Are the correlates of active school transport context-specific?

Citation for published version: Larouche, R, Sarmiento, OL, Broyles, ST, Denstel, KD, Church, TS, Barreira, TV, Chaput, J-P, Fogelholm, M, Hu, G, Kuriyan, R, Kurpad, A, Lambert, EV, Maher, C, Maia, J, Matsudo, V, Olds, T, Onywera, V, Standage, M, Tremblay, MS, Tudor-Locke, C, Zhao, P & Katzmarzyk, PT 2015, 'Are the correlates of active school transport context-specific?', International Journal of Obesity Supplements, vol. 5, no. S2, pp. S89-S99. https://doi.org/10.1038/ijosup.2015.25 DOI: 10.1038/ijosup.2015.25 Publication date: 2015 Document Version Peer reviewed version Link to publication University of Bath General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Download date: 18. Jun. 2020 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 Original Article Are the Correlates of Active School Transport Context-specific? Richard Larouche1, PhD, Olga L. Sarmiento2, MD, MPH, PhD, Stephanie T. Broyles3, PhD, Kara D. Denstel3, MPH, Timothy S. Church3, MD, PhD, Tiago V. Barreira3,4, PhD, Jean-Philippe Chaput1, PhD, Mikael Fogelholm5, ScD, Gang Hu3, MD, PhD, Rebecca Kuriyan6, PhD, Anura Kurpad6, MD, PhD, Estelle V. Lambert7, PhD, Carol Maher8, PhD, José Maia9, PhD, Victor Matsudo10, MD, PhD, Tim Olds8, PhD, Vincent Onywera11, PhD, Martyn Standage12, PhD, Mark S. Tremblay1, PhD, Catrine Tudor-Locke3,13, PhD, Pei Zhao14, MD, and Peter T. Katzmarzyk3, PhD; for the ISCOLE Research Group 1 Children’s Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada 2 School of Medicine, Universidad de los Andes, Bogotá, Colombia 3 Pennington Biomedical Research Center, Baton Rouge, LA, USA 4 Syracuse University, Syracuse, NY, USA 5 Department of Food and Environmental Sciences, University of Helsinki, Helsinki, Finland 6 St. Johns Research Institute, Bangalore, India 7 Division of Exercise Science and Sports Medicine, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa 8 Alliance for Research In Exercise Nutrition and Activity (ARENA), School of Health Sciences, University of South Australia, Adelaide, Australia 9 2 CIFI D, Faculdade de Desporto, University of Porto, Porto, Portugal 10 Centro de Estudos do Laboratório de Aptidão Física de São Caetano do Sul (CELAFISCS), Sao Paulo, Brazil 11 Department of Recreation Management and Exercise Science, Kenyatta University, Nairobi, Kenya 12 Department for Health University of Bath, Bath, United Kingdom 13 Department of Kinesiology, University of Massachusetts Amherst, Amherst, USA 14 Tianjin Women’s and Children’s Health Center, Tianjin, China Running head: Correlates of active school transport in children Address for Correspondence and Reprints: Richard Larouche, PhD; Children’s Hospital of Eastern Ontario Research Institute, 401 Smyth Road, Ottawa, ON,Canada,K1H 8L1; E-Mail: rlarouche@cheo.on.ca; Telephone: 1-613-737-7600 ext. 4191; Fax: 1-613-738-4800. 1 39 Abstract 40 Background/Objectives: Previous research consistently indicates that children who engage in 41 active school transport (AST) are more active than their peers who use motorized modes (car or 42 bus). However, studies of the correlates of AST have been conducted predominantly in high- 43 income countries and have yielded mixed findings. Using data from a heterogeneous sample of 44 twelve country-sites across the world, we investigated the correlates of AST in 9-11 year olds. 45 Methods: The analytical sample comprised 6 555 children (53.8% girls), who reported their 46 main travel mode to school and the duration of their school trip. Potential individual and 47 neighborhood correlates of AST were assessed with a parent questionnaire adapted from 48 previously validated instruments. Multilevel generalized linear mixed models (GLMM) were used 49 to examine the associations between individual and neighborhood variables and the odds of 50 engaging in AST while controlling for the child’s school. Site moderated the relationship of 51 seven of these variables with AST; therefore we present analyses stratified by site. 52 Results: The prevalence of AST varied from 5.2% to 79.4% across sites and the school-level 53 intra-class correlation ranged from 0.00 to 0.56. For each site, the final GLMM included a 54 different set of correlates of AST. Longer trip duration (e.g. ≥16 min vs. ≤15 min) was 55 associated with lower odds of AST in eight sites. Other individual and neighborhood factors 56 were associated with AST in three sites or less. 57 Conclusion: Our results indicate wide variability in the prevalence and correlates of AST in a 58 large sample of children from twelve geographically, economically and culturally diverse 59 country-sites. This suggests that AST interventions should not adopt a “one size fits all” 60 approach. Future research should also explore the association between psychosocial factors 61 and AST in different countries. 62 Key Words: active travel, social-ecological models, built environment, safety, multi-national 63 64 Trial Registration: ClinicalTrials.gov: Identifier NCT01722500 2 65 66 Introduction The majority of children and youth worldwide fail to meet current physical activity (PA) 67 guidelines.1,2 The promotion of active school transport (AST) may be part of a multifaceted 68 strategy to address the current physical inactivity crisis. There is consistent evidence showing 69 that children who engage in AST are more active than those using motorized travel modes.3,4 70 Recent research also suggests that children engaging in AST may accrue psychosocial benefits 71 such as improved well-being5 and better cognitive performance6. At the population level, a 72 switch from motorized travel to AST could substantially reduce greenhouse gas emissions 73 associated with the school trip.7 74 Despite the reported benefits, the prevalence of AST has decreased markedly during the 75 last few decades in several middle-8-10 and high-income countries11-14. Onywera et al.15 also 76 reported that Kenyan children are less likely to engage in AST than their parents were at the 77 same age. Furthermore, Kenyan children living in urban areas were much more likely to use 78 motorized travel modes than their rural counterparts.15,16 While these studies were limited by a 79 small sample size, they provide preliminary evidence that AST may also be decreasing in low- 80 income countries as a result of the PA transition.17 Therefore, a better understanding of the 81 correlates of AST is warranted to inform future interventions aiming to reverse these trends and 82 improve children’s health. 