseases

and diseases of the circulatory system (which includes CI-ID), cancer, and all external

causes of death combined. Data were available for 1977 through 1994 in the IBGE

(1993) and DATASUS4 (1997) resources. Slight increases were observed for the

cardiovascular disease, cancer and external causes. Chor et al. (1995) pointed out that
Sindies on the quality of information on death certificates are not available in Brazil;
therefore, the data should be viewed as trends in mortality rates. Nevertheless, preventive
StratEgies should be taken in order to identify high risk groups and those with multiple
risks,

The leading causes of death change across the life span (PAHO, 1994a). Causes

\

4
D1‘\"I‘ASUS is the part of the Unified Health System that deals with statistical data of
mortality, hospital admissions, and reimbursement to the private health sector.

60

of death by age group are as follows: (1) conditions originating in the perinatal period for
infants under 1 year of age; (2) infectious and parasitic diseases for children aged 1-4; (3)
external causesfor persons aged 5-39 years; and (4) diseases of the circulatory system for

persons 40+ years (Figure 2).

Malnutrition

The prevalence of malnutrition (defined as weight-for-age below -2 z scores) in
children under 5 years in Brazil was 18.4% in 1989, varying from 2.5% in the South
region to 12.8% in the Northeast region, and from 13.6% in the low income class to 1.496
in the highest income class (Monteiro et al., 1992). There has been a downward trend in
the prevalence of malnutrition in children under ‘5 years of age in all geographic regions
and all economic strata (Monteiro et al., 1992).

Malnutrition is associated with decreased immunologic function due to lower
Protein availability. This depressed immunocompetence increases the risk of infections as
well as increases the recovery time from an infection (Kerpel-Fronius, 1983). Infections
SUCh as diarrhea, pneumonia and measles are responsible for the high mortality rates in
Chi‘dI‘en under 5 years of age, especially in the Northeast region where malnutrition is

more prevalent (Monteiro et al., 1995a).

overnutrition and overweight

A nutritional transition to a high fat and high refined sugar diet is also evident in

Brazil (Monteiro et al., 1995b). Obesity is becoming a health problem, but few studies

61

have been carried out to determine the prevalence in the population and unfortunately,
data are not available for older children and adolescents.

The prevalence of overweight (based on weight-for-height above 2 z scores) in 0-

6 year old children is 2.2% (PAHO, 1994a). Using data from two National Surveys of
Households, 1974 and 1989, Monteiro et al. (1995b) showed that the prevalence of
overweight (defined as weight-for-height > 2 z-scores for children and BMI > 30 kg/m2
for adults) remained stable in children 1 to 4 years (sexes combined, 46%). However. the
prevalence decreased in males and increased in females.

In adults 25 to 64 years of age, the prevalence of overweight increased both in
sex-Specific or combined sex analyses. The data also showed that the greatest increase in
the prevalence of adult overweight occurred in women of low SES (Figure 3). Data from
the Ministry of Health (1993) showed that 32% of the Brazilian adult population are
overweight, based on a BMI >25 kg/m2 (41% males and 59% females). Obesity (BMI>30
kg/mz) was found in 8% of adults ('71 3% females).

The recent preliminary results from the Survey on Standards of Life (IBGE on-
line. 1 998), using data collected in 1996/97, showed similar trends of obesity in adults:
290/0 of the sample was overweight-while 9.8% were obese (BMI >25 kg/m2 and BMI
> 30 kg/m2 criteria, respectively). More females were overweight/obese than males,
alth(nigh the comparison with previous data showed that there was a greater increase in
the prevalence in males. There is a higher prevalence of overweight/obesity in the
southeast region compared to the Northeast. The prevalence of overweight/obesity

1ncreases with increasing income; however the highest prevalence was observed in the

4th QUintile ofincome.

Hypertension

No direct prevalence data on hypertension are available in Brazil, but it is
estimated to range from 11 to 20% (Ribeiro et al., 1988). The Ministry of Health (1993)
estimated the prevalence of hypertension at 15% for individuals ages 20+ years. Klein
(1981, 1985, cited in Lolio, 1990) reported prevalences of hypertension in two samples of
males and females aged 20 to 74 years, and including patients under control of
hypertension. One sample was from Porto Alegre, Rio Grande do Sul, South Brazil, and
the other from Volta Redonda, Rio de Janeiro, Southeast Brazil. The prevalence of
hypertension (systolic blood pressure 2 160 mm Hg, and/or diastolic blood pressure 2 95

mm Hg) was 11.3% in Porto Alegre and 10.1% in Volta Redonda. Hypertension is the
third most common diagnoses in tempOrary working disability, and it is the leading cause
in Permanent working disability (Ribeiro et al., 1988).

A higher prevalence of hypertension was observed in blue collar workers than in
white collar workers, with prevalences ranging from 2.9 to 17.6% (Cordeiro et al., 1993).
AS a part of the Multicenter Study in Latin America, a total of 1157 individuals 15 to 64
years from Porto Alegre had their blood pressure, CHD risk factors and SES variables
(education, income, and social class) measured. The less privileged males showed a
higher prevalence of hypertension, and the inverse was observed in females (Duncan et
31.4 1 993b). Hypertension was more prevalent in Blacks (26.4%) than in Whites (18.4%)
from Pelotas, Rio Grande do Sul (Piccini and Victora, 1994). Dressler (1991) suggested

that part of the racial/ethnic group differences in blood pressure may be socioculturally

medi ated.

Cigarette smoking

Data on the use of tobacco in Brazil showed that 23.9% (13.9% males and 10%

females) of the sampled subjects were smokers (Ministry of Health, 1993). Except for the
age group 50+ years, women had a higher prevalence of smoking than men. This was
also true at younger ages, showing that more women start to use cigarettes earlier in life.
The prevalences of smoking in children 10-14 years and 15-19 years were approximately
2% and 5% for females and 1% and 4% for males. The highest prevalence was observed
for the 30-49 years group. Average usage in males was 11-20 cigarettes a day, and that
for females was 5-10 cigarettes a day.-The prevalence in males has declined in the last
decade, but the reverse has occurred in females (Achutti, 1988, cited in Briscoe, 1990).

Although females start smoking earlier than males, they tend to smoke fewer cigarettes

per day.

Exte mal causes of mortality

External causes of death have increased in Brazil in the last 50 years from 2.6% to
9.3°/o (Camargo et al., 1995). Males are in general more prone to external causes of
mOrtality than females. The prevalence increases with age, peaking at age group 15-44
Years (PAHO, 1994b). The major external cause is motor vehicle traffic accidents, which
are most frequently alcohol-related, especially in younger ages. There is an increase in
alcol’lol consumption in young people in countries such as Brazil. A 1989 survey of
Brazilian high school students in urban areas, indicated that 25 to 54% drank alcoholic

beVEI‘ages. (PAHO, 1994a).

64

The increase in urbanization and drug use contribute to the increase in homicides.
the second most frequent cause of adolescent deaths. Males have higher risk of homicide
than females. Homicides represent 4.8% of the mortality in children 10-14 years; the
proportionate mortality rate increases to 25.8% in the 15-19 year group (DATASUS on-

line, 1997).

Physical activity level and hours of TV viewing

Preliminary data from the Survey on Standards of Life (IBGE on-line, 1998)
ShOWed that only 19.2% of the sample was physically active, i.e., exercised regularly
during the week. The males were more active than females: 26% and 12.7%,
respectively. In this group, 7.9% of individuals exercised at least 3 times a week for 30 or
more minutes (10.8% for males and 5.2% for females). Leisure and aesthetics were the
reasons for regular exercising reported by 14.3% of the surveyed individuals. Data from
R10 Grande do Sul showed that 47% of middle class and 25% of lower class adults
exercise regularly (Achutti et al., 1988,. cited by Briscoe, 1990).

Mello et al. (1998) studied the engagement in exercise in a group of 1,000 people
from the city of Sao Paulo. They found that 31.3% participated in physical activities
re:f'aularly. Walking was the most reported activity (36.4%) followed by soccer (17.3%)
and aerobics (16%). Thirty one percent of individuals exercised daily, 29.1% exercised 3
t0 6 times a week and 35.8% 1 to 2 times‘a week. The authors did not report the
diStI‘i bution in age or sex categories. Nevertheless, they stated that the sample was

comI>Osed of individuals from all social classes and from all geographical regions in the
city.

65

Few studies of physical activity on Brazilian adolescents compare students of low

and high SES (Matsudo et al., 1996; Andrade et al., 1996). Matsudo et al. (1996)
investigated the physical activity level of adolescent males (mean age= 13 years) of high
and low SES. The questionnaire included a self activity rating, a self-report of present
activity level, hours of television viewing, duration of regular walking, walking pace, and
perceived exertion. Low SES students were more likely to consider themselves as very
active than high SES students (45.6% and 25.4%). High SES subjects more often
classified themselves as sedentary. The self activity rating showed that low SES
adolescents consider themselves more active than sex and age peers more frequently than
high SES males (42.9% and 29.5%, respecrively). Low SES students walked for a longer
duration than high SES adolescents, and no differences were observed for walking pace
or TV viewing (mean values 4.2 and 3.6 hrs/day, respectively).

Using the same methodolbgy, Andrade et al. (1998) analyzed the physical activity

lev€313 of low and high SES females (mean age= 12 years). They found that low SES
females tend to report being more sedentary than high SES girls (9% and 5.7%
respectively). More high SES girls reported regular activity. On the other hand, low SES
girls were frequently more active than their peers in the high SES group (29.2% vs
12- 80/0). As in low SES boys, the low SES girls also showed a longer duration in walking,
Wit l'lout significant difference in the walking pace. Low SES females were more likely to
013 SSify themselves as equally or more active than sex and age peers (87.6% and 80.60%
for low and high SES, respectively). Average TV viewing was 4.1 hrs/day for low SES

girls and 3.9 hrs/day for peers 'of high SES.

One hundred and forty one children from a high SES area (mean age= 13.1 and

66

12.75 years for boys and girls, respectively) had their physical activity profile
investigated by Andrade et al. (1996). Fifty three percent of boys and 64.7% of girls
reported being regularly active. For the self-activity rating, 59.2% of the boys and 47.8%
of girls considered themselves as active as same sex and age peers. Walking was
performed by approximately 70% of the sample. Average TV viewing observed was
3.6il -9 hrs/day for males and 3.9i2.0 hrs/day for females. Results using the same
methodology in a low SES sample basically showed similar findings (Matsudo et al.,

1998),

The city of the study: N iteréi, Rio de Janeiro

Niteroi, a city of 51 square miles, is located in the Southeast area of Rio de
Janeiro State. The city is separated from the city of Rio de Janeiro by the Guanabara Bay,
a distance of 11 miles. The temperature ranges from 148° C to 322° C (59°—900 F) during
the year. The city is divided into five administrative regions: North, East, Bay beaches.
Ocean beaches, and Pendotiba, and the two beach regions are the wealthiest regions in
the City (Appendix C).

The population in Niteroi was estimated as 436,155 inhabitants (46.9% males) in
the 1 991 Brazilian Census. The annual population growth for the city was 0.85% at that
time_ The population in Niteroi is also young, where 24.5% is under 15 years of age. On
the Other hand, 7.6% of the population is over 65 years. These two proportions show that

Niteroi has a relatively older population structure than the general Brazilian population.
The age group 10-19 years represents 17% of the population. Table 8 shows the

dlStribution of teenagers in the five administrative regions. Sixty-eight percent of the

67

inhabitants are white, 8% are black, 22% are Pardo and the remaining 2% of individuals
are either of Asian or of Native Brazilian origin (IBGE, 1996). The estimated population
of 14 and 15 year old males and females in 1997 is shown in Table 9. The median
education of adults is 9 years, which is higher than the Brazilian population
(PMN/CECITEC, 1994)

Niteroi is considered a urban city and approximately 30% of its income is related
to services. Naval industry and fishery are also contributors to the city income; however,
these contributions are decreasing over the years (Government of Brazil on-line, 1998).

As mentioned before, the health status of the population varies within Brazil
according to the degree of development of the area. In this way, the two parameters for
measuring health status, life expectancy at birth and infant mortality show better status
for Niteroi compared to the whole country. Life expectancy at birth is estimated as 70
Years while infant mortality is 16/1,000 (65.5 years and 51 6/1,000 respectively for the
country). In 1993, proportionate mortality rate for infectious disease was 2.8%, while the
values for neoplasms, cardiovascular diseases and external causes were 14.5%. 33.1%
and 1 2.5%, respectively (PMN-CECITEC, 1996b). These values are comparable to the
state data and are somewhat lower for infectious disease and higher for cardiovascular
diseases comparing to whole country data. The prevalence of malnutrition" in children
from birth to 5 years of age was investigated by Marins et al. (1995). They found that

20- 5% of children were malnourished, while 2.7% of them were classified as moderately

and severely malnourished.

_\

5 -
USlng Gomez criteria and NCHS reference data.

68

In terms of leisure physical activity opportunities, Niteroi offers several options.
There are several beaches (in the Guanabara bay as well as in the Open sea), where
swimming and beach sports can be performed. At some beaches, sports programs such as
beach volleyball and soccer, are offered to children and adolescents at nominal cost.
Sports programs in different modalities are also offered at the Caio Martins Sports
Complex and at the Fluminense Federal University with low or no cost to the participant.
Some of the schools carry their own sports programs and programs are also provided by
private sports clubs. Few parks are available for outdoor activities, and as a urban city,

the neighborhood does not always offer safe areas for physical activity of children.

Methodological aspects

Sample

The public school system in Niteréi includes 71 State and Municipal schools. The
criteria for a school entering in the study included having an 8th grade class, a physical
edUlcation teacher, and a basic sports facility. Using the first criterion, 32 schools were
eligible. The schools were grouped according to the five major administrative regions;
however, one of these regions (East) had no eligible schools. After obtaining the
Permission to carry out the study in the schools from both the State and the Municipal
Departments of Education (Appendix D), the investigator visited half of the eligible
SChOols, based on accessibility and on the region in which they were located. The sample
was intended to be composed as close to the proportion of adolescents in each

admi nistrative region.

The visits resulted in the exclusion of three schools; another three schools were

69

selected. Because all but two of the schools had morning classes, scheduling
measurement days was limited. Data collection was also restricted by a teacher’s strike.
The final number of schools in the study was 16 and altogether, 329 individuals were
measured and tested (126 males and 203 females). The distribution of schools and

su b j ects across the five administrative regions is shown in Table 10.

In each school, the investigator contacted the principal, explained the study and
asked for permission to collect the data in the school. A summary copy of the study
proposal was given to the school principal for the school file. After permission was
granted, the investigator talked to the 8th grade physical education teacher and requested
Permission to make the measurements during his/her classes. Several days prior to the
data_ collection, the investigator visited all the 8th grade classes in the school to invite the
Stud ents to participate in the study and to distribute the parental consent form (Appendix

E, both the English and Portuguese versions). Participation in the study was voluntary.

On the day of measurement, only those who had a signed parental consent form
were asked about health conditions, such as diabetes, orthopedic limitations for running,
respi ratory diseases (e. g. asthma). After signing self-assent (Appendix F, both the English
and Portuguese versions), the subject was included in the study.

The students were divided into two sub-groups (4 or 5 individuals each), one for
anthropometry and blood pressure measurements which were done first, while the other
grOUp completed the sociodemographic data form, the self-assessment of sexual
m aturation, and the physical activity questionnaire. When all the students finished both

see'tions, the 12-minute run was performed. Two research assistants were trained to

a . o o n o a
q r‘riinister the physrcal actrvrty questionnaire and the sexual maturation form, as well as

70

to get the distances on the 12 minute run.
A few days later, each participant received a card with some of his/her data
(height, weight, estimated percentage of body fat, blood pressure and the distance run in

12 minutes). School principals also received a report with the results for the school.

Variables and Measurement techniques

Sociodemographic Data

Sex-

The sex of the subject was recorded on an identification form (Appendix G),
Whi c h also contained the name of the individual and other sociodemographic data.
Cit/‘0)Iological age

The age of the individual was calculated based on birth date and the date of the
asses sment, to the nearest 0.01 year, according to Eveleth and Tanner (1976).

SOC i671 class - socioeconomic status (SES)

Three measures of social class are commonly used in Brazil: occupation,
eduCation, and income. These three indicators are both independent and interdependent.
They may be used separately or in a composite index. Cooper (1984) pointed out that

eduCation and occupation variables may not be comparable across races, and income
Wollld be the best indicator of social class. Although income is the best indicator of
ac(less to resources, income assessment is a sensitive issue, especially for those in low the
SE 8. Lombardi et al. (1998) proposed a social class classification for epidemiological

St I O I I
L: Q ies in Brazrl based on occupation and education, and also on the relations to the

IQ
p Quction of goods. Individuals in the middle class are owners or shareholders of

71

medium to small enterprises, or are engaged in occupations that requires a college degree.
such as university professors. Persons in the working class are those without a college
degree, and are either directly or indirectly related to the production of goods (typical and
atypical, respectively). Examples of these two categories are bank tellers and carpenters
for in the atypical and typical working class, respectively.

Social class was estimated by using occupation of the head of the household. The
parent who signed the consent provided the information on occupation and on the status
of employment (employed or self-employed). The occupation of the head of household
was categorized according to the classification in the Appendix H. This classification was
creat ed by applying the categories proposed by Lombardi et al. (1988) and by using the
IBGE list of occupations (1997, on-line), yielding the following categories: upper class,
middle class, atypical working class, and typical working class. The atypical working
Class and typical working class are similar to the American classification of lower middle
elaSS and lower class, respectively. For simplification, the American classification will be
used -

The subject was also asked if he/she was in the work force. If the subject worked,

the occupation was classified in the same manner as that for the head of household. Other

SOCiodemographic data, such as number of individuals in the household and the number

of Siblings, were also obtained.

72

I

Race
Race was self-reported. The subject classified his/her race by using one of the
three categories routinely used in Brazilian census: Black, White, and Pardo. Due to the
lower number of Black subjects, that group was combined with the Pardo group to form

the Non-White group.

A n thropometry

Sta/II)? (standing height)

Stature was measured with a portable anthropometer to the nearest 0.1 centimeter.
It was measured with the individual standing erect without shoes and with the shoulders
relaxed. The head waspositioned in the Frankfort horizontal plane. The distance from the
floor to the vertex of the .skull was measured.
Sirri; {g height

Sitting height was also measured with the portable anthropometer. The individual
was Sitting erect on a table with the legs freely hanging over the table edge. The head was
POSit ioned in the Frankfort horizontal plane. The Sitting height was the distance from the
table to the vertex of the skull.
B 0d), mass

Body mass was measured on a spring scale to the nearest 454 grams (1 1b.). The
Subject was wearing light clothes and no shoes. Although the standard technique involves
the use of a beam scale, the use of a spring scale is recommended for fieldwork when

§Te is no practical alternative.

Ski} {fold thicknesses

Six skinfolds were measured to the nearest 0.2 millimeters using a Holtain caliper.

Each skinfold was measured three times and the median value was used in the analyses.

All measures were taken in the right side of the body. The individual was standing during

the measurement.

a.

Triceps: it was measured on the back of the arm over the triceps muscle at a level
midway between the olecranon and acromial processes.
Biceps: it was measured on the anterior aspect of the arm over the biceps muscle at

the same level as the triceps skinfold.

. Subscapular: it was measured on the back just beneath the inferior angle of the

scapula, following the natural cleavage line of the skin.
Abdominal: it was measured as horizontal fold 3 centimeters lateral and 1

centimeter inferior to the umbilicus.

. Medial calf: it was measured on the inside of the calf at the level of maximum

circumference.

Suprailiac: it was measured immediately above the iliac crest in the midaxillary

line.

( ’rC'umferences

Three circumferences were measured using a flexible, non-stretchable tape. The

measurement was taken to the nearest 0.1 centimeter. The individual was standing while

t . .
he measurements were taken. Each Circumference was measured three times and the

‘ Q(lian value was used in the analyses.

3. Arm (relaxed): it was measured on the relaxed right arm at the site of the triceps

74

skinfold. The arm hanged relaxed with the side during measurement. The tape was
in contact with the skin without compressing the underlying sofi tissues.

b. Waist: it was measured at the level of the natural waist, the narrowest part of the
torso. The clothing was minimal not to interfere with the measurement. No breath
holding or contraction of the abdominal muscles was permitted during the
measurement.

0 - Hip: it was measured at the level of maximal protusion of the buttocks. Gluteal
contraction was avoided during the measurement. The measurement was taken
over light clothing.

Deri 1 Red variables

a. E Stimated Leg Length (Subischial length): Using both stature and sitting height, leg
lengt 11 was estimated by subtracting sitting height from stature.

b- S i tting height/stature ratio (SHSR): SHSR was used to provide an estimate of relative
leg 1% ngth. It was calculated as

sitting height
stature

 

SHSR = x 100

c. BOdy mass index (BMI): BMI was calculated as

 

k
BMI (kg/m2) ____ body mass (2g)
stature (m )
d - Sum of skinfolds: It was calculated by summing the values of the 6 skinfolds (ZéSF).
e -

Mid-upper arm muscle area (UAMA): It was derived with the following equation,

us i rig arm circumference (C) and triceps skinfold (T):

75

 

2
Upper arm muscle area (cmz) = m

Upper arm muscle area was also calculated by using both triceps and biceps (B)
ski nfolds, as follows:

2

TC
Upper arm muscle area (cmz) = E;— C - -2—(T + B)

1“. “’aist-hip ratio (WHR): It was calculated by dividing waist circumference by hip
circumference.

g. Trunk-extremity ratio (TER): It was calculated as the sum of three trunk skinfolds
divi ded by the sum of the three extremity skinfolds.

_ subscapular + suprailiac + abdominal
TER- . .
tnceps + calf + biceps

 

Sex“ al maturation

Maturity status was determined by self-assessment of secondary sex
ch'éll‘acteristics using the protocol of the Fels Research Institute (1993), which uses breast
and pubic hair development for girls and genital and pubic hair development for boys.
The stages are those described by Tanner (1962) with illustrations from the Dutch
rnational survey (van Wieringen et al., 1971). Breast and genital development were

assessed using stages from 1 to 5, while pubic hair development was assessed using
Stéiges 1 to 6 (Appendices I and J). Females were also asked to report the age at menarche

IQ
t he nearest month.

76

When completing the sexual maturation forms, the individual went to a separate
area, so that his/her privacy was guaranteed. After completion of the forms, he/she was
asked to place them into an envelope, thus no other person could see the reported values.

The maturity stage based on breast and genital development was the variable used in the

analyses.

C o mponents of Health-related Physical Fitness

Ca rdiorespiratory fitness
The subject walked/ran for 12 minutes in a school area specially marked to
ind i cate distances, after the purpose and directions for the test were explained. In most of
the schools, the running area was a volleyball court (9x18m); however, the available area
rflufb’ed from 9x9m to 30x18m. Each running area was divided into 6 marked sub-areas.
The distance covered in the 12 minutes was assessed by counting the total number of
com plete laps and by adding the number of sub-areas the subject covered.
Warm-up. and cool-down activities were done respectively, before and after the
‘331 - Motivation was given tothe subjects during the performance of the test by the
it“v'estigator, her assistant and other participants in the study. The distance was reported to
the nearest meter. In general, the students were used to run laps around the court as
Warm—up activity in physical education classes. No more than 10 subjects ran at a time,
Si t'lce each recorder was responsible for a maximum of 5 subjects per bout of the 12
m i hute run.
Physical fitness categories were determined by sex- and age-specific quintiles of

d i . . . .
S‘ance run in 12 minutes. Quintile cutoff pomts by age group and sex are shown m

77

Table 34. Individuals in the first quintile (lowest 20% for distance run in 12 minutes) are
included in the risk group for low fitness; those in the other four quintiles form the not at
risk group. Therefore, by definition, 20% of the sample is at risk for low

ca rdiorespiratory fitness.

Body composition
Body composition was viewed as fatness. It was determined by using the sum of 6

sk i nfolds (Z6SF) and the BMI. The cut-off points for risk for overweight and obesity
were defined as the 2 85th and 2 95th percentiles of the BMI, respectively, as

recommended by Himes and Dietz (1994). The values are shown in Table 1 1.

Selected Risk factors for Cardiovascular disease

Blood Pressure
Blood pressures were measured with an aneroid sphygmomanometer, after the
subj ect had his/her anthropometric data collected, to prevent higher values due to anxiety.
TWO measures of SBP and DBP were taken 3 to 5 minutes apart and the average was
‘1st as the estimate of the BP level (National High Blood Pressure Education Program
WOrking Group on Hypertension Control in children and adolescents, 1996). Cuff size
Was appropriate for arm size. The pressure within the cuff was released at a rate of
ap proximately 2 to 3 mm Hg per second. SBP was defined as the first Korotkoff sound
and DBP as the fifth sound (disappearance of sounds). Both values were reported to the

I) §Grest 2 mm Hg. High normal blood pressure was defined as values above the 90th

78

percentile for age-specific reference data, while hypertension was defined as values

above the respective 95th percentile.

