For individual use only. Duplication or distribution prohibited by law.

Active Living; Youth

Effect of a School Choice Policy Change on Active Commuting to Elementary School John R. Sirard, PhD; Kelsey McDonald, PhD; Patrick Mustain, MPH, MA; Whitney Hogan, MPH; Alison Helm, MPH Abstract Purpose. The purposes of this study were to assess the effect of restricting school choice on changes in travel distance to school and transportation mode for elementary school students. Design. Study design was pre-post (spring 2010–fall 2010) quasi-experimental. Setting. Study setting was all public elementary schools in Minneapolis, Minnesota. Subjects. Subjects comprised approximately 20,500 students across 39 schools. Intervention. Study assessed a school choice policy change that restricted school choice to a school closer to the family’s home. Measures. School district transportation data were used to determine distance to school. Direct observations of student travel modes (two morning and two afternoon commutes at each time point) were used to assess transportation mode. Analysis. Chi-square and independent-sample t-tests were calculated to describe the schools. Repeated measures general linear models were used to assess changes in travel distance to school and observed commuting behavior. Results. Distance to school significantly decreased (1.83 6 .48 miles to 1.74 6 .46 miles; p ¼ .002). We failed to observe any significant changes in morning (þ.7%) or afternoon (.7%) active commuting (both p ¼ .08) or the number of automobiles in the morning (7 autos per school; p ¼ .06) or afternoon (þ3 autos per school; p ¼ .14). Conclusion. The more restrictive school choice policy decreased distance to school but had no significant effect on active commuting. Policy interventions designed to increase active commuting to school may require additional time to gain traction and programmatic support to induce changes in behavior. (Am J Health Promot 2015;30[1]:28–35.) Key Words: Physical Activity, Transportation, Children, Neighborhoods, Prevention Research. Manuscript format: research; Research purpose: policy intervention testing; Study design: quasi-experimental; Outcome measure: behavioral; Setting: school; Health focus: physical activity; Strategy: policy; Target population age: youth; Target population circumstances: education/income level, geographic location, and race/ethnicity

PURPOSE Several U.S.1–6 and international7–11 studies have demonstrated that students who walk to school are more physically active than those who do not. Although some differences are noted by gender and age stratifications, the evidence consistently supports the hypothesis that actively commuting students are more physically active than non–active commuters, independent of other sources of physical activity, and across study locations and methodologies. Therefore, exploring opportunities to increase the number of children actively commuting to school may help increase youth physical activity levels.12 In the United States, recent studies indicate that automobiles and school buses account for 50% and 32% of trips to school, respectively; walking and bicycling accounted for only 10% and 3%, respectively.13 Using the National Personal Transportation Survey to track school travel modes from 1969 to 2001, a recent study reported that active commuting (AC) to school

John R. Sirard, PhD; Kelsey McDonald, PhD; Patrick Mustain, MPH, MA; Whitney Hogan, MPH; and Alison Helm, MPH, are with the Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota. John R. Sirard, PhD, is with the Department of Kinesiology, School of Public Health and Health Sciences, University of Massachusetts Amherst. Kelsey McDonald, PhD, is with the Centre for Urban Epidemiology, Institute for Medical Informatics, Biometry and Epidemiology, University Clinic Essen, University of DuisburgEssen, Essen, Germany. Patrick Mustain, MPH, MA, is with the Yale Rudd Center for Food Policy and Obesity, Yale University, New Haven, Connecticut. Whitney Hogan, MPH, is with the Peer Health Program, Bowdoin College, Brunswick, Maine. Alison Helm, MPH, is with MN Community Measurement, Minneapolis, Minnesota. Send reprint requests to John R. Sirard, PhD, Department of Kinesiology, School of Public Health and Health Sciences, University of Massachusetts Amherst, 110 Totman Building, 30 Eastman Lane, Amherst, MA 01003; [email protected]. This manuscript was submitted May 10, 2013; revisions were requested September 30, 2013 and January 24, 2014; the manuscript was accepted for publication April 2, 2014. Copyright Ó 2015 by American Journal of Health Promotion, Inc. 0890-1171/15/$5.00 þ 0 DOI: 10.4278/ajhp.130510-QUAN-236

