VOORHEES, CAROLYN C.1; ASHWOOD, SCOTT2; EVENSON, KELLY R.3; SIRARD, JOHN R.4; RUNG, ARIANE L.5; DOWDA, MARSHA6; MCKENZIE, THOMAS L.7
Recent reviews have shown the potential of active transport, such as walking and biking to and from school, to contribute to overall physical activity levels in elementary and middle school youth (5,6).
Transportation-related activity, accumulated along with other forms of physical activity throughout the day, has the potential to reduce the chronic health risks associated with an inactive lifestyle and obesity. However, little is known about specific community design attributes that influence the likelihood that students walk to school.
Krizek et al. (13) proposed a conceptual model of community design attributes that may be unique to youth, such as issues related to opportunity, time constraints, and parental permissiveness. It is unclear whether neighborhood features are different for youth related to walking to school compared with adults walking for utilitarian or leisure purposes. Three recent reviews discussed hypothetical factors relating to children's trips to school (17) and environmental determinants of active travel in youth (1,25). Panter et al. (25) present a novel framework that incorporates multilevel physical environment factors, both existing and hypothesized, which may either directly or indirectly relate to walking outcomes in youth. These include attributes of the travel route such as directness, safety, urban form, and destinations. Sirard and Slater (30) also present a conceptual framework for children's travel behavior, which hypothesizes that the perceived physical environment directly impacts frequency of active commuting. However, much of the empirical research in this area (11,21,22) relates to features that make neighborhoods more walkable for adults, including such measures as land use mix or convenient walking destinations, existence and quality of sidewalks, block size, street connectivity, population density, pedestrian safety, and crime (28).
More than one-third of US children aged 9-15 yr live within a mile of school, and less than half walk or bike to school once a week (15). In addition, the long-term trends show that the distances children must travel to school are increasing over time, whereas their active transportation rates are decreasing (10,16). A national US survey (18) and a multicenter trial ancillary study (6), as well as large Canadian (14) and Irish studies (23), showed that distance and safety concerns were major barriers to children walking to school.
Features related to walking behavior have been measured in a variety of ways, using surveys of individual's perceptions, audit instruments, and geographic information systems (GIS). Few studies of active transportation to school include both objective and perceived measures (8,12,18,29). Thus, it is important to consider a comprehensive set of neighborhood design features that may relate to other forms of activity such as walking to school. The current study assesses the associations between perceived and objectively measured neighborhood variables, including safety and environmental features and walking to and from school in adolescent girls living close to school. Because participants were eighth-grade girls from a large, geographically and ethnically diverse cohort (36), this study could have implications for community design features that facilitate walking to or from school for adolescents.
Trial of Activity in Adolescent Girls (TAAG) is a multicenter cluster-randomized trial, funded by the National Heart, Lung, and Blood Institute (NHLBI), to test the effectiveness of a school and community-linked intervention to reduce the usual decline in moderate-to-vigorous physical activity among adolescent girls (31). In collaboration with NHLBI staff, and coordinated by the University of North Carolina, Chapel Hill, healthy sixth-grade girls, aged 11-12 yr, were recruited from 36 schools participating in TAAG at field centers located in Baltimore, MD; Minneapolis/St. Paul, MN; Columbia, SC; Tucson, AZ; San Diego, CA; and New Orleans, LA; in the fall/winter of 2003. A random sample of 60 girls was targeted for recruitment from each of the 36 schools. Girls in the sample, who were unable to read and understand English, had been told by a physician to avoid physical activity, or had other medical contraindications, were considered ineligible and were replaced by other eligible girls in the school. Each participating institution's human subjects review board approved the study protocol. Written consent to participate was obtained from one parent, and assent was obtained from each participant. Parental consent and student assent were obtained for eligible girls for an average recruitment rate of 80% for the baseline sixth-grade sample and 89% of the original sixth-grade cohort consented for eighth-grade measurement (31). We could not investigate walking to or from school in the sixth-grade cohort or examine change in walking status longitudinally because these particular questions were only included on the eighth-grade questionnaire. Therefore, this study examined eighth-grade girls (n = 3443). From this original sample, we were able to geocode 2952 addresses; from these 2952 girls, only those living 1.5 miles or less from school were included in the final analyses (n = 890). Girls living more than 1.5 miles from school were deemed to live too far to have the "opportunity" to walk to or from school and were excluded. The distance between the girls' homes and their schools was calculated using ArcGIS (Redlands, CA).
