Background: To assess how perceptions of the community built environment influence support for community policies that promote physical activity (PA). Methods: A national cross-sectional survey assessed perceptions of the local built environment and support of community policies, including school and workplace policies, promoting PA. A random digit–dialed telephone survey was conducted in US counties selected on Behavioral Risk Factor Surveillance System data for high or low prevalence of obesity and inactivity. A total of 1208 subjects were interviewed, 642 from high-prevalence counties and 566 from low-prevalence counties. Analyses were stratified by county prevalence of obesity and inactivity (high or low). Linear models adjusted for covariates were constructed to assess the influence of built environment perceptions on policy support. Results: Perception of more destinations near the residence was associated with increased support for community policies that promote PA, including tax increases in low-prevalence (obesity and inactivity) counties (P < .01). Positive perception of the workplace environment was associated (P < .001) with increased support for workplace policies among those in high-, but not low-, prevalence counties. Conclusions: Support for community policies promoting PA varies by perception of the built environment, which has implications for policy change.

Obesity increases the risk for developing numerous costly and life-threatening conditions, including coronary heart disease, type 2 diabetes, and hypertension.15 It is a leading preventable cause of death in the United States.6 Increasing physical activity (PA) and decreasing sedentary behavior are key recommendations for reducing the risk of obesity and related comorbidities.7 As individual behavioral modifications have limited population impact, environmental and policy approaches are considered efficient ways to address obesity.8,9 The Community Preventive Services Task Force recommends several environmental and policy strategies to increase PA and reduce obesity, including increasing the availability of healthy food and increasing neighborhood walkability.10 Recent recommendations include combining active transportation systems with land use and environmental design interventions to increase PA.11 Policy approaches to promoting PA include national, state, county, and municipal plans, such as the National Physical Activity Plan, and state obesity plans.1214 These plans address increasing activity and reducing obesity by suggesting policies across a variety of sectors, including business, community recreation, land use, transportation, school, and early child care education. Such plans provide guidelines for various health promoting strategies, including reducing screen time, incentivizing active commuting, and increasing public facilities and programming for PA. The PA can also be promoted through changes to the environment with master planning processes that guide the development of physical environments to promote healthy PA behaviors.15,16 These include adding bicycle trails and bike lanes to roadways as part of smart growth policies and planning principles (diverse housing types, mixed land use, housing density, compact development patterns, and levels of open space).11

Characteristics of built environments have been linked to chronic diseases and can have a long-term impact on those who interact with them.1720 Changing the environment to support PA takes coordinated efforts, often at multiple levels of society. When individuals recognize that their environments promote PA, they are more likely to use those environments for PA2123 and may therefore be more likely to support policies that promote PA.

Although there are many limitations when examining the effectiveness of infrastructure improvements to increase PA, recent reviews indicate that environmental interventions should be supported.2426 Polices are one mechanism through which investment in the built environment can be encouraged.27 Decision makers can work to create supportive environments that provide opportunities for PA, provide funding, and coordinate efforts through laws specifying councils or boards to share resources and information.28

Knowledge of the relationship between individual perception of the built environment and support for policies that promote PA is essential to help public health practitioners and community leaders craft policies to promote PA with the support of community residents. In a recent article by Cradock et al,29 it was shown that support for policies promoting PA was related to investments in the built environment infrastructure around transportation. However, little is known regarding the relationship between neighborhood environments and public support for policies that promote PA in other areas of the built environment.

This study examines how people view policies promoting PA and their neighborhood environment. It was conducted by the Physical Activity Policy Research Network, a national research network funded by the Centers for Disease Control and Prevention to study the effectiveness of policy on population PA. The objective of this analysis is to examine how public support for community policies that promote PA varies by the respondents’ perceptions of their local environment, specifically regarding the food, work, and neighborhood built environment, among a national sample of respondents from counties of high- and low-obesity and inactivity prevalence.

