Disentangling Associations of Neighborhood Street Scale Elements With Physical Activity in Mexican School Children

Transcription

1 615389EABXXX / Environment and BehaviorLee et al. research-article2015 Transportation & Land Use Disentangling Associations of Neighborhood Street Scale Elements With Physical Activity in Mexican School Children Environment and Behavior 2016, Vol. 48(1) SAGE Publications Reprints and permissions: sagepub.com/journalspermissions.nav DOI: / eab.sagepub.com Rebecca E. Lee 1, Erica G. Soltero 2, Alejandra Jáuregui 3, Scherezade K. Mama 4, Simón Barquera 3, Edtna Jauregui 5,6, Juan Lopez y Taylor 5, Luis Ortiz-Hernández 7, and Lucie Lévesque 8 Abstract Promoting outdoor play and participation in sports and organized physical activities in children may depend on neighborhood characteristics. This study investigated associations between neighborhood streets and physical activities among Mexican children (N = 1,509, 6-11 years). Child sociodemographic characteristics and physical activity were measured in schools in Guadalajara (n = 10), Mexico City (n = 13), and Puerto Vallarta (n = 3), Mexico, in Street segments within an 800 m radius around each school were measured 1 Arizona State University, Phoenix, AZ, USA 2 University of Houston, TX, USA 3 Instituto Nacional de Salud Pública, Cuernavaca, México 4 The Pennsylvania State University, University Park, PA, USA 5 Universidad de Guadalajara, Mexico 6 Secretaria de Salud, Guadalajara, México 7 Universidad Autónoma Metropolitana Xochimilco, México, México 8 Queen s University, Kingston, ON, Canada Corresponding Author: Rebecca E. Lee, Center for Health Promotion and Disease Prevention, College of Nursing and Health Innovation, Arizona State University, 300 North 3rd Street, Phoenix, AZ 85004, USA. releephd@yahoo.com

2 Lee et al. 151 using the Pedestrian Environment Data Scan. Most (75.8%) played outdoors; 47.4% participated in sports and 40% in organized physical activities. Fewer path obstructions and more pedestrian amenities were associated positively with outdoor play. Greater street cleanliness, more pedestrian amenities, and more path obstructions were associated with less participation in sports or organized activities. Walkability was negatively associated with all physical activities. Fostering safe and appealing streets may help promote outdoor play time, but not sports participation, for Mexican children. Keywords physical activity, children, gender, safety, perception of safety, parents, Mexico, outdoor play, organized sports Physical activity (PA) is an international public health priority (National Physical Activity Plan Congress, 2015, February 23-24; World Health Organization, 2014, February). For children, time spent outside is linked to PA, making the neighborhoods of places where children spend their time, like schools, important places for PA (Collins, Al-Nakeeb, Nevill, & Lyons, 2012; Ferreira et al., 2007). In Mexico, almost 70% of children aged 10 to 14 years actively commute to school an average of 10 min each way, suggesting that most children who attend public school do so in their home neighborhoods and are exposed to neighborhood street-scale features along the route (Jauregui, Medina, Salvo, Barquera, & Rivera-Dommarco, 2014). It is not known how children in low- and middle-income countries (LMIC), like Mexico, may be influenced by neighborhood environmental characteristics that influence PA (Davison & Lawson, 2006). Micro-level, street-scale features, such as measures of walkability, traffic speed and volume, access to recreation facilities, land use mix, and residential density along with residence closer to schools and access to green spaces, have been associated with PA in children (Ding, Sallis, Kerr, Lee, & Rosenberg, 2011; McCrorie, Fenton, & Ellaway, 2014). However, these associations are not consistent, and the quandary between the associations of neighborhood variables to PA in some studies but not others has hampered the development of a well-specified theoretical or conceptual model to guide this field (Buck et al., 2015; Carson, Rosu, & Janssen, 2014). Ecologic systems models, such as the Ecologic Model of Physical Activity (EMPA), posit that environmental factors directly and indirectly shape and modify PA as part of a dynamic, complex system (Lee & Cubbin, 2009; Sallis & Owen, 1997; Spence & Lee, 2003). The interactions among environmental and individual characteristics in complex systems may account, in part, for the inconsistencies observed