Data collectors participated in a centralized training to ensure standardization of survey administration procedures across all 36 study schools in the six field sites.
Any Walking to or from School during Weekday Variable Description
The mode of transportation to and from school was assessed by asking the following question: During the last 7 d not including today, how many days did you walk A) to school? B) from school? Response options for both questions ranged from 0 to 5 d. These answers were reduced to a variable representing any weekday walking trips to or from school. Frequency of biking was also assessed; however, less than 1% of participants reported biking, so this was not included in our definition.
Perceived Neighborhood Variable Descriptions
Perceived neighborhood environment questions were taken from a 10-item questionnaire that asked about safety (e.g., safe to walk or jog in neighborhood, see walkers/bikers from homes on street, traffic, crime, other children playing outdoors, lighting); aesthetics (i.e., having many interesting things to look at in the neighborhood); and access to facilities near home (e.g., places to walk to from home, sidewalks, trails) (6). Response options for each item were on a five-point scale (disagree a lot, disagree a little, neither agree or disagree, agree a little, or agree a lot). Two-week test-retest reliabilities, using the five-level responses with a separate sample of middle school girls, ranged from 0.37 to 0.58 (weighted k coefficients) for these items (6). For these analyses, five-level responses were collapsed into three categories: disagree, neither, and agree. One indicator was set equal to 1 for girls who "agreed" or "strongly agreed" with perception and for girls who "neither agreed nor disagreed" with each statement. The baseline comparison was girls who "disagreed" or "disagreed strongly." Traffic and crime items were reverse coded.
Perception of facilities (perceived active destinations) located near home was measured by asking girls to respond to a list of 14 facilities, which had been identified during formative work to be active recreation places common to middle school girls in these 36 communities (9). The following questions was asked: "Is it easy to get to and from this place from home or school?" (yes or no). To match the perceived destinations to the objectively measured destinations, only 8 of the original 14 destinations were used in our perceived destinations measure. The facilities listed include basketball courts, golf courses, martial arts studios, parks, tracks, swimming pools, walking/biking trails, and dance studios. These were scored by adding the total number of facilities or destinations to which the participant could easily get to (range = 0-9) (7). The reliability of these measures is reported elsewhere (6,7).
Objective Neighborhood Variable Descriptions
We geocoded the TAAG participants' addresses using ArcGIS software and created a neighborhood, defined as the area within a 0.5-mile street network buffer surrounding each girl's home. By combining secondary data sources, we created several objective measures for each neighborhood.
Neighborhood socioeconomic status.
Socioeconomic status (SES) of the neighborhood was defined with the Townsend Index (34), using data from the 2000 US Census. The components of the Townsend Index are the percentage of the workforce who is unemployed, average persons per household, percentage of houses not owner-occupied, and percentage of households with no vehicle. The component values for a specific girl were interpolated proportionally from the block group data (weighted average of the component values across the block groups within a 0.5-mile street network buffer around the girl's home, with weights proportional to the block group's area in the buffer). Each of the four component scores was standardized (z scores) and summed to form the Townsend Index, with higher values of the index representing greater levels of material deprivation (e.g., lower neighborhood SES).