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For individual use only. Duplication or distribution prohibited by law. decreased from 40.7% to 12.9%, whereas automobile commuting increased from 17.1% to 55.0%. For elementary schools the percent of AC trips was 15%.14 The low levels of AC have persisted through 2009 (12.7% overall; 13.1% for elementary school students).15 Consistent use of an automobile to transport children from place to place may be necessary because of safety, long travel distances, and time constraints. However, the use of auto transportation for trips that could be completed safely by walking or bicycling may establish a pattern of automobile dependence that could carry over to adolescence and adulthood.16 Reducing this dependence at an early age may establish a lifelong pattern of active transportation that could contribute to maintaining or regaining an appropriate balance between energy intake and expenditure.17 The strongest and most consistent factor associated with AC to school is the commute distance.14,17–25 However, school district policies designed to increase the racial and ethnic diversity of schools, and federal mandates stressing school accountability (e.g., ‘‘No Child Left Behind Act’’) have steered some school districts toward a system in which families can choose a school that is not necessarily the closest (neighborhood) school. In 2007, 46% of students in the United States had some type of school choice available to them, ranging from 33% in the Northeast to 55% in the West. Among those with school choice, approximately 25% of families chose a public school that was not their designated school.26 Therefore, the issue of school choice affects many families across the country. Although these policies are designed to encourage diversity and school accountability, they are at odds with a call from active school commuting advocates for a return to more traditional neighborhood schools.2 Also, the increased busing costs associated with broad school choice policies are often untenable given the budget deficits currently faced by many municipalities. Often, distance to school is considered in a context of new school location, with schools in closer proximity to students’ homes associated

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with shorter travel distances. Another method of decreasing school travel distance in urban centers might be via large-scale policy change to restrict school choice, encouraging more children to attend their neighborhood school, possibly increasing active transportation to school.27–29 A recent cross-sectional study observed a significant association between school choice and travel mode: students attending magnet schools (further from their homes) used bus or auto transportation in higher proportions than students in nonmagnet schools.30 The purpose of this study was to expand on those previous cross-sectional findings by assessing the effect of a more restrictive school choice policy on changes in (1) travel distance to school for students in Minneapolis kindergarten through fifth grade (K-5) and kindergarten through eighth grade (K8) schools, and (2) prevalence of AC to Minneapolis K-5 and K-8 schools. We hypothesized that travel distance to school would decrease and the proportion of students actively commuting would increase following implementation of the new school choice policy.

METHODS Design This was a quasi-experimental study with the goal of observing distance and AC changes in all K-5 and K-8 public schools affected by a more restrictive school choice policy. Baseline measures were collected before the policy change and then again after the policy change had gone into effect. No similar school district was available to serve as a control group. Sample All public schools located within the Minneapolis Public School District encompassing the fifth grade were recruited to participate in the study. This included all K-5 and K-8 schools (N ¼ 44). After obtaining school district approval, the principals and transportation coordinators of each school were contacted via letter, phone, and e-mail in order to acquaint them with the study. Project staff also visited each school to introduce the study and answer questions. A letter describing the study along with staff

contact information was provided in the district-wide parent newsletter. A total of 44 schools were invited to participate. Passive consent was required for a school’s participation, meaning administrators could opt out of the study, but no action was required to be included. Four schools were excluded from the study: (1) FA school is a magnet school and includes students from other districts; (2) HC school was excluded at the principal’s request; (3) RB school only serves students with specific emotional and behavioral needs; and (4) MM school serves students in grades kindergarten through 12 and would have been the only school with older adolescent students capable of driving themselves to and from school. In addition, two schools (AA and AS) shared the same building and had the same arrival and dismissal times. For our purposes, these schools were considered one school, and all students from AA and AS were combined and counted as attending AS. This study was reviewed and approved by the University of Minnesota’s Institutional Review Board, the Minneapolis Public School District, and by the individual principals at each school. Measures School Observations. Observations were completed in the spring and fall of 2010, before and after the policy change went into effect. At baseline and follow-up, each school was observed four times: twice during the morning arrival and twice during the afternoon dismissal. To limit the effects that unfavorable weather might have on student commuting habits, the observations were performed toward the end of the spring semester (April– May 2010), and in the first 2 months of the fall semester (mid-September–midNovember 2010). Furthermore, observations were canceled on days with heavy rain and rescheduled if possible. Fall observations began 2 weeks after the new school year began in order to give families and students time to develop an established method of commuting. Parents were notified through school Web sites and weekly newsletters (including Spanish, Hmong, and Somali translations) that observations would be conducted, but

