Sao Paulo Health Survey (ISA)
This study used data from the Sao Paulo Health Survey. Data collection was completed in 2015, with 4,043 participants who lived in five health administrative areas in Sao Paulo city. The sampling process has been described in more detail elsewhere [15]. Briefly, the survey used a multi-stage sampling design: from the five health administration areas in Sao Paulo, 150 census tracts were randomly selected, and then households were sampled randomly from each tract. The data were collected using face-to-face interviews in households.
Georeferencing resulted in 3,145 participants adults (aged 18 years or more) having their residential address geocoded [16]. More details can be obtained from other publications [16-18]. This dataset will be used such as the baseline of the longitudinal study denominated of “ISA: Physical Activity and Environment” that will have such as objective to verify the relationship between the built environment and physical activity in adults from Sao Paulo city, Brazil.
Sedentary Behavior
Sedentary behavior data were collected using the International Physical Activity Questionnaire (IPAQ) [19] and measured on the basis of two questions: 1) Total sitting time on a usual weekday; 2) Total sitting time on a usual weekend day. For analysis, two outcomes were used: 1) Continuous measures of minutes of sitting time in a typical weekday; and 2) Continuous measures of minutes of sitting time in a typical weekend day.
Mix of Destinations
Walkable destinations within each participant’s residential catchment were captured using georeferencing procedures [16-18] applied to publicly available datasets, and included eleven destinations: 1. Bus stops; 2. Train/subway stations; 3. Parks; 4. Squares; 5. Public recreation centres; 6. Bike paths; 7. Primary health care units; .8 Supermarkets; 9. Food stores; 10. Bakeries; and 11. Coffee shops. The dataset for items 1 to 8 pertain to places in 2016 and were obtained mainly from the open site GEOSAMPA <http://geosampa.prefeitura.sp.gov.br/PaginasPublicas/_SBC.aspx>, and items 9 to 11 were sourced from the Health Surveillance Registration database from Sao Paulo city associated with the National Economic Activity Classification in November 2016.
We calculated the number of destinations within a 500m radial buffer of each participant’s home address. This distance based on previous studies conducted in Sao Paulo city with the same sample that found significant associations between the built environment and walking [18]. The destinations types were operationalized in three steps: 1) By calculating the median number of each type of facility within the 500m buffers; 2) For each type of facility, the participants were grouped into two categories using a median-split (1=above median; 0=at or below median); 3) By calculating a mix of destination score adding all results of step 2. This process has been described in more detail elsewhere [18].
Covariates
We used age (18-29 years, 30-39 years, 40-49 years, 50-59 years, 60 years or more), education (incomplete elementary school, incomplete high school, complete high school, incomplete undergraduate or above), marital status (singles, married/with partners, separated/widowers), obesity (in two categories: BMI <30 kg/m2 or above), physical activity (<150 minutes per week or above evaluated by IPAQ long form), self-report of diseases diagnosed by physicians (none or at least one of the following: hypertension; diabetes; myocardial infarction; cardiac arrhythmia; other heart disease; cancer; arthritis, rheumatism or arthrosis; osteoporosis; asthma or asthmatic bronchitis; emphysema, chronic bronchitis or chronical obstructive pulmonary diseases; rhinitis; chronic sinusitis; other lung disease; tendonitis, repetitive strain injury or work-related musculoskeletal disorders; cerebral vascular accident or stroke; spine disease or spine problem), smoking status (yes or no), car or motorcycle ownership (yes or no); time living in the same residence ( <1 year, ≥1 year or <5 years, >5 years), and region where people lived in Sao Paulo city (North, South, Midwest, Southeast, and East). These covariates were selected based on the findings of systematic reviews about sedentary behavior correlates in adults [8, 12, 14] and in original study that examined the relationship between walking for transportation and physical activity [18].
Statistical Analysis
The analysis uses two outcomes: 1) minutes of sitting time during a typical weekday; 2) minutes of sitting time during a typical weekend day. We used descriptive statistics to examine how sitting time was distributed by each of the environmental, sociodemographic, and health-related variables.
We calculated the mean of differences between minutes of sitting timing in a typical weekday and in a typical weekend day according to tertile scores of the mix of destinations (lowest tertiles x middle/highest tertiles).
Linear multilevel analysis of the association between mix of destinations and sitting time were conducted in three stages: 1) Firstly, without adjustment; 2) Secondly, with simultaneous adjustment for all the covariates; and 3) with interactions between gender and mix of destination based on the work of Owen et al.[11]. The multilevel analysis accounted for clustering within census-tracts and households. All analyses were conducted using Stata version SE 12.1. (StataCorp LP, College Station, USA). We used the xtmixed command for linear models and the results are presented as beta coefficients (β) with 95% confidence intervals.
Ethics Approval
The Ethics Committee of the School of Arts, Sciences, and Humanities at the University of Sao Paulo approved the study (process number 55846116.6.0000.5390).