Neighborhood Public Transportation Access and Adolescent Body Mass Index: Results from the FLASHE Study

Abstract

Background:

Prior investigators have examined the relationship between neighborhood public transportation access and physical activity among adolescents, but research is lacking on the association with obesity in this age group. This study examines the association between neighborhood public transportation access and adolescent BMI using a national sample.

Methods:

We used cross-sectional data from the Family Life, Activity, Sun, Health, and Eating study, a national survey (2014) that assessed physical activity and diet, among adolescents (aged 12–17 years, N = 1737) and their parents. We ran crude and adjusted linear regression models to test the association between neighborhood-level public transportation access (less prevalent and prevalent) and individual participant-level BMI z-scores.

Results:

The analytic sample included 336 adolescents (50% female; 69% had healthy weight; 28% had overweight or obesity). Adjusted models showed a positive relationship between high public transportation access and adolescent z-BMI (b = 0.25, confidence interval [95% CI]: −0.01 to 0.50). In stratified analyses, high public transportation access was associated with higher z-BMI for high school students (b = 0.57, 95% CI: 0.23–0.91), males (b = 0.48, 95% CI: 0.09–0.87), and adolescents in households with an income below $99,999 (0.29, 95% CI: 0.02–0.56).

Conclusion:

Neighborhood public transportation access is associated with adolescent BMI, but the direction of this association varies across urban adolescent demographic subgroups. Further research is needed to clarify the relationships between individual and social-environmental factors that impact public transportation access and its association with adolescent BMI.

Introduction

Overweight and obesity rates increased drastically among American youth from 1999 to 2016, particularly among adolescents.¹ Simultaneously, there has been a decline in physical activity with few American youth meeting national guidelines for physical activity.² Sharp declines in physical activity typically begin in early adolescence,³ and evidence indicates they are much steeper in the United States compared with other nations.³ This suggests that adolescence is a critical time for physical activity promotion among United States youth.

The socio-ecological model provides a framework for understanding the dynamic interactions among individual, interpersonal, community, and societal factors that might influence adolescent obesity and physical activity.⁴ Many adolescents experience barriers to a healthy weight and physical activity, including lack of social support from family and friends,⁵ financial constraints,⁶ and built environment factors.⁷ Earlier studies that addressed adolescent physical activity have focused on addressing some of these barriers with an emphasis on intrapersonal and interpersonal factors.^8,9 Interventions have also addressed parental influences via family-based lifestyle interventions,¹⁰ using health behavior change techniques that involve education, goal-setting, and self-monitoring.¹¹ Although these interventions have had some success in increasing adolescent physical activity and improving overall BMI, they do not address neighborhood environmental factors such as walkability, proximity to parks, and public transportation, that impact adolescent physical activity.¹²

Adolescents have less autonomy over their behavior and mobility compared with adults, so they are more likely to be affected by their neighborhood environment. Given that the relationship between neighborhood environment barriers to youth physical activity and BMI is understudied,¹³ it is important to understand environmental factors that affect adolescent physical activity.

Neighborhood public transportation is an important environmental factor, specific to urban areas, that impacts physical activity. Public transportation users engage in up to 19 minutes of active transportation, or daily transport-related physical activity, walking to and from transit.¹⁴ Public transportation also provides access to physical activity resources, such as parks and recreation, green spaces, and community programs.^15,16 Although earlier studies have examined the relationship between neighborhood public transportation access and physical activity among adolescents,^17–19 research is lacking on the association with BMI in this age group. Living in an area with more neighborhood public transportation opportunities has the potential for both increasing physical activity minutes and subsequently reducing adolescent BMI. In addition, the relationship between public transportation access and physical activity may have a stronger negative association among older adolescents, boys vs. girls, and higher area-level poverty given prior research demonstrating greater public transportation use among these groups.²⁰

However, although low-income populations report higher usage of public transportation, they have lower access to public transportation overall.^19,21,22 Therefore, this study aims to examine the association between neighborhood public transportation access and adolescent BMI drawing from a national sample. We also will test the moderating effect of age, sex, and socioeconomic status on the public transportation access–adolescent BMI association.

