Skip directly to search Skip directly to A to Z list Skip directly to site content
CDC Home

PCD Logo

ORIGINAL RESEARCH

Effect of Changes to the Neighborhood Built Environment on Physical Activity in a Low-Income African American Neighborhood

Jeanette Gustat, PhD, MPH; Janet Rice, PhD; Kathryn M. Parker, MPH; Adam B. Becker, PhD, MPH; Thomas A. Farley, MD, MPH

Suggested citation for this article: Gustat J, Rice J, Parker KM, Becker AB, Farley TA. Effect of changes to the neighborhood built environment on physical activity in a low-income African American neighborhood. Prev Chronic Dis 2012;9:110165. DOI: http://dx.doi.org/10.5888/pcd9.110165.

PEER REVIEWED

Abstract

Introduction
Obesity is a public health problem that is due in part to low levels of physical activity. Physical activity levels are influenced by the built environment. We examined how changes in the built environment affected residents’ physical activity levels in a low-income, primarily African American neighborhood in New Orleans.

Methods
We built a 6-block walking path and installed a school playground in an intervention neighborhood. We measured physical activity levels in this neighborhood and in 2 matched comparison neighborhoods by self-report, using door-to-door surveys, and by direct observations of neighborhood residents outside before (2006) and after (2008) the interventions. We used Pearson χ2 tests of independence to assess bivariate associations and logistic regression models to assess the effect of the interventions.

Results
Neighborhoods were comparable at baseline in demographic composition, choice of physical activity locations, and percentage of residents who participated in physical activity. Self-reported physical activity increased over time in most neighborhoods. The proportion of residents observed who were active increased significantly in the section of the intervention neighborhood with the path compared with comparison neighborhoods. Among residents who were observed engaging in physical activity, 41% were moderately to vigorously active in the section of the intervention neighborhood with the path compared with 24% and 38% in the comparison neighborhoods at the postintervention measurement (P < .001).

Conclusion
Changes to the built environment may increase neighborhood physical activity in low-income, African American neighborhoods.

Top of Page

Introduction

Obesity is a serious and widespread problem in the United States. Nearly 34% of American adults are obese, and 68% are either obese or overweight (1). In 2007, Louisiana had the third highest rate of obesity in the nation; according to self-reported data, 30.7% of adults were obese (2). Youth and adults in New Orleans are less likely to be physically active than those in the rest of the country (3). Among adults in New Orleans, 38% met the recommended levels of physical activity (PA) compared to 49% nationally. These findings were even more pronounced among low- and moderate-income African Americans (2).

Features of the built environment influence the propensity to be physically active (4-7). PA can be facilitated or constrained by the built environment, although the relationship between individual factors, social factors, and the physical environment is complex and not well understood (8). Making changes to the built environment should be considered as a means of addressing the related problems of obesity and physical inactivity (9). The Institute of Medicine has identified improvements to the built environment that can encourage walking and bicycling, such as a well-connected network of off-street trails and paths and paths connecting destinations for such activity, as a priority (10).

The Prevention Research Center (PRC) at Tulane University works to identify and address physical and social environmental factors that influence the obesity epidemic and has an overall goal of reducing obesity and its associated health problems. In this project, the Partnership for an Active Community Environment (PACE), the PRC worked with neighborhood-based community groups to create improvements to the built environment that would facilitate PA. Taking a community-based participatory research approach (11), we recruited members of local organizations to participate in the project steering committee. The objective of this study was to asses the effect of improvements to the built environment on the PA levels of residents in a low-income, African American community.

Top of Page

Methods

We used a serial cross-sectional study design to evaluate the effect of the installation of a path and playground on community-wide PA. We conducted cross-sectional assessments at baseline (fall of 2006) and follow-up (fall of 2008). The changes to the built environment occurred in 2007. The study protocol received approval from the Tulane University institutional review board.

Setting

In keeping with principles of community-based participatory research (11), we chose the PACE project intervention neighborhood on the basis of long-standing relationships between neighborhood leaders and Tulane University investigators (12). Meeting monthly, members of local community organizations and Tulane University faculty and staff formed the PACE steering committee (Appendix). The committee helped establish boundaries for the intervention neighborhood, identified 2 comparison neighborhoods, and chose to install a walking path and support the installation and use of a school playground as the built environment interventions. Neighborhoods were matched as closely as possible on proportion of homes owned by their residents, education level and annual household income of residents, percentage of African American residents, and similarity of the built environment, including housing and business type (13).