Cigarette smoking habits
The subject was asked to report if she/he used cigarettes. If she/he did, the
average number of cigarettes smoked per day was recorded in the following categories: <

1» 2'5, 6-10, 11-19, 2 20 cigarettes (adapted from CDC, 1995).

Physical activity level

Habitual physical activity was assessed with a modified version of the Physical
Activity Qu estionnaire for older children (PAQ— C), which classifies individuals into
aetiVity categories from 1 to 5 (Kowalski, 1997). The questionnaire was modified to
exclude Sports that are not played in Brazil. The assessment of hours of TV viewing in
the week Was included in the questionnaire, but did not contribute to the final activity
score (Appendix K, both English and Portuguese versions)-

Each item in the questionnaire was scored on a 5-point scale. For question I, the
aCIiVitY checklist, it was necessary to transform the score into a S-point scale, by dividing
the total nu mber of points on the item by the number of activities in the checklist.
including activities under the other section. A similar procedure was needed for question
13* the activity on each day of the week, where the total points are divided by 7. The final
score was obtained by the average of items 1 through 7, 9 and 13 (Kowalski, personal
communication. September 14, 1995).

The physical activity score represents a spectrum from the very inactive (l) to the

79

very active (5 ‘). Based on the score, the subject was classified as inactive, when his/her
score was < 3 , or active (2 3). This classification indicated individuals not at risk and at

risk for phy si cal inactivity (active and inactive, respectively).

Overweight
The BMI was used to identify individuals who were at risk for overweight (2 85th
percentile) and who were overweight (2 95th percentile). Age- and sex-specific cut-off

POintS Suggested by Himes and Dietz (1994) are indicated in Table 11.

Fat distribution

Relative fat distribution was determined by the waist-hip and trunk-extremity

ratios.

Nutritional status

Mid-upper arm muscle area for height was used as an indicator of nutritional
status, aGeording to the criteria of Frisancho (1990, p. 51). Mild malnutrition was defined
as an Upper arm muscle area between the 5th and 15th percentiles, while severe

malnumtion was defined as an upper arm muscle area below the 5th percentile.

80

Technical Error of measurement

All anthropometric and blood pressure measurements were taken by the
researcher. To assess intra-observer measurement variability, 4.5 % of the sample (15
individuals) was remeasured within one week after the first assessment (Table 12). The

technical error of measurement, 0, , was calculated after Malina (1995a):

I 2
0.6 2 Z d a
2n

where d is the difference of replicate measures and n is the number of measured pairs.

 

Intra~observer measurement error for blood pressure (Table 13) was calculated by

using the rel i ability of a single measurement (Foster, 1980), as follows:

2,: Ms, - MSw
MSA + MSW

wher e MSA is the mean square among subjects and MSW is the mean square within

SUbJeCtS from the one-way analysis of variance table.
Int raclass correlation coefficients were calculated for determining reliability

(Baumgartner, 1991) of the anthropometric variables. The formula for the intraclass
correlation coefficient (rim) is as follows:

- = (MSX - MS“')
‘l‘llra MSA.

 

T . . . . .
he Closer the intraclass correlation coefficrent 15 to one, the smaller the error introduced

by the Observer. The reliability coefficients are presented in Table 14.

81

The t echnical errors of measurement for anthropometric variables were similar to
those reported in national surveys in the US. (Malina, 1995a) and in the Quebec Family
Study Canada (Bouchard, 1985). The measurement errors for blood pressure were lower
than those from the Bogalusa Heart Study (Foster et al., 1980). All of the intraclass
correlations for anthropometric and blood pressure variables in this study were similar to
those reported by Bouchard (1985) and Foster (1980), except for DBP, where the
intraclass correlation was lower (F059). No other comparative data are available for
measurement variation in blood pressure; therefore, it is not known if this low coefficient

is usual.

Statistical Analyses

Sex-— and age-specific descriptive statistics were performed for all variables. Race
SPeCific AN COVAS were done for anthropometric variables, adjusting for age and stage
0f sexual maturation. Chi-square test for independence was used to analyze the
distribution of race and SES for individuals at risk and not at risk for each risk factor
variable. I~.<)gistic regression analyses were also performed to calculate the odds-ratio
(OR), adjusting for chronological age and sexual maturation. Only the significant ORs
are reported ANCOVAs were also performed to compare variables of individuals at risk
and “Ct at risk for inactivity and low fitness, adjusting for chronological age and sexual
maturation. Variables with skewed distributions, i.e., zesr, BMI. TER. physical activity
score, 12 minute run, SBP, and UAMA, were loglo transformed to either eliminate or
reduce 8kewness.

SES was used as a dummy variable to express three categories: middle class,

82

lower middle class and lower class. Lower class was the reference category. In cases
where occupation of the head of the household was not sufficiently specific, lower class
was assigned - Data were not included when the head of the household was out of the
work force.

The variables were grouped as either predictor or dependent variables for the
statistical analyses. The predictors were SES and race. The variables, 12 minute run and
physical activity score, were dependent variables in some of the analyses and predictors

in others. The dependent variables were the selected risk factors: SBP, DBP, BMI, Z6SF.

TER, and WHR.

The analyses for the specific questions were as follows:

1. To what extent do sociodemographic characteristics predict selected risk factors for
CHD and health-related physical fitness in adolescents?

MUItiple regression analyses were performed using each selected risk factor or
each healtl"l--related fitness component as the dependent variable, and SES, race and sex as
the PrediCtOrs. Chronological age and stage of sexual maturation were entered as
covariateg This analysis attempted to estimate the variance in CHD risk factors and

health-relat ed fitness that is accounted for by the sociodemographic characteristics.

2' What is the relationship between cardiorespiratory fitness and selected risk factors for
CHD “1 adolescents?
Multiple regression analyses were performed using each selected risk factor as the

d . . . . .
ependent variable and cardiorespiratory fitness as the predictor. Chronological age. sex

and sexual maturation were entered as covariates. The purpose of this analysis was to
estimate the proportion of the variance in the CHD risk factors that is explained by

cardiorespiratory fitness.

3. What is the relationship between level of physical activity and selected risk factors for
CHD in adolescents?
Multiple regression analyses were performed using each selected risk factor as the
dependent variable and physical activity as the predictor. Chronological age, sex and
sexual maturation were entered as covariates. This analysis attempted to estimate the

variance in CHD risk factors that is accounted for by estimated level of physical activity.

4. Do risk factors for CH1) cluster within individuals?
The Chi-square test for independence was used to test the clustering of risk
faCIOTS for CHD within individuals, following the procedures of Raitakari et al. (1995) in

the Young Finns Study.

84

CHAPTER 4

RESULTS

The first part of the chapter presents the descriptive results for anthropometric,
health-related physical fitness and cardiovascular risk variables. The subsequent parts

present results of the specific analyses of the research questions addressed in the study.

Descriptive Statistics

Demographic Characteristics

The sample included 329 students (126 males and 203 females) from the public
5eh001 System in Niteroi, Rio de Janeiro. The distribution of sample by age and sex is
Show“ in Table 15. The age- and sex-specific samples, respectively, 1.5% and 2.5%, of
the estimat ed population of boys and girls in 1997. Therefore, the findings in this study
ShOUId be generalized with caution.

Table 16 shows the self-reported race of the subjects. The race distribution differs
Significant ly (p<0.01) from the expected distribution as reported by the 1991 Census
(IBGE: 1 996). The proportion of White subjects is lower, while the proportions of Blacks
and PardOS are higher in each sex, assuming that the proportion in the entire population is
“mam among 14-15 year old individuals.

SOcioeconomic status was determined by the occupation of the head of household.

classifying the subjects as middle class, lower middle or lower class. The majority of the

85

subjects came from the lower class (Table 17). Most of the individuals came from
families where the head of the household worked as a manual laborer. Males were more
likely to be the head of the household; females, however, were responsible for 8% to 14%
of the households (Table 18), which is a lower proportion than 26.3% in the Niteroi
population (PMN/CECITEC, 1996). Table 19 shows the distribution of SES by race.
There were no differences among males; however, Non-White females were more likely
to be from lower class, i.e., almost all (86%) of the Non-White females came from the
lower class- No comparative data are available to explain this higher percentage of Non-
Whites in the lower class among females only.

The age groups show similar trends in the number of children per couple and of
members in the household (Table 20). These numbers are higher than the mean values for
Niteréi (2 and 3.5, respectively, PMN/CECITEC, 1994), which may be explained by the
tendency of larger families in poorer groups. The number of people working in the
hOUSChOId i s also similar among the age groups; however, no reference data for Niteroi
are available. Table 21 shows the work status of the subjects. The majority of subjects are

students and are not in the work force. But, more boys are part-time workers than girls.

regardles s of age.

Anthropometric data

Tables 22 and 23 show the descriptive anthropometry for males and females,
1' ° . . .
espeCIIVQIy. For males, there are no Significant differences between the two age groups
f .
or any 0fthe variables. However, the data show that older males tend to have lower

Values for all skinfolds, and higher values for the TER.

86

For females, there are significant differences for the abdominal skinfold.
estimated arm muscle area (S,+Sb), and hip circumference, where older girls have higher
values. Although the other skinfolds are not significantly different, the trend is for thicker
skinfolds in the older group.

There is a trend for an increase in body weight and the BMI with age. even
though the differences are not statistically significant. The greater estimated arm muscle
area (Sr+Sb) in older girls is indicative of increased muscularity with age, which also
contributes to greater body weight and BMI.

Comparisons by race, adjusting for chronological age and sexual maturation
(Tables 24 and 25), show. no significant differences among males, except for the TER;
non-Whites have higher values than Whites (1.37 vs 1.27 mm/ mm, p<0.05). Non-Whites
also show a trend of lower SHSR than Whites; however, the difference is not significant
(p=0.07).

For females. Non-whites have a higher waist circumference than Whites (p<0.05).
Differences are also evident for the BMI, the subscapular skinfold, and the sum of the
subscapul ar and triceps skinfolds, as they approach significance (p=0.06, p=0.07. and
[PO-OZ re Spectively). Non-Whites show higher values than Whites, suggesting that Non-

Whites tend to be heavier and to have more subcutaneous fat (Table 25).

Sexual “1 aturation

Distributions of the self-reported stages of sexual maturation are presented in
T . . . .
able 26 - The majority of the students rated themselves in the advanced maturity stages.

F0 ' . .
r 31113, the advanced stages are consrstent With the age at menarche.

87

Ninet y-six percent of the girls were post-menarcheal (Table 27). Since not all the
girls had reached menarche, mean age at menarche of the complete sample is actually
slightly higher than the value presented. Nevertheless, the mean is within the range of
ages at menarche (12.2 to 14.0 years) reported for Brazilian girls (Duarte, 1993). Figure 4
shows the relative distribution of the ages at menarche in the sample. Compared to the
data from C olli (1988), the current sample shows a higher percentage of girls in the age
groups up to 12 years.

Analyses of secondary sexual characteristics by race in both sexes, age at
menarche and distribution of ages at menarche for females show no significant
differences between Whites and non-Whites (Tables 28 and 29, Figure 5, respectively).
Although the distribution was not significantly different among White and Non—White
females for stage of secondary sexual characteristics, the data suggest that more Non-

Whites are at a higher maturity stage than Whites.

Cardio respiratory fitness

Cardiorespiratory fitness was assessed by the 12-minute run. Data were not
available for all subjects due to either weather conditions (rain on the day of the test), or
to absenc e on the day the run was scheduled (in two schools the run was performed on a
different day than the rest of the data collection). Only two females could not finish the
‘35? There are no differences between age groups in both sexes or between White and

Non-White subjects (Table 30).
Sex- and age-specific quintiles of distance run in 12 minutes are shown in Table

31. Individuals in the first quintile are included in the low fitness risk group and those in

88

the other four quintiles form the not at risk group. Therefore, by definition, 20% of the

subjects are at risk for low fitness.

Selected risk factors for cardiovascular disease

Blood Pre ssu re

B 1 00d pressures of males and females are presented in Table 32. There are no
difference sin males by age group in systolic (SBP) or diastolic (DBP) blood pressures.
In female S, only SBP is statistically significant; older girls show higher values. Data for

. DBP suggest that older girls also have higher values, although the difference is not

significant

Using age- and sex-specific cut-off values for high normal blood pressure (shown
in Tab! e 4), the prevalence of high blood pressures is as follows: 14 year old males have a
prevalence of 7.5% for high normal BP (3% SBP and 4.5% DBP), while 15 year old
males have a prevalence of 6.7% for high normal BP and of 3.3% for hypertension. There
are no cases of either high normal BP or (hypertension among younger females, while

Older fem ales show a prevalence of 2.9% for high normal BP (Table 33 .

Cigarette smoking

The subjects responded to a question on cigarette smoking habits (Table 34).
None Of the younger males used cigarettes at the time of the investigation, while about
7% 0f the older males smoked. Only one (1%) 14 year old and three (3%) 15 year old

females smoked. There are no differences (p > 0.05) in cigarette use between age or

89

racial group S -

Physical activity

Results for the physical activity questionnaire and hours of TV viewing are shown
in Table 3 5 - The mean number of hours of TV viewing is higher than the 2 hours
recommended by the American Academy of Pediatrics (Gortmarker et al., 1996).
Younger girls watch more TV than older girls (p< 0. 05). There are no differences in
physical activity scores between age groups in each sex; however, the values represent
low level S of activity for the subjects. The majority of subjects of both sexes are
classified as either inactive (males) or very inactive (females, Table 36). There are no
differences in physical activity scores by race in any age or sex group.

Risk for inactivity was estimated by the physical activity score, with values <3
being at risk and 2 3 not at risk. Most subjects were at risk for inactivity; prevalences
varied from 83% to 94 % (Table 37).

Theifrequency with which the subjects performed specific physical activities and
the ranks are shown in Tables 38 through Table 41 for males and females, 14 and 15
years or age, respectively. For males in both age groups, the most performed activity is
soccer, a traditional sport in Brazil. The next three most performed activities are jogging
or running‘ bicycling and walking for exercise.

The top physical activity for females differs by age group. Younger girls

participate more in dance and walking for exercise, while older girls report more walking
{OT exercise and dance (inverted position by rank). Bicycling and jogging or running are

the third and fourth activities in rank for both age groups of girls. Capoeira, a martial art

90

originated in Brazil, is the only activity reported by all sex and age groups. Judo and

jujitsu are also performed by the different sex and age groups.

Overweight/ obesity

The prevalences of overweight and obesity are presented in Table 42. Younger
boys show a prevalence of 7.6% for overweight and no cases of obesity. Overweight is
present in 5.0% of older boys and obesity in 1.7%. Among females, 9% of 14 year olds
are overweight, whileonly 1% is obese. For older females, the prevalences of overweight
and obesity are 6.8% and 1.9%, respectively. The prevalence of overweight is higher in
the younger groups in both sexes than in the older subjects. There are no differences in

the prevalence of overweight or obesity by race in either sex.

Nutritional status

The prevalence of undernutrition is shown in Table 43. Younger males present lower
prevalences of both mild and severe undernutrition compared with older males. The
reverse is evident among females, where older girls have lower prevalences of mild and
severe undernutrition. The prevalences of mild and severe undernutrition are almost
identical for both males and females in the 14 year old group. Among 15 year olds, males

are more likely to be undernourished than females

91

The effects of sociodemographic characteristics on health-related physical fitness

and on selected CHD risk factors.

The first question investigates the proportion of variance in specific components
of health-related physical fitness and on selected risk factors explained by
sociodemographic variables. Each risk factor and fitness component was analyzed
separately by multiple regression analysis using sex, SES and race as the predictors.

All of the dependent variables except DBP were log to transformed to either
correct or decrease the skewness of data. Chronological age and sexual maturation were
entered as covariates. Stepwise regression analyses were also performed to determine
which variables contributed most to the explained variance.

Tables 44 to 51 show the results of the analyses. Regression coefficients (beta) for
the sociodemographic variables were low, showing small effects on the risk factors and
on health-related physical fitness. Except for DBP, all stepwise regression analyses
included the variable sex. On the other hand, race was not entered in any stepwise
analysis.

Sex explained 2% to 41% of the variance of the variables under study. The
contribution of sex is greater to cardiorespiratory fitness and WHR (R2=O.41), followed
by ZéSF (R2=O.34). The explained variance for the BMI, TER, physical activity and SBP
varies from 2% to 6%. Males have higher cardiorespiratory fitness. physical activity
level, TER, WHR, SBP and DBP than females, while they have lower BMI and zesF

Ageis a significant variable only for SBP. explaining 2% of the variance (Table

49). Older subjects tend tohave higher SBP. Sexual maturation enters in the stepwise

regression for the BMI and 26SF, explaining 6% and 5% of the variance, respectively.
More mature individuals show higher BMI and £6SF values.

Although race is not significant in any analyses, there is a trend for higher values
of cardiorespiratory fitness, the BMI, the Z6SF, the WHR the TER and both BPs in Non-
White subjects. For physical activity, the tendency is reversed, i.e., Whites showed higher
values.

The only SES variable included in the stepwise analyses is lower middle class for
the physical activity score (Table 51). Lower middle class explains 3% of the variance in
the physical activity score. Adolescents in this class are somewhat more active than their
peers.

Sociodemographicvariables, chronological age, sexual maturation or sex did not
influence DBP (Table 50). The fiill regression model is not significant and no variables

enter in the stepwise analysis.

Comparison by risk status

Groups at risk and not at risk by sex were identified for each risk factor.
Comparisons of the sociodemographic variables were performed using AN OVA for age
and the Chi-square test of independence for race and SES. Logistic regression analysis
adjusting for chronological age and sexual maturation was used to obtain the odds-ratio,

and only the significant ORs are reported. The results are shown in Tables 52 through 58.
Age is a significant variable for cigarette smoking in boys, DBP in girls, and low fitness

in boys. The effects of race are noted in female overweight and physical activity, and

male DBP. The only risk factor that is affected by SES is physical activity in females.

There are no differences among the sociodemographic variables for cigarette
smoking in either sex (Table 52), except that male smokers are older than non-smokers.
The prevalence of smoking (2.7%) is low, but differences might not have been found due
to the low prevalence (low power of the analysis). Nevertheless, prevalence of smoking is
expected to be low in the age groups considered.

For both sexes, overweight individuals are more likely to be Non-White, although
the difference is significant (p<0.05) only in females (Table 53). Logistic regression
shows an OR of 4.37 for Non-White females.

There are no differences byrace or SES for SBP in either sex group (Table 54).
On the other hand, DBP shows differences between White and Non-white males. Non-
Whites are more likely to have elevated values. In females, those at risk are older than
peers not at risk (Table 55). Logistic regression was performed for DBP using the whole
sample and adjusting for sex and sexual maturation, due to unstable ORs when analyzed
by sex. The results are shown in Table 56. Older individuals, males and Non-Whites are
more likely to have elevatedDBP. Although not significant, the ORs for SES suggest that
individuals at higher classes are less prone to elevated DBP.

More Non-White females are at risk for inactivity than White girls. There also are
differences by SES, where the lower middle class has proportionately more active
females (Table 57). There are no differences for males. For the risk of low fitness, males
not at risk are younger (p< 0.05); the OR was 3.1 (Table 58). There are no differences in

azny of the sociodemographic variables in females.

94

Effects of cardiopulmonary fitness on selected CHD risk factors

Multiple regression analyses were performed similarly, but separately, to estimate
the variance in the selected risk factors explained by cardiopulmonary fitness on the. In
each analysis, the risk factor was the dependent variable and the 12 minute run was the
predictor variable. Due to skewness, the 12 minute run values were loglo transformed.
Chronological age, sexual maturation, age and sex were entered into all models as
covariates; depending on the risk factor in the model, other covariates were also included.
SES was included as a covariate in the analyses of physical activity level due to the
effects observed in the previous analyses. For the BMI, the E681: and UAMA were
entered as covariates to adjust for different effects of fat and muscle mass on the BMI.
Z6SF was entered as a covariate in the analysis of the trunk-extremity skinfolds ratio, as
fat distribution is dependent of overall fatness. WHR was controlled for the BMI, also to
adjust for overall fatness. For BPs, body weight was entered as a covariate to control for
the positive effects weight has on BP. Stepwise regression analyses were done to identify
the variables that contributed the most to the explained variance.

Cardiorespiratory fitness is significantly related to the Z6SF and the physical
activity level (p<0.001). The partial R2 is 0.07 for £6SF and 0.10 for physical activity,
respectively (Tables 59 and 60). The regression coefficient for the £651: is negative,
showing an inverse relationship between these two variables, i.e., fatter youngsters have

lower cardiorespiratory fitness. The results of the multiple and stepwise regression
analyses for the risk factors not affected. by cardiorespiratory fitness are shown in Tables

61 through 65.

Comparison between risk groups

Comparisons of risk factors between individuals at risk and not at risk of low
cardiorespiratory fitness were performed with sex-specific ANCOVA, controlling for
chronological age and sexual maturation. Results are shown in Tables 66 and 67, for
males and females, respectively. In males, individuals at risk are older than those not at
risk (p<0.05). Those at risk also have higher values for the Z6SF (p<0.05). All smokers
are in the group at risk (p<0.001). Although there are no significant, those at risk tend to
have higher values for the BMI, and both SBP and DBP, and lower values for physical
activity level.

Females at risk show differences in the 12 minute run, physical activity and fat-
related variables: BMI, 26SF, TER and WHR, although for the latter variable the
difference is not significant (p=0.08). Physical activity is lower in females at risk. while
the fat related variables are higher in girls at risk for low fitness. There is no age
difference between the two groups. In contrast to bOys. all of the smokers are in the group

at no risk.

Effects of physical activity level on selected CHD risk factors

Individual analyses were performed to estimate the variance accounted for by
physical activity level on the risk factors. Each risk was entered into the analysis as the
dependent variable, physical activity as the predictor, and sex, chronological age and
sexual maturation as covariates. Similar to the analyses of cardiorespiratory fitness, other

covariates were included: race and body weight for BP, 26SF for TER and BMI, UAM A
96

for BMI, and BMI for WHR. Stepwise regression identified the variables that most
explained the variance. Tables 68 through 74 show the results of the analyses.

For all risk variables but the BMI, physical activity shows a negative relationship,
i.e., lower levels of physical activity indicated higher values of the risk variable.
Nevertheless, beta coefficients are close to zero except for DBP (Table 73) and BMI

(beta =0, Table 68).

Comparison between risk groups

Sex-specific ANCOVAs adjusting for chronological age and sexual maturation
were performed to compare risk variables in individuals at risk and not at risk for
inactivity (Tables 75 and 76). In males, only DBP is significantly different between the
two groups. Individuals at risk for inactivity show higher values for DBP. Once again. all
smokers were at risk for inactivity (Table 75).

All‘differences between risk groups are not significant in females (Table 76). A
trend of higher values is evident for the £6SF, TER. WHR, SBP and DBP among girls at
risk, even though these differences are not significant. As with boys, all smokers are

individuals at risk.

Clustering of risk factors

In order to investigate the clustering of risk factors in the sample, an analysis of
subjects with complete risk data was performed by listing all of the possible

combinations of risk factors: inactivity, elevated blood pressures, overweight. smoking.

97

and low fitness level, and by determining their frequencies in the sample. Expected
frequencies were calculated according to the individual probabilities of each risk factor
based on its occurrence in the sample (Raitakari et al., 1995). Then, the Chi-square test
for independence was performed.

The clustering of risk factors shOws that 5% of females and 12% of males are free
of risks (Table 77). In those with risks, the majority present just one risk factor (68.2%
and 63.3% for females and males, respectively). The maximum cluster of risk factors in
females was 3 and for males it was 4. The distributions of risk factors are shown in
Tables 78 and 79 for males and females, respectively. The most frequent risk factor is
inactivity for both sexes, individually or clustered. The Chi-square analyses, however.
show no statistical significance.

Sex-specific ANCOVAs by number of clustered'risks, controlling for
chronological age and sexual maturation, compared subjects with different number of risk
factors. The following trend is suggested:' as the number of clustered risk factors
increases, the mean value for~ each of the variables also increases, except for
cardiorespiratory fitness and physical activity level, where values decrease (Tables 80
and 81). All variables show significant differences (p < 0.05), except WHR in males and

TER in both males and females.

98

CHAPTER 5

DISCUSSION

The purpose of this chapter is to compare the results of this study, presented in the
previous chapter, to available data in the literature. The primary focus is comparisons
with Brazilian data; however, data on Brazilian adolescents are not always available. The
discussion is organized in the sequence of the research questions: General characteristics
of the sample; the status of growth and sexual maturation; and CHD risk factors in

adolescents.

General characteristics of the sample

It is characteristic of the Brazilian public school system in elementary and
secondary education to have students coming from low SES families. As this study
surveyed public schools, most of the students were from working class families.
Although the classification relied on occupation instead of income, it could detect the
higher frequency of low SES individuals. From occupation data, it was possible to infer
family income and access to different resources, without actually asking for income
information. Inquiring about income is a sensitive issue, especially in times of economic
crisis and low wages.