We hypothesize that in areas with less public transportation access, adolescents will have higher BMIs. We also hypothesize that older adolescents, boys, and areas with higher area poverty will show a stronger relationship between public transportation access and adolescent BMI. Findings from this research have the potential to inform the development of targeted interventions for promoting neighborhood public transportation access among US adolescents, particularly among subgroups at high risk for obesity and physical inactivity, to ultimately reduce chronic health disparities across the lifespan.

Methods

Study Sample

We used data from the Family Life, Activity, Sun, Health, and Eating (FLASHE) study. Detailed information about the FLASHE study design and measure development has been previously reported.²³ In brief, the FLASHE study is a cross-sectional national survey conducted in 2014 that assessed cancer-preventative behaviors, including physical activity and diet, among adolescents (aged 12–17 years, n = 1737) and their parents. The FLASHE study includes self-reported data and geocoded variables derived from the American Community Survey (2010–2014) census tract level data at various home circular network distances. The sample was drawn from an Ipsos Consumer Opinion Panel representative of the US general population on sex, education, income, age, household size, and region.

Parent–adolescent dyads were eligible if the adolescent was between 12 and 17 years old, the parent was above the age of 18, and the dyad lived together at least 50% of the time. If there was more than one eligible adolescent in the household, one was randomly selected for the study. We only included adolescents who live in an urban (city) area (7.15%) in the study because those who lived in suburban (3%), town (0.46%), and rural (0.53%) areas had very low percent neighborhood public transportation access, our main predictor variable, which is similar to other studies that show public transit access is much lower outside of city environments.^24–26

Participants were excluded based on each inclusion criteria (e.g., suburban/town/rural [n = 1238] and incomplete BMI z-score [n = 163], geocoded measures [n = 206], and covariates of interest [n = 165]) yielding a final analytic sample of 336 participants. The FLASHE was reviewed and approved by the US Government's Office of Management and Budget, National Cancer Institute's Special Studies IRB, and Westat's, Inc.

Measures

The primary outcome, BMI z-score, was drawn from individual parental-reported anthropometric adolescent data including sex, age (in months), height, and weight.²⁷ The variable uses the CDC SAS Program for computing BMI z-scores (standard deviations [SDs]) for each adolescent based on parental reports of adolescent height, weight, age, and sex. Given that our analytic sample includes adolescents in all weight categories, we use BMI z-score instead of BMI expressed as a percentage of the 95th percentile, which is a better measure of adiposity for children with obesity.²⁸

The primary predictor, neighborhood public transportation access, was based on the percentage of adult workers 16 years or older using public transportation regularly within the adolescent's circular home network (750 m), which was based on parent-reported home addresses. Adolescents living in areas where a higher percentage of adults using public transportation should have increased access to public transportation than those living in areas where public transportation is less common for working adults. This measure is drawn from the American Community Survey (2010–2014) census tract-level data based on the question, “How did this person usually get to work last week?”²⁹ Given the right-skewed distribution (median = 2%; tertiles = 1% and 5%; range = 0%, 76%) neighborhood public transportation access was categorized by combining the two lowest tertiles as less prevalent (0%–5%) and highest tertile as prevalent (>5%).

For this geocoded variable as well as geocoded covariates, a home circular network of 750 m was used. Sensitivity analyses performed to assess alternate home network geocoded variables (400, 500, 800, 1000, and 1200 m) were not found to impact estimates.

Individual-level parental-reported covariates include grade level, sex, race/ethnicity, and household income. Based on data showing that older adolescents use public transportation more than younger adolescents,^20,30,31 we categorized adolescent age by middle school (6th–8th grade) and high school (9th–12th grade). Race/ethnicity was precategorized in the FLASHE dataset as non-Hispanic White, non-Hispanic Black, Hispanic, and other. The household income variable is categorized from $0 to $99,999 or $100,000 or more.

