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Keywords:

  • child;
  • environment;
  • neighborhood;
  • physical activity;
  • recreation

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References

Objective: The purpose of this study was to examine the association of perceived physical neighborhood factors with physical activity, sedentary behavior, and BMI among adolescent girls.

Research Methods and Procedures: Sixth grade girls (n = 1554) completed a questionnaire on neighborhood factors (e.g., safety, esthetics, access to physical activity resources). The dependent variables included non-school metabolic equivalent weighted moderate to vigorous physical activity (MW-MVPA) and non-school sedentary behavior, both measured using accelerometry, and BMI.

Results: The following neighborhood factors were associated with lower BMI: seeing walkers and bikers on neighborhood streets, not having a lot of crime in the neighborhood, seeing other children playing outdoors, having bicycle or walking trails in the neighborhood, and access to physical activity facilities. The absolute contribution for the average girl for each of these neighborhood factors was relatively small, with none of these factors exceeding 0.8 kg/m2 BMI units. The following neighborhood factors were associated with higher MW-MVPA: having well-lit streets at night, having a lot of traffic in the neighborhood, having bicycle or walking trails in the neighborhood, and access to physical activity facilities. Girls with ≥9 places to go for physical activity had 14.0% higher non-school MW-MVPA than girls with ≤4 places.

Discussion: This study identified several neighborhood factors associated with non-school MW-MVPA and BMI, but none of the factors explored were associated with non-school sedentary behavior. Of all of the neighborhood factors we examined, reporting more physically active destinations contributed the largest absolute amount to the average girl's non-school MW-MVPA, according to this cross-sectional study.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References

A recent evidence-based review recommended that school-age children should obtain 60 minutes or more of moderate to vigorous physical activity (MVPA)1 daily (1). Despite this and other similar recommendations (2), physical activity among adolescents remains suboptimal. In addition, girls exhibit lower levels of physical activity than boys and experience steeper declines during adolescence (3, 4, 5). This, in part, is contributing to the rise in obesity during adolescence (6, 7, 8, 9). Understanding the correlates of physical activity during this time period can inform the development of interventions.

In a comprehensive review of physical activity correlates of youth (10), environmental or neighborhood variables were the least often studied when considering an array of other types of measures. Since this 2000 review, a growing number of studies have explored neighborhood factors associated with physical activity among youth. Studies of this type are important because one cannot presume that associations with neighborhood factors are similar for adults as with youth (11). One might hypothesize, for example, that seeing other children playing outside in the neighborhood might be an important correlate of physical activity among youth, but not adults. Because few studies have examined these factors in youth, we are only beginning to understand the directions of these relationships.

Among the published studies, only a few utilized objective measures rather than self-reported measures of physical activity (12, 13). Furthermore, fewer studies have focused on correlates of sedentary behavior. Of the two identified studies that explored the association of neighborhood factors with self-reported sedentary behavior and physical activity, interestingly, both found a lack of association with sedentary behavior but some promising associations with physical activity (14, 15). Moreover, exploring BMI or obesity as an independent variable is often not presented because most studies focus on the relationship of neighborhood factors to physical activity behavior only.

The purpose of this study was to examine the association of neighborhood factors with physical activity, sedentary behavior (both occurring after school and on weekends, heretofore referred to as non-school), and BMI among adolescent girls. Specifically, we hypothesized that neighborhood safety, neighborhood esthetics, and access to physical activity facilities near home would be positively associated with non-school physical activity and negatively associated with non-school sedentary behavior and BMI.

Research Methods and Procedures

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References

Data Collection

Participants were adolescent girls in the sixth grade recruited from 36 schools located in Arizona, California, Louisiana, Maryland, Minnesota, and South Carolina and who were participating in the Trial of Activity in Adolescent Girls (TAAG). TAAG is a multicenter school-randomized trial designed to test an intervention to reduce the usual decline in MVPA in middle school girls (16). The data used for this study were collected at baseline in fall 2003. The coordinating center is located at the University of North Carolina (Chapel Hill), and the field centers include the University of Arizona, San Diego State University, Tulane University, University of Maryland College Park, University of Minnesota, and University of South Carolina. The National Heart, Lung, and Blood Institute Project Office Staff collaborated with investigators to conduct the study. Geographic Information Systems data for the study were collected at the RAND Corporation. Parents or guardians provided written informed consent, and the girls also provided written assent. This study was approved by the Institutional Review Boards at each field center, the Coordinating Center, and at RAND.

Physical Activity and Sedentary Behavior Measures

An Actigraph (model no. AM7164) accelerometer was used to measure physical activity and sedentary behavior. This device is made by Manufacturing Technologies Inc. (http:mtiactigraph.com; Fort Walton, FL) and is a small, lightweight, technically reliable (17) uniaxial accelerometer. Participants wore the monitor on their right hip secured by a belt to measure accelerations in the vertical plane.

Trained and certified TAAG staff members distributed the accelerometers and provided detailed verbal and written instructions on when and how to wear the accelerometers over a 6-day period. Girls were asked to remove the monitor only for sleeping, bathing, or swimming. Data were collected and stored in 30-second epochs. Half-minute counts were used instead of full-minute counts based on the expectation that the shorter interval would be more sensitive to fluctuations in activity levels. If counts were recorded as zero for 20 minutes or more, then it was assumed that the participant was not wearing the accelerometer.

Accelerometer readings were processed using methods similar to those reported by Puyau et al. (18). Sedentary behavior was defined for counts 0 to 50 per 30-second epoch. Readings between 51 and 1500 per 30 seconds were defined as light physical activity, and readings above 1500 to 2600 counts per 30 seconds were defined as moderate and >2600 as vigorous. This threshold for MVPA had the optimal sensitivity and specificity for discriminating brisk walking from less vigorous activities in eighth grade girls (19). Occasional missing accelerometry data within a girl's 6-day record were replaced by imputation based on the expectation maximization algorithm (20). Counts above 1500 per 0.5 minutes were converted into metabolic equivalents (METs) using a regression equation developed from a TAAG substudy (19, 21); the sum of METs over time provided MET minutes per day of MVPA, where 1 MET minute represents the MET of energy expended sitting at rest for 1 minute. This provided more weight to vigorous activities when compared with moderate activities. For example, an activity corresponding to 7 METs performed for 10 minutes would receive a value of 70 MET-weighted (MW) MVPA minutes. For the analyses, accelerometer data were only used after school (2 pm to 12 am on weekdays) and on weekends because we hypothesized that the neighborhood environmental factors would only affect non-school activity.