The sex and age groups had similar number of members and working members in
the household, and number of children per couple. On the other hand, more males were
part-time workers than females. No comparisons can be made to the Brazilian data on

99

working and studying youth, because it is stratified into age groups 10-14 years and
15-17 years by household income. The sample included more Non-White individuals
than expected from the 1991 Census for Niteroi. The higher frequency of Non-Whites
and the composition of the sample varying between 1.5% and 2.5% of the estimated

population for Niteroi, thus limit the generalization of the present findings.

The status of growth and sexual maturation of adolescents

Growth status is an indicator of health and nutritional status of a population and it
is generally assessed by stature and body weight. The findings of this study are compared
to US. NCHS reference data (Hamill et al., 1979) as suggested by WHO (1983).
Additionally, the results are also compared to data from the 1978 Santo Andre Study (SA.
Marques et al., 1982), which also allows for comparisons of sexual maturation and
skinfold thickness as well (Colli, 1988; Goldberg, 1986). The SA data are reported in two
groups: R1+R2, which represents subjects from lower SES, and R the total SA sample.
Data from the 1989 Brazilian National Health and Nutrition Survey (PNSN, Anjos et al..
1998), the 1985/86 Rio de Janeiro Public School Survey (RJ-SS, Einsenstein, 1997) and
the 1996/97 Survey of Standards of Life (SSL, IBGE On-line, 1998) are also included in

comparisons when appropriate.

Stature

For stature (Table 82), 14 year old boys show similar medians to NCHS and

SA(R); in contrast, boys 15 years of age present consistently lower values in all

100

percentiles for both NCHS and SA(R). However, the median of 15 year old boys is
similar to the median reported in the Survey on Standards of Life (SSL, IBGE On-line.
1998), which presents more recent data on adolescents. The stature medians for boys in
this study are consistently higher than those of boys from SA (R1+R2) and RJ-SS.

For females of both ages, the results are similar to SA(R) and to SSL; they are
somewhat lower than NCHS values, the patterns being consistent in all percentiles. As
observed in boys, girls in the present study are taller than their peers from SA(R1+R2)

and RJ-SS (Table 83).

Body weight

Except for the 14 year old boys, body weight is lower than the SA(R) and NC HS
in both sexes, although the 95th percentile tends to be higher than the SA(R). In the
former group, body weightis consistently higher than SA(R) and similar to NCHS (Table
82). When comparing to SA(RI+R2) and RJ-SS, both males and females are heavier for

all percentiles (Tables 82 and 83). .

Secular changes in stature and body weight

The higher values in stature of subjects in this study, who are predominantly from
lower SES, compared to the SA(R1+R2), which is also of a lower SES, poses a question
on the occurrence of secular changes in stature in individuals from low SES. The time
between studies is 19 years and improvements have accompanied economic growth over
this span. Secular changes were also empirically investigated based on SHSR of the two

groups. Mean values for stature, and the percentiles for sitting height and estimated leg

10]

length are not reported for the SA Study. The latter two variables are presented only for
the entire group (SA(R)). Therefore, the mean stature value was estimated by adding
sitting height and estimated leg length, followed by the calculation of SHSR (Table 84).

SHSR is consistently lower in the subjects of both sexes and ages in this study.
Assuming that the difference holds true in the SA(RI+R2) group, it suggests that secular
changes in stature are occurring with the characteristic decrease in SHSR, i.e., an increase
in relative leg length. However, it should be noted that the racial distribution of the
sample is not given for the SA Study. In the present study, about half of subjects are Non-
White and it is known that Blacks have lower SHSR (Malina and Bouchard, 1991). On
the other hand, Brazilian Non-Whites tend to be over represented in low SES groups;
consequently, the relative proportion of Non-Whites in the SA sample may not be much
different from that in this study sample.

The higher body weights in the present study also suggest secular changes for
lower SES individuals. These increases in weight address the question of the relationship
between the increase in body weight and increase in body fat. Comparisons of skinfold
thicknesses (triceps and subscapular) were made between this study and SA(RI+R2), as
well as SA(R) and NCHS (Table 85). However, comparison of data across studies should
consider differences in skinfolds thicknesses due to random variation in manufacture of
skinfold calipers (Malina, 1995a).

Boys 14 years of age show higher mean values of the triceps and subscapular
skinfolds than SA(R) and SA(RI+R2). For 15 year old boys, both skinfolds are higher
than in the SA(RI+R2) group. The triceps is similar to the SA(R) and the subscapular is

higher. Comparisons to the NCHS values show that both skinfolds are similar in l4-year-

102

old boys and lower in boys 15 years of age.

In girls, there is a different pattern. For both ages, the skinfolds are consistently
similar to SA(RI+R2) girls and lower than SA(R), except for a lower triceps in 15 year
old girls in this study. For both ages, triceps means for the NCHS are higher than in this
study, while subscapular values are similar.

Sex-specific patterns are suggested from the skinfold data. For females, the
increase in body weight seems to be independent of an increase in subcutaneous fat, as
the values of both the triceps and subscapular are similar in this study and SA(RI+R2)
samples (non significant t-tests). The pattern for males is that the increase in body weight
is partially related to an increase in fatness, as shown by consistently higher values of the
triceps and subscapular skinfolds in both ages. Comparison of the means showed
significant differences (p< 0.05) for the triceps and subscapular skinfolds in the 14 year
old and the subscapular skinfold in the 15 year old males.

The increase in fatness may be related to the nutritional transition in progress in
Brazil as well as the increase in life standards (health care services, food availability.
sanitation and education, Monteiro et al., 1995c). However, these facts do not occur
selectively for males. The reaSon for observing an increase in fat only in the boys may
rest on the greater stability for the effects of environment on growth in females than in

males (Tanner, 1990).

Body mass index

The SA does not provide the 50th percentiles for the BMI; for comparison, they
were calculated from median values of body weight and stature. These values are also
presented in Tables 82 and 83. BMIs in this study are similar to those in the SA(R) and
SA(RI+R2) samples as well to the NCHS, PNSN, and RJ-SS, except for the 14 year old

boys, where the BMI is higher than the SA(RI+R2), PNSN and RJ-SS.

Sexual maturation

Figure 6 shows that 14 year old boys are more mature than those from the SA(R),
while fewer 15 year old boys reported being in the post-pubertal stage (stage 5 of genital
development). In both age groups, girls are more frequently in pubertal stages (stages 2, 3
and 4 of breast development) than SA(R). The fact that 14 year old boys are more mature
than peers in SA(R) may explain the higher values of stature, weight and BMI observed
in this group

These differences in the distribution of pubertal stages may be due to the methods
used in each study. In SA, sexual maturation was assessed by physicians, while in this
study by self-assessment. A low concordance (23%-45%) between physician and
individual assessment for breast stage 4 was found by Matsudo and Matsudo (1994).
indicating a difficulty in identifying the correct stage. Mean age at menarche in this study
is similar to that in SA(R) and SA(RIer2) as well to other Brazilian samples (Duarte,
1993). Therefore the apparent difference in distribution of sexual maturation may be only

due to misclassification.

104

Matsudo and Matsudo (1994) reported high concordance for genital stage 4 in
males (84%-86%). However, concordance could not be computed for stage 5 because no
males reported being in that stage, even though the age of males included in the study
ranged from 6 to 26 years. This fact suggests that Brazilian males in the study of Matsudo
and Matsudo ( 1994) tended to underestimate their genital development. Underestimation
could explain the lower frequency in genital stage 5 in 15 year old boys in this study.

On the contrary, Williams et al. (1988) pointed out that younger American boys
(10.3-14.8 year of age) tended to overestimate their stage of sexual maturation.

Therefore, there is also a possibility that 14 year old boys in this study overestimate their
stage, which would explain the higher percentage of mature boys than in SA. As shown
by Carvalho (1990), the major discordance was between one stage (47% and 42% for 14
and 15 year old boys, respectively). If similar misclassification had occurred in this study,

the sexual maturation distribution would be more similar to the SA.

Nutritional status

The prevalences of undernutrition (mild and severe forms combined) vary from
12.9% to 30% (Table 43 ). As Johnston and Ouyang (1991) noted, whenever international
reference data are applied to developing countries, there is an overestimation of
prevalence of undernutrition. Therefore, it is assumed these high prevalences represent an
overestimation.

The only data available for older children is for 10-14 years of age (Henao et al.,
1992). In 218 children 10-14 years of age from the Ilhabela Longitudinal Study. 30% of

boys and 3-2% of girls were undernourished by the criterion UAMA for height < 15th

105

percentile.

Boys are more likely to be undernourished than girls in this study. This increased
likelihood may be related to a greater sensitivity to environmental changes in males, so
that they are more prone to develop undernutrition if the environment is adverse.

The WHO (1995) suggests that nutritional status of adolescents be evaluated
based on BMI for age. For thinness, which assesses acute malnutrition, the criterion is a
value below the 5th percentile of the US. reference. Anjos et al. (1998) presented the
distribution of BMI values for the Brazilian population. The values for the 5th percentile
tend to be lower in the Brazilian population compared to US. reference data. Johnston
and Ouyang (1991) pointed out that local reference data can not be used with confidence
because they include undernourished individuals in the sample as well. Nevertheless, for
the analyses of malnutrition both Brazilian and American data were used. The cutoff
points and the prevalences are shown in Tables 86 and 87, respectively.

In males, the prevalence of undernutrition by the US. criteria is higher in both
ages than the prevalence by the Brazilian criteria. This difference is expected, as the
cutoff points are 'lower for Brazilian BMI distribution. The reverse occurs for females.
where higher prevalences are observed for the Brazilian BMI criteria because of the
higher cutoff points. Females also show a lower prevalence of undernutrition than males
by the BMI for age criterion.

Using the BMI by age criteria, the prevalences in both sexes and age groups are
much lower than by the UAMA for height criteria. The BMI is affected by variation in
sexual maturation. During puberty the relationship between stature and weight is altered

due to the different timing of-the spurts of stature and weight (Malina, 1995a).

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Comparing individuals of the same stature but different maturity status, the more mature.
in general, will show higher weight; therefore, if he/she were undernourished, there
would be more chances of having BMI appropriate for the age. This is a limitation of the
criteria.

On the other hand, the UAMA for height criteria are based on estimated muscle
mass, which is decreased in undernutrition. Even in more mature individuals, muscle
mass would be affected by undernutrition. Although the prevalences of undernutrition are
higher by these criteria compared to the BMI for age, most cases are classified as mild

and may be the result of overestimation.

The status of health-related physical fitness

As expected for individuals going through puberty, sex differences are found in
the present study; males have higher values of cardiorespiratory fitness. Krahenbuhl
(1985) has suggested that these differences are partially explained by higher muscle mass
and levels of physical activity in males. However, stepwise regression analysis (Table 74)
showed that the variance in cardiorespiratory fitness was explained in part by Z6SF (69/8)
and physical activity (4%), but not by UAMA, an indicator of muscle mass. The subjects
were classified as normally nourished and undernourished based on UAMA, and there
were no differences in cardiorespiratory fitness between the two nutritional groups in
both sexes. This contrasts the findings of Desai et al. (1984) and Spurr and Reina (1986)
in Brazilian and Colombian children, respectively. These authors found lower V02 max
in undernourished individuals. However, the definition of undernutrition and the measure

of cardiorespiratory fitness varied between the studies and the present study.

l07

It is known that applying international reference data may result in overestimation
of malnutrition; therefore, similar values of cardiorespiratory fitness in this study between
the two nutritional groups may be due to problems in classification. To rule out the
occurrence of classification bias, AN COVAs by sex were done using three categories of
nutritional status: normal, mild and severe undernutrition, adjusting for chronological age
and sexual maturation. Once again, there were no differences among the nutritional
groups. These results suggest that the 12-minute run may not be a sufficiently powerful
cardiorespiratory fitness test to discriminate differences in fitness levels in normally and
undernourished children, but it is also possible that there is actually no differences in
cardiorespiratory fitness among these individuals.

Fatness is related to poor performance in activities where the body mass has to be
displaced. Negative correlations have been reported for the sum of skinfolds and
cardiorespiratory fitness, ranging from —0.156 to —0.273 (Pate et al., 1989) and —0.07 to ~—
0.40 (Malina et al., 1995a). In this study, the correlations between 26SF and the 12-
minute run were —0.34 and —0.21 for males and females. These values are well within the
range of those in the cited studies. '

There are no racial differences in cardiorespiratory fitness in either sex. The
results contrast to the findings of Farrell et al. (1987) and Pivamik et a1. (1995), where
Non-White adult and adolescent women had lower cardiorespiratory fitness as measured
by a maximal treadmill test. The non-significant difference may be due to the different
methods (treadmill vs 12-minute run) for assessing cardiorespiratory fitness. As
Armstrong and Welsman (1994) have noted, 12-minute run performance is influenced by

motivation and by the subject’s judgment of pace. Therefore, a maximal effort is less

108

 

 

likely to occur compared to the treadmill test, resulting in no significant differences being
detected. Nevertheless, Non-Whites girls would be expected to have lower
cardiorespiratory fitness due to their somewhat higher body weight and Z6SF. It should
be noted that Pardo and Black individuals, with various degrees of admixture, form the
Non-White group in this study. It may be that comparisons to Black American groups are
not appropriate.

Comparisons to Brazilian studies on growth variables and the 12-minute run are
shown in Table 88. The 14 year old boys and 15 year old girls have consistently higher
values for stature and body weight than the subjects from the other Brazilian studies. The.
14 year old girls are taller but not consistently heavier than their peers. However, similar
results are observed for the BMI and the 12-minute run. Compared to US. percentiles for
the 12-minute run (AAHPERD, 1980), this and all the other Brazilian samples have
lower values for the 50th percentile, indicating that, on average, Brazilian youth have
lower cardiorespiratory fitness than American youth.

The relationship between physical activity and cardiorespiratory fitness is only
low to moderate. The proportion of variance in cardiorespiratory fitness due to physical
activity in this study, 4%, is similar to the 3%reported by Morrow and Freedson (1994).
Boys at risk for low fitness are more likely to be older (OR=3.1) than those not at risk.
All females and 91% of boys at risk are sedentary individuals. On the other hand. the
majority of sedentary boys and girls are not at risk of low cardiorespiratory fitness since
risk was defined as belonging to the lowest quintile of the 12 minute run. The use of a
criterion-referenced standard classification would probably increase the percentage of

sedentary individuals at risk for low fitness.

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The status of CHD risk factors

Low prevalences of CHI) risk factors in the Niteroi sample are observed among
boys and girls. The exceptions are low cardiorespiratory fitness and inactivity, due
probably to the definitions used in this study. It is good to have low prevalence of CHD
risk factors among adolescents, especially because risk factors tend to track into
adulthood. On the other hand, the low prevalence may have generated low statistical
power in the analyses, limiting the results of this study, as well as the generalization of

the findings.

Cigarette smoking

The prevalence of cigarette smoking in the sample is low in both sexes, 2.7% and
1.8% for males and females, respectively. Most subjects smoked an average of 2-5
cigarettes a day. The prevalences are comparable to those from the 1989 PNSN (Ministry
of Health, 1993) for children 10-14 years and 15-19 years of age, although the older boys
in this study have somewhat higher values (Table 89). Proportionally, smoking is more
frequent among boys, which contrasts the PNSN data, in which females have slightly
higher prevalences for the adolescent as well as for young adult years.

Duncan et al. (1993a) found prevalences of smoking of 20% in females and
almost 40% in males 15-19 years old in a representative sample surveyed in the middle of
the 19805 in Porto Alegre, South Brazil. The 1989 Brazilian survey of high school
students (9th to 11th grades) in urban areas showed prevalences ranging from 44% to
75% (PAHO, 1994). The use of different criteria of smoking may result in differences in

110

prevalence and poses limitations in comparing studies. The same criteria were used in the
present study and in Duncan et al. (1993a). The lower prevalences in this study may be
due to the fact that older subjects were also included in the Porto Alegre group. It is also
plausible that the effects of anti-smoking campaigns, more widely used in this decade, are
taking effect.

Once the smoking habit is established during early and middle adolescence. it
seems fortunate to have low prevalences in late adolescent subjects, especially because
smoking status in adolescence tends to track into adulthood (Hunter et al., 1986; Twisk et
al., 1995). Nevertheless, since smoking status was self-reported in this study, there is also
a possibility of underreporting.

In American adolescents of both sexes, the prevalence of smoking is higher in
Whites than in Blacks (Greenlund et al., 1996; CDC, 1998). There is no significant effect
of race in this study. but different patterns were observed between males and females.
Female smokers were more likely to be White, while the opposite was noted in males. A
low power analysis due to low prevalence may explain the non-significant difference. No
stratified data by race were found for the Brazilian population; therefore, no conclusions
can be made about these findings.

A higher prevalence of smoking of lower educational and/or low SES background
is described for adult Americans (CDC, 1993, Hunter et al., 1995) as well as for
Brazilians (Duncan et al., 1993b). These SES effects are also observed in Scottish,
American, Finnish adolescents (Glendinning et al., 1994; Lowry et al., 1996; Leino et al..
1996) and in this study, especially in males. Glendinning et al. (1994) described a

positive relationship between parental and subject smoking status. Therefore, the higher

111

prevalence of smoking among low SES adolescents, who are classified by parental SES

status, may be associated with a higher prevalence among their parents.

Blood Pressure

The expected age— and sex-specific differences are observed in this study. SBP
and DBP increased with age in both sexes, although only SBP in girls is significantly
different. Boys tend to have higher blood pressures than girls. Age explains 2% of the
variance in SBP while sex explains 5%. However, these variables do not explain any
portion of the variance in DBP.

Comparisons to data for Brazilian children from The Rio de Janeiro Study
(Pozzan et al., 1997) are shown in Table 90. Sex— and age-specific median SBP are
somewhat lower in this study, while the reverse is observed for DBP. Comparisons to
US. data compiled by Rosner ,et al. (1993) and to the data from the study of
Cardiovascular Risk in Young F inns (Dahl et al., 1985) show that SBP mean in this study
are similar to the US. data in both sexes, while Finnish boys and girls tend to have higher
SBP values. For DBP, males and females in this study tend to have slightly higher values.
especially the males. As for SBP, Finnish adolescents show higher DBP values (Table
91).

The reasons for higher DBP values in this study in comparison to Brazilian and
international data are not known. Body weight of 14 year old boys are similar to US.
data, while older males tended to be lighter than Americans and Finnish boys. Brazilian
boys tended to be shorter than American and Finnish counterparts. Females also tend to

be shorter and lighter than other females in the US. and Finland. Both boys and girls in

112

this study have similar values of the BMI to the Finnish children. Therefore, differences
in body size can not explain the higher values in DBP.

Rosner et al. (1993) reported that levels of blood pressure are affected by various
factors including type of sphygmomanometer, number of observations used and the
criteria for DBP (fourth or fifih Korotkoff sound, K4 or K5). All studies applied the K5
criterion. There are differences for number of observations and sphygmomanometers
used among the studies. In the US. data, Rosner et al. (1993) used the first measured
value; The Rio de Janeiro Study used the last of three measures; while the Young Finns
Study used the mean of three; and this study used the mean of two measures. An aneroid
sphygmomanometer was used in this study while the others used either a mercury or a
random-zero. The extent of the effects of these factors in the blood pressure findings is
unknown. Nevertheless, the differences are small.

Body weight is associated with blood pressure; overweight/obese individuals are
at higher risk for developing hypertension. The Niteroi data show that the effect of
weight on blood pressure accounted for 3% of variance in SBP and 6% in DBP.
ANCOVA adjusting for chronological age, sexual maturation, and race showed that
overweight females had higher blood pressures than those at normal weight; however.
these differences were not observed in males (Table 92). In this regard, Krieger and
Landsberg (1995) suggested that body weight has a greater effect on blood pressure in
females than in males.

In adults, there is a positive relationship between upper body fat distribution and
hypertension (Krieger and Landsberg, 1995); however, Siervogel and Baumgartner

(1988) suggested that the relationship may be less important in adolescents than in adults.

ANCOVA controlling for chronological age, sexual maturation and race showed that
boys at risk for elevated blood pressure have higher values of WHR than those not at risk
(p <0.01, Table 93). Similar values of both variables were observed in females.

Racial differences in blood pressure are already observed in early childhood.
Black individuals tend to have higher values for both SBP and DBP, but differences are
not always significant (Alpert and Fox, 1993). The data from this study show no
significant effects of race on blood pressures, after controlling for chronological age.
sexual maturation and body weight (Table 94). However, individuals at risk for elevated
blood pressure are more likely to be Non-whites in all age— and sex groups (Tables 54, 55
and S6).

SES is also related to blood pressures. Mean blood pressures and the prevalence
of hypertension are greater among those in lower SES classes by income, occupation or
education classification (Winkleby et al., 1990, 1992). Differences in mean BPs were not
observed in this study, i.e., SES did not explain any proportion of variance in both SBP
and DBP. For those at risk for elevated blood pressure, 70% of males and 100% of
females were in the lower SES group. Although these findings are in agreement with the
literature, it must be noted that the majority of the sample is from a lower SES
background. This may result in insufficient statistical power to either find differences in
mean blood pressures or in the distribution of elevated blood pressures among these
groups.

There are no significant effects of cardiorespiratory fitness on blood pressure in
this sample. Alpert and Wilmore (1994) suggested that no statistical significance is

achieved in most studies controlling for a measure of size or fatness. Després et al. (1990)

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also suggested that the association between blood pressure and fitness is weaker during
childhood and adolescence. Blood pressure was adjusted for body weight in this study.
All relationships between blood pressures and the 12-minute run are positive except for
DBP in females. Other studies presented either positive relationships for SBP and DBP in
both sexes (Gillum, 1989) or negative correlation coefficients (Sallis et al.. 1988) for
children 14-15 years and 11.8 years of age, respectively.

Comparing individuals at risk for low fitness to those not at risk, no differences
are observed for either blood pressure values afier controlling for chronological age,
sexual maturation and body weight. Kwee and Wilmore (1990) found significant
differences between 8 to 15 year old boys in the lower and those at the higher quartile of
cardiorespiratory fitness assessed by a treadmill maximal test. There were no differences
among blood pressures in children 10-17 years who passed or failed the Physical Best
criterion for cardiorespiratory fitness (Bazzano et al., 1992). Similar blood pressures were
reported for 14 year old adolescents in categories of high and low running performance
(Bergstrdm et al., 1997).

There is an inverse association between physical activity and blood pressure, but
physical activity level only explains variance in DBP (3%). Sallis et al. (1988) also
observed a negative relationship. except for SBP in boys; however, the correlation
coefficients were not significant. ‘

Among individuals at risk for inactivity, only DBP in males was significantly
higher than in the active counterparts, although sedentary youngsters tended to show

slightly higher blood pressures. The lack of significant differences may be due to the

small number of active adolescents in bOth sex groups, resulting in low power in the

115

analysis. Similar differences in blood pressure by activity levels were reported by

Strazzullo et al. (1988) for children 11.3 years of age.

Overweight

Overweight was determined using the BMI as the criterion. As discussed for
undernutrition. the use of an international reference potentially results in misclassification
of subjects. In the case of overweight, the misclassification appears to move in the
direction of underestimation. Anjos et al. (1998) showed that Brazilian values for the
95th percentile of the BMI were consistently lower than those for the US. population.
and that the differences were greater among males.

The prevalence of overweight is presented in Table 95 using both cutoff points. In
females, the prevalence is somewhat higher with the Brazilian cutoff A discrepancy in
prevalence is clearly observed among males, where prevalences by the Brazilian criterion
are at least two times greater than by the US. criterion in both age groups. A possible
reason for the discrepancy may be that some individuals classified as overweight by the

Brazilian criterion have more muscle mass (which would increase the BMI) than those
Classified by both criteria. ANCOVA, controlling for chronological age and sexual
maturation. showed that individuals classified as overweight only by the Brazilian cutoff
tended to have larger estimated mid-upper arm muscle area, but the difference was not

Significant (38.0 cm2 versus 36.4 cm2 for those classified by both criteria).

By the Brazilian criterion, the prevalence of overweight was higher in boys than
in girls. This suggests that the use of this criterion may be inappropriate for boys since it

is well established that overweight is more prevalent in females. This is also true for the

116

Brazilian population. The highest prevalence occurs in 14 year old boys, who are
consistently taller and heavier than other Brazilian samples, and also have higher BMI
values. In addition, median body weight and the BMI in these boys are similar to those of
American boys. so that an overestimation of overweight by the Brazilian criterion is
expected for this age. Comparisons of the triceps and subscapular skinfolds indicate
similar conclusions.

On the other hand, in 15 year old boys the values for. body weight, stature. BMI,
and the triceps and subscapular skinfolds are within values reported in otherBrazilian
studies and somewhat lower than NCHS values. However, the prevalence by the
Brazilian criterion is twice the prevalence by the US. criterion. The fact that there are no
comparative data for Brazilian adolescents limits the discussion and emphasizes the use
of US. reference data.