83 While previous research has consistently shown that a greater distance between home and 84 school is strongly associated with motorized travel, the literature is less consistent regarding the 85 influence of other environmental factors on children’s travel behavior.18-20 Almost all of the 86 studies included in these reviews have been conducted in high-income countries, and often in a 87 single city. Limited variability in environmental characteristics may partly explain lack of 88 significant associations reported in many single-site studies.21 Furthermore, it is unclear if 89 associations observed in high income countries can be generalized to low and middle income 90 countries in which little is known about the correlates of AST.22 The heterogeneity in the 3 91 measurement of environmental attributes also makes comparison of results across studies 92 difficult.20,23 Hence, there is a clear need for studies examining the correlates of AST using a 93 consistent methodology in environmentally diverse countries. 94 Therefore, our study had two primary objectives. First, we aimed to describe school travel 95 behavior in a large sample of 9-11 year olds from 12 different countries who participated in the 96 International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE).24 Of 97 particular interest, the ISCOLE was conducted using standardized methods in geographically, 98 economically and culturally diverse country-sites. Second, we investigated the individual and 99 environmental correlates of AST among those participants using multilevel models. 100 Methods 101 Setting 102 The ISCOLE investigated the influence of behavioral settings and the physical, social, and 103 policy environments on the observed relationship between lifestyle and weight status among 9- 104 11 year old children living in the following 12 country-sites: Australia (Adelaide), Brazil (São 105 Paulo), Canada (Ottawa), China (Tianjin), Colombia (Bogota), Finland (Helsinki, Espoo and 106 Vantaa), India (Bangalore), Kenya (Nairobi), Portugal (Porto), South Africa (Cape Town), the 107 United Kingdom (UK; Bath and North East Somerset) and the United States (US; Baton 108 Rouge).24 These countries represent five major geographic regions of the world and include low, 109 middle and high income countries. Ethical approval was obtained at the coordinating center 110 (Pennington Biomedical Research Center) and from relevant research ethics boards in each 111 site. Written informed consent was obtained from parents (or legal guardians), and child assent 112 was also obtained before participation in the study. Data were collected from September 2011 113 through December 2013. 114 Participants 115 116 Based on a priori sample size calculations, recruitment targeted a sex-balanced stratified sample of 500 children in each site with minimal variability around 10 years of age.24 To 4 117 maximize variability within site, participating schools were selected in areas that differed in 118 socioeconomic status and level of urbanization (urban and suburban). Further details about the 119 sampling strategy are available elsewhere.24 120 Of the 7 372 children enrolled in ISCOLE, 6 872 remained in the analytical dataset after 121 exclusion of participants for whom information on school travel mode (n=70), school travel time 122 (n=2), parent education (n=389) and motor vehicle ownership (n=39) was not available. 123 Descriptive analyses were conducted with this analytical dataset (N=6 872). However, because 124 our intent was to examine the within-school differences between active and motorized travelers, 125 we also excluded schools in which either 0% or 100% of children engaged in AST (k=22 126 schools; n=317 children). Therefore, the analytical sample for all regression analyses consisted 127 of 6 555 children. Included participants were slightly younger (10.5 vs. 10.4 years; p<.001), and 128 had lower scores on the land use mix – diversity subscale (2.8 vs. 3.0; p<.001) described below. 129 Chi-squared tests identified differences between included and excluded participants for parental 130 education (p<.001) as well as for seven single items related to parental neighborhood 131 perceptions (see Supplementary Table 1 for more details). However, effect sizes for all these 132 differences are trivial (Cohen’s d ≤ 0.19 and Cramer’s V ≤ .052). 133 Measure of Travel Mode 134 Trained study staff administered a child questionnaire in schools.24 Travel mode was 135 assessed with one item (“in the last week you were in school, the MAIN part of your journey to 136 school was by”). Response options were: 1) walking; 2) bicycle, rollerblade, skateboard, 137 scooter; 3) bus, train, tram, underground, or boat; 4) car, motorcycle, or moped; 5) other. 138 Children also reported the time that it usually took them to travel to school. Categories were: 1) 139 <5 minutes; 2) 5-15 minutes; 3) 16-30 minutes; 4) 31 minutes to 1 hour; 5) >1 hour. These 140 questions were adapted from the Health Behavior in School-aged Children study.25 141 142 5 143 144 Individual Factors Socio-demographic variables were obtained through a parent questionnaire.24 Parents 145 reported the mother’s and father’s highest level of education (six levels), the number of 146 functioning motorized vehicles (five levels), and the child’s gender. Parental social support for 147 their child’s PA was examined with 4 items which were averaged to create a social support 148 scale (Cronbach α=0.71; Table 1). 