Methods

Design and Sample

This study used a one-time, cross-sectional assessment of selected US counties. A random digit-dial, computer-assisted telephone interview method was used to sample 1218 individuals, using both landline and cellular frames covering the target counties. The landline frame yielded approximately 75% of the interviews, with the remaining interviews from the cellular frame. All respondents confirmed that they resided in the target counties. In an attempt to achieve maximum variability in responses regarding opinions and perceptions of the built environment and community policies promoting PA, counties were selected for inclusion in the sampling frame if they were classified as either having a high prevalence of both obesity and inactivity or low prevalence of obesity and inactivity. US counties were identified as having a high prevalence of obesity and inactivity or low prevalence of obesity and inactivity, based on data from the Census and the Behavioral Risk Factor Surveillance System.30,31 Adults were sampled from 884 counties with a high prevalence of obesity and inactivity and 171 counties with low prevalence of obesity and inactivity. Detailed information on the sample is provided in a previously published article.32 Briefly, individuals from these counties were sampled disproportionately to the population size, so that an equal number of interviews would be completed in the high-obesity, high-inactivity and low-obesity, low-inactivity counties. The response rate ranged between 38% and 46% for landline calls and between 9% and 27% for cellular calls, representing the percentage of completed interviews achieved out of all attempted sample records. Human subject’s approval was obtained from the Washington University in St. Louis Institutional Review Board.

Measures

Policies

Four scales were generated to summarize support for policies promoting PA in (1) schools; (2) the community, including items about tax increases; (3) the community, excluding items about tax increases; and (4) the workplace. The scale on community policies was examined with and without items about policies tied to tax increases (without specifying details on the type, form, or amount of tax increase) to explore whether the financial aspect had an impact on the respondent’s support for policies. All questions about support for policies promoting PA were assessed with yes or no responses and asked of all respondents. Items within each scale were summed for each respondent, and the average score was used.32 Higher scores indicated increased support. The items for each scale are found in Table 1.

Table 1

Items included in the Policy and Environment Scales

Policy scales (# items, Cronbach α)Items included in scale
Community policy scale with items supporting tax increasesa (6, α = .699)Does respondent support (1) city funds used for public PA facilities, such as pools, recreation centers, and walking trails; (2) public transit; (3) street improvements for bicycles/pedestrians; (4) community use of school facilities; (5) city tax increases for public PA facilities; (6) city tax increases for public transit?
Community policy scale without items supporting tax increasesa (4, α = .456)Same as above without items 5 and 6.
Workplace policy scalea (4, α = .707)Does respondent support employers providing (1) time for PA, (2) incentives for PA, (3) facilities for PA, and (4) incentives for active commuting to work?
School policy scalea (6, α = .719)Does respondent support (1) daily PE in day care, (2) daily PE in elementary schools, (3) daily PE in middle schools, (4) daily PE in high schools, (5) daily recess, (6) funding for programs to encourage walking and bicycling to school?
Neighborhood environment scales
 Food environmentb (6, α = .938)In the neighborhood near where you live, (1) is it easy to buy FFVs? (2) Is there high-quality fresh produce? (3) Is there a large selection of FFVs? (4) Is it easy to buy low-fat products? (5) Are there high-quality low-fat products? (6) Is there a large selection of low-fat products?
 Neighborhood safetyc (2, α = .542)In your neighborhood (1) you feel safe going out at night, (2) there are many stray dogs (–).
 Neighborhood aestheticsc (5, α = .284)In your neighborhood, there (1) is a lot of traffic (–), (2) is a lot of trash (–), (3) are many people outside, (4) are many people outside being physically active, (5) are many interesting things to see while walking.
 Neighborhood destinationsc (5, α = .751)In your neighborhood, there (1) are many destinations for shopping within walking distance from home, (2) is a public transit stop within a 10- to 15-min walk, (3) are sidewalks on most streets, (4) are many four-way intersections, (5) are facilities such as special lanes or trails to bicycle
 Workplace environmenta,d (13, α = .770)Does your current workplace offer (1) time for PA, (2) facilities for PA, (3) showers, (4) lockers, (5) bike storage, (6) healthy food, (7) financial incentives for PA, (8) personal health services: fitness tests or counseling, (9) group health services: exercise classes or health fairs, (10) subsidized: health club membership, (11) health insurance, (12) sports team sponsorship, (13) other facility or service?

Abbreviations: (–), reverse coded; FFVs, fresh fruit and vegetables; PA, physical activity; PE, physical education.

aDichotomous response choice: yes, no. bFive-point response choices: strongly disagree, disagree, neither, agree, strongly agree. cFour-point response choices: strongly disagree, disagree, agree, strongly agree. dOnly asked of individuals reporting employment full- or part-time.

