Correlates of Walking for Travel in Seven European Cities: The PASTA Project
2019; National Institute of Environmental Health Sciences; Volume: 127; Issue: 9 Linguagem: Inglês
10.1289/ehp4603
ISSN1552-9924
AutoresMireia Gascón, Thomas Götschi, Audrey de Nazelle, Esther Gracia, Albert Ambrós, Sandra Márquez, Oriol Marquet, Ione Ávila-Palència, Christian Brand, Francesco Iacorossi, Elisabeth Raser, Mailin Gaupp-Berghausen, Evi Dons, Michelle Laeremans, Sonja Kahlmeier, Julián Sánchez, Regine Gerike, Esther Anaya-Boig, Luc Int Panis, Mark Nieuwenhuijsen,
Tópico(s)Urban Green Space and Health
ResumoVol. 127, No. 9 ResearchOpen AccessCorrelates of Walking for Travel in Seven European Cities: The PASTA Project Mireia Gascon, Thomas Götschi, Audrey de Nazelle, Esther Gracia, Albert Ambròs, Sandra Márquez, Oriol Marquet, Ione Avila-Palencia, Christian Brand, Francesco Iacorossi, Elisabeth Raser, Mailin Gaupp-Berghausen, Evi Dons, Michelle Laeremans, Sonja Kahlmeier, Julian Sánchez, Regine Gerike, Esther Anaya-Boig, Luc Int Panis, and Mark Nieuwenhuijsen Mireia Gascon Address correspondence to Mireia Gascon, Barcelona Institute for Global Health (ISGlobal), Parc de Recerca Biomèdica de Barcelona—PRBB, C/Doctor Aiguader, 88, 08003 Barcelona, Spain. Telephone: 0034 932147363. Email: E-mail Address: [email protected] Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Spain , Thomas Götschi Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland , Audrey de Nazelle Centre for Environmental Policy, Imperial College London, London, United Kingdom , Esther Gracia Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Spain , Albert Ambròs Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Spain , Sandra Márquez Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Spain , Oriol Marquet Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Spain , Ione Avila-Palencia Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Spain , Christian Brand Transport Studies Unit, University of Oxford, Oxford, United Kingdom , Francesco Iacorossi Agenzia Roma Servizi per la Mobilità, Rome, Italy , Elisabeth Raser Institute for Transport Studies, University of Natural Resources and Life Sciences, Vienna, Austria , Mailin Gaupp-Berghausen Institute for Transport Studies, University of Natural Resources and Life Sciences, Vienna, Austria , Evi Dons Centre for Environmental Sciences, Hasselt University, Hasselt, Belgium Flemish Institute for Technological Research (VITO), Mol, Belgium , Michelle Laeremans Flemish Institute for Technological Research (VITO), Mol, Belgium Transportation Research Institute, Hasselt University, Hasselt, Belgium , Sonja Kahlmeier Physical Activity and Health Unit, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland , Julian Sánchez Centre for Environmental Policy, Imperial College London, London, United Kingdom , Regine Gerike Institute of Transport Planning and Road Traffic, Technische Universität (TU) Dresden, Dresden, Germany , Esther Anaya-Boig Centre for Environmental Policy, Imperial College London, London, United Kingdom , Luc Int Panis Flemish Institute for Technological Research (VITO), Mol, Belgium School for Mobility, Hasselt University, Hasselt, Belgium , and Mark Nieuwenhuijsen Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Spain Published:18 September 2019CID: 097003https://doi.org/10.1289/EHP4603Cited by:12AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack CitationsCopy LTI LinkHTMLAbstractPDF ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground:Although walking for travel can help in reaching the daily recommended levels of physical activity, we know relatively little about the correlates of walking for travel in the European context.Objective:Within the framework of the European Physical Activity through Sustainable Transport Approaches (PASTA) project, we aimed to explore the correlates of walking for travel in European cities.Methods:The same protocol was applied in seven European cities. Using a web-based questionnaire, we collected information on total minutes of walking per week, individual characteristics, mobility behavior, and attitude (N=7,875). Characteristics of the built environment (the home and the work/study addresses) were determined with geographic information system (GIS)-based techniques. We conducted negative binomial regression analyses, including city as a random effect. Factor and principal component analyses were also conducted to define profiles of the different variables of interest.Results:Living in high-density residential areas with richness of facilities and density of public transport stations was associated with increased walking for travel, whereas the