Association between obesity and sedentary behavior in adults

Associação entre a obesidade e o comportamento sedentário em adultos

Abstract

Although sedentary behavior (SB) is related to the development of metabolic diseases, there is still no consensus in literature about the association between accelerometer-based SB and obesity, especially adjusted for cardiovascular risk factors and level of daily physical activities. The aim was to evaluate the association between obesity and SB adjusted for potential confounders in adults. Data from 780 participants of the Epidemiology and Human Movement (EPIMOV) Study were analyzed. Body weight, body mass index (BMI), and fat body mass as percentage (%FBM) (bioelectrical impedance) were obtained and, then, used to stratify participants. SB was objectively measured using triaxial waist-worn accelerometers placed above the dominant hip during waking hours for at least four consecutive days (4-7 days). SB and its pattern were not significantly different between obesity groups. Although SB presented some significant correlations with obesity, the correlation and determination coefficient indicated weak association between SB and obesity (e.g., BMI and %FBM). Obesity presented little or no association with SB and its pattern after adjustment for potential confounders, especially when SB is measured through accelerometry.

Key words
Body composition; Motor Activity; Obesity; Sedentary Behavior

Resumo

Embora o comportamento sedentário (CS) esteja relacionado ao desenvolvimento de doenças metabólicas, ainda não há consenso na literatura sobre a associação entre o CS avaliado diretamente por acelerometria e a obesidade, especialmente quando essa relação é ajustada por fatores de risco cardiovascular e nível de atividade física. Objetivou-se avaliar a associação entre CS e obesidade ajustada por potenciais confundidores em adultos. Foram analisados os dados de 780 participantes do Estudo Epidemiológico sobre o Movimento Humano (EPIMOV). Dados relativos à massa corporal, índice de massa corporal (IMC) e porcentagem de gordura corporal (%GC) (bioimpedância elétrica) foram obtidos e, então, utilizados para estratificar os participantes. O CS foi medido objetivamente por meio de acelerômetros triaxiais colocados sob o quadril dominante durante as horas de vigília por, pelo menos, quatro dias consecutivos (4-7 dias). O CS e seu padrão não foram significativamente diferentes entre os grupos de obesidade. Embora o CS tenha apresentado algumas correlações significativas com a obesidade, o coeficiente de correlação e determinação indicou uma fraca associação entre o CS e a obesidade (por exemplo, IMC e %GC). A obesidade apresentou pouca ou nenhuma associação com o CS e seu padrão após o ajuste para potenciais fatores de confusão, principalmente quando avaliado com acelerômetro.

Palavras-chave
Composição Corporal; Atividade Motora;; Obesidade; Comportamento Sedentário

INTRODUCTION

Obesity is a growing worldwide public health problem, which is associated with cardiovascular problems, diabetes, cancer and other diseases¹. The energy imbalance between calorie consumption and expenditure is the main trigger of obesity². Increased time in sedentary behavior (SB), as well as reduced level of daily physical activities results in decreased total energy expenditure³.

The benefits of physical activity and its inverse relationship with all-cause mortality are widely described in literature⁴. However, adopting SB (e.g. activities with energy expenditure ≤ 1.5 Metabolic Equivalent of Task (METs) or sitting and lying postures) differs from being physically inactive, both of which may or may not coexist⁵. The English expression “Active Couch Potato” refers to the subject who meets the minimum recommended physical activity, but spends much time in SB, which can be equally harmful⁵. Thus, SB should gain attention in public health policies and recommendations on physical inactivity and disease prevention⁶ since SB is an independent predictor of premature mortality⁷ and has potential for the development of metabolic diseases due to the absence of prolonged muscle contraction⁸.

Triaxial accelerometry provides an objective measure of SB and its pattern such as total time, sedentary breaks and bouts, which also influence metabolic health and cardiovascular risk⁹,¹⁰. There is still no consensus in literature about the association between accelerometer-based SB and obesity, especially when adjusted for associated comorbidities and level of daily physical activities. Our hypothesis is that obesity is not associated with SB after adjustment for commonly associated comorbidities. Thus, we aimed to evaluate the association between obesity and presence of SB adjusted for potential confounders in adults. Secondarily, the association between obesity and level of daily physical activities was also evaluated.

METHODS

Study design and participants

This is a cross-sectional study with 780 participants aged ≥ 20 years (297 men and 493 women) selected from the Epidemiological and Human Movement Study (EPIMOV), approved by the local university ethics and research committee, No. 186.796. The EPIMOV Study is a population-based epidemiological study whose main objective is to evaluate the association between SB and physical inactivity and the development of hypokinetic chronic diseases.

The sample was selected for convenience and recruited through folders and social media. All participants were informed about the risks and discomforts related to the research protocol and signed the Informed Consent Form.