We also considered neighborhood flood levels at the time of Hurricane Katrina (August 29, 2005) to ensure that a similar level of damage and rebuilding in each of the neighborhoods existed (14). The chosen neighborhoods were reoccupied and in the process of being rebuilt by the time baseline assessments were conducted in the fall of 2006. All 3 neighborhoods were urban communities composed of single-family homes, apartments in old houses, small apartment complexes, and small businesses, and had sidewalks on nearly every street, although many were in poor condition. The intervention neighborhood and 1 comparison neighborhood each had a single large playground, both of which were taken over by the Federal Emergency Management Agency and used as a trailer park, making them unavailable for public use during the study period. There was no such space in the other comparison neighborhood. Streets and sidewalks were the only available places for PA in the neighborhoods. The schools in each neighborhood all had fenced, locked, concrete-slab yards with no playground equipment. One comparison neighborhood was approximately 1.5 miles and the other 5.4 miles from the intervention neighborhood. Because railroad tracks divided the intervention neighborhood, the steering committee recommended 2 interventions, 1 on each side of the tracks: the path in Area A and the playground in Area B (Figure 1).

Map showing the neighborhoods described previously

Figure 1. Partnership for an Active Community Environment Study Areas, New Orleans, Louisiana.

Intervention

The PACE steering committee chose to install a walking path in Area A of the intervention neighborhood (INA). In November 2007, PACE and the city of New Orleans built an 8-foot-wide path of 6 blocks on a grassy, tree-filled median of a wide neighborhood boulevard. The path connected a park outside the intervention area to a commercial corridor.

In another intervention, in May 2007, KaBoom! (http://kaboom.org), a national nonprofit organization, installed a playground with the help of community members and organizations on the back lot of a local elementary school in Area B of the intervention neighborhood (INB). The PACE project paid supervisors to keep the fenced playground open after school hours and on weekends from summer 2007 through spring 2009.

Household survey

We measured self-reported PA in the intervention neighborhood and the 2 comparison neighborhoods through interviewer-administered household surveys conducted door to door (survey instruments available upon request to corresponding author). The sampling plan consisted of 2 separate stratified random samples of households before (September 2006 through February 2007) and after (October 2008 through January 2009) the walking path and the playground were built. A total of 6,497 households were in the 3 areas; 3,115 in the intervention neighborhood, 943 in the first comparison neighborhood, and 2,439 in the second comparison neighborhood. Trained interviewers orally administered the survey, which assessed the community social environment, the community physical environment, and self-reported PA, health and well-being, height, weight, and demographic characteristics. Interviewers randomly selected from each household 1 English-speaking adult aged 18 to 70 who had lived in the neighborhood for at least 3 months. Interviewers made up to 12 attempts to reach that person. People were excluded if they did not speak English, had not lived in the neighborhood for at least 3 months, or were outside the age range. PA questions covered walking for leisure, walking for transportation, and engaging in other activities such as bicycling or jogging. We also asked about use of specific locations for such activity.

At baseline, we sampled 778 households and conducted 499 interviews (response rate, 64.1%): 113 (out of 184) in INA, 111 (out of 174) in INB, 159 (out of 255) in comparison neighborhood 1 (CN1), and 116 (out of 165) in comparison neighborhood 2 (CN2). Of people sampled, we were unable to contact 112 (14.4%): 36 in INA, 21 in INB, 33 in CN1, and 22 in CN2. At follow-up, we sampled 900 households and conducted 692 interviews (response rate, 76.9%): 144 (out of 179) in INA, 192 (out of 253) in INB, 169 (out of 204) in CN1, and 187 (out of 264) in CN2. Of people sampled, we were unable to contact 109 (12.1%): 22 in INA, 26 in INB, 9 in CN1, and 52 in CN2.