3 152 Environment and Behavior 48(1) in the extant literature and suggest that continued investigation of these relationships is warranted (Brownson, Hoehner, Day, Forsyth, & Sallis, 2009; Pikora, Giles-Corti, Bull, Jamrozik, & Donovan, 2003; Rydin et al., 2012). Research is needed to elucidate differences in findings related to measurement (self-report vs. objective audits), operational definitions, and the almost exclusive focus of research on high-income countries (Giles-Corti, Kelty, Zubrick, & Villanueva, 2009). Common strategies for increasing PA in children include increasing the amount of outdoor play and promoting participation in sports and organized physical activities (SOPA; Cleland et al., 2008; Collins et al., 2012; Hermann et al., 2006; Jauregui et al., 2011; Vella, Cliff, Okely, Scully, & Morley, 2013). Nevertheless, most studies have quantified PA as a single measure of the daily amount of moderate or greater intensity PA (Carver, Timperio, & Crawford, 2008; Carver, Timperio, Hesketh, & Crawford, 2010; de Vries, Bakker, van Mechelen, & Hopman-Rock, 2007; Kligerman, Sallis, Ryan, Frank, & Nader, 2007). It may be that specific aspects of environments may impact specific types of PAs differently (Salmon & Timperio, 2007). For example, time spent playing outside might be more sensitive to street-scale features that affect pedestrian safety and neighborhood suitability for PA. In contrast, participating in SOPA may be less sensitive to street-scale features and more sensitive to availability of PA facilities where these activities might be done (Prins, Oenema, van der Horst, & Brug, 2009). The relationship between environment and PA in children has been poorly documented in most LMIC, including Mexico, which has an emerging influence in North America and is burdened with the highest childhood obesity rate in the world (Background Note: Mexico, 2010; Holub et al., 2013; The State of Food and Agriculture, 2013). The Mexican population is at high risk for developing diseases related to physical inactivity over the life course (Acosta- Cazares & Escobedo-de la Pena, 2010; Jauregui et al., 2014; Villalpando et al., 2010). This study aimed to determine how micro-level, street-scale features of the school neighborhood might be differentially associated with outdoor play versus participation in SOPA. We expected that factors that influenced the safety and suitability of the neighborhood environment, such as walkability and street-scale features, would be associated with outdoor play, whereas access to recreation facilities would be associated with participation in SOPA. Methodology Participants Mexican school children (N = 1,509) participated in a multisite investigation of neighborhood and health behaviors in Guadalajara (n = 10 schools),

4 Lee et al. 153 Mexico City (n = 13 schools), and Puerto Vallarta (n = 3 schools), Mexico, in 2012 (range = children per school, M = 73). Public schools were referred by the State of Jalisco Secretaría de Educación or selected by virtue of participating in another study of policy implementation (Gharib et al., 2015). School addresses were located through the Directorio de Escuelas en México and mapped using Google Maps (Directorio de Escuelas, 2013). Children who were enrolled in Grades 3, 4, or 5 (ages 6-11), present on measurement day, ambulatory, apparently healthy, had parental consent, and provided assent were enrolled. Procedures and protocols were approved by the appropriate institutional review boards at the Instituto Nacional de Salud Pública de México, Queen s University, Universidad de Guadalajara, and University of Houston. Measures Individual measures. Demographic characteristics were measured using a modified version of the School Physical Activity and Nutrition (SPAN) survey distributed to parents for completion (Hoelscher, Day, Kelder, & Ward, 2003). The survey was translated and back translated by native Spanish, bilingual speakers; reviewed by Mexican members of the multinational team; and pilot tested for readability in Mexico. Items measured child age, gender, annual household income, and the number of children and the total number of people in the household. The SPAN assessed days of outdoor play (number of days that a child played outdoors for 30 min), number of sports teams in the past year, and other organized PAs or lessons (e.g., martial arts, dance, gymnastics, soccer, baseball, or tennis) which has shown reliability in previous work (Hoelscher et al., 2003). To increase the robustness of models, a single variable for SOPA was created by classifying children who did not play on any sports team and did not take part in any other organized PAs during the past 12 months as 0, and those children who participated in any as 1. Neighborhood environment. In each school neighborhood, all arterial street and a random sample of 25% of residential street segments within an 800 m radius around each school was assessed using an abbreviated version of the Pedestrian Environment Data Scan (PEDS), following established training and assessment protocols (Clifton, Livi Smith, & Rodriguez, 2007; Lee, Booth, Reese-Smith, Regan, & Howard, 2005; Lee, Mama, McAlexander, Adamus, & Medina, 2011; Lee, Mama, Medina, Ho, & Adamus, 2012; McMillan, Cubbin, Parmenter, Medina, & Lee, 2010; Parmenter, McMillan, Cubbin, & Lee, 2008; Rodríguez, Brisson, & Estupiñán, 2009; Soltero,