BMI

Trained and certified TAAG staff members collected height and weight data. Height was measured twice using a Shorr height board, and the average of the two measures was calculated. If the difference in the two measures was 1 cm or greater, a third measure was taken, and the two closest measures were averaged. Weight was measured twice using a Seca Model 880 weight scale, and the average of the two measures was calculated. If the difference in the two measures was 0.5 kg or greater, a third measure was taken, and the two closest measures were averaged. BMI was calculated by dividing weight in kilograms by the square of height in meters. We also categorized girls according to their BMI and age using the 2000 Centers for Disease Control and Prevention growth charts for U.S. girls, into overweight (≥95th percentile) and at risk for overweight (≥85th percentile) (22).

Survey Instrument

Data collectors participated in a centralized training to ensure that standardized procedures, scripts, and protocols were used. Students completed the self-administered questionnaire at school, supervised by the data collectors. A standardized introduction to the survey was read, and data collectors were available for questions.

Measures of neighborhood factors were taken from a questionnaire developed and examined for test-retest reliability during the pilot phase of the TAAG Study (23). The 10 items asked about perceived safety (e.g., safe to walk or jog in neighborhood, see walkers/bicyclists from homes on street, traffic, crime, other children playing outdoors, lighting), esthetics (i.e., many interesting things to look at in the neighborhood), and access to facilities near home (e.g., places to walk to from home, sidewalks, trails). For each of the 10 items, the response options on a five-point scale were disagree a lot, disagree a little, neither agree or disagree, agree a little, or agree a lot. Two-week test-retest reliability, on a separate sample of sixth and eighth grade girls using the five-level responses, ranged from 0.37 to 0.58 (weighted κ coefficients) for these items (23). For analysis, these five-level answers were collapsed into three categories: disagree, neither, and agree. We chose a priori to analyze each of the 10 items separately. Although we were able to group the items into three broad domains, we thought it important to describe the individual characteristics and their associations of the neighborhood separately. We also explored whether any scales could be developed with the 10 items, in conjunction with our pilot work with girls of this age (23). Our results indicated that we could derive a safety scale by adding the sum of four items [e.g., safe to walk or jog, walks and bikers can be seen at night, there is a lot of crime (reverse coded), and streets well lit at night] and dividing by 4 to obtain the mean, ranging from 1 (less safe) to 5 (most safe). If any item was missing, then the scale was set to missing. The Cronbach's α coefficient for this measure, indicating internal consistency between items, was 0.56.

Girls were also provided a list of 14 facilities and asked: “Is it easy to get to and from this place from home or school?” (yes or no). The listed facilities included the following: basketball court; beach or lake; golf course; health club; martial arts studio; playing field (soccer or softball); park, recreation center, or YMCA/YWCA; track; skating rink (ice, roller, or inline); swimming pool; walking, biking, or hiking path or trail; tennis court; and dance or gymnastic club. These were scored by adding the total number of facilities to which the participant easily could get to (possible score range, 0 to 14). These 14 locations corresponded to recreational activities that similar girls of this age and location reported most often, identified through the formative work of the TAAG study (24).

Measures of Covariates

Race/ethnicity was self-reported on the survey. Date of birth was collected on the parental consent forms, and age was calculated from the date of birth to the date of completion of the survey. Each school provided the percentage of sixth, seventh, and eighth graders on free or reduced-price lunches. Generally, students whose families earn <200% of the poverty level were eligible for this program.

A neighborhood socioeconomic index, described in detail elsewhere (25), was created using neighborhood-level U.S. census data. Three different census block-group level indicators from the census were standardized: the percentage of households above the poverty line, the percentage of employed persons in the labor force over 16 years of age, and the percentage of persons over the age of 25 years with more than a high school diploma (Cronbach's α = 0.88). These three factors were then combined into an index and interpolated for the circular area delimited by a 0.5-mile radius around each girl's geocoded residence.

Statistical Analysis

A random sample of 60 eligible sixth grade girls per school were invited to participate in TAAG measurements at baseline. Reasons for ineligibility were: unable to read and understand English, told by a doctor to avoid exercise, or other medical contraindication. Parental consent and student assent were obtained for 1721 of the 2160 eligible girls for an average recruitment rate of 80%. One hundred eighteen girls did not provide complete accelerometer data, 47 home addresses could not be geocoded, and two did not complete the questionnaire, leaving 1554 for these analyses. The addresses of the girls were geocoded to map their location using ArcGIS 9.0 (ESRI, Redlands, CA).

All analyses were conducted using the mixed procedure in SAS version 9.1 (SAS Institute, Inc., Cary, NC) (26). The data can be considered to have a hierarchical structure, in which girls (Level 1) are nested within schools and schools (Level 2) are nested within study site. Therefore, to determine whether neighborhood factors were associated with our outcomes, school and site were treated as random effects in a hierarchical linear model. In all of our statistical models, Level 1 (i.e., girl level) fixed effect covariates included: the race/ethnicity of participants, defined with indicator variables [e.g., Hispanic, black, white, and other (this variable includes the remaining race/ethnic categories due to their sample size)]; each girl's neighborhood socioeconomic index; and BMI for the physical activity outcomes or non-school MW-MVPA for models with BMI as the outcome.

The percentage of students at each school on free or reduced-price lunch was included as a school Level 2 (i.e., school level) fixed effect. Because BMI and non-school MW-MVPA might be along the causal pathway to their respective outcomes, including them as covariates may have led us to overcontrol and attenuate the associations. Therefore, all models were reexamined without non-school MW-MVPA or BMI as a girl-level fixed effect covariate.

We used log-transformed versions of non-school MW-MVPA and BMI as dependent variables because we found that this corrected substantial skew of residuals in these models. Subsequently, the corresponding exponentiated parameter estimates for fixed effects are understood as the percentage difference in the dependent variable per unit change in the predictor. To express these effects in terms of their original units, we also calculated the average difference in minutes over the 6-day monitoring period of non-school MW-MVPA and BMI for a girl with the mean value of these variables. Sedentary behavior did not require any transformations; thus, the estimates for fixed effects are expressed directly in minutes over the 6-day monitoring period. We also calculated odds ratios (ORs) using multilevel logistic models using the SAS GLIMMIX function with two outcomes: overweight and at-risk of becoming overweight.

