Abstract
Background
Physical inactivity plays a role in the acquisition of heart disease, type 2 diabetes, and breast and colon cancer. The impact of such noncommunicable diseases on low- and middle-income countries is a major global health concern, but most studies in this area have focused on high-income countries. A better understanding of the factors that may influence physical activity in low- and middle-income countries is needed.
Purpose
This study describes the prevalence of cycling and walking for transportation and their association with personal and environmental factors in adults from three state capitals in Brazil.
Methods
In 2008–2009, a random-digit-dialing telephone survey was conducted with residents (aged ≥18 years) of Curitiba, Vitoria and Recife, sampled through a clustered multistage sampling process. Walking and cycling for transportation, perception of the environment related to physical activity, and demographic and health characteristics were collected. Poisson regression was used to examine associations between cycling and walking for transportation with covariates stratified by cities. All analyses were conducted in 2011.
Results
The prevalence of bicycling for transportation was 13.4%; higher in Recife (16.0%; 95% CI=13.7, 18.4) compared to Curitiba (9.6%; 95% CI=7.8, 11.4) and Vitoria (8.8%; 95% CI=7.34, 10.1); and 26.6% for walking regularly as a mode of transportation. The adjusted analysis showed that cycling is positively associated with being male (prevalence ratio [pOR]=3.4; 95% CI=2.6, 18.4) and younger (pOR =2.9; 95% CI=1.8, 4.9) and inversely associated with having a college degree (pOR =0.3; 95% CI=0.2, 0.4). Walking for transportation is inversely associated with having a college degree (pOR =0.6; 95% CI=0.5, 0.8). No strong evidence of association was found of environmental indicators with walking or bicycling.
Conclusions
The prevalence of active commuting was low and varied by city. Personal factors were more consistently associated with bicycling than with walking, whereas perceived environmental features were not related to active commuting.
Introduction
Noncommunicable diseases are the leading cause of death around the world, and their impact on low- and middle-income countries is a major global health concern.1 Physical inactivity causes 6%–10% of the major noncommunicable diseases including coronary heart disease, type 2 diabetes, and breast and colon cancer.2 Despite calls for action and global initiatives, physical inactivity is still highly prevalent.3 It is estimated that one third of the adult population worldwide does not reach recommended levels of physical activity.4
Brazil, like other emerging economies, has experienced a rapid change in terms of urbanization and transportation over the last decades.5 In this context, one of the most remarkable changes is the shift from public and collective transportation to a more private and individualized mode. As a result, the incidence of traffic-related injuries and deaths has increased, as has the prevalence of respiratory diseases and the level of stress associated with residing in urban areas.6–9 Another potential negative effect is an increase in physical inactivity due to lower amounts of activity obtained through daily transportation.10
Bicycling and walking are not only a promising way to promote physical activity but also provide benefits to the individual and communities.11–13 Initiatives and interventions to increase these modes of transportation have been documented in the peer-reviewed14 and gray literature,15 and they are usually designed to take into account environmental and individual characteristics. However, nearly all the information available on these characteristics originated from high-income countries, particularly from regions where walking and bicycling for transportation are more prevalent.16–18
The limited evidence available from low- and middle-income countries shows that walking and cycling are more prevalent among men and in low-income groups,19–21 and that only a few built and perceived environmental characteristics are associated with these commuting choices.20,22,23 Moreover, available evidence usually comes from specific locations and populations (e.g., workers). In addition, the variation across locations should also be considered. For instance, recent findings have shown that walking for transportation in Latino communities in the U.S. is more prevalent among women and varies according to length of time one has lived in the country.24 Another study reported that walking for transportation is greater in areas with higher urbanization and during summer months compared to winter months, indicating that commuting behaviors are affected by the environment.25
The results of commuter cycling and walking studies are diverse, in part because the assessed environmental attributes and measures have varied considerably across the studies. In addition, they are mainly limited to high-income countries, as recently shown.4 Therefore, a better understanding of the factors that may influence bicycling and walking for transportation in middle-income countries, such as Brazil, is needed. Such data will be valuable for targeting and designing interventions (e.g., identifying subgroups and potential moderators) to promote active modes of transportation,26 which could help reverse the increase in physical inactivity. The aims of the current study were to describe the prevalence of cycling and walking for transportation; and to identify the associated personal and perceived environmental correlates in residents from three state capitals from various regions of Brazil.