Using street centerline data from the US Census Bureau (TIGER), we created three network connectivity indices for each girl's neighborhood. The neighborhood buffers were overlaid on the street centerline data, and we selected the streets and intersections that fell within the street network buffer to compute the individual indices. There are several measures of street connectivity that are based on ratios of street segments and intersections (4). We computed three types of measures. The α index measures the proportion of circuits or loops within a network relative to the maximum number of routes possible, given the number of intersections. The β index is a simple ratio of intersections to street segments. The γ index is the proportion of street segments relative to the maximum possible given the number of intersections. All three of these indices are measures for how connected a network is (2,4,37). Because the individual indices were strongly correlated, but measured slightly different aspects of connectivity, we standardized and combined them together into a single factor (Cronbach α = 0.99).
In addition to the connectivity of the street network within a neighborhood, block size was also computed. Using the street centerline data, three measures of block size were computed and combined together into a single factor. The measures were average street length, the average area within a block, and the average block perimeter (3,4). The block size factor combined these three measures (Cronbach α = 0.95). Block size was hypothesized to measure different characteristics than street connectivity because smaller block sizes may be more appealing for walkers than larger block sizes.
Using the US Census block groups, the total population within each neighborhood was computed from year 2000 census data by taking the weighted average of the component populations across the block groups within a 0.5-mile buffer around the girl's home, with weights proportional to the block group's area in the street network buffer. We calculated the density by dividing this total by the area of the street network buffer (∼1.57 miles2).
Percent African American and Hispanic.
In addition to computing the total population within each neighborhood, we also computed the total African American and Hispanic populations from the 2000 US Census block group data. These were used to calculate the percent African American and percent Hispanic within each 0.5-mile street network buffer.
Land use mix.
A land use mix measure was computed for each neighborhood from national land use and land cover-classified satellite imagery. The measure used was the Simpson Index of Diversity that captures the diversity of land use and land cover within the neighborhood. This index is commonly used in ecological studies as well as in economic studies, where it is also called the Herfindahl Index (26). It was calculated as 1 minus the chance that 2 plots of land have the same land use value for each buffer. Thus, higher values correspond to greater diversity.
To identify physical activity destinations within a neighborhood, both InfoUSA and an online yellow page site (www.smartpages.com) were used to identify possible businesses appropriate for physical activity within zip codes near a girl's address. Both sources were then compared, and businesses that appeared in only one of the two sources were called for verification. Both sources provided address data that were used to geocode the businesses and identify those that fell within 1 mile of each girl's address. Eight types of destinations were common to both lists and the perceived measure: basketball courts, golf courses, martial arts studios, parks, tracks, swimming pools, walking/biking trails, and dance studios. A composite variable was created for each girl, which was the sum of the nine destinations (from InfoUSA or yellow pages) that matched the perceived destinations from the student survey.
The data in these analyses had a hierarchical structure, with girls nested within schools and schools nested within sites. As such, mixed-effects logistic models were used to analyze the relationships between individual perceptions and measures of neighborhood characteristics and binary walking to school outcomes while accounting for the correlations at each level of nesting by treating school and site as random effects. The neighborhood was not treated as an additional level of nesting because there was only one observation for each neighborhood defined as the 0.5-mile street network buffer around each girl's home. Treatment (intervention) effects were accounted for in all analytic models because this cohort participated in the group randomized intervention trial. All analyses were conducted using the xtmelogit procedure in Stata version 10 (College Station, TX).
We analyzed the relationship between each of our measures individually and walking to school. In addition, we combined perceived, objective, and sociodemographic variables into a single multivariate model to determine the contribution of each of the perceived and objective variables to any active commuting (walking to or from school) during the weekday for girls living within 1.5 miles of school. Of the 890 girls living within 1.5 miles of school, 500 (56.2%) walked to or from school at some point during the past week. The Wald χ2 test and the percent correctly predicted were analyzed as fit indices for the combined model. In addition to the destination measures, the models included controls for the neighborhood perception measures as well as the objective neighborhood measures and race/ethnicity of the girls.