Participants with previous diagnosis of heart or lung disease and/or with difficulties to perform physical activity due to osteoarticular, neurological or musculoskeletal problems were excluded.

Measures

• Clinical and sociodemographic assessment

Anamnesis about regular medication use and health problems was conducted. In addition, all participants answered the previously validated physical activity readiness questionnaire (PAR-Q)¹¹. The following cardiovascular risk factors were considered: Family history of cardiovascular diseases (incidence of acute myocardial infarction in first-degree relatives), arterial hypertension, hyperglycemia or diabetes, already installed hypercholesterolemia or dyslipidemia, smoking (self-reported current smoking or having smoked at least 100 cigarettes during life), overweight or obesity and level of physical activity as recommended¹², which were adapted for direct evaluation by triaxial accelerometry in this study.

• Anthropometric evaluation and body composition

Body mass and height were obtained using stadiometer (TOLEDO, São Paulo, Brasil). Then, body mass index (BMI) was calculated¹³. Waist, hip and neck circumferences were also measured, as previously recommended¹³.

Body composition was assessed using tetrapolar electrical bioimpedance (310e, BIODYNAMICS, Detroit, USA). All prior determinant procedures for non-compromise and test accuracy were followed, which include not using diuretic drugs for 7 days, fasting for at least 4 hours, do not drink alcohol 48 hours prior to test, urinate at least 30 minutes before test and remain 10 minutes in supine position prior to test¹⁴.

Two electrodes were applied on the dorsal region of the dominant hand and other two on the dominant foot. Participants were evaluated in supine position with arms and legs abducted at 30º and 45º respectively. All procedures followed manufacturer’s manual instructions. Resistance and reactance were obtained. Lean body mass and fat body mass as total and percentage were calculated by means of previously validated specific equation¹⁵.

Participants were stratified according to fat body mass as percentage (%FBM) as follows: mild obesity was considered from 25 to 30%, moderate from 30 to 35%, high from 35 to 40% and morbid above 40% for women. For men, mild obesity was considered from 15 to 20% of FBM, moderate from 20 to 25%, high from 25 to 30 and morbid above 30%¹⁴.

• Accelerometer-based sedentary behavior and physical activity

Previously validated triaxial accelerometer (GT3X+, Actigraph, Pensacola, FL, EUA) was used to assess SB and daily physical activity ^16,17.

Participants were instructed to use the device in the dominant hip for 7 consecutive days during waking hours without removing it, except for water-related activities or during night sleep.

Data from participants who used the device for at least four days for 12 consecutive hours per day were considered valid ¹⁷. As recommended, participants who presented less than 150 minutes of moderate-to-vigorous physical activity (MVPA) during weekdays were considered physically inactive¹². SB and its pattern were obtained as previously described^{9, 17-19}.

Briefly, SB was considered as activities with less than 100 counts per minute (cpm) and sedentary bouts as activities that remain at least 5 minutes. For sedentary breaks, activities with more than 100 cpm (transitions from sedentary to active phase) were considered. Accelerometer non-use time was defined as at least 60 minutes of zero counts, e.g. between 0 and 100 cpm. SB and its pattern were calculated as minutes or minutes/week, and as absolute values and percentages of the total time. The total duration of sedentary series (min) was corrected for total accelerometer use time^17-19.

Physical activity intensity thresholds were as follows: light (≤ 3.00 METs or <1952 counts), moderate (3.00-5.99 METs or between 1952-5724), vigorous (6.00-8.99 METs or between 5725-9498 counts) and very vigorous physical activity (9.00 METs or >9498 counts)¹⁹. Daily .physical activity was calculated as minutes, hours and percentages of the total time.

Energy expenditure was also obtained in METs and Kcal from counts using Freedson’s equation through the physical activity intensity ¹⁸.

Statistical analysis

Statistical analysis was performed using SPSS® version 23 (IBM SPSS Corp., Armonk, NY, USA). Data were descriptively analyzed and presented as mean ± standard deviation or as median (interquartile range) according to the Kolmogorov-Smirnov test. Categorical variables were described as frequency and percentages.

Sample size was calculated based on the number of independent variables of interest for inclusion in the multiple regression model, which indicates at least 135 subjects for the present study. Sex, age (years), hypertension, dyslipidemia, diabetes, %FBM and current smoking were included in multivariate models.

At first, correlations between physical activity and SB and the other variables under study were analyzed using Pearson’s or Spearman’s correlation coefficients and simple linear regression.

The sample was stratified according to %FBM. ANOVA was used for comparison of physical activity and SB between groups. The analysis was adjusted for age (years), sex and main confounders (e.g., cardiovascular risk factors). For comparison of categorical variables, the χ² test was used.