Methods
The cities of Curitiba, Vitoria, and Recife are state capitals located in the southern, southeast, and northeast regions of Brazil, respectively. The capitals differ not only geographically but also in regard to their social and built environment (Table 1). Curitiba has the lowest inhabitant/car ratio, indicative of higher traffic density, whereas Recife has the highest population density and inhabitant/car ratio. Of the three, Recife has the highest average temperature, crime rate, Gini index (a measurement of the income distribution of a country's residents), and unemployment rate, indicative of higher social inequity. Despite these differences, the capitals have similarities in their demographics (e.g., percentage of women), physical activity environment and policies. All cities have community physical activity programs, which to some extent are linked to their environmental features (e.g., parks, plazas and beaches).27–30
Table 1.
Description of city's social and environmental characteristicsa
| Characteristics | Curitiba | Recife | Vitoria |
|---|---|---|---|
| Population, n | 1,851,215 | 1,561,659 | 320,156 |
| Women aged >18 years (%) | 53.5 | 55.6 | 54.5 |
| Unemployment (%)b | 4.6 | 12.2 | 6.8 |
| Area (Km2) | 435 | 217 | 93 |
| Population density (Inhabitants/Km2) | 4255.7 | 7196.6 | 3442.5 |
| Average temperature (degrees Fahrenheit) | 62 | 78 | 76 |
| Automobile fleet (units) | 867,066 | 307,166 | 109,305 |
| Inhabitants/cars | 2.1 | 5.1 | 2.9 |
| Crime rate (Homicides/100,000 inhabitants) | 45.5 | 87.5 | 75.4 |
| Human Development Index (HDI) | 0.85 | 0.79 | 0.85 |
| Gini index | 0.59 | 0.68 | 0.61 |
Source: Brazilian Institute of Geography and Statistics (IBGE, 2010)
Percentage of the population aged ≥16 years, economically active and not employed.
Population and Sample
In each city, a random-digit-dialing telephone survey was carried out with the same sampling methodology used by the Brazilian Chronic Disease Risk Factor Surveillance.31 The surveys conducted in Curitiba during 2008 and in Recife during 2007 were part of Project GUIA (Guide for Useful Interventions for Physical Activity in Brazil and Latin America), a large initiative aimed at understanding physical activity promotion in Brazil.32 Another survey was carried out in 2009 to evaluate a local physical activity program in Vitoria as part of another project using the methods in surveying.30
Participants were non-institutionalized residents of the three cities (n=6166), who had resided for at least 1 year in the same neighborhood and were aged ≥18 years. Respondents were selected through a stratified and clustered multistage sampling process. Response rates were 60.5% (Curitiba), 75.2% (Vitoria) and 64.5% (Recife). The sampling procedure was similar in all three cities with some differences in the stratification process due to specific characteristics of the city. IRB approval was obtained prior to data collection from São Paulo Federal University, Pontiff Catholic University of Parana in Curitiba, and Washington University in St. Louis.
Measures and Data Collection
Trained interviewers administered a standardized questionnaire. All the interviewers had experience in administering telephone population surveys and received a 2-day training before the start of each survey. The International Physical Activity Questionnaire (IPAQ), long-form33 was used to obtain information on walking and cycling for transportation, which were considered the outcome variables. Walking for transportation was categorized according to the most recent recommendations for physical activity and health (≥150 minutes/week).34 Cycling for transportation was dichotomized into two categories (yes vs no).
Covariates included sociodemographic and health characteristics as well as perceived environment indicators. Age was divided into three categories: 18–34 years, 35–54 years, and ≥ 55 years. Education was classified as less than high school, having completes high school, or above. Marital status was classified as single, married or living together, or other (widowed/separated/divorced). BMI was based on self-reported weight and height and was grouped into two categories: underweight and normal weight (BMI <25) versus overweight/obese (BMI ≥25).
The Neighborhood Environment Walkability Scale (A-NEWS) was used to obtain perceptions on environment related to physical activity.35 The response categories were adapted and dichotomized (yes vs no). This modified version was previously used in other face-to-face 36 and phone surveys with adequate reliability.22 Only the measures comparable across the three data sets were used, including perceptions of safety (walking/bicycling during the day and night), traffic conditions, and presence of sidewalks.