same characteristics at the work/study area were less strongly associated with the outcome when the residential and work/study environments were entered in the model jointly. A walk-friendly social environment was associated with walking for travel. All three factors describing different opinions about walking (ranging from good to bad) were associated with increased minutes of walking per week, although the importance given to certain criteria to choose a mode of transport provided different results according to the criteria.Discussion:The present study supports findings from previous research regarding the role of the built environment in the promotion of walking for travel and provides new findings to help in achieving sustainable, healthy, livable, and walkable cities. https://doi.org/10.1289/EHP4603IntroductionLack of physical activity is among the 10 leading risk factors for mortality worldwide and is a key risk factor for obesity and other noncommunicable diseases (NCDs), such as cardiovascular diseases, cancer, and diabetes (WHO 2018). Indeed, it is estimated that people who are insufficiently active have between a 20% and 30% increased risk of premature death compared with people who are sufficiently active (WHO 2018). According to a study conducted in 2015, physical inactivity costs 80.4 billion euros per year in Europe, which is equivalent to 6.2% of all European health spending. The authors also estimated that by 2030 these costs could be as high as 125 billion euros (ISCA and Cebr 2015). Moreover, a recent study including almost 2 million participants worldwide showed that physical inactivity levels have increased in high-income countries in the last 15 y (Guthold et al. 2018). The study noted that “if current trends continue, the 2025 global physical activity target (a 10% relative reduction in insufficient physical activity) will not be met” and urged the implementation of policies to increase population levels of physical activity worldwide (Guthold et al. 2018).The WHO (2018) recommends that in a typical week adults perform at least 150 min of moderate-intensity aerobic physical activity (which includes walking) or, alternatively, at least 75 min of vigorous-intensity aerobic physical activity or an equivalent combination of moderate- and vigorous-intensity activity. Although walking for travel purposes is an easy and healthy way to reach the recommended levels of physical activity, in the last century the increasing use of motorized modes of transport (e.g., car, motorbike) has contributed to the drop in levels of physical activity among the general population and has led to other traffic-related health problems such as air and noise pollution (Giles-Corti et al. 2016; Nieuwenhuijsen 2016). In the past few years, many studies have been conducted to evaluate possible determinants that contribute to the use of active modes of transport, particularly walking (Christian et al. 2013; Christiansen et al. 2016; D’Haese et al. 2015; Kerr et al. 2016; Knuiman et al. 2014; Marquet et al. 2017; Marquet and Miralles-Guasch 2015; Smith et al. 2017; Sugiyama et al. 2012; Wasfi et al. 2017; Yang 2016). However, these studies often contained small sample sizes and most of them focused on a particular domain of influence (e.g., policy context, built environment, social environment, personal or trip attributes) or were heterogeneous regarding the methods followed to assess both exposures and outcomes (Dons et al. 2015; Götschi et al. 2017). Moreover, the majority of these studies were conducted in Australia and the United States, with fewer conducted in other regions worldwide, including Europe (Sugiyama et al. 2012), where the built environment characteristics of the cities are significantly different (Dons et al. 2015; Kelly et al. 2017).The European Commission–funded Physical Activity through Sustainable Transport Approaches (PASTA) project is a multinational, interdisciplinary research project aiming to understand the correlates of active travel behavior as well as potential confounders and mediators (Dons et al. 2015; Gerike et al. 2016; Götschi et al. 2017). Although PASTA is a longitudinal study, with several waves of assessment, the present study used data from the baseline questionnaire only. The main aim of the present cross-sectional study was to explore the correlates of walking for travel in seven European cities, using a common protocol in all cities, and including a range of correlates such as the built environment (both around the residence and the work or study locations) and the social environment as well as personal characteristics and trip attributes. We also explored whether there were