A 3-step hierarchical regression analysis was performed to investigate the association between %FBM or BMI and physical activity or SB. In Step 1, only %FBM or BMI was used to obtain unadjusted coefficients. Step 2 contained Step 1 plus age and sex. Finally, Step 3 used Step 2 plus arterial hypertension, diabetes, dyslipidemia and current smoking. The α error probability was set at 5% for aforementioned tests.

RESULTS

The sample was mainly composed of middle-aged women. Eutrophic participants were younger when compared to obese groups. The prevalence of diabetes was higher in morbid obesity in comparison to the other groups. The same was observed for prevalence of hypertension in high and morbid obesity and for prevalence of dyslipidemia in moderate, high and morbid obesity (Table 1).

Table 1
Sample characteristics (n = 780).

		Obesity stratified according to Fat Body Mass (%)
		Eutrophic (n=74)	Mild obesity (n=114)	Moderate obesity (n=198)	High obesity (n=153)	Morbid obesity (n=241)
Age (yr)		30 ± 9	36 ± 13	43 ± 13	48 ± 12	49 ±13
Male		31 (41.9)	44 (38.6)	55 (27.8)	57 (37.3)	110 (41.5)†
Female		43 (58.1)	70 (61.4)	143 (72.2)#	96 (62.7)	141 (58.5)
Hypertension		2 (2.7)	6 (5.3)	25 (12.6)	36 (23.5)*‡	70 (29)*‡†
Diabetes		0 (0)	5 (4.4)	16 (8.1)	19 (12.4)	43 (17.8)‡†
Dyslipidemia		3 (4.1)	12 (10.5)	52 (26.3)*‡	54 (35.3)*‡	91 (37.5)*‡
Smoking		6 (8.1)	12 (10.5)	26 (13.1)	19 (12.4)	27 (11.2)
BMI (kg/m²)		22 ± 2	23 ± 2	26 ± 4	29 ± 3	33 ± 6
Waist circumference (cm)		73 ± 8	77 ± 8	84 ± 11	94 ± 8	102 ± 14
Waist-to-hip ratio		0.7 ± 0.05	0.8 ± 0.08	0.8 ± 0.07	0.9 ± 0.08	0.8 ± 0.07

• Note. Data were expressed as mean ± standard deviation and frequency (%). BMI: body mass index.
• ^*
  (p ≤ 0.05) vs. eutrophic;
• ^‡
  (p ≤ 0.05) vs. mild obesity;
• ^†
  (p ≤ 0.05) vs. moderate obesity; €: (p ≤ 0.05) vs. high obesity;
• ^#
  (p ≤ 0.05) vs. morbid obesity.

The proportion of physically inactive participants was 23% for the eutrophic group, 26% for the mild obesity, 25% for the moderate obesity group, 35% for the high obesity group and 38% for the morbid obesity group (Table 1).

SB pattern was similar for all groups, except for sitting time (%) in the high obesity group when compared to the eutrophic group, and standing time (%) in all groups when compared to the morbid obesity group (Table 2).

Table 2
Results for accelerometer-based sedentary behavior and accelerometer-based physical activity

