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Predictors and Patterns of Physical Activity From Transportation Among United States Youth, 2007-2016

  • Emily M. D'Agostino
    Correspondence
    Address correspondence to: Emily M. D'Agostino, Dr.PH., Department of Family Medicine & Community Health, Duke University School of Medicine, 8872200 W. Main St., Office 623, Durham, NC 27710.
    Affiliations
    Department of Family Medicine and Community Health, Duke University School of Medicine, Durham, North Carolina

    Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina
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  • Sarah C. Armstrong
    Affiliations
    Department of Family Medicine and Community Health, Duke University School of Medicine, Durham, North Carolina

    Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina

    Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina

    Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina
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  • Emily P. Alexander
    Affiliations
    Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina
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  • Truls Østbye
    Affiliations
    Department of Family Medicine and Community Health, Duke University School of Medicine, Durham, North Carolina

    Duke Global Health Institute, Duke University School of Medicine, Durham, North Carolina
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  • Cody D. Neshteruk
    Affiliations
    Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina
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  • Asheley C. Skinner
    Affiliations
    Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina

    Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina
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      Abstract

      Purpose

      Physical activity is strongly associated with health benefits in youth, although wide disparities in physical activity persist across sex, race/ethnicity, and income. Active transportation is an important source of youth physical activity. We aimed to describe active transportation patterns for United States adolescents and young adults ages 12–25 years across sociodemographic and weight status characteristics.

      Methods

      Cross-sectional secondary data analyses were based on self-reported transportation-related physical activity using the National Health and Nutrition Examination Survey, 2007-2016.

      Results

      Of the sample (n = 8,680; population represented, N = 57,768,628), 4,300 (49.5%) were adolescents (12-17 y), and 4,380 (50.4%) were young adults (18-25 y). Male adolescents were more likely to participate in any (risk ratio [RR] = 1.3; 95% confidence interval [CI], 1.16-1.40) and daily (RR = 1.3; 95% CI, 1.06-1.63) active transportation than females. Black (RR = 1.1; 95% CI, 1.01-1.31) and Hispanic (RR = 1.2; 95% CI, 1.05-1.31) adolescents were more likely to engage in any active transportation than whites. Young adult males were more likely to participate in any (RR = 1.3; 95% CI, 1.20-1.50) and daily (RR = 1.3; 95% CI, 1.08-1.55) active transportation than females. Young adults with a lower family income, and both adolescents and young adults with a lower household education, were more likely to engage in any and daily active transportation. We also observed an inverse relationship between weight class and active transportation participation.

      Conclusion

      Active transportation was higher in males, minority, and lower income youth. Our study findings provide evidence for physical activity interventions, suggesting active transportation is a feasible target for low-income and minority youth to reduce physical activity disparities and support optimal health.

