Title: Gender Differences in Physical Activity, Sedentary Behavior … · 2008-07-15 · Title: Gender Differences in Physical Activity, Sedentary Behavior and Its Relation to Body

Title: Gender Differences in Physical Activity, Sedentary Behavior and Its Relation to

Body Composition in Active Brazilian Adolescents

Authors: Julia Aparecida Devide Nogueira * 1 and Teresa Helena Macedo da Costa 2

Address: 1College of Physical Education, University of Brasília, Brazil; 2Nutrition

Biochemistry Laboratory, Department of Nutrition, University of Brasília, Brazil.

* Corresponding author

Email: Julia Nogueira – [email protected]; Teresa da Costa – [email protected]

Running head: Physical activity and body composition in adolescents

Key Words: body mass index, physical activity level (PAL), exercise

Page 1 of 21 Journal of Physical Activity and Health © Human Kinetics, Inc.

mailto:[email protected]

mailto:[email protected]

2

Abstract

Background: Body weight and composition are determined by genotype, environment,

and energy balance. Physical activity or sedentary behavior have different associations

with body weight, fat mass and fat-free mass; a relationship that is not clear in

adolescents. The aim of this study was to test the associations between gender, physical

activity, sedentary behavior and body composition in physically active adolescents.

Methods: Weight, height and skinfold thickness were measured in 326 physically active

boys and girls aged 11-15y. All subjects answered a questionnaire assessing their usual

daily activities for the last month. Time spent on each activity was used to estimate the

physical activity level (PAL).

Results: PAL was associated with body composition after adjustment for age and

maturation, with differences between genders. For boys, PAL was positive and

significantly associated with body mass index (BMI) and fat free mass index (β = 0.14

and 0.15, respectively). For girls, PAL was negative and significantly associated with

BMI and fat mass index (β = -0.11 and -0.75, respectively). Sedentary behavior,

expressed by hours of TV, videogame and computer use, was not associated with any

body composition outcome for either gender.

Conclusion: The accumulated amount of physical activity, but not of sedentary behavior,

was related to body composition in active adolescents.

Page 2 of 21Journal of Physical Activity and Health © Human Kinetics, Inc.

3

Introduction

The determination of body weight and composition is multifactorial and includes

genetic and behavioral components.1 During adolescence, body weight and composition

are regulated by normal growth and maturation2 in association with the dietary habits and

physical activity (PA)/sedentary behavior (SED) that account for the total energy intake

and expenditure, respectively.1 Changes in body composition patterns in populations

occur as a result of lifestyle transitions. It is not clear if the increase in overweight and

obesity in children3, adolescents4,5, and adults1 in recent decades is caused by a decrease

in PA level; an increase in SED time such as television watching, videogame and

computer use; an increase in food intake; or a combination of these factors.1,6

PA is a multidimensional human behavior that is difficult to assess precisely under

free-living conditions, and it is especially challenging in children and adolescents because

of their more complex activity patterns.6,7 Techniques for assessing free-living PA can be

grouped into two broad categories: subjective (observation and questionnaires), and

objective (heart rate, calorimetry, double labeled water and motion sensors). All

techniques present some constraints, and the choice of the most appropriate method to

use, aside from considerations of precision and validity, are dictated largely by practical,

financial and logistical considerations.6 Objective methods are generally preferable in

assessing PA in children. However, in many cases, self-reporting is the only feasible

method of assessing PA in epidemiological studies, and it can provide a valid overall

estimate of the total amount of PA.6,8

There are also several techniques to assess body composition. The body mass

index (BMI) cut-off for age and gender9 is widely used to screen for under and


4

overweight adolescents in epidemiological surveys because of the ease with which body

weight and height can be measured.10 However, BMI does not take into account the

composition of body weight. In spite of the statistical validity of BMI in children and

adolescents, the additional assumption that greater BMI values are equivalent to greater

body fat mass (FM) in this age group is less well supported by evidence.11 A way to

overcome this limitation is to use skinfold thickness measurements to provide

information on relative FM and fat-free mass (FFM). It has been shown that skinfold

thickness is a practical and valid way to estimate FM and FFM in population-based field

studies.12,13 Moreover, FM and FFM can be expressed adjusted for height; as FM index