Built Environment

Individual perceptions of the neighborhood built environment were summarized in 5 scales: (1) food environment, (2) safety, (3) aesthetic appeal, (4) destinations/facilities, and (5) workplace environment. Higher scores indicate more appealing environments with more destinations, healthier food, or safer environments. Measures were scored on 5- and 4-point responses from strongly agree to strongly disagree. The 5-point responses had a neutral response option. Questions about the workplace environment were asked only of those who reported being employed full time or part time. The items for each scale are found in Table 1. The items indicating negative features (ie, trash, stray dogs) were reverse coded. Additional details on scale development are described elsewhere.32

Covariates

Body mass index (BMI, in kilograms per square meter) was calculated from self-reported weight and height. PA was assessed by self-report, separately for moderate and vigorous activity, reported as episodes per week and time per episode.33 The psychometric properties of the questions used to assess PA have been reported previously.34,35 Total time spent in moderate and vigorous activity was calculated, and respondents were then categorized based on whether or not they met national recommendations for PA of either 150 minutes of moderate activity or 75 minutes of vigorous activity per week.36 Other covariates included race (white, black, other), education (less than high school degree, high school degree or higher), employment status (employed, unemployed, retired), home ownership (own, rent, other), and median household income for county of residence.

Analysis

Scores for the policy support scales were used for the outcome measures, and the scores for the built environment scales were the primary exposures of interest. Frequencies and means of respondent characteristics from low-obesity and inactivity counties were compared with those of the respondents from high-obesity and inactivity counties using chi-square tests for categorical variables and t tests for continuous variables. Linear regression models were constructed with policy support scales as the dependent variables and built environment scales as the independent variables. These models were stratified according to whether the respondent resided in a low or high obesity and inactivity prevalence county. Statistical adjustment was performed for covariates, including BMI, PA, race, education, employment status, home ownership, and median household income for the county of residence within each stratum of county obesity and inactivity status. A post-hoc linear regression analysis was applied to evaluate associations of the neighborhood destination scale with items in the community policy scale including support for tax increases, and to evaluate associations of the workplace environment scale with items in the community policy scale excluding support for tax increases and with items in the workplace policy scale. These models were also adjusted for individual BMI, PA, race, education, employment status, home ownership, and median household income for the county of residence.

Results

Sample Characteristics

A total of 1218 interviews were completed. Ten individuals were excluded for providing implausible responses for weight, leaving 642 in high-obesity, high-inactivity counties and 566 in low-obesity, low-inactivity counties. The survey participants from the counties with a high prevalence of obesity and inactivity had higher BMIs and were less educated, less active, and more likely to be black, unmarried, and unemployed compared with those from counties with a low prevalence of obesity and inactivity (Table 2). Median household income for the high-obesity, high-inactivity counties was 35% lower than in the low-obesity, low-inactivity counties (P < .001). Perceptions of the neighborhood environment, including the food environment, safety, aesthetics, and destinations/facilities, were all significantly higher (P < .001) among those in the low-obesity and inactivity counties compared with those in the high-obesity, high-inactivity counties (Table 3). Support for policies that promote PA in the community, both with and without tax items, was higher among those from low-obesity, low-inactivity counties (P < .001; Table 3).

Table 2

Characteristics of Study Subjects Sampled From HH and LL Counties

VariablesHH (N = 642)LL (N = 566)P value*
Age, mean (SD), y57.02 (15.6)56.10 (16.2).32
BMI, mean (SD), kg/m228.45 (6.0)26.33 (5.1)<.001
Race, n (%)<.001
 White522 (83.3)501 (91.3)
 Black77 (12.3)13 (2.4)
 Other28 (4.4)35 (6.3)
Gender, n (%).11
 Male250 (38.9)246 (43.5)
 Female392 (61.1)320 (56.5)
Education, n (%)<.001
 College or more323 (50.4)447 (79.0)
 High school or less318 (49.6)119 (21.0)
Marital status, n (%).02
 Married323 (57.8)314 (64.8)
 Other marital status236 (42.2)171 (35.2)
Housing, n (%).62
 Rent522 (81.8)453 (80.2)
 Own101 (15.8)100 (17.7)
 Other15 (2.4)11 (2.0)
Employment, n (%)<.001
 Employed265 (41.4)314 (55.8)
 Unemployed185 (28.9)100 (17.8)
 Retired190 (29.7)149 (26.4)
Median county income, mean (SD), US dollars38,966 (7,269)60,255 (17,198)<.001
Vigorous PAa, n (%)231 (36.6)298 (52.7)<.001
Moderate PAa, n (%)110 (17.4)145 (25.9)<.001
Vigorous or moderate PAa, n (%)266 (42.5)347 (61.3)<.001
Sedentary activity, mean (SD), h/d4.76 (3.8)4.94 (4.6).40