	Obesity stratified according to Fat Body Mass (%)
	Eutrophic (n=74)	Mild obesity (n=114)	Moderate obesity (n=198)	High obesity (n=153)	Morbid obesity (n=241)
Sedentary behavior
Total number of sedentary bouts	66 (28)	66 (30)	58 (28)	66 (32)	61 (32)
Total time of sedentary bouts (min)	1,228.3 (571.7)	1,277.9 (653.0)	1,112.8(601.7)	1,303.9 (704.3)	1,219.9 (712.4)
Average duration of each sedentary bouts (min)	18.3 (-2.6)	18.8 (-2.2)	18.8 (2.5)	19.2 (-2.7)	19.3 (-2.7)
Daily average duration of sedentary bouts (min)	221.6 (-78.6)	226.9 (-96.8)	207.4(-86.6)	229.7 (-99.3)	223.3 (-98.1)
Total number of sedentary breaks	65 (28)	65 (30)	57 (28)	65 (32)	60 (32)
Total duration of sedentary breaks (min)	7,348.9 (2,679.5)	7,013.7 (1,877.7)	7,058.4 (1,990.9)	6,991.9 (-2,409.9)	7,306.7 (2,586.3)
Average duration of each sedentary break (min)	131.4 (-71.7)	137.7(-94.5)	165.4 (-162.9)	135.0 (-89.2)	174.1 (-207.2)
Average duration of sedentary breaks (min)	1,379.9 (488.4)	1,299.1 (376.9)	1,391.1 (479.9)	1.,292.4 (499.7)	1,453.2 (638.5)
Average time in sedentary behavior (% total)	74.0 (-6.1)	74.1 (-6.2)	72.0 (-7.2)	73.1 (-7.6)	73.8 (-6.5)
Standing time (% total)	24.0 (-8.2)†€#	24.2 (7.2)€#	21.4 (7.5)#	20.5 (6.9)#	17.5 (-7.4)
Sitting time (% total)	22 (8)	23 (7)	24.1 (-8.1)	26.0 (-7.6)*	24.7 (-8.3)
Sitting time during weekdays (min) ¥	305.3 (-189.0)	315.9 (-202.3)	298.5 (-220.5)	294.5 (-198.5)	338.6 (-222.5)
Sitting time during weekend (min) ¥	249.5 (-181.0)	272.2 (-206.9)	246.6 (-193.2)	266.5 (-172.6)	318.1 (-200.8)†
Physical activity level
Average energy expenditure (Kcal)	2.102 (1.224)	2.034 (1.213)	2.278 (1.291)	2.625 (1.560)‡	2.627 (1.533)‡
Average energy expenditure/day (Kcal)	379 (204)	373 (208)	427 (200)	480 (256)*‡	492 (240)*‡
Average energy expenditure (METs)	1.2 (0.1)	1.2 (0.1)	1.2 (0.1)	1.2 (0.1)	1.2(0.1)
Physical activity
Light (% total)	20.1 (5.1)	20.5 (5.8)	22.6 (6.2)*	21.7 (6.1)	21.7 (5.4)
Moderate (% total)	5.1 (2.1)	4.8 (2.5)	4.9 (2.4)	4.8 (3.1)	4.2 (2.4)
Vigorous (% total)	0.4 (0.7)#	0.4 (0.9)€#	0.2 (0.7)	0.2 (0.6)	0.1 (0.4)
Very vigorous (% total)	0.1 (0.3)‡†€#	0.0 (0.0)	0.0 (0.1)	0.0 (-0.2)	0.0 (-0.1)
Moderate-to-vigorous (% total)	5.7 (2.5)#	5.3 (2.9)	5.3 (2.6)#	5.1 (3.3)	4.4 (2.5 )
Average MVPA/day (min)	50.6 (203.9)#	48.3(28.1)#	47.0 (23.1)#	45.4 (29.3)	38.8 (23.7)
Number of steps/min	10.2 (3.3)#	9.7 (3.6)#	9.7 (3.4)#	9.2 (4.0)#	7.9 (3.0)
Total number of steps	8.021(3.959-16.278)#	7.732(4.747-16.318)#	8.124(4.360-13.647)#	7.453(3.185-14.279)	6.634(3.338-12.205)

• Note. Data were expressed as median (95% interval confidence).
• ^*
  (p ≤ 0.05) vs. eutrophic;
• ^‡
  (p ≤ 0.05) vs. mild obesity;
• ^†
  (p ≤ 0.05) vs. moderate obesity;
• ^€
  (p ≤ 0.05) vs. high obesity;
• ^#
  (p ≤ 0.05) vs. morbid obesity.
• ^¥
  self-reported.

Regarding physical activity, there was a downward trend of decrease in all measurements as adiposity increases, especially in very vigorous physical activity, MVPA (%), and MVPA per day. The eutrophic group showed higher physical activity when compared to the other groups (Table 2).

The number of steps per minute was higher in all groups compared to morbid obesity. High total number of steps in eutrophic, mild and moderate obesity groups was also found. The average duration of sedentary bouts, standing time (%) and sitting time (%) correlated with %FBM and BMI (p ≤ 0.01) (Table 3).

Table 3
Results for bivariate analysis between obesity and sedentary behavior or physical activity.