(FMI) and FFM index (FFMI), respectively. They are equivalent concepts to the BMI;

mathematically, BMI (kg/m2) = FFMI (kg/m2) + FMI (kg/m2).11

So far, results associating PA, SED and body composition in youths are not

consistent.3,14-17 Part of the problem lies in the differences in methodology, imprecision of

the measuring tools, analysis of different outcome variables and different adjustments for

confounding variables.18 Also, any possible relationship is heavily confounded during the

pubertal years by normal physiological changes in body composition, specially because

these changes are different between genders.6 There is no firm scientific recommendation

about the type and amount of PA needed to maintain health, promote optimal growth and

maintain a healthy body composition during adolescence.6 More studies assessing PA and

body composition in different age groups and in transitional nations are encouraged.4

This study was developed to explore and clarify the associations between gender, PA,

SED and body composition in physically active adolescents from Brazil.


5

Methods

Subjects

This study was approved by the Ethics Committee of the Ministry of Health of

Brazil. Each adolescent and parent gave written informed consent prior to participation.

Three hundred and twenty six physically active adolescents aged 11-15y were

randomly selected from a list of participants maintained by the following Brazilian Sports

Federations: handball, volleyball, basketball, indoor soccer, soccer, Olympic gymnastics,

swimming, track and field, judo, and tennis. Adolescents enrolled in Federations are

eligible to participate in local or regional level competitions for that sport.

General characteristics, PA and SED

All participants answered a questionnaire assessing: (I) their socio-economic status

based on items in the house (number of rooms, electronics, automobiles, etc.) and

parents’ educational levels; (II) the signs of biological maturation, defined as presence of

armpit hair for boys or menarche for girls; and (III) their usual daily activities for the past

month. The daily activity questionnaire consisted of 15 questions on: (a) number of hours

per day and days per week, including weekends, spent on training; (b) number of hours

per day and days per week, including weekends, spent on other PAs; (c) number of hours

per day spent sleeping, at school, and on SED (TV, videogame and computer use) during

weekdays and weekends. Trained interviewers administered the questionnaires.

Reported hours of participation in each activity were multiplied by the number of

days they were performed and divided by 7; therefore, results were presented as hours per

day. Boys and girls were stratified into those who trained less than one hour per day and

those who trained one or more hours per day. Physical inactivity was defined as less than


6

300 minutes per week of moderate to vigorous intensity PA practice (training).19 Partial

activity ratio (PAR) was calculated by time spent on each activity times the energy costs

of activities, expressed as multiples of basal metabolic rate. Then PAR was added and

divided by 24 to give physical activity level (PAL) by the FAO/ WHO/ UNU factorial

approach.20

Anthropometry

Following the questionnaires, anthropometric measurements were performed in

triplicate according to the Anthropometric Standardization Manual.21 Body weight was

measured using a digital scale (Plenna model MEA 07400, USA) to ± 0.1 kg. Body

height was measured with a wall-mounted stadiometer (Seca model 208, Germany) to ±

0.5 cm. Triceps and subscapular skinfolds were measured using a Harpender caliper

(CMS Weighing Equipments, UK) to ± 0.2 mm. The average of the closest two

measurements was used for analysis.

Body mass index (BMI; weight (kg) / height2 (m)) was calculated and analyzed.

Cut-off points for age and gender according to the International Obesity Task Force

(IOTF) were used to screen for overweight and obese adolescents.9

FM (%) was calculated using the Boileau, Lohman and Slaughter equation for

males and females aged 8 to 29 years old.22 From the %FM value, FM and FFM values

were calculated and they were also expressed as FM index (FMI; FM (kg) / height2 (m))

and FFM index (FFMI; FFM (kg) / height2 (m)). The validity of this approach has been

previously demonstrated for comparisons of populations where mean height is similar.11


7

Statistical Analysis

To study the factors associated with body composition, PA and SED, an adjusted

linear multiple regression model was used. For each gender the variables BMI, FFMI or

FMI were used as dependent variables. Independent variables were: hours of SED; hours

of training; and PAL. For both genders the effect of age and maturation were controlled.