A greater increase in the prevalence of overweight among adult Brazilian males
than in females is observed in a comparison of 1989 PNSN and 1996/97 SSL (IBGE On-
line, 1998). The Brazilian BMI percentiles used to classify overweight individuals comes
from the PNSN data; therefore, it is reasonable to believe that the BMI distribution is

becoming more skewed towards higher values. causing an overestimation of prevalence
in a comparison of recent data to PNSN percentiles. In females, the prevalence by each
criteria does not differ significantly because there are smaller differences between the
Brazilian and US. BMI distributions.
There is no standard definition of overweight and obesity. In this study, the
Classification of individuals at risk overweight and overweight was based on the cutoff

values suggested by Himes and Dietz (1994) and referred by the WHO (1995). However,

117

different cutoff points and distributions are applied in the literature, making it difficult to
compare data on overweight/obesity.

In both sexes, overweight youngsters are more likely to be Non-Whites than
Whites. Data from the Bogalusa Heart Study only show systematic racial variation in
girls, where Blacks have a higher BMI (Webber et al., 1995). On the contrary, Malina
(1993a) showed that, by the BMI criterion, White 12-17 year old boys and girls have a
higher prevalence of obesity. Malina (1993a) also suggests that estimates of prevalence
due to ethnicity vary according to the obesity indicator used.

As higher prevalences are observed with increases in income quintiles among
Brazilian adults, it was expected that more middle class adolescents would be
overweight. In this study. the prevalence of overweight is higher in middle class females
and similar values are observed between lower middle and lower classes. Overweight is
more prevalent in lower class males, although the difference between prevalence in
middle and lower classes is small.

There is a positive association between obesity and hours of TV viewing. The
major link seems to be the decreased physical activity while watching TV. However. TV
viewing is also associated with increased food intake, by increasing the opportunity for

snacking especially high-caloric foods. A survey of nutrition behavior among middle-

c: lass secondary students (1 1th grade) in Manaus, North Brazil, showed that 23% of

Subjects often had snacks while watching TV (Doyle and Feldman, 1997). Thus, the
increase in food intake during TV watching may be not a major concern among Brazilian

adolescents.

A significant dose-response effect of time of TV viewing and prevalence of

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obesity was observed only in females in the present study, where each additional hour of
TV viewing increased the risk for overweight to 20% (OR=1.20, adjusted for
chronological age, sexual maturation and race). The OR for males was 1.13, and not
significant (C .I.= 0.85 - 1.52). Table 96 shows the prevalence of overweight by hours of
TV viewing. Obesity is found in subjects who watch TV for at least 3 hs/day; however,
the linear trend is significant only for females.

The mean hours of TV viewing was 4.4 hs/day and 4.9 hs/day for males and
females, respectively, in the present study. These means are slightly higher than those
reported for the Brazilian children, especially in girls. Among 13 years old youth, females
watch 3.9 hs/day while males of the same age watch 3.6 hs/day (Andrade et al., 1996).
Boys from the lower class spend, on average, 4.2 hs/day watching TV; peers from the
upper middleclass watch TV for 3.6 hs/day (Matsudo et al., 1996).

Boys in the present study have similar values to boys of lower class, which is
expected since the majority of the participants come from lower class households. The
higher values observed in females may be explained by the difference in social class
between the girls from this study and those from Andrade et al. (1996). The latter sample

came from a middle class background. The variation in TV viewing between social
Classes is probably due to the different access to leisure options. Adolescents from lower
Class may have fewer options due to limited financial resources and rely more often on
TV viewing for leisure. At least one TV set is found in almost all households in the
Southeast region of Brazil (IBGE On-line, 1998).
Brazilian children watch more TV than American and Canadian youth. The

estimated mean time for TV watching in the US. is between 2 and 3 hs/day (Pate et al.,

119

1994). However, Gortmaker (1996) reported the mean value of TV viewing as 4.8 hs/day,
in a sample of children 10 to 15 years of age. Katzmarzyk and Malina (1998) reported
mean times of 2.0hs/day and 3.6 hs/day for 10-14 year old youth engaged and not
engaged in sports, respectively (p<0.001). Data from the Quebec Family Study (1979-
1981) showed that Canadian boys and girls spent 2.3 hs/day and 2.4 hs/day, respectively.
watching TV (Kartzmarzyk et al., 1998).

Part of this higher values in TV watching is explained by the fact that Brazilian
children spend less time in school, since the school day has 5 hours. Thus, children have
more fiee time, which may be spent in front of a TV set if they have fewer options for
leisure activities.

Physical inactivity is negatively related to overweight. In this sample of Brazilian
adolescents, Spearman correlations show are —0. 1 6 for boys and —0.1 1 for girls.
However, with the PAQ-C protocol, females and almost all males were classified as
inactive. Even a small change in the activity category from very inactive to inactive.
shows a decrease in the prevalence of obesity. Comparing physical activity, overweight
and TV viewing, 76.9% of the very inactive and 66.7% of the inactive overweight girls
watch 5+ hs/day. On the other hand. only in the very inactive overweight group, the

majority (66.7%) of boys spend 5+hs/days watching TV. Long periods of TV watching

potentially take time away from physical activity, but it is possible that children are
spending much of their spare time in front of TV due to lack of choices in activity
pursuits. Preliminary analysis of sport participation of adolescents in this study shows

that many subjects did not participate in sports due to lack of opportunity.

Fat distribution

There are sex differences for both the TER and WHR in this study sample; boys
have higher ratios than girls, indicating proportionally more subcutaneous fat on the
trunk. The TER and WI-R were similar by age group or by sexual maturity status
(pubertal vs post pubertal, controlling for age). Beunen et al. (1994) showed that late
maturing 13 to 17 year old boys have lower TER; however, this study did not find
differences between sexual maturity groups. These contrasting findings may be due to the
assessment of maturation. Beunen et al. (1994) used age at peak height velocity as the
maturity indicator, while the present study used self-reported secondary sex
characteristics. The possible bias due to misclassification has been previously discussed.

Hammer et al. (1991) indicated a lower WHR in more mature girls aged 10 to 15
years (age was a covariate in the analyses of covariance). Self-assessment of sexual
maturation was also the indicator of maturity status. The same trend was not observed in
the girls from this study. The variability in sexual maturation was smaller in this study

because these girls were older.

Racial differences in fat distribution have been reported. Black adolescent males
and females tend to have more fat centrally distributed (Malina, 1996a). In this study.
Non-White boys also had higher values of TER (p<0.05). No differences were noted
among Non-White girls. Similar values of TER and WHR were found when data were

St ratified by social class, in both boys and girls.

Cardiorespiratory fitness and level of physical activity showed no significant

relationship to fat distribution in this study. In males at risk for low fitness, there were no

differences for TER and WHR. In contrast, females at risk had higher TER (p<0.05) and
121

WHR (p=0.08). Similarly, females at risk for inactivity also had higher TER and WHR
than those not at risk. There were no differences in fat distribution between active and
inactive boys.

Deutsch et al. (1985) described a relationship between obesity and fat distribution.
Obese adolescents and obese young adults had more centrally distributed fat than non-
obese individuals. This study showed the same trends. Youngsters at risk for overweight
had higher TER and WHR, after controlling for chronological age and sexual maturation
(Table 97). Only the difference in WHR in boys was significant, although it approaches
significance in girls.

Data on fat distribution in Brazilian adolescents are based only on the
subscapular-triceps skinfold ratio (STR Guedes, 1993). Adolescents in this study have
consistently higher STR values. The triceps skinfolds are similar in males from the two
samples; however, the subscapular skinfolds are lower in Guedes’ boys. yielding a lower
STR (Table 98). Girls in the present. study tend to have lower values for the triceps and
higher values for the subscapular skinfold. These differences result in a higher STR than

those for the females in Guedes (1993). Additionally, boys and girls in this study are
taller and heavier, and have a higher BMI than their peers (Table 88). This indicated that
the adolescents in the present study have more overall fat and more centrally distributed

fat as well.

Physical activity

Recent Brazilian data on physical activity patterns of adults show that 26% of

males and almost 13% of females are regularly active (IGBE On-line, 1998). When using

122

the PAQ-C scores to classify individuals as active or sedentary, the majority of
adolescents in this study are in the sedentary category.

The prevalence of inactivity is 85% for boys and 84% for girls. The high values
are somewhat corroborated by the long periods of TV watching in the sample. Although
there are no significant differences between individuals at various levels of physical
activity, TV viewing time tends to be higher in inactive subjects, especially in girls. One
of the reasons for a lack of differences between sedentary and active boys and girls is that
only few subjects are classified as moderately active with maximum PAQ-C scores of 3 .8
and 3.7, respectively, forboys and girls.

Another factor that contributes to inactivity is part-time working, since it
decreases the free time after school. Twelve percent of boys and 6% of girls are part-time
workers, and almost all of them are classified as sedentary (93.8% and 100%,
respectively). Differences in physical activity scores are found only in females, where
part-time workers have lower scores compared to those not in the work force (1.6 vs 2.0,

p<0.01).

The PAQ-C does not account for labor activity. Therefore, part-time workers may

be actually getting their physical activity inthe workplace. More boys tend to work in
manual activities. such as janitor and car washer, than in non-manual occupations (sales
Counter clerk, for example). The reverse is observed for females; they more frequently
Work in non-manual job positions. Consequently, individuals who are engaged in manual
Occupations have higher amounts of physical activity in their daily life due to job related
Chores, even though they are classified a s inactive by the PAQ—C.

Social factors also influence the physical activity patterns of adolescents.

Anderssen and Wold (1992) noted that the pattern of physical activity of 7th grade boys
and girls was associated with the activity level of their best friend and parents, and with
direct support (encouragement) and help (in organizing activity) from parents.
Considering the high prevalence of inactivity observed in Brazil, it may be expected that
the influence of peers and parents is in the direction of low levels of activity. On the other
hand. direct support and help from parents tend to be sex-specific, with boys being more
motivated to be active (Anderrsen and Wold, 1992). This may also be true in Brazilian
society.

The physical environment also influences physical activity. Environments rich in
resources for physical activity may facilitate activity, while those with poor resources
pose a barrier to regular activity (Sallis et al., 1997). Unsafe neighborhoods have negative
effects on physical activity, especially in children and adolescents. Unfortunately, large
cities in Brazil are known for high crime rates. Although Niteroi is a medium size city. it
also has problems with safety. Therefore, not many children are permitted to play on the
streets, which contributes to a decrease in the opportunities for physical activity.

The availability of convenient facilities also has an effect on physical activity.
As most of subjects in this study come from lower SES families, they depend on facilities
and programs that have no or low cost. Niteroi has few such facilities and programs. and
the available programs do not serve as many children as desired. Trost et al. (1997)
showed that community sports are the strongest predictor of vigorous and moderate to
vigorous physical activity in 11-14 year old rural boys and girls of South Carolina, US.
Bungum and Vincent (1997) found similar results in 14-18 year old females. As stated

previously. lack of opportunity to participate in sports was reported as a barrier to activity

124

by many students in the sample.

This high prevalence of inactivity is of concern, especially because levels of
physical activity tend to decrease through the adolescent years. Consequently, these
sedentary children maybe prone to become sedentary adults. A higher probability for
inactivity among these children when they are adults is also expected due to the marginal
tracking of physical activity from adolescence to adulthood (Malina, 1993b; Raitakari et
al., 1994; Mechelen and Kemper, 1995). However, data are lacking on events and factors
that influence physical activity and inactivity in Brazil.

The mean values for the physical activity score (PAQ-C) are lower in both sexes
in the present study than those reported by Kowalski et al. (1997) in youth from 8 to 14
years. As physical activity decreases with age during adolescence, this may explain the
lower scores obserVed in this study, since the subjects are older than the sample upon
which the PAQ-C was validated.

The lower scores may also indicate that the questionnaire is not sensitive to
cultural differences related to the perception of physical activity in Brazilian youth. Few
other studies on physical activity have been carried out among Brazilian youngsters. and
this study was the first to use this instrument in Brazil. The evidence, therefore, may be
not sufficient to conclude that the PAQ-C is not a valid method of assessing physical

activity habits in Brazilian adolescents. In fact, the correlation between the self-reported
activity rating6 and the PAQ-C score was 0.40 (p<0.001), which is slightly lower than the

coefficient of 0.63 reported by Kowalski et al. (1997). Table 100 shows the PAQ-C

 

6 Question 12 in the modified questionnaire (Appendix K), which is not included in the

Computation of the score.
125

scores by categories of the self-reported activity rating. For both sexes, PAQ-C scores
increase as the subjects classify themselves in to higher activity categories than peers.
Racial differences in physical activity levels are reported in the literature (Aaron
at al., 1993; CDC, 1993, 1995), where Non-Whites are likely to be less active. There are
no differences in physical activity scores among White and Non-White boys and girls in
this study. Non-White females are more likely to be sedentary (OR=3.7. C .I.= 0.96-
1397) than Whites, and the reverse is apparent in males (ORxon.\vhhe‘-‘ 0.81. CI. =0.29-
2.20).
Individuals from the low SES tend to be less active than those from higher
classes, when either education or income is used as the indicator of SES (CDC, 1993;
Stephens and Caspersen, 1994). Occupation of the head of household was the indicator of
SES in this study. Significant differences in physical activity levels are found only in
females, although the lower SES boys tend to have lower scores. In both sexes, physical
activity is greater in subjects from lower middle class (Table 101). A possible explanation
is that boys and girls from this class show the lowest proportion of part-time workers.
Since working subjects have lower activity scores, having fewer workers in the group
may increase the mean score. However, the trend of more part-time workers among
middle class females. is contradictory to Brazilian data showing that the number of part-
time working students decreases as the household income increases (Table 7). The results
of the regression analyses show that lower middle class explains about 3% of the variance
on the physical activity score (Table 51).
Soccer, the most popular sport in Brazil, is the most performed physical activity

among males in both age groups. Among girls, dance and walking for exercise are the on

126

the top of the rank, although the first and second places in rank are inverted for each age
group. Walking for exercise is the most performed activity by older females. Bicycling
and jogging or running for exercise are other popular activities in both sex and age
groups. Except for soccer, these activities are non-competitive.

Other studies have shown that jogging/running and bicycling are consistently
popular activities among adolescent males, while walkinO, jogging/running and dance
(ballroom) are the favorite activities among females (Shephard, 1986; Ross et al., 1985;
Huang, 1994). In the Bogalusa Heart Study (Myers et al., 1996), the most performed
activities among White and Black boys and girls enrolled in 5th to 8th grade were
walking, walk/run, and running. Calisthenics and bicycling were also frequent activities.

Four Brazilian studies on physical activity patterns among adolescents in $50
Paulo were available at the time of this study (Andrade et al., 1996, 1998; Matsudo et al.,
1996, 1998). These studies used a self-reported activity rating, and time spent and pace of
walking as the measures of physical activity. Unfortunately, results of theses studies are
presented only as abstracts so that the specifics are not available. The results were
presented only as relative distributions.

Although PAQ-C’ has a question on self-activity rating and the $50 Paulo studies
also used a self-activity rating, there is no guarantee that the questions were formulated in
t he same way. and thus evaluate the same perception. This may explain the differences in
t he relative distribution of the categories of self-activity rating observed in the present
study and in the $50 Paulo studies.

Another limitation is that the authors use broad age ranges: 1 1-18 years. and 10-

1 5 years, while the other two studies provide only a mean age. Nevertheless, it was

127

assumed that the age ranges are also wider than in the present study. These age groups
contrast the limited range, 14-15 years, in this study, thus comparisons are difficult since
the results are not age-stratified. The relevance of using age-stratified physical activity
levels is rather obvious, i.e., activity levels, on average, decrease with increasing age with
the steepest decline occurring in the adolescent years.

There is only one similarity between the data from this study and those from 550
Paulo. A lower percentage of low7 SES girls classified themselves as less active than
peers than those in the high SES (31.8% vs 53.4%, respectively). The same trend is
observed among males in this study, 15.6% vs 21.4% for the low and high SES.
respectively. However, data arevnot available for high SES boys from Sao Paulo. Whether
or not the perception of activity levels differs between low and high SES individuals

remains to be investigated.

Clustering risk factors

Because the chance for disease occurrence increases as the number of risk factors
for CHD are clustered within an individual, it is important to identify those who are at
greater risk for a clustering of risk factors. In the present study, the clustering of risks was
assessed by the distribution of clustered risks. The analysis was based on the distribution
of five risk factors: inactivity, overweight, smoking, elevated blood pressure. and low
fitness. Few subjects were free of risks (12% and 5%, in males and females,

respectively). Most subjects had a single risk factor.

7 Low SES is defined here as a combination of lower middle class and lower class.

128

Inactivity was the most frequent risk factor. either individually or clustered.
However, caution may be used since the physical activity score may be not represent the
actual level of physical activity of Brazilian adolescents. Inactivity was the only risk for
59% of males and 66% of females. Inactivity and low fitness clustered in 1 1% of males
and 16% of females. An overweight and inactivity cluster was observed in approximately
5% of the subjects. As the number of risk variables increased, the number of subjects
decreased. No subject had all five risk factors.

Adolescents with clustered risk factors may be at higher risk to cluster risk factors
in adulthood. In the Bogalusa Heart Study, Myers et al. (1995) showed that young adults
(19-32 years) who clustered high values of blood pressures, ponderal index (a measure of
body fatness) and total cholesterol, had higher age-, sex-, and race-specific z-scores for
these risk factors in childhood (5-14 years). The likelihood of clustering risk factors in
adulthood increases with the increasing number of risk factors during childhood, even
though the presence of any single risk in childhood increases the probability of clustering
risk factors in adulthood by six times compared to those who have no risk in early ages.

Other data from the Bogalusa Heart Study (Bao et al., 1994) also showed that
individuals at the highest tertile of the ponderal index had higher tracking of the multiple
risk index (SBP, insulin and total cholesterol/ HDL ratio) than those at the lowest tertiles
in a 8-year follow-up. The baseline ages ranged from 5-17 years. Also, the tracking of
multiple risks was stronger in children with clustered risk factors than those with a single
risk.

Increased body weight, BMI and triceps skinfold thickness in childhood (8-18

years) are associated with the early development of coronary artery calcification in young

129

adult males. Higher SBP is an additional predictor of coronary artery calcification in
females (Mahoney et al.. 1996). Therefore, the analyses of the clustering of risk factors in
young individuals may help to identify of those with higher probability for future CHD
event

It has been reported that Black girls and White boys aged 5 to 14 years in the
Bogalusa Heart Study had the greatest proportions of clustering of blood pressures.
overweight and cholesterol (Webber et al., 1979). Even though the subjects in this study
are older than those from the Bogalusa Heart Study and no data on cholesterol levels
were taken, similar findings are suggested in females. Fewer Non-white girls had none or
one risk factor, while for 2 and 3 clustered risks they showed higher proportions. Non-
White and White males, on the other hand, had similar values for a number of clustered
risk factors, except for the 4 clustered risks, where all of the boys were Non-White.

Analyses by SES showed no clear pattern.

CHAPTER 6

SUMMARY AND CONCLUSIONS

Summary

This study investigated the status of selected coronary heart disease (C I-ID) risk
factors and components of health-related physical fitness in Brazilian adolescents. taking
into account race, SES and sexual maturation. Data were collected from October to
December 1997 and from March to May 1998 in public schools in Niteroi, Rio de
Janeiro, Brazil. All 8th grade students aged 14-15 years were eligible to participate. A
total of 329 subjects (126 males and; 203 females) were included in the study. Data on
each subject included sociodemographic characteristics (sex, chronological age.
socioeconomic status, and race), anthropometry (stature, sitting height, body mass.
skinfolds at the biceps, triceps, subscapular, abdominal, medial calf, and suprailiac sites.
circumferences of the arm, waist, and hip), (selected risk factors (blood pressures,
cigarette smoking, physical activity’level, overweight, low fitness, subcutaneous fat
distribution), components of health-related physical fitness (cardiorespiratory fitness and
body composition), and self-assessed sexual maturation. The following variables were
derived: estimated leg length, sitting height/stature ratio, body mass index (BMI), sum of
six skinfolds, upper arm muscle area, waist-hip circumference ratio, and trunk-extremity
skinfold ratio.

The analyses included descriptive statistics (means, standard deviations.

Comparison of means. frequencies) for each variable; multiple regression analyses to
explore the relationships of sociodemographic variables with CHD risk factors and

131

health-related physical fitness; the relationship of cardiorespiratory fitness and physical
activity level with CHD risk factors; and Chi square test of independence to determine
clustering of risk factors among adolescents. Data were analyzed with the SPSS package.
Technical errors of measurement for anthropometry were similar to those reported
for national surveys in the United States (Malina. 1995). Measurement errors for blood
pressures was lower than in the Bogalusa Heart Study (Foster et al., 1980). Intraclass
correlations for anthropometry and blood pressures were comparable to those from the
Quebec Family Study (Bouchard, 1985) and the Bogalusa Heart Study (Foster et al.,
1980).
Within the limitations of the study, the major findings of the study are as follows:
Race has no relationship with cardiorespiratory fitness, level of physical activity.
or CHD risk factors. However. Non-Whites tend to. show a higher prevalence of elevated
BPs and overweight. and also more centrally distributed subcutaneous fat. Non-White
males are more likely to be smokers, while the reverse occurs among females. Non-White
females tend to cluster risk factors more frequently than Whites.
SES does not explain any part of the variance in cardiorespiratory fitness or in
CHD risk factors. Lower middle class status explained 3% of the variance in physical
activity level. Lower SES individuals are at higher risk for smoking, elevated BPs and
inactivity. Middle class girls and lower class boys are more likely to be overweight.
Individuals in lower SES tend to be more at risk than subjects from other SES classes.
Cardiorespiratory fitness has no relationship with BPs. BMI, TER, or WHR.
Cardiorespiratory fitness is inversely related to £6SF, correlations varying from —0.34 to

—O-21 in both sexes. Physical activity has a slight positive relationship with

cardiorespiratory fitness, explaining 4% of the variance. Boys at risk for low fitness tend
to have lower physical activity scores, higher BPS, and significantly higher 26SF.
Significantly lower physical activity scores, higher BMI, £681: and TER are
characteristic of females at risk. BPs also tend to be higher among these girls. Boys and
girls at risk for low fitness tend to show an adverse risk profile.

Physical activity does no explain any part of the variance in Z6SF, BMI. TER.
WHR, or SBP. Physical activity is inversely related to DBP (R2=0.03) and cardiovascular

fitness (R2= 0.04). Inactive boys tend to Show higher BP values, while girls tend to have

lower CF and higher 26SF, TER WHR and BPs. As with individuals at risk for low

fitness, inactive subjects also tend to have an adverse risk profile.

Risk factors tend to cluster among adolescents; only 12% of boys and 5% of girls
were free of risk. The majority had a single risk factor: inactivity, and all clusters
i ncluded inactivity. No one clustered with 5 risks, and no female had 4 clustered risks.
The prevalence of clustered risks was 24.8% for boys and 26.8% for girls. Non-White
females had higher proportions of clustered risks, suggesting that they may be at higher

ri sk for CHD. There were no differences among boys. Clustering of risk factors was

S i milar among individuals of different SES.
Conclusions

The following conclusions can be derived from the present study:

1 The sociodemographic variables race and social class have no relationship with

cardiorespiratory fitness or the selected CHD risk factors. The exception is physical

u

l" .5

activity level, in which lower middle class status explains 3% of variance.
Non—White individuals and those from the lower class are more likely to present a
higher risk profile.

Cardiorespiratory fitness is only related to Z6SF and level of physical activity.

Boys and girls at risk for low fitness tend to show an overall adverse risk profile.

In addition to the relationship with cardiorespiratory fitness, physical activity level is
inversely related to DBP.

Individuals at risk for inactivity tend to show an overall adverse risk profile.

Risk factors cluster within approximately 25% of the adolescents. Inactivity is present
in all clusters. Few individuals cluster 3 and 4 risk variables. Non-White females tend
to cluster risk factors more frequently than Whites, suggesting that they may be at

higher risk for CHD.

Recommendations

This study has generated data on physical activity levels and other risk factors for

CHD among Brazilian adolescents. Comparative data for other samples of Brazilian
adolescents are presently limited. Therefore, it is difficult to make specific comparisons
and to respond to questions indicated by the results. Further research addressing these

questions is required and should include large sample sizes of randomly selected

i ndividuals.

This study has demonstrated a high prevalence of inactivity among Brazilian

adolescents. It may be due to the questionnaire used to estimate physical activity in this

St udy. Therefore, more research should look at the validity of questionnaires to assess

134

physical activity for international comparisons. The development of a more culturally
sensitive instrument is indicated. Research should also focus on physical activity levels

from childhood through adolescence. The present study is limited to individuals 14-15

years of age.

The determinants of inactivity in Brazilian adolescents should also be
investigated. Since most of the individuals in this study come from lower SES families, it
is possible that the high inactivity is due to limited access to activity programs and
facilities, among other factors. Therefore, it seems appropriate to increase the
opportunities for activity for adolescents in the community, and the school the logical
place to provide such programs in addition to the regular physical education. However.
many public schools have limited resources to run activity programs, including physical
education.