Percent area poverty (percent of network living <200% of the poverty line) was included as an area-level covariate. This variable is available in the FLASHE geocoded dataset and is based on the participant's home address. We categorized percent area poverty into tertiles for the analysis: low (0%–29%), moderate (29%–46%), and high (46%–100%) area poverty to simplify interpretations of the variables and analyses.³²

Statistical Analysis

We used SAS version 9.4 (2021) to analyze the data. We calculated means and SDs for all continuous variables and frequencies and percentages to summarize categorical variables. We ran crude and adjusted linear regression models to test the association between neighborhood-level public transportation access (less prevalent and prevalent) and individual participant-level BMI z-scores. Adjusted models included individual grade level, sex, race/ethnicity, household income, and area-level poverty as covariates. We also ran the adjusted linear regression models stratified by grade level, sex, household income, and percent area poverty.

Results

Descriptive Results

Table 1 shows the sample baseline characteristics. The analytic sample included 336 adolescents of middle school (45%) and high school (55%) ages evenly distributed by sex (50% female). Over half of the participants had healthy weight (69%), whereas over a quarter had overweight or obesity (28%). On average participants had a BMI z-score of 0.44 (1.04), lived in home-circular networks with 7.15% of adults using public transportation and 39% of the population <200% of the Federal Poverty Line. As given in Figure 1, the overall BMI z-score was highest in non-Hispanic Black and middle school adolescents. A comparison of sociodemographic factors among included and excluded populations showed no difference between age, sex, race, BMI z-score, and weight categories (Table 2). As expected, those in the excluded population, living in a suburb, town, and rural area had lower average neighborhood public transportation usage (2%) compared with those in the included, urban population (7%). The excluded population also had lower area poverty (30%) compared with the included population (39%).

Figure 1.

Tabulated mean BMI z-score by sex, age, and race/ethnicity. The overall BMI z-score was highest in Black and middle school adolescents. Error bars indicate standard error.

Table 1.

Baseline Characteristics of Analytic Sample

Variable	Overall, n (%)/M (SD) (n = 336)
Age
Middle school^a	151 (45%)
High school^a	185 (55%)
Sex
Female^a	169 (50%)
Male^a	167 (50%)
Race/ethnicity
Non-Hispanic White^a	223 (67%)
Non-Hispanic Black^a	55 (16%)
Hispanic^a	32 (10%)
Other^a	25 (7%)
BMI and weight class
BMI z-score^b	0.44 (1.04)
Underweight^a	9 (3%)
Healthy weight^a	233 (69%)
Overweight^a	55 (16%)
Obese^a	39 (12%)
Household income
$0 to $99,999	270 (80%)
$100,000 or more	66 (20%)
Home-Circular Network (HC0750) geocoded variables
Neighborhood public transportation access^b,c	7 (13%)
Population below 200% of the Federal Poverty Line^b,c	39 (18%)

Categorical variables summarize by sample size and percent.

Continuous variables summarized by mean and SD.

Geocoded variables summarized as percent of HC0750 Network and SD.

SD, standard deviation.

Table 2.

Baseline Characteristics of Included and Excluded Analytic Sample

Variable	Overall, n (%)/M (SD)
Variable	Included (n = 336)^a	Excluded (n = 918)^b
Age
Middle school^c	151 (45%)	364 (40%)
High school^c	185 (55%)	554 (60%)
Sex
Female^c	169 (50%)	462 (50%)
Male^c	167 (50%)	456 (50%)
Race/Ethnicity
Non-Hispanic White^c	223 (67%)	573 (62%)
Non-Hispanic Black^c	55 (16%)	156 (17%)
Hispanic^c (%)	32 (10%)	95 (10%)
Other^c	25 (7%)	94 (10%)
BMI and Weight Class
BMI z-score^d	0.44 (1.04)	0.36 (1.09)
Underweight^c	9 (3%)	39 (4%)
Healthy weight^c	233 (69%)	627 (68%)
Overweight^c	55 (16%)	144 (16%)
Obese^c	39 (12%)	108 (12%)
Household income
$0 to $99,000	270 (80%)	712 (78%)
$100,000 or more	66 (20%)	206 (22%)
Home-Circular Network (HC0750) geocoded variables
Neighborhood public transportation usage^d,e	7 (13%)	2 (4.02%)
Population below 200% of the Federal Poverty Line^d,e	39 (18%)	30 (14.80%)

Included population (city).