Dependent variables were transformed for the neperian logarithm scale to minimize the

variability and to approach the normal distribution. A high multicolinearity between

hours of training and PAL was observed. PAL was chosen to perform the analysis.

All variables used in the study were checked for normality of distribution before the

analyses, and appropriate transformations were applied when necessary. Values were

expressed as means and standard deviations. Differences in variables were tested by

paired Student’s t tests. SPSS 7.0 for WINDOWS (SPSS Inc, Chicago, USA) was used

for the calculations. Statistical significance was accepted at p < 0.05.

Results

The population studied had a mean age of 13.0 (SD 1.0) years. Most participants

were from medium (22%), medium-high (51%) or high (19%) socio-economic classes,

with a monthly average family income of US$ 645.00, US$ 978.00 and over US$

1,500.00, respectively. The adolescents had been in school for an average of 5.5 (SD 1.2)

years and had a mean 2.8 (SD 2.3) years of previous experience in physical training. On

weekdays they spent an average of 4.9 (SD 0.3) hours per day in school and 1.5 (SD 0.9)

hours per day studying at home. Participants spent a mean 8.5 (SD 1.1) hours sleeping


8

per day. There were no significant differences between boys (n = 204) and girls (n = 122)

in any of the above characteristics.

However, although mean age was similar, significantly (p = 0.03) more girls (66%)

reported having the signs of maturation than boys (53%). Hours of training per day, PAL,

and fat mass (index and %) were also significantly different between genders, being

higher for girls than boys (Table 1).

Mean BMI was in the normal range according to the IOTF cut-offs; however,

14.7% of boys and 13.1% of girls were classified as overweight or obese according to

these criteria. No significant differences were found in age, height, PA (hours of training

or PAL) and SED patterns between overweight and obese subjects and their lean

counterparts. However, weight and FM (% and index), but also FFMI were significantly

higher in overweight and obese boys and girls than in their lean counterparts (all p <

0.001).

To further investigate the influence of gender and PA on body composition, the

group was stratified into those who trained less than one hour per day (<300min/week)

and those who trained one or more hours per day (Table 2). Boys who trained one or

more hours per day were significantly older (p=0.019) than boys who trained less, and

girls who trained one or more hours per day were significantly younger (p=0.037) than

girls who trained less; so, the results presented were adjusted for age and maturation.

Boys who trained one or more hours per day had significantly higher PAL, weight,

height, BMI and FFMI than boys who trained less. Girls who trained one or more hours

per day had significantly higher PAL and FFMI, lower FM% and spent significantly

fewer hours on SED.


9

Multiple linear regression models with activity (PAL) and SED measurements as

independent variables and BMI, FFMI and FMI as the outcome variables are shown in

Table 3. Each model included age and maturation as covariates. In boys, PAL was

significantly and positively associated with BMI and FFMI. In girls, PAL was

significantly and negatively associated with BMI and FMI. No significant associations

were found between body composition and SED for boys and girls.

Discussion

This is, to our knowledge, the first study to assess the relation of gender, PA, BMI,

FMI and FFMI in a relatively large sample of pre-pubertal and pubertal Brazilian

adolescents involved in physical training, from beginners with five months experience to

very experienced ones, with up to nine years experience. Although not representative of

the Brazilian adolescent population, the sample reflects the nature of participation in

sports in developing countries, where low-income subjects are naturally excluded by lack

of time due to work and/or lack of money for equipments, clothes, and transportation to

and from the training place.

The sample was homogeneous, with boys and girls being similar in socio-economic

status, age, BMI and FFMI. However, more girls reported the signs of maturation and

they presented higher amounts of fat (expressed as FM% and FMI). During adolescence,

girls usually enter puberty earlier than boys and the BMI increase for girls is the

consequence of an increase in both FFM and, more pronounced, in FM after menarche.2

During adolescence, age is not only related to physical growth and maturation,23 but also

related to participation in PA. Younger girls and older boys trained more than their


10

counterparts. Studies reported a decline in PA with age during adolescence especially

among females.24-27 For the reasons above, results of PA, PAL, SED, and body

composition were adjusted for age and maturation.