different patterns of association between those participants working (full- or part-time) or studying and those not working (e.g., unemployed, retired) or studying.Materials and MethodsStudy Design and PopulationDetails of the PASTA project are provided elsewhere (Dons et al. 2015; Gaupp-Berghausen et al. 2019; Gerike et al. 2016). Briefly, PASTA pursues a mixed-method and multilevel approach that is consistently applied in seven case study cities (Antwerp, Barcelona, London, Örebro, Rome, Vienna, and Zurich) following a common protocol. The PASTA framework distinguishes hierarchical levels for various factors (i.e., city, individual, and trips), and three main domains or pathways that influence active mobility behavior (and physical activity), namely socio-geographical factors, socio-psychological factors, and rationale- or mode choice-related factors (Dons et al. 2015).A standardized recruitment strategy was developed for all cities using an opportunistic approach (e.g., press releases, postcards and leaflets; direct targeting of local stakeholders and community groups; extensive use of social media). To minimize attrition, a user engagement strategy was developed, including incentivizing participation with a lottery. The lottery was done every 3 months, with each city deciding how to award the incentives (cash or vouchers). Those participants with a greater number of completed questionnaires for the previous 3 months had a greater chance of winning. Örebro (Sweden) was the only city that did not do a lottery (nor any other kind of incentive) because it was not allowed due to its workplace recruitment particularities (Dons et al. 2015; Gaupp-Berghausen et al. 2019). Participants had to be at least 18 y of age (at least 16 y in Zurich) and to live, work, study, or regularly travel (i.e., at least once a week) in the PASTA city of interest (Dons et al. 2015). Individual-level information and correlates of active mobility were investigated through a large-scale longitudinal web-based survey ( http://pastaproject.eu/fileadmin/editor-upload/sitecontent/City_survey/PASTA-questionnaires.pdf). The baseline questionnaire allowed the collection of sociodemographic, individual, household, health, and attitudinal variables. Information on mobility and physical activity habits was gathered through the use of questions on the frequency of use of different modes of transport and the use of the Global Physical Activity Questionnaire (GPAQ). In total, 10,691 participants answered the baseline questionnaire (Gaupp-Berghausen et al. 2019). However, 2,701 participants were excluded because they did not have acceptable GPAQ indicators based on the validation criteria established by the GPAQ guideline (WHO n.d.). Of the remaining 7,990, 115 participants were excluded because they did not provide a home address at baseline and, therefore, indicators for their residential built environment characteristics were not available. A total of 7,875 participants were included in our main analyses. Out of those, 6,957 participants also provided work or study addresses and were included in our secondary analyses. The rest of the participants, n=918, reported not working or studying and were therefore not included in the secondary analysis. For each partner city, the relevant permission to collect, store, and process data was obtained from the local ethics committees. On enrollment, participants registered on the PASTA website and gave informed consent [see the participant information sheet in the Overview of PASTA Questionnaires ( http://pastaproject.eu/fileadmin/editor-upload/sitecontent/City_survey/PASTA-questionnaires.pdf)]. Further details can be found in the paper by Dons et al. (2015).Outcome AssessmentWe followed the GPAQ standard procedures to validate the answers provided by the participants (WHO n.d.) and to calculate our outcome variable of interest, minutes of walking per week for travel, which was the result of combining the GPAQ questions “In a typical week, on how many days do you walk for at least 10 min continuously to get to and from places?” and “Typically, how much time do you spend walking on such a day?”Correlates of Walking for TravelAccording to the PASTA framework of active travel behavior (Götschi et al. 2017), we considered many correlates that could potentially be associated with walking, including those related to the built environment, social context, and individual-level factors. Residential- and work/study-address built environment characteristics were systematically gathered in each city by collecting publicly available geographic information system (GIS)-based data along with information from other data sources