Data Analysis
A descriptive analysis of cycling and walking according to personal and environmental factors, stratified by cities, was conducted. A description of bivariate analysis was performed using Poisson regression between cycling and walking for transportation and covariates stratified by cities. Poisson regression was chosen due to the low prevalence of cycling observed in the sample. Finally, multivariate analyses were carried out using Poisson hierarchic regressions between cycling and walking for transportation and covariates stratified by city (Level 1= demographics; Level 2=BMI and perceived health; Level 3 = all covariates plus perceived environment variables). Interaction terms were created to assess the effect modification by city. The group of commands “svy” was used in Stata 10.0 software to account for the complex sampling design using sampling weights for gender and age. All analyses were conducted in 2011.
Results
Study Population Characteristics
The study population consisted predominantly of women and adults aged 18–34 years (Table 2). Education level, marital status, and perceived health varied across the cities. Of the three cities, Recife had the highest percentage of residents in the low-education category, whereas Vitoria had the highest relative frequency in the highest education category. Being married was the most common marital status in Curitiba and Vitoria but not in Recife. Recife had the highest percentage of residents in the poor or regular health categories, compared to the other two cities. Obesity/overweight status was similar across the cities; with normal weight being the most frequent response.
Table 2.
Demographic characteristics of participants according to the city of residence
| Variables and Categories | Curitiba | Recife | Vitória | Combined | ||||
|---|---|---|---|---|---|---|---|---|
| n | % (95% CI)a | n | % (95% CI)a | n | % (95% CI)a | n | % (95% CI)a | |
| Gender | ||||||||
| Women | 1329 | 62.6 (59.8, 65.3) | 1285 | 56.3 (52.4, 60.1) | 1276 | 62.2 (59.8, 64.6) | 3890 | 58.8 (56.3, 61.4) |
| Men | 768 | 37.4 (34.7, 40.2) | 761 | 43.7 (39.9, 47.6) | 747 | 37.8 (35.4, 40.2) | 2276 | 41.2 (38.6, 43.7) |
| Age categories | ||||||||
| 55+ | 625 | 15.6 (14.1, 17.2) | 585 | 18.3 (16.1, 20.5) | 611 | 20.2 (18.5, 22.0) | 1821 | 17.4 (16.0, 18.8) |
| 35-45 | 861 | 37.3 (34.7, 39.9) | 761 | 34.1 (30.7, 37.4) | 798 | 35.0 (32.7, 37.2) | 2420 | 35.3 (33.1, 37.5) |
| 18–34 | 611 | 47.0 (44.1, 49.9) | 700 | 47.6 (43.8, 51.5) | 614 | 44.8 (42.2, 47.3) | 1925 | 47.3 (44.8, 49.8) |
| Education level | ||||||||
| <High | 671 | 28.6 (26.2, 31.1) | 631 | 46.1 (42.1, 50.1) | 492 | 20.4 (18.5, 22.2) | 1794 | 38.5 (35.7, 41.2) |
| High school | 724 | 41.2 (38.4, 44.1) | 765 | 38.2 (34.9, 41.6) | 652 | 33.6 (31.2, 36.0) | 2141 | 39.2 (36.9, 41.4) |
| >High school | 692 | 30.1 (27.5, 32.7) | 612 | 15.7 (14.0, 17.4) | 879 | 46.0 (43.6, 48.5) | 2183 | 22.4 (20.8, 23.9) |
| Marital status | ||||||||
| Other | 376 | 9.3 (8.0, 10.6) | 342 | 10.9 (8.7, 13.0) | 367 | 10.9 (9.6, 12.2) | 1085 | 10.3 (8.9, 11.6) |