To explore the relative importance of these variables with each of our outcomes, we selected neighborhood variables with significance at least p < 0.10 and checked for collinearity among them. None of the variables were collinear, using the guide of obtaining a condition index value of 30 or greater (27). These variables were then added to a full model, also including the variables described previously (i.e., school, site, BMI or non-school MW-MVPA, neighborhood socioeconomic index, percentage on free or reduced-price lunch, and race/ethnicity of the girls). The neighborhood variables were dropped one by one until only significant neighborhood variables (p < 0.10) remained in a final model.

For these analyses, adjustment for multiple tests was not performed. For all models presented, the normality assumption appeared valid based on examination of the RxP plots (i.e., plot of residuals vs. predicted values) at the first or girl level and the Q-Q plot of the residuals at all levels (i.e., girl, school, and site).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References

Sample Characteristics and Perceptions of Neighborhoods

Characteristics of the study sample are included in Tables 1 and 2. The sixth grade girls in this study, on average, were 11.8 years old with a BMI of 20.9 kg/m2. According to the Centers for Disease Control and Prevention growth charts for U.S. girls of their age and BMI (22), 34.5% (n = 547) were at risk of being overweight (≥85th percentile), and 16.9% (n = 268) were overweight (≥95th percentile). On average, they engaged in 1.6 hours over 6 days of non-school MW-MVPA and 28.1 hours per 6 days of non-school sedentary behavior. Almost one-half of the girls included in these analyses were white, followed by Hispanic and black. Across the 36 schools participating in the study, the average percentage of students on free or reduced-price lunches was 36.9% (interquartile range, 13% to 50%).

Table 1.  Characteristics of participants in the analysis (n = 1554)
 %n
Site  
 Tucson, AZ14.5225
 San Diego, CA18.8293
 New Orleans, LA17.0265
 Baltimore, MD14.6227
 Minneapolis, MN17.5272
 Columbia, SC17.6274
Race/ethnicity  
 Asian, Native Hawaiian, or Pacific Islander3.960
 Black21.0326
 Native American0.711
 Multi-racial7.2112
 Hispanic22.0342
 White45.2703
 Missing0.12
Is it easy to get to and from a … (yes)  
 Park69.91088
 Playing field64.81008
 Swimming pool60.3939
 Walking, biking, or hiking path or trail57.2890
 Basketball court56.4877
 Tennis court41.9652
 Track41.6647
 Skating rink (ice, roller, or inline)34.6538
 Recreation center31.3487
 Beach or lake29.8464
 Dance or gymnastics club29.7462
 Golf course23.8371
 Health club22.6352
 Martial arts studio19.9310
Table 2.  Mean, median, and IQ range of measures describing participants in study
 MeanMedianIQ Range
  • IQ, interquartile; MVPA, moderate to vigorous physical activity; MW, metabolic equivalent weighted.

  • *

    Access to physical activity facilities sum score combines the total number of physical activity facilities easy to get to.

  • Measured in minutes over the 6-day monitoring period.

Age11.811.811.5 to 12.1
Access to physical activity facilities sum score*6.16.04 to 8
Safety score3.84.03.25 to 4.5
BMI (kg/m2)20.919.617.3 to 23.3
Non-school activity from accelerometer   
 Sedentary1683.51670.21474.4 to 1900.4
 Light1399.91398.61242.3 to 1567.2
 MVPA97.184.358.6 to 119.8
 MW-MVPA610.8514.8352.7 to 739.5

Participants reported that the physical activity facilities that were easiest to get to and from home or school were a park (70%), followed by playing fields and swimming pools (Table 1). The hardest facilities to get to and from home or school were martial arts studios (20%), followed by health clubs and golf courses. On average, girls reported being able to get to and from 6 of 14 physical activity facilities (Table 2).

Between 61% and 72% of the girls agreed or strongly agreed that, in their neighborhoods, it was safe to walk and jog, that walkers and bikers could be seen from homes, that they see other kids often playing outside, and that there were places they liked to go within walking distance (Table 3). Approximately one-half of the girls agreed or strongly agreed that, in their neighborhoods, the streets were well lit at night and that there were biking or walking trails in their neighborhood, sidewalks on most streets, and interesting things to look at in the neighborhood. Between 75% and 79% disagreed or strongly disagreed that in their neighborhood traffic makes it hard to walk and that there was a lot of crime.

Table 3.  Survey percentage and association of neighborhood factors (independent variables) with nonschool MW-MVPA and BMI (dependent variables)
  Non-school MW-MVPABMI
Survey item%% difference in non-school MW-MVPAMean difference in non-school MW-MVPA (minutes*)p-value% difference in non-school MW-MVPAMean difference in BMI (kg/m2)p-value
  • MW-MVPA, metabolic equivalent weighted moderate to vigorous physical activity. Models are adjusted for school, site, BMI or non-school MW-MVPA, neighborhood socioeconomic index, percentage free or reduced-price lunch, and race/ethnicity of girls. Agree combines responses of “agree a little” and “agree a lot.” Disagree combines responses of “disagree a little” and “disagree a lot.” Access to physical activity facilities sum score combines the total number of physical activity facilities easy to get to. Because our dependent variables were log transformed, estimates are expressed in terms of percentage difference in the dependent variable per unit change in each covariate. To translate these estimates back into their original units, we calculated the difference for the average girl by multiplying the estimate by the average value of the dependent values. Average BMI was 20.9 kg/m2, and average MW-MVPA was 609.6 minutes per 6 days.

  • *

    Minutes of MW-MVPA are summed over the 6-day monitoring period.

  • Estimate probability, p < 0.10.

  • Estimate probability, p < 0.05.