After adjustments, PAL showed associations with body composition outcomes in

physically active adolescents with differences for gender. For boys, PAL was positive

and significantly associated with BMI and FFMI. For girls, PAL was negative and

significantly associated with BMI and FMI. Usually, studies report associations between

PAL and FM for males but not for females,3,18 but also report higher levels of PA for

boys than girls.15,17,18,28 In this study, hours of training and PAL were significantly higher

for girls than boys, what can help to explain the negative association of PAL with FM for

girls. Adolescents who engage in larger amounts of PA tend to have lower FM than those

with lower amounts of PA.28 In our study, the group that presented the highest number of

hours spent on training, the gymnasts, was composed exclusively of girls. PA type and

intensity may also play a role in the definition of the type of change in body composition;

short-term high-intensity PA is related to an increase in FFM, and long-term moderate-

intensity PA is related to a decrease in FM in adults.6,22 This is an aspect that needs to be

further investigated in adolescents.

Approximately 32% of the participants reported ≥4 hours of SED per day, and

fewer SED hours were reported by girls who trained one or more hours per day. Other

studies also report a high amount of inactivity among contemporary children and

adolescents14,15,29 but some report an association of SED and body composition

(obesity).30,31 SED, although high for both genders, was not associated with body

composition in our study.


11

The inconsistent findings that do or do not link PA, SED and body composition can

be related to several factors.3,14-17 Firstly, methodological limitations are present in the

measurement of PA and SED. Self-reported PA and SED are associated with recall bias,

which is particularly problematic in children and adolescents. On the other hand,

observational procedures may interfere with spontaneous activity patterns, require

extreme control of observer reliability, and can be expensive and time-consuming,

making them only suitable for small to medium sized samples.6,18

Secondly, PA data analysis in children and adolescents who differ in body size is

influenced by whether PA is expressed in terms of body movement or energy

expenditure.7 PAL predominantly reflects the energy cost of habitual PA and some

authors advocate that when determining the relation of PA with adiposity, PA should be

expressed as body movement, not as energy expended.32 However, it may be that the

energy cost of habitual activity is equally important, in terms of body composition, as the

extent of physical movement.33 Indeed, in this study PAL was shown to correlate and

associate with some body composition outcomes more strongly than hours of training.

Thirdly, results will differ accordingly to the body composition outcome variable

chosen to integrate the analysis. Some studies documenting the recent trends in body

composition often rely on BMI alone to define overweight or obesity.14,15,29 This study

showed that FMI and FFMI, BMI components, presented differences by gender. The

analysis using these two components was shown to be more appropriate and should be

chosen whenever possible, since the excess of body fat is the specific factor associated

with obesity and the increased disease risk.34


12

Other limitations that should be considered in interpreting the findings from the

present study are: the cross-sectional nature of the data does not allow the establishment

of a causal relation; the precision of predicted FM can be limited by the uncertain

availability of generalizable, valid, reliable, cross-validated prediction equations for

various age, sex, and racial groups;35 and although we controlled for some potential

confounders, other unmeasured confounders such as hereditary components (parents’

body composition, genetic variations) might have a substantial influence in explaining the

observations.36

Conclusions

Results from this study are noteworthy since data on PA, SED and body

composition of adolescents of transitional nations are lacking. For these adolescents, PA

was related to body composition; PAL was associated with higher BMI and FFMI in boys

and with a lower BMI and FMI in girls. More significant differences in body composition

were seen in the group of adolescents who had one or more hours of PA per day.

Although active, these contemporary Brazilian adolescents presented high amounts

of SED. However, SED was not related to body composition in this group. It may be that

SED is a more appropriate intervention target to improve body composition on those who

are less active than our study population. These findings should assist National and

International Health Organizations to develop policies and programs focusing on the

promotion of PA as a healthy behavior in relation to growth and body composition

among contemporary active adolescents. Other studies are required to further investigate

PA type and intensity, and dietary patterns in relation to body composition and health.