      Keywords

      Implications and Contribution
      Active transportation is an important physical activity source, although national youth active transportation patterns have not been reported. This study found that active transportation participation among youth was higher in males, minority and lower income youth, suggesting active transportation is a feasible target for reducing health disparities and promoting health.
      See Related Editorial on p.187
      Physical activity is strongly associated with health benefits in youth, including improved cardiovascular, musculoskeletal, socioemotional and mental health, and reduced risk of obesity, asthma, diabetes, and depression [
      • Janssen I.
      • Leblanc A.G.
      Systematic review of the health benefits of physical activity and fitness in school-aged children and youth.
      ]. Developing healthy physical activity habits early in life reduces health disparities [
      CSDH
      Closing the gap in a generation: Health equity through action on the social determinants of health. Final report of the Commission on social determinants of health.
      ,
      WHO
      Prevention of cardiovascular disease: Guidelines for assessment and management of cardiovascular risk.
      ,
      WHO
      Promoting physical activity in schools: An important element of a health-promoting school.
      ,
      WHO
      Prevalence of insufficient physical activity.
      ]. However, the United States (US) is one of the lowest performing countries globally in youth physical activity attainment [
      • Tremblay M.S.
      • Gray C.E.
      • Akinroye K.
      • et al.
      Physical activity of children: A global matrix of grades comparing 15 countries.
      ,
      • Katzmarzyk P.T.
      • Denstel K.D.
      • Beals K.
      • et al.
      Results from the United States 2018 report card on physical activity for children and youth.
      ]. Notable declines in youth physical activity are observed in US youth with increasing age, particularly from adolescence to young adulthood, and wide disparities persist based on sex, race/ethnicity, and income [
      • Armstrong S.
      • Wong C.A.
      • Perrin E.
      • et al.
      Association of physical activity with income, race/ethnicity, and sex among adolescents and young adults in the United States: Findings from the national health and Nutrition examination survey, 2007-2016.
      ]. For example, 49% of white youth ages 6–17 years old engage in at least 60 minutes of moderate-to-vigorous physical activity at least 5 days per week compared with just 42% of black and 45% of Hispanic youths [
      National Physical Activity Plan Alliance
      The 2018 United States report card on physical activity for Children and youth.
      ]. Low-income youth are also less likely than higher income youth to participate in organized sports (70% vs. 88%) [
      National Physical Activity Plan Alliance
      The 2018 United States report card on physical activity for Children and youth.
      ]. Low rates of moderate-to-vigorous physical activity in nonwhite and low-income youth predict significant disparities in youth health-related physical fitness by race and income [
      • Konty K.J.
      • Day S.E.
      • Larkin M.
      • et al.
      Physical fitness disparities among New York City public school youth using standardized methods, 2006-2017.
      ].
      Youth active transport, defined as walking or using a bicycle to get to and from places [
      • Katzmarzyk P.T.
      • Denstel K.D.
      • Beals K.
      • et al.
      Results from the United States 2018 report card on physical activity for children and youth.
      ], is also in decline [
      • McDonald N.C.
      Active transportation to school: Trends among US schoolchildren, 1969–2001.
      ]. Active transportation is an important source of daily physical activity for youth and a significant contributor to routine youth physical activity and its associated health benefits [
      • Carver A.
      • Timperio A.
      • Crawford D.
      Perceptions of neighborhood safety and physical activity among youth: The CLAN study.
      ,
      • Larouche R.
      • Faulkner G.E.
      • Fortier M.
      • Tremblay M.S.
      Active transportation and adolescents’ health: The Canadian health measures survey.
      ]. Fifteen percent and 18% of total physical activity in US adolescent (12-19 y) males and females, respectively, is accounted for by active transportation [
      • Moseley C.A.
      • Skinner A.C.
      • Perrin E.M.
      • et al.
      Adolescent and young adult recreational, occupational, and transportation activity: Activity recommendation and weight status relationships.
      ]. Estimates from studies of European youth report similar estimates (15%–18%) of total physical activity attributable to commuting by biking and walking [
      • Chillón P.
      • Gottrand F.
      • Ortega F.B.
      • et al.
      Active commuting and physical activity in adolescents from Europe: Results from the HELENA study.
      ]. However, prior studies indicate that only 23% of 12- to 19-year-old US youth walk or bike at least 10 minutes continuously 5–7 days per week for transportation [
      • Katzmarzyk P.T.
      • Denstel K.D.
      • Beals K.
      • et al.
      Results from the United States 2018 report card on physical activity for children and youth.
      ]. Also, Hispanic (28%) and non-Hispanic black (16%) youth have the highest rates of active transportation to school, compared with white youth (9%) [
      • McDonald N.C.
      Critical factors for active transportation to school among low-income and minority students: Evidence from the 2001 national household travel survey.
      ], indicating that fostering safe and accessible opportunities for youth to engage in active transportation may be leveraged to reduce physical activity barriers and related health disparities [
      • Zimmerman S.
      • Lieberman M.
      • Kramer K.
      • Sadler B.
      At the Intersection of active transportation and equity: Joining Forces to Make Communities Healthier and Fairer.
      ,
      • Hong M.K.
      • Wilcox L.
      • Machado D.
      • Olson T.A.
      • Simoneaux S.F.
      Care partnerships: toward technology to support teens' participation in their health care. Proceedings of the SIGCHI Conference on Human Factors in Computing systems.
      ].
      Areas with a high proportion of minority and low-income residents, however, have low access to physical activity recreation, neighborhood walkability, and safe/well maintained active transportation routes [
      • Gordon-Larsen P.
      • Nelson M.C.
      • Page P.
      • Popkin B.M.
      Inequality in the built environment underlies key health disparities in physical activity and obesity.
      ,
      • Carlson J.A.
      • Saelens B.E.
      • Kerr J.
      • et al.
      Association between neighborhood walkability and GPS-measured walking, bicycling and vehicle time in adolescents.
      ]. In light of recent calls directed at health professionals to guide programs and policies that promote active transportation to reduce disparities [
      • Asabigi K.
      New Technologies and Automakers have the potential to Eliminate transportation as a Major factor contributing to health disparities, and provide additional benefits.
      ], population-level prevalence and individual-level predictors of active transportation in US youth are needed. This study therefore aimed to describe active transportation patterns for US adolescents and young adults ages 12–25 years across individual-level sociodemographic and weight status characteristics.

      Methods

      Population

      We drew data from years 2007–2016 of the National Health and Nutrition Examination Survey (NHANES; n = 8,680; population represented, N = 57,768,628). These years included consistent questions about physical activity for transportation in the adolescent and young adult population. NHANES is a multistage probability sample of the noninstitutionalized United States population and allows estimates that represent the US population. NHANES includes several components; here, we use the physical examination and the in-home interview. NHANES is described in detail elsewhere [
      CDC
      National health and Nutrition examination survey. 2020 [cited 2020 July 22].
      ]. We included adolescents and young adults aged 12-25 who responded to questions regarding transportation-related physical activity. This study was approved by the Duke Health Institutional Review Board.

      Measures

      Main outcomes

      The two main outcomes included participation in any and daily transportation-related physical activity. Physical activity is measured based on self-report. Questions were asked of adolescents 12–15 years during the physical examination, without parent assistance. Those 16 years and older responded for themselves during the in-home interview. We used two questions focused on physical activity that occurs for transportation. First, individuals are asked:
      The next questions exclude the physical activity at work that you have already mentioned. Now I would like to ask you about the usual way (you travel/SP travels) to and from places. For example to school, for shopping, to work. In a typical week (do you/does SP) walk or use a