5 154 Environment and Behavior 48(1) Mama, Pacheco, & Lee, 2015). The PEDS instrument objectively measures the quality and condition of street segments, is publicly and freely available on the internet, has shown adequate reliability, and has been previously described (κ >.70; Clifton et al., 2007; Lee et al., 2011; Lee et al., 2012). PEDS variables measured pedestrian facilities (sidewalks, path obstructions, path condition, pedestrian amenities, and pedestrian traffic buffers); street attributes (posted speed limits, traffic control devices, traffic volume, and number of traffic lanes); number of land uses in the segment, including presence of recreation facilities; and street cleanliness (absence of litter, graffiti, and poor building maintenance). Geographic Information Systems (GIS) data from the urban cartographic boundary files, the demographics database from the 2010 Census of the Instituto Nacional de Estadística y Geografía, were used to define neighborhood contextual variables (National Institute of Statistics and Geography, 2014). All GIS variables were generated using ArcInfo Workstation 9.31 and ArcGIS Desktop 10.0 to describe census tracts in which schools were located. The Urban Poverty Index (UPI; educational attainment, household income, household size, population density) from the Consejo Nacional de Población was measured at the census tract level (Bustos, 2011). The UPI classifies census tracts into five categories from very high to very low. Socioeconomic status (SES) was calculated by weighting the buffer proportion of each census tract UPI score by the total population within the buffer. Walkability. The number of land uses per segment was collected from neighborhood audits using the PEDS (Clifton et al., 2007). A land use mix index was calculated using a three-category land use entropy score considering residential, office, and commercial land uses (Frank et al., 2010). The proportion of commercial land use was also calculated (number of segments with commercial land use / total number of measured segments; Frank et al., 2010). Street connectivity data were obtained from the urban roadway database from the Instituto Federal Electoral. Connectivity was defined as the number of intersections per square kilometer and was calculated by dividing the number of street intersections by the total buffer area (Lee, Mama, Adamus-Leach, & Soltero, 2015). Residential density data were obtained from the 2010 Census of the Instituto Nacional de Estadística y Geografía. Residential density was defined as the number of households per square kilometer and was calculated by dividing the total number of households within the buffer by total buffer area. Walkability was defined as the combination of audit measured land use mix and commercial land use with GIS measures of connectivity and residential density, as previously described (Frank et al., 2010; Jauregui et al., 2015).

6 Lee et al. 155 Analyses All analyses were run using STATA v13 SE (Stata Corp, College Station, TX, USA). Variables were analyzed descriptively with frequencies or means. A two-phase analysis was conducted with the goal of creating a parsimonious model. The selection of environmental variables in models was based on theoretical and empirical criteria. Initial preliminary covariate-adjusted, single-environmental variable, multi-level logistic regression models were run to explore associations between neighborhood-level variables and PA outcomes, adjusted for city, neighborhood SES, and individual demographic covariates. All environmental variables were introduced as continuous variables; however, path obstructions, posted speed limits, low volume roads, pedestrian amenities, and recreation facilities were introduced as tertiles, because this parameterization provided the best fit. Next, a full model was run for PA outcomes including the same covariates listed above and significant (p <.050) environmental variables and interactions from the previous phase or those considered theoretically relevant. Walkability, recreation facilities, and low traffic volume roads were retained in full models based on previously reported associations (Ding et al., 2011; Giles-Corti et al., 2009). Models including environmental variables that were not significantly associated (.05 p <.20) with PA outcomes in the first phase were also tested with the purpose of not excluding environmental variables that could achieve significance after controlling for other variables in the full model. If significance was not reached, these variables were not kept in final models. To maximize power, we merged two environmental variable tertiles (1 and 2, or 2 and 3) into one single category if no differences were observed in the relationships between individual tertiles and PA outcomes. A two-level random-intercept model was run using the melogit command in STATA. Neighborhood buffers were introduced as the clustering variable. Final models were tested for specification error, goodness of fit, and collinearity. Differences between unadjusted means and proportions and regression model estimates for main effects and interactions were considered significant if p <.05. As more than 40% of families did not report family income, we completed the modeling strategy for both the full sample and the subsample with available income data separately. All final models had a mean variance inflation factor <2.5. Results A total of 1,509 surveys were collected; 188 were excluded for missing data (gender, age, or outdoor play), leaving 1,321 cases for analysis. Of those, 41.6% (n = 549) did not have reported income data, yielding a subsample of