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For individual use only. Duplication or distribution prohibited by law. the exact dates of the observations were not provided, so that commuting mode would not be affected by the expected presence of the observers. No observations were performed on International Walk to School Day. Also, most schools had after-school programs that prevented some students from being counted during the afternoon commute. It was assumed that since these students would need to be signed out by a parent/guardian, most would be traveling from school by automobile. The observation method used in this study to measure commuting behaviors has been used in a previous study.31 Detailed maps of each school and the immediate surrounding area were obtained using Google maps (https:// maps.google.com). Research assistants visited each school prior to observation to (1) talk with the transportation coordinator regarding AC practices and to identify available entrances and any paths or means of access to the school that were not readily apparent, and (2) walk the perimeter of the school grounds (including the perimeter of playgrounds and playing fields) to determine where to place observers and how many would be needed. The intention was to provide observers with the best sight lines for all possible ways of getting to the school’s entry points. Still, if a parent parked his or her car around a corner and then walked with his or her child or dropped the child off at that point, the child would have been counted as walking. Although this is certainly possible, anecdotal reports from observers indicate that there was little suspicion of this actually happening. Any errors introduced from such activities are believed to have been very minimal. A team of two to six trained research staff observed distinct locations around each school, making sure to cover auto and bus drop-off/pick-up areas as well as all possible walking/bicycling routes. In the mornings, observation teams were in place 30 minutes before school started and continued to count the late arrivals until 15 minutes after school started. In the afternoons, observers were in place 15 minutes before school dismissal and remained 30 minutes afterward. Each observer was assigned to observe a specific geographic area

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around the school. The method of transportation for students arriving or leaving school was recorded on tally sheets. Individual students getting on and off buses were not counted because of time constraints. Following observations, each observation sheet was tallied by two different research staff and then entered in duplicate into a database. Any inconsistencies between data inputs were investigated and corrected. The variables recorded included the number of students walking, number of students biking (this included rollerblades, scooters, and skateboards), number of students dropped off/picked up by cars, total number of cars, and number of buses. The temperature (degrees Celsius) and weather (coded as sunny, cloudy, light rain, snow) were also recorded at each observation. Transportation Survey. To account for factors not related to the policy change that might affect the prevalence of AC, a school transportation survey was developed to assess new school-level programs and infrastructure developments related to AC. Surveys were mailed on two separate occasions in fall 2010 to each school’s transportation coordinator, along with an explanatory letter describing the purpose of the survey, and an addressed and stamped return envelope. Following multiple e-mail reminders and in-person visits to schools, 32 of the 39 participating schools (82%) returned surveys. Eight questions asked whether the school participated in AC promotional and educational programs, such as ‘‘International Walk to School Day’’ and ‘‘Safety training (for walkers, cyclists, drivers, etc).’’ Nine questions asked whether there were infrastructure projects at or around the school, such as ‘‘Paint/repaint crosswalks,’’ ‘‘Building of sidewalks or paths,’’ and ‘‘installation of stop signs.’’ The survey asked the respondents to report on the presence of these programs and infrastructure projects that occurred in the past (spring 2010), were occurring in the present (fall 2010), and were projected for the future (spring 2011). The presence of programs and infrastructure developments was coded as binomial variables that were summed to provide a score for new features in

fall 2010. Based on the distribution of scores, schools were dichotomized as instituting at least one new AC effort versus no new efforts (1 vs. 0). Distance to School. Distance to school was obtained from the school district’s transportation database. Distance was calculated based on travel along the street network between the school and each student’s home address. To ensure the accuracy of these distance values, a random sample of 500 students was chosen from the database in the spring and fall. For these random samples the distance to school along the street network for each student was calculated using geographic information system software (ArcGIS 9.3, Esri, Redlands, California). In both spring and fall, there was no significant difference between the distances from the two methods (spring: t ¼ .265, p ¼ .86; fall: t ¼ .131, p ¼ .90). Therefore, the school district’s database was used to investigate changes in distance to school following the school choice policy change. In addition, we also calculated the percent of students at each school within four walk zones: .25 miles, .50 miles, 1.0 mile, and 2.0 miles. School Characteristics. School-level demographics were obtained through the school district Web site and included enrollment, race/ethnicity distribution (70% minority was considered a high-minority school), and percent of students eligible for free or reduced school meals (70% eligible was considered a lower-income school). Intervention The intervention for this study was a policy change implemented in a large, urban school district. The more restrictive school choice policy change went into effect during the summer of 2010. Prior to this change, the Minneapolis school district followed a full school choice model, meaning that parents rank order the schools they would like their child to attend for the upcoming school year, regardless of the schools’ proximity to their home. If there is room at the first choice, the child is enrolled in that school and provided bus transportation if requested by the parents. In fall 2010 the school district moved to a more limited

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Table 1 School-Level Demographics for All Study Schools (N ¼ 39) and by Geographic Zone Zone (Fall 2010) 1 (North; n ¼ 13)

All Schools

2 (Southeast; n ¼ 10)

3 (Southwest; n ¼ 16)

Variable

Spring 2010

Fall 2010

Spring 2010

Fall 2010

Spring 2010

Fall 2010

Spring 2010

Fall 2010

Mean enrollment (SD) High minority, %*† High free lunch, %*†

518 (206) 61.5 48.7

527 (192) 53.8 48.7

464 (126) 84.6 76.9

473 (116) 76.9 76.9

552 (331) 60.0 40.0

547 (298) 50.0 40.0

541 (158) 43.8 31.3

558 (161) 37.5 31.3

* High-minority schools are those where .70% of students identified as nonwhite. High free lunch schools are those where .70% of students identified as qualifying for free/reduced-price lunch. † Significant difference among zones for both time points, Mantel-Haenszel v2 p  0.04.