| Married | 1199 | 56.0 (53.1, 58.9) | 940 | 42.9 (39.2, 46.5) | 1053 | 50.4 (47.9, 52.9) | 3192 | 48.0 (45.5, 50.4) |
| Single | 522 | 34.7 (31.8, 37.7) | 764 | 46.3 (42.4, 50.1) | 603 | 38.7 (36.1, 41.2) | 1889 | 41.7 (39.2, 44.3) |
| Perceived health | ||||||||
| Poor/Regular | 541 | 24.6 (22.2, 26.9) | 774 | 37.8 (34.3, 41.4) | 608 | 27.7 (25.5, 29.8) | 1923 | 32.5 (30.3, 34.8) |
| Good | 963 | 48.0 (45.1, 50.8) | 822 | 41.6 (37.7, 45.5) | 771 | 38.8 (36.3, 41.2) | 2556 | 43.8 (41.3, 46.3) |
| Very good/excellent | 592 | 27.5 (25.0, 30.0) | 450 | 20.6 (17.8, 23.4) | 631 | 33.6 (31.2, 35.9) | 1673 | 23.7 (21.7, 25.6) |
| BMI | ||||||||
| Overweight/Obese | 912 | 60.2 (57.4, 62.9) | 830 | 58.1 (54.1, 62.1) | 888 | 56.7 (54.2, 59.2) | 2630 | 58.8 (56.3, 61.4) |
| Normal | 1133 | 39.8 (37.1, 42.6) | 1115 | 41.9 (37.9, 45.9) | 1.01 | 43.3 (40.8, 45.8) | 3258 | 41.2 (38.6, 43.7) |
| Bike use | ||||||||
| Yes | 160 | 9.6 (7.8, 11.4) | 248 | 16.0 (13.7, 18.4) | 166 | 8.8 (7.4, 10.1) | 574 | 13.4 (11.8, 14.9) |
| No | 1937 | 90.4 (88.6, 92.2) | 1798 | 84.0 (81.6, 86.3) | 1857 | 91.2 (89.9, 92.6) | 5592 | 86.6 (85.1, 88.2) |
| Walking for transportation regularly (150min/week) | ||||||||
| Yes | 458 | 23.9 (21.4, 26.4) | 480 | 27.4 (23.7, 31.1) | 477 | 23.8 (21.7, 25.9) | 1415 | 26.0 (23.6, 28.3) |
| No | 1639 | 76.1 (73.6, 78.6) | 1551 | 72.6 (68.9, 76.3) | 1499 | 76.2 (74.1, 78.3) | 4689 | 74.0 (71.7, 76.4) |
| Sidewalks in the streets nearby | ||||||||
| Yes | 1556 | 70.7 (68.0, 73.3) | 1762 | 81.1 (77.5, 84.8) | 936 | 46.7 (44.2, 49.2) | 4254 | 75.9 (73.6, 78.2) |
| No | 541 | 29.3 (26.7, 32.0) | 284 | 18.9 (15.2, 22.5) | 1036 | 53.3 (50.8, 55.8) | 1861 | 24.1 (21.8, 26.4) |
| Traffic make it difficult to cycle/walk | ||||||||
| Yes | 1130 | 54.9 (52.1, 57.8) | 968 | 43.6 (40.0, 47.3) | 1231 | 62.1 (59.7, 64.6) | 3329 | 48.5 (46.1, 51.0) |
| No | 967 | 45.1 (42.2, 47.9) | 1077 | 56.4 (52.7, 60.0) | 692 | 37.9 (35.4, 40.3) | 2736 | 51.5 (49.0, 53.9) |
| Safe to cycle/walk during the night | ||||||||
| Yes | 775 | 37.2 (34.5, 40.0) | 806 | 44.4 (40.5, 48.2) | 408 | 21.6 (19.5, 23.7) | 1989 | 40.8 (38.3, 43.4) |
| No | 1322 | 62.8 (60.0, 65.5) | 1240 | 55.6 (51.8, 59.5) | 1530 | 78.4 (76.3, 80.5) | 4092 | 59.2 (56.6, 61.7) |
| Safe to cycle/walk during the day | ||||||||
| Yes | 1760 | 84.8 (82.8, 86.8) | 1551 | 79.5 (77.0, 82.0) | 1128 | 58.2 (55.7, 60.7) | 4439 | 80.6 (78.9, 82.3) |
| No | 337 | 15.2 (13.2, 17.2) | 495 | 20.5 (18.0, 23.0) | 816 | 41.8 (39.3, 44.3) | 1648 | 19.4 (17.7, 21.1) |
Weighed prevalence
Overall, cycling for transportation was 13.4% and was higher in Recife (16.0%; 95% CI=13.7%, 18.4) compared to Curitiba (9.6%; 95% CI=7.8, 11.4) and Vitoria (8.8%; 95% CI=734, 10.1). An interaction for cycling prevalence was found between Recife (prevalence OR [pOR]=1.32; 95% CI=1.03, 1.98) and Curitiba. One of four participants (26.6%) walked regularly as a mode of transportation. A lower prevalence was observed in Curitiba (23.9%) and Vitoria (23.8%) compared to Recife (27.4%), although they did not differ statistically.