such as weather data and population statistics and by means of stakeholder interviews (Dons et al. 2015). The rest of the information was collected through the web-based questionnaire previously mentioned.Individual characteristics. A wide range of individual characteristics were collected. Based on previous literature (Christian et al. 2013; Christiansen et al. 2016; D’Haese et al. 2015; Kerr et al. 2016; Knuiman et al. 2014; Marquet et al. 2017; Marquet and Miralles-Guasch 2015; Smith et al. 2017; Sugiyama et al. 2012; Wasfi et al. 2017; Yang 2016), we included the following variables in the base model because these individual characteristics have been shown to strongly influence travel model choices: age, gender, level of education [high education: education above secondary school (yes/no)], employment status (full-time, part-time, student, not working), access to car or a van [hereafter referred to as access to a car (never, sometimes, always)], and access to a bicycle or an electric bicycle (e-bicycle) [hereafter referred to as access to a bicycle (yes/no)].Built environment characteristics. The same built environment characteristics were included in the present analysis for both the residential and the work/study addresses, using a 300-m radial buffer. Table 1 provides the complete details on how each indicator was calculated and/or defined. Briefly, using a diversity of sources depending on the variable of interest and the sources available in each city [Navteq (2012), Open Street Map (OSM) and local layers (2015–2017), or census/neighborhood data (2011–2016)], we obtained information for street-length density (in meters per kilometer squared), street connectivity (in intersections per kilometer squared), building-area density (in meters squared per kilometer squared), population density (in number of inhabitants per kilometer squared), facilities density (in number of facilities per kilometer squared), facilities richness (in number of facilities type/total number facilities), density of public transport stations (in number of stations per kilometer squared), distance to the nearest public transport station (in meters). Levels of the air pollutants particulate matter ≤2.5μm in aerodynamic diameter (PM2.5) and nitrogen dioxide (NO2) (both in micrograms per cubic meter) were estimated based on land-use regression models (de Hoogh et al. 2016), and surrounding greenness was defined based on the normalized difference vegetation index [NDVI; images from the years 2015–2016 (Nieuwenhuijsen et al. 2014), which go from −1 (less green) to 1 (more green)]. We used land-cover map Corine (2006) to assess distance (in meters) and area (in kilometers squared) of the closest major (≥0.5ha) green space, access to a major green space [i.e., location is <300m from a major green space (yes/no)], distance (in meters) and area (in kilometers squared) of the closest major (≥0.5ha) blue space, and access to a major blue space [i.e., location is 1 (in order to exclude cul-de-sacs) (n/km2)]Navteqa street intersections data (2012)Building-area density (m2/km2)OSM / local layers (2015–2017)bPopulation density (n inhabitants/km2)Census / neighborhood data (2011–2016)cFacility density index [number of points of interest (POIs) (n facilities/km2)]Navteqa POI data set (2012). For full list of POIs see https://tinyurl.com/PASTA-POIFacility richness index [number of different facility types (POIs) present, divided by the maximum potential number of facility types specified (n facility types/74)]Navteqa POI data (2012). For full list of POIs see https://tinyurl.com/PASTA-POIDensity of public transport stations (n of public transport stations/km2)OSM (and local data if available; 2015–2017)dDistance to the nearest public transport station (m)OSM (and local data if available; 2015–2017)dPM2.5 (μg/m3)PM2.5 land-use regression models incorporating satellite-derived and chemical transport modeling data (de Hoogh et al. 2016)eNO2 (μg/m3)NO2 land-use regression models incorporating satellite-derived and chemical transport modeling data (de Hoogh et al. 2016)eSurrounding greenness (NDVI)Landsat Satellite Images (2015–2016)fGreen and blue spaces indicatorsLand-cover map Corine 2006 (available for the whole of Europe for both urban and rural areas)Note: NDVI, normalized difference vegetation index; NO2, nitrogen dioxide; OSM, Open Street Maps ( https://www.openstreetmap.org/export); PM2.5, particulate matter ≤2.5μm in aerodynamic diameter.aNavteq is licensed data under ArcGIS software. This data is prepared for routing analysis over Europe. It contains data on Streets and Points of Interest (POIs), so it identifies a wide range of categories in which