13

Acknowledgements

This project was partially funded by Finatec (Fundação de Empreendimentos Científicos

e Tecnológicos) and Funpe (Fundo de Pesquisa), University of Brasilia, Brazil. The

author JADN received scholarships from CAPES (Coordenação de Aperfeiçoamento de

Pessoal de Nível Superior) and CNPq (Conselho Nacional de Desenvolvimento Científico

e Tecnológico), Brazil. We thank Eliene Souza and Clislian Silva for helping in data

collection.


14

References:

1. World Health Organization: Obesity: Preventing and Managing the Global Epidemic

- report of a WHO consultation on obesity. Geneva: WHO, 1998.

2. Rogol AD, Clark PA, Roemmich JN. Growth and pubertal development in children

and adolescents: effects of diet and physical activity. Am. J. Clin. Nutr.

2000;72:S521-528.

3. Ball EJ, O'Connor J, Abbott R, Steinbeck KS, Davies PSW, Wishart C, Gaskin KJ,

Baur LA. Total energy expenditure, body fatness, and physical activity in children

aged 6-9y. Am. J. Clin. Nutr. 2001;74:524-528.

4. Wang Y, Monteiro C, Popkin BM. Trends of obesity and underweight in older

children and adolescents in the United States, Brazil, China, and Russia. Am. J.

Clin. Nutr. 2002;75:971-977.

5. Watts K, Beye P, Siafarikas A, Davis EA, Jones TW, O'Driscoll G, Green DJ.

Exercise training normalizes vascular dysfunction and improves central adiposity

in obese adolescents. J. Am. Coll. Cardiology. 2004;43:1823-1827.

6. Livingstone MBE, Robson PJ, Wallace JMW, McKinley MC. How active are we?

Levels of routine physical activity in children and adults. Proc. Nut. Soc.

2003;62:681-701.

7. Ekelund U, Yngve A, Brage S, Westerterp KR, Sjostrom M. Body movement and

physical activity energy expenditure in children and adolescents: how to adjust for

differences in body size and age. Am. J. Clin. Nutr. 2004;79:851-856.

8. Sallis JF, Saelens BE. Assessment of physical activity by self-report: status,

limitations, and future directions. Res. Q. Exerc. Sport. 2000;71:S1-14.


15

9. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for

child overweight and obesity worldwide: international survey. Br. J. Nutr.

2000;320:1240-1243.

10. Malina RM, Katzmarzyk PT. Validity of the body mass index as an indicator of the

risk and presence of overweight in adolescents. Am. J. Clin. Nutr. 1999;70:S131-

136.

11. Wells JCK, Coward WA, Cole TJ, Davies PSW. The contribution of fat and fat-free

tissue to body mass index in contemporary children and the reference child. Int. J.

Obes. 2002;26:1323-1328.

12. Eckhardt CL, Adair LS, Caballero B, Avila J, Kon IY, Wang JZ, Popkin BM.

Estimating body fat from anthropometry and isotopic dilution: A four-country

comparison. Obes. Res. 2003;11:1553-1562.

13. van Loan MD. Assessment of fat-free mass in teen-agers: use of TOBEC

methodology. Am. J. Clin. Nutr. 1990;52:586-590.

14. Dennison BA, Erb TA, Jenkins PL. Television viewing and television in bedroom

associated with overweight risk among low-income preschool children.

Pediatrics. 2002;109:1028-1035.

15. Eisenmann JC, Bartee RT, Wang MQ. Physical activity, TV viewing, and weight in

U.S. youth: 1999 Youth Risk Behavior Survey. Obes. Res. 2002;10:379-385.

16. Goran MI, Reynolds KD, Lindquist CH. Role of physical activity in the prevention of

obesity in children. Int. J. Obes. Relat. Metab. Disord. 1999;23:S18-33.


16

17. Rennie KL, Livingstone MBE, Wells JCK, McGloin A, Coward WA, Prentice AM,

Jebb SA. Association of physical activity with body-composition indexes in

children aged 6-8 y at varied risk of obesity. Am. J. Clin. Nutr. 2005;8:13-20.

18. Ekelund U, Neovius M, Linné Y, Brage S, Wareham N, Rossner S. Associations

between physical activity and fat mass in adolescents: the Stockholm Weight

Development Study. Am. J. Clin. Nutr. 2005;81:355-360.