The environmental characteristics varied in all three cities. For instance, although more than half of the participants reported that traffic does not make cycling/walking more difficult, this proportion was lower in Vitoria (37.9%) than in the other two cities. Three quarters of the participants reported having sidewalks in the streets, with a lower proportion reported in Vitoria (46.7%). Finally, perceived safety for cycling/walking during the night was lower in Vitoria compared to the other cities.
Individual and Environmental Correlates of Cycling for Transportation
Table 3 shows the results of the adjusted Poisson regression analysis on cycling for transportation. The associations in the adjusted analysis showed that men were 3.4 times more likely to cycle than women. Younger respondents were roughly three times more likely to cycle than were older participants. Participants with a higher level of education were 70% less likely to cycle than those in the lower education category, in all three cities. Other covariates were not consistently associated with cycling. For instance, normal weight status was associated with cycling only in the pooled analysis (pOR=1.7, 95% CI=1.3, 2.3) and in Recife (pOR=1.7, 95% CI=1.3, 2.3). No evidence of strong association was found for marital status, perceived health, and all four environmental indicators.
Table 3.
Personal and environmental factors associated with cycling for transportation in Curitiba, Vitoria and Recife, Brazil, pOR (95% CI)
| Variables | Curitibaa | Recifea | Vitoriaa | Combinedb | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Gender | Women | Ref | Ref | Ref | Ref | ||||||||
| Men | 2.8 | 1.9 | 4.1 | 3.7 | 2.7 | 4.9 | 3.4 | 2.6 | 4.9 | 3.4 | 2.6 | 4.4 | |
| Age categories, years | 55+ | Ref | Ref | Ref | Ref | ||||||||
| 35–45 | 1.8 | 1.1 | 3.4 | 3.1 | 1.7 | 4.6 | 2.0 | 1.2 | 3.3 | 2.6 | 1.7 | 4.2 | |
| 16–34 | 2.4 | 1.2 | 4.7 | 3.4 | 1.7 | 6.7 | 2.6 | 1.5 | 4.6 | 2.9 | 1.8 | 4.9 | |
| Education level | <High | Ref | Ref | Ref | Ref | ||||||||
| High school | 0.7 | 0.4 | 1.1 | 0.8 | 0.6 | 1.1 | 0.6 | 0.4 | 0.8 | 0.8 | 0.6 | 1.0 | |
| >High school | 0.3 | 0.1 | 0.5 | 0.3 | 0.2 | 0.5 | 0.2 | 0.1 | 0.3 | 0.3 | 0.2 | 0.4 | |
| Marital status | Other | Ref | Ref | Ref | Ref | ||||||||
| Married | 1.1 | 0.5 | 2.4 | 0.7 | 0.4 | 1.1 | 1.5 | 0.7 | 3.2 | 0.8 | 0.5 | 1.2 | |
| Single | 1.6 | 0.7 | 3.7 | 0.6 | 0.4 | 1.1 | 1.6 | 0.7 | 3.5 | 0.8 | 0.5 | 1.3 | |
| Perceived health | Poor/Regular | Ref | Ref | Ref | Ref | ||||||||
| Good | 1.3 | 0.7 | 2.2 | 1.0 | 0.7 | 1.4 | 0.7 | 0.5 | 1.1 | 1.1 | 0.8 | 1.4 | |
| Very good/excellent | 1.7 | 0.9 | 2.9 | 1.2 | 0.8 | 1.7 | 0.9 | 0.6 | 1.5 | 1.3 | 0.9 | 1.7 | |
| BMI | Overweight/Obese | Ref | Ref | Ref | Ref | ||||||||
| Normal | 1.4 | 0.9 | 2.0 | 1.9 | 1.4 | 2.8 | 1.1 | 0.8 | 1.5 | 1.7 | 1.3 | 2.3 | |
| Sidewalks in the streets nearby | Yes | Ref | Ref | Ref | Ref | ||||||||
| No | 1.1 | 0.7 | 1.6 | 0.9 | 0.6 | 1.4 | 0.9 | 0.7 | 1.3 | 0.9 | 0.7 | 1.3 | |
| Traffic make it difficult to cycle/walk | Yes | Ref | Ref | Ref | Ref | ||||||||
| No | 1.0 | 0.7 | 1.5 | 0.8 | 0.6 | 1.1 | 1.2 | 0.8 | 1.6 | 0.9 | 0.7 | 1.1 | |
| Safe to cycle/walk during the night | Yes | Ref | Ref | Ref | Ref | ||||||||
| No | 1.1 | 0.7 | 1.6 | 1.2 | 0.8 | 1.6 | 1.0 | 0.7 | 1.4 | 1.1 | 0.8 | 1.4 | |
| Safe to cycle/walk during the day | Yes | Ref | Ref | Ref | Ref | ||||||||
| No | 1.1 | 0.6 | 1.9 | 0.7 | 0.5 | 1.0 | 0.9 | 0.7 | 0.8 | 0.6 | 1.1 | ||
Weighed prevalence OR adjusted for Gender, Age categories, Education level, Marital status, Perceived health and BMI;
Weighed prevalence OR adjusted for Gender, Age categories, Education level, Marital status, Perceived health, BMI and City.
Individual and Environmental Correlates of Walking for Transportation
