AWM: Eating Frequency and Patterns (2013)


Peixoto Mdo R, Benico MH, Jardim PC. The relationship between body mass index and lifestyle in a Brazilian adult population: A cross-sectional survey. Cad Saude Publica. 2007 Nov; 23(11): 2,694-2,740.



PubMed ID: 17952262
Study Design:
Cross-Sectional Study
D - Click here for explanation of classification scheme.
Quality Rating:
Neutral NEUTRAL: See Quality Criteria Checklist below.
Research Purpose:

To measure the prevalence of excess weight and obesity in the adult population in the city of Goiânia, Goiás State, Brazil, associated with variables related to socio-demographic conditions, lifestyle, physical activity, eating frequency and habits and body mass index (BMI).

Inclusion Criteria:
  • Houses were selected by two-stage probabilistic sampling of clusters
  • The first stage consisted of identifying 115 census tracts as defined by the National Census Bureau (IBGE) and used in the National Household Sample Survey (PNAD, 1998) in the urban area of Gioânia
  • The second stage of a selection from the households, considering the number of households in each census tract and the total sample size
  • Starting from the calculation of sample size per census tract, households were chosen by lot in a random and systematic manner
  • Only one resident per household was chosen to be interviewed among household members older than 18 years.
Exclusion Criteria:

Among the houses randomly chosen, some were excluded due to:

  • 121 addresses were classified as non-existent (the address was not located or the individual no longer lived at that address)
  • Other houses were located, but in most of these cases, no interview could be performed since the residents refused to answer the questions
  • Other less frequent causes of losses were empty houses or the impossibility of locating the residents after three visits on different days and different times
  • Pregnant women and mothers of infants less than six months of age were excluded to avoid data interpretation errors.
Description of Study Protocol:


The size of the sample in Goiânia was calculated by considering a population of 1,004,098 inhabitants, the overall prevalence of arterial hypertension in Brazil (20% of the adult population), 95% confidence interval (95% CI) and a 10% estimated error. 


Cross-sectional study. 

Statistical Analysis

  • The data were analyzed separately for men and women and the results were expressed initially as percentages, means and confidence intervals (95% CI)
  • The frequencies of individuals in the different BMI categories (underweight, normal, overweight and obesity) were calculated
  • To evaluate associations between study variables and the dependent variable (BMI), simple linear regression and Spearman analysis (specifically utilized for food consumption) were performed to estimate the independent effect of the control variables on the outcome (BMI), using hierarchical multiple linear regression analysis
  • In addition to the variables showing significant associations with BMI, those with P<0.2 according to the bivariate analysis were added to multiple modeling procedure
  • The World Health Organization theoretical model for obesity determinants were adapted to this study and served as a guideline to structure the blocks of variables and consequently the order in which these blocks entered the model
  • Block sequence: 
    • Socioeconomic and cultural variables
    • Demographic variables
    • Lifestyle
    • Physical activity
    • Eating habits
  • In the hierarchical model, variables from one block were adjusted to the variables from blocks at preceding levels, as well as to variables from the same level
  • Variables with P<0.05 were kept in the model, while others were used for adjustment purposes only
  • Stratified analyses were corrected by complex delineation of the sample, use of the set of svy commands that took the sample's complex structure into account
  • Sample weights associated with each sample cluster and the effect of the sampling design were incorporated into the analyses
  • A 5% significance level was used.


Data Collection Summary:

Timing of Measurements

June to December 2001.

Dependent Variables


Independent Variables

  • Lifestyle variables:
    •  Smoking
    • Alcohol intake
    • Physical activity
  • Physical activity variables:
    • Occupational physical activity
    • Occupational physical activity
    • Leisure time physical activity
    • Sedentary behavior
  • Food intake variables:
    •  Food frequency questionnaire
    • Change in diet for health reasons within the previous 12 months. 

Control Variables

Sociodemographic variables:

  • Sex
  • Age
  • Parity
  • Presence of spouse/partner
  • Schooling
  • Family income
  • Information on healthy eating in the past year.
Description of Actual Data Sample:
  • Initial N: 1,994
  • Attrition (final N): 1,252
  • Age: 
Men Women
Mean 95% CI Mean 95% CI
38.4 37.3; 39.5 38.1 37; 39.1
  • Ethnicity: Brazilian
  • Other relevant demographics: 35% men and 65% women
  • Anthropometrics: 
  Mean for Men Mean for Women
Weight 70.7kg 61.3kg
Height 169.6cm 157.4kg
BMI 24.6 24.8
  • Location:  Goiânia, Goiás State, Brazil.