149 Neighborhood Factors 150 Parents completed a home and neighborhood environment questionnaire available 151 elsewhere.24 The questionnaire included items related to social capital, the food environment, 152 the physical activity environment, and the built environment and it was adapted from the 153 Neighborhood Impact on Kids study26 and other validated questionnaires27-29. Following 154 Sampson et al.29, collective efficacy was assessed as the sum of two 5-item subscales, 155 specifically neighborhood cohesion (Cronbach α=0.75) and neighborhood response (α=0.75). 156 To reduce the number of independent variables, items that were identical (or very similar) to 157 those used in the Neighborhood Environment Walkability Scale for Youth (NEWS-Y)27 were 158 assigned to the corresponding subscale of the NEWS-Y. Items were scored as recommended 159 by the NEWS-Y developers (http://www.drjamessallis.sdsu.edu/Documents/NEWS- 160 Yscoring.pdf) and reverse coded when necessary to ensure that higher scores indicate greater 161 walkability/safety. Subscales were used as potential correlates of AST provided that they had 162 satisfactory internal consistency (e.g. α>0.70) in the overall sample and in the majority of 163 countries. Three subscales satisfied this criterion: land use mix-diversity (4 items; α=0.81), 164 neighborhood recreation facilities (9 items; α=0.85), and crime safety (5 items; α=0.86). The 165 remaining12 items were analyzed individually because principal component analysis failed to 166 reveal components with acceptable internal consistency. 167 168 6 169 170 Data Treatment Children’s travel mode was dichotomized as active (walk, bicycle, etc.) vs. motorized (car, 171 bus, etc.). Socio-economic variables were recoded based on the observed frequency 172 distributions. Mothers’ and fathers’ education was categorized as less than high school, high 173 school/college, or university. Then the highest level of education in the household was used in 174 analyses. Motorized vehicle ownership was categorized as 0, 1, and ≥ 2. School travel time was 175 dichotomized as ≤15 min vs. ≥16 min. The 12 single items were recoded as “agree” or 176 “disagree”. 177 Statistical Analyses 178 We used multilevel generalized linear mixed models (GLMM) with a binomial distribution and 179 logit link to examine the individual and environmental correlates of children’s travel mode.30 We 180 intended to explore the within-school differences between active and motorized travelers. To 181 produce unbiased estimates of the within-school effects and to control for endogeneity (i.e., 182 correlation between school random effects and the covariates included in the model), we treated 183 schools as fixed effects and limited our analysis to students from schools with variation in travel 184 behavior. The Hausman test supported a fixed-effects specification (p<0.001). Prior to pooling 185 data across sites, we verified whether country-site moderated the relationships between the 186 potential correlates of AST and children’s travel mode by fitting a site by correlate interaction 187 term in GLMMs adjusted for gender, school travel time and the school within site interaction. 188 Interactions were considered significant if p<.10, due to the reduced statistical power. We found 189 significant interactions with the following 7 variables: school travel time (p<.001), motorized 190 vehicle ownership (p=.043), and the single items “there is a bus, subway or train stop within 191 easy walking distance” (p=.085) “there are many places to go within easy walking distance” 192 (p=.033), “there are sidewalks on most streets” (p=.077), “most drivers go faster than the posted 193 speed limit” (p=.008), and “traffic makes it difficult or unpleasant for my child to walk” (p=.094). 7 194 Therefore, we present site-specific models wherein school, gender, parental education and 195 school travel time were mandatory variables. 196 To reduce the likelihood of excluding variables that may achieve statistical significance at 197 p<.05, but only after adjustment for other covariates, we used a liberal p<.20 threshold for 198 inclusion of variables in the site-specific multivariable model.25 Then, a backward selection 199 approach was used to remove non-significant variables (p>.05). As a result of the backward 200 selection process, the final site-specific models include a different set of variables in each site. 201 An alternative analytical strategy would have been to force all variables that have achieved 202 statistical significance in at least one site into the models. However, the latter strategy resulted 203 in poor-fitting models with frequent problems of quasi-complete separation, probably due to the 204 sparse distribution of some of the parent-perceived variables. Therefore, the backward selection 205 approach was preferred. Analyses were conducted with IBM SPSS version 22 (Armonk, NY). 206 Degrees of freedom were calculated with the Sattherthwaite31 method. 207 Results 208 Socio-demographic characteristics of the participants are shown in Table 2. A total of 6 872 209 participants (3 701 girls and 3 171 boys aged 10.4±0.6 years) were included in analyses. There 210 were large differences between sites in site-level socio-demographic indicators and in 211 household motorized vehicle ownership and parental education. Overall, 42.1% of children 212 reported engaging in AST with large differences between sites in travel mode and trip duration 213 (Figures 1 and 2). The highest rates of AST were observed in Finland (79.4%) and Colombia 214 (73.8%), and the lowest in India (5.2%) and the US (10.8%). The highest proportion of trips 215 made by bus/train/van was noted in India (61.8%) while the highest percentage of trips made by 216 car was noted in Australia (63.8%). Conversely, the highest proportion of cycling was found in 217 Finland (24.4%) while the highest proportion of walking was noted in Colombia (71.6%) The 218 school level intra-class correlation coefficient ranged from 0.00 in India to 0.56 in Colombia. 