Adjusted Poisson regressions on walking for transportation are presented in Table 4. Education level was the only covariate consistently associated with walking across all three cities, showing that people in the highest education category are less likely to walk regularly compared to those with less education. Other covariates showed divergent patterns. For instance, married and single respondents were roughly twice as likely to walk regularly in the combined data set and also in Recife but not in the other cities. Positive perception of health was associated with walking in the pooled analysis, and in Curitiba. All four environmental indicators, age, and BMI did not present strong associations with walking for transportation.
Table 4.
Personal and environmental factors associated with walking for transportation in Curitiba, Vitoria and Recife, Brazil, pOR (95% CI)
| Variables | Curitibaa | Recifea | Vitoriaa | Combinedb | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Gender | Women | Ref | Ref | Ref | Ref | ||||||||
| Men | 1.0 | 0.8 | 1.2 | 0.9 | 0.7 | 1.2 | 1.0 | 0.8 | 1.2 | 0.9 | 0.8 | 1.1 | |
| Age, years | 55+ | Ref | Ref | Ref | Ref | ||||||||
| 35–45 | 1.0 | 0.8 | 1.3 | 1.1 | 0.8 | 1.6 | 1.0 | 0.8 | 1.3 | 1.1 | 0.9 | 1.4 | |
| 16–34 | 1.1 | 0.8 | 1.5 | 1.1 | 0.7 | 1.7 | 1.0 | 0.8 | 1.4 | 1.1 | 0.8 | 1.4 | |
| Education level | <High | Ref | Ref | Ref | Ref | ||||||||
| High school | 0.9 | 0.7 | 1.1 | 1.0 | 0.7 | 1.4 | 0.9 | 0.7 | 1.1 | 0.9 | 0.8 | 1.2 | |
| >High school | 0.6 | 0.5 | 0.9 | 0.6 | 0.4 | 0.8 | 0.6 | 0.4 | 0.7 | 0.6 | 0.5 | 0.8 | |
| Marital status | Other | Ref | Ref | Ref | Ref | ||||||||
| Married | 1.2 | 0.8 | 1.6 | 1.6 | 1.1 | 2.4 | 0.7 | 0.6 | 0.9 | 1.4 | 1.1 | 1.8 | |
| Single | 1.4 | 0.9 | 2.0 | 2.0 | 1.3 | 3.0 | 0.9 | 0.6 | 1.2 | 1.7 | 1.3 | 2.2 | |
| Perceived health | Poor/Regular | Ref | Ref | Ref | Ref | ||||||||
| Good | 1.1 | 0.8 | 1.5 | 1.1 | 0.8 | 1.4 | 1.1 | 0.9 | 1.4 | 1.1 | 0.9 | 1.3 | |
| Very good/excellent | 1.4 | 1.0 | 1.8 | 1.3 | 0.9 | 1.8 | 0.9 | 0.7 | 1.2 | 1.3 | 1.0 | 1.6 | |
| BMI | Overweight/Obese | Ref | Ref | Ref | Ref | ||||||||
| Normal | 1.2 | 0.9 | 1.4 | 1.2 | 0.9 | 1.8 | 1.2 | 0.9 | 1.4 | 1.2 | 0.9 | 1.5 | |
| Sidewalks in the streets nearby | Yes | Ref | Ref | Ref | Ref | ||||||||
| No | 1.1 | 0.8 | 1.4 | 1.3 | 0.9 | 1.9 | 1.0 | 0.8 | 1.2 | 1.2 | 0.9 | 1.5 | |
| Traffic makes it difficult to cycle/walk | Yes | Ref | Ref | Ref | Ref | ||||||||
| No | 0.9 | 0.7 | 1.2 | 0.9 | 0.8 | 1.2 | 1.0 | 0.8 | 1.2 | 1.0 | 0.8 | 1.1 | |
| Safe to cycle/walk during the night | Yes | Ref | Ref | Ref | Ref | ||||||||
| No | 1.0 | 0.8 | 1.3 | 1.2 | 0.9 | 1.6 | 1.0 | 0.8 | 1.3 | 1.2 | 0.9 | 1.4 | |
| Safe to cycle/walk during the day | Yes | Ref | Ref | Ref | Ref | ||||||||
| No | 1.2 | 0.9 | 1.6 | 0.9 | 0.7 | 1.3 | 1.0 | 0.8 | 1.2 | 1.0 | 0.8 | 1.3 | |
Weighed prevalence OR adjusted for Gender, Age categories, Education level, Marital status, Perceived health and BMI;
Weighed prevalence OR adjusted for Gender, Age categories, Education level, Marital status, Perceived health, BMI and City.
Discussion
This study describes the prevalence of cycling and walking for transportation and their association with personal and environmental factors across three state capitals in Brazil. This is one of the first studies to analyze active commuting in a low- to middle-income country in a large sample. The prevalence of cycling for transportation was 13.4%, and one of four (26%) participants walked regularly as a mode of transportation. These prevalences varied among cities and were associated with gender, age and education. Health and environmental indicators were not consistently associated across all the cities and were more frequently associated with walking than bicycling.
Overall, the prevalence of cycling for transportation was higher in Recife compared with that in Curitiba and Vitoria. The prevalence of cycling for transportation remained higher than that observed in North America, where it is four37 to six times38 lower. However, when compared to some Europeans countries, figures in the present study are low. For example, survey data from Belgium16, Germany39 and Austria40 has shown prevalences of 55.0%, 44.6% and 41.4% respectively, which are roughly three times higher than the findings from these Brazilian cities. Comparisons with other middle- and low-income countries are limited by the scarce data available, as recently noted in a comprehensive review.4 Yet, a prevalence of 15% has been reported in Bogota (Colombia),20 which is similar to the overall prevalence found in the current study.