choice policy. The city was divided into three zones (zone 1 ¼ North, zone 2 ¼ Southeast, and zone 3 ¼ Southwest), and parents chose a school within their home zone. Parents could still choose a school outside their zone if they provided transportation for their child. Public magnet schools (public schools offering special instruction unavailable elsewhere, designed to attract a diverse student body) also continued to enroll students from across the city. Based on school district characteristics in the spring, this policy change was estimated to affect 23% of the student population, approximately 4715 students (Minneapolis Public Schools, unpublished data, 2009). Analysis School-level descriptive statistics (mean 6 SD) were calculated for all schools combined; differences between spring and fall 2010 (before and after the policy change) were tested using a dependent t-test for mean enrollment and v2 for percent of high-minority schools and percent of low-income schools. The same descriptive statistics were calculated stratified by the new geographic zones used to divide the city according to the new policy implementation. Repeated measures general linear models were used to identify any changes in distance to school, the percent of students within specified walk zones, and observed changes in commuting behavior, across all schools and by geographic zone. Models for observed commuting behavior also controlled for new programs and environmental features that could affect AC to school. To examine potential sensitivity to nonnormal dis-

American Journal of Health Promotion

tributions and influence of extreme values, medians for distance to school are also reported. The signed rank test was calculated to corroborate the general linear model analyses for differences between the two time points and changes in distance within geographic zones. Statistical Analysis Software (version 9.2, SAS Institute Inc, Cary, North Carolina) was used for all analyses. Analyses were considered significant using p  .05.

RESULTS At both time points, school-level demographic variables and those for distance to school, observed AC, and observed automobile traffic approximated the normal distribution. Schoollevel demographics at each time point and by geographic zone are presented in Table 1. Mean school enrollment, across all schools, did not significantly change from spring 2010 (N ¼ 518 6 206 students per school) to fall 2010 (N ¼ 527 6 192 students per school; dependent t-test ¼ 1.37, p ¼ .179; Table 1). School-level high-minority status did not significantly change from spring (n ¼ 24 schools) to fall (n ¼ 21 schools) 2010 (McNemar v2 ¼ 3.0, p ¼ .083), and the number of schools with a high percentage of students qualifying for free or reduced-price lunch did not change during the study time frame (n ¼ 19 schools at both time points). Average enrollment numbers per school were not significantly different among the three zones (p ¼ .473), but zone 1 (‘‘North’’) had a significantly greater proportion of high-minority and low-income schools

compared with the other zones (Mantel-Haenszel v2 ¼ 4.30, p ¼ .038 for high-minority status, and v2 ¼ 5.65, p ¼ .017 for low-income status) at both time points. Therefore, this policy change does not appear to have differentially affected ethnic and economic diversity in these elementary schools. Across all schools, mean distance to school significantly decreased (p ¼ .002) from spring 2010 (1.83 6 .48 miles) to fall 2010 (1.74 6 .46 miles; Table 2). There was no significant interaction with geographic zone, indicating that the decrease in distance to school was similar across zones. Our analyses using medians and the signed rank test also found a statistically significant decrease in distance to school across all schools (p ¼ .004), but also found a significant decrease in zone 3 (zone 1, p ¼ .17; zone 2, p ¼ .07; and zone 3, p ¼ .03). The percent of students across all schools within the various walk zones changed in the hypothesized direction (i.e., schools had more students within the shorter walk zones in the fall), but only the change in distance for the largest walk zone (within 2 miles of school) was statistically significant (p ¼ .004). No significant interaction with geographic zone was observed, indicating that the increase in the percentage of students living within 2 miles of their school was relatively similar across all three zones. Changes in directly observed transportation modes are presented in Table 3. From spring to fall there was no significant change in the proportion of students actively commuting overall for morning and afternoon

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Table 2 Distance to School Across All Schools and by Geographic Zones Zone 1 (North; n ¼ 13)

All Schools Variable Distance to school, miles Mean (SD)* Median (95% confidence interval)† Percent within walk zone 0.25 miles 0.50 miles 1.00 mile 2.00 miles*

2 (Southeast; n ¼ 10)

Spring 2010

Fall 2010

Spring 2010

Fall 2010

Spring 2010

Fall 2010

1.83 (0.48) 1.81 (1.68–1.99)

1.74 (0.46) 1.67 (1.59–1.89)

1.82 (0.32) 1.93 (1.63–2.01)

1.75 (0.27) 1.68 (1.58–1.91)