219 Regardless of travel mode, school trips were generally quicker in high income countries. Among 8 220 active travelers, the proportion of children reporting trips ≥ 16 minutes ranged from 11.8% in 221 Canada to 33.6% in Kenya. The majority of motorized travelers reported trips ≤ 15 minutes in all 222 countries except India, Colombia and Kenya. 223 Descriptive characteristics for the environmental variables are shown in Table 3. In general, 224 high-income countries had better crime safety and collective efficacy scores than low-income 225 countries, but this pattern was not apparent for other subscales. A greater proportion of parents 226 expressed concerns about traffic safety aspects than about walkability aspects (i.e., street 227 connectivity, presence of sidewalks, etc.). The neighborhood environment was generally rated 228 more poorly in the US than in other high income countries. 229 Multivariable site-specific models are shown in Table 4. In general, gender was not 230 associated with AST, except in Canada, where girls were about half as likely to engage in AST 231 as boys. Motor vehicle ownership was negatively associated with AST in 3 out of 12 sites: 232 China, Portugal and South Africa. Parental education was associated with AST only in the US 233 where children of less educated parents were more likely to engage in AST. The number of 234 siblings was not associated with AST except in Brazil and South Africa where children who had 235 1 sibling were less likely to engage in AST than those who had ≥2 siblings. In both countries, 236 the likelihood of AST did not differ between children who had no siblings and those who had 237 either 1 or ≥2 siblings. Child-reported school travel time was negatively associated with AST in 8 238 sites: Brazil, Canada, China, Finland, India, Kenya, South Africa, and the US. 239 Relationships between the social environment and children’s travel mode varied across 240 countries. Parental social support for PA was positively associated with AST only in India. Each 241 unit increase in the collective efficacy subscale was associated with about 20% lower odds of 242 AST in China. In contrast, each unit increase in the crime safety subscale was associated with 243 65% higher odds of AST, but only in Finland. 244 245 With respect to road safety constructs, parental perception that the speed of traffic is usually slow was associated with lower odds of AST among British children (OR=0.39). Australian 9 246 children whose parents perceived that the traffic makes it difficult/unpleasant for walking and 247 that there are crosswalks and signals on busy streets were almost half as likely to engage in 248 AST as children whose parents disagreed with these items. Counter-intuitively, Brazilian 249 children whose parents disagreed that most drivers go faster than the speed limit were about 250 half as likely to engage in AST. In contrast, the opposite relationship was found in Australia and 251 India. 252 Associations between indicators of neighborhood walkability and AST also varied across 253 sites. Each unit increase in the land use mix – diversity subscale was associated with higher 254 odds of AST in Canada (OR=1.38), but lower odds in China (OR=0.76). Children whose parents 255 perceived that there is a transit stop within walking distance were about twice as likely to 256 engage in AST in the US; however, they were about half as likely to do so in Portugal. The 257 perception that there are many places to go within walking distance was positively associated 258 with AST in Australia and the UK (OR=1.77 and 1.81 respectively), while the opposite was 259 found in Colombia (OR=0.61). South African children whose parents perceived that there are 260 not too many dead end streets were more than 3 times as likely to engage in AST. Similarly, 261 Finnish children whose parents reported that there are many routes for getting from place to 262 place were about 3 times as likely to be active travelers. Finally, the presence of sidewalks was 263 associated with about two times higher odds of AST in Portugal. 264 Discussion 265 Our primary objectives were to describe school travel behavior in a large heterogeneous 266 sample of children from 12 different country-sites and to investigate the individual and 267 environmental factors associated with AST. Across sites, between 0 and 52% of the variance in 268 travel mode was explained by school-level factors. We also noted very large differences both 269 within and between sites in children’s travel behavior. For instance, the prevalence of AST was 270 almost 20 times higher in Finland compared to India. Previous reviews have also noted 271 substantial differences between countries in the rates of AST.2,32,33 However, these reviews 10 272 were limited by heterogeneity in the measurement of travel behavior. Our findings suggest that 273 differences between countries are not an artifact of methodological differences in the 274 assessment of travel behavior. 275 Given these large differences between countries and the consistent associations observed 276 between AST, accelerometry-measured PA3 and indicators of adiposity34 in ISCOLE, 277 investigating the correlates of AST in this sample is of particular interest. We found that travel 278 time was the most consistent correlate of AST. Specifically, children reporting trips of 16 279 minutes or more were less likely to engage in AST in 8 out of 12 sites. Moreover, in the entire 280 sample, the association between travel time and travel mode was moderated by country-site 281 (p<.001), suggesting that the “acceptable” duration of an active trip varies across country-sites. 