The primary outcome for these analyses was self-reported walking to or from school on any weekday (56% of the sample reported "yes"; Table 1). Most girls agreed that they have places they like to walk (65%), have sidewalks (68%), have safe places to walk to (70%), see walkers/bikers from home (65%), often see kids playing (63%), and disagreed that there is too much traffic (69%) or a lot of crime (70%). Biking/walking trails (48%), interesting things to look at (44%), and well-lit streets (43%) were less common. The total perceived and actual active destinations were similar (mean = 4.62 and 4.06, respectively). Most girls lived about 1 mile (0.92 mile) from school.
For girls who lived within 1.5 miles of their school, the average surrounding neighborhood was 16.9% African American and 6.3% Hispanic, with a population density of 2265.8 people per square mile (Table 1). The average land use index was 0.45, meaning that these neighborhoods were relatively diverse, with a 45% chance that two randomly sampled plots will have the same land use value. The sample of girls was racially and ethnically diverse with 42% white, 21% African American, and 25% Hispanic.
Walking to/from school was associated with several perceived and objective neighborhood variables in the unadjusted analysis comparing those who walk to/from school and those who do not (Table 2). Perceived measures related to walking to/from school included students' reporting they agreed that there were "places they like to walk" and disagreed that there was "a lot of crime" in their neighborhood. Objective neighborhood measures related to walking to/from school included more actual activity destinations, shorter distance to school, less neighborhood deprivation (higher SES) measured by the Townsend Index, higher percent Hispanic residents, lower population density, higher street connectivity, and smaller block size.
When combining perceived, objective, and sociodemographic variables in a nested mixed-effects logistic regression model (Table 3), several measures remained as significant predictors of weekday walking to or from school. Girls who perceived that they had places to walk and who perceived their neighborhood was safe were nearly twice as likely to walk to or from school compared with girls who did not. For the objective measures, girls with more total destinations available in their neighborhood were almost 1.4 times as likely to walk to or from school. Girls from neighborhoods with larger block sizes were 20% less likely to walk. Girls from neighborhoods with a higher percentage of Hispanics were slightly more likely to walk to or from school.
In addition to estimates of the relationship between our measures and the probability of walking to or from school, Table 3 includes information on the structure and fit of our model. The random effects estimates at the bottom of the table provide information on whether the model specification is appropriate. These are estimates of the variation around the intercept that can be explained by each of the nesting levels. When the population was restricted to girls who live within 1.5 miles of school, that variation is estimated to be zero. Variation due to school is still significant, however, suggesting that a model with random effects for school (but not site) is necessary. There is also evidence that the models fit the data well. The Wald χ2 statistics indicates that our model is a significant improvement over a model with just an intercept. Furthermore, comparing predicted and actual choices suggests that the model fits the data well. There is slightly more than 75% agreement between a girl's predicted choice to walk to or from school and her actual choice.
Whereas conceptual models have hypothesized direct relationships with active commuting for perception variables, this study provides evidence that some individual perceptions and objective design features were associated with walking to or from school in this sample of adolescent girls. For this study, girls who lived in closer proximity to school were selected because they were more likely to have a choice in whether to commute to school actively or not. The SES measure (Townsend Index of deprivation) used in this study showed in the multivariate model that girls from higher SES (lower deprivation) neighborhoods were more likely to walk to or from school on weekdays. Neighborhood ethnic composition was significantly associated with walking to or from school. Girls from neighborhoods that contained a higher percentage of Hispanic residents were slightly more likely to walk to or from school. This may be related to unmeasured variables such as fewer transportation resources and social or cultural factors such as family approval or peer group perceptions (18,20). Future studies have the opportunity to investigate the impact of these unmeasured variables. Other studies investigating neighborhood SES and active commuting to school have had either null or positive results depending on the population (12,33).