Physical activity observations

We used adapted SOPLAY (System for Observing Play and Leisure Activity in Youth) methods to objectively measure neighborhood PA on streets, sidewalks, and outside public areas on every block in each of the 3 neighborhoods (15). The basis of the SOPLAY system is momentary time sampling; it uses group time sampling techniques (15) and counts the number of people and their PA levels during play and leisure opportunities. Observers can use this technique in a neighborhood setting, where spontaneous activity of varying levels occurs among a changing number of people. Our interest was entire neighborhoods, so observers drove through neighborhood streets to count people. We established driving routes to cover all streets. Trained observers scanned the blocks, counting the numbers of sedentary, walking (moderate PA), and very active (vigorous PA) people, including youth and adult men and women. The protocol defined “vigorous” as any activity that was more active than walking, including running, lifting, bicycling, pushing, carrying, and dancing. We recorded and controlled for contextual factors such as weather. Unusual events such as street parties were noted. During observations, we divided the intervention neighborhood into 2 sections (INA and INB).

Data collectors conducted PA observations in the afternoons between 4:00 pm and 6:00 pm, 3 days per week (Thursday, Saturday, and Sunday), for 6 weeks (from October through early December, excluding the week of Thanksgiving) in 2006 and 2008.

Analysis

We examined self-reported PA in several ways. Respondents indicated dichotomously (yes/no) whether they walked for transportation and walked for leisure (which included walking for exercise or walking a dog). We created 2 binary variables (1 for transportation and 1 for leisure) to indicate walking at least 30 minutes per day for at least 5 days per week and created a single binary variable to indicate walking for transportation, leisure, both, or not walking. Results were similar for the 3 approaches and were, therefore, only reported for the first. Frequencies of other forms of PA were low and were not included.

We compared neighborhoods at baseline and follow-up for the proportion of observed people that were moderately or vigorously active, for self-reported PA and location of activity, and for sociodemographic characteristics. We treated the 2 sections of the intervention neighborhood as separate neighborhoods, which required 3 dummy variables to code the intervention neighborhood.

We computed Pearson χ2 statistics to explore the bivariate relationships and used logistic regression to explore the effect of the intervention. We considered age as a confounder and potential effect modifier. Regression models included neighborhood, time, and neighborhood-by-time interactions. If the neighborhood-by-time interaction was significant, we used post hoc tests to determine whether the intervention neighborhood sections changed more than the comparison neighborhoods. We set significance at P < .05 and used SAS version 9.1 (SAS Institute, Inc, Cary, North Carolina) or Stata version 9.0 (StataCorp LP, College Station, Texas) for analyses. Everyone who took the survey completed it, and less than 5% of responses were missing. No imputation was conducted.

Top of Page

Results

Survey respondents from the 3 neighborhoods were similar demographically at baseline (Table 1). Walking was the most common PA, and participation in any other activity was low (data not shown). Self-reported walking for both transportation and leisure increased from baseline to follow-up for most neighborhoods (Table 2). Because this increase was similar for each neighborhood, the neighborhood-by-time interactions were not significant. Age was neither a confounder nor an effect modifier (data not shown).

People from all neighborhoods reported that they most frequently exercised at sidewalks, mall or stores, and streets (Table 3). No significant neighborhood-by-time interactions were found. Walking trail use increased slightly but nonsignificantly (from 21.9% to 29.6%) in INA.

We found a significant neighborhood-by-time interaction between baseline and follow-up for the proportion of people observed who were active. A significant increase in the proportion of people engaged in moderate and vigorous activity was noted in INA between baseline (36.7%) and follow-up (41.0%) (Pearson χ2 test, P < .001) (Figure 2).

chart

Figure 2. Percentage of people observed in intervention and comparison neighborhoods engaged in moderate and vigorous physical activity, at baseline (2006) and follow-up (2008), Partnership for an Active Community Environment Project, New Orleans, Louisiana. Neighborhood-by-time interaction was significant (χ2 test, P = .001), and differences between baseline and follow-up for INA and CN1 were significant (Pearson χ2 test, P < .001). Abbreviations: INA, Area A of intervention neighborhood (path); INB, Area B of intervention neighborhood (playground); CN1, comparison neighborhood 1; CN2, comparison neighborhood 2. [A tabular version of this figure is also available.]