Abbreviations: BMI, body mass index; HH, high-obesity, high-inactivity; LL, low-obesity, low-inactivity; PA, physical activity.

aMeets recommended amount of physical activity, either 75 minutes of vigorous activity per week or 150 minutes per week of moderate activity.

*P values from t test or χ2 when appropriate.

Table 3

Built Environment Perception Scales and Policy Support Scales of Study Subjects Sampled From HH and LL Counties

HH (N = 642)LL (N = 566)
VariablesMean (SD)Mean (SD)P value*
Built environment perception scales
 Neighborhood food scale3.57 (1.27)4.22 (1.08)<.001
 Neighborhood safety scale3.20 (0.84)3.57 (0.62)<.001
 Neighborhood aesthetic scale2.83 (0.64)3.32 (0.56)<.001
 Neighborhood destination scale1.78 (0.73)2.48 (0.94)<.001
 Workplace environment scalea0.21 (0.21)0.30 (0.22)<.001
Policy support scales
 School policy0.90 (0.18)0.91 (0.19).38
 Community policy with tax increases0.70 (0.27)0.80 (0.24)<.001
 Community policy no tax increases0.78 (0.25)0.88 (0.20)<.001
 Workplace policy0.65 (0.34)0.66 (0.33).55

Abbreviations: HH, high-obesity, high-inactivity; LL, low-obesity, low-inactivity.

aOnly assessed for those currently working. N = 183 (HH), N = 191 (LL).

*P values from t test.

Built Environment Perception and Policy Scales

Positive perception of the food environment was associated with support of policies that promote PA, including community policies with tax increases in the unadjusted model for low-obesity, low-inactivity counties, but did not achieve significance after adjusting for BMI, PA, race, employment status, home ownership, and median county income (Table 4). Perception of neighborhood safety was not significantly associated with any of the policy scales (P > .05). Perception of neighborhood aesthetics was significant in the unadjusted model for policies that promote PA, including community policies with items supporting tax increases and for workplace policies among low-obesity, low-inactivity counties. However, these associations were not maintained after adjustment. Perception of neighborhood destinations was associated with support for community policies with tax increases in the unadjusted model for both the high-obesity, high-inactivity and low-obesity, low-inactivity counties. This association remained for the low-obesity, low-inactivity counties after adjustment (P < .01). The destination scale was associated with support for community policies without tax increases in the unadjusted model, but this association did not remain after adjustment. The perception of the work environment among those employed either full- or part-time was significantly associated with support for community policies with tax increases and workplace policies that promote PA in high-obesity, high-inactivity counties in both the unadjusted and adjusted models (P < .05 and P < .001, respectively). The association between work environment perceptions and these policy scales (community policies without taxes and workplace policies) was not seen in the models in low-obesity, low-inactivity counties.

Table 4

Relationship Between the Built Environment Scales and Support for Policies That Promote Physical Activity Among Individuals in HH and LL Counties