282 While trip duration and distance can both be conceived as indicators of “generalized travel cost” 283 from a behavioral economic perspective35, trip duration is partly dependent on the chosen travel 284 mode, so our results should be interpreted cautiously. Nevertheless, it is worth noting that 285 previous research indicates that distance depends on many factors including parent/child school 286 choice, parental neighborhood selection, availability of walking/cycling paths that may provide 287 shortcuts, and the policies that govern school choice, bussing eligibility, and where new schools 288 are built.36,37 Therefore, a social-ecological approach targeting multiple levels of influence will 289 likely be needed to overcome the distance barrier.36 290 Of particular importance, we observed that country-site was an important moderator. 291 Specifically, when pooling the data across the 12 sites, the relationship between seven of the 292 independent variables examined and AST was moderated by study site. Furthermore, each of 293 the 12 site-specific multivariable models included a different combination of correlates. These 294 findings suggest that, to increase the prevalence of AST, context-specific interventions should 295 be preferred over a “one size fits all” approach. 296 The heterogeneity in the correlates of AST across countries may be partly attributable to the 297 diversity of the country-sites. It has been suggested that the lack of motorized alternatives could 11 298 explain the relatively high prevalence of AST in low and middle income countries.2,38 299 Nevertheless, we found a negative relationship between motorized vehicle ownership and AST 300 only in China, Portugal and South Africa. Furthermore, despite a high country-level rate of 301 motorized vehicle ownership, Finland had the largest prevalence of AST. The high prevalence 302 of AST in Finland has been attributed to a combination of factors including favorable social 303 norm39, supportive policies40 and high quality walking and cycling infrastructure41. In contrast, a 304 “culture of convenience”, wherein the socially acceptable distance for walking to/from school is 305 thought to be less than 1.6 km may partly explain the low prevalence of AST among Canadian 306 children.42 Perceived convenience of driving has also been described as a key reason why 307 children are driven to school in other studies.43-45 Unfavorable social norms and the perception 308 of what constitutes “good parenting” may create so-called “social traps” in which driving begets 309 driving.46 In these social traps, parents who previously did not drive their children to school start 310 to do so because they perceive that, otherwise, others will not view them as “good parents”. 311 While we found that the correlates of children’s travel behavior varied markedly across sites, 312 it is noteworthy that the International Physical Activity and the Environment study found that the 313 environmental correlates of walking, PA, and body mass index among adults were generally 314 consistent across diverse study sites, including some middle income countries.47-49 The 315 environmental factors that encourage active travel and PA among adults – such as density, land 316 use mix, street connectivity, and composite measures of neighborhood walkability47,49-51 – may 317 not be as relevant, or more variable, for children. 318 Previous reviews have noted that, apart from a consistent negative association between 319 distance and AST, studies of the built environment constructs associated with children’s AST 320 have reported conflicting results.18-20 Moreover, a recent meta-analysis of studies examining the 321 relationship between objective measures of the built environment and PA revealed a strong 322 moderating effect of age.52 While neighborhood walkability was positively associated with 15 323 year olds’ PA, there was a small negative association for 9 year olds and inconsistent results for 12 324 12 year olds. A potential explanation for these findings is that high walkability areas may also be 325 characterized by heavy traffic, thereby decreasing parental willingness to allow their child to 326 travel on foot or bike.53 327 It is also worth noting that the ISCOLE questionnaire focused on parents’ perceptions of 328 their home neighborhood. Beyond the home neighborhood, the characteristics of the route 329 to/from school and those of the school neighborhood may also influence travel mode choice.54 330 Therefore, some of our counter-intuitive findings related to walkability and traffic safety aspects 331 may be due to the presence of other barriers beyond the home neighborhood. This may be 332 compounded by the variability in individuals’ perceptions of their home neighborhood 333 boundaries.55,56 Another potential explanation is that parents may have interpreted some 334 questions differently in different countries. Furthermore, some counter-intuitive findings could 335 also be explained by reverse causality. Parents of active travelers may be more worried about 336 their child’s safety en route to/from school, and chauffeuring children may be viewed as a 337 strategy to mitigate these fears.46,57 Given that causality cannot be inferred from our cross- 338 sectional study, future prospective studies are needed to test this hypothesis. 