Excluded population in study (rural, town, suburb).

Categorical variables summarize by sample size and percent.

Continuous variables summarized by mean and standard deviation.

Geocoded variables summarized as percent of HC0750 Network and standard deviation.

Association Between Public Transportation and BMI z-Score

Table 3 shows that after adjusting for age, race/ethnicity, household income, and neighborhood poverty, there was a positive relationship between high public transportation access and adolescent z-BMI (b = 0.25, 95% confidence interval [CI]: −0.01 to 0.50). In stratified analyses, there was a positive public transportation access-z-BMI association for high school and male adolescents (b = 0.57, 95% CI: 0.23–0.91 and b = 0.48, 95% CI: 0.09–0.87). In stratified analyses, high public transportation access was associated with higher z-BMI for adolescents with a household income below $99,999 subgroups (0.29, 95% CI: 0.02–0.56). No relationships were observed between public transportation access and adolescent z-BMI among middle school students, females, and race/ethnic youth and adolescents living in a household with an income above $100,000. Sensitivity analyses utilizing a different prevalence threshold categorization for low vs. high public transportation usage (7.5% vs. 5%) revealed similar findings (Supplementary Table S1).

Table 3.

Stratified Models of the Association Between Neighborhood Public Transportation Access and BMI z-Score

	BMI z-score^a
	Point estimate	95% CI
Overall	0.25 ^*	−0.01 to 0.50
Age
Middle school^b (%)	−0.23	−0.63 to 0.16
High school^b (%)	0.57 ^*	0.23 to 0.91
Sex
Female^b (%)	−0.001	−0.33 to 0.33
Male^b (%)	0.48 ^*	0.09 to 0.87
Race/ethnicity
Non-Hispanic White^b (%)	0.23	−0.07 to 0.54
Non-Hispanic Black^b (%)	0.28	−0.37 to 0.94
Hispanic^b (%)	−0.77	−1.82 to 0.29
Other^b (%)	0.67	−0.74 to 2.09
Household income
$0 to $99,999	0.29 ^*	0.02 to 0.56
$100,000 or more	0.07	−0.72 to 0.85

Boldface indicates statistical significance of (^*p < 0.05).

Overall model was adjusted for age, sex, race/ethnicity, household income, and percent of the HC0750 living 200% below the poverty level. Age stratified models were adjusted for, sex, race/ethnicity, household income, and percent of the HC0750 living 200% below the poverty level. Sex stratified models were adjusted for age, race/ethnicity, household income, and percent of the HC0750 living 200% below the poverty level. Race/ethnicity stratified models were adjusted for age, sex, household income, and percent of the HC0750 living 200% below the poverty level. Household income stratified models were adjusted for age, sex, and percent of the HC0750 living 200% below the poverty level.

CI, confidence interval.

Discussion

This study examined the association between neighborhood public transportation access and BMI z-score among a national sample of adolescents living in urban areas. The association between neighborhood public transportation access and adolescent BMI was not in the hypothesized direction. Stratified analyses revealed that this association was present among males, high school students, and among adolescents living in households with an income below $99,000; however, associations were in the opposite direction of our hypothesis.

We hypothesized that adolescents in areas with higher neighborhood public transportation access would have lower BMIs. We expected the magnitude of association to be higher among older adolescents and males because they are more likely to use public transportation. Specifically, older adolescents are more independent, and adolescent girls are less likely to walk in certain areas at certain times of the day because of the risk of gender-based violence.^20,30,31,33 However, our analyses yielded results opposite of our hypothesis, showing a positive relationship between public transportation access and BMI, particularly among males and high school (vs. middle school) students. A possible explanation for our findings may be that males and older adolescents have an increased personal sense of safety and mobility relative to females and younger adolescents, and thus potentially greater access to energy-dense foods (e.g., fast food, convenience stores), which may contribute to higher BMIs.³⁴

In this sense, greater mobility may be associated with higher weight status among urban male adolescents and those in high school compared with middle school, especially given that the relationship between diet and obesity is stronger than physical activity. This interpretation of our findings is supported by prior literature showing that there are healthy food deserts in urban areas where public transit may also be more prevalent.^35–37

Neighborhood public transportation access may be also associated with other neighborhood factors that affect adolescent obesity that we did not account for, such as neighborhood safety and crime. Neighborhood crime also is an important predictor of youth mobility, transit use, and physical activity.^38–40 Youths in high crime areas are less likely to use public transportation or engage in active transportation,⁴⁰ which could contribute to increased BMI as observed in this study.