Summary of Results:

Key Findings

Baseline Information

  • In men, approximately 42% of men showed excess weight (31.2% overweight and 10.7% obese).  However, only 26% presented above-normal abdominal adiposity.
  • In women, approximately 43% presented both excess weight (29.2% overweight and 13.9% obese) and increased abdominal adiposity.
  Men Women
Smoking 27.7% (±2.4) 19% (±2.2)
Alcohol consumption 60.3% (±3.1) 37.9% (±2.1)
Sedentary habits during leisure time 50.7% (±2.7) 71.1%(±2.7)

Results Impacting BMI

  • For men, bivariate analysis of age, income, presence of a spouse/partner, smoking cessation and meat consumption were positively associated (P<0.05) with BMI
  • In contrast, vegetable consumption and leisure time and commuting physical activity were inversely associated (P<0.05) with BMI
  • For women, age, parity, smoking cessation, change in diet in the previous year, use of sweeteners and meat consumption were positively correlated (P<0.05) with BMI
  •  However, height, schooling and consumption of alcoholic beverages, eggs, fat, grains, fruit and sweets showed a negative association (P<0.05).

Variables and Bivariate Analysis

Variables with P<0.2 in the bivariate analysis and which were added to the multiple regression analysis were:

  • For men and women: 
    • Food intake during the previous year
    • Physical activity at work
    • Number of meals
    • Habit of eating meals in front of the TV
  • For men only: 
    • Alcohol consumption
    • Dietary changes
  • For women only: Leisure time physical activity.

Demonstration of the Independent Influence of Sociodemographic Variables

  • For men, smoking and alcohol consumption remained as adjustment variables, since multiple regression analysis resulted in P<0.2
  • The same occurred with the variables martial status and use of sweeteners in the women's model
  • In men, BMI increased with age. Higher income men showed a higher mean BMI than those in the lowest quartile. Leisure time and commuting physical activities were inversely correlated with BMI.
  • Consumption of four or more meals per day was also inversely associated with BMI; frequency of vegetable intake showed a slightly negative association (P=0.09) and meat intake showed a positive association (P=0.01) with BMI
  • In women, BMI increased markedly and progressively with age; mean BMI in former smokers was higher than for non-smokers
  • Sedentary behavior (watching TV more than six hours per day) favored increased BMI, while frequency of grain consumption showed a negative association as did diet change during the previous year
  • Meanwhile, meat consumption was positively associated with BMI.
Author Conclusion:
  • The objectives of this study were to evaluate the prevalence of overweight and obesity and estimate associations between variables related to socio-demographic conditions, lifestyle, physical activity, eating habits, food consumption frequency and BMI in a wide range of individuals. The higher proportion of women in the sample might suggest a selection bias, but when comparing the distribution of this population with census data, it is indicated that the sample was representative of the adult population of Goiânia. 
  • Comparing data from this study to a prior nationwide study showed an increase in overweight and obesity in both men and women. For developing countries, the WHO recommends a mean BMI of 23kg/m2, while for developed countries the recommended mean BMI is 21kg/m2. In general, mean BMI is increasing in both sexes, at all schooling and income levels and in all age brackets except the youngest. This highlights the relevance of obesity as a public health problem and the need to identify social and behavioral factors related to excess body weight.
  • For socio-demographic variables, increased BMI went hand-in-hand with higher income for men only, corroborating the results of previous studies. Other studies have indicated that excess weight and obesity are inversely correlated with socioeconomic status in developed countries, especially among women.  However, in developing countries, these characteristics are positively correlated with the population's socioeconomic status. 
  • In another study, in was demonstrated that for many women, belonging to the lowest socioeconomic class provides strong protection against obesity in countries with low per capita gross national product, but is a strong risk factor in more developed economies. It is important to note the high obesity prevalence among lower-income women, as this has been demonstrated in selected urban areas of Brazil's Southeast, the country's most economically developed region.
  • This study failed to show an association between BMI and schooling for either sex. Contrary to expectations, individuals with more schooling did not achieve better weight control, regardless of their presumed higher degree of knowledge on health and healthy eating habits. BMI increased significantly with the number of children in women, but this influence lost statistical significance after controlling for age. 
  • Other studies have also failed to show a correlation between parity and BMI among women. In this study, marital status was associated with obesity according to the bivariate analysis for men. However, the effect of this variable lost statistical significance after multiple regression analysis following adjustment for socioeconomic variables.
  • Bivariate analysis showed a negative association for both men and women between alcohol consumption, regardless of the amount and BMI. Other epidemiological studies show mixed results on the association between alcohol consumption and BMI. No difference was found in BMI for smokers vs. non-smokers for either men or women. The association between smoking and BMI is also controversial in epidemiological research. In this study, former smokers of both sexes had higher BMI than non-smokers and current smokers. However, after multiple regression analysis the associations only remained significant in women. This finding is consistent with a longitudinal study.
  • No association was observed between BMI and occupational physical activity for either sex, which was consistent with other studies. Most men used motor vehicles for commuting and had a higher BMI than those who walked for road a bicycle. Also, in this study, multiple analyses showed that for women, watching TV for prolonged periods significantly raised their BMI. Women who watched more TV were less physically active during their leisure time and consumed fruits and vegetables less frequently than their more active counterparts (P<0.001).
  • Regarding food frequency, for women, multiple regression analysis showed that only the consumption of grains (negative association) and meat (positive association) remained statistically significant. For men, only meat consumption showed a positive correlation. Similar results were observed in other cross-sectional studies. 
  • Along with excess calories, the number of daily meals appeared to affect the regulation of body weight and lipogenesis, since obese individuals frequently tend to eat a great deal of food during a single meal usually later in the day. In this case, the habit of having four or more meals per day was a protective factor against excess weight among men, a result that reinforced the premise that the eating pattern associated with obesity is irregular and or disorganized. However, the habit of having one or more meals in front of the TV was not correlated with BMI in either sex.
  • This study demonstrated the prevalence of excess weight in the adult population of Goiânia. It identified the factors independently associated with BMI. Prevalence of excess weight was high with 41.9% of men and 43% of women, increasing with age in both men and women, and with income in men only.  Active lifestyle (among men) and consumption of grains (among women) and less meat appear to offer increased protection against higher BMI. Considering that excess weight is a risk factor for cardiovascular diseases, more aggressive public policies are needed to change this strong national trend in Brazil.
Funding Source:
Government: Brazilian National Research Council
Reviewer Comments:

The authors note the following limitations:

  • The plain questionnaire on food frequency used in this study consisted of two components, a list of foods and the frequency with which they were consumed. Researchers could identify the frequency with which foods or food groups were consumed without classifying individuals according to nutrient or energy consumption. For the current study to be economically viable and methodologically adequate, the researchers opted to study only the frequency of food consumption by using an appropriate methodology for analysis of this type of questionnaire on intake frequency.
  • The cross-sectional design did not allow determining exactly the time interval between the independent and dependent variables. Thus, one bias in cross-sectional studies is reverse causality. Nevertheless, the design did not show the prevalence and distribution of target variables and allowed testing established hypotheses and proposed others for subsequent testing, employing more adequate designs.  
  • The method used in this study to evaluate the contribution of physical activity did not allow a more precise analysis of energy expenditure, mainly since it was unable to measure the number of hours spend per day in different kinds of physical activity. Neither was the method able to take physical activity at home into account. Despite these limitations, an inverse correlation between BMI and leisure time physical activity was observed in men, but not in women. This agreed with the results of other studies.
  • The lack of correlation between the frequency of consumption of sweets and BMI may have reflected dietary changes by individuals with high BMI who are attempting to lose weight, but could also result from under-reporting or even omission of information about the frequency of consumption of these foods known to be highly caloric. Thus, the lack of association between behavioral variables and BMI may suffer the influence of both the study design and biased information.
Quality Criteria Checklist: Primary Research
Relevance Questions
  1. Would implementing the studied intervention or procedure (if found successful) result in improved outcomes for the patients/clients/population group? (Not Applicable for some epidemiological studies) Yes
  2. Did the authors study an outcome (dependent variable) or topic that the patients/clients/population group would care about? Yes
  3. Is the focus of the intervention or procedure (independent variable) or topic of study a common issue of concern to dieteticspractice? Yes
  4. Is the intervention or procedure feasible? (NA for some epidemiological studies) Yes
Validity Questions
1. Was the research question clearly stated? Yes
  1.1. Was (were) the specific intervention(s) or procedure(s) [independent variable(s)] identified? Yes
  1.2. Was (were) the outcome(s) [dependent variable(s)] clearly indicated? Yes
  1.3. Were the target population and setting specified? Yes
2. Was the selection of study subjects/patients free from bias? Yes
  2.1. Were inclusion/exclusion criteria specified (e.g., risk, point in disease progression, diagnostic or prognosis criteria), and with sufficient detail and without omitting criteria critical to the study? Yes
  2.2. Were criteria applied equally to all study groups? Yes
  2.3. Were health, demographics, and other characteristics of subjects described? Yes
  2.4. Were the subjects/patients a representative sample of the relevant population? Yes
3. Were study groups comparable? N/A
  3.1. Was the method of assigning subjects/patients to groups described and unbiased? (Method of randomization identified if RCT) N/A
  3.2. Were distribution of disease status, prognostic factors, and other factors (e.g., demographics) similar across study groups at baseline? N/A
  3.3. Were concurrent controls or comparisons used? (Concurrent preferred over historical control or comparison groups.) N/A
  3.4. If cohort study or cross-sectional study, were groups comparable on important confounding factors and/or were preexisting differences accounted for by using appropriate adjustments in statistical analysis? N/A
  3.5. If case control study, were potential confounding factors comparable for cases and controls? (If case series or trial with subjects serving as own control, this criterion is not applicable.) N/A