339 Finally, it is worth noting that the effect of parental perceptions on their child’s travel 340 behavior may be indirect. Therefore, to inform the development of future AST interventions, 341 greater attention should be paid to the mediators of children’s travel behavior. To date, few 342 studies have conducted mediation analyses. Nevertheless, Lu and colleagues58 found that 343 parental self-efficacy mediated the relationship between parent-perceived barriers and parents’ 344 intention to encourage their child to engage in AST. These results suggest that increasing 345 parent’s self-efficacy may be a promising strategy, especially in an environment that is 346 conducive to AST. 347 The cross-sectional design which precludes causal inferences is the main limitation of our 348 study. Second, while participating schools were purposefully selected in areas that differed in 349 terms of socioeconomic status and urbanisation, the samples are not nationally-representative. 13 350 Third, the reliability and validity of the questions used to assess travel mode and trip duration 351 are unknown. While reported school travel mode generally shows high test-retest reliability and 352 convergent validity between children and parents22, children’s perception of their school travel 353 time may be inaccurate at the individual level.59 School travel time may also be limited as a 354 proxy for distance because it depends on the chosen travel mode (e.g., it should take more time 355 to walk than to drive a given distance). Unfortunately, distance was not measured in ISCOLE, 356 so it was impossible to control for distance in our analyses. Fourth, while the parent 357 questionnaire was developed based on validated surveys, it included only a subset of items 358 from the NEWS-Y survey, and the wording of items differed. To minimize this limitation, we have 359 examined the internal consistency of our subscales overall and within each site, and we have 360 used only those subscales that showed satisfactory consistency. Fifth, included and excluded 361 participants differed in several environmental variables. However, effect sizes were trivial to 362 small, so these differences likely had limited impact on our results. Finally, although the 363 correlates of walking and cycling may differ, we did not analyse these modes separately due to 364 the scarcity of cycling in most sites. 365 To our knowledge, this is the first investigation of the correlates of AST in such a diverse 366 range of country-sites. Previous studies of the correlates of AST had mostly been conducted in 367 high-income countries, with very few studies conducted among African children22, or children 368 from developing countries more generally. Of particular interest, we identified that country-site 369 was an important moderator of the relationship of individual and environmental variables with 370 AST. Understanding the moderators of health behavior can help identify what works (or may 371 work) for whom.60 Finally, the very large sample size and the use of multilevel models are other 372 important strengths of our study. 373 Conclusion 374 We found large differences in the prevalence and correlates of AST among children from 12 375 diverse country-sites across the world, challenging generally held belief that there is a common 14 376 (or universal) set of correlates of AST. Interestingly, study site moderated the relationship 377 between 7 of the independent variables considered and children’s travel behavior. Therefore, 378 policy-makers, urban/transport planners and public health workers should not assume that built 379 environment interventions that are effective in one setting (or in one population) will necessarily 380 work elsewhere. As such, these stakeholders should consider collaborating with researchers to 381 identify the correlates of AST at the local level before implementing interventions. Future multi- 382 country studies should examine the role of variables such as home-school distance, social 383 norms and perceived convenience as potential correlates of AST. Furthermore, there remains a 384 need for studies to identify relevant mediators that could be targeted in future interventions. 385 386 15 387 Acknowledgements 388 We wish to thank the ISCOLE External Advisory Board and the ISCOLE participants and their 389 families who made this study possible. RL holds a postdoctoral fellowship from the Canadian 390 Institutes of Health Research. A membership list of the ISCOLE Research Group and External 391 Advisory Board is included in Katzmarzyk, Lambert and Church. An Introduction to the 392 International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE). Int J Obes 393 Suppl. (This Issue). 394 ISCOLE was funded by The Coca-Cola Company. The funder had no role in study design, data 395 collection and analysis, decision to publish, or preparation of the manuscript. 396 16 397 Funding and Conflict of Interest Statement 398 MF has received a research grant from Fazer Finland and has received an honorarium for 399 speaking for Merck. AK has been a member of the Advisory Boards of Dupont and McCain 400 Foods. RK has received a research grant from Abbott Nutrition Research and Development. VM 401 is a member of the Scientific Advisory Board of Actigraph and has received an honorarium for 402 speaking for The Coca-Cola Company. TO has received an honorarium for speaking for The 403 Coca-Cola Company. The authors reported no other potential conflicts of interest. 404 17 405 References 406 1. Hallal P, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U, for the Lancet Physical 407 Activity Series Working Group. Global physical activity levels: surveillance progress, pitfalls, 408 and prospects. Lancet 2012;380(9838):247-257. 409 2. Tremblay MS, Gray CE, Akinroye K, Harrington DM, Katzmarzyk PT, Lambert EV, et al. 410 Physical activity of children: a global matrix of grades comparing 15 countries. J Phys Act 411 Health 2014;11(Suppl. 1):S113-S125. 412 3. Denstel KD, Broyles ST, Larouche R, Sarmiento OL, Barreira TV, Chaput J-P, et al. Active 413 school transport and weekday physical activity in 9-11 year old children from 12 countries. 