Safety       
 It is safe to walk or jog in my neighborhood       
  Disagree15.3      
  Neither12.90.84.80.880.30.10.81
  Agree71.87.042.30.01−0.9−0.20.63
 Walkers and bikers on the streets in my neighborhood can easily be seen by people in their homes       
  Disagree14.8      
  Neither19.30.95.70.82−0.3−0.70.02
  Agree65.92.213.10.61−1.6−0.30.27
 There is a lot of crime in my neighborhood       
  Agree14.7      
  Neither10.1−3.1−19.00.44−4.6−1.00.001
  Disagree75.1−4.4−26.80.03−1.6−0.30.08
 My neighborhood streets are well lit at night       
  Disagree29.5      
  Neither16.74.124.80.330.30.10.85
  Agree53.86.841.30.001−0.5−0.10.74
 There is so much traffic that it makes it hard to walk in my neighborhood       
  Agree13.7      
  Neither8.9−5.1−30.80.35−1.9−0.40.02
  Disagree78.7−5.1−31.00.10−2.3−0.50.01
Safety index: continuous (based on the first four safety questions) 4.628.00.010.00.10.67
 I often see other girls or boys playing outdoors in my neighborhood       
  Disagree18.9      
  Neither11.49.960.00.02−1.9−0.40.17
  Agree69.70.63.50.91−2.3−0.50.001
Aesthetics       
 There are many interesting things to look at while walking in my neighborhood       
  Disagree24.0      
  Neither20.82.112.70.52−1.3−0.30.39
  Agree55.21.37.70.75−0.6−0.10.71
Physical Activity Facilities and Destinations       
 There are many places I like to go within easy walking distance of my home       
  Disagree20.3      
  Neither18.20.00.30.99−1.2−0.20.56
  Agree61.54.929.70.19−1.0−0.20.42
 There are sidewalks on most of the streets in my neighborhood       
  Disagree32.0      
  Neither8.16.036.60.29−2.4−0.50.04
  Agree59.94.728.90.05−1.5−0.30.07
 There are bicycle or walking trails in my neighborhood       
  Disagree37.1      
  Neither13.2−3.0−18.50.60−1.5−0.30.08
  Agree49.87.545.80.001−1.7−0.40.001
Access to physical activity facilities sum score: quartiles (score)       
 Quartile 1 (0 to 4)26.6      
 Quartile 2 (5 to 6)30.71.710.30.69−3.3−0.70.003
 Quartile 3 (7 to 8)20.413.581.90.01−1.6−0.30.02
 Quartile 4 (9 to 14)22.315.393.20.003−1.5−0.30.39
Access to physical activity facilities sum score       
 Continuous 1.59.10.0030.00.00.80

Associations of Neighborhood Factors with Physical Activity, Sedentary Behavior, and BMI

The adjusted associations for the neighborhood factors are presented in Table 3 for non-school MW-MVPA and BMI and in Table 4 for non-school sedentary behavior. In the text, we highlight associations reaching a significance level of p < 0.05 for any agree or disagree responses for the models shown in Tables 3 4 5. We reran the models presented in Tables 3 and 4, not adjusting for BMI (for models with non-school MW-MVPA or sedentary behavior as the outcome) or non-school MW-MVPA (for the models with BMI as the outcome). In all cases, the interpretations did not change, although often the coefficients were slightly attenuated. In Table 5, we further explore BMI as an outcome by categorizing it two different ways to obtain the odds of those overweight and those at risk of overweight. Lastly, in Table 6, we explore the relative importance of the neighborhood factors on the physical activity and BMI outcomes.

Table 4.  Association of neighborhood factors (independent variables) with non-school sedentary behavior (dependent variable)
 Non-school sedentary behavior
Survey itemDifference in non-school sedentary behavior (minutes*)p-value
  • MW-MVPA, metabolic equivalent weighted moderate to vigorous physical activity. Models are adjusted for school, site, BMI or non-school MW-MVPA, neighborhood socioeconomic index, percentage free or reduced-price lunch, and race/ethnicity of girls. Agree combines responses of “agree a little” and “agree a lot.” Disagree combines responses of “disagree a little” and “disagree a lot.” Access to physical activity facilities sum score combines the total number of physical activity facilities easy to get to.

  • *

    Minutes of MW-MVPA are summed over the 6-day monitoring period.

  • Estimate probability: p < 0.10.

  • Estimate probability: p < 0.05.

Safety  
 It is safe to walk or jog in my neighborhood  
  Disagree  
  Neither−12.70.68
  Agree22.40.32
 Walkers and bikers on the streets in my neighborhood can easily be seen by people in their homes  
  Disagree  
  Neither9.00.75
  Agree32.70.15
 There is a lot of crime in my neighborhood  
  Agree  
  Neither8.80.80
  Disagree23.60.31
 My neighborhood streets are well lit at night  
  Disagree  
  Neither−19.10.47
  Agree−22.50.25
Safety index: continuous (based on the first four safety questions)10.10.36
 There is so much traffic that it makes it hard to walk in my neighborhood  
  Agree  
  Neither37.80.29
  Disagree41.00.08
 I often see other girls or boys playing outdoors in my neighborhood  
  Disagree  
  Neither35.30.27
  Agree6.60.75
Aesthetics  
 There are many interesting things to look at while walking in my neighborhood  
  Disagree  
  Neither−0.70.98
  Agree−0.50.98
Physical activity facilities and destinations  
 There are many places I like to go within easy walking distance of my home  
  Disagree  
  Neither−21.10.44
  Agree−40.50.06†
 There are sidewalks on most of the streets in my neighborhood  
  Disagree  
  Neither−49.80.15
  Agree−12.30.54
 There are bicycle or walking trails in my neighborhood  
  Disagree  
  Neither15.80.57
  Agree−3.80.84
Access to physical activity facilities sum score: quartiles (score)  
 Quartile 1 (0 to 4)  
 Quartile 2 (5 to 6)−17.50.45
 Quartile 3 (7 to 8)3.60.89
 Quartile 4 (9 to 14)−23.40.35
Access to physical activity facilities sum score  
 Continuous−2.40.35
Table 5.  Association of neighborhood factors (independent variables) with overweight and at-risk-for-overweight (dependent variables) using ORs with 95% CIs
 At-risk-for-overweightOverweight
Survey itemOR95% CIOR95% CI
  • OR, odds ratio; CI, confidence interval; MW-MVPA, metabolic equivalent weighted moderate to vigorous physical activity. Models are adjusted for school, site, non-school MW-MVPA, neighborhood socioeconomic index, percentage free or reduced-price lunch, and race/ethnicity of girls. Agree combines responses of “agree a little” and “agree a lot.” Disagree combines responses of “disagree a little” and “disagree a lot.” Access to physical activity facilities sum score combines the total number of physical activity facilities easy to get to. At-risk-for-overweight is defined as BMI ≥85th percentile. Overweight is defined as BMI ≥95th percentile.

  • *

    OR probability: p < 0.10.

  • OR probability: p < 0.05.