7 156 Environment and Behavior 48(1) 772 cases with complete income information. Some (n = 79) did not report information on SOPA, yielding a subsample of 731 cases with income for that outcome and full sample of 1,242. No differences were found in demographic or PA variables between the subsample and the full sample (p >.05); however, children in the subsample were younger by 2.3 months (p =.001). A total of 2,977 segments ( per neighborhood) were measured; 10.7% were arterial streets. Descriptive characteristics are presented in Table 1. Outdoor Play Initial covariate-adjusted single-environmental models showed that path obstructions were negatively associated with outdoor play, and an interaction between low traffic volume roads and gender was found in the full sample and subsample (p <.05; Table 2). These and other theoretically relevant variables (walkability and recreation facilities) or variables with a p value <.2 (amenities, data not shown) were retained in the final adjusted model for outdoor play (Table 3). The final subsample adjusted model showed that walkability (odds ratio [OR] = 0.91, p <.05) and having 24% segments with path obstructions (OR = 0.43, p <.05) were associated with lower odds for outdoor play. Boys in schools with neighborhoods with a high proportion of low traffic volume roads (>55%) were 2.14 times more likely to play outdoors compared with peers in schools in neighborhoods with a lower proportion (p <.05). This relationship was not significant in girls. A high proportion of pedestrian amenities (>22%) was associated with 2.21 times higher odds for outdoor play. No other differences by gender were found. Similar but marginally significant relationships were observed in the full sample final adjusted model for path obstructions (p =.052, data not shown) and low volume roads (p =.067, data not shown); however, walkability and amenities were not associated with outdoor play. No other relationships were observed. Sports and Other PAs Initial models showed that the walkability score, sidewalk buffer, path obstructions, and amenities were negatively associated with participation in SOPA in the full sample and subsample (p <.05; Table 4). These and other theoretically relevant variables (low volume roads and recreation facilities) or variables associated with a p value <.2 (cleanliness, data not shown) were retained in the final adjusted model for participation in SOPA (Table 5). In the subsample, the adjusted models showed that the walkability score was negatively associated with participation in SOPA (OR = 0.91, p <.05), and that having a street with fair or good cleanliness (OR = 0.17, p <.05) or a high

8 Lee et al. 157 Table 1. Sociodemographic and Neighborhood Descriptive Characteristics in a Sample of Mexican Urban Children (N = 1,321). M or n 95% CI or % Sociodemographic characteristics Age (years) 9.63 [9.57, 9.68] Gender Male Female Income a Less than $5, MXN $5, $9, MXN $10, MXN or more Number of children in the household 2.5 [2.41, 2.54] More than People in the household More than Outdoor play Yes 1, No Sports participation b Yes No Organized activities b Yes No School neighborhood characteristics Walkability c Low (< 1.0) Medium ( 1.0, 1.0) High(>1.1) % of segments with sidewalks Low (<70%) Medium (70%-90%) High (>90%) % of segments with posted speed limits Low (<9%) Medium (9%-12.5%) High (>12.5%) (continued)

9 158 Environment and Behavior 48(1) Table 1. (continued) M or n 95% CI or % % of segments with traffic control devices Low (<15%) Medium (15%-40.0%) High (>40.0%) Cleanliness Poor 26 2 Fair Good % of segments with path obstructions Low (<24%) Medium (24%-55%) High (>55%) Path condition Poor Fair Good % of segments with low volume roads Low (<56%) Medium (56%-65%) High (>66%) % of segments with recreation facilities None (0%) Some (0.1%-5%) High (>5%) % of segments with pedestrian amenities Low (<9%) Medium (9%-22%) High (>22%) Mean number of traffic lanes 1 or More than Urban poverty index Very low Low Medium High Note. n = sample size; 95% CI = 95% confidence interval; SOPA = sports and organized physical activities. a Based on n = 772 with available income data. b Based on N = 1,242 with available SOPA data. c Composite measure of street connectivity, residential density, commercial land use, and land use mix (Frank et al., 2010).