The results also show that activities such as soccer for boys and dance and
Walking for girls are among the most popular among this sample of Brazilian adolescents.
These activities should be included in activity programs for this age group to enhance the
adherence to the programs. The increase in regular physical activity is important for all
adOlescents, especially those having risk factors for CHD. Although the prevalence of
ri 3k factors is low in this age group, those at a higher risk may benefit the most.
This study also demonstrated that Non-White and lower SES adolescents are
ITjot‘e likely to present risk factors, individually or clustered. Further studies should
i IlV’estigate risk factors in more detail, including blood lipids, family aggregation of risks.
and tracking of the risk variables. Research also needs to examine access to preventive

ea 1 th servrces. especrally for those at higher risk.

Considering that being Non-White and from lower SES increase the probability of
having CHD risk factors and, in the case of females, also having increased probability for
clustering risk variables, these individuals may have a special need for health care
services. The lower class depends almost exclusively on public health facilities. In the
few last decades, investments in health have been decreasing in Brazil. Therefore, these
individuals may be not getting the required health attention, thus firrther increasing risk
for CHD, especially because individuals at risk during adolescence tend to track into
adulthood. A better understanding of risk factors in youth will help provide a sound basis
for the design of strategies for disease prevention and health promotion in youth. and for

the more effective use of preventive health resources and services in the developing

country that Brazil still is.

 

TABLES

Table 1. Relative distribution (%) of stages of genital and breast development in Brazilian

males and females, respectively, by age group from 10 to 19 years.

 

 

 

 

 

Stage
Age (years) 1 2 3 4 5
Genital development
10 - 38.2 58.2 3.6 0 0
11 - 16.1 69.9 11.8 1.6 0.5
12 ~- 6.7 56.5 26.7 8.8 1.3
13 - 3.0 30.2 33.7 26.0 7.1
14 — 1.2 12.4 26.8 37.3 22.3
15 ~- 0.3 4.9 9.7 36.1 49.1
16 ~ 0 0.5 3.2 30 66.3
17 2 0 0.3 1 1 15.2 83 4
l8 - 0 0 03 62 935
19 — 0 0 0 1.8 982
Breast develOpment

10 2 47.2 34.4 13.5 3.7 1.2
’1 ~ 14.7 31.1 31.6 14.2 8.4
12 x 5.2 15.7 33.1 24.0 22.0
13 ~ 1.1 5.0 16.2 32.5 45.2
’4 x 0.3 0.5 4.6 25.7 68.9
’5 x 0 0 2.7 16.8 80.5
16 x 0.3 0 0.3 16.5 82.9
1 7 ~ 0 0 0 12.6 82.9
18 x 0 0 0 13.2 86.8
i 0 0 7.2 92.8

 

A d apted from Colli (1988).

Table 2. Relative distribution (%) of stages of pubic hair stages in Brazilian males and

females, respectively, by age group from 10 to 19 years.

 

 

 

 

Stage
Age(years) I 2 3 4 5 6
Males
10 -— 78.8 20.6 0.6 0 0 0
11 ~ 61.3 31.7 4.8 2.2 0 0
1.2 + 38.1 43.0 13.0 5.7 0 '
13 2 16.7 35.4 20.9 23.8 2.0 1.2
14 - 7.0 17.6 21.6 40.6 8.2 4.9
15 - 2.6 4.6 11.0 45 14.6 22.2
16 ~. 0.3 0.5 2.6 39.5 15.1 41.9
17 —~ 0.3 0.3 0.8 26.8 13.0 58.8
18 .2 o 0 0 17.1 12.9 70.0
19 4 o 0 0 14.5 7.1 78.4
males
10 ~ ‘ 49.1 36.8 9.8 3.7 0.6 0
11 ~ 8.4 34.2 25.3 11.6 10.5 0
12 ~ 7.7 22.6 21.2 24.5 23.4 0.6
13 ~ 1.5 7.2 12.7 24.6 52.9 1.1
1" ~ 0.3 1.5 2.8 16.7 74.6 4.1
15 ~— 0.3 1.3 1.6 8.9 82.4 5.4
’6 ~ 0 0 0 3.5 85.6 10.8
17 ~ 0 0 0 3.5 85.6 10.8
’8 ~— 0 0 0 1.3 84.1 14.6
& 0 0 0 2.1 784 19.5

 

Adapted from C0111 (1988)-

Table 3. Correlations between self and physician assessments of secondary characteristics

in several Brazilian adolescents by sex.

 

 

Females Males
Comparison of trials Breast Pubic Hair Genitals Pubic Hair
Self trial 1 vs Ilia/2 0.97 0.97 0.93 0.99
Physician, trial 1 vs trial 2 0.98 0.97 0.76 0.89
S'elfvsphysician, trial 1 0.89 0.92 0.80 0.84
.S'elf vs physician, trial 2 0.93 0.92 0.79 0.84

 

.Adapted from Matsudo and MatsUdo (1994).

Table 4. Percentile values for high normal blood pressure and hypertension by age and

sex during adolescence.

 

 

 

\
High Normal Blood Pressure (mm Hg) Significant Hypertension (mm Hg)
(>90th percentile) (> 95th percentile)
\ Age Systolic Diastolic Systolic Diastolic
1 3~15 years 130—135 80-85 136-143 86-91
\

 

Adapted from the Second Task Force on Blood Pressure in Children (1987).

140

Table 5. Relative composition (%) of the Brazilian population by race and geographic

 

 

 

region.
Geographic Regions
Race North* Northeast Southeast South Central West
PVhites 27.4 29.4 66.0 82.8 47.4
Blacks 1.5 5.3 5.9 3.0 3.2
Pardos 71.0 65.3 27.1 13.6 49.2
Asians 0.1 0.0 1.0 0.6 0.2

 

Data for the North region are from urban areas; no data are available for rural areas.
Adapted from IBGE (1993).

Table 6. Mean wages by occupational category and sex in Brazil, 1981-1990.

 

 

 

Sector (MMW)“
Occupation Public Private Sci/Lemp/owd
\

Males
PrOfessional and managerial 17.3 16.3 20.5
Technicians and related support 5.7 6.7 9.8
I\/IE‘=lnual 3.1 3.2 4.9

Females
Pro fessional and managerial 9.1 9.0 4.5
TQChnicians and related support 4.1 4.1 5.1
wal ' 1.6 1.9 2.1

 

‘Adapted from Oliveira (1993) a MMW: minimum monthly wage (U331 10)
141

Table 7. Work and study status of Brazilian adolescents by household income.

 

Age groups

 

1 0-1-l years (%)

15-1 7 years (%)

 

 

Household Only Working Only Only Working Only
Income studying and working studying and working
(Mb/1W“) studying studying
< 0.25 59.6 14.7 12.3 26.5 14.9 39.8
0.25-0.50 63.8 11.7 11.7 28.6 15.2 39.8
0.50-1.00 73.2 10.3 8.0 30.7 20.3 34.8
1.00-2. 00 83.1 8.3 4.0 36.8 24.8 28.4
>200 91.5 5.1 1.2 56.1 23.8 14.5
\

 

Adapted from IPEA (1996). " MMW (monthly minimum wage).

Tab] e 8. Number and percentage of adolescents 10-19 years of age in the five

adm l nistrative regions of Niteroi.

N

Adolescent Population

 

 

Ad ministrative region N %
"VOW? 30176 40
15“" 38309 3 8
06“”? Beaches 9072 12
Bay beaches 65 10 9
Pmdoti 17a 915 1

\\

Adapted fiom pMN/CECITEC (1996a).

142

Table 9. Estimated population of 14 and 15 year old youth in Niteroi in 1997.

 

Sex

 

 

Age (years) Males Females

N %a N %“
14 + 3965 0.8 4073 0.9
15+ 4145 0.9 4157 0.9

\

a Pro1301‘tional to the entire population. Data from DATASUS On-line (1998).

Table 1 0. Number of schools and subjects according to the administrative areas in

Nitero'i.

\

Number of schools

Number of subjects (%)

 

 

Ad In inistrative region Eligible Included Males Females
\
North 15 5 44 (34.9) 52 (25.6)
Ocean Beaches 2 2 13 ( 16.2) 33 (16.2)
Bag-Beaches 11 7 58 (46.1) 99 (48.8)
Pendotiba 4 2 11 (8.7) 19 (94)
\

 

Table 1 1 - .Age- and sex-specific cut-off points of the body mass index (BMI) for the risk

of overwei ght and for overweight as recommended by Himes and Dietz (1994).

 

 

 

BMI (kg/m2)
At risk. for overweight Ovem'eight
Age (years) Males Females Males Females
14 ~ 23 24 27 28
15 2 24 24 28 29

 

144

Table 12. Intra-observer technical errors of measurement for anthropometric variables.

 

Measurement Error

 

 

V'ariables T his study HES-Ill a HHANES b Quebec [Quit/[1'
Study ‘
Height (cm) 0.6 0.5 1.3 0.3
Weight (kg) 0.7 0.4
Sitting height (cm) 0. 5 0. 5 0.6

( ircumferences (cm)

Relaxed arr); 0 . 4 0.4 0. 7

Waist 1.2 1.3

Hip 0.9 1.2

Skinfolds (I771?!)

Triceps 0.6 0.8 1.6 1.2
Biceps 0, 7 l
Calf 1.1 1.4 2.7 1.9
Subscapu/ar 0,6 I ,8 2.2 l .4
Suprailiac: 1.1 1.9 3.3 2.1
Abdominal 0.7 2
.W’ (4’6 -5'/\’info/ds 2 . 7 4-9

jxtremigv ’runlr ratio (mm mm) 0.1 0~ 1
/"—_\

 

" U 5- Health Examination Survey, Cycle III (Malina, 1995a). b US. Hispanic Health and
Hptrltion Examination Survey (Malina, 1995a). ° Bouchard (1985).

145

Table 13. I ntra-observer measurement errors for blood pressures.

 

Measurement Error (mm Hg)

 

 

Blood Pressure (mm Hg) This study Bogalusa Heart Studyu
Systolic 3 . 7 5 .0
Diastolic 3 . 5 5 .0

 

“ Measurement errors in the 1976-1977 sample (Foster et al.. 1980).

146

 

Table 14. Int raclass correlation coefficients for the anthropometric and blood pressure

 

 

 

variables.
Intraclass R
Variables This study Quebec Family Bogalusa Heart
Study“ Studyh
Height (cm /' 1 1 1
Weight flrg) 1 1 l
Sitting height (cm) 1
Circumfere) zces (cm)
Relaxed arm 0.96
Waist . 0. 76
Hip l

Skinfolds (mm)

.4 bdomiual 0 . 9 7 0.98
Biceps 0.89 0.96
Calf 1 0.94
Subscapula;~ 0,99 0.98
Suprailiac 0.96 0.96
Triceps 097 0.98 0.99
Sum of 6 skinfolds 0.98 0.99
f /’ ””k'ex ’ r entity ratio (mm. mm) 1 0-97
Elam, Pressure (mm Hg)
’s‘ystolic 0.83 0.84
fliastoli c 0.59 0.79

 

a» Bouchard (1985) 1‘ Foster et al. (1980)

147

Table 15. Age distribution and descriptive statistics for age of the study sample by sex.

 

 

 

 

Sex
Male Female
Age (years) N Mean SD Median N Mean SD Median
14+ 66 14.6 0.3 14.6 100 14.6 0.3 14.6
15~ 60 15.4 0.3 15.4 103 15.4 0.3 15.4
Total 126 15.0 0.5 14.9 203 15.0 0.5 15.0

 

 

Table 16. C omposition of the study sample by race within each sex.

 

 

Age group (years)

 

 

 

 

1 4 — 15 — Total
Race N % N % N %
Males
White 35 53 27 45 62 49
Mixed 316101: (Pardo) 18 27 31 52 49 39
8"” 12 18 2 3 14 1 1
Missing data 1 2 0 0 1 1
Tom” 66 60 126
Females
1”” ’ e 49 49 46 45 95 47
Wired Black (Pardo) 43 43 43 42 86 42
fliack 8 8 14 14 22 1 1
"2" Total 100 103 203

 

F

148

Table 17. Di stribution of socioeconomic status categories in the study sample by age

group and sex.

 

Age group (years)

 

 

 

1 -l _ 15 *
Socioeconomic status ' N % N 9'9
Males
Middle Class:8 7 10.6 7 l 1.7
Tower middle class" 13 19.7 13 21 .7
Lower flags“ 3 6 54.5 29 48.3
N0! in work forcead 4 6.1 5 8 . 3
Missing or not well defined 6 9. 1 6 10
Total 66 6O
Females

Middle class“ 6 6 9 8. 7
Lower middle classb 12 12 13 12.6
Lower classr‘ 59 59 61 59.2
Not in work forcead 8 8 4 3 .9
MISS”? 0" not well defined 15 15 16 [5'5
T 0/0/ 1 00 1 03

A

 

.1 owners and shareholders of medium and small enterprises, executive, administrative.
managet\al and professional occupations. btechnicians and related support, sales, and
adm‘FfiStTative support (including clerical) occupations. claborers, craft, operatives and

$9Wice Workers. d retired or unemployed head of household, housewife under pension.

149

Table 18. Heads of the household of the study sample by age group and sex.

 

 

 

Age group (years)

 

 

14* 15+
Head of household N % N %
Males
Male pare)” 59 89 55 92
Female parent 7 1 1 5 8
Total 66 60
Females
Male pare) It 86 86 90 87
Female par‘enf 14 14 13 13
T oial 100 1 03

 

 

150

Table 19. Distribution of subjects by social class (socioeconomic status) by race by sex.

 

 

 

Race
White Non- White
Social Class N (%) N (%)
Males"l
Middle 7 (13.2) 5 (9.1)
Lower Middle 14 ( 26.4) 1 1 (20.0)
Lower 32 (60.4) 30 (70.9)
Femalesb
Middle ’ 9 (11.7) 6 (6.5)
Lower Middle 17 (22.1) 7 (7.5)
Lower 51 (66.2) 80 (86.0)

 

“ Chi-square= 1.35, df=2,p>0.05
b Chi-square= 9.75', df=2, p <0.01

151

Table 20. Characteristics of the households of the study sample by age and sex.

 

Age group (years)

 

 

14* 15 '
N Mean SD N Mean SD
Males
Number of children per couple, n 66 2.5 1.4 60 2.8 1.7
Members in the household, 11 66 4.3 1.3 60 4.5 1.7
Workers in the household, 11 66 1.7 0.8 60 1.7 1.0
Females
Number of children per couple, n 100 3.0 1.7 103 2.8 1.4
Members in the household, 11 100 4.3 1.2 103 4.6 2.5
Workers in the household, 11 99 1.7 0.8 103 1.8 0.9

 

Table 21. Working status of the subjects by age group and sex.

 

Age group (years)

 

 

15 -
Working status N % N %
Males
Not in work force 59 89.4 50 83.3
Part—time workers 7 10.6 9 15.0
Missing data 0 0 1 1 .7
Total 66 60
Females
Not in workforce 93 93 .0 98 95. 1
Part-time workers 7 7.0. 5 4. 9
Total 100 103

 

152

 

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156

Table 26. Distribution (%) of self-assessed stages of secondary sex characteristics in

males and females by age.

 

 

 

Stage
Age groups (years) 1 2 3 4 5
Males
Genital
14- O 3.] 4.6 63.1 29.2
15- O O 6.7 63.3 30.0
Pubic hair
14* o 3.1 3.1 18.5 75.4
15+ 0‘ 0 3.3 20.0 76.7
Females
Breast
14: O 3.0 22.0 61.6 13.1
15— O O 23.5 58.8 17.6
Pubic hair
14~ O 3.1 4.1 38.] 54.6
15* 0 O 2.9 35.0 62.1

 

Table 27. Menarcheal status and descriptive statistics for age at menarche by age group

and in the total sample.

 

Menarcheal status

 

 

Yes No Age at menarche (years)

Age (years) N % N % N b Mean SD Median
14* a 93 93.0 6 6.0 91 12.4 1.4 12.6
15* 102 99.0 1 1.0 100 12.4 1.4 12.6
Total 195 96.5 7 3.5 191 12.4 1.4 12.6

 

. . . . h .
" missmg data for one subject. several SUbJQCtS could not recall when menarche was

attained.

158

Table 28. Distribution (%) of self-assessed stages of secondary sex characteristics in

males and females by race, age groups combined.

 

 

 

Stage
Race 1 2 3 4 5
Males
Genitaf1
White 0 3.2 3.2 64.5 29.0
Non- White 0 0 . 8. 1 62.9 29.0

Pubic hairb

White 0 3.3 4.9 18.0 73.8
Non- White 0 0 1.6 20.6 77.8
Females
Breasf
White 0 2.1 25.5 61.7 10.6
Non-White 0 0.9 20.6 58.9 19.6
Pubic haird
White 0 0 4.2 43.2 52.6
Non-White 0 2.8 2.8 29.9 64. 5

 

a Chi-square=3.30, df=3, ns b Chi—square=3.30, df=4, ns
° Chi-square=3.70. df=3, ns d Chi-square=7.30, df=4, ns

159

Table 29. Menarcheal status and descriptive statistics for age at menarche by race. age

groups combined.

 

Race

Menarcheal status

 

Age at menarche (years)

Mean SD Median

 

Whitea
Non- White

Yes No
N % N %
89 94.7 5 5.3
106 98.1 2 1.9

12.5 1.3 12.6
12.3 1.4 12.6

a missing data for one subject. b several subjects could not recall when menarche was

attained.

160

Table 30. Distance covered in the 12-minute run by age and sex, and by race and sex, age

groups combined.

 

Distance run in 12 minutes (meters)

 

 

Males Females
N Mean SDa N Mean SD
Age Gears)
[4 -~ 63 2248.0 286.2 90 1743.8 251.6
15- 55 2252.2 418.9 92 1737.6 272.9
Total sample 118 2249.9 352.7 182 1740.7 261.9
Raceb
White 59 2245.4 46.5 83 1733.4 29.0
Non-White 57 2265.7 47.3 97 1750.7 26.8

 

3 SE for the race analyses.

maturation.

b ANCOVA adjusting for chronological age and sexual

161

Table 31. Quintile cutoff points of cardiorespiratory fitness by age group and sex.

 

Quintiles cutoff points of the12 minute run (m)

 

Sex 1 2 3 4 5
14+ years
Males 1574.0 1999.2 2196 2357.4 2515.4
Females 1233.0 1510.6 1665 1783.8 1935.0
15+ years
Males 1549.0 1886.2 2128.6 2379.0 2503.4
Females 1154.0 1526.4 1665.0 1774.6 1963.8

 

162

 

 

 

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164

Table 34. Cigarette smoking status by age group and sex.

 

Cigarette smoking status

 

 

N0 Y e5 (average number of cigarette per day)
<1 2 to 5 6 to 10
Age (years) N % N % N % N %
Males

14+ 66 100.0 0 0 0 O 0 0

15+3 55 91.7 1 1.7 2 3.3 1 1.7
Females

14+ 3 97 97.0 0 O 1 1.0 O 0

15+ " 96.1 1 1.0 1 1.0 1 1.0

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166

Table 36. Physical activity status in the past 7 days by age group and sex.

 

 

 

Age (years)
1 4 - 15 2
Category (score) N % N %
Males
Very inactive ( 1) 22 33 .3 21 3 5 .O
Inactive (2) 35 53.0 29 48.3
Moderate/y active (3 ) 9 13 . 6 10 16. 7
Females

Very inactive ( 1) 55 55.0 51 49.5
Inactive ( 2) 3 9 3 9.0 46 44.7
Moderately active ( 3 ) 6 6.0- 6 5.8

 

Table 37. Prevalence of the risk for inactivity by age group and sex.

 

Age (years)

 

 

144 15*
Risk for inactivitya N % N %
Males
Not at risk 9 13.6 10 16.7
At risk 57 86.4 50 83.3
Females
Not at risk 6 6 6 5.8
At risk 94 94 97 94.2

_

 

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at I’lSk when physrcal act1v1ty score <3.

167

Table 38. Relative frequency of different physical activities in 14 year old males.

 

 

Frequency of physical activity (%) T0181
percentage
Activity No 1-2 3-4 5-6 7 ‘2 9f Rank
parUCIpants
Soccer 24.2 22.7 24.2 9.1 19.7 75.8 I
Jogging or running 3 7.9 28.8 12. 1 9. l 12. l 62. 1 2
Walkingfor exercise 40.9 12.1 19.7 6.1 21.2 59.1 3
Bicycling 40.9 12.1 19.7 6.1 21.2 59.1 3
Tag 48.5 34.8 4.5 4.5 7.6 51.5 5
Skipping 68.2 10.6 7.6 1.5 12.1 31.8 6
Volleyball 80.3 15.2 1.5 1.5 1.5 19.7 7
Dance 81.8 9.1 7.6 1.5 18.2 8
Swimming 84.8 10.6 3.0 1.5 15.2 9
Basketball 87.9 9.1 3.0 12.1 10
Skateboarding 89.4 4.5 1.5 4.5 10.6 1 1
Creative Playground 89.4 7.6 3.0 10. 6 l l
Dodgeba/ly 95.5 3.0 1.5 4.5 13
Aerobics 97.0 3.0 3.0 14
Other activities

(‘apoeiraa 0 4.0 0 l .0 5.0
Handball 0 0 1 .O 0 1 .0
Roller-skating 1.0 1.0 O 0 2.0
Gymnastics 0 1.0 0 1.0 2.0

 

" capoeira is a martial art originated in Brazil.

168

Table 39. Relative frequency of different physical activities in 15 year old males.

 

Frequency of physical activity (%)

Total
percentage of

 

Activity No 1-2 times 3-4 times 5-6 times 7 2+ times participants Rank
Soccer 25.0 31.7 11.7 8.3 23.3 75.0 1
Jogging or running 38.3 25.0 20.0 5.0 1 1.7 61.7 2
Walking for exercise 45.0 23.3 8.3 3.3 20.0 3&0 3
Bicycling 46.7 18.3 15.0 3.3 16.7 53.3 4
Tag 60.0 23.3 3.3 5.0 8.3 40.0 5
Skipping 66.7 6.7 5.0 3.3 18.3 33.3 6
Dance 73.3 15.0 1.7 3.3 6.7 26.7 7
l-'()lleyball 75.0 15.0 3.3 3.3 3.3 25.0 8
Swimming 81.7 15.0 1.7 1.7 18.3 9
Skateboarding 83.3 1 1.7 1.7 3.3 16.7 10
Basketball 86.7 11.7 1.7 13.3 11
Creative Playground 88.3 6.7 3.3 1.7 l 1.7 12
Aerobics 91.7 3.3 3.3 1.7 8.3 13
Dodgeball 93.3 3.3 1.7 1.7 6.7 H
Other activities
Capoeira 0.9 1.8 0 0 2.7
Judo 0 0.9 0 0 0.9

 

“ capoeira is a martial art originated in Brazil.

169

Table 40. Relative frequency of different physical activities in 14 year old females.

 

Frequency of physical activity (%)

Total
percentage of

 

Activity No 1-2 times 3-4 times 5-6 times 7 2 times participants Rank
Dance 36.0 30.0 11.0 7.0 16.0 64.0 1
Walkingfor exercise 38.0 30.0 8.0 4.0 20.0 62.0 2
Bicycling 52.0 21.0 10.0 3.0 14.0 48.0 3
Jogging or running 63 .0 19.0 7.0 4.0 7.0 37.0 4
Volleyball 72.0 15.0 8.0 2.0 3.0 28.0 5
Soccer 77.0 15.0 3.0 1.0 4.0 23.0 6
Dodgeball 77.0 15.0 3.0 2.0 3.0 23.0 6
Tag 80.0 12.0 4.0 2.0 2.0 20.0 8
Aerobics 82.0 12.0 5.0 1.0 0.0 18.0 9
Skipping 85.0 7.0 3.0 2.0 3.0 15.0 10
Swimming 88.0 9.0 1.0 2.0 12.0 11
Creative Playground 90.0 8.0 1.0 1.0 10.0 12
Basketball 94.0 4. 0 2 .0 6.0 13
Skateboarding 96.0 1 .0 l .O 2.0 4. 0 l 4

Other activities
Capoeira‘iI . 0 0 0 1.5 1.5
Judo 0 0 1.5 0 1.5
Jujitsu 0 1.5 1.5 0 3.0
Surfing 3 .0 0 0 0 3.0
F utvolley b 1.5 0 0 0 1.5
Roller-skating 0 1 .5 0 1 .5 3 .0

 

° capoeira is a martial art originated in Brazil. b futvolley is a game where basic volleyball

rules are applied however the ball is touched by the foot, trunk or head as in soccer.

170

Table 41. Relative frequency of different physical activities in 15 year old females.