Approximately one of four participants walked regularly as a mode of transportation. A slightly lower prevalence was observed in Curitiba and Vitoria (~24%) compared with Recife (27.4%). Comparisons with other studies and settings are difficult, mainly because of three factors. First, walking for transportation is rarely analyzed separately from cycling as a mode of transport;4 second, the vast majority of the available evidence that analyzed such outcomes separately comes from high-income countries;4 and third, such studies have rarely used the most recent physical recommendation cut-point as an outcome variable.34 Despite these factors, the results are consistent with previous evidence. For instance, data from the U.S. National Health Survey have shown that 28.2% of adults walk for transportation for at least 10 minutes. More recently, a study with Latino adults from San Diego CA showed that 29% of respondents adhered to current physical activity guidelines through walking for transportation.39
Prevalence of cycling and walking for transportation in these three Brazilian cities should be interpreted in light of contextual changes. For instance, a recent survey conducted in 22 African countries showed that physical activity for transport, including walking, contributes largely to overall physical activity.41 Notably, this contribution was lower in countries with a medium or high human development index ,41 suggesting a country-specific effect. In fact, the social and economic changes experienced by Latin America, including rapid urbanization42 and a steady increase in car ownership might have influenced patterns of active transportation.43 Such changes may partially explain the relatively low prevalence found compared to that in European countries, where such characteristics (e.g., urbanization and car ownership) have been relatively stable over the past decade. In the U.S., increasing prevalence of physical inactivity is partially explained by declining rates of transportation-related physical activity.10
Finally, city-specific differences should be considered in light of the results of the current study. The prevalence of cycling in Recife was almost twice that in Curitiba. Recife also has a slightly higher prevalence of walking, but the difference is not as great. Recife has the lowest human development index, and the highest crime rate, population density, unemployment rate, and social inequality; therefore, it is likely that physical activity is a needed means of transportation regardless of safety. In fact, physical activity for transport is more prevalent in areas with a higher population density and low income20; however, higher crime rates have shown an inconsistent association with both cycling and walking for transportation.44
Personal characteristics were associated with physical activity for transportation, which is consistent with the current body of evidence. Several studies11,20,37,38 have reported that gender (female), age and schooling are inversely associated with cycling for transportation while walking for transportation is more frequent in groups with a lower level of education.17,18,20 Cycling and walking for transportation were not associated with perceived environment. This lack of association with perceived-environment variables could be explained by contextual, design and measurement limitations of this study. Since crime is highly prevalent in all cities, regular bikers or walkers may be more likely to report an unsafe environment. As previously noted, Recife and Vitoria have the highest crime rates among the cities, supporting the hypothesis that lack of safety is over-reported among those who are regular walkers.