the different POIs (e.g., schools, libraries, cinemas, banks, restaurants) are included. (See the full list in this link: https://tinyurl.com/PASTA-POI.)bThe source of information varied across cities: Antwerp: local layer (2015) for city center and OSM (2016) for addresses outside the city; Barcelona: local layer (2013) and OSM (2017) for addresses outside the city; London: local layer (2016); and Örebro, Rome, Vienna and Zurich: OSM (2017).cThe source of information varied across cities: Antwerp, Barcelona, London, Rome, and Vienna: National Census (2011), Örebro: local layer (2015); and Zurich local and regional layer (2016).dThe source of information varied across cities: Antwerp: OSM (2016); Barcelona: local layer (2011) and OSM (2017) for addresses outside the city; London: local layer (2011); Örebro: OSM (2017) but local layer (2015) for bus stations; Rome: OSM (2017); Vienna: OSM (2017); and Zurich: OSM (2017).eThe NO2 and PM2.5 air pollution grids [100-m resolution; annual means (μg/m3)] used are from the Europe-wide models for these pollutants, developed for 2010. Models are based on routine air pollution monitoring data (AIRBASE database) incorporating satellite-derived and chemical transport model estimates, and road and land-use data. Both NO2 and PM2.5 models explained ∼60% of spatial variation in measured NO2 and PM2.5 concentrations (de Hoogh et al. 2016). ( http://www.sahsu.org/content/data-download.)fWe followed the positive health effects of the natural outdoor environment in typical populations in different regions in Europe (PHENOTYPE) project (Nieuwenhuijsen et al. 2014) protocol to select the images from LANDSAT within the greenest period and having the lowest cloud cover. Green season was considered to be from March to July 2015. However, if additional usable images were needed, these were obtained from the following year, 2016. Different images were merged to cover all the study area, and if different images overlapped in the same area, we selected the one without clouds and having the highest pixel value. Following this process, we were able to completely cover the area of study.Social norms and mobility culture in the neighborhood. Three different questions were used to determine the community context of each individual with regard to walking (Götschi et al. 2017): a) “Most people who are important to me think that I should walk for travel,” b) “In my neighborhood walking is well regarded, and c) “In my neighborhood it is common for people to walk for travel.” Response options were on a 5-point Likert-type scale with 1 for “very much disagree” to 5 for “very much agree.”Values and attitude toward walking for travel. Two sets of questions were used to evaluate, on the one hand, the importance of certain criteria when choosing a mode of transport to travel and, on the other hand, the opinion about walking for travel in relation to different criteria. In particular, participants had to report the level of importance to them (5-point Likert-type scale from “not important” to “very important”) of the following criteria: short travel time, lower travel cost, higher travel comfort, safer travel (with regard to traffic), safer travel (with regard to crime), lower exposure to air pollution, privacy, personal health benefits, low environmental impact, flexible departure time, more predictable time, and journey reliability. Regarding opinion about walking for travel, the questionnaire asked “With your day-to-day travel needs in mind, would you say that walking for travel” (5-point Likert-type scale from “very much disagree” to “very much agree” for each item): saves time, is comfortable, is safe (with regard to traffic), is safe (with regard to crime), is unpleasant due to high levels of air pollution, offers privacy, offers personal health benefits, offers flexibility (e.g., with regard to departure time), and offers a predictable travel time.Transport habits. The question “How often do you currently use each of the following methods of travel to get to and from places? (walk, bicycle or e-bicycle, motorcycle or moped, public transport, car or van)” was used to evaluate the influence of transport habits on the minutes spent walking per week and also to understand behavioral patterns of mobility. There were six possible answers: never, less than once a month, 1–3 d/month, 1–3 d/week, daily or almost daily, don’t know (this last answer was treated as missing).Statistical AnalysisMultiple imputation of the data. Because there were some participants with missing information for some of the variables of interest (mostly between 0% and 6.6%, except income, which had 21.9% of missing values; see Tables