This study addressed the association between neighborhood public transportation access and BMI z-score in adolescents. Limited research has examined this topic, although prior studies have demonstrated a positive relationship between public transportation and physical activity in boys and girls aged 12–17 years.⁴¹ Our findings demonstrate that neighborhood public transportation access is positively associated with BMI z-score in high schooler and male adolescents, and negatively associated with BMI z-score in Hispanic adolescents. This study also extends prior research on sex disparities in transportation vulnerability and adolescent BMI.⁴²

Limitations

The FLASHE study is cross-sectional; therefore, causality cannot be assigned to the associations discussed in this article. To fully understand this relationship, longitudinal data are needed. In addition, data on neighborhood public transportation access for individuals aged 16 and older was used as a proxy for area-level youth transportation vulnerability based on prior literature,¹⁷ although future studies should draw from additional measures of transportation vulnerability including biking/walking path safety, connectivity, and reliability of public transportation in the home area. Because individual census tracts were not given at the participant level, we could not account for the nested nature of individual and neighborhood-level data. Owing to this limitation, we could also not link via census tract to other datasets that could provide a richer view of public transportation. In addition, the study is limited by the use of adult neighborhood public transportation access as a proxy for adolescent neighborhood public transportation access.

Given that the BMI z-score is based on parent-reported anthropometric data, we acknowledge the potential bias in reporting this measure and measurement error in the outcome of interest. The study is also limited owing to the lack of measurement of key confounding variables including parental education and crime. We were not able to include a variable on crime in our analyses because it was not available in the FLASHE data. Finally, although we hypothesized that adolescents living in areas with higher public transportation access would have increased physical activity,¹⁷ and thus would have lower BMIs, we knowledge the bidirectional nature of the relationship between physical activity and BMI as a limitation in our analyses.

Conclusion

Our findings suggest that neighborhood public transportation access is associated with adolescent BMI, although the direction of this association varies across urban adolescent demographic subgroups. These findings could be pointing to other factors, such as household poverty or neighborhood crime that may impact the association between neighborhood public transit and adolescent BMI. Future research is needed to clarify the relationships between individual and social-environmental factors that impact public transportation access and its association with adolescent BMI.

Impact Statement

Neighborhood public transportation access in urban settings was associated with higher BMI among adolescents, particularly among males, older, and lower-income adolescents. We hypothesize that independence and associated mobility in urban areas may increase access to energy-dense foods, especially given that diet is a stronger predictor of obesity than physical activity.

Authors' Contributions

The authors made substantial contributions to this study. I.G. conceptualized and designed the study, contributed to the analysis and interpretation of data, prepared the tables and results for publication, drafted the initial version of the article, and reviewed and revised the article. E.M.D. conceptualized and designed the study, contributed to the analysis and interpretation of data, and critically reviewed and revised the article. K.I.P. and A.C.S. conceptualized and designed the study and interpretation of the data, and reviewed and revised the article, reviewed and revised the article. C.D.N. reviewed and revised the article. All authors approved the final version of the article as submitted and agree to be accountable for all aspects of the work.

Funding Information

No funding was received for this article.

Footnotes

Author Disclosure Statement

No competing financial interests exist.

Supplementary Material

References

Skinner

, Ravanbakht

, Skelton

, et al. Prevalence of obesity and severe obesity in US children, 1999–2016. Pediatrics, 2018; 141(3):e20173459.

Hills

, Andersen

, Byrne

. Physical activity and obesity in children. Br J Sports Med, 2011; 45(11):866–870.