HHLL
UnadjustedAdjustedaUnadjustedAdjusteda
BE perceptionβSEβSEβSEβSE
Support for school policies
 Food−0.0040.006−0.0070.0060.0140.0080.0150.009
 Safety−0.0020.0110.0090.010−0.0010.016−0.0160.014
 Aesthetic0.0180.0120.0170.0120.0070.0150.0130.016
 Destination0.0120.0110.0060.0120.0080.0090.0050.010
 Workplaceb0.0760.0620.0370.0630.0800.0640.0850.070
Support for community policies including tax increases
 Food−0.0070.009−0.0080.0090.024*0.0100.0200.011
 Safety−0.0010.016−0.0140.014−0.0180.0210.0240.019
 Aesthetic0.0280.0180.0210.0180.040*0.0190.0310.020
 Destination0.054***0.0160.0260.0170.044***0.0110.040**0.012
 Workplaceb0.1450.0970.1510.1000.0130.078−0.01090.084
Support for community policies without tax increases
 Food−0.0020.008−0.0020.0080.0170.0080.0080.009
 Safety−0.0050.014−0.0120.013−0.0250.0170.0070.015
 Aesthetic0.0260.0160.0180.0160.0060.0150.0020.016
 Destination0.045***0.0140.0280.0150.028**0.0090.0190.010
 Workplaceb0.196*0.0840.227*0.0870.0440.064−0.0100.067
Support for workplace policies
 Food−0.0090.012−0.0090.012−0.0040.014−0.0100.015
 Safety−0.0380.021−0.0190.018−0.0040.030−0.0270.025
 Aesthetic−0.0030.0230.0030.024−0.054*0.026−0.0520.028
 Destination0.041*0.0200.0160.0220.0220.0160.0110.017
 Workplaceb0.468***0.1060.459***0.1110.1140.1080.0830.116

Abbreviations: BE, built environment; BMI, body mass index; HH, high-obesity, high-inactivity; LL, low-obesity, low-inactivity; PA, physical activity; SE, standard error.

aAdjusted for individual BMI, physical activity, race, employment status, home ownership, and median income for the county of residence for each respondent.

bOnly assessed for those currently working.

*P < .05. **P < .01. ***P < .001.

Destination and Workplace Environment Perceptions Scales With Support for Specific Community and Workplace Policies That Promote PA

To further explore the significant associations between the perceptions of the environment and the policy scales, individual items of the community policies with tax increases scale, the community policies without taxes increases scale, and workplace policies scale were examined (Table 5). Associations with the neighborhood destinations and facilities scale were observed for several of the individual items in the community policies scale with tax increases. In fully adjusted models, those who perceived more destinations and facilities in their neighborhood were significantly more likely to support allocation of funds (P = .002 and P = .0002 for high-obesity, high-inactivity prevalence counties and low-obesity, low-inactivity prevalence counties, respectively) and raising taxes (P < .0001 in low-obesity, low-inactivity prevalence counties) to build and maintain public transit. In addition, the destination scale was associated with support for allowing community use of school PA facilities after hours among low-obesity, low-inactivity prevalence counties (P = .02).

Table 5

Associations Between Unit Increase in the Destination and Work Environment Scales Each With Individual Items From the Community Policy and Workplace Policy Scales Among Respondents in HH and LL Counties

HHLL
Policy itemsβcSEPβcSEP
Destination scale
 Cities should . . .
   . . . have to accommodate bicyclists/pedestrians in infrastructure improvements−0.0040.019.83−0.0100.015.51
   . . . allocate funds to build/maintain PA facilitiesa,b0.0080.019.680.0160.014.27
   . . . increase taxes to build/maintain these PA facilitiesa0.0100.030.730.0400.024.10
   . . . allocate funds to build/maintain public transita0.0920.030.0020.0790.021.0002
   . . . increase taxes to build/maintain public transita0.0460.030.130.1000.025<.0001
   . . . allow community use of school PA facilities after hours0.0110.027.69−0.0280.012.02
Workplace scaled
 Cities should . . .
   . . . have to accommodate bicyclists/pedestrians in infrastructure improvements0.0900.122.460.0140.116.91
   . . . allocate funds to build/maintain PA facilitiesa,b0.2140.116.070.1140.089.21
   . . . allocate funds to build/maintain public transita0.2760.178.12−0.0450.144.75
   . . . allow community use of school PA facilities after hours0.3210.158.04−0.0300.082.71
 Employers should . . .
   . . . provide time during the workday for employees to exercise0.3290.177.060.1090.175.54
   . . . provide incentives to encourage employees to exercise0.1470.116.210.0970.128.45
   . . . provide facilities or places to exercise at the worksite0.8290.175<.00010.0810.177.65
   . . . provide incentives for active commuting to work0.4510.178.010.0630.150.67

Abbreviations: BMI, body mass index; HH, high-obesity, high-inactivity; LL, low-obesity, low-inactivity; SE, standard error.

aThe survey question asked about the respondent’s city of residence. bPhysical Activity facilities were defined as a noncomprehensive list, including walking trails, swimming pools, recreation centers, and bike paths. cModels were adjusted for BMI, physical activity, race, education, employment status, home ownership, and median income in the county of residence. dOnly assessed for those currently working.