414 Int J Obes 2015; XXXX 415 4. Larouche R, Saunders T, Faulkner GEJ, Colley RC, Tremblay MS. 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Children’s main school travel mode stratified by country-site in the International Study of Childhood Obesity, Lifestyle and the Environment (N=6872). Note: other modes included active modes such as running and jogging, motorized modes such as the school van, matatu, bus feeder, pedicab, and non-active non-motorized modes such as being a passenger on a bicycle. These travel modes were classified as active or motorized/inactive as appropriate. Figure 2. Children’s school travel duration stratified by country-site in the International Study of Childhood Obesity, Lifestyle and the Environment (N=6872).Note: the top panel shows travel duration for active travelers and the bottom panel shows travel duration for motorized travelers. 24 Table 1. Internal consistency and descriptive statistics for the neighborhood scales used in the International Study of Childhood Obesity, Lifestyle and the Environment (N=6 555) Title and number of items Neighborhood cohesion (5 items assessed as 5-point Likert scales)* Items “People around my neighborhood are willing to help their neighbors” “This is a close-knit neighborhood” “People in my neighborhood can be trusted” “People in my neighborhood generally don't get along with each other” (reverse coded) “People in my neighborhood do not share the same values, attitudes or beliefs”(reverse coded) Neighborhood Stem: how likely is it that your neighbors would do response (5 something about it? items assessed as “If a group of neighborhood children were skipping 5-point Likert school and hanging out on a street corner” scales)* “If some children were spray-painting graffiti on a local building” “If a child was showing disrespect to an adult” “If there was a fight in front of your house and someone was being beaten or threatened” “Suppose that because of budget cuts the fire station closest to your home was going to be closed down by the city” Crime safety (5 “I'm afraid of my child being taken or hurt by a items assessed as stranger on local streets” 4-point Likert “I'm afraid of my child being taken or hurt by a scales)† stranger in my yard, driveway, or common area” “I’m afraid of my child being taken or hurt by a stranger in a local park” “I'm afraid of my child being taken or hurt by a known "bad" person (adult or child) in my neighborhood” “There is a high crime rate” Land use mix – Stem: About how long would it take you to walk from diversity (4 your home to the nearest places listed below? items assessed as “Convenience/corner store/small grocery 5-point Likert store/bodega” scales)† “Supermarket” “Fast food restaurant” “Non-fast food restaurant” Neighborhood Stem: About how long would it take you to walk from recreation your home to the nearest places listed below? facilities scale (9 α Mean (SD) .75 3.47 (0.84) .75 3.57 (0.86) .86 2.41 (.87) .81 2.75 (1.03) .85 3.46 (0.94) 25 items assessed as “Indoor recreation or exercise facility (public or 5-point Likert private)” scales)† “Beach, lake, river, or creek” “Bike/hiking/walking trails, paths” “Basketball court (including half-court)” “Other playing fields/courts” “Small public park” “Large public park’ “Public playground with equipment” “School with recreation facilities open to the public” Social support Stem: During a typical week, how often do you or .71 2.60 (0.92) scale (4 items another adult in the household: assessed as 5“Watch your child participate in physical activity or point Likert sports?” scales) “Encourage your child to do sports or physical activity” “Provide transport to a place where your child can do physical activity or play sports” “Do a physical activity or play sports with your child” α = Cronbach’s alpha. *The scores for these two subscales were added to obtain a collective efficacy score (Sampson et al., 1997) †For these subscales, questionnaire items that were conceptually-similar to those used in the Neighborhood Environment Walkability Scale for Youth (NEWS-Y; Rosenberg et al., 2009) were assigned to the corresponding NEWS-Y subscale. Then, the internal consistency of the resulting subscales was assessed for the overall analytical sample (N=6,555) and the analytical samples of each country-site. 26 Table 2. Descriptive Characteristics of Participants Stratified by Study Site in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE). Australia (Adelaide) Brazil (São Paulo) Canada (Ottawa) China (Tianjin) Colombia (Bogota) Finland (Helsinki, Espoo & Vantaa) India (Bangalore) Kenya Portugal (Nairobi) (Porto) South Africa (Cape Town) UK (Bath & North East Somerset) US (Baton Rouge) Total N=512 N=498 N=559 N=544 N=911 N=496 N=600 N=552 N=672 N=437 N=469 N=622 N=6 872 Sociodemographic characteristics World bank classificationa High income Uppermiddle income High income Uppermiddle income Uppermiddle income High income Lowermiddle income Low income High income Uppermiddle income High income High income N/A Gini indexb 35.2 (1994) 54.7 (2009) 32.6 (2000) 42.6 (2002) 55.9 (2010) 26.9 (2000) 33.4 (2005) 47.7 (2005) 38.5 (1997) 63.1 (2009) 36.0 (1999) 40.8 (2000) N/A Motor vehicles per 1,000 inhabitantsc 687 198 605 37 58 534 15 21 509 159 526 809 N/A Estimated road traffic death rate per 100,000 inhabitantsd 6.1 22.5 6.8 20.5 15.6 5.1 18.9 20.9 11.8 31.9 3.7 11.4 N/A 10.8 (0.4) 10.5 (0.5) 10.5 (0.4) 9.9 (0.5) 10.4 (0.6) 10.4 (0.4) 10.4 (0.5) 10.2 (0.7) 10.4 (0.3) 10.2 (0.7) 10.9 (0.5) 10.0 (0.6) 10.4 (0.6) Male 45.9 48.0 41.9 53.1 49.3 46.8 47.0 46.4 44.0 42.3 43.9 43.1 46.1 Female 54.1 52.0 58.1 46.9 50.7 53.2 53.0 53.6 56.0 57.7 56.1 56.9 53.9 11.3 24.3 2.0 32.9 31.5 2.8 4.7 14.1 46.6 47.1 2.8 8.5 19.8 47.9 53.2 27.7 44.7 50.9 55.2 21.8 45.1 32.9 39.8 51.8 44.1 42.8 40.8 22.5 70.3 22.4 17.6 41.9 73.5 40.8 20.5 13.0 45.4 47.4 37.4 None 2.3 30.1 3.8 9.7 75.5 9.9 4.3 44.2 10.7 47.6 4.3 8.4 23.2 1 22.5 47.8 