19. Biddle S, Cavill N, Sallis J. Young and active? Young people and health-enhancing

physical activity evidence and implications. London: Health Education Authority;

1998.

20. FAO, WHO, UNU. FAO/WHO/UNU Expert Consultation: Human energy

requirements. Geneva: WHO, 2001.

21. Lohman TG, Roche AF, Martorell R. Anthropometric Standardization Reference

Manual. Champaign, IL: Human Kinetics Publishers, 1988.

22. Boileau RA, Lohman TG, Slaughter MH. Exercise and body composition of children

and youth. Scand. J. Sports Sci. 1985;7:17-27.

23. Roemmich JN, Clark PA, Weltman A, Rogol AD. Alterations in growth and body

composition during puberty. 1. Comparing multicompartment body composition

models. J. Appl. Physiol. 1997;83:927-935.

24. Anderson RE, Crespo CJ, Bartlett SJ, Cheskin LJ, Pratt M. Relationship of physical

activity and television watching with body weight and level of fatness among

children: results from the Third National Health and Nutrition Examination

Survey. JAMA. 1998;279:938-942.


17

25. Hoos MB, Gerver WJM, Kester AD, Westerterp KR. Physical activity levels in

children and adolescents. Int. J. Obes. 2003;27:605-609.

26. Kann L, Kinchen SA, William BL. Youth Risk Behavior Surveillance - United States,

1997. Report CDC Surveillance Summary. Morb. Mortal. Wkly. 1998;47:1-89.

27. Sallis JF. Age-related decline in physical activity: a synthesis of human and animal

studies. Med. Sci. Sports Exerc. 2000;32:1598-1600.

28. Gutin B, Yin Z, Humphries MC, Barbeau P. Relation of moderate and vigorous

physical activity to fitness and fatness in adolescents. Am. J. Clin. Nutr.

2005;81:746-750.

29. Dowda M, Ainsworth BE, Addy CL, Saunders R, Riner W. Environmental

influences, physical activity, and weight status in 8- to 16-year-olds. Arch.

Pediatr. Adolesc. Med. 2001;155:711-717.

30. Arluk SL, Branch JD, Swain DP, Dowling EA. Childhood obesity´s relationship to

time spent in sedentary behavior. Military Medicine. 2003;168:583-586.

31. Must A, Tybor DJ. Physical activity and sedentary behavior: a review of longitudinal

studies of weight and adiposity in youth. Int. J. Obesity. 2005;29:S84-S96.

32. Rowlands AV, Ingledew DK, Eston RG. The effect of type of physical activity

measure on the relationship between body fatness and habitual physical activity in

children: a meta analysis. Ann. Hum. Biol. 2000;27:479-497.

33. Abbott RA., Davies PSW. Habitual physical activity and physical activity intensity:

their relation to body composition in 5.0-10.5-y-old children. Eur. J. Clin. Nutr.

2004;58:285-291.


18

34. Gutin B, Barbeau P, Owens S, Lemmon CR, Bauman M, Allison J, Kang H, Litaker

MS. Effects of exercise intensity on cardiovascular fitness, total body

composition, and visceral adiposity of obese adolescents. Am. J. Clin. Nutr.

2002;75:818-826.

35. Chumlea WC, Guo SS, Kuczmarski J, Johnson CL, Leahy CK. Body composition

estimates from NHANES III bioelectrical impedance data. Int. J. Obes. Relat.

Metab. Disord. 2002;26:1596-1609.

36. Bouchard C, Malina RM, Pérusse L. Genetics of the Fitness and Physical

Performance. Champaign, IL: Human Kinetics Publishers, 1997.


20

Table 1 – Descriptive characteristics of physically active Brazilian adolescents.