1.91 (0.55) 2.12 (1.52–2.31)

1.75 (0.49) 1.92 (1.41–2.10)

3.6 12.3 31.7 64.2

3.8 12.8 32.8 67.3

3.1 10.3 29.3 64.6

2.9 10.2 29.1 66.5

3.4 12.8 30.6 60.8

3.8 13.3 32.4 65.7

(2.3) (6.6) (14.5) (18.8)

(2.7) (7.4) (16.0) (18.3)

(1.5) (4.1) (8.9) (13.3)

(1.5) (2.9) (7.6) (12.0)

(1.9) (7.5) (18.7) (23.0)

(2.3) (7.5) (19.7) (20.1)

* Significant difference between spring and fall across all schools; dependent t-test p  0.004. † Significant difference between spring and fall across all schools; signed rank test p , 0.004. ‡ Significant difference between spring and fall for zone 3; signed rank test p ¼ 0.03.

combined (.2%, p ¼ .51), as well as separately in the morning (þ.7%, p ¼ .08) or in the afternoon (.7%, p ¼ .08). A similar pattern was observed when separating AC into walking and bicycling. In addition, there was no significant change in the number of automobiles in the morning (7 autos per school, p ¼ .06) or in the afternoon

(þ3 autos per school, p ¼ .14). Although the observation of seven fewer automobiles in the morning was not statistically significant, such a small change, if real, could have a meaningful impact at the district level. For example, an average reduction of seven automobiles during the morning commute would translate to 273 fewer

cars per day dropping off children across all 39 schools. Furthermore, these results may have been attenuated by lower recorded temperatures during morning and afternoon commutes in the fall compared with the spring (spring morning, 10.58C 6 3.38C; fall morning, 8.68C 6 3.28C, p ¼ .008;

Table 3 Changes in Directly Observed Transportation Modes From Spring to Fall 2010 Across All Schools (N ¼ 39) and by Geographic Zone; Mean (SD)* Zone All Schools Variable Students actively commuting, % Morning Afternoon Students walking, % Morning Afternoon Students bicycling, % Morning Afternoon† No. of automobiles, mean (SD) Morning Afternoon Students traveling by auto, % Morning Afternoon Schools with new active commuting efforts, %

1 (North; n ¼ 13)

2 (Southeast; n ¼ 10)

3 (Southwest; n ¼ 16)

Spring 2010

Fall 2010

Spring 2010

Fall 2010

Spring 2010

Fall 2010

Spring 2010

Fall 2010

9.9 (6.7) 9.1 (5.7) 10.7 (8.3) 8.7 (5.6) 8.0 (4.9) 9.4 (6.9) 1.2 (1.8) 1.1 (1.6) 1.3 (2.2) 122 (57.3) 88 (48.2) 34 (12.3) 16.8 (5.9) 23.8 (9.6) 9.7 (3.6) —

9.6 (6.5) 9.8 (6.7) 9.4 (7.2) 8.7 (5.5) 8.7 (5.3) 8.8 (6.5) 0.9 (1.5) 1.1 (2.2) 0.6 (1.1) 118 (50.7) 81 (39.2) 36.7 (15.2) 16.4 (5.2) 22.5 (7.8) 10.3 (3.9) 51.3

7.7 (4.6) 6.9 (2.9) 8.4 (6.7) 7.0 (3.8) 6.6 (2.5) 7.5 (5.4) 0.6 (1.3) 0.3 (0.5) 0.9 (2.3) 98 (40.8) 67 (30.6) 30 (13.3) 15.7 (5.3) 21.7 (8.7) 9.7 (3.4) —

6.9 (3.5) 6.8 (3.5) 7.0 (3.8) 6.6 (3.3) 6.5 (3.4) 6.7 (3.6) 0.3 (0.4) 0.3 (0.5) 0.3 (0.5) 91 (33.6) 63 (22.6) 28 (12.7) 14.5 (4.6) 20.3 (6.7) 8.7 (3.5) 46.2

10.0 (6.6) 9.1 (6.1) 10.9 (7.7) 9.1 (6.1) 8.0 (5.5) 10.2 (7.3) 0.9 (1.0) 1.1 (1.4) 0.7 (0.8) 112 (53.2) 80 (45.3) 33 (12.2) 15.2 (4.2) 20.4 (5.1) 10.0 (4.5) —

10.9 (7.9) 9.8 (6.1) 12.0 (9.9) 10.0 (6.8) 8.8 (5.3) 11.1 (8.6) 0.9 (1.7) 1.0 (1.8) 0.9 (1.8) 112 (48.9) 77 (40.3) 35 (12.6) 15.6 (4.0) 20.5 (4.8) 10.7 (4.5) 50