		%FBM	BMI
		r	r
Sedentary behavior
	Total number of sedentary bouts	-0.01	-0.04
	Total time of sedentary bouts (min)	0.01	0
	Average time of sedentary bouts (min)	0.11**	0.10**
	Maximal time of sedentary bouts (min)	0.08*	0.07*
	Minimal time of sedentary bouts (min)	-0.01	0.01
	Daily mean of sedentary bouts (min)	0.01	0
	Total number of sedentary breaks	-0.01	-0.04
	Total time of sedentary breaks (min)	0.01	0.02
	Average time of sedentary breaks (min)	0.03	0.05
	Maximal time of sedentary breaks (min)	0	0.05
	Minimal time of sedentary breaks (min)	0.01	-0.02
	Daily mean of sedentary breaks (min)	0	0.06
	Average time in sedentary behavior (h)	0	-0.05
	Average time in sedentary behavior (% total)	-0.01	-0.03
	Standing time (% total)	˗0.02**	-0.34**
	Sitting time (% total)	0.12**	0.11**
	Sitting time during weekdays (min) ¥	-0.01	0.05
	Sitting time during weekend (min) ¥	0.05	0.10**
Physical activity
	Average energy expenditure (Kcal)	0	0.23**
	Average energy expenditure/day (Kcal)	0	0.30**
	Average energy expenditure (METs)	˗0.17**	0.23**
	Light physical activity (h)	0.08*	0.30**
	Moderate physical activity (h)	˗0.13**	0.02
	Vigorous physical activity (h)	˗0.23**	0.03
	Very vigorous physical activity (h)	˗0.17**	˗0.12**
	Light physical activity (% total)	0.12**	˗0.16**
	Moderate physical activity (% total)	˗0.15**	˗0.11**
	Vigorous physical activity (% total)	˗0.24**	0.12**
	Very vigorous physical activity (% total)	˗0.17**	˗0.11**
	Average moderate-to-vigorous physical activity (h/total)	˗0.18**	˗0.16**
	Average moderate-to-vigorous physical activity (% total)	˗0.21**	˗0.11**
	Average moderate-to-vigorous physical activity/day (min)	˗0.20**	˗0.15**
	Number of steps/min	˗0.23**	˗0.15**
	Total number of steps	˗0.18**	˗0.15**

• Note. BMI: body mass index. FBM: fat body mass.
• ^¥
  self-reported;
• ^**
  p < 0.01;
• ^*
  p < 0.05.

Physical activity variables correlated with %FBM and BMI, except for light physical activity. The average daily total energy expenditure was significant only when correlated with BMI (Table 3).

After adjustment, accelerometer-based SB variables were less associated with %FBM than self-reported SB variables (Table 4).

Table 4
Results for multivariate analysis.

Outcomes	Predictors
	Fat Body Mass (%)		BMI
	Unadjusted coefficient	Adjusted coefficient	Unadjusted coefficient	Adjusted coefficient
Average duration of sedentary bouts (min)	0.033*	0.005	˗0.042*	0.005
Standing time (% total)	˗0.256*	˗0.426*	˗0.470*	˗0.575*
Sitting time (% total)	0.122*	0.061	0.156	0.053
Sitting time during weekdays (min) ¥	˗0.406	4.176*	1.859	˗0.052*
Sitting time during weekend (min) ¥	1.222	3.874*	3.007*	4.570*
Average moderate-to-vigorous physical activity (% total)	˗0.064*	˗0.055*	˗0.064*	3.800*
Average number of steps	˗0.015*	˗0.019*	˗0.022*	˗0.023*

• Note.
• ^¥
  self-reported; **: p < 0.01;
• ^*
  p < 0.05.

Subsequently, in hierarchical regression, the %FBM coefficient remained significant in relation to accelerometer-based variables, except for average duration of sedentary bouts. However, the %FBM coefficient became significant when controlled for age, sex (Step 2) and cardiovascular risk factors (Step 3) to both self-reported sitting time during weekdays and weekend (Table 5).

Unlike %FBM, the BMI coefficient remained significant for standing time and sitting time during weekend. Regarding the average duration of sedentary bouts, MVPA and sitting time (%) became non-significant. Finally, the BMI coefficient remained significant in relation to sitting time during weekdays (Table 5).

Table 5
Results for hierarchical regression analysis with %FBM or BMI as predictors.

Outcomes		Variables entered
		Step 1			Step 2			Step 3
		ꞵ	R²	∆R²	ꞵ	R²	∆R²	ꞵ	R²	∆R²
%FBM
	Average duration of sedentary bouts (min)	0.143**	0.021	-	0.128*	0.042	0.021	0.121	0.044	0.003
	Standing time (% total)	-0.298**	0.089	-	-0.546**	0.140	0.051	-0.557**	0.150	0.010
	Sitting time (% total)	0.148**	0.022	-	0.165*	0.038	0.016	0.173*	0.042	0.004
	Sitting time during weekdays (min) ¥	-0.019	0.000	-	0.235**	0.062	0.061	0.223**	0.069	0.007
	Sitting time during weekend (min) ¥	0.068	0.005	-	0.242**	0.028	0.023	0.219**	0.040	0.012
	Average moderate-to-vigorous physical activity (% total)	-0.250**	0.062	-	-0.259**	0.063	0.001	-0.248**	0.077	0.014
	Average number of steps	-0.277**	0.077	-	-0.467**	0.103	0.026	-0.457**	0.114	0.011
BMI
	Average duration of sedentary bouts (min)	0.122**	0.015	-	0.061	0.039	0.024	0.047	0.043	0.004
	Standing time (% total)	-0.346**	0.120	-	-0.371**	0.125	0.006	-0.392**	0.139	0.014
	Sitting time (% total)	0.114**	0.013	-	0.060	0.037	0.024	0.062	0.042	0.004
	Sitting time during weekdays (min) ¥	0.062	0.004	-	0.163**	0.067	0.063	0.162**	0.072	0.005
	Sitting time during weekend (min) ¥	0.112**	0.013	-	0.172**	0.035	0.022	0.161**	0.045	0.010
	Average moderate-to-vigorous physical activity (% total)	-0.209**	0.044	-	-0.154**	0.163	0.119	-0.125	0.171	0.009
	Average number of steps	-0.279**	0.078	-	-0.283**	0.127	0.049	-0.246**	0.141	0.014