Positive perception of the work environment among those employed full- or part-time was associated with support for community use of school PA facilities after hours among high-obesity, high-inactivity prevalence counties (P = .04). Work environment was also positively associated with support for workplace policies promoting PA among high-obesity, high-inactivity prevalence counties, specifically for providing facilities to exercise at the worksite (P < .0001) and for providing incentives for active commuting to work (P = .01) (Table 5).

Discussion

In this analysis of a national survey, an individual’s perception of elements of the built environment in their neighborhood of residence or in their workplace was found to be associated with support for policies that promote PA, including measures associated with tax increases. Positive perception of the neighborhood environment, specifically the presence of destinations and facilities within the neighborhood, was associated with increased support for community policies that promote PA in counties with low-obesity and inactivity prevalence. Positive perceptions of the workplace environment were associated with increased support for policies that promote PA in the workplace among those in high-obesity and inactivity-prevalent counties.

The socioecologic model considers the connections between the individual and his or her immediate family, community, neighborhood, and regulatory influences on health.37 The neighborhood community built environment is a part of this and can include commercial or retail establishments, parks, playgrounds, and streets, where individuals have contact on a daily basis.38 As these features are not typically privately owned, they are supported through community measures, taxes, and public funding allocations. Policy makers must prioritize the use of limited resources. It is important that decisions about the use of funding be made with the optimal health of residents in mind. The present analysis has shown that when residents perceive their environment has destinations and facilities for PA, they support policies promoting PA in their communities when it involves building or maintaining public transit. This support extends to supporting increased community taxes to pay for such facilities for those in healthier communities (low obesity and low inactivity).

Community policies that promote PA might include complete streets policies that promote bicycle lanes, public transit additions or improvements; joint use agreements with local schools making school space available to the public; and construction or improvement of parks and recreation facilities.39,40 Such policies that promote community enhancements can provide improvements to infrastructure and facilities that improve access to destinations. The present study found that community destinations and facilities were perceived to be more numerous in healthier communities (communities with low-obesity and inactivity levels) and were also associated with support for community policies that promote PA. When further examined, community destinations were associated with greater support for public transit expenditures in both high-obesity, high-inactivity and low-obesity, low-inactivity counties and support for tax increases to fund public transit in low-obesity, low-inactivity counties. Availability of neighborhood destinations and facilities is something that policy makers could include in appropriation decisions with the support of their communities.

The worksite is a place where adults spend a substantial amount of time outside the home. The present study found no significant associations between any of the neighborhood environment perceptions and support for workplace policies. More studies need to explore the relationship between home and workplace support for PA.41,42 However, significant associations were found between perceptions of the work environment and support for the community and workplace policies that promote PA. Elements of the built environment in the workplace have the opportunity to influence PA during a large portion of workers’ daily lives. Workplaces have many opportunities to promote healthy activity behaviors in employees.43 Employers also have a strong incentive to promote healthy behavior among employees as this may lead to savings in both health care and related costs.4447 In the present study, those who perceived their work environment as conducive to PA demonstrated support for employers providing facilities for exercise at the worksite and incentives for active commuting in communities with high obesity and high inactivity. Identifying the elements of employee perception of the workplace environment associated with their support for PA policy in the workplace could facilitate and promote healthier workplace behavior. Often, the first step in increasing PA in the workplace is making PA possible in the workplace with engaged organizational leadership that encourage PA with a supportive work culture and environment.48 In addition, employees indicating a supportive workplace environment were found to also support policies that allow the use of school facilities for PA. It is possible that these employees appreciate the value of institutional support for PA. Using schoolsas places for PA, through joint use agreements, can be an effective strategy for increasing PA in the community.49

Limitations of the study include the self-reported nature of the data and the assessment of perceptions of the built environment. Although perceptions represent important truths to the respondent, they may not accurately depict physical conditions. In addition, self-reported support for various policies may or may not translate to an actual vote for a policy. The data are cross-sectional and inference to causation, and the direction of association is limited. Furthermore, individuals who value features that support a healthy lifestyle may choose to live in areas with those environmental supports. However, individuals should still be able to assess the features of the built environment whether or not they live in environments conducive to PA.50,51 Also, the data do not reflect a complete assessment of a resident’s neighborhood environment, including that fact that details about school environments were not assessed. Generalizability of the results may be limited, as the respondents were primarily middle-aged, white, female, and had high rates of unemployment.