10 Lee et al. 159 Table 2. Initial Preliminary Single-Environmental Variable Exploratory Models for Outdoor Play, Adjusted. Outdoor play Subsample Full sample (n = 722) a (N = 1,321) b OR 95% CI OR 95% CI Walkability score c 0.96 [0.90, 1.02] 0.97 [0.93, 1.03] % of segments with sidewalk 0.79 [0.35, 1.77] 0.81 [0.40, 1.63] % of segments with sidewalk 0.76 [0.38, 1.49] 0.73 [0.41, 1.29] buffer Path condition Poor 1 1 Fair 0.52 [0.21, 1.37] 0.61 [0.28, 1.57] Good 0.87 [0.31, 2.46] 0.66 [0.28, 1.57] % of segments with path obstructions <24% %-55% 0.52 [0.32, 0.86] 0.68 [0.49, 0.95] >55% 0.41 [0.17, 0.99] 0.54 [0.27, 1.09] % of segments with posted speed limits Low (<9%) 1 1 Medium (9%-12.5%) 1.13 [0.74, 1.72] 1.23 [0.86, 1.76] High (>12.5%) 1.31 [0.80, 2.14] 1.23 [0.85, 1.79] % of segments with traffic 1.1 [0.30, 4.02] 0.77 [0.27, 2.16] control devices % of segments with crossing aids 1.48 [0.66, 3.34] 1.16 [0.59, 2.29] Street cleanliness Poor 1 1 Fair 0.84 [0.26, 2.71] 0.85 [0.31, 2.37] Good 1.31 [0.35, 4.94] 0.96 [0.32, 2.94] % of segments with low volume roads d Girls <56% %-65% 0.94 [0.57, 1.48] 0.92 [0.49, 1.80] >66% 0.74 [0.44, 1.14] 0.71 [0.38, 1.42] Boys <56% %-65% 0.92 [0.54, 1.58] 0.96 [0.48, 1.90] >66% 1.41 [0.83, 2.41] 1.56 [0.77, 3.15] Number of traffic lanes 0.92 [0.68, 1.28] 1.05 [0.82, 1.34] (continued)

11 160 Environment and Behavior 48(1) Table 2. (continued) Outdoor play Subsample Full sample (n = 722) a (N = 1,321) b OR 95% CI OR 95% CI % of segments with amenities <9% 1 1 9%-22% 0.89 [0.53, 1.48] 1.05 [0.71, 1.55] >22% 1.02 [0.60, 1.72] 0.87 [0.59, 1.27] % of segments with recreation facilities None %-5% 1.15 [0.72, 1.84] 1.15 [0.81, 1.62] >5% 0.67 [0.40, 1.12] 0.74 [0.52, 1.08] Note. The OR is interpreted as the decrease or increase in the amount of time children spend on outdoor play, associated with 1 unit/category increase in the independent. Hence, an OR of 1.10 indicates an increase of 10% in outdoor play associated with 1 unit/category increase in the environmental characteristic. An OR of 0.90 likewise indicates a decrease of 10%. Numbers in bold indicate p value <.05. N, n = sample size; OR = odds ratio; 95% CI = 95% confidence interval; SES = socioeconomic status. a Model controls for gender, age, neighborhood SES, city, and family income. b Model controls for gender, age, neighborhood SES, and city. c Composite measure of street connectivity, residential density, commercial land use, and land use mix (Frank et al., 2010). d p value of interaction term Gender by Low volume roads <.05 in the full sample and <.1 in the subsample. percentage (9% or more) of pedestrian amenities (OR = 0.51, p <.05) was associated with lower odds of participating in SOPA. Similar associations were found in the full sample for pedestrian amenities; however, street cleanliness was only marginally associated with participation in SOPA, and walkability was not associated with participation in SOPA. No differences by gender or income were found. No other relationships were observed. Discussion This study aimed to identify neighborhood factors associated with outdoor play and participation in SOPA in Mexican children. Findings provided partial support for our hypotheses demonstrating that attending school in neighborhoods with fewer path obstructions, more pedestrian amenities, and low traffic volume was associated with more outdoor play, somewhat consistent