11.7 (7.9) 10.9 (6.8) 12.5 (9.7) 9.9 (6.4) 9.2 (5.8) 10.5 (7.8) 1.8 (2.2) 1.7 (2.0) 2.0 (2.6) 148 (63.2) 111 (54.3) 38 (11.3) 18.6 (6.9) 27.5 (11.4) 9.6 (3.2) —

11.0 (7.1) 12.1 (8.2) 9.8 (7.2) 9.7 (5.9) 10.3 (6.3) 9.1 (6.5) 1.3 (1.8) 1.9 (2.9) 0.8 (1.0) 144 (52.7) 100 (42.4) 44 (15.5) 18.4 (5.8) 25.4 (9.5) 11.3 (3.5) 56.3

* All models control for the number of new active commuting efforts. † Significant difference between spring 2010 and fall 2010 across all schools, p  0.05.

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Table 2, Extended

Zone 3 (Southwest; n ¼16) Spring 2010

Fall 2010

1.79 (0.56) 1.71 (1.50–2.09)

1.72 (0.59) 1.54‡ (1.41–2.03)

4.2 13.5 34.2 66.1

(3.0) (7.6) (15.8) (20.7)

4.4 14.6 36.1 68.9

(3.4) (9.4) (18.6) (22.1)

spring afternoon, 19.78C 6 5.38C; fall afternoon, 16.08C 6 6.18C, p ¼ .004). The number of new AC environmental and programmatic efforts was similar across zones (mean 6 SD: zone 1, .8 6 1.1; zone 2, 1.3 6 1.8; zone 3, 1.3 6 1.6; p ¼ .62). Therefore there was no significant evidence indicating that changes in travel modes were due to differential changes in AC efforts among the geographic zones.

DISCUSSION According to the 2010 U.S. Census, there are more than 32 million children in U.S. elementary schools (http://www.census.gov/hhes/ school/data/cps/2010/tables.html). As mentioned in the Introduction, about 46% of these families will be in an area with school choice (~14.7 million students), and of those families, approximately 25% will choose a school that is not their neighborhood school (~3.7 million students).26 Therefore, strategies to promote attendance at more proximal schools, such as by restricting school choice to within a geographic zone, may provide a policy-level strategy to make AC for school a more realistic possibility for a large number of families while maintaining ethnic and economic diversity. The purpose of this study was to determine the impact of a more restrictive district-wide school choice policy on AC to elementary schools.

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The policy change was an effort to decrease district transportation costs by compelling parents to choose a more proximal school. It was estimated that approximately 23% of students would be affected by the school choice policy change, yet we observed only a small decrease in the average distance students lived from school, and no significant net increase or decrease in the percent of children actively commuting to school. There was no evidence to suggest that more programmatic and environmental efforts to support AC resulted in a greater increase in AC, likely because of the similar changes for the three geographic zones. Further, the changes in travel modes did not differ based on school-level characteristics, such as race/ethnicity distribution (high- vs. low-minority status) or economic disadvantage (high vs. low free school lunch eligibility). Distance to School. The policy change evaluated here did result in an overall decrease in distance to school (.09 miles), but, in contrast to previous cross-sectional research, this did not translate into a greater percentage of children actively commuting to or from school. The cross-sectional research suggests that distance to school is a primary predictor of AC behavior: children living closer to school are more likely to actively commute to school. This observation is true whether distance is objectively measured by GIS methods19,23,25 or based on parents’ perceptions.17,18,20–22 Research indicates that smaller, neighborhood schools built prior to the mid-1980s have greater proportions of AC students.24,27,28 New, larger schools are frequently built on the edge of a city or town because of lower land and maintenance costs. The increased distance and the lack of pedestrian infrastructure that accompany these new schools prevent students from actively commuting to them. In urban areas that are already developed, school siting has less relevance for distance to school, and the policy change evaluated in the current study may be one option to decrease school commute distances. One potential reason for the discrepancy between the cross-sectional studies and the current study could

be that although overall distance to school decreased, the percentage of children living within the shorter ‘‘walk zones’’ (.25, .50, and 1.0 miles from the school; Table 2) did not significantly change. Therefore, the small decrease in distance to school, across all students in the district, was not enough to induce the expected increase in AC. Transportation Mode and Changing Behavior. There was no net change in AC. Because of the timing of our postpolicy change assessment, the results might show the lower limit for such a policy change to alter behavior. The effect may continue to accrue as parents and students become more accustomed to their new school commute. Furthermore, some parents may have elected to keep their child in their old school (and provided their own transportation) after the policy change instead of transferring their child to a new school that might be within AC distance from their home. Until those students graduate from their current K-5 or K-8 school, the effect of this policy change may not be fully realized. Some parents may have switched schools but the decrease in the distance to school may not have been large enough to be within a perceived acceptable walking or bicycling distance. Additional efforts would be needed to overcome such a perception and entrenched pattern of behavior. The issue of changing the habitual morning and afternoon routines is important to consider when interpreting our results. In the spring, it might take families additional time to change from their winter commute pattern (i.e., more auto commuting) to a more active commute (e.g., have to get the bike out of the back of the garage and/ or find helmets, get in the habit of walking with other children). It is possible that this change in behavior does not happen until the very end of school, or even the start of summer. Additionally, students may have been walking and biking during the summer and continued those methods of travel into the fall. Such a behavior pattern, if it exists, would result in the current study overestimating the increase in AC to school. However, data collection occurred as late in the spring and as early in the fall as possible to avoid the