Safety    
 It is safe to walk or jog in my neighborhood    
  Disagree1.0 1.0 
  Neither1.0(0.7, 1.5)1.1(0.7, 1.8)
  Agree0.8(0.6, 1.1)1.1(0.7, 1.5)
 Walkers and bikers on the streets in my neighborhood can easily be seen by people in their homes    
  Disagree1.0 1.0 
  Neither0.9(0.6, 1.3)0.8(0.5, 1.2)
  Agree0.8(0.6, 1.1)0.8(0.6, 1.2)
 There is a lot of crime in my neighborhood    
  Agree1.0 1.0 
  Neither0.7(0.5, 1.1)0.6*(0.3, 1.1)
  Disagree0.9(0.7, 1.3)0.9(0.6, 1.3)
 My neighborhood streets are well lit at night    
  Disagree1.0 1.0 
  Neither1.1(0.8, 1.5)1.3(0.9, 2.0)
  Agree1.0(0.8, 1.3)1.2(0.9, 1.6)
Safety index: continuous (based on the first four safety questions)1.0(0.9, 1.2)1.2*(1.0, 1.4)
 There is so much traffic that it makes it hard to walk in my neighborhood    
  Agree1.0 1.0 
  Neither1.1(0.7, 1.7)0.9(0.5, 1.6)
  Disagree0.9(0.7, 1.2)0.8(0.6, 1.1)
 I often see other girls or boys playing outdoors in my neighborhood    
  Disagree1.0 1.0 
  Neither0.9(0.6, 1.3)0.8(0.5, 1.4)
  Agree0.8*(0.6, 1.0)0.9(0.6, 1.3)
Aesthetics    
 There are many interesting things to look at while walking in my neighborhood    
  Disagree1.0 1.0 
  Neither0.8(0.6, 1.2)1.0(0.7, 1.5)
  Agree0.9(0.7, 1.1)0.9(0.7, 1.3)
Physical activity facilities and destinations    
 There are many places I like to go within easy walking distance of my home    
  Disagree1.0 1.0 
  Neither0.9(0.6, 1.3)1.1(0.7, 1.7)
  Agree0.8(0.6, 1.1)1.0(0.7, 1.4)
 There are sidewalks on most of the streets in my neighborhood    
  Disagree1.0 1.0 
  Neither0.9(0.6, 1.4)1.1(0.6, 1.9)
  Agree0.9(0.7, 1.2)0.8(0.6, 1.1)
 There are bicycle or walking trails in my neighborhood    
  Disagree1.0 1.0 
  Neither1.0(0.7, 1.4)0.9(0.6, 1.4)
  Agree0.9(0.7, 1.2)0.9(0.7, 1.2)
Access to physical activity facilities sum score: quartiles (score)    
 Quartile 1 (0 to 4)1.0 1.0 
 Quartile 2 (5 to 6)0.8*(0.6, 1.0)0.8(0.5, 1.1)
 Quartile 3 (7 to 8)1.0(0.7, 1.3)0.7*(0.4, 1.0)
 Quartile 4 (9 to 14)0.7(0.5, 1.0)0.8(0.6, 1.3)
Access to physical activity facilities sum score    
 Continuous1.0(0.9, 1.0)1.0(0.9, 1.0)
Table 6.  Multivariable association of neighborhood factors (independent variables) with non-school MW-MVPA and BMI (dependent variables)
Survey itemNon-school MW-MVPABMI
 % difference in non-school MW-MVPAMean difference in non-school MW-MVPA (minutes*)p-value% difference in non-school MW-MVPAMean difference in BMI (kg/m2)p-value
  • MW-MVPA, metabolic equivalent weighted moderate to vigorous physical activity. Models are adjusted for all other neighborhood factors listed along with school, site, BMI or non-school MW-MVPA, neighborhood socioeconomic index, percentage free or reduced-price lunch, and race/ethnicity of girls. Agree combines responses of “agree a little” and “agree a lot.” Disagree combines responses of “disagree a little” and “disagree a lot.” Access to physical activity facilities sum score combines the total number of physical activity facilities easy to get to, and the referent is Quartile 1 (1 to 4). Because our dependent variables were log transformed, estimates are expressed in terms of percentage difference in the dependent variable per unit change in each covariate. To translate these estimates back into their original units, we calculated the difference for the average girl by multiplying the estimate by the average value of the dependent values. Average BMI was 20.9 kg/m2, and average MW-MVPA was 609.6 minutes per 6 days.

  • *

    Minutes of MW-MVPA are summed over the 6-day monitoring period.

  • Estimate probability: p < 0.10.

  • Estimate probability: p < 0.05.

Safety      
 Walkers and bikers on the streets in my neighborhood can easily be seen by people in their homes      
  Neither   −2.3−0.50.06
  Agree   −0.6−0.10.67
 There is a lot of crime in my neighborhood      
  Neither   −3.7−0.80.001
  Disagree   −0.9−0.20.41
 My neighborhood streets are well lit at night      
  Neither4.225.80.29   
  Agree3.923.60.07   
 There is so much traffic that it makes it hard to walk in my neighborhood      
  Neither−5.6−33.80.29   
  Disagree−7.0−42.90.02   
 I often see other girls or boys playing outdoors in my neighborhood      
  Neither   −0.6−0.10.62
  Agree   −1.6−0.30.001
Physical activity facilities and destinations      
 There are bicycle or walking trails in my neighborhood      
  Neither−3.5−21.10.5−0.6−0.10.54
  Agree4.829.30.02−1.4−0.30.003
Access to physical activity facilities sum score: quartiles (score)      
Quartile 2 (5 to 6)1.710.30.68−2.9−0.60.01
Quartile 3 (7 to 8)12.978.30.01−1.1−0.20.18
Quartile 4 (9 to 14)14.084.90.004−0.9−0.20.63

Safety

Several of the safety items were associated with higher non-school MW-MVPA, including agreeing that it is safe to walk or jog in the neighborhood and agreeing that streets are well lit. Perceptions that crime was not a problem (i.e., disagreeing that there is a lot of crime in the neighborhood) was associated with lower levels of non-school MW-MVPA. Perceptions that traffic is not a problem (i.e., disagreeing that there is too much traffic) was associated with lower BMI. Agreeing that they could see kids playing outside was associated with lower BMI and lower odds of being at risk for overweight. For every additional unit on the four-item safety scale (ranging from 1, less safe, to 5, more safe), non-school MW-MVPA increased by 4.6% or by 9.1 non-school MW-MVPA minutes per 6 days. No significant associations were identified with safety measures and non-school sedentary behavior (Table 4).

Aesthetics

Having many interesting things to look at, the single item relating to esthetics of the neighborhood, was not associated with any of the outcomes under study.