• Note. Variables entered: %FBM (Step 1); %FBM, Age and Sex (Step 2); %FBM, Age, Sex, Hypertension, Diabetes, Dyslipidemia and Current smoking (Step 3).
• ^**
  p < 0.01;
• ^*
  p < 0.05.
• ^¥
  self-reported.

DISCUSSION

The present study investigated the association between SB and obesity categorized by %FBM free from confounding effect of cardiovascular risk factors in adults. To our knowledge, the association related to %FBM instead of BMI is still scarce, especially when SB is measured through accelerometry. After adjustment, accelerometer-based SB variables were less associated with %FBM than self-reported SB variables, which may be linked to the low self-report accuracy. As expected, obesity was more significantly associated with level of physical activity than SB. Thus, obesity has little or no association with SB after adjustment for main confounders.

Our sample was mainly composed of middle-aged women. The presence of cardiovascular risk factors was similar to those presented by the Brazilian population for current smoking-related prevalence in all groups, but low prevalence of arterial hypertension and diabetes, except for high and morbid obesity²⁰.

The association between SB, physical activity and obesity has recently been the focus of several studies. Although obesity was associated with average duration of sedentary bouts objectively measured, our results showed no significant differences of accelerometer-based SB between obesity groups, as previously described for BMI, which only correlated with self-reported TV time²¹. In a review study, Carneiro et al.²² suggested that the obese population has higher daily and total energy expenditure. However, when considering body composition (fat-free mass), there are no differences between obese and non-obese subjects, which may occur due to the presence of unhealthy behaviors such as low physical activity and high fat intake²².

It was observed that high BMI, but not high %FBM, was also related to long duration of sedentary bouts²³, which reinforces what literature has yet to clarify. Our results shows that sitting time correlates with increased obesity level, which differs from a recent study suggesting that sitting time was not significantly associated with obesity⁵.

As expected, vigorous and very vigorous physical activity, as well as number of steps, was lower in high and morbid obesity groups. These findings agree with literature, since MVPA is associated with low BMI, subcutaneous and visceral adipose tissue²⁴.

Our results showed that the average daily energy expenditure was higher, the higher the obesity level. DeLany²⁵ recently discussed these findings and suggested that they may be explained not by increased metabolic efficiency, but by the high energy expenditure to perform activities due to high body mass. The author also pointed out that these points could mask the lower levels of physical activity performed by these subjects.

Although SB correlates with %FBM, it does not seem to be sufficient to determine obesity, unlike physical activity, especially MVPA. A large NHANES study including more than 5,000 participants found that slight differences in daily MVPA (e.g., 6 minutes) between groups had significant impact on the risk of obesity, which did not occur with the total time spent on SB or watching TV ²⁶.

In contrast to our results, Werneck et al.²⁷ concluded that sedentary bouts no longer than 4 minutes and high sedentary behavior breaks are associated with adiposity, thus contributing with obesity in adolescents ²⁷. However, the sample was composed of 389 adolescents (10-14 years), mainly male, using skinfold thickness, waist circumference and BMI to characterized adiposity. In addition, sedentary bouts were divided into 3 main groups: 1-4mins, 5-14mins, ≥15mins, whereas we obtained more time in SB and the average duration of SB were greater than the last interval. Although SB as % was similar to ours, the sample presented major differences in comparison to the sample of this study, as well as methods, which explains the opposite results. Similar to our study, Myers et al.²⁸ observed negative correlation between physical activity and all body composition measurements (body mass, FBM, and waist circumference and BMI). The authors showed that the correlation between adiposity and SB was attenuated when adjusted for MVPA. Thus, that the MVPA is more important than SB in adiposity, which reinforce our results.