S1–S3 for further details on the proportions of observations with missing data for questionnaire and built environment variables, respectively) and assuming that data was missing at random (MAR), we followed multiple imputation procedures prior to analyzing the data in order to avoid loss of participants (Royston 2005). The procedure of the imputation process and the variables considered are detailed in Table S4. Briefly, we conducted multiple imputations by chained equations, carrying out 20 imputations with 10 cycles for each imputation that generated 20 complete data sets. For the imputation process, we used many more variables than the ones finally included in the analyses in order to have the richest information possible (see Table S4). Because the demographic composition and the built environment characteristics varied among cities (see Tables S1–S3), the imputations were carried out separately for each city, and afterward the seven databases were merged into one single database. We analyzed the data sets following the standard combination rules for multiple imputations, which consist of three phases: a) imputation (i.e., creating multiply imputed data), b) completing data analysis of multiply imputed data, and c) pooling of individual analyses from phase 2 using Rubin’s combination rules (Marshall et al. 2009; Rubin 1987).Negative binomial regression analysis. Negative binomial regression analyses including city as a random effect were conducted to obtain incidence rate ratios (IRRs) in order to explore the correlates of minutes of walking per week for travel, the outcome variable of interest. As explained in the “Individual Characteristics” section, we created a base model that included age, gender, level of education, employment status, access to a car, and access to a bicycle (Table 2). Then, all potential correlates of walking were included one by one to the base model to evaluate the association with minutes of walking per week. All built environment characteristic variables were scaled to the mean [thus, IRRs were derived using the standard deviation (SD) as the exposure contrast] except surrounding greenness, for which we used the interquartile range (IQR), and access to green spaces and access to blue spaces, which were binary variables. Street length, connectivity, building area, population, facilities, and public transport stations are expressed per kilometer squared (density). However, in terms of interpretation, the reader might desire to use the indicators per area of the buffer (area of a 300-mbuffer=0.2809 km2). In this case, the SD of each of these variables has to be multiplied by 0.2809 [e.g., if the SD of street-length density is 7,031 m/km2, then the new value for the area of the buffer is 1,975m]. In addition, some of the 5-point Likert-type variables had very low prevalence in some of the categories of reference and were therefore recategorized into four or three categories instead of five for the purpose of this analysis. The criteria to collapse categories was whether the category or the sum of two or more categories reached a prevalence of at least 5% within each city. (The original categories are described in Table S1, whereas the new categories are provided in the tables of the supplemental material including the associations with the outcomes.) These variables were modeled using categorical indicator terms with a single reference category. These same analyses were conducted for the total study population (N=7,875) and the working/studying population (n=6,957), which additionally had information on the built environment characteristics at the place of work or study.Table 2 Description of the variables included in the base model of the associations between correlates of walking for travel and minutes of walking per week (whole study population, N=7,875).Table 2 lists the variables in the first column. The second and third columns list description, minutes of walking per week (mean SD) by category. The adjacent columns list I R R (95 percent) and p values for association.VariableDescriptionMinutes walking per week (mean, SD) by categoryAssociationIRR (95% CI)p-ValueAge {y [mean (min–max)]}39.6 (16.1–91.4)—1.00 (0.99, 1.00)0.76Gender (%) Male47.1172 (382)1 Female52.9186 (352)1.03 (0.91, 1.15)0.66High level of education (%) Noa27.3213 (406)1 Yesa72.7166 (350)0.82 (0.72, 0.93)<0.001Employment status (%) Full-time worker61.6164 (368)1 Part-time worker16.6150 (293)0.91 (0.77, 1.07)0.24 Student14.1215 (333)0.98 (0.81, 1.18)0.81 Not workingb7.8275 (417)1.65 (1.32, 2.06)<0.001Access to a car or van (%) Never22.7247 (432)1 Sometimes26
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