Physical Activity Facilities and Destinations

Agreeing that their were many places within easy walking distance of home was not associated with non-school MW-MVPA or BMI but approached significance for less sedentary behavior. Agreeing that their neighborhood had sidewalks on most of the streets and trails was associated with 4.7% (on average, 28.9 non-school MW-MVPA minutes per 6 days) and 7.5% (on average, 45.8 non-school MW-MVPA minutes per 6 days) higher non-school MW-MVPA, respectively. Reporting bicycle or walking trails in one's neighborhood was inversely associated with BMI. The number of physical activity places that a girl could get to was added together, with a maximum of 14, and divided into quartiles. Girls with 9 to 14 places to go for physical activity (Quartile 4) had 15.3% higher non-school MW-MVPA than girls with four or fewer places (Quartile 1). Girls with five to six (Quartile 2) or seven to eight (Quartile 3) places to go for physical activity had lower BMI compared with girls with four or fewer places (Quartile 1), and this measure was not associated with sedentary behavior. Furthermore, those with 9 to 14 places to go for physical activity (Quartile 4) were also at a lower odds of being at risk for overweight (OR, 0.7; p = 0.03) compared with those with four or fewer places (Quartile 1).

Relative Importance of Neighborhood Factors

To explore the relative importance of neighborhood factors (i.e., 10 neighborhood factors and quartiles of access to physical activity facilities) with BMI, non-school MW-MVPA, and sedentary behavior, we computed final models retaining significant neighborhood factors while controlling for important covariates (Table 6). The following neighborhood factors were associated with lower BMI (p < 0.10): seeing walkers and bikers pass by people in their homes, not having a lot of crime in the neighborhood, seeing other children playing outdoors, having bicycle or walking trails in the neighborhood, and access to physical activity facilities. The absolute contribution for the average girl for each of these neighborhood factors was relatively small, with none of these factors exceeding 0.8 kg/m2 BMI units.

The following neighborhood factors were associated with higher MW-MVPA: having well-lit streets at night, having a lot of traffic in the neighborhood, having bicycle or walking trails in the neighborhood, and access to physical activity facilities. Girls in the highest quartile of places to go had 14.0% higher non-school MW-MVPA than girls in the lowest quartile. This translated into 84.9 more non-school MW-MVPA minutes per 6 days for an average girl in the highest quartile compared with the lowest quartile of having places to go.

For the final model predicting at risk for overweight, only quartiles of access to physical activity facilities were retained; thus, this model is identical to the single neighborhood factor models presented in Table 5. Because there were no significant predictors identified for sedentary behavior and being overweight, these were not further explored.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References

This is one of the first studies to explore the association of neighborhood characteristics in relationship to objective measures of BMI, physical activity, and sedentary behavior among adolescent girls. Neighborhood settings are believed to influence health behaviors, including physical activity and diet, through many avenues, such as by setting norms and expectations about behaviors, by reinforcing norms and expectations by providing easy access to facilities related to health behaviors, and by establishing rules and regulations. In this study, we found that having more destinations for physical activity, reporting trails, sidewalks, and well-lit streets in the neighborhood, and perceiving the neighborhood as safe to walk or jog were neighborhood features associated with higher levels of non-school MW-MVPA. Having more destinations for physical activity and being able to see other children play outside was associated with lower BMI.

Safety

The association between perceptions of safety and non-school MW-MVPA may reflect a dynamic relationship between norms and the physical environment. When more people are out using the streets, they tend to be safer because anti-social activity is less likely to occur with many observers who would not tolerate such behaviors. When streets are safer, parents may be more likely to encourage their children to be active outdoors. In this study, when considering the final models, perception of crime in the neighborhood was not associated with non-school MW-MVPA. A next step would be to understand whether parental perceptions are concordant with the girl's perceptions of safety and whether the parental perceptions might be associated with their physical activity. We were unable to obtain objective measures of crime across all of these study areas, so it is not known whether these more objective measures might have been associated with physical activity.

At least three studies assessed safety in an objective manner (e.g., without relying on self-report) to explore associations with self-reported physical activity. First, in San Antonio, TX, the density of violent crimes within 0.5 miles of the homes of seventh grade girls, but not boys, was associated with less outdoor physical activity (28). Similarly, self-reported perception of lower safety was associated with less outdoor physical activity for girls but not boys. The correlation between the density of violent crimes and their own subjective assessment of safety for outdoor play was low. For girls, it appeared that both the perceived and objective measures of crime and safety were important correlates of outdoor physical activity. Second, in a national study of adolescents, a high prevalence of objectively measured serious neighborhood crimes was associated with less self-reported MVPA (14). A third study in Chicago that examined neighborhood safety and crime and found that youth 11 to 16 years of age with lower neighborhood safety (5-item scale developed from a community survey of adults) and higher social disorder (7-item scale of people loitering, fighting, drinking, selling drugs, or prostitution observed and coded from video tapes) reported lower levels of physical activity (29). Other studies relying on self-reported neighborhood safety also found it to be associated with an increased risk of childhood overweight among 7-year-old children (30) but a lower BMI among fourth graders (31).

Heavy traffic also relates to perceptions of safety. In a study conducted in the United Kingdom, 11- to 16-year-olds who reported busy traffic near their home were less likely to report their neighborhood as a safe place to walk after dark or as a safe place for children to play outside (32). This led us to hypothesize that reports of higher traffic would be associated with lower physical activity and higher obesity. However, we found that perceiving that traffic was not a problem was associated with lower non-school MW-MVPA only. Again, it would be of use to concurrently explore parental perceptions of traffic in conjunction with the girl's perceptions.

Seeing other children playing outdoors was grouped under safety but might also be related to social norms or the social environment. This measure was associated with lower BMI but not with non-school MW-MVPA or sedentary behavior. Through what mechanism could seeing other kids play be related to obesity? People often copy each other, a well-described phenomenon, and it could be that youth playing outside would have lower obesity than those who do not. Simply being exposed to such youth may stimulate behaviors that would result in maintaining similar appearances.

Aesthetics

Two studies have found that having interesting things to look at in the neighborhood was associated with self-reported physical activity among sixth and eighth grade U.S. girls (23) and among 7th to 12th grade Portuguese adolescents (33). In addition, a Dutch study found that adolescents who lived in a more attractive neighborhood, based on self-report, had a more positive attitude toward being physically active (34). However, in this study, having many interesting things to look at in the neighborhood was not associated with any of the activity or obesity outcomes in sixth grade girls. Two other studies of adolescents attempted to quantify esthetics in an objective way. In a study mentioned earlier, conducted among 11- to 16-year-old youth living in Chicago, neighborhood physical disorder (10-item scale of types of trash and graffiti observed and coded from videotapes) was not significantly associated with self-reported physical activity (although the authors state that the association went in the “right” direction) (29). In another study of U.S. adolescent Boy Scouts, objectively measured tidiness (e.g., garden maintenance, verge maintenance, and road cleanliness) of their neighborhood was not associated with accelerometer-measured sedentary behavior or MVPA (12). It is not clear how these objective measures of esthetics might relate to the perceived measures that we collected.