Additionally, the overall population density is high, which could offer a more walkable environment45 but also increase the exposure to greater traffic volume. Finally, three of four participants reported sidewalks in the streets nearby. These characteristics could reduce the variability of the exposure (e.g., safety and sidewalks), while the cross-sectional design may affect the direction of associations. The combination of these factors may help to explain the lack of association. Nonetheless, the available literature is not conclusive on the association between safety from crime and traffic with physical activity for transportation,44 which also makes it difficult to draw evidence-based hypotheses.
This is not the case for the presence of sidewalks, which is positively associated with physical activity for transport.17,46 However, the studies reporting such association employed objective environmental measures (e.g., GIS), limiting the comparisons that can be made to the current findings. Moreover, the measure employed did not account for the quality of the sidewalks, which may also be an important factor in whether they are used, as reported in the literature.44 Finally, the outcome variables were self-reported, although providing reliable information on the total physical activity volume 47 does not capture details in travel time and physical activity intensity (e.g., walking speed), which may be important factors for light and moderate levels of activity such as cycling and walking.
Several strengths should be noted in this study. First, the sample size was large enough to provide precise prevalence estimates and to detect small effects on the exposure variables. Commuting physical activity was analyzed as a separate outcome, which is rarely reported in the literature, particularly in low- or middle-income countries.4 All the analyses were conducted to account for the main confounders.
This study offered a unique opportunity to compare various social and environmental characteristics across three main state capital cities. Findings should help to disentangle the myriad factors related to active transportation in low- to middle-income countries with high rates of urbanization. The standardization of the outcome measure and independent variables allows comparability across studies. Additionally, these findings suggest that lower-income populations and women are potential subgroups that need intensive intervention for promoting active transportation regardless of country income.
Although environmental correlates were not associated with commuting physical activity, this research could be supplemented with natural experiments in urban planning and transportation as observed in some cities in Latin America.20,27 In that sense, researchers should explore whether changes in policies and practices (e.g., improvements in street lighting, safety of street crossing, and sidewalk continuity) in similar settings are effective in promoting active transportation.
Conclusion
The prevalence of cycling and walking for transportation was low in the current study compared to that found in certain high-income countries, particularly in Europe, and was slightly higher in the cities with the most unequal and unsafe environments. Personal factors were more consistently associated with bicycling than with walking, whereas perceived environmental features were not associated with active commuting.
Acknowledgments
This study was funded through the CDC's Prevention Research Centers Program contract U48/DP001903 (Applying Evidence-Physical Activity Recommendations in Brazil). The findings and conclusions in this article are those of the author(s) and do not necessarily represent the official position of the CDC. The authors thank all members of Project GUIA for their valuable contribution and input. The authors are also thankful for the contribution of CAPES (Coordenação de Aperfeiçoamento Pessoal de Nível Superior) for funding researchers from Brazil.
Footnotes
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