We observed a slight, significant increase in vigorous activity from 10.5% to 13.7% in that same area (Pearson χ2 test, P < .001) (Figure 3).

chart

Figure 3. Percentage of people observed in intervention and comparison neighborhoods engaged in vigorous physical activity, at baseline (2006) and follow-up (2008), Partnership for an Active Community Environment Project, New Orleans, Louisiana. Neighborhood-by-time interaction was significant (χ2 test, P = .001), and the difference between baseline and follow-up for INA was significant (Pearson χ2 test, P < .001). Abbreviations: INA, Area A of intervention neighborhood (path); INB, Area B of intervention neighborhood (playground); CN1, comparison neighborhood 1; CN2, comparison neighborhood 2. [A tabular version of this figure is also available.]

We analyzed the combined proportion of moderate and vigorous activity for youth and adults separately and the patterns of moderate and vigorous activity were similar (combined proportions shown) (Figure 2).

The playground was open for 81 weeks, from July 2007 through April 2009. The daily counts of playground users ranged from 1 to 114 (mean daily count, 25 users; data not shown).

Top of Page

Discussion

We found that after a walking path was installed in a low-income neighborhood, the proportion of people observed who were active in that area increased. Observed activity decreased in the other areas during the same period. Self-reported activity also increased for residents in both intervention areas on streets, for everyone in parks, and for those in INA on a walking trail. However, only the change in park use was significant. The modest increases in observed activity around the path suggest that improvements to the built environment may have had this effect and are encouraging.

It must be noted that the evaluation of the path was not targeted around the path. We hypothesized that environmental changes available to the entire community would lead to increases in PA throughout the neighborhood, as norms for PA changed. However, we could not focus data collection where the built environment was altered because the interventions were decided after baseline data were gathered. Regardless, we were able to show a modest increase in both observed moderate and vigorous activity combined from 36.7% to 41.0% and in observed vigorous activity from 10.5% to 13.7%.

Randomized, controlled trials involving changes to the built environment are difficult to conduct, so most research has been cross-sectional. Our study used cross-sectional assessments before and after the interventions were implemented. In a review article relating features of the built environment to PA in majority African American populations, associations have not been consistent (16). We were unable to find other studies that used objectively measured PA to evaluate interventions to the built environment in primarily African American settings. Objectively measured activity is a better measure of the changes occurring in the neighborhood (17). We also saw some correspondence with the self-reported findings on increase in walking trail use. Our finding is encouraging and confirms cross-sectional studies that find a relationship between features in the built environment and an increase in PA.

Trails and other outdoor recreational spaces in the physical environment promote PA, as evidenced by this study and others (6,18,19). Community-based interventions such as implementing walking paths and trails are a strategy to increase PA levels of residents. A key component to a successful walking path or trail is the ability of residents to have access near their homes and to provide connectivity by linking to destinations (17,20-23). The path in this study was centrally located in the neighborhood and connected a well-used park to a commercial district with markets, shops, and businesses that the neighborhood residents frequent.

Environmental interventions aimed at increasing PA have shown modest results. Fitzhugh et al found an association between objectively measured PA and increased PA after installation of a greenway/trail (24). Their study placed observers in a stationary position in the neighborhood and only captured people being active at the observed location. Our methods allowed us to observe the activity of the entire neighborhood and to count sedentary as well as active people.

The study has several limitations. First, respondents tend to overestimate duration and intensity of PA when the accounts are self-recalled (25). However, systematic observations such as the ones we used are reliable measures of the use of a place or space (26) and may overcome the limitations of self-report (27). Second, when neighborhood is the unit of analysis, randomization is nearly impossible to accomplish, and matching for comparison is even more difficult than matching individuals, making it challenging to control for confounding variables. However, we chose 2 comparable neighborhoods, using census data and the subjective experience and perceptions of steering committee members. The data suggest that these efforts were successful and that communities were well-matched on key variables. Third, the PA observations were limited to streets and yards of houses. Parking lots, areas of commercial lots, and the fenced area of the school yard were not captured in the observations. We were not able to determine whether the users of the school playground were simply displaced from the streets where they may have otherwise played or were new users who were not previously playing outside. Fourth, evaluations of the neighborhoods were broad and encompassed the entire area both in sampling households throughout the neighborhood for the survey and in observing every street in each neighborhood for PA. A more targeted evaluation of the area where the interventions were implemented may have yielded a better measure of effect.