Boys (n = 204) Girls (n = 122) p

Characteristics M SD M SD Boys vs. girls

Age (years) 12.9 1.0 13.1 1.1 NS

Training (h/d) 0.9 0.6 1.3 0.9 <0.001

SED (h/d) 3.3 1.9 3.2 1.9 NS

PAL 1.55 0.15 1.64 0.22 <0.001

Weight (kg) 48.4 11.0 48.5 9.7 NS

Height (cm) 158.2 10.9 157.8 8.2 NS

BMI (kg/m2) 19.2 2.7 19.3 2.9 NS

FMI (kg/m2) 3.1 1.6 4.3 1.8 <0.05

FFMI (kg/m2) 16.1 1.6 15.0 1.5 NS

FM (%) 15.7 5.9 21.7 6.5 <0.001

SED, sedentary behavior; PAL, physical activity level; BMI, body mass index; FMI, fat mass index; FFMI, fat-free mass index; FM, fat mass;

M, mean; SD, standard deviation; p, level of significance by Student’s t test.


21

Table 2 – PA, SED and body composition of physically active Brazilian adolescents separated by hours of training and adjusted for age and

maturation.

Boys Girls

Training (h/d) < 1 (n = 146) ≥ 1 (n = 58) < 1 (n = 67) ≥ 1 (n = 55)

M 95%CI M 95%CI p M 95%CI M 95%CI p

Training (h/d) 0.6 0.5 to 0.7 1.6 1. 6 to 1.7 <0.001 0.6 0.5 to 0.8 2.1 1.9 to 2.2 <0.001

SED (h/d) 3.3 3.0 to 3.6 3.3 2.9 to 3.8 NS 3.6 3.2 to 4.1 2.8 2.2 to 3.3 <0.05

PAL 1.48 1.46 to 1.50 1.72 1.69 to 1.75 <0.001 1.49 1.45 to 1.52 1.82 1.78 to 1.86 <0.001

Weight (kg) 47.0 45.5 to 48.6 51.9 49.4 to 54.3 <0.001 47.9 45.8 to 50.0 49.2 46.9 to 51.6 NS

Height (cm) 157.1 155.7 to158.5 161.1 158.9 to163.3 <0.01 157.0 155.2 to 58.7 158.8 156.8 to 160.8 NS

BMI (kg/m2) 18.9 18.5 to 19.3 19.8 19.1 to 20.5 <0.05 19.4 18.7 to 20.1 19.4 18.7 to 20.2 NS

FMI (kg/m2) 3.0 2.8 to 3.3 3.3 2.9 to 3.7 NS 4.6 4.2 to 5.1 4.0 3.5 to 4.5 NS

FFMI (kg/m2) 15.9 15.6 to 16.1 16.5 16.1 to 16.9 <0.01 14.8 14.4 to 15.1 15.4 15.0 to 15.8 <0.05

FM (%) 15.5 14.5 to 16.4 16.1 14.6 to 17.7 NS 23.3 21.8 to 24.8 19.7 18.1 to 21.4 <0.01

PA, physical activity; SED, sedentary behavior; PAL, physical activity level; BMI, body mass index; FMI, fat mass index; FFMI, fat-free mass

index; FM, fat mass; M, mean; CI, confidence interval; p, level of significance by Student’s t test.


22

Table 3 –Multivariate analysis examining the association of PA and SED with BMI, FMI and FFMI in physically active Brazilian adolescents.

BMI (kg/m2) 1 FMI (kg/m2) 1 FFMI (kg/m2) 1

Models * β SE p β SE p β SE p

Boys (n=204)

PAL 0.14 0.05 <0.05 -0.02 0.02 NS 0.15 0.04 <0.001

SED (h/d) 0.00 0.01 NS 0.00 0.00 NS 0.01 0.00 NS

Girls (n=122)

PAL -0.11 0.05 <0.05 -0.75 0.16 <0.001 0.04 0.04 NS

SED (h/d) 1 -0.01 0.01 NS -0.03 0.02 NS -0.00 0.00 NS

*All models were adjusted for age and maturation; 1 Measures were log transformed.

PA, physical activity; SED, sedentary behavior; PAL, physical activity level; BMI, body mass index; FMI, fat mass index; FFMI, fat-free mass

index; β, unstandardized regression coefficients; SE, standard errors; p, level of significance by Student’s t test.


Documents

Title: Gender Differences in Physical Activity, Sedentary Behavior … · 2008-07-15 · Title: Gender Differences in Physical Activity, Sedentary Behavior and Its Relation to Body