Allison

, Adlaf

, Dwyer

, et al. The decline in physical activity among adolescent students: A cross-national comparison. Can J Public Health, 2007; 98(2):97–100.

Bronfenbrenner

Ecological Systems Theory. Jessica Kingsley Publishers: Philadelphia, PA; 1992.

Clary

, Lewis

, Limb

, et al. Weekend and weekday associations between the residential built environment and physical activity: Findings from the ENABLE London study. PLoS One, 2020; 15(9):e0237323.

Finkelstein

, Petersen

, Schottenfeld

. Promoting children's physical activity in low-income communities in Colorado: What are the barriers and opportunities?. Prev Chronic Dis, 2017; 14:E134.

Bracy

, Millstein

, Carlson

, et al. Is the relationship between the built environment and physical activity moderated by perceptions of crime and safety?. Int J Behav Nutr Phys Act, 2014; 11(1):24.

Yuksel

, Şahin

, Maksimovic

, et al. School-based intervention programs for preventing obesity and promoting physical activity and fitness: A systematic review. Int J Environ Res Public Health, 2020; 17(1):347.

O'Kane

, Carlin

, Gallagher

, et al. A study protocol for a clustered randomised controlled trial to evaluate the effectiveness of a peer-led school-based walking intervention on adolescent girls' physical activity: The Walking in ScHools (WISH) study. BMC Public Health, 2020; 20(1):541.

10.

Bean

, Caccavale

, Adams

, et al. Parent involvement in adolescent obesity treatment: A systematic review. Pediatrics, 2020; 146(3):e20193315.

11.

Rose

, Barker

, Maria Jacob

, et al. A systematic review of digital interventions for improving the diet and physical activity behaviors of adolescents. J Adolesc Health, 2017; 61(6):669–677.

12.

Wang

, Cai

, Wu

, et al. What childhood obesity prevention programmes work? A systematic review and meta-analysis. Obes Rev, 2015; 16(7):547–565.

13.

Weihrauch-Blüher

, Kromeyer-Hauschild

, Graf

, et al. Current guidelines for obesity prevention in childhood and adolescence. Obes Facts, 2018; 11(3):263–276.

14.

Besser

, Dannenberg

. Walking to public transit: Steps to help meet physical activity recommendations. Am J Prev Med, 2005; 29(4):273–280.

15.

Cole

, MacLeod

, Spriggs

. Health impact assessment of transportation projects and policies: Living up to aims of advancing population health and health equity?. Annu Rev Public Health, 2019; 40:305–318.

16.

Handy

, Boarnet

, Ewing

, et al. How the built environment affects physical activity: Views from urban planning. Am J Prev Med, 2002; 23(2 Suppl):64–73.

17.

Granados

, Haderer

, D'Agostino

, et al. The association between neighborhood public transportation usage and youth physical activity. Am J Prev Med, 2021; 61(5):733–737.

18.

Chaix

, Kestens

, Duncan

, et al. Active transportation and public transportation use to achieve physical activity recommendations? A combined GPS, accelerometer, and mobility survey study. Int J Behav Nutr Phys Act, 2014; 11:124.

19.

D'Agostino

, Armstrong

, Alexander

, et al. Predictors and patterns of physical activity from transportation among United States youth, 2007–2016. J Adolesc Health, 2021; 69(2):263–271.

20.

Yeung

, Wearing

, Hills

. Child transport practices and perceived barriers in active commuting to school. Transp Res Part A Policy Pract, 2008; 42(6):895–900.

21.

Heaps

, Abramsohn

, Skillen

. Public transportation in the US: A driver of health and equity. Health Affairs Health Policy Brief. 2021. Available from: https://www.healthaffairs.org/do/10.1377/hpb20210630.810356/

22.

Litman

. Evaluating transportation equity: Guidance for incorporating distributional impacts in transport planning. Inst Transp Eng ITE J, 2022; 92(4):43–49.

23.

Nebeling

, Hennessy

, Oh

, et al. The FLASHE Study: Survey development, dyadic perspectives, and participant characteristics. Am J Prev Med, 2017; 52(6):839–848.