This study has several strengths. The study design included 2 comparison communities, subjective and objective measures of the outcome variables, and 2 waves of data collection. The communities were chosen using objective considerations as well as via the participation of members of the steering committee, who had "insider" perspectives. The interventions were community generated or supported, making them more likely to be accepted and used (11). Contextual variables unique to the time and place (post-Katrina New Orleans) were carefully considered and effectively measured.

Built environment changes, such as easily accessible paths that lead to destinations, can provide more opportunities for PA in primarily African American neighborhoods and others where infrastructure has been allowed to fall into disrepair or was not initially installed. Such increases in opportunity can lead to increases in population-level PA. Environmental interventions can affect many people, over a long period, which programmatic interventions cannot do. Cities and planning commissions should prioritize places for people to be active that are in or near the neighborhoods where people live and consider simple adaptations to existing infrastructure. Resident participation in decision making about the nature and location of environmental changes may be necessary to ensure that changes lead to changes in behavior. As PA levels decrease across the country and in specific communities, creating supportive environments for activity is important. Walking paths or trails may provide a long-term, sustainable, and low-cost strategy to increase the PA of residents in low-income neighborhoods.

Top of Page

Acknowledgments

This study was part of the core research project of the Prevention Research Center at Tulane University School of Public Health and Tropical Medicine and was funded by Centers for Disease Control and Prevention Cooperative Agreement no. 1-U48-DP-000047. The authors thank the PACE steering committee for their dedicated time to the project.

Top of Page

Author Information

Corresponding Author: Jeanette Gustat, PhD, MPH, Prevention Research Center at Tulane University School of Public Health and Tropical Medicine, 1440 Canal St, Ste 2000, New Orleans, LA 70112. Telephone: 504-988-1029. E-mail: gustat@tulane.edu.

Author Affiliations: Janet Rice, Kathryn M. Parker, Prevention Research Center at Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana; Adam B. Becker, Consortium to Lower Obesity in Chicago Children, Chicago, Illinois; Thomas A. Farley, New York City Department of Health and Mental Hygiene, New York, New York.