In our study, SB was associated with obesity stratified by %FBM. However, it is possible to observe that SB presented little or no influence on obesity when examining correlation and determination coefficients and comparing adjusted and unadjusted coefficients. Although significant correlations between SB (e.g., standing time, sitting time and average duration of sedentary bouts) and obesity were found, the association was weak, especially when compared to those observed for physical activity. Our results are in agreement with the systematic review and meta-analysis of prospective studies carried out by Campbell et al.²⁹, who concluded that the association between SB and body weight and obesity is weak, inconsistent and lacking in significance. Thus, our study reinforces that obesity has more consistent association with physical activity than SB, which corroborates previous findings²⁹.

According to Curry and Thompson³⁰, both self-reported physical activity and sedentary time are underestimated when compared to accelerometer-based measurements. This may be due to the lack of physical activity participation cultural context and terminology, the difficult of measuring how much physical activity is compatible with a given intensity, the struggle to remind the amount of time-spent sitting, and the limited knowledge about moderate-to-vigorous daily-living activities. Therefore, our findings showed conflicting and inconsistent adjusted and unadjusted coefficients, especially self-reported SB, which may be attributed to the main limitations of the use of questionnaires to inquire the amount of activities of the daily living.

Some limitations and strengths of the present study should be considered. The convenience sample may explain the higher proportion of women. Moreover, the study design does not allow cause and effect interference. Therefore, how much SB is a cause or consequence of obesity needs further clarification by longitudinal studies. However, SB and physical activity were objectively evaluated using triaxial accelerometer. Taking to account BMI limitations, we choose to use FBM to stratify our sample, which is one of the main strengths of our study.

CONCLUSIONS

Sedentary behavior and its pattern were not significantly different between obesity groups. Although SB presented some significant correlations with obesity, correlation and determination coefficient indicated weak association between SB and obesity (e.g., BMI and %FBM). Obesity presents little or no association with SB and its pattern after adjustment for potential confounders, especially when measured through accelerometry. Finally, obesity has more important association with level of physical activity than with SB.

COMPLIANCE WITH ETHICAL STANDARDS

• Funding

  This research received financial support by Fundação de Amparo à Pesquisa de São Paulo (FAPESP - #2011/07282-6 e #2015/20830-3).

• Ethical approval

  Ethical approval was obtained from the Federal University of São Paulo Human Research Ethics Committee – No. 186.796/2013 and the protocol was written in accordance with standards set by the Declaration of Helsinki.

How to cite this article

• Gonze BB, Ostolin TLVP, Sperandio EF, Arantes RL, Gagliardi ART, Romiti M, Dourado VZ. Association between obesity Bras Cineantropom Desempenho Hum 2021, 23:e75420. DOI: http://doi.org/10.1590/1980-0037.2021v23e75420