 

 

Frequency of physical activity (%) Total
percentage of
Activity No 1-2 3-4 5-6 7 «- ParllC'PamS Rank
Walking for exercise 28.2 35 .0 10. 7 5.8 20.4 71 .8 1
Dance 43.7 25.2 9.7 5.8 15.5 56.3 2
Bicycling 52.4 17.5 15.5 6.8 7.8 47.6 3
Jogging or running 65 .0 24.3 5.8 1 .9 2.9 3 5.0 4
Soccer 71.8 17.5 5.8 1.9 2.9 28.2 5
Volleyball 80.6 10.7 3.9 1.0 3.9 19.4 6
Skipping 83.5 6.8 2.9 1.9 4.9 16.5 7
Dodgeball 85.4 10.7 2.9 1.0 14.6 8
Tag 87.4 6.8 3.9 1.0 1.0 12.6 9
Aerobics 87.4 6.8 1.9 1.0 2.9 12.6 9
Swimming 87.4 7.8 1.9 1.0 1.9 12.6 9
Creative Playground 92.2 4.9 1.9 1.0 7. 8 12
Basketball 95.1 3.9 1.0 4.9 13
Skateboarding 99. 0 l .0 1 .0 l 4
Other activities

Handball 1 .6 0 0 0 1 .6
(‘apoeiraa 0 0 1 .6 0 1 .6

Judo 1.6 0 0 0 l .6

Jujitsu 1.6 1.6 0 O 3 2

Table tennis ' 1.6 0 0 O 1 .6

Tennis 0 l .6 0 O l .6

 

"’ capoeira is a martial art originated in Brazil.

171

Table 42. Prevalence of the risk for overweight and of obesity by age group and sex.

 

 

 

BMI Criteria 3
Age (years) Risk N %
Males
14+
overweight b 5 7. 6
15+
overweight 3 5.0
obesity C l 1.7
Females
14+
orient-[eight 9 9. 0
obesity ' 1 i 1.0
15 -
overweight 7 6.8
obesity 2 1.9

 

" According to Himes and Dietz (1994). b BMI > 85th percentile for age.
‘ BMI > 95th percentile for age.

172

Table 43. Prevalence of undernutrition be age group and sex.

 

 

 

Age (years)
14+ 15*
Nutritional status a N % N %
Males
Normal nutrition 5 3 80. 3 42 70. 0
Mild malnutritibn b 9 13 .6 1 1 18.3
Severe malnutrition C 4 6.1 7 1 1.7
Females
Normal nutrition 79 79.0 89 86.4
Mild malnutrition 14 14.0 1 1 10.7

Severe malnutrition 7 _ 7.0 3 2.9

 

3 Criteria by Frisancho (1990). b Mild malnutrition- is defined as UAMA for height
between 5th and 15th percentile. c Severe malnutrition- is defined as UAMA for height

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181

Table 52. Mean and relative distribution (%) of sociodemographic variables by risk status

of cigarette smoking by sex.

 

Risk for cigarette smoking3

 

 

Variables No risk A! risk [2
Males
N (%) 105 (97.3) 3 (2.7)
Age (years)b
Mean 14.94 15.46 0.02‘
SD 0.49 0.34
Race (N (%)) ‘
White 52 (98.1) 1 (1.9) ns
Non-White 52 (96.3) 2 (3.7)
Socioeconomic status (%)
Middle class 11 (100.0) 0 ns
Lower middle class 25 (100.0) 0
Lower class 69 (95.8) 3 (4.2)
Females

N(%) 165 (98.2) 3 (1.8)
Age Gears)
Mean 15.02 14.91 ns
SD 0.53 0.66
Race (N( %))
White 74 (97.4) 2 (2.6) ns
Non-White 91 (98.9) 1 (1.1)
Socioeconomic status (%)
Middle class 14 (93.3 1 (6.7) ns
Lower middle class 23 (100.0) 0
Lower class 128 (98.5) 2 (1.5)

 

“ Not at risk = non-smoker, at risk=smokers b ANOVA

*p <0.05 . ns=not significant.

182

Table 53. Mean and relative distribution (%) of sociodemographic variables by risk status

of overweight by sex.

 

Risk for overweight8

 

 

Variables No risk At risk p ORb C I ‘
Males
N(%) 100 (91.7) 9(8.3)
Age (i'eai‘syd
Mean 14.97 1489 ns
SD 0.49 0.53
Race (N(%))
White 51 (96.2) 2 (3.8) ns
Non-White 48 (87.3) 7 (12.7)
Socioeconomic status (%)
Middle class 11 (91.7) 1 (8.3) ns
Lower middle class 24 (96.0) 1 (4.0)
Lower class 65 (90.3) 7 (9.7)
Females
N/%) 153 (90.0) 17 (10.0)
Age (years)
Mean 15.01 15.06 ns
SD 0.51 0.66
Race (1W %))
White 74 (96.1) 2 (3.9) 0.02"
Non-White 79 (84.9) 14 (15.1) 4.37 1.20-1582

Socioeconomic status (%)

Middle class
Lower middle class
Lower class

13 (86.7) 2 (13.3
22 (91.7) 2 (8.3)
118 (90.1) 13 (9.9)

ns

 

“ Not at risk: below the 85th percentile of BMI for sex and age. b Logistic regression
adjusting for chronological age and sexual maturation. “ 95% confidence interval.

d ANOVA

*p <0.05 , ns=not significant.

183

Table 54. Mean and relative distribution (%) of sociodemographic variables by risk status

of systolic blood pressure by sex.

 

Risk for elevated systolic blood pressure3

 

 

Variables No risk A! risk p
Males
N(%) 106 (97.2) 3 (2.7)
Age (years)b
Mean 14.95 15.1] ns
SD 0.49 0.68
Race (N ( %))
White 52 (98.1) 1 (1.9) ns
Non-White 53 (96.4) 2 (3.6)
Socioeconomic status (%)
Middle class 12 (100.0) 0 ns
Lower middle class 23 (92.0) 2 (8.0)
Lower class 71 (98.6) 1 (1.4)
Females

N (%) 168 (98.8) 2 (1.2)
Age (years)
Mean 15.01 15.46 ns
SD 0.53 064
Race (N. (%))
White 77 (100.0) 0 ns
Non-White 91 (97.8) 2 (2.2)
Socioeconomic slams (%)
Middle class 15 (100.0) 0 ns
Lower middle class 24 (100.0) 0
Lower class 129 (98.5) 2 (1 .5)

 

"Not at risk= SBP value below 85th percentile for age. b ANOVA ns=not significant.

184

Table 55. Mean and relative distribution (%) of sociodemographic variables by risk status

of diastolic blood pressure by sex.

 

Risk for elevated diastolic blood pressure8

 

 

Variables No risk A I risk p
Males
N (%) 102 (93.6) 7 (6.4)
Age (years) b
Mean 14.94 15.22 ns
SD 0.49 0.53
Race (N ( %/)
White 53 (100.0) 0 0.01‘
Non-White 48 (87.3) 7 (12.7)
Socioeconomic slams (%)
Middle class 12 (100.0) 0 ns
Lower middle class 24 (96.0) 1 (40)
Lower class 66 (91 .7) 6 (8.3)
Females

N (%) 168 (98.8) 2 (1.2)
Age (years)
Mean 15.01 15.92 0.02‘
SD 0.52 0.01
Race (N ( %))
White 77 (100.0) 0 ns
Non-White 91 (97.8) 2 (2.2)
Socioeconomic status (%)
Middle class 15 (100.0) 0 ns
Lower middle class 24 (100.0) 0
Lower class 129 (98.5) 2 (1.5)

 

“ not at risk= DBP value below 85th percentile for age. b ANOVA

“p <0.05 , ns=not significant.

185

Table 56. Logistic regression analysis of elevated diastolic blood pressure by

sociodemographic variables.

 

 

Variables Odds Ratio3 Confidence Intervalh
Age (years) 11.9 2.1-67.9
Sex (male) 6.9 1.6-30.9
Race (Non-ll’hiie) 9.9 1.2-83.9
Socioeconomic status
Middle class 0.5 004-56
Lower middle class 04 004-3 .9

 

“ Logistic regression adjusting for sexual maturation.

186

Table 57. Mean and relative distribution (%) of sociodemographic variables by risk status

of inactivity by sex.

 

Risk for inactivity“

 

 

Variables No risk At risk 1) ORb C1 °
Males
N (%) 18 (16.5) 91(83.5)
Age (years)d
Mean 15.07 14.94 ns
SD 0.45 0.50
Race ( N%/)"
White 8 (15.1) 45 (84.9) ns
Non-White 9 (16.4) 46 (83.6)
Socioeconomic slams (%)c
Middle class 2 (16.7) 10 (83.3 ns
Lower middle class 4 (16.0) 21 (84.0)
Lower class 12 (16.7) 60 (83.3)
Females
N (%) 9 (5.3) 161 (94.7)
Age (years)
Mean 14.91 15.02 ns
SD 0.48 0.53
Race ( N(%))
White 7 (9.1) 70 (90.9) 0.04 ‘"
Non-White 2 (2.2) 91 (97.8)
Socioeconomic status (%)
Middle class 0 15 (1000) < 0.01 *
Lower middle class 5 (20.8) 19 (79.2) 0.11 0 03-043
Lower class 4 (3.1) 127 (96.9)

 

" Not at risk= physical activity score below 3. b Logistic regression adjusting for
chronological age and sexual maturation ° 95% Confidence interval. cChi-square

*p <0.05 , ns=not significant

187

Table 58. Mean and relative distribution (%) of sociodemographic variables by risk status

of low cardiorespiratory fitness by sex.

 

Risk for low fitnessa

 

 

Variables No risk At risk p ORb C I ‘
N (%) 86 (78.9) 23 (21.1)
Age (years)d
Mean 14.90 15.16 0.02‘ 310 l 15-8.35
SD 0.46 0.56
Race 6N" ( %))c
White 39 (73.6) 14 (26.4) ns
Non-White 46 (83.6) 9 (16.4)
Socioeconomic status (%)e
Middle class 11 (91.7) 1 (8.3) ns
Lower middle class 22 (88.0) 3 (12.0)
Lower class 53 (73.6) 19 (26.4)

Females

N (%) 136 (80.0) 34 (20.0)
Age (years)
Mean 15.03 14.94 ns
SD 0.51 0.57
Race (N ( %))
White 63 (81.8) 14 (18.2) ns
Non-White 73 (78.5) 20 (21.5)
Socioeconomic status (%)
Middle class 13(86.7) 2 (13.3 ns
Lower middle class 20(83.3 4 (16.7)

Lower class

103 (78.6) 28 (21.4)

 

" Not at risk= 12 minute run above the first quintile. b Logistic regression adjusting for

chronological age and sexual maturation. ° 95% Confidence interval.

" C hi-square

' p <0.05 , ns=not significant.

188

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Table 66. Comparisons of risk factors in males at risk and not at risk of low

cardiorespiratory fitness“.

 

Low fitnessb

 

 

Variables No risk At risk F p

N (%) 85 (79.4) 22 (21.6)

Age (years)

Mean 14.9 15.2 5.49 0.02"

SD 0.5 0.6

12 minute run (m)

Mean 2387.7 1732.5 106.50 <0.001‘
. SD 28.2 56.3

Physical activity score

Mean 2.3 2.1 2.43 ns

SD 0.1 0.1

Body mass index (kg. m”)

Mean 19.6 20.5 1.66 ns

SD 0.3 0.6

Sum of six skinfolds (mm)

Mean 55.0 71.3 7.68 0.01"

SD 2.6 5.2

I 'runk-extremio' ratio (mm - mm )

Mean 1.33 1.26 1.00 ns

SD 0.0 . 0.06

Waist-hip ratio (cm cm)

Mean 80.83 79.82 1.18 ns

SD 0.41 0.82

Systolic blood pressure (mm Hg)

Mean 109.9 112.2 1.08 ns

SD 1.0 1.9

Diastolic blood pressure (mm H g)

Mean 66.7 68.0 0.43 ns

SD 0.9 1.8

Smoking status (%)

No 85 (80) 19 (17) 12.06c <0.001‘

Yes 0.0 3 (3)

 

“ not at risk= 12 minute run above the first quintile. b ANCOVA adjusting for
chronological age and sexual maturation. cChi—square value. p < 0.05 , ns=not
significant.

196

Table 67. Comparisons of risk factors in females at risk and not at risk of low
cardiorespiratory fitness“.

 

Low fitnessb

 

 

Variables No risk At risk F p
N (%) 136 (80.0) 34 (20.0)
Age (years)
Mean 15.0 14.9 0.80 ns
SD 0.5 0.6
12 minute run (m)
Mean 1819.5 1399.4 120.41 <0.001‘
SD 17.1 34.2
Physical activity score
Mean 2.1 1.8 7.97 0.01“
SD 0.0 0.1
Body mass index (kg m")
Mean 19.9 21.3 6.18 0.01‘
SD 0.2 0.5
Sum of six skinfolds (mm) _
Mean 84.9 97.0 6.68 0.01“
SD 2.1 4.2
Trunk-extremity ratio (mm mm)
Mean 1.20 1.29 - 4.68 0.03"
SD 0.02 0.04
Waist-hip ratio(cm cm)
Mean 73.67 74.99 3.20 ns
SD 0.33 0.66
systolic blood pressure (mm Hg)
Mean 105.9 107.0 0.53 ns
SD 0.7 1.3 '
Diastolic blood pressure (mm Hg)
Mean 65.7 66.9 1.08 ns
SD 0.5 1.1
Smoking status (%)
No 131 (78) 34(20) 0.78 ‘ ns
Yes 3(2) 0

 

“ not at risk= 12 minute run above the first quintile. b ANCOVA adjusting for
chronological age and sexual maturation. CChi-square value. p < 0.05 . ns=not
significant.

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204

Table 75. Comparisons of risk factors in males at risk and not at risk for inactivity“.

 

 

 

Inactivityb

Variables Not at risk At risk F p
N(%) 18 (16.8) 89 (83.2)
Age (years)
Mean 15.1 14.9 1.16 ns
SD 0.5 0.5
12 minute rim (m)
Mean 2333.2 2236.8 1.02 ns
SE 86.9 38.9
Body mass index (kg'm3) ‘
Mean 19.2 19.9 0.77 ns
SE 0.7 0.3
Sum of six skinfolds (mm)
Mean 57.2 58.6 0.05 ns
SE 5.9 2.6
Trunk-extremity ratio (mm mm)
Mean 1.34 1.31 0.01 ns
SE 0.07 0.03
Waist-hip ratio (cm cm)
Mean 80.5 80.7 0.03 ns
SE 0.9 ' 0.4
Systolic blood pressure (mm H g)
Mean 108.3 110.8 1.18 ns
SE 2.1 0.9
Diaslolic blood pressure (mm H g)
Mean 63.1 67.7 4.82 0.03“
SE 1.9 0.9
Smoking slams (%)c
No 18 (16.7) 86 (80.3) 0.15d ns
Yes 0 3 (2.8)

 

“ physical activity score <3 b ANCOVA adjusting for chronological age and sexual
maturation. ‘ missing data for 1 subject. d Chi-square value

* p<0.05, ns=not significant.

a

Table 76. Comparisons of risk factors in females at risk and not at risk for inactivity .

 

 

 

Inactivityb

Variables Not at risk At risk F p
Nl%) 9 (5.3) 161(947)
Age (years)
Mean 14.9 15.0 0.37 ns
SD 0.5 0. 5
12 minute mm (m)
Mean 1855.0 1728.7 1.99 ns
SE 86.9 20. 5
Body mass index (kg m')
Mean 19.5 20.2 0.65 ns
SE 0.9 0.2
Sum of six skinfolds (mm)
Mean 75.3 88.0 2.24 ns
SE 8.3 1.9
Trunk-extremity ratio/mm mm)
Mean 1.16 1.2" 0.41 ns
SE 0.10 0.02
ll'aisI-hip ratio (cm cm)
Mean 72.5 74.0 1.39 ns
SE 1.3 0.3
Systolic blood pressure (mm Hg)
Mean 104.2 106.2 0.59 ns
SE 2.6 0.6
Diastolic blood pressure (mm Hg)
Mean 62.9 66.1 2.01 ns
SE 2.1 0.5
Smoking slams (%)C
No 8 (4.8) 157 (93.5) 0.15d ns
Yes 0 3 (1.8)

 

“ physical activity score <3. b ANCOVA adjusting for chronological age and sexual
maturation. ° missing data for 2 subjects. d Chi-square value

p<0.05, ns=not significant.

206

Table 77. Clustering of risk factors by sex.

 

 

 

Sex
Males Females

Number of risks N °/o N %
None 14 12.0 9 5 .0
One 74 63.2 122 68.2
Two 24 20. 5 44 22.3
Three 3 2.6 8 4. 5
Four 2 1.7 0 0
Five 0 O O 0

 

207

Table 78. Distribution of risk factors and clusters in males.

 

 

Risk
Numb" [3P Overweight Inactivity Smoking LOW Observed. % Expected]
of risks ‘ - 3 fitness frequenC} frequcnq
None — - - - - 14 12.0 12.9
0”" + - - - - 2 1.7 l 2
risk
_ + - - - 1 0.9 l 2
- - + - - 69 59.0 65 5
- - — + - O O 4
- - - - + 2 1.7 3 5
TWO + + ., - - - O 0.]
risks
+ - -r— - - 4 3.4 6 l
+ _ - T - O 0.04
T - - - + O 0.23
_ + + - - 5 4.3 5 4
- + - + - O 0 02
_ .1. _ - + O O 23
- - + + - O l 64
- - + - + 13 11 l 15 l
- - - + + 0 0.08

 

Table 78. Continued.

 

 

Risk
Number Low Observed Expcctcd
of risks BF Overweight Inactivity Smoking fitness frequency % frequenC}
Three + + + - - 0 O .4 7
risks
+ + - + - 0 0 . 002
+ + - - + 0 0 . 02
+ - + + - 0 0. l 2
+ - + - + l 0 . 9 l .4
+ - - + + 0 0.00 7
- + + + - O O. l 4
- + + - + l 0 . 9 l .29
- + - + + O 0.00 7
- - + + + 2 l .7 O . 3 5
Four
. + + + + - 0 0. 0 1 2
risks
+ + + - + 2 1.7 0.12
+ + - + + 0 0.0006
+ - + + + 0.03 5
- + + + -+- 0 0.0 3 5
Fire -+- + -+- + + O 0 . 003 5
risks

 

Chi-square=2.68, df=3, p>0.05

209

Table 79. Distribution of risk factors and clusters in females.

 

 

Risk
Number of Low Observed % Expected
risks BF Ovenreight Inactivity Smoking fitness frcguency frequenq'
None - - - - - 9 5.0 6. 27
One + - - - - O 0.1 1
risk
- + - - — 0 0.72
- - + - - 1 19 66.5 1 18. I4
- — - + - 0 0.1 l
- - - - + 0 1.61
I.“ + + - - - 0 0.01
risks
+ - + - - l 0.6 2.15
+ _ - + - 0 0.002
+ - - - + O 0.04
- + + - - 10 5.6 12.53
- —+- - -‘r - O 0.01
- + - - + O 0. l6
- - + + - 3 1.7 2.15
_ - + - + 29 16.2 29.17
- - - + + O 0.36

 

210

Table 79. Continued.

 

 

Risk
Number Low Observed % Expected
of risks 31’ Overweight Inactivity Smoking fitness frequency freunHC}
Three + + + - - l 0. 6 0.22
risks
+ + - + - 0 0.0002
- + — - + 0 0 . 0 l
+ - + + - 0 0 . 3 6
+ - + - + 1 0.6 0.52
+ - - + + 0 0.0005
- + + - + 6 3 .4 3 .04
- + - + + 0 0.004
- - + + + 0 0. 54
f Our
. + 4— + + - 0 0.004
risks
+ + + - + 0 0. 54
+ -r - + + 0 0.004
+ - + + + 0 0 . 05
- + + + + 0 0.0005
l’ii'e + + + + + 0 0.00009
risks

 

Chi-square =3.40, df=3, p>0.05

211

Table 80. Comparisons of selected variables in males grouped by number of clustered

risk factors.

 

Number of risk factors

 

Variables" 0 l 2 3 4
N 14 74 24 3 2
Body mass index (kg m")
Mean 18.4 19.2 21.1 21.1 24.0
SE 0.7 0.3 0.5 1.3 1.5
Sum of six skinfolds (mm)
Mean 51.4 52.2 73.8 75.6 4.3
SE 6.0 2.5 4.7 11.3 12.9
T rank-extremity ratio (mm mm)
Mean 1.3 1.3 1.3 1.1 1.5
SE 0.1 0.0 0.1 0.1 0.2
Waist-hip ratio( cm cm)
Mean 81.1 80.0 81.7 81.5 842
SE 1.0 0.4 0.8 1.9 2.1
Systolic blood pressure (mm Hg)
Mean 106.4 109.9 110.7 114.4 125.0
SE 2.3 1.0 1.8 4.3 4.9
Diastolic blood pressure (mm Hg)
Mean 61.9 66.6 70.7 63.3 80.6
SE 2.1 0.9 1.6 4.0 4.5
Physical activity score
Mean 3.3 2.2 2.0 2.1 1.
SE .1 0.1 0.1 0.2 0.3
Cardiovascular fitness (m)
Mean 2369.4 2361.6 1979.9 1742.9 1708.5
SE 80.7 35.5 65.6 153.4 174.2

 

“ AN COVA adjusting for chronological age and sexual maturation.

212

Table 81. Comparisons of selected variables by number of clustered risk factors in

 

 

females.
Number of risk factors

Variablesa 0 1 2 3
N 9 122 44 8
Body mass index (kg m")
Mean 19.4 19.5 21.4 26
SE 0.6 0.2 0.3
Sum of six skinfolds (mm)
Mean 74.6 81.8 93.7 145.9
SE 6.1 1.8 3.1 7.5
Trunk-extremity ratio (mm mm)
Mean 1.2 1.2 1.3 1.3
SE 0.1 0.0 0.0 0.1
Waist-hip ratio (cm cm)
Mean 73.9 73.6 74.8 75.5
SE 1.1 0.3 0.6 1.4
Systolic blood pressure (mm Hg)
Mean 104.5 105.7 107.7 112.9
SE 2.2 0.7 1.1 2.8
Diastolic blood pressure (mm Hg)
Mean ' 62.9 65.7 66.4 72.8
SE 1.8 0.5 0.9 2.2
Physical activity score
Mean 3 .3 2.0 1.8 .8
SE 0.1 0.0 0.1 .2
Cardiovascularfitness (m)
Mean 1867.9 1835.6 1505.5 1438.3
SE 68.3 19.6 33.1 77.2

 

“ ANCOV A adjusting for chronological age and sexual maturation.

213

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216

Table 85. Means, standard deviations, and medians for skinfold thicknesses of Brazilian

adolescents by sex and age.

 

 

Triceps (mm) Subscapular (mm)
Age (years) Study Mean SD Median Mean SD Median
Males

14 + This study 10.3 4.0 9.8 9.3 3.8 8.2
Santo Andre (R1+R2)a 8.9 3.3 7.6 6.9 3.0 6.3

Santo Andre (R)b 10.0 4.3 9.1 7.7 3.7 6.8
NHANES IIc 10.4 5.8 9.0 8.9 5.3 7.0

15 + This study 9.1 3.4 7.9 9.2 3.4 8.6
Santo Andre' (R1+R2) 8.4 2.8 7.6 7.0 1.7 6.7

Santo Andre (R) ‘ 9.2 3.4 8.2 8.0 3.2 7.2
NHANES II 10.1 7.2 7.5 10.0 8.2 7.5

Females

14 + This study 15.2 4.9 14.3 12.7 4.4 11.7
Santo Andre (R1+R2) 15.7 5.8 14.3 12.9 6.2 11.2

Santo Andre (R) 16.7 5.9 15.6 13.6 6.6 11.5
NHANES II 17.6 7.5 17.0 13.2 7.7 10.5

15 + This study 15.6 4.5 14.6 13.6 4.4 12.4
Santo Andre (R1+R2) 16.4 5.3 16.2 14.0 5.6 12.9

Santo Andre' (R) 17.6 5.3 17.1 14.6 6.3 13.2
NHANES II 17.1 7.1 16.5 13.1 6.9 10.5

 

" Santo Andre (R1+R2) sample is composed by lower SES subjects. b Santo Andre (R)
is the entire sample. ° Najjar and Rowland (1987).

217

Table 86. Comparison of recommended cutoff points for undernutrition and overweight

for the body mass index (BMI) in Brazilian and US. adoleScents by age and sex.

 

Percentiles of BMI by age (kg/m2)

 

 

Brazil“ US.b
Age (years) 5th 85th 5th 85th
Males
14 + 15.4 20.8 16.2 23.0
15+ 15.7 21.7 16.6 24.0
Females
14 + 16.1 23.3 15.7 24.0
15 + 17.1 23.6 16.0 24.0

 

“ Anjos et al. (1998). b Himes and Dietz (1994).