  3.6. If diagnostic test, was there an independent blind comparison with an appropriate reference standard (e.g., "gold standard")? N/A
4. Was method of handling withdrawals described? Yes
  4.1. Were follow-up methods described and the same for all groups? Yes
  4.2. Was the number, characteristics of withdrawals (i.e., dropouts, lost to follow up, attrition rate) and/or response rate (cross-sectional studies) described for each group? (Follow up goal for a strong study is 80%.) Yes
  4.3. Were all enrolled subjects/patients (in the original sample) accounted for? Yes
  4.4. Were reasons for withdrawals similar across groups? N/A
  4.5. If diagnostic test, was decision to perform reference test not dependent on results of test under study? N/A
5. Was blinding used to prevent introduction of bias? No
  5.1. In intervention study, were subjects, clinicians/practitioners, and investigators blinded to treatment group, as appropriate? N/A
  5.2. Were data collectors blinded for outcomes assessment? (If outcome is measured using an objective test, such as a lab value, this criterion is assumed to be met.) No
  5.3. In cohort study or cross-sectional study, were measurements of outcomes and risk factors blinded? No
  5.4. In case control study, was case definition explicit and case ascertainment not influenced by exposure status? N/A
  5.5. In diagnostic study, were test results blinded to patient history and other test results? N/A
6. Were intervention/therapeutic regimens/exposure factor or procedure and any comparison(s) described in detail? Were interveningfactors described? ???
  6.1. In RCT or other intervention trial, were protocols described for all regimens studied? N/A
  6.2. In observational study, were interventions, study settings, and clinicians/provider described? Yes
  6.3. Was the intensity and duration of the intervention or exposure factor sufficient to produce a meaningful effect? ???
  6.4. Was the amount of exposure and, if relevant, subject/patient compliance measured? ???
  6.5. Were co-interventions (e.g., ancillary treatments, other therapies) described? N/A
  6.6. Were extra or unplanned treatments described? N/A
  6.7. Was the information for 6.4, 6.5, and 6.6 assessed the same way for all groups? N/A
  6.8. In diagnostic study, were details of test administration and replication sufficient? N/A
7. Were outcomes clearly defined and the measurements valid and reliable? Yes
  7.1. Were primary and secondary endpoints described and relevant to the question? Yes
  7.2. Were nutrition measures appropriate to question and outcomes of concern? Yes
  7.3. Was the period of follow-up long enough for important outcome(s) to occur? N/A
  7.4. Were the observations and measurements based on standard, valid, and reliable data collection instruments/tests/procedures? Yes
  7.5. Was the measurement of effect at an appropriate level of precision? Yes
  7.6. Were other factors accounted for (measured) that could affect outcomes? Yes
  7.7. Were the measurements conducted consistently across groups? Yes
8. Was the statistical analysis appropriate for the study design and type of outcome indicators? Yes
  8.1. Were statistical analyses adequately described and the results reported appropriately? Yes
  8.2. Were correct statistical tests used and assumptions of test not violated? Yes
  8.3. Were statistics reported with levels of significance and/or confidence intervals? Yes
  8.4. Was "intent to treat" analysis of outcomes done (and as appropriate, was there an analysis of outcomes for those maximally exposed or a dose-response analysis)? N/A
  8.5. Were adequate adjustments made for effects of confounding factors that might have affected the outcomes (e.g., multivariate analyses)? Yes
  8.6. Was clinical significance as well as statistical significance reported? Yes
  8.7. If negative findings, was a power calculation reported to address type 2 error? N/A
9. Are conclusions supported by results with biases and limitations taken into consideration? Yes
  9.1. Is there a discussion of findings? Yes
  9.2. Are biases and study limitations identified and discussed? Yes
10. Is bias due to study's funding or sponsorship unlikely? Yes
  10.1. Were sources of funding and investigators' affiliations described? Yes
  10.2. Was the study free from apparent conflict of interest? Yes