REFERENCES

• ¹
  Abelson P, Kennedy D. The obesity epidemic. Science 2004;304(5676):1413.
• ²
  World Health Organization [Internet]. Obesity and overweight [cited 2020 Jun 3]. Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight
  » https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight
• ³
  Hill JO, Wyatt HR, Reed GW, Peters JC. Obesity and the environment: Where do we go from here? Science 2003;299(5608):853-5.
• ⁴
  Physical activity guidelines advisory committee report, 2008: To the Secretary of Health and Human Services. Nutr Rev 2009;67(2):114-20.
• ⁵
  Owen N, Healy GN, Matthews CE, Dunstan DW. Too much sitting: The population health science of sedentary behavior. Exerc Sport Sci Rev 2010;38(3):105–13.
• ⁶
  Hamilton MT, Healy GN, Dunstan DW, Zderic TW, Owen N. Too little exercise and too much sitting: Inactivity physiology and the need for new recommendations on sedentary behavior. Curr Cardiovasc Risk Rep 2008;2(4):292–8.
• ⁷
  Schmid D, Ricci C, Leitzmann MF. Associations of objectively assessed physical activity and sedentary time with all-cause mortality in US adults: The NHANES study. Plos One 2015; 10(3):e0119591.
• ⁸
  Hamilton MT, Hamilton DG, Zderic TW. Role of low energy expenditure and sitting in obesity, metabolic syndrome, type 2 diabetes, and cardiovascular disease. Diabetes 2007;56(11):2655-67.
• ⁹
  Atkin AJ, Gorely T, Clemes SA, Yates T, Edwardson C, Brage S, et al. Methods of Measurement in Epidemiology: Sedentary Behaviour. Int J Epidemiol 2012;41(5):1460-71.
• ¹⁰
  Kim Y, Welk GJ, Braun SI, Kang M. Extracting Objective Estimates of Sedentary Behavior from Accelerometer Data: Measurement Considerations for Surveillance and Research Applications. Plos One 2015;10(2):e0118078.
• ¹¹
  Thomas S, Reading J, Shephard RJ. Revision of the Physical Activity Readiness Questionnaire (PAR-Q). Can J Sport Sci 1992;17(4):338-45.
• ¹²
  American College of Sports Medicine Position Stand. The recommended quantity and quality of exercise for developing and maintaining cardiorespiratory and muscular fitness, and flexibility in healthy adults. Med Sci Sports Exerc 1998;30(6):975-91.
• ¹³
  Boileau RA. Advances in body composition assessment. Cad Saude Publica 1993;9(suppl 1):S116–7.
• ¹⁴
  Costa RF. Composição corporal: teoria e prática da avaliação. 2001;184–184.
• ¹⁵
  Segal KR, Van Loan M, Fitzgerald PI, Hodgdon JA, Van Itallie TB. Lean body mass estimation by bioelectrical impedance analysis: a four-site cross-validation study. Am J Clin Nutr 1988;47(1):7-14.
• ¹⁶
  Brooks AG, Gunn SM, Withers RT, Gore CJ, Plummer JL. Predicting walking METs and energy expenditure from speed or accelerometry. Med Sci Sports Exerc 2005;37(7):1216–23.
• ¹⁷
  Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, Mcdowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc 2008;40(1):181–8.
• ¹⁸
  Freedson PS, Melanson E, Sirard J. Calibration of the Computer Science and Applications, Inc. accelerometer. Med Sci Sports Exerc 1998;30(5):777–81.
• ¹⁹
  Healy GN, Dunstan DW, Salmon J, Cerin E, Shaw JE, Zimmet PZ, Owen N. Breaks in sedentary time: beneficial associations with metabolic risk. Diabetes Care 2008;31(4):661-6.
• ²⁰
  Brasil. Vigilância de Fatores de Risco e Proteção para Doenças Crônicas por Inquérito Telefônico – VIGITEL - 2018 [Internet]. 1998 [cited 2020 Jun 3]. Available from: http://bvsms.saude.gov.br/bvs/publicacoes/vigitel_brasil_2018
  » http://bvsms.saude.gov.br/bvs/publicacoes/vigitel_brasil_2018
• ²¹
  Coombs NA, Stamatakis E. Associations between objectively assessed and questionnaire-based sedentary behaviour with BMI-defined obesity among general population children and adolescents living in England. BMJ Open 2015;5(6): e007172.
• ²²
  Carneiro IP, Elliott SA, Siervo M, Padwal R, Bertoli S, Battezzati A, et al. Is Obesity Associated with Altered Energy Expenditure? Adv Nutr 2016;7(3):476–87.
• ²³
  Gupta N, Hallman DM, Mathiassen SE, Aadahl M, Jørgensen MB, Holtermann A. Are temporal patterns of sitting associated with obesity among blue-collar workers? A cross sectional study using accelerometers. BMC Public Health 2016;16:148.
• ²⁴
  Yoshioka M, Ayabe M, Yahiro T, Higuchi H, Higaki Y, St-Amand J, et al. Long-period accelerometer monitoring shows the role of physical activity in overweight and obesity. Int J Obes 2005;29(5):502–8.
• ²⁵
  Delany JP, Kelley DE, Hames KC, Jakicic JM, Goodpaster BH. High energy expenditure masks low physical activity in obesity. Int J Obes 2013;37(7):1006–11.
• ²⁶
  Maher CA, Mire E, Harrington DM, Staiano AE, Katzmarzyk PT. The independent and combined associations of physical activity and sedentary behavior with obesity in adults: NHANES 2003-06. Obesity 2013;21(12):E730-E737.
• ²⁷
  Werneck AO, Silva ECA, Bueno MRO, Vignadelli LZ, Oyeyemi AL, Romanzini CLP, et al. Association(s) between objectively measured sedentary behavior patterns and obesity among Brazilian adolescents. Pediatr Exerc Sci. 2019; 31(1):37-41.
• ²⁸
  Myers A, Gibbons C, Finlayson G, Blundell J. Associations among sedentary and active behaviours, body fat and appetite dysregulation: Investigating the myth of physical inactivity and obesity. Br J Sports Med 2017;51(21):1540–5.
• ²⁹
  Campbell SDI, Brosnan BJ, Chu AKY, Skeaff CM, Rehrer NJ, Perry TL, et al. Sedentary Behavior and Body Weight and Composition in Adults: A Systematic Review and Meta-analysis of Prospective Studies. Sports Med 2018;48(3):585-595.
• ³⁰
  Curry WB, Thompson JL. Comparability of accelerometer- and IPAQ-derived physical activity and sedentary time in South Asian women: A cross-sectional study. Eur J Sport Sci 2015;15(7):655–62.

Publication Dates

• Publication in this collection
  05 Apr 2021
• Date of issue
  2021

History

• Received
  03 July 2020
• Accepted
  20 Oct 2020