218

Table 87. Prevalence of undernutrition by sex and age according to different BMI

 

 

 

criteria.
Prevalence of malnutrition (%)
Age (years) Brazilian 31%] criteria3 U. 5. BMI criteriab
Males
14 + 6 0 13.6
15 + 3.3 8 3
Females

14 + 6.0 4.0
15 + 2.9 1.0

 

" Anjos et al. (1998). b Himes and Dietz (1994).

219

 

 

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4.48. 2.... on Now 3 new. :8 $4.. .48: 8.8.6
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4.4m4 3.48 ...o. o: 09. m... 0.8. 38 c 22.2.0
8.3. 0.3.2 2.. ...: ....4 w... 4.8. .82. .288:
N28 342 ....N 3. ...: 9?. mo 88. 333...... _ I
Om :83. Om :85. Om :83. .Dm :85. 33% Ammmmbwwfli

 

AS. 5: o...:.E-m.

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Ea. ESSm

5:88.28 8.28.5 :. E... 22...: m: $8.... .Co§.q8..c..p_8 cc: 8% 35.. 5.. .28 8.3.8600 .3. 03:...

220

Table 89. Prevalence of cigarette smoking by age group and sex in the present study and

in the Brazilian National Health and Nutrition Survey (PNSN).

 

Prevalence (%)

 

 

Age (years) This study . PNSN 21
Males

10-14 1.0

14* o

15 6.7

15-19 4.0
Females

10-14 2.0

14" 1.0

15 ” 3.0

15—19 5.0

a Ministry of Health (1993).

221

Table 90. Comparison of blood pressures in Brazilian adolescents by age and sex.

 

Median blood pressure (mm Hg)

Systolic Diastolic

 

This study The Rio de Janeiro This study The Rio de Janeiro

 

Age (years) Study a Stu dya
Males

l4~ 107.5 115.0 67.0 59.0

l5~- 112.5 117.0 65.0 61.0
Females

14-- 104.0 110.0 65.5 60.0

15+ 106.0 110.0 66.0 62.0

 

“ Pozzan et al. ( 1997)

222

Table 91. Comparison of blood pressures of the study sample with adolescents in the U. S.

and Finland by age and sex.

 

Blood pressure (mm Hg)

 

 

This Study U. 5.3 Finland b
Age (years) Mean SD Mean SD Mean SD
Symohc
Males
14* 109.5 8.1 110.7 12.2
15+ 110.9 9.5 112.3 10.3 120.0 12.0
Females
14~ 105.2 6.9 108.0 11.1
15* 107.6 8.6 107.6 11.3 117.0 10.0
Diastolic
Males
I-I-— 67.8 6.8 59.2 14.1
15 »- 66.3 9.7 ' 61.0 12.0 69.0 11.0
Females
14~ 65.3 6.2 62.9 12.5
15~ 66.7 6.4 61.2 10.6 70.0 9.0

 

" Rosner et al. (1993)
1‘ Dahl et al. (1985)

to
IO
U)

Table 92. Blood pressure by weight status and sex in the study sample.

 

Weight status

 

 

Normal Overweight
Blood pressure '
(mm Hg) Mean“ SE Mean‘11 SE F value p
Males
Systolic 110.3 0.8 110.6 2.9 0.01 ns
Diastolic 66.9 0.8 68.2 2.8 O. 16 ns
Females

systolic 105.8 0.6 111.7 1.9 8.85 0.003”
Diastolic 656 0.5 69.9 1.5 7.56 0.007%“

 

“ ANCOVA adjusting for chronological age, sexual maturation and body weight.

$p<0.05. ns=not significant.

Table 93. Fat distribution and risk for elevated blood pressure by sex in the study sample.

 

Elevated blood pressure3

Not at risk 1 At risk F ratio [2

 

 

Fat distribution Mean SE Mean SE
Males
TER (mm mm) 1.28 1.02 1.39 1.06 1.79 ns
WHR (cm cm) 80.17 1.00 83.75 1.01 8.89 < 0.01 *
Females
TER (mm mm) 1.20 1.01 1.21 1.10 0.01 ns
WHR (cm cm) 73 .96 1.00 73.45 1.03 0.06 ns

 

“ ANCOV A adjusting for chronological age, sexual maturation and race. p < 0.05

224

Table 94. Blood pressure by sex and race in the study sample.

 

 

 

Race
White Non-white
Blood pressure (mm Hg) Meana SE Mean SE F value p
Males
Systolic 109.8 1 .2 1 10.9 1 .2 0.48 ns
Diastolic 66.7 1.1 67.1 1.1 0.06 ns
Females
Systolic 105 .4 O. 8 106.6 0. 8 1 .08 ns
Diastolic 65.6 0.7 65.9 0.6 0.12 ns

 

“ ANCOVA adjusting for chronological age, sexual maturation and body weight.

ns=not significant.

Table 95. Prevalence of overweight by age and sex in the study sample according to

Brazilian and US. criteria for the body mass index (BMI).

 

Prevalence of overweight (%)

Age (years) Brazilian BMI criteriaa

U. S. Bil/[I criteriab

 

Males
14 + 27.3
15 r 13.3

Females
14 4- 12.0
15 -*- 10.7

7.6
6.7

10.0
8.7

 

" Anjos et al. (1998). b Himes and Dietz (1994)-

225

Table 96. Prevalence of overweight by hours of TV viewing in each sex.

 

 

Overweight
TV viewing (hs/day) N %
Malesa
<1 7 0
2.0-2.9 23 0
3-3.9 24 1
4-49 23 4
5+ 48 4
Femalesb
<1 24 0
2 0-29 22 0
3-39 37 2
4-49 25 3 .0
5+ 94 14

 

“ Chi-square =6.33, df=4, p>0.05 Chi-square linear=2.60, df=1, p>0.05
1” Chi-square =8.99, df=4, p=0.06 Chi-square 1inear=8.61, df=1, p<0.05

226

Table 97. Relative fat distribution in males and females of the study sample at risk and

not at risk for overweight.

 

 

 

Overweighta
Not at risk At risk
Relative fat distribution Meanb SE Meanb SE F ratio p
Males
Trunk-extremity ratio (mm mm) 1 .3 1 0. O3 1 .3 9 0.09 0.83 ns
Waist-hip ratio (cm cm) 80.3 0 3 85.0 1.2 14.80 < 0.001
Females

Trunk-extremity ratio (mm mm) 1 22 0.02 1.26 0.06 0.37 ns
”inst-hip ratio (cm cm) 73.8 0 3 75.7 0.9 3.6 0.06

 

a defined as values > 85th percentile for age and sex.

b ANCOVA adjusting for chronological age and sexual maturation.

p < 0.05, ns=not significant.

227

Table 98. Skinfold thicknesses and the subscapular/triceps ratio by sex and age in this

study and a comparable sample of Brazilian adolescents.

 

 

 

This study Guedes (1993)

Variable Mean SD Mean SD
Triceps skinfold (mm)
14+ years 10.3 4.0 10.7 5.7
15+ years 9.1 3.4 9.6 5.2
Sttbscapttlar skinfold (mm)
14+ years 9.3 3.8 8.0 5.0
15+ years 9.2 3.4 7.9 4.7
Subscapitlar-triceps ratio (mm mm)
14+ years 0.933 0.21 0.79 0.19
15+ years 1.05 . 0.25 0.85 0.20

Females

Triceps skinfold (mm)
14+ years 15.2 4.9 16.3 6.3
15+ years 15.6 4.5 17.9 7.0
Sttbscapular skinfold (mm)
14+ years 12.7 4.4 11.1 4.8
15+ years 13.6 4.4 12.6 5.4
Sitbscapttlar-triceps ratio (mm mm)
14+ years 0.85 0.17 0.69 0.16
15+ years 0.88 0.19 0.72 0.17

 

“ significant age difference (p<0.01).

228

Table 99. Physical activity scores in the study sample by social class and sex, age groups

combined.

 

Physical activity scorea

 

 

Social class Mean SE F value p
Males
Middle class 2.3 0.2 1.69 ns
Lower middle class 2.5 0.]
Lower class 2.2 0.1
Females

Middle class 1.8 0.1 3 78 003’
Lower middle class 2.3 0.1
Lower class 2.0 0.1

 

" ANCOVA adjusting for chronological age, scores vary from 1 to 5.

1p< 0.05, ns=not significant.

Table 100. Physical activity score and self-activity rating in the study sample by sex, age

groups combined.

 

Physical activity scorea

 

Activity rating Mean SD
Males
Less active than peers 1.9 b 0.5
As active as peers 2.3 b 0.5
More active than peers 2.6 b 0.6
Females
Less active than peers 1.7 ° 0.5
As active as peers 2.1 0.6
More active than peers 2.3 0.5

 

" scores vary from 1 to 5, adjusted by chronological age
b differences among the 3 groups (p<0.01)

° difference between the less active group and the other groups (p<0. 001)

[Q
DJ
O

Table 101. Physical activity scores by social class and sex.

 

Physical activity scorea

 

 

Social class Mean SE F value /)
Males
Middle class 2.3 0.2 1.69 ns
Lower middle class 2.5 0.1
Lower class 2.2 0.1
Females

Middle class 1.8 0.1 3.78 0. 03
Lower middle class 2.3 0.1
Lower class 2 . O O. 1

 

“ ANCOVA adjusting for chronological age, scores vary from 1 to 5.

*p< 0.05. ns=not significant.

2

FIGURES

 

 

 

g 25.0 - M Infectious and parasitic diseases
'2 Neoplasms
‘- 1 .
E‘ ‘0'” Diseases ofthe circulatory system
’3 . - External causes
5 1) 0 ‘
é ‘ -\ ‘ W
E 10.0 ‘ M
E " - x ._
.2 5.0- “~~--_._._-

0.0 . 1 . l

1975 1980 1985 1990 1995

Time (years)

Figure 1. Proportionate mortality rates for several conditions in Brazil from 1977 through
1994. Drawn from data reported by IBGE (1992) and DATASUS on-line (1997).

External causes combined include motor accidents, homicides, suicides, drowning, etc.

l’i'olmnionnlc lllt)l'lil|ll_\ rnlcs ("0)

1

I All other causes combined

U Enema] causes

I Diseases of the circulatory system
I Neoplasms

I Infectious and parasitic diseases

 

1-4 yrs 5-9 yrs 10-14 vs 15-19 yrs 20-29 )1s 30-39 yrs 4049 yrs 50-59)1s 60-69 yrs 70-79 yn 80 _\'r.\

Age groups (years)

Figure 2. Variation in proportionate mortality rates through the life span in Brazil. Drawn

from data reported in DATASUS on-line (1996).

233

16-

12- . ____,

 

30° 0 poorest
------ 40° 0 intcnncdiatc

— ' 30°orichcst

l’rcvarlcncc ul'm'crwcighl (°o)

 

 

0 1 T U I
1970 1975 1980 1985 1990

Sun‘e} years

Figure 3. Prevalence of overweight (BMI > 30 kg/mz) in adults 25-64 years (sexes

combined) by socioeconomic status. Drawn from data reported by Monteiro et al. (1995).

 

U)
U |

1:1 This study
a C6111 (1988)

 

(a)
O

 

 

N
Ur

 

 

 

 

'JI

 

10 1

Q. 3.0..

8+ 9+ 10+ 11+ 12+ 13+ 14+ 15+ 16+ 17+

 

 

 

Relative distribution (%)
JI

 

 

 

 

  

 

 

 

 

   

' 1

Reported ages at menarche (years)

Figure 4. Relative distribution of reported ages at menarche in the present study and that

ofColli (1988) .

APPENDICES

to
D)
U]

APPENDIX A

APPROVAL OF THE UNIVERSITY COMMITTEE ON RESEARCH

INVOLVING HUMAN SUBJECTS

 

OFFICE OF
RESEARCH
AND
GRADUATE
STUDIES

ersitv Committee on
Research lnvolvi
Human Subjects
(UCRIHS)

Chga‘w State University
1rt=strahon Building
333' '.ans:ng. Michigan

48824.1046

517/355-2180
310(517/432-1171

v» :1 ;an State University
-: ‘5.'I.'u.'xona/ Diversdyi
{ice/lence in Action

1 5 3n airinmttveactlon.
2" -:C:cr.'unily institution

 
 
  
 
  

MICHIGAN STATE

UNIVERSITY

August 5, 1997

TO: Robert M. Malina
213 IM Sports Circle

RE: IRB#: 97-491
TITLE: CORONARY HEART DISEASE RISK FACTORS AND HEALTH
RELATED PHYSICAL FITNESS OF ADOLESCENTS IN
NITEROI, RIO DE JANEIRO, BRAZIL
REVISION REQUESTED: N/A
CATEGORY: FULL REVIEW
APPROVAL DATE: 08/04/97

The University Committee on Research Involving Human Subjects'(UCRIHS)
review of this project is complete._ I am pleased to adVise that the
rights and welfare of the human subjects appear to be adequately
rotected and methods to obtain informed consent are appropriate.
herefore, the UCRIHS approved this preject and any reViSions listed
above.

RENEWAL: UCRIHS approval is valid for one calendar year, beginning with
the approval date shown above. Investigators planning to
continue a project beyond one year must use the green renewal
form (enclosed with t e original approval letter or when a
project is renewed) to seek ugdated certification. There is a
maXimum of four such expedite renewals poseible. Investigators
wishing to continue a prOJect beyond that time need to submit it
again or complete reView.

REVISIONS: UCRIHS must review any changes in procedures involving human
subjects, rior to initiation of t e change. If this is done at
the time o renewal, please use the green renewal form. To
reVise an approved protocol at any other time during the year,
send your written request to the. CRIHS Chair, requesting revised
approval and referenCing the preject's IRB # and title. Include
in your request a description of the change and any revised
instruments, consent forms or advertisements that are applicable.

PROBLEMS/ .

CHANGES: Should either of the followin arise during the course of the
work, investigators must noti UCRIHS promptly: (1) roblems
(unexpected Side effects, comp aints, etc.) involving uman
subjects or (2) changes in the research environment or new
information indicating greater risk to the human sub ects than
eXisted when the protocol was previously reviewed an approved.

If we can be of any future help, please do not hesitate to contact us
at (517)355-2180 or FAX (517)432-1171.

avid E. Wright, P .D.
UCRIHS Chair

Rosane C. Rosendo da Silva

APPENDIX B

to
b)
00

APPENDIX B

MAP OF BRAZIL

AC - Acre

  

AM - Amazonas
Northeast BA _ Bahia
CE - Curé
DF - Distrito Federal (Brasflia)
ES - Espirito Santa
- GO - Goiés
; ‘ PB MA - Maranhéo

PE MT - Mato Grosso

MS - Mato Grosso do Sul
MG - Minas Gerais-
PA - Paré
PB - Paraiba
PR - Parané
PE - Pernambuco
Pl - Piaui
RN - Rio Grande do Norte
RS - Rio Grande do Sul
RJ - Rio de Janeiro
R0 - Rondfinia
RR - Roraima
SC - Santa Catarina
BRAZIL SP - Séo Paulo
' SE - Sergipe
TO - Tocantins

Figure 5. Map of Brazil. Adapted from University of Whales (1997, on-line).

239

APPENDIX C

APPENDIX C

MAPS OF NITEROI, RIO DE JANEIRO

I: a y 5' . . ,1 ‘ ‘ Niteréi

" Atlantic Ocean

 

Figure 6. Map of the state of Rio de Janeiro, showing the location of Niteroi. Adapted
from PMN/CECITEC (l996a).

241

 

Pendofiba

Figure 7. Map of the administrative regions of Niteroi. Adapted from PMN/CECITEC
(19963)

242

APPENDIX D

APPENDIX D

LETTERS OF CONSENT FROM STATE AND MUNICIPAL DEPARTMENTS

OF EDUCATION IN NITEROI

nnvsnnn no esmno no mo DE JANEIRO
SECRETARIA ne ESTADO DE enucncAn
COORDENADORIA neninnAL METROPOLITANA u

 

C O M U N I C A D O

Niteréi, 24 de marco de l9é8.

Comunicamos que a Professora ROSANE CARLA ROSEBDO DA SIEVA,
esté AUTQRIZADA a continuar a coleta de dados sobre "FATORES DE
RISCO PARA A DOENCA CORANARIANA E A APrIDzo FISICA relacionado a

sai'ide em adolescentes da Rede delica de Ensino de Nitero'i.

Atenciosamente,

   
 

We

a 41in Correa JCaciel

oordenadora Regional
E/Cll05.R

lint. 291.4224

It is a consent for Rosane C Rosendo da Silva to collect the data for the research
(‘HD risk factors and health related physical fitness of adolescents in the State public

schools in Niteroi.

Sincerely,

245

PREFEITURA DE NITEROI

Fundaeio Municipal de Educaeio
SUPERINTENDENCIA DE DESENVOLVIMENTO DE ENSINO

oric1o FSDE N“ 70 /98

Naomi, 05 de fevereiro de 1998.

Sr.“ Diretora,

Aprcscntamos 'a Professora Rosane Carla
Rosendo da Silva, assistcntc da Universidade Federal Fluminense que prepara seu
doutoramento pcla MICHIGAN STATE UNIVERSITY, na area do Educaeao Fisica.
Para tanto UCCCSSIIafil avaliar cstudantcs da 8a série, motivo pelo qual solicitou esta
autorizagfio. Contando com sua colaboragao,
Atcnciosamente

Azania. 4%‘-%

Nelson Ricardo da Costa e Silva

u’Vc/sm ’Ricardo do C «in e (Sr/nu
Oorcnto do Plancjamonto Escolzr o
Dounvolvlmonta Curricular . fSDEIFME
' MIL 31595-0

Dear Principal:
We introduce Rosane C Rosendo da Silva Assistant Professor at Universidade

Federal Fluminense, who 15 conducting a research for her doctoral degree in Kinesiology

at Michigan State University. We agree that she will be measuring the 8th graders in your

school, and we ask for your cooperation. -
Sincerely,

246

APPENDIX E

APPENDIX E

PARENTAL CONSENT

( ‘oronary heart disease risk factors and health-related physical fitness of adolescents in

Nileréi, Rio de Janeiro, Brazil

Consent Form
I am conducting a study to assess risk factors for coronary heart disease and

components of health-related physical fitness in adolescents 14 to 16 years of age, as part
of the requirements for the doctoral degree at Michigan State University, East Lansing,
Michigan, USA.

I am inviting your son/daughter/ward to participate in the study, which is being
conducted in the physical education program at his/her school. In order to participate in

the study your child has to have your written approval.

If you permit your child to participate, he/she will be tested on several activities.
None of the tests offer any physical or psychological risk, except the 12 minute walk/run
which involves minimal risk. To minimize the risk, this test will be performed after an
appropriate wann-up and will be supervised by both the researcher and the physical
education teacher. The child will be instructed to stop the test if he/she feel too tired to
continue, or if she/he feels dizziness. Please let us know if your child has any respiratory

disease such as asthma, diabetes or any other condition for which physical exercise is not

recommended.

Other tests include measures of height, weight, body circumferences, and body

fatness. Questionnaires on the habitual physical activity, sexual maturity status, cigarette

248

smoking and sociodemographic data, such as age and parental occupation, will be
completed. The approximate time for participation is about one hour. Control of data
collection will be performed during the study and some of the adolescents will be re-
evaluated for a second time, and in this case another hour will be spent. Since the tests
will be conducted during the physical education class, every effort to minimize the loss of
academic teaching time will be made.

All of the information gathered for this study will be strictly confidential. Only
the researcher will have the access to the data. Individual data may be shared with you,
the parent or guardian, should your adolescent wish.

The study does not involve any financial costs to the subject or to the family. All
materials needed for testing will be provided; however, the adolescent needs to be
appropriately dressed for physical education class on the test day.

If you have any question regarding this study, you can contact Rosane Rosendo da
Silva at Univ. Federal F luminense, Dep. de Educaeao F isica, Campus do Valonguinho,
Niterdi, Rio de Janeiro, tel 620-8080, ramal 371 or Dr. Robert M. Malina at the Institute
for the Study of Youth» Sports, Michigan State University, East Lansing, Michigan,
48825, phone 001-517-355-7620.

You may keep the above portion of the form and return only the signed portion.
Please return this form as soon as possible. Your cooperation is greatly appreciated.

Sincerely,
Rosane C Rosendo da Silva

249

Consent Form

Coronary heart disease risk factors and health-related physical fitness of adolescents in
Nitero'i, Rio de Janeiro, Brazil

I have decided to allow my son/'daughter/ward

 

to participate in a study to assess the risk factors for coronary heart disease and
components of health-related physical fitness in adolescents 14 to 16 years of age that
will be conducted in regularly scheduled school day in the physical education class. My
signature indicates that I have read the above information, or have had someone read the
above information to me, and have given permission for my son/daughter/ward to
participate in the study. I understand that my child may withdraw at any time ( I can
choose to discontinue his/her participation in the study) without any negative

consequences to me or my family in my present or future relations with Michigan State

 

 

 

 

University.
Signature of parent/guardian Date
Signature of investigator Date

Current occupation of father
( ) employed ( ) self-employed

Current occupation of mother
( ) employed ( ) self-employed

 

Please Sign and return this page.

250

Autorizacao dos Pais

Farores de risco para a doenea coronariana e aptifiofisica relacionada a made em

adolescentes de Niteroi, Rio de Janeiro, Brasil

Sr. Responsavel, estou conduzindo um estudo que avalia os fatores de risco de
doenca coronariana e a aptidao fisica de adolescentes de 14 e 15 anos de idade, como
trabalho de final de curso de pos-graduacao na Universidade do Estado de Michigan,
Estados Unidos. For 1550, estou convidando o (a) seu (sua) filho(a) para participar do
estudo, que sera realizado na escola que ele(a) freqiienta . Para participar no estudo o seu
filho(a) precisa de sua autorizacao, de acordo com o procedimento solicitado pelo Comité
de Etica de Pesquisa da universidade.

A participacao no estudo envolve varias medidas. Nenhuma delas oferece risco de
lesoes fisicas. O teste de corrida em 12 minutos é o unico que envolve risco minimo de
lesoes. Para diminuir esse risco o teste sera realizado apos uma sessao de aquecimento. e
sera supervisionado por um professor de Educaeao Fisica. Seu/sua filho(a) sera
orientado(a) a parar o teste se sentir cansaco para continuar o teste, on se sentir algum
tipo de mal-estar. Por favor nos notifique se seu/sua filho(a) tiver alguma doenea em que
a atividade fisica intensa nao seja recomendada, tal como asma on diabetes.

Outros testes incluem medidas de altura, peso, circunferéncias e dobras de

gordura subcutanea. Questionarios sobre a atividade fisica habitual, nivel de
desenvolvimento dos caracteres sexuais secundarios, uso de cigarros e dados socio-

demograficos tais como idade e ocupacao dos pais serao preenchidos. A tempo

251

aproximado para esses testes e de 1 hora. Alguns adolescentes serao medidos em duas
ocasioes para controle de qualidade das medidas.

Todas as informacoes coletadas nesse estudo sao estritamente confidenciais.
Apenas a pesquisadora tera acesso aos dados, que poderao ser requisitados pelo
responsavel atrave's do adolescente. Em caso de publicaeao do trabalho, a anonimidade
sera tambem garantida.

O estudo nan envolve nenhum gasto para o participante ou sua familia. Todos os
materiais necessarios para os testes serao providenciados, pore'm o adolescente devera
utilizar roupas apropriadas para a aula de Educacao fisica no dia do teste.

Se vocé tiver qualquer pergunta sobre o estudo, vocé pode entrar em contato com
uma das seguintes pessoas:

Prof 3 Rosane Rosendo
Univ. Federal Fluminense
Dep. de Educacao Fisica
Campus do Valonguinho
Niteroi, Rio de Janeiro

tel 620-8080, ramal 371

Prof. Dr. Robert Malina

Director

Intitute for the Study of Youth Sports
Michigan State University

East Lansing, MI 48825

tel. (001517) 355-7620

Dr. David Wright

Chair

Universtity Committee on Research involving Human Subjects
Michigan State University

East Lansing, MI 48825

tel (001517) 355-2180

A folha que contem a sua permissao devera ser devolvida a fim de que seu/sua

filha possa participar do estudo. Desde ja agradeeo a sua colaboracao.

Atenciosamente,

Rosane Rosendo

to
U:
D)

Formulario de Consentimento

Eu permito que meu/minha filho(a)

 

participe do estudo F atores de risco para a doenea coronariana e aplidaofisica
re/acionada a .sazide em adolescentes de Nitero'i, Rio de Janeiro, Brasil, que avalia
fatores de risco de doenca coronariana e aptidao fisica de adolescentes de 14 e 15 anos de
idade e que sera realizado na escola em que ele(a) estuda. Entendo que men/minha
filho(a) pode sair do estudo em qualquer momento que eu ou ele(a) decidir nao mais
participar, sem que haja nenhuma conseqiiéncia negativa para ele(a) ou para mim, em

minha relacoes com a UFF ou com a Universidade do Estado de Michigan, no presente

 

 

 

 

ou fiJtUI‘O.
Assinatura do pai, mac ou responsavel Data
Assinatura da pesquisadora Data

Profissao atual do pai:

 

( )empregado ( ) trabalho por conta propria

Profissao atual da mae:

 

( ) empregado ( )trabalho por conta prépria

Por favor devolva esta pagina para a pesquisadora.