38.3 44.1 21.6 45.0 32.5 33.3 42.4 27 36.2 32.0 34.6 2 or more 75.2 22.1 58.0 46.1 2.9 45.2 63.2 22.5 46.9 25.4 59.5 59.6 42.2 Agee Gender Highest parent education <High School Complete high-school or some college ≥Bachelor degree Motorized vehicle ownership Number of siblings 27 None 6.8 19.6 11.3 67.9 7.8 14.7 23.0 9.4 27.7 5.8 11.1 10.0 18.0 1 44.2 42.3 51.5 28.0 33.8 38.8 64.8 27.8 53.0 37.6 45.0 29.1 41.2 2 or more 49.0 38.1 37.2 4.1 58.4 46.6 12.2 62.9 19.3 56.6 43.9 60.8 40.8 24.1 39.0 34.2 22.2 71.6 54.8 3.8 40.9 27.1 57.9 50.3 9.8 36.9 7.2 1.0 0.7 10.1 1.8 24.4 1.3 2.9 1.0 0.9 12.2 0.5 4.9 4.5 32.3 38.3 7.5 18.4 13.1 61.8 27.9 12.1 4.8 3.2 34.8 22.2 63.8 26.7 26.5 55.1 7.5 7.5 33.0 23.6 58.9 36.3 33.9 54.3 34.8 0.4 1.0 0.4 5.0 0.8 0.2 0.0 4.7 0.9 0.0 0.4 0.7 1.2 85.2 68.7 74.8 65.6 61.7 79.0 37.2 56.5 84.1 70.7 79.5 70.3 68.8 14.8 31.3 25.2 34.4 38.3 21.0 62.8 43.5 15.9 29.3 20.5 29.7 31.2 0.18 0.25 0.31 0.09 0.56 0.24 0.00 0.38 0.11 0.55 0.10 0.27 N/A School transport characteristics School travel mode Active Walking Bicycle, roller-blade, skateboard, scooter Motorized Bus, train, tram, underground or boat Car, motorcycle or moped f Other Travel time ≤15 minutes ≥ 16 minutes School-level ICCg for school travel mode a World Bank Data at country level: World Development Indicators 2012. The World Bank: Washington, DC; 2012. World Bank Data: Gini index at country level c World Bank Data at country level: Motor vehicles (per 1,000 people) include cars, buses, and freight vehicles but not two-wheelers. d World Health Organization data: Global status report on road safety 2013 e Mean and Standard Deviation. f Other includes school van, matatu, bus feeder, riding on the top tube of the bike's frame, pedicab and wheelchair g ICC: Intra-class correlation coefficient, calculated in an “empty” model with only school entered as a random effect (Cerin, 2011). b 28 Table 3. Parent-perceived Environmental Characteristics Stratified by Study Site in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE). Australia (Adelaide) Brazil (São Paulo) Canada (Ottawa) China (Tianjin) Colombia (Bogota) Finland (Helsinki, Espoo & Vantaa) India (Bangalore) Kenya (Nairobi) Portugal (Porto) South Africa (Cape Town) UK (Bath & North East Somerset) US (Baton Rouge) Total N=512 N=498 N=559 N=544 N=911 N=496 N=600 N=552 N=672 N=437 N=469 N=622 N=6 872 2.8 2.2 2.8 2.5 2.3 2.5 3.0 2.5 2.5 2.7 2.6 2.9 2.6 (0.8) (0.9) (0.8) (1.0) (0.8) (0.8) (0.9) (1.0) (0.9) (1.1) (0.8) (1.0) (0.9) 7.0 6.3 7.7 7.5 6.7 7.4 7.0 6.9 6.9 6.7 7.4 7.3 7.1 (1.3) (1.2) (1.4) (1.2) (1.5) (1.3) (1.3) (1.5) (1.4) (1.7) (1.3) (1.7) (1.5) 3.3 2.8 2.8 2.3 1.9 3.0 2.6 2.9 2.8 3.3 2.8 3.3 2.8 (1.0) (0.9) (1.0) (0.8) (0.7) (1.0) (0.8) (1.0) (1.0) (0.9) (0.9) (1.1) (1.0) 3.1 3.7 2.5 3.6 3.3 2.5 3.8 4.2 3.9 4.0 3.2 3.8 3.5 (0.9) (0.9) (0.8) (0.9) (0.6) (0.8) (0.8) (0.7) (0.8) (0.8) (0.7) (1.0) (0.9) 2.6 2.1 3.0 2.2 1.6 3.4 2.5 2.3 2.4 1.9 2.9 2.6 2.4 (0.7) (0.6) (0.7) (0.7) (0.7) (0.6) (0.7) (0.8) (0.7) (0.8) (0.7) (0.8) (0.9) There are shops, stores, markets or places to buy things within easy walking distance (% agree) 75.5 86.4 74.3 92.4 94.1 79.6 93.1 85.2 83.9 83.8 87.9 45.1 82.1 There is a bus, subway or train stop within easy walking distance (% agree) 89.8 90.0 96.1 78.8 84.2 98.4 88.7 76.4 89.2 79.8 96.6 43.8 83.8 There are sidewalks on most streets (% agree) 86.5 92.9 85.4 88.0 97.3 91.0 73.8 67.1 80,6 83.3 91.3 60.9 83.4 There are NOT many dead end streets (% agree) 79.5 74.1 87.2 86.6 93.2 81.8 71.0 64.0 77,6 73.8 77.3 65.5 78.4 There are many different routes for getting from place to place (% agree) 86.1 84.5 90.7 85.6 95.7 88.7 81.7 77.6 85.0 82.7 85.3 71.5 85.1 The speed of traffic is usually slow [<30 mph](% agree) 79.4 69.1 82.4 42.7 57.5 52.3 63.5 45.3 63.5 59.4 52.7 6.4 61.0 Social support for PA subscale (range 1-5) Collective efficacy subscale (range 2-10) Land use mix – diversity subscale (range 1-5) Neighborhood recreation facilities subscale (range 15) Crime safety subscale (range 1-4) 29 There are many interesting things to look at while walking in my neighborhood (% agree) 73.3 55.2 73.7 49.9 52.4 76.8 55.8 58.6 54.8 44.3 68.7 53.6 59.3 Streets have good lighting at night (% agree) 57.8 74.7 72.9 77.4 77.7 76.6 82.9 46.8 73.2 67.0 83.7 62.8 71.5 There are crosswalks and signals on busy streets (% agree) 55.7 68.6 81.6 83.0 47.9 79.3 65.4 32.8 72.4 67.8 76.9 43.7 63.4 There are many places to go within walking distance (% agree) 64.8 68.2 75.9 72.4 53.5 69.1 71.6 60.1 40.1 51.1 69.8 34.3 60.0 Most drivers go faster than the posted speed limits (% disagree) 34.2 25.4 28.7 56.3 27.7 33.1 27.6 34.9 34.9 24.4 28.7 25.9 31.9 The traffic makes it difficult or unpleasant to walk (% disagree) 67.6 39.4 74.0 46.0 49.7 84.0 30.4 52.5 53.1 39.1 58.6 56.5 54.0 Note: results for the subscales are reported as mean (SD). All items were coded so that higher scores indicate greater walkability/safety. 30 Table 4. Correlates of Active School Transport Stratified by Study Site in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE). Australia (Adelaide) Brazil (São Paulo) Canada (Ottawa) China (Tianjin) Colombia (Bogota) Finland (Helsinki, Espoo & Vantaa) India (Bangalore) Kenya (Nairobi) Portugal (Porto) South Africa (Cape Town) UK (Bath & North East Somerset) US (Baton Rouge) N=496 N=430 N=551 N=541 N=834 N=439 N=559 N=537 N=639 N=336 N=456 N=532 0.72 (0.461.13) 1.17 (0.721.92) 0.46 (0.290.74)** 0.98 (0.661.47) 1.00 (0.641.57) 1.38 (0.792.40) 0.58 (0.16-2.17) 0.68 (0.441.06) 0.85 (0.581.26) 1.11 (0.562.21) 1.18 (0.741.89) 1.01 (0.591.71) 1.05 (0.641.70) 1.04 (0.492.23) 0.96 (0.511.83) 0.94 (0.451.97) 0.82 (0.471.42) 1.29 (0.237.37) 0.84 (0.471.48) 1.13 (0.572.22) 1.01 (0.492.10) 1.05 (0.462.38) 0.94 (0.521.70) 1.08 (0.225.38) 1.15 (0.691.92) 1.87 (0.824.26) 1.30 (0.732.34) 1.38 (0.762.51) 1.41 (0.444.48) 1.30 (0.354.80) 1.25 (0.762.05) 2.85 (0.4816.78) 2.