Physical Activity Facilities and Destinations

The Guide to Community Preventive Service's recommends creation of or enhanced access to places for physical activity, combined with informational outreach services to increase physical activity (35). This was certainly supported in this study because it was the strongest factor associated with non-school MW-MVPA. The findings in the literature are mixed on this issue, in part due to varying measures used to indicate access to physical activity facilities and also varying age groups and populations under study (13, 23, 28, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44). Interestingly, in a study of 11- to 15-year-old boys and girls, the number of physical activity facilities in a 1-mile network neighborhood buffer was associated with MVPA measured with an accelerometer for girls, but not for boys (13). The present study also found that adolescent girls who reported trails in their neighborhood had higher non-school MW-MVPA and lower BMI. It seems intuitive that those with access to trails in their neighborhood would be more active and have lower obesity.

Limitations

The work reported here provides support for continuing to explore the role of the neighborhood on girls’ physical activity and BMI. However, this study is limited by several factors. First, other important confounders may exist that we did not account for in the adjusted models. Second, in this exploratory study, we have tested many associations but chose not to adjust for multiple testing because we had specific a priori hypotheses and considered this study exploratory. Therefore, significance should be interpreted with caution, and replication of results is needed. Third, it is important to note that the measure of access to physical activity facilities related to spatial features (e.g., proximity, density) and ignored specific features of those facilities, including esthetics, safety, cost, and age-appropriate offerings. Further refinement of this simple measure might prove useful. Fourth, some of the self-reported items under study showed only moderate reliability from our pilot work (23), and the safety scale displayed only moderate internal consistency. A better understanding of why this is the case and how to improve on these measures is indicated. Lastly, this study is cross-sectional in design, so the direction of the relationships is not known.

Conclusion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References

In conclusion, this study identified several neighborhood factors associated with non-school MW-MVPA and BMI. Interestingly, none of the factors explored were associated with non-school sedentary behavior. Further work is needed to understand why the differential effect might be the case. Of all the neighborhood factors explored, having places to go for physical activity was most strongly associated with non-school MW-MVPA. This study adds to a small but growing body of knowledge on the neighborhood level correlates of adolescent physical activity. To date, we identified only one study of youth that compared perceived and objectively measured neighborhood factors with each other and in association with physical activity (28). Other studies of this type would also be useful for different ages and geographies. Longitudinal studies are also needed to account for the temporality of the measures and to understand what might mediate perceptions about neighborhoods.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References

This work was funded by NIH/National Heart, Lung, and Blood Institute Grants R01HL071244, U01HL-66845, HL-066852, HL-066853, HL-066855, HL-066856, HL-066857, and HL-066858. We thank Scott Ashwood, Adrian Overton, and Kimberly Ring for input and assistance on the data analyses and Terry Conway, Marsha Dowda, David Murray, Charlotte Pratt, and the anonymous reviewers for helpful critiques. We thank the girls who participated in the study; the project coordinators for participant recruitment; and the members of TAAG Steering Committee, including Russell Pate (University of South Carolina, Columbia, SC), Deborah Rohm-Young (University of Maryland, College Park, MD), Leslie Lytle (University of Minnesota, Minneapolis, MN), Timothy Lohman (University of Arizona, Tucson, AZ), Larry Webber (Tulane University, New Orleans, LA), John Elder (San Diego State University, San Diego, CA), June Stevens (University of North Carolina, Chapel Hill, NC), and Charlotte Pratt (National Heart, Lung, and Blood Institute).

Footnotes
  • 1

    Nonstandard abbreviations: MVPA, moderate to vigorous physical activity; TAAG, Trial of Activity in Adolescent Girls; MET, metabolic equivalent; MW, MET weighted; OR, odds ratio.