254

APPENDIX F

255

APPENDIX F

SUBJECT ASSENT

( ‘oronaiji' heart disease risk factors and health-related physical fitness ofadolescents in
Nitero'i, Rio de Janeiro. Brazil

Assent Form For The Subjects

1, , understand that my parents have given permission

 

for me to participate in the study concerning coronary heart disease risk factors and
health-related physical fitness of adolescents, under the direction of Rosane Rosendo da
Silva.

I understand that measures of my body will be taken, that I will give information
about my physical activity habits and sexual maturity status, and that I will have to
perform a 12 minute walk/run. I also understand that any information about what I say or
do will not be given to anybody else, and will only be used for the purpose of this study.

My involvement in this study is voluntary, and I have been told that 1 may
withdraw from participation at any time without penalty and loss of benefit to myself.
When I sign this page, I am indicating that I am agreeing to participate in this study and

that I understand what will be required of me.

 

Signature of the subject Date

 

_

Signature of principal investigator Date

Please sign and return this page.

256

F ormulario de Concordancia na Participaeiio do Estudo

Fatores de risco para a doe/lea coronariana e aptidc‘iofisica relacionada a .s‘aiide em

adolescentes de Nitero'i, Rio de Janeiro, Brasil

Eu, , compreendo que meus pais consentiram

 

a minha participacao no estudo que avalia fatores de risco de doenca coronariana e
aptidao fisica de adolescentes de 14 e 15 anos de idade, sob direeao da Prof 9 Rosane
Rosendo.

Compreendo que medidas do meu corpo que serao realizadas e que eu informarei
sobre minha atividade fisica regular e o estagio de maturaeao sexual, além de executar o
teste de 12 minutos. Eu tambem entendo que qualquer das informaeoes sobre aos meus
resultados nao serao divulgados a ninguém, e que serao utilizados apenas nesse estudo.

Minha participacao nesse estudo é voluntaria, e eu fui informado que posso
desistir de participar a qualquer momento, sem nenhum prejuizo a minha pessoa.
Assinando este documento eu indico que concordo em participar do estudo e que entendo

o que me sera solicitado.

 

 

Assinatura do participante Data

 

 

Assinatura da pesquisadora Data

Porfai'or devolra esta pagina para a pesquisadora.

257

APPENDIX G

258

APPENDIX G

IDENTIFICATION FORM

Sociodemographic data

 

Name: Sex: ( ) Male ( )
Female
Date of birth: / / Date of testing / /

 

 

Race: ( ) White ( )Black ( )Pardo
How many people live in your household?

How many brothers and sister do you have?

 

Do you work outside your home? ( ) yes ( ) no

If yes, what is your occupation?

 

Health status

Do you have any disease that prevents you from exercising? ( ) yes ( ) no

 

If yes, what is the disease?
Do you have any orthopedic problem that limits your running ability? ( ) yes ( )no

If yes, what is the problem?

 

Do you smoke cigarettes frequently? ( ) yes ( ) no
If yes, what is the average number of cigarettes per day?

()<1()2-5 ()6-10 ()11-20 ()220

I0
U1
\0

FICHA DE IDENTIFICACAO

Dados sécio—demogréficos

Escola: Turma:

 

 

Sexo: ( ) masculino ( )feminino Data de nascimento:

 

Cor: ( )Branca ( ) Preta ( ) Parda

Quantos irmaos e irmas vocé tem?

 

Quantas pessoas moram na sua casa?

 

Vocé trabalha fora de casa? ( ) sim ( ) nao

Se sim. qual e' a sua ocupacao?

 

Na sua casa, quantas pessoas trabalham fora?

 

Dados de saude

Vocé tem a1 guma doenea que impeca a pratica regular de exercicios fisicos?

( ) sim ( )nao Se sim. qual e' a doenca?

 

Vocé tem algum problema ortopedico que limita a sua capacidade de correr?

( ) sim ( ) nao Se sim, qual é o problema?

 

V'océ fuma regularmente?
( ) sim ( ) nao
Se sim, qual e' a media de cigarros fumados por dia?

()<1 ()2-5 ()6-10 ()11-20 ()220

Data do teste: / l

APPENDIX H

261

APPENDIX H

CLASSIFICATION OF SOCIAL CLASS BY HEAD OF HOUSEHOLD
OCCUPATION
A. High class:
Owners and shareholders of large enterprises
B. Middle class:
Oi-l'ners and shareholders of medium and small enterprises
Executive, administrative and managerial
Accountants and auditors
Administrators, education
Administrators, protective services
Financial managers
Management-related occupations
Managers, medicine and health
Managers, real state
Marketing, advertising, and public relations managers
Personnel and labor relations managers
Public officials and administrators
Purchasing managers
Professional
Architects
College and university teachers

Computer systems analysts

262

Dentists
Educational and vocational counselors
Elementary and secondary school teachers
Engineers
Lawyers and judges
Librarians
Mathematical and computer scientists
Natural scientists
Physicians
Physicians’ assistants
Registered nurses
Social scientists and urban planners
Therapists
C. Working Class
Atypical (Lower middle class)
Technicians and related support
Clinical laboratory technologists and technicians
Computer programmers
Dental technicians
Drafting occupations
Electrical and electronic technicians
Engineering technologists and technicians

Health technologists and technicians

263

Licensed practical nurses
Sales occupations
Cashiers
Insurance sales
Real state sales
Sales counter clerks
Sales representatives, except retail
Administrative support, including clerical
Bank tellers
Bookkeepers, accounting and auditing clerks
Computer operators
Data entry keyers
Duplicating, mail and other office machine Operators
File clerks
General office clerks
Postal clerks
Receptionists
Secretaries
Supervisors
Teacher’s aides
Telephone operators

Typists

264

Typical (Lower class)

Precision production, craft, and repair
Automobile body repairers
Bakers
Carpenters
Construction trades
Electricians
Locksmith and safe repairers
Mechanics and repairers
Painters
Plumbers
Precision production occupations
Tile setters
Typesetters

Operators, fabricators, and laborers
Freight, stock, and material handlers
Laborers, except construction
Machine operators, assemblers, and inspectors
Motor vehicle operators
Solderers and brazers

Stevedores

Farming, forestry. and fishing
Animal caretakers
Farm operators and managers
Fishers, hunters, and trappers
Forestry and logging
Gardeners
Other agricultural and related occupations

Sentice Occupations
Child care, not private household
Cooks
Doorkeepers
Dressmakers
Elevator operators
Firefighters
Food preparation and service
Garbage collectors
Guards and private police
Hairdressers and cosmetologists
Housekeepers
Janitors and cleaners
Launderers and ironers
Maids

Nursing aides, orderlies, and attendants

266

Police and detectives

Police officers

Porters

Private household cleaners and servants
Private household, child care

Street vendors

Vehicle washer

Waiters and waitresses

267

APPENDIX I

268

APPENDD( I

THE SELF-ASSESSMENT OF SEXUAL MATURITY - MALES

General Instructions

 

Please read each description of a stage and compare it with the corresponding
drawing and photograph opposite. Then assign a stage to yourself. Most will be easily
able to decide that the correct stage is one of a possible two or three. Read the
descriptions for these again and check the photographs and drawings again before making
a decision.

Most of the drawings and photographs are for boys who are not circumcised. Try
not to let this difference affect your choice of stages. You may wish to used a mirror to

help assign the correct stages.

269

BOYS
Grades of genital development
The genitalia to be graded are the penis and the scrotum and the testes which can
be felt within the scrotum. If you consider there are differences from one side of your
body to the other, which is not unusual, assign a stage based on the average of the two

sides.

 

 

 

 

STAGE 1: The penis, scrotum, and testes are of the same size and proportion as
in early childhood.

STAGE 2: The scrotum and testes have enlarged. The size of each testis can be
judged by looking at the scrotum and also by feeling each testis through the skin of the
scrotum. The skin of the scrotum becomes thinner, wrinkled and slightly red, but this is
difficult to see in a photograph. There is little or no change in the penis.

STAGE 3: The penis is longer than in early childhood but there is little change in
thickness. The scrotum and testes are larger than in STAGE 2. The scrotum now hangs
down further below the base of the penis.

STAGE 4: The penis is further enlarged in length and breadth. The end of the
penis becomes conical and there is an enlargement where this part (the glans) joins the
rest of the penis. The scrotum and testes are fithher enlarged and the skin of the scrotum
is darker.

STAGE 5: The penis, scrotum and testes are adult in size and shape.

STAGES OF GENITAL DEVELOPMENT

BOYS

V

 

271

STAGES IN GENITAL DEVELOPMENT
BOYS

Check the stage that best represents your development

 

'u/
- T
I
V
b/ a
M :
I
I
'1'
v
5"

272

Stages in development of pubic hair

Pubic hair grows near the middle where the thighs meet the stomach. It develops
during pubescence and becomes colored and curly.

STAGE 1: No pubic hair. The hair in this region does not differ from that over
the front of the stomach.

STAGE 2: There is sparse, long, slightly colored hair that is straight or only
slightly curled. It is at base of the penis or on the scrotum. This stage is difficult to see in
a photograph, particularly if the person has fair hair. Therefore, rely more on the written
description and the drawing than on the photograph.

STAGE 3: The pubic hair is considerably darker, coarser, and more curled. The
hair is around the base of the penis.

STAGE 4: The pubic hair is now adult in type (colored similarly to cranial hair,
coarse, curled), but the area covered is considerably smaller than in most adults. The
pubic hair does not spread to the inner surface of the thighs; that is. It is not extend over
the folds where the thighs join the stomach.

STAGE 5: The pubic hair is adults in type and quantity. It covers a triangular area
with a straight upper margin ( ). The pubic hair spreads to the inner
surfaces of the thighs across the folds where the thighs join the stomach, but it does not
extend up onto the front of the stomach beyond a straight line.

STAGE 6: The pubic hair is not restricted to a triangular area but extends up to

the front of the stomach so that the area covered is no longer triangular.

273

STAGES IN DEVELOPMENT OF PUBIC HAIR

BOYS

 

274

 

STAGES IN DEVELOPMENT OF PUBIC HAIR
BOYS

Check the stage that best represents your development

\7’

 

 

 

275

APPENDIX J

276

APPENDIX J

THE SELF-ASSESSMENT OF SEXUAL MATURITY - FEMALES

GENERAL INSTRUCTIONS

Please read each description of a stage and compare it with the corresponding
drawing and photograph opposite. Then assign a stage to yourself. Most will be easily
able to decide that the correct stage is one of a possible two or three. Read the description

for these again and check the photographs and drawings again before making a decision.

277

 

Stages of breast development
Please use the mirror to help assign the correct stages. In many girls, one breast is
more developed than the other. If this is so for you, record an average stage for the two

breasts. The parts of the breast you need to examine are shown in the drawing below.

iii...
(r \\

.1 g. ,Ateosa
: e! ’ ‘
I ll“

    
 

N ionic-

Breast

STAGE 1: Only the nipple is higher than the general level of the chest wall in this
area, as in a child.

STAGE 2: There is an elevation of the areola, which is the colored area around
the nipple. The areola is larger than in childhood and fairly hard tissue can be felt deep to
it.

STAGE 3: There is further enlargement of the breast and the areola, without any
separation of their contours. The breast is now clearly feminine in appearance.

STAGE 4: The nipple and the areola project from the general level of the breast to
form a mound.

STAGE 5: The breast is now adult. The nipple projects from the general contour

but the areola does not. The areola is markedly colored.

 

Stages of development f pubic hair

Public hair grows near the middle where the thighs meet the stomach. It develops
during pubescence and becomes colored and curly.

STAGE 1: No public hair. The hair in this region does not differ from tat on the
front of the stomach.

STAGE 2: There is sparse, long, slightly colored hair in the public area that is
straight or only slightly curled. It is mainly along the labia. The labia are the rounded
folds of the opening of the vagina. This stage is difficult to see in a photograph,
particularly if the person has fair hair. Therefore, rely on the written description and the
drawing than on the photograph.

STAGE 3: The pubic hair is considerably darker, coarser, and more curled than in
Stage 2. The public hair spreads sparsely over the pubic area beyond the labia.

STAGE 4: The pubic hair is now adult in type (colored similarly to cranial hair.
coarse, curled), but the area covered is considerably smaller than in most adults. There is
spread to the inner surfaces of the thighs, that is, the pubic hair does not extend over the
folds where the thighs join the stomach.

STAGE 5: The pubic hair is adult in quantity and type. The area covered is an
inverted triangle ( ). The pubic hair extends to the inner surfaces of the
thighs. In some girls, the pubic hair extends up the front of the stomach in Stage 5.

STAGE 6: In about 10 percent of adults there is further extension of pubic hair to

the sides or up onto the front of the stomach.

281

( ,

STAGES IN DEVELOPMENT 0G PUBIC HAIR

GIRLS

 

282

 

STAGES IN DEVELOPMENT OF PUBIC HAIR
GIRLS

C heck the stage that best represents your development

 

APPENDIX K

284

APPENDIX K

PHYSICAL ACTIVITY QUESTIONNAIRE

Name: Age: Sex : M _ F
Date:

 

We are trying to find out about your physical activity levels that you have done IN THE LAST 7
D_AY_S_ (in the last week). This includes sports or dance that make you sweat or make your legs
feel tired. or games that make you breathe harder, like tag, skipping, running. climbing and
others.

REMEMBER:

A. There are no right or wrong answers-- this is not a test.

B. Please answer all the questions as honestly and accurately as you can -- this is very important.

 

1. PHYSICAL ACTIVITY

Have you done any of the following activities in the PAST 7 DAYS (last week)? If yes. how
many time?

** Tick Only One Box Per Row **

 

No 1-2 3—4 5-6 7 times or more
Skipping [:1 El I: l:l El
Creative Playground [:1 E] El D l]
Tag E] D D l: l:l
Walking for exercise l: D [:1 El D
Bicycling El 1:1 El 1:1 [:1
Jogging or running 1:1 [:1 D E] l:
Aerobics D l: El El l:l
Swimming l:l l:l El E1 El
Dance El El 1:1 El E1
Skateboarding l:l D D El [:1
Soccer El l:l [I D D
Volleyball Cl l:l 1:1 D El
Basketball Cl l:l l:l E] El
Dodgeball l3 l:l l:l l:l l:l
Other

[:1 D [:1 1:1 [:1

El [:1 1:1 1:] D

 

2. In the lasr 7 days, during your physical education (PE)classes. how often were you very actix e
(playing hard, running. jumping, throwing)?

 

I don’t do PE. ............................................................................. [:1

Hardly ever ............................................................................. El check
Sometimes ............................................................................. [I one
Quite often ............................................................................. 1:] only
Always ............................................................................. 1:]

3. In the last 7 days. what did you do most of the time AT RECESS?

Sat down (talking. reading, doing school work) ............................................ [:1

Stood around or walked around ............................................ [:1 check
Ran or played a little bit ............................................ E] ‘ one
Ran around and played quite a bit ............................................ C] only
Ran and played hard most of the time ............................................ (:1

4. In the last 7 days. what did you normally do AT LUNCH (besides eating lunch)?

Sat down (talking. reading. doing school work) ............... j ............................. [I

Stood around or walked around ............................................ 1:] check
Ran or played a little bit ............................................ [:1 one
Ran around and played quite a bit ............................................ C] only
Ran and played hard most of the time ............................................ [:1

5. In the last 7 days. on how many days RIGHT AFTER SCHOOL, did you do sports, dance. or
play games in which you were very active?

None ............................................ [:l

1 time last week . ............................................ I: check
2 or 3 times last week ............................................ 1:] one

4 times last week i ............................................ [:1 only
5 times last week ............................................ El

6. In the last 7 days. on how many EVENINGS did you do sports, dance. or play games in which
you were very active?

None ............................................ 1:]

1 time last week ............................................ 1:] check
2-3 times last week ............................................ 1:] one
4-5 times last week ............................................ 1:] only
6-7 times last week ............................................ 1:]

286

7. ON THE LAST WEEKEND. how many times did you do sports. dance. or play games in
which you were very active?

None ........................................................................ D

1 time ........................................................................ I] check
2-3 times ........................................................................ [:1 one
4-5 times ........................................................................ [I only
6 or more times ........................................................................ D

8. On the average. about how many hours per day do you watch television? hours;

9. Which ONE of the following describes you best for the last 7 days?

** Read ALL FIVE statements before deciding on the one answer that describes you **
A). All or most of my free time was spent doing things that

involve little physical effort (e. g., watching TV, doing

homework, playing computer games or Nintendo) ....................... (:1

B) I sometimes (1 - 2 times last week) did physical things

in my free time (e. g., played sports, went running.

swimming. bike riding. did aerobics). ....................... I: check
C) I often (3 - 4 times last week) did physical things in one
my free time ....................... Cl only
D) I quite often (5 - 6 times last week) did physical things

in my free time ....................... [I

E) I very often ( 7 or more times last week) did physical

things in my free time ....................... D

10. How fit (in good shape) do you think you are compared to others your age and sex?

Very fit .......................................................................... 1:]

Fitter than most .......................................................................... 1:] check
About average .......................................................................... B one
Less fit than most .......................................................................... [:1 only
Very unfit .......................................................................... 1:]

1 1. Were you sick last week, or did anything prevent you from doing your normal physical
activities“?

Yes ............................................................................................................................... E]

No ................................................................................................................................ [:1

If yes, what prevented you?

 

12. Compared to other children your age and sex, how would you rate yourself as to how

physically active you were in the last 7 days?
A) I was much less active than others
B) I was a little less active than others
C) I was about the same as others

D) l was a little more active than others
E) l was much more active than others

.................................................

.................................................

.................................................

ooooooooooooooooooooooooooooooooooooooooooooooooo

.................................................

D
[:1
El
CI
1:1

check
one
only

13. Mark how often you did physical activity (like playing sports, games. doing dance or any

other physical activity) for each day last week.

None Little bit
A) Monday [:1 E]
B) Tuesday [I [:1
C) Wednesday El 1:]
D) Thursday [:1 1:]
13) Friday [:1 1:1
F) Saturday (:1 D
G) Sunday [:1 1:]

Medium

UCIDDCIEJE]

Often

DDDDDDD

Very often

DDDDDDD

Questionario sobre atividade fisica regular

 

Nome: p Idade: Sexo: M _ F
Data:

 

Gostaria de saber que tipos de atividade fisica vocé praticou NOS ULTIMOS SETE DIAS (nessa
ultima semana). Essas atividades incluem esporte e danca que faeam vocé suar en que faeam vocé
sentir suas pemas cansadas. ou ainda jogos (tais como pique), saltos. corrida e outros. que facam
vocé se sentir ofegante.

LEMBRE-SE:

A. Nae existe eerto ou errado - este questionario n50 e’ um teste.

B. Por favor responda a todas as questoes de forma sincera e precisa - é muito importante para o
resultado.

 

1. ATIVIDADE FISICA

Vocé fez alguma das seguintes atividades nos ULTIMOS 7 DIAS (na semana passada)? Se sim.

quantas vezes?

** Marque apenas um X por atividade **
Nenhuma

’T‘
N
U)
I
4:.
'Ji
1
O\

7 vezes ou mais
Saltos

Atividade no parque ou playground
Pique

Caminhada

Andar de bicicleta

C error on trotar

Ginastiea aerobica

Nataeao

Danea

Andar de skate

Futebol

Voleibol

Basquete

“Queimado”

Outros (liste no espaeo)

DDDDDDDDDDDDDD

 

DD'DDDDDDDDDDDDDD
DD DDDDDDDDDDDDDD
DD DDDDDDDDDDDDDD
DD DDDDDDDDDDDDDD

DD

 

2. Nos ultimos 7 dias, durante as aulas de Educacao Fisica. o quanto vocé foi ativo (iogou
intensamente, correu, saltou e arremessou)?

Eu nao faco as aulas ............................................................................. 1:]

Raramente ............................................................................. 1:] marque
Algumas vezes ............................................................................. 1:] apenas
F reqiientemente ............................ . ................................................ [:l uma
Sempre ............................................................................. 1:1

3. Nos ultimos 7 dias, o que vocé fez na maior parte do RECREIO?

F icou sentado (conversando, lendo, ou fazendo trabalho de casa) .............. [:l

Ficou em pe. parade on andou .............. D marque
Correu ou jogou um pouco .............. E] apenas
Correu ou jogou um bocado .............. 1:] uma

C orreu ou jogou intensamente a maior parte do tempo .............. 1:] opcao

4. Nos ultimos 7 dias, o que vocé fez norrnalmcnte durante o horarie do almeeo (alem dc
almocar)?

F icou sentado (co‘nversando. lendo. ou fazendo trabalho de casa) .............. 1:]

F icou em pe'. parade ou andou .............. E] marque
Correu ou jogou um pouco .............. C] apenas
Correu eu jogou um bocado .............. 1:1 uma

C orreu eu jogou intensamente a maior parte do tempo .............. D opeao

5. Nos ultimos 7 dias, quantos dias da semana vocé praticou algum esporte. danea. ou jogos em
que vecé foi muito ativo. LOGO DEPOIS DA ESCOLA?

Nenhum dia ........................................ [j

1 vez na semana passada ........................................ E] marque
2 cu 3 vezes na semana passada ........................................ D apenas
4 vezes na semana passada ........................................ [:1 uma

5 vezes na semana passada > ........................................ D opeéie

6. Nos ultimos 7 dias. quantas vezes vocé praticou algum esporte, danca, ou jogos em que yoce
foi muito ativo. A NOITE?

Nenhum dia ........................................ D

1 vez na semana passada ........................................ El marque
2-3 vezes na semana passada ........................................ [:1 apenas
4-5 vezes na semana passada ........................................ Cl uma
6-7 vezes na semana passada ........................................ [:1 opcfio

7. NO ULTIMO FINAL DE SEMANA quantas vezes vocé praticou algum esporte. danea. ou
jogos em que vocé foi muito ativo?

Nenhum dia ........................................ E]

1 vez ........................................ [:1 marque
2-3 vezes ........................................ 1:] apenas
4-5 vezes ........................................ Cl uma

6 on mais vezes ........................................ D opcao

8. Em media quantas horas vocé assiste televisao por dia? horas.

9. Qual das epeees abaixo melhor representa vocé nos ultimos 7 dias?

** Leia TODAS AS 5 afirmativas antes de decidir qual é a melhor opeao”

A). Todo ou quase todo o meu tempo livre eu utilizei fazendo

coisas que envelvem pouco esforco fisico (assistir TV, fazer

trabalho de casa. jogar videogames) .................. 1:]
B) Eu pratiquei alguma atividade fisica (1-2 vezes na ultima

semana) durante o meu tempo livre (ex. Praticeu esporte,

cerreu. nadou. andou de bicicleta. fez ginastica aerobica) .................. [:1 marque
C) Eu pratiquei atividade fisica no men tempo livre (3-4 apenas
vezes na semana passada) .................. 1:] uma
D) Eu geralmente pratiquei atividade fisica no meu tempo Opeao
livre (5-6 vezes na semana passada) .................. [:1

E) Eu pratiquei atividade fisiCa regularmente no meu tempo
livre na semana passada (7 on mais vezes) .................. 1:]

10. C omparando voeé com outras pessoas do mesma idade e sexo, como vocé se considera?

Muite mais em forma ...................................................................
Mais em forma ................................................................... marque

[:1

Cl
Igualmente em forma ................................................................... (:1 apenas

Cl

D

Menos em forma ................................................................... uma

C ompletamentc fora de forma ................................................................... opeao

l l. Vocé teve alguma problema de saude na semana passada que impediu que vocé fosse
normalmente ativo?

Sim ............................................................................................................... El

Se sim. o que impediu vocé de ser normalmente ativo?

 

12. Comparando vocé com outras pessoas da mesma idade e sexo, come vocé se classifica em
funeao da sua atividade fisica nos ultimos 7 dias?

 

A) Eu fui muito menos ativo que os outros ................................................. 1:]

B) Eu fui um pouco menes ativo que es outros ................................................. [:1 marque
C) Eu fui igualmente ativo ................................................ El apenas
D) Eu fui um pouco mais ativo que os outros ................................................ 1:] uma
E) Eu fui muito mais ativo que os outros ................................................ C] opedo

l3. Marque a freqiiéncia em que vocé praticou atividade fisica (esporte. jogos. danea ou outra
atividade fisica) na semana passada.
Nenhuma vez Algumas vezes Poucas vezes Diversas vezes Muitas vezes

Segunda El (:1 D D D
Terea D l: I] l: D
Quarta 1:1 1:1 1:1 1:1 1:1
Quinta E] E El l:l 1:1
Sexta [:1 El E] [I D
Sébado [j [:1 El 1:] El
Domingo l:l 1:] l:l El 1:1

IO
\0
IO

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