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For individual use only. Duplication or distribution prohibited by law. coldest weather conditions and the very start and end of the school year. Our results should also be considered in light of an unfavorable change in temperature and the amount of daylight (spring data collection was closer to the longest day of the year) from spring (pre) to fall (post). Therefore, the results of the current study may be underestimating the changes in AC if daylight and weather conditions were more stable. Conducting a study that measured AC 1 year apart would be needed to address these issues of seasonality, but that was not possible within the time frame of the current study. Indeed, with quasiexperimental research and large-scale policy changes, such as the one focused on here, a substantial lag may exist between policy implementation and observed change in individuallevel behaviors. Therefore, future studies investigating such policy changes should plan for periodic follow-up data collection, analysis, and evaluation efforts for up to several years after a new policy or environmental change goes into effect. Strengths and Limitations. The strengths of this study include the comprehensive scope of the project by incorporating almost all of the K-5 and K-8 schools within a large, urban school district. Distance to school was objectively measured and direct observation by trained research staff was used to record actual commuting behavior, rather than relying on self-reports or proxy reports of usual or past-week behavior. There are limitations to this study that need to be considered. This study was conducted in one northern Midwest city that previously had a very open school choice policy. Although most U.S. states offer some form of school choice, the effect of such a policy change in other districts and in other states is unclear. Most schools had after-school programs that prevented some students from being counted during the afternoon commute. No bus service was provided for students staying after school for extended-care programs or club activities. Although we hypothesize that most of these students would be picked up by automobile, it was not possible to

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obtain this information. Also, because we did not collect individual-level observation data, we are not able to determine who was changing his or her behavior and to what extent he or she changed. Observations were canceled (and rescheduled) on days with heavy rain (n ¼ 3 observations in the spring and n ¼ 4 observations in the fall). If fewer children actively commuted on these days, the true prevalence of AC at the observed schools may be slightly lower than reported here (spring, 9.9%; fall, 9.6%). Lastly, Minneapolis’ full school choice policy and busing policy may not be the norm, with many school districts opting for more limited choice programs and also limited, if any,

SO WHAT? Implications for Health Promotion Practitioners and Researchers What is already known on this topic? Shorter distance to school is the strongest correlate of active commuting to school. The ability to reduce travel distance to school is limited in urban areas. Restricting school choice is a potential tool for reducing travel distance, possibly allowing for more active commuting to school and promoting physical activity and other benefits. What does this article add? The more restrictive school choice policy was an effective means of decreasing distance to school. However, using multiple observations in nearly all elementary schools in the district, this policy change was not associated with increased active commuting or decreased automobile traffic. To observe significant changes in individual-level behavior, additional time and programmatic efforts may be needed for these types of policy and environmental changes to reach their full impact. What are the implications for health promotion practice or research? We demonstrated that a more restrictive school choice policy affected the strongest correlate of active commuting to school—distance. Other municipalities and school districts may use these results to support their efforts to limit school choice as a means of increasing active commuting to school while maintaining school diversity.

busing to those choice schools. Therefore, the generalizability of these findings may be limited. Still, the results of the current study may inform other districts that either have the same policies or are contemplating such full school choice and busing policies. Although such policies are in line with school accountability legislation, they also appear to sacrifice proximity to school. Conclusion. The current study observed a significant decrease in average distance to school, yet no net change in AC behavior following the implementation of a more restrictive school choice policy. The policy change resulted in limited segmentation of the school district into just three geographic zones, which allowed for continued diversity in the schools while also reducing transportation costs for the school district. Additional research is needed on policy changes at the district, city, and state levels that may have an impact on AC to school. However, our findings may help other policymakers, city planners, community leaders, and parents to adopt a similar policy change. Acknowledgments The authors wish to thank the Minneapolis Public Schools Research, Evaluation and Assessment Office and Transportation Office; the administrators, faculty, resource officers, parents, and students from the participating schools; and the Minneapolis Safe Routes Network. This study was funded by a grant from the Robert Wood Johnson Foundation’s Active Living Research Rapid Response funding program no. 67295.