24.

McDaniels

, Harley

, Beach

. Transportation, accessibility, and accommodation in rural communities. In: Disability and Vocational Rehabilitation in Rural Settings: Challenges to Service Delivery. (Harley DA, Ysasi NA, Bishop ML, Fleming AR. eds.) Springer International Publishing: Cham; 2018; pp. 43–57.

25.

Tomej

, Liburd

. Sustainable accessibility in rural destinations: A public transport network approach. J Sustain Tour, 2020; 28(2):222–239.

26.

Vitale Brovarone

, Cotella

. Improving rural accessibility: A multilayer approach. Sustainability, 2020; 12(7):2876.

27.

Division of Nutrition

, and Obesity, National Center for Chronic Disease Prevention and Health

Promotion

. A SAS Program for the 2000 CDC Growth Charts (ages 0 to <20years) 2019. Available from: https://www.cdc.gov/nccdphp/dnpao/growthcharts/resources/sas.htm (Last accessed January 4, 2023).

28.

Cole

, Faith

, Pietrobelli

, et al. What is the best measure of adiposity change in growing children: BMI, BMI%, BMI z-score or BMI centile?. Eur J Clin Nutr, 2005; 59(3):419–425.

29.

Public use microdata samples (PUMS). United States Census Bureau. https://www.census.gov/programs-surveys/acs/microdata.html. Updated December 15, 2022. Accessed January 4, 2023.

30.

Broberg

, Salminen

, Kyttä

. Physical environmental characteristics promoting independent and active transport to children's meaningful places. Am J Health Promot, 2013; 38:43–52.

31.

Carver

, Timperio

, Hesketh

, et al. Are safety-related features of the road environment associated with smaller declines in physical activity among youth?. J Urban Health, 2010; 87(1):29–43.

32.

DeCoster

, Gallucci

, Iselin

A-MR

. Best practices for using median splits, artificial categorization, and their continuous alternatives. J Exp Psychopathol, 2011; 2(2):197–209.

33.

Althoff

, Sosič

, Hicks

, et al. Large-scale physical activity data reveal worldwide activity inequality. Nature, 2017; 547(7663):336–339.

34.

Kern

, Auchincloss

, Stehr

, et al. Neighborhood prices of healthier and unhealthier foods and associations with diet quality: Evidence from the multi-ethnic study of atherosclerosis. Int J Environ Res Public Health, 2017; 14(11):1394.

35.

Gittelsohn

, Trude

, Poirier

, et al. The impact of a multi-level multi-component childhood obesity prevention intervention on healthy food availability, sales, and purchasing in a low-income urban area. Int J Environ Res Public Health, 2017; 14(11):1371.

36.

Hager

, Cockerham

, O'Reilly

, et al. Food swamps and food deserts in Baltimore City, MD, USA: Associations with dietary behaviours among urban adolescent girls. Public Health Nutr, 2017; 20(14):2598–2607.

37.

Rummo

, Wu

, McDermott

, et al. Relationship between retail food outlets near public schools and adolescent obesity in New York City. Health Place, 2020; 65:102408.

38.

Kneeshaw-Price

, Saelens

, Sallis

, et al. Neighborhood crime-related safety and its relation to children's physical activity. J Urban Health, 2015; 92(3):472–489.

39.

Marquet

, Hipp

, Alberico

, et al. Short-term associations between objective crime, park-use, and park-based physical activity in low-income neighborhoods. Prev Med, 2019; 126:105735.

40.

Goon

, Kontulainen

, Muhajarine

. Neighborhood built environment measures and association with physical activity and sedentary time in 9-14-year-old children in Saskatoon, Canada. Int J Environ Res Public Health, 2020; 17(11):3837.

41.

Bezold

, Stark

, Rundle

, et al. Relationship between Recreational Resources in the School Neighborhood and Changes in Fitness in New York City Public School Students. J Urban Health, 2017; 94(1):20–29.

42.

Eaton

, Kann

, Kinchen

, et al. Youth risk behavior surveillance—United States, 2011. MMWR Surveill Summ, 2012; 61(4):1–162.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB