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Journal of Exercise Physiologyonline
October 2013
Volume 16 Number 5
Official Research Journal of
Editor-in-Chief
Tommy
the American
Boone, PhD,
Society
MBA
of
Exercise
Physiologists
Review
Board
Todd Astorino, PhD
ISSN 1097-9751
Julien Baker,
PhD
Steve Brock, PhD
Lance Dalleck, PhD
Eric Goulet, PhD
Robert Gotshall, PhD
Alexander Hutchison, PhD
M. Knight-Maloney, PhD
Len Kravitz, PhD
James Laskin, PhD
Yit Aun Lim, PhD
Lonnie Lowery, PhD
Derek Marks, PhD
Cristine Mermier, PhD
Robert Robergs, PhD
Chantal Vella, PhD
Dale Wagner, PhD
Frank Wyatt, PhD
Ben Zhou, PhD
Official Research Journal
of the American Society of
Exercise Physiologists
ISSN 1097-9751
JEPonline
Association Between High Adiposity and
Sociodemographic Factors in Adolescents
André L. Araújo1, Randall J.A.F. Melo1, Fabiano F. Silva1,2, Renato A.
Souza1, Giseli Minatto3, Andreia Pelegrini4, Bianca T. Ramallo5,
Cristiano Mostarda5, Wellington R.G. Carvalho5
1
Grupo de Estudos e Pesquisa em Ciências da Saúde (GEP-CS), Instituto
Federal de Educação, Ciência e Tecnologia (IFSULDEMINAS),
Muzambinho, MG, Brazil, 2Biomedical Engineering Institute. Universidade
Camilo Castelo Branco, São José dos Campos, SP, Brazil, 3Universidade
Federal de Santa Catarina, Centro de Desportos, Núcleo de Pesquisa em
Cineantropometria e Desempenho Humano, Florianópolis, SC, Brazil,
4
Universidade Estadual do Estado de Santa Catarina, Centro de Ciências
da Saúde e do Esporte, Grupo de Estudo e Pequisa em Cineantropometria,
Florianópolis, SC, Brazil, 5Universidade Federal do Maranhão,
Departamento de Educação Física, São Luís, MA, Brazil
ABSTRACT
Araújo AL, Melo RJAF, Silva FF, Souza RA, Minatto G, Pelegrini
A, Ramallo BT, Mostarda C, Carvalho WRG. Association Between
High Adiposity and Sociodemographic Factors in Adolescents.
JEPonline 2013;16(5):99-106. The purpose of this study was to
investigate the association between adiposity and sociodemographic
factors such as gender, age, socioeconomic status, and area of
residence in adolescents 11 to 16 yrs of age living in a Brazilian city of
the medium human development index (IDH index). Anthropometric
variables that were measured included body mass, height, sum of
skinfold thickness of the triceps and subscapular (Σ2DC) to calculate
the percentage of body fat (%BF). The Mann-Whitney U Test, chisquare test, and binary logistic regression were used to evaluate the
data. Statistical significance was set at P<0.05. The findings indicate
that increased body fat was observed in 11.8% of the sample. The
girls showed higher Σ2DC (P=0.001) and %BF (P=0.001) than the
boys. There were no statistical associations between the increased
body fat and the other sociodemographic factors. The findings
suggest that it is important to implement health education programs
for this age group to help decrease the adolescents’ predisposition to
elevated body fat.
Key Words: Anthropometry, Body Composition, Obesity
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INTRODUCTION
Recently, Chaves et al. (5) reported on the occurrence of a nutritional transition in Brazil that involves
socioeconomics and demographics factors associated with hypercaloric diets (21). In less than two
decades, young children and the adolescent population have gone from being nutritionally deficient to
the prevalence of obesity (9). The result is a high incidence of diseases, especially in regards to the
adverse effects on health in young adults (6) with increased morbidity and mortality. The obesity
epidemic is a problem that affects both the urban and rural areas of Brazil (20). Findings from studies
conducted in the United Kingdom and North Ireland (26) and Brazil (17) as to the socioeconomic
status that leads to obesity and a reduction in healthy lifestyles.
There are numerous anthropometric measures to identify the person who is obese. Aside from
various imaging techniques (e.g., magnetic resonance imaging, computerized tomography scanning,
and dual X-ray absorptiometry), and bio-impedence analysis, the skinfold measurement test is a
common method to determine percentage of body fat and body composition (17,18,30). Yet, while
there are no perfect tests to measure obesity, especially since each has various limitations, it is
nonetheless important to identify the individual who is obese because the intra-abdominal fat is a risk
factor for disease.
Given this context, it is critical that preventive actions are taken to help obese teenagers who face
statistically greater risks of becoming obese adults (29). In addition, there is a lack of studies in the
Campestre city (Minas Gerais, Brazil) that have investigated levels of adiposity and associated
factors during adolescence. Thus, the aim of this study was to investigate the association between
adiposity and sociodemographic factors (gender, age, socioeconomic status, and area of residence)
in adolescents 11 to 16 yrs of age living in a Brazilian city of the medium human development index
(IDH index).
METHODS
Subjects
This study consisted of 68 adolescents aged 11 to 16 yrs with a mean age of 13.1 ± 1.2 yrs. Thirtyone (45.6%) were males and 37 (54.4%) were females. A descriptive cross-sectional analytical study
was conducted in the Campestre city (Minas Gerais, Brazil). The study followed the guidelines for
human subject research in accordance with Resolution 196/96 of the National Health Council. The
sample for this study was composed of adolescents 11 to 16 yrs of age. The subjects were enrolled in
the 2011 public school year. They were residents in urban and rural areas of the city. The Campestre
city has a Human Development Index (HDI) of 0.759 that ranks as a medium HDI (13,22).
Procedures
In the sociodemographic questionnaire, specific information about the subjects’ gender, date of birth,
socioeconomic status, and area of residence was recorded. Chronological age was established in
accordance with the procedures of Eveleth and Tanner (8). The economic profile of each subject was
verified in accordance with the criteria of the Brazilian Association of Business and Research – ABEP
(2). The instrument that was used estimates the purchasing power of households and classifies the
subjects’ as A, B, C, D, and E in accordance with their accumulation of material goods, housing
conditions, number of domestic servants, and education level of the head of household.
Anthropometric measurements were performed in accordance to the standard procedures (15). Body
weight was measured in kilograms (kg) using a digital scale (Seca®) with a resolution of 100 grams
(g). Height was measured in meters (m) using a vertical stadiometer (Seca®) to the 0.1 centimeters
101
(cm). Skinfold thickness of the posterior arm (triceps, TR) and the subscapular region (SE) were
measured in the right hemibody using a caliper (Holtain Tanner-Whitehouse Skinfold Caliper, UK)
From these data, the sum of TR +SE (Σ2DC) was used to estimate relative body fat (%BF) by the
equations of Slaughter et al. (24). For the classification of %BF, the criteria proposed by Fitnessgram
(25) that identifies high adiposity adolescents who exceed cut off points that establish the healthy
zone were used.
Statistical Analyses
The data were analyzed using descriptive procedures (mean, median, standard deviation, and
frequency distribution) in the software SPSS (Statistical Package for Social Sciences, Inc., Chicago,
IL, USA). Data normality was verified using the Kolmogorov-Smirnov test. The data were not normally
distributed even after applying the logarithmic transformation (Log10). For comparisons between
means and proportions, the Mann-Whitney U Test for independent samples and Chi-square and
Fisher’s Exact tests were used, respectively. Noting that the prevalence of adiposity was lower than
20%, binary logistic regression was used to examined the associations between high body adiposity
with sociodemographic factors (gender, age, economic status, and area of residence), for estimating
odds rations (OR), and the confidence intervals (CI 95%). All variables were entered into the
regression model, which used the method of selection Backward-wald. The level of significance was
set at P≤0.05.
RESULTS
Of the 68 subjects studied, 30 (44.1%) lived in urban areas and 38 (55.9%) lived in the rural area in
the Campestre city. Following analysis of the anthropometric and body composition measures (Table
1), statistically significant differences were observed between the genders only for Σ2DC (P=0.001)
and %BF (P=0.001) with the higher values for girls versus boys. The anthropometric variables of
weight and height did not differ between the genders (P>0.05).
Table 1. Chronological Age, Anthropometric, and Body Composition According to Gender.
Variables
Male (n=31)
Female (n=37)
Age (yrs)
M ± SD
13.2 ± 1.3
Md
13.0
CI (95%)
12.7-13.7
M ± SD
13.0 ± 1.1
Md
13.0
CI (95%)
12.6-13.4
Body Mass (kg)
50.3 ± 11.8
51.0
46.0-54.6
54.1 ± 11.9
53.8
50.1-58.0
Height (m)
1.61 ± 0.1
1.63
1.57-1.65
1.57 ± 0.1
1.58
1.54-1.60
Σ2DC (mm)
17.2 ± 6.1
15.0
14.9-19.4
25.8 ± 10.5*
24.2
22.3-29.3
BF (%)
15.9 ± 5.2
14.7
14.0-17.8
22.5 ± 6.7*
22.1
20.2-24.7
M: mean; ±SD: standard deviation; Md: median; Σ2DC: sum of skinfold thicknesses of triceps and subscapular;
and percentage of body fat (%BF); *P≤0.05 indicates difference between genders (Mann-Whitney U Test).
The high adiposity was observed in 11.8% of the sample. No associations were observed (P>0.05) in
the high adiposity with the variables studied (Table 2).
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Table 2. Data of Absolute (n) and Relative (%) Frequence of High Adiposity According to
Sociodemographic Factors.
Adequate Adiposity
High Adiposity
Variables
Gender (n=68)
Female
Male
Age (yrs) (n=68)
11 to 13
14 to 16
Socioeconomic Status (n=68)
Low (D+E)
Mean (C)
Residense Area (n=68)
Urban
Rural
n
%
n
%
33
27
89.2
87.1
04
04
10.8
12.9
P value
0.540
0.588
39
21
88.6
87.5
05
03
11.4
12.5
0.522
12
48
92.3
87.3
01
07
7.7
12.7
36
24
94.7
80.0
02
06
5.3
20.0
0.068
Fisher’s Exact Test
Table 3 shows the odds ratios of association between body adiposity and high sociodemographic
factors. There were no statistically significant observations in both the crude and adjusted analysis for
high body fat with sociodemographic factors (P>0.05).
Table 3. Association Between Adiposity and High Sociodemographic Factors Assessed by
Odds Ratio and Confidence Interval of 95%.
High Adiposity
Variables
OR
Gender
Female
Male
Age (yrs)
11-13
14-16
Socioeconomic status
Low (D+E)
Mean (C)
Area of residence
Urban
Rural
CI 95%
P
OR*
CI 95%
0.790
1.0
1.2
0.395
2.0
1.0
0.3-5.30
0.4-9.9
0.889
1.0
1.1
0.837
1.0
0.8
0.2-5.10
0.2-4.2
0.616
1.0
1.75
0.603
1.8
1.0
0.2-15.60
0.2-17.4
0.080
1.0
4.5
0.8-24.20
P
0.057
1.0
5.6
0.9-32.8
OR: odds ratio; CI 95%: confidence interval of 95%, P: level of statistical significance; *OR: adjusted for the
sociodemographic factors (gender, age, socioeconomic status, and area of residence). Binary logistic
regression.
103
DISCUSSION
This study examined the association between high adiposity and sociodemographic factors (gender,
age, socioeconomic status, and area of residence) in adolescent mans and females, 11 to 16 yrs of
age, living in a city of medium HDI. The findings indicate that there were higher values of Σ2DC and
%BF for girls than boys. In general, the values were consistent with other studies (4,11,27), and could
be explained by sexual dimorphism. The differences between the genders occur in response to
genetic determinants, hormonal and environmental influences, working since the prenatal period and
presenting variability in its progression at the time of puberty (27).