12 Lee et al. 161 Table 3. Final Full Models of Environmental Correlates of Outdoor Play in Mexican Urban Children, Adjusted. Subsample Full sample (n = 772) a (N = 1,321) b OR 95% CI OR 95% CI Walkability score c 0.89 [0.82, 0.98] 0.99 [0.93, 1.06] Proportion of segments with path obstructions <24% % 0.43 [0.24, 0.77] 0.68 [0.46,1.00] Proportion of segments with low volume roads d Girls <56% % 0.98 [0.55, 1.74] 0.86 [0.54, 1.37] Boys <56% % 2.14 [1.07, 4.3] 1.66 [0.97, 2.88] Recreation facilities None A few or some (>0%) 1.17 [0.75, 1.83] 1.00 [0.69, 1.43] Amenities 22% >22% 2.38 [1.24, 4.55] 0.96 [0.63, 1.47] Note. The OR is interpreted as the decrease or increase in the amount of time children spend on outdoor play, associated with 1 unit/category increase in the independent. Hence, an OR of 1.10 indicates an increase of 10% in outdoor play associated with 1 unit/category increase in the environmental characteristic. An OR of 0.90 likewise indicates a decrease of 10%. Numbers in bold indicate p value <.05. N, n = sample size; OR = odds ratio; 95% CI = 95% confidence interval. a Multilevel model adjusted for gender, age, neighborhood poverty index, city, and family income. Wald χ 2 = 23.99, p =.02 b Multilevel model adjusted for gender, age, neighborhood poverty index, and city. Wald χ 2 = 29.9, p =.0049 c Composite measure of street connectivity, residential density, commercial land use and land use mix (Frank et al., 2010). d p value for interaction term Gender by Low volume roads <.05 in both adjusted models with previous research using different instruments in other countries (de Vries et al., 2007). These factors may enhance safety, which can influence PA in children, particularly girls (Carver et al., 2010). However, in contrast to hypotheses, presence of neighborhood recreation facilities was not associated with participation in SOPA, unlike another study (Prins et al., 2009). Lower walkability was associated with higher outdoor play and more SOPA participation in

13 162 Environment and Behavior 48(1) Table 4. Initial Preliminary Single-Environmental Variable Exploratory Models for SOPA, Adjusted. SOPA Subsample (n = 731) a Full sample (N = 1,242) b OR 95% CI OR 95% CI Walkability score c 0.92 [0.86, 0.99] 0.89 [0.85, 0.95] % of segments with sidewalk 0.48 [0.20, 1.18] 0.43 [0.17, 1.08] % of segments with sidewalk buffer 0.46 [0.22, 0.96] 0.47 [0.21, 1.02] Path condition Poor 1 1 Fair 0.74 [0.30, 1.79] 0.85 [0.34, 2.1] Good 0.99 [0.36, 2.72] 0.96 [0.35, 2.64] % of segments with path obstructions Low (<24%) 1 1 Medium (24%-55%) 0.48 [0.28, 0.83] 0.51 [0.30, 0.84] High (>55%) 0.43 [0.18, 1.05] 0.49 [0.20, 1.17] % of segments with posted speed limits Low (<9%) 1 1 Medium (9%-12.5%) 1.05 [0.64, 1.72] 1.18 [0.71, 1.96] High (>12.5%) 1.51 [0.88, 2.57] 1.30 [0.78, 2.18] % of segments with traffic control devices 0.31 [0.08, 1.21] 0.53 [0.12, 2.23] % of segments with crossing aids 0.82 [0.32, 2.09] 0.9 [0.36, 2.30] Street cleanliness Poor 1 1 Fair 0.39 [0.12, 1.31] 0.55 [0.17, 1.83] Good 0.84 [0.21, 3.33] 0.92 [0.24, 3.51] % of segments with low volume roads 0.85 [0.15, 4.97] 0.63 [0.12, 3.36] Number of traffic lanes 0.72 [0.52, 1.02] 0.82 [0.59, 1.15] % of segments with amenities <9% 1 1 9%-22% 0.84 [0.53, 1.34] 0.74 [0.44, 1.25] >22% 0.42 [0.26, 0.67] 0.52 [0.30, 0.91] % of segments with recreation facilities None %-5% 1.13 [0.67, 1.91] 1.06 [0.63, 1.79] >5% 0.71 [0.39, 1.30] 1.04 [0.58, 1.89] Note. The OR is interpreted as the decrease or increase in the amount of time children spend on outdoor play, associated with 1 unit/category increases in the independent variable. Hence, an OR of 1.10 indicates an increase of 10% in SOPA associated with 1 unit/category increase in the environmental characteristic. An OR of 0.90 likewise indicates a decrease of 10%. Numbers in bold indicate p value <.05. SOPA = sports and other organized physical activities; N, n = sample size; OR = odds ratio; 95% CI = 95% confidence interval; SES = socioeconomic status. a Model controls for gender, age, number of children in the household, neighborhood SES, city, and family income. b Model controls for gender, age, number of children in the household, neighborhood SES, and city. c Composite measure of street connectivity, residential density, commercial land use, and land use mix (Frank et al., 2010).