One of the strengths of this study includes the unique ability to examine the perceptions of the environment with support for policies that promote PA. This is one of the first studies to examine this relationship. This link between perceptions of the environment and support for policies that promote PA indicate that such policies may be successfully implemented even when associated with tax increases. In addition, the national scope of the sample is a strength. Respondents were sampled from counties defined by high- and low-obesity and activity levels that would most likely add to the variability in the responses.

Conclusions

This study has demonstrated that the perception of more neighborhood destinations and facilities is associated with increased support for community policies that promote PA in both high- and low-obesity and inactivity-prevalent counties. This information will be important for community leaders and public health professionals, as modification of elements of the built environment may facilitate the implementation of more popular and, ultimately, successful policies for the promotion of PA. In addition, this study demonstrates that perception of the workplace environment is associated with support for workplace policies that promote PA in high-obesity, high-inactivity counties. Workplace policies promoting PA may be an important method of increasing PA in communities, as many individuals spend the majority of their waking hours away from home in their workplaces.

Acknowledgments

This study was funded by the Centers for Disease Control and Prevention (CDC) Cooperative Agreement (nos. U48DP005050, U48DP001903, U48DP00531) from the CDC, Prevention Research Centers Program; Special Interest Projects 10-09, 09-09; and the Physical Activity Policy Research Network. The findings and conclusions of this article are those of the authors and do not necessarily represent the official position of the CDC.

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If the inline PDF is not rendering correctly, you can download the PDF file here.

Gustat is with the Department of Epidemiology, Tulane Prevention Research Center, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA. Anderson is with the Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA. O’Malley is with the Department of Global Community Health and Behavioral Sciences, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA. Hu is with the Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN. Tabak, Valko, and Eyler are with Brown School, Prevention Research Center in St. Louis, Washington University in St. Louis, St. Louis, MO. Goins is with the Division of Preventive and Behavioral Medicine, University of Massachusetts Medical School, Worcester, MA. Litt is with the University of Colorado Boulder, Boulder, CO.

Gustat (gustat@tulane.edu) is corresponding author.
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    Laine J, Kuvaja-Köllner V, Pietilä E, Koivuneva M, Valtonen H, Kankaanpää E. Cost-effectiveness of population-level physical activity interventions: a systematic review. Am J Health Promot. 2014;29:71–80. PubMed ID: 25361461 doi:10.4278/ajhp.131210-LIT-622

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    Gustat J, O’Malley K, Hu T, et al. Support for physical activity policies and perceptions of work and neighborhood environments: variance by BMI and activity status at the county and individual levels. Am J Health Promot. 2014;28(3)(suppl):S33–S43. PubMed ID: 24380463 doi:10.4278/ajhp.130430-QUAN-216

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    Merrill RM, Richardson JS. Validity of self-reported height, weight, and body mass index: findings from the National Health and Nutrition Examination Survey, 2001–2006. Prev Chronic Dis. 2009;6(4):A121. PubMed ID: 19754997

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    Fitzgerald EA, Frasso R, Dean LT, et al. Community-generated recommendations regarding the urban nutrition and tobacco environments: a photo-elicitation study in Philadelphia. Prev Chronic Dis. 2013;10:E98. PubMed ID: 23764347 doi:10.5888/pcd10.120204

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    Roof K, Oleru N. Public health: Seattle and King County’s push for the built environment. J Environ Health. 2008;71(1):24–27. PubMed ID: 18724501

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    Moreland-Russel S, Eyler A, Barbero C, Hipp JA, Walsh H. Diffusion of complete streets policies across US communities. J Public Health Manag Pract. 2013;19:S89–S96. doi:10.1097/PHH.0b013e3182849ec2

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    Tabak R, Hipp JA, Dodson EA, Yang L, Adlakha D, Brownson RC. Exploring associations between perceived home and work neighborhood environments, diet behaviors, and obesity: results from a survey of employed adults in Missouri. Prev Med Rep. 2016;4:591–596. PubMed ID: 27843759 doi:10.1016/j.pmedr.2016.10.008

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