References

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For individual use only. Duplication or distribution prohibited by law. 6. Sirard JR, Riner WF Jr, McIver KL, Pate RR. Physical activity and active commuting to elementary school. Med Sci Sports Exerc. 2005;37:2062–2069. 7. Alexander LM, Inchley J, Todd J, et al. The broader impact of walking to school among adolescents: seven day accelerometry based study. BMJ. 2005;331: 1061–1062. 8. Cooper AR, Page AS, Foster LJ, Qahwaji D. Commuting to school: are children who walk more physically active? Am J Prev Med. 2003;25:273–276. 9. Cooper AR, Andersen LB, Wedderkopp N, et al. Physical activity levels of children who walk, cycle, or are driven to school. Am J Prev Med. 2005;29:179–184. 10. Cooper AR, Wedderkopp N, Wang H, et al. Active travel to school and cardiovascular fitness in Danish children and adolescents. Med Sci Sports Exerc. 2006; 38:1724–1731. 11. Mackett RL, Lucas L, Paskins J, Turbin J. The therapeutic value of children’s everyday travel. Transp Res Part A Policy Pract. 2005;39:205–219. 12. Tudor-Locke C, Ainsworth BE, Popkin BM. Active commuting to school: an overlooked source of children’s physical activity? Sports Med. 2001;31:309–313. 13. Federal Highway Administration. Our Nation’s Travel: 1995 NPTS Early Results Report. Washington, DC: US Dept of Transportation; 1995. 14. McDonald NC. Active transportation to school: trends among US schoolchildren, 1969–2001. Am J Prev Med. 2007;32:509– 516.

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15. McDonald NC, Brown AL, Marchetti LM, Pedroso MS. US school travel, 2009: an assessment of trends. Am J Prev Med. 2011; 41:146–151. 16. Cooper AR, Jago R, Southward EF, Page AS. Active travel and physical activity across the school transition: the PEACH project. Med Sci Sports Exerc. 2012;44:1890– 1897. 17. Black C, Collins A, Snell M. Encouraging walking: the case of journey-to-school trips in compact urban areas. Urban Stud. 2001; 38:1121–1141. 18. Centers for Disease Control and Prevention. Barriers to children walking to or from school–United States, 2004. MMWR Morb Mortal Wkly Rep. 2005;54:949– 952. 19. Ewing R, Schroeer W, Greene W. School location and student travel. Transp Res Rec. 2004;1895:55–63. 20. Merom D, Tudor-Locke C, Bauman A, Rissel C. Active commuting to school among NSW primary school children: implications for public health. Health Place. 2006;12:678–687. 21. Metcalf B, Voss L, Jeffery A, et al. Physical activity cost of the school run: impact on schoolchildren of being driven to school (EarlyBird 22). BMJ. 2004;329:832–833. 22. McMillan TE. The relative influence of urban form on a child’s travel mode to school. Transp Res Part A Policy Pract. 2007; 41:69–79. 23. Schlossberg M, Greene J, Phillips P, et al. School trips: effects of urban from and distance on travel mode. J Am Plann Assoc. 2006;72:337–346.

24. Sirard JR, Slater ME. Walking and bicycling to school: a review. Am J Lifestyle Med. 2008;2:372–396. 25. Timperio A, Ball K, Salmon J, et al. Personal, familial, social and environmental correlates of active commuting to school. Am J Prev Med. 2006; 30:45–51. 26. Planty M, Hussar W, Snyder T, et al. The Condition of Education 2009. Publication NCES 2009-081. Washington, DC: National Center for Education Statistics; 2009. 27. Braza M, Shoemaker W, Seeley A. Neighborhood design and rates of walking and biking to elementary school in 34 California communities. Am J Health Promot. 2004;19:128–136. 28. Kouri C. Wait for the Bus: How Lowcountry School Site Selection and Design Deter Walking to School and Contribute to Urban Sprawl. Charleston, SC: South Carolina Coastal Conservation League; 1999. 29. Sirard JR, Alhassan S, Spencer TR, Robinson TN. Changes in physical activity from walking to school. J Nutr Educ Behav. 2008;40:324–326. 30. Wilson EJ, Marshall J, Wilson R, Krizek KJ. By foot, bus or car: children’s school travel and school choice policy. Environ Plann A. 2010;42:2168–2185. 31. Sirard JR, Ainsworth BE, McIver KL, Pate RR. Prevalence of active commuting at urban and suburban elementary schools in Columbia, SC. Am J Public Health. 2005; 95:236–237.

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Effect of a School Choice Policy Change on Active Commuting to Elementary School.

The purposes of this study were to assess the effect of restricting school choice on changes in travel distance to school and transportation mode for ...
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