In a cross-sectional study, Gültekin et al. (11) evaluated 332 boys and 269 girls. They showed clear
evidence of sexual dimorphism in the pattern of body fat, with girls showing higher body fat than boys.
Carvalho et al. (4) in a more recent cross-sectional study involving public school students of both
genders, 8 to 18 yrs, observed that with advancing age higher values of body fat mass and %BF for
girls than boys. In the present study, the body composition variables followed an expected result. Our
findings indicate a high adiposity in 11.8% of the subjects, which is higher than the 3 to 11.2%
reported in the other studies (12,20) and yet, it is lower than some studies (1,14,17) with a range of
17.4% to 25.6%.
It is known that obesity in adolescence tends to persist into adulthood with increased morbidity (19)
and psychological problems. Accordingly, changes in lifestyle, especially regarding physical activity
can help prevent and treat obesity. This thinking is substantiated by the work of Ronque et al. (23),
whose findings suggest that adequate aerobic fitness may contribute to the reduction of risk factors
for cardiovascular disease (7) as well as assist in the control of body fat.
In the present study, no significant association was observed between body composition and high
sociodemographic factors. This is in agreement with earlier studies (4,12,14). In 2011, Minatto et al.
(17) in a cross-sectional study with adolescent boys and girls (14 to 17 yrs of age) from a town with a
low to medium HDI showed the prevalence of inadequate body composition was 24.1%. Inadequate
indices of adiposity were more prevalent among adolescent males from medium to high
socioeconomic strata; whereas, the age of 14 to 15 was a protective factor against inadequate body
composition compared to older ages (16 to 17 yrs of age). Terres and colleagues (28) reported that
there was no association of body composition with 15 to 18 yrs of age. But, they did report higher
rates of adiposity in 15 to 16 yrs olds versus 17 to 18 yrs olds. Additionally, Martorell et al. (16)
showed that the country's economic development seemed to interfere in the magnitude of the
association between body composition and sociodemographic data. The literature suggests that
adolescents living in urban areas have a higher risk of inadequate body composition compared to
those living in the rural area (20).
To our understanding, this study is the first cross-sectional, descriptive analysis of sociodemographic
factors and adiposity in adolescent subjects in the Campestre city. While the lack of studies with
similar criteria makes it difficult to compare our results, the findings do provide important information
that adds to the discussion regarding adolescents who have experienced high adiposity between 11
and 16 yrs of age. As to the likelihood of the subjects developing future health problems (10), our
results should serve as a warning of the association between adiposity and cardiovascular disease,
diabetes mellitus, and hypertension (3,28).
104
Limitations
This study has the limitations inherent of cross-sectional studies: (a) the establishment of a causal
relationship between the outcome and the variables investigated; (b) the loss of part of the sample
(79.6%) for non-compliance by the parents; (c) not attending at school on the day of the assessment;
and (d) the bias that resulted from the subjects who failed to completely fill out the socioeconomic
questionnaire.
CONCLUSIONS
The findings indicate that increased body fat was observed in 11.8% of the sample. The girls showed
higher Σ2DC and %BF than the boys. There were no statistical associations between the increased
body fat and the other sociodemographic factors. The findings suggest that it is important to
implement health education programs for this age group to help decrease the adolescents’
predisposition to elevated body fat.
ACKNOWLEDGMENTS
The authors thank the generous cooperation of the volunteers participating in this study.
Address for correspondence: Carvalho W.R.G., PhD, Departamento de Educação Física,
Universidade Federal do Maranhão, Av. dos Portugueses, 1966 – Cidade Universitária do Bacanga,
São Luis – MA, Brazil, 65080-805, Brazil, Phone: (55) (98) 8153-0239, Email: [email protected]
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