Top of Page

References

  1. Flegal MK, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999-2008. JAMA 2010;303(3):235-41.
  2. Behavioral Risk Factor Surveillance System survey data. Atlanta (GA): US Department of Health and Human Services, Centers for Disease Control and Prevention; 2007.
  3. Brener ND, Kann L, Garcia D, MacDonald G, Ramsey F, Honeycutt S, et al. Youth risk behavior surveillance — selected Steps communities, 2005. MMWR Surveill Summ 2007;56(2):1-16.
  4. Durand CP, Andalib M, Dunton GF, Wolch J, Pentz MA. A systematic review of built environment factors related to physical activity and obesity risk: implications for smart growth urban planning. Obes Rev 2011;12(5):e173-82.
  5. Papas MA, Alberg AJ, Ewing R, Helzlsouer KJ, Gary TL, Klassen AC. The built environment and obesity. Epidemiol Rev 2007;29:129-43.
  6. Heath G, Brownson RC, Kruger J, Miles R, Powell KE, Ramsey LT, et al. The effectiveness of urban design and land use and transport policies and practices to increase physical activity: a systematic review. J Phys Act Health 2006;3(Suppl 1):S55-76.
  7. Kahn EB, Ramsey LT, Brownson RC, Heath G, Howze EH, Powell KE, et al. The effectiveness of interventions to increase physical activity: a systematic review. Am J Prev Med 2002;22(4S):73-107.
  8. Transportation Research Board. Does the built environment influence physical activity? Examining the evidence (special report 282). http://www.TRB.org. Accessed May 13, 2008.
  9. Sallis JF, Bauman A, Pratt M. Environmental and policy interventions to promote physical activity. Am J Prev Med 1998;15(4):379-97.
  10. Report brief: local government actions to prevent childhood obesity. Washington (DC): Institute of Medicine; 2009.
  11. Israel BA, Schulz AJ, Parker EA, Becker AB. Review of community-based research: assessing partnership approaches to improve public health. Annu Rev Public Health 1998;19:173-202.
  12. Becker AB. Project BRAVE: engaging youth as agents of change in a youth violence prevention project. Community Youth Development 2005;Fall:39-52.
  13. US Census Bureau (2000). Profile of General Demographic Characteristics: 2000: New Orleans, Louisiana. http://factfinder.census.gov/servlet/AdvGeoSearchByListServlet?_command=getResults&_programYear=50&_geoAreaType=140&_treeId=420&_disp_order=1007&_currentGeoAreaType=140&_geoIdsHierarchy=62976427|63058632|50|Y&_placename=14000US22071001900&_geoBucketId=50&_lang=en. Accessed March 15, 2006.
  14. Orleans Parish September 11th flood extent with neighborhood and major roads. Greater New Orleans Community Data Center; 2005. http://www.gnocdc.org. Accessed January 17, 2012.
  15. McKenzie TL, Marshall SJ, Sallis JF, Conway TL. Leisure-time physical activity in school environments: an observational study using SOPLAY. Prev Med 2000;30(1):70-7.
  16. Casagrande SS, Whitt-Glover MC, Lancaster KJ, Odoms-Young AM, Gary TL. Built environment and health behaviors among African Americans: a systematic review. Am J Prev Med 2009;36(2):174-81.
  17. McCormack G, Giles-Corti B, Lange A, Smith T, Martin K, Pikora TJ. An update of recent evidence of the relationship between objective and self-report measures of the physical environment and physical activity behaviours. J Sci Med Sport 2004;7(1 Suppl):81-92.
  18. Lee C, Moudon AV. Correlates of walking for transportation or recreation purposes. J Phys Act Health 2006;3(Suppl 1):S77-98.
  19. Booth KM, Pinkston MM, Poston WS. Obesity and the built environment. J Am Diet Assoc 2005;105(5 Suppl 1):S110-7.
  20. Lieber SR, DR F. Physical and social conditions affecting recreating site preferences. Environ Plan A 1985;17(12):1613-25.
  21. Lindsey G, Han Y, Wilson J, Yang J. Neighborhood correlates of urban trail use. J Phys Act Health 2006;3(Suppl 1):S139-157.
  22. Roemmich JN, Epstein LH, Raja S, Yin L, Robinson J, Winiewicz D. Association of access to parks and recreational facilities with the physical activity of young children. Prev Med 2006;43(6):437-41.
  23. Lovasi GS, Hutson MA, Guerra M, Neckerman KM. Built environments and obesity in disadvantaged populations. Epidemiol Rev 2009;31:7-20.
  24. Fitzhugh EC, Bassett DR, Evans MF. Urban trails and physical activity: a natural experiment. Am J Prev Med 2010;39(3):259-62.
  25. Prince SA, Adamo KB, Hamel ME, Hardt J, Gorber SC, Tremblay M. A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. Int J Behav Nutr Phys Act 2008;5:56.
  26. McKenzie TL, Cohen DA, Sehgal A, Williamson S, Golinelli D. System for observing play and recreation in communitites (SOPARC): reliability and feasibility measures. J Phys Act Health 2006;3(Suppl 1):S208-222.
  27. De Greef K, Van Dyck D, Deforche B, De Bourdeaudhuij I. Physical environmental correlates of self-reported and objectively assessed physical activity in Belgian type 2 diabetes patients. Health Soc Care Community 2011;19(2):178-88.

Top of Page

Tables

Return to your place in the textTable 1. Baseline Demographic Data and Self-Reported Body Mass Index (BMI), Household Survey Respondents in Intervention and Comparison Neighborhoods, New Orleans, Louisiana, Partnership for an Active Community Environment Project, 2006
Characteristic INA (n = 105)a INB (n = 108) CN1 (n = 150) CN2 (n = 110)
African American, %b 85.7 91.7 96.7 100.0
Female, % 54.7 63.9 65.3 60.4
Employed, % 61.2 50.9 49.7 60.6
≥GED/high school graduate, % 82.9 76.2 80.4 88.3
Age, mean (SD),c y 41.6 (14.3) 47.0 (14.0) 43.5 (13.6) 45.5 (14.1)
Annual income ≥$20,000, % 36.0 46.7 33.6 46.2
BMI, mean (SD), kg/m2
Male 27.9 (6.7) 27.6 (6.8) 26.3 (4.8) 26.8 (5.0)
Female 27.7 (8.0) 29.6 (6.0) 30.0 (8.5) 30.1 (7.6)

Abbreviations: INA, intervention neighborhood A (path); INB, intervention neighborhood B (playground); CN1, comparison neighborhood 1; CN2, comparison neighborhood 2; GED, general educational development certificate; SD, standard deviation.
a Differences between total numbers in table and methods section result from item nonresponse.
b Difference between neighborhoods at baseline, P < .001 (Pearson χ2 test).
c Difference between neighborhoods at baseline, P = .03 (Pearson χ2 test).