14 Lee et al. 163 Table 5. Environmental Correlates of Sports or Organized Activities Participation in Mexican Urban Children, Adjusted. Subsample (n = 731) a Full sample (N = 1,242) b OR 95% CI OR 95% CI Walkability score c 0.91 [0.84, 0.98] 0.96 [0.89, 1.04] % of segments with sidewalk buffer 1.76 [0.69, 4.52] 1.15 [0.44, 3.03] % of segments with path obstructions 50% or less >50% 0.70 [0.41, 1.18] 0.67 [0.41, 1.12] Street cleanliness Poor Fair or good 0.17 [0.05, 0.66] 0.28 [0.07, 1.05] % of segments with amenities <9% % 0.51 [0.32, 0.83] 0.57 [0.34, 0.95] Recreation facilities None % or more 0.90 [0.59, 1.38] 0.92 [0.59, 1.44] Low traffic volume roads <56% >=56% 0.99 [0.63, 1.57] 0.83 [0.51, 1.35] Note. The OR is interpreted as the decrease or increase in the amount of time children spend on outdoor play, as the independent variable increases with 1 unit. Hence, an OR of 1.10 indicates an increase of 10% in outdoor play as the environmental characteristic increases with 1 unit. An OR of 0.90 likewise indicates a decrease of 10%. Numbers in bold indicate p value <.05. N, n = sample size; OR = odds ratio; 95% CI = 95% confidence interval. a Multilevel model adjusted for gender, age, grade, number of children in the household, neighborhood poverty index, city, environmental variables listed in the table and family income. Wald χ 2 = 53.4, p value <.001 b Multilevel model adjusted for gender, age, grade, number of children in the household, neighborhood poverty index, city, and environmental variables listed in the table. Wald χ 2 = 50.26, p value <.001 c Composite measure of street connectivity, residential density, commercial land use, and land use mix (Frank et al., 2010). the subsample, but not the full sample. In contrast, others have reported more PA in youth in more highly walkable neighborhoods (Kligerman et al., 2007). Research in higher income countries has shown positive associations between neighborhood walkability and PA (moderate or greater PA and active transportation) in youth (Ding et al., 2011; Kligerman et al., 2007). In this study, this relationship was inverse or not found in both outdoor play and

15 164 Environment and Behavior 48(1) participation in SOPA. Previous evidence in a Mexican sample has suggested negative relationships between the walkability score, as defined for highincome countries, and total PA or active commuting to school (Jauregui et al., 2015; Salvo et al., 2014). Our walkability index was adapted to use audit information to calculate land use mix and commercial land use, which might be more accurate and timely than GIS derived data. Walkability focuses on destinations and the ability to reach them by walking; however, for outdoor play, the presence of destinations may mean increased traffic and less safety in LMIC. Removing the retail proportion or land use mix did not change the negative relationship of walkability with PA. Connectivity was the individual component most strongly and negatively associated with PA. Residential density and land use mix were also negatively related, although less strongly, and the individual relationship with retail proportion was equivocal. One possible explanation for this might be that in Mexican neighborhoods children may play in driveways (Umstattd Meyer, Sharkey, Patterson, & Dean, 2013). This practice is especially frequent in lower income neighborhoods where streets are small, well connected, and in poor condition, as observed in this study: most neighborhoods had streets with relatively low cleanliness, including litter, graffiti, and poorly maintained buildings. It is unclear how measures of walkability should be applied to youth PA research in LMIC without further theoretical and practical development. Perhaps the hypothesized relationships differ by country and method of assessment. Others have reported that quality, more than quantity of PA resources, may drive use (Lee et al., 2015). Instead, we found something potentially more intriguing. Neighborhoods of schools that had higher walkability, greater cleanliness, and more pedestrian amenities had children with lower participation in SOPA, and neighborhoods with more path obstructions had higher participation in SOPA. Perhaps the seemingly unsavory quality of a neighborhood environment that was less clean and less favorable for pedestrians and cyclists encouraged parents to enroll their children in after school activities that minimized their time in that environment. There are few studies investigating the relationship between school neighborhood street-scale features and participation in SOPA, and future investigations should pursue this. Others have reported that neighborhoods with sidewalk features that are more favorable for pedestrians activity tend to have more active children; perhaps neighborhoods that have cleaner streets with fewer incivilities are more playable and foster unstructured types of outdoor PA (Buck et al., 2015; Jago, Baranowski, & Baranowski, 2006; Jago, Baranowski, Zakeri, & Harris, 2005; Moore, 1987). Strengths of this study include drawing a sizable sample from three diverse geographic regions of Mexico and objective neighborhood data based on