  • The costs of publication of this article were defrayed, in part, by the payment of page charges. This article must, therefore, be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References
  • 1
    Strong, W., Malina, R., Blimkie, C., et al. (2005) Evidence based physical activity for school-age youth. J Pediatr. 146: 732737.
  • 2
    Fulton, J., Meenakshi, G., Galuska, D., Rattay, K., Caspersen, C. (2004) Public health and clinical recommendations for physical activity and physical fitness: special focus on overweight youth. Sport Med. 34: 581599.
  • 3
    Kimm, S., Glynn, N. W., Kriska, A. M., et al. (2002) Decline in physical activity in black girls and white girls during adolescence. New Engl J Med. 347: 709715.
  • 4
    Goran, M. (1998) Measurement issues related to studies of childhood obesity: assessment of body composition, body fat distribution, physical activity, and food intake. Pediatrics 101: 505518.
  • 5
    Centers for Disease Control and Prevention (2003) Physical activity levels among children aged 9 to 13 years: United States, 2002. MMWR Morb Mortal Wkly Rep. 52: 785788.
  • 6
    Ogden, C., Carroll, M., Curtin, L., McDowell, M., Tabak, C., Flegal, K. (2006) Prevalence of overweight and obesity in the United States, 1999–2004. JAMA 295: 15491555.
  • 7
    Hedley, A., Ogden, C., Johnson, C., Carroll, M., Curtin, L., Flegal, K. (2004) Prevalence of overweight and obesity among US children, adolescents, and adults, 1999–2002. JAMA 291: 28472850.
  • 8
    Troiano, R., Flegal, K. (1998) Overweight children and adolescents: description, epidemiology, and demographics. Pediatrics 101: 497504.
  • 9
    Dollman, J., Norton, K., Norton, L. (2005) Evidence for secular trends in children's physical activity behavior. Br J Sports Med. 39: 892897.
  • 10
    Sallis, J., Prochaska, J., Taylor, W. (2000) A review of correlates of physical activity of children and adolescents. Med Sci Sports Exerc. 32: 963975.
  • 11
    Krizek, K., Birnbaum, A., Levinson, D. (2004) A schematic for focusing on youth in investigations of community design and physical activity. Am J Health Promot. 19: 3338.
  • 12
    Jago, R., Baranowski, T., Zakeri, I., Harris, M. (2005) Observed environmental features and the physical activity of adolescent males. Am J Prev Med. 29: 98104.
  • 13
    Norman, G., Nutter, S., Ryan, S., Sallis, J., Calfas, K., Patrick, K. (2006) Community design and access to recreational facilities as correlates of adolescent physical activity and body mass index. J Phys Act Health 3 (Suppl 1), S118S128.
  • 14
    Gordon-Larsen, P., McMurray, R., Popkin, B. (2000) Determinants of adolescent physical activity and inactivity patterns. Pediatrics 105: 18.
  • 15
    Norman, G., Schmid, B., Sallis, J., Calfas, K., Patrick, K. (2005) Psychosocial and environmental correlates of adolescent sedentary behaviors. Pediatrics 116: 908916.
  • 16
    Stevens, J., Murray, D., Catellier, D., et al. (2005) Design of the Trial of Activity in Adolescent Girls (TAAG). Contemp Clin Trials 26: 223233.
  • 17
    Metcalf, B., Curnow, J., Evans, C., Voss, L., Wilkin, T. (2002) Technical reliability of the CSA activity monitor: The EarlyBird Study. Med Sci Sports Exerc. 34: 15331537.
  • 18
    Puyau, M., Adolph, A., Vohra, F., Butts, N. (2002) Validation and calibration of physical activity monitors in children. Obes Res. 10: 150157.
  • 19
    Treuth, M., Schmitz, K., Catellier, D., et al. (2004) Defining accelerometer thresholds for activity intensities in adolescent girls. Med Sci Sports Exerc. 36: 12591266.
  • 20
    Catellier, D., Hannan, P., Murray, D., et al. (2005) Imputation of missing data when measuring physical activity by accelerometry. Med Sci Sports Exerc. 37: S555S562.
  • 21
    Schmitz, K., Treuth, M., Hannan, P., et al. (2005) Predicting energy expenditure from accelerometry counts in adolescent girls. Med Sci Sports Exerc. 37: 155161.
  • 22
    Centers for Disease Control and Prevention (2000) CDC Growth Charts: United States: 2000 National Center for Health Statistics and the National Center for Chronic Disease Prevention and Health Promotion Atlanta, GA.
  • 23
    Evenson, K. R., Birnbaum, A. S., Bedimo-Rung, A. L., et al. (2006) Girls’ perception of physical environmental factors and transportation: reliability and association with physical activity and active transport to school. Int J Behav Nutr Phys Act 3: 28.
  • 24
    Grieser, M., Vu, M., Bedimo-Rung, A., et al. (2006) Physical activity attitudes, preferences, and practices in African American, Hispanic, and Caucasian girls. Health Educ Behav. 33: 4051.
  • 25
    Cohen, D., Ashwood, S., Scott, M., et al. (2006) Proximity to school and physical activity among middle school girls: The Trial of Activity in Adolescent Girls Study. J Physical Activity Health 3 (Suppl 1), S129S138.
  • 26
    Littell, R., Milliken, G., Stroup, W., Wolfinger, R. (2006) SAS System for MIXED models SAS Institute Cary, NC.
  • 27
    Belsley, D., Kuh, E., Welsch, R. (1980) Regression Diagnostics: Identifying Influential Data and Sources of Collinearity John Wiley New York.
  • 28
    Gomez, J., Johnson, B., Selva, M., Sallis, J. (2004) Violent crime and outdoor physical activity among inner-city youth. Prev Med. 39: 876881.
  • 29
    Molnar, B., Gortmaker, S., Bull, F., Buka, S. (2004) Unsafe to play? Neighborhood disorder and lack of safety predict reduced physical activity among urban children and adolescents. Am J Health Promot. 18: 378386.
  • 30
    Lumeng, J., Appugliese, D., Cabral, H., Bradley, R., Zuckerman, B. (2006) Neighborhood safety and overweight status in children. Arch Pediatr Adolesc Med. 160: 2531.
  • 31
    Romero, A., Robinson, T., Kraemer, H., et al. (2001) Are perceived neighborhood hazards a barrier to physical activity in children? Arch Pediatr Adolesc Med. 155: 11431148.
  • 32
    Mullan, E. (2003) Do you think that your local area is a good place for young people to grow up? The effects of traffic and car parking on young people's views. Health Place 9: 351360.
  • 33
    Mota, J., Almeida, M., Santos, P., Ribeiro, J. (2005) Perceived neighborhood environments and physical activity in adolescents. Prev Med. 41: 834836.
  • 34
    de Bruijn, G., Kremers, S., Lensvelt-Mulders, G., de Vries, H., van Mechelen, W., Brug, J.. (2006) Modeling individual and physical environmental factors with adolescent physical activity. Am J Prev Med. 30: 507512.
  • 35
    Kahn, E., Ramsey, L., Brownson, R., et al. (2002) The effectiveness of interventions to increase physical activity: a systematic review. Am J Prev Med. 22: 73107.
  • 36
    Gordon-Larsen, P., Nelson, M., Page, P., Popkin, B. (2006) Inequality in the built environment underlies key health disparities in physical activity and obesity. Pediatrics 117: 417424.
  • 37
    Dunton, G., Jamner, M., Cooper, D. (2003) Assessing the perceived environment among minimally active adolescent girls: validity and relations to physical activity outcomes. Am J Health Promot. 18: 7073.
  • 38
    Romero, A. (2005) Low-income neighborhood barriers and resources for adolescents’ physical activity. J Adolesc Health 36: 253259.
  • 39
    Fein, A., Plotnikoff, R., Wild, C., Spence, J. (2004) Perceived environment and physical activity in youth. Int J Behav Med. 11: 135142.
  • 40
    Adkins, S., Sherwood, N., Story, M., Davis, M. (2005) Physical activity among African-American girls: the role of parents and the home environment. Obes Res. 12 (suppl): 3845S.
  • 41
    Trost, S., Pate, R., Ward, D., Saunders, R., Riner, W. (1999) Determinants of physical activity in active and low-active, sixth grade African American youth. J School Health 69: 2934.
  • 42
    Trost, S., Pate, R., Ward, D., Saunders, R., Riner, W. (1999) Correlates of objectively measured physical activity in preadolescent youth. Am J Prev Med. 17: 120126.
  • 43
    Bungum, T., Pate, R., Dowda, M., Vincent, M. (1999) Correlates of physical activity among African-Americans and Caucasian female adolescents. Am J Health Behav. 23: 2531.
  • 44
    Neumark-Sztainer, D., Story, M., Hannan, P., Tharp, T., Rex, J. (2003) Factors associated with changes in physical activity: a cohort study of inactive adolescent girls. Arch Pediatr Adolesc Med. 157: 803810.