 

Return to your place in the textTable 2. Self-Reported Walking at Baseline (2006, n = 476) and Follow-Up (2008, n = 665),a by Neighborhood, New Orleans, Louisiana, Partnership for an Active Community Environment Projectb
Neighborhood Walk for Transportation, % Walk for Leisure, %
Baseline Follow-Up Baseline Follow-Up
Intervention A (path) 29.3 34.8 60.0 65.3
Intervention B (playground) 24.8 36.9 63.3 61.5
Comparison 1 31.3 40.5 61.3 70.4
Comparison 2 19.8 31.1 57.7 68.9

a Differences between total numbers in table and methods section result from item nonresponse.
b Neighborhood-by-time interaction was not significant.

 

Return to your place in the textTable 3. Percentage of Survey Respondents Who Reported Exercising at Specific Locations, by Neighborhood, at Baseline (2006, n = 473) and Follow-Up (2008, n = 666), New Orleans, Louisiana, Partnership for an Active Community Environment Projecta,b
Location Baseline, % Follow-Up, %
INA INB CN1 CN2 INA INB CN1 CN2
Sidewalk 61.5 53.2 60.0 51.8 52.1 52.9 54.4 52.8
Mall or storec 35.2 45.0 54.7 47.3 31.9 38.7 34.4 40.6
Street 40.4 32.1 46.7 42.7 41.5 41.7 41.9 37.2
Parkc 27.6 14.7 30.7 18.2 34.5 26.2 38.4 36.5
Walking trail 21.9 21.1 25.3 17.3 29.6 15.0 21.4 20.8
Equipment in homec 21.9 23.8 19.3 22.7 13.4 13.4 11.9 14.4
Indoor gymd 21.9 18.4 21.5 18.2 11.4 9.6 13.1 18.3
Track at local school 10.5 8.3 9.3 9.1 6.3 4.3 6.3 14.4

Abbreviations: INA, intervention neighborhood A (path); INB, intervention neighborhood B (playground); CN1, comparison neighborhood 1; CN2, comparison neighborhood 2.
a Differences between total numbers in table and methods section result from item nonresponse.
b There were no significant neighborhood-by-time interactions.
c Overall differences between baseline and follow-up, P < .001 (Pearson χ2 test).
d Overall differences between baseline and follow-up, P = .002 (Pearson χ2 test).

Top of Page

Appendix

Return to your place in the textAppendix. Member Organizations Represented in the Partnership for an Active Community Environment Steering Committee

  1. All Citizens Together
  2. Bunny Friend Neighborhood Association
  3. Bywater Hospital (2004-2005)
  4. Crescent City Peace Alliance
  5. Dillard University (2004-2005)
  6. Douglass Community Coalition (2004-2005)
  7. Faubourg St. Roch Improvement Association
  8. Holy Angels/Marionites
  9. Small Axe Urban Farms
  10. St. Claude Merchants’ Association (2004-2006)
  11. St. Roch Neighborhood Association
  12. St. Roch Community Church
  13. Steps to a Healthier Louisiana (a project of the New Orleans City Health Department and the Louisiana Public Health Institute)
  14. The Renaissance Project (2004-2007)
  15. Tulane University School of Public Health and Tropical Medicine
  16. Urbanheart (2004-2005)

Top of Page



The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions.

 
For Questions About This Article Contact pcdeditor@cdc.gov
USA.gov: The U.S. Government's Official Web PortalDepartment of Health and Human Services
Centers for Disease Control and Prevention   1600 Clifton Rd. Atlanta, GA 30333, USA
800-CDC-INFO (800-232-4636) TTY: (888) 232-6348 - Contact CDC–INFO
A-Z Index
  1. A
  2. B
  3. C
  4. D
  5. E
  6. F
  7. G
  8. H
  9. I
  10. J
  11. K
  12. L
  13. M
  14. N
  15. O
  16. P
  17. Q
  18. R
  19. S
  20. T
  21. U
  22. V
  23. W
  24. X
  25. Y
  26. Z
  27. #