16 Lee et al. 165 carefully conducted neighborhood environmental audits. Cities and neighborhoods were not selected randomly, owing to feasibility constraints and the wishes of the local authorities. In the analysis, we used school neighborhood buffers instead of home-based buffers, assuming that children lived near schools, and because we were unable to collect residential information of children owing to safety concerns from our government partners. In Mexico, most children and adolescents walk or bike to school, which suggests that children s homes are near their schools (Jauregui et al., 2014). It is plausible that most children in Mexico attend schools in their neighborhoods and are exposed daily to this environment. Future studies should consider more technologically advanced approaches, such as using geographic tracking devices to better understand daily neighborhood exposures of children (Chaix et al., 2013). Although parent reports of children s behavior are a limitation, the SPAN survey is a reliable tool, and this study relied on appropriate cultural adaptation and pilot testing as described herein. The sample was drawn from primarily urban and suburban neighborhoods, thus results may not be generalizable to rural or indigenous communities. Most (79%) of the Mexican population lives in urban and suburban settings, increasing representativeness (Poblacion urbana (% del total), 2015). Income information was only available for 41.1% of the cases. The same models were run for both the full sample and the subsample, and similar relationships were found with the notable exception that walkability was negatively associated with PA in the subsample, and not associated at all in the full sample; however, the direction of the association was similar. The cut points for the environmental variables were selected to enhance the model fit. The number of neighborhoods assessed in this study limits the amount of available power; thus, the capacity of the models to support additional categories and deeper investigation of environmental variables (via quartiles or quintiles) was not possible. Given the absence of a well-defined theoretical framework in this field, individual associations could be over-adjusted, because variables in the causal pathways (e.g., other environmental variables) may be controlled for, leading to fewer significant associations. The individual components used to define the walkability score do not correspond exactly to those used in previous definitions of walkability (Frank et al., 2010), which limits comparability. Micro-level, street-scale features of the neighborhood environment are associated with PA, and may determine whether children are able to play outdoors or participate in SOPA. Fostering safe and appealing streets and outdoor spaces for child play time should be a priority to increase PA in Mexican children. This is particularly important in a population where more children play outdoors, rather than participate in SOPA, as suggested by these findings. It seems many potential solutions to the enduring challenge of increasing PA

17 166 Environment and Behavior 48(1) might lie in the improvement of street safety and quality via appropriate regulations and their enforcement. Practitioners are needed to improve neighborhood outdoor spaces where children play and promote SOPA programming, but additional research is needed to clarify these relationships, particularly in understudied and vulnerable LMIC, like Mexico. Authors Note R.E.L. conceived of the study, obtained grant funding, led training, and co-led data collection, data interpretation, and writing of the manuscript. E.G.S. co-led training and assisted with data collection, analyses, interpretation, and writing of the manuscript. A.J. led analyses and assisted with interpretation and writing of the manuscript. S.K.M. co-led training and data collection, and assisted with data collection and writing of the manuscript. S.B., E.J., and J.L.T. assisted with study conceptualization; L.O.H. assisted with training and interpretation, and writing of the manuscript. L.L. assisted with interpretation and writing of the manuscript. All authors reviewed and approved a final version of the manuscript prior to its submission. Acknowledgments The authors wish to acknowledge the State of Jalisco Secretaría de Salud and Secretaría de Educación for their assistance in identifying schools and neighborhoods for investigation. The authors also wish to acknowledge the many students and trainees in the United States and Mexico who helped in data collection, entry, and processing. Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was made possible by a Fulbright-García Robles Core Scholar Fellowship and a grant from the National Cancer Institute (1R13CA162816) awarded to Dr. Lee, and by a grant (CIHR GIR ) awarded to Dr. Lévesque and Dr. Barquera from the Canadian Institutes for Health Research (CIHR) Institute of Population and Public Health and the Public Health Agency of Canada Strategic Initiatives and Innovations Directorate (PHAC-SIID). References Acosta-Cazares, B., & Escobedo-de la Pena, J. (2010). High burden of cardiovascular disease risk factors in Mexico: An epidemic of ischemic heart disease that may be on its way? American Heart Journal, 160, doi: /j. ahj

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