Our findings that perceived safety was an independent predictor of walking to/from school is consistent with recent reviews (5,7,35). Another study suggests that "concerns about strangers" are a major barrier for parents and may have particular salience for adolescent girls, as was recently found in an urban sample of older male and female adolescents (27). A recent study in Australian adolescents confirms similar findings to our study (1). Similarly, neighborhood SES also relates to safety in that parents who live in higher SES neighborhoods that are perceived to be safe may be more likely to allow their child to walk to school. Neighborhood resources and block size were also related to active commuting in our study, where girls with more physical activity destinations near home and those from neighborhoods with smaller block sizes were nearly twice as likely to walk to or from school compared with girls with few destinations and larger block sizes. In addition, participants' perceptions of "places they like to walk" were also a significant independent predictor and consistent with the findings for total destinations. The objective measure of total active destinations and the perceived measure of places girls like to walk may be similar, but capturing different things as the objective measure includes business establishments and the perceived measure may include places such as friends' houses. The girls who were more likely to actively commute to school also lived in neighborhoods with smaller street blocks (shorter street segments, smaller total area within the block, and shorter perimeters) in their neighborhood, which is supported in the urban planning literature as a desirable design feature for walking (35). Distance to school was also a significant deterrent to walking to or from school in this study and, as a previous TAAG study showed, reduced the potential for moderate-to-vigorous physical activity (2).
Strengths and limitations.
This is one of the first studies to report both on multiple-level factors assessed from the adolescents' perspective and on objective factors in the neighborhood environment related to walking to or from school, in a large, geographically diverse sample of adolescents. The geographic and racial/ethnic diversity and the low dropout rate of the cohort add to the generalizability of our results and reduce possible selection bias. However, our final sample included only girls who live close to school, and this may be considered a limitation. The sample includes girls who had the "opportunity" to walk. Beyond the objectively measured environmental features, we relied on self-reported walking behavior and perceptions of the neighborhood or environment. Other studies have reported or hypothesized some of these factors as supportive of walking in adult populations (5,22,24); however, few have reported on these objective and perceived relationships in adolescents, specifically related to walking behavior (1,12). The factors we investigated, such as safety, aesthetics, and neighborhood design features, have particular salience for walking to and from school. Many of these environmental features may not have any relationship to higher intensity levels of weekday "out-of-school" physical activity, which may be performed at a specific location (e.g., playing fields, indoor gyms) rather than near home or school. This impact may be an underestimate because all of our independent GIS variables (e.g., street connectivity, density, SES) were calculated within 1 mile of home, missing a portion of the total distance for girls in some schools who may have walked more than 1 mile. The reason for this is that the majority of school busing policies relate to students who live greater than 1 mile from school; those who live within 1 mile or less are expected to find private transportation (walk or parent/guardian transport).
Using GIS methods could further specify destinations that are on the travel route to school. Also, there may be other perceived or objective variables not measured, as well as stronger measures of individual or family SES. Longitudinal studies are needed to explore temporal relationships, and other studies could include biking separately because other neighborhood design features may relate specifically to biking versus walking to and from school.
Although this study is cross-sectional, and findings from this study will need to be confirmed in larger, longitudinal studies, we, nonetheless, found several important measures associated with walking to or from school. The findings, once confirmed, may have policy implications similar to those recently discussed by McMillan (19) and Tester (32), such as expanding initiatives already underway to create and maintain safe routes to school as well as considering new construction that incorporates features of neighborhood design by limiting block size in new housing developments, considering local policies that encourage higher density residential development close to schools, or building schools in the middle of neighborhoods. In addition, policy efforts are underway to increase mixed use development in residential neighborhoods to facilitate walking for utilitarian purposes or pleasure. Despite the built environment initiatives underway, safety, land use, and school location issues could be considered together because intervening on one without the other will not be likely to change walking behavior.
This work was funded by National Institutes of Health/NHLBI grant No. R01HL71244 (Deborah Cohen, PI) and by TAAG grants U01HL66845, U01HL66852, U01HL66853, U01HL66855, U01HL66856, U01HL66857, and U01HL66858.
We thank the girls who participated in the study, the project and measurement coordinators for participant recruitment and measurement, and the members of TAAG Steering Committee.
Results of the present study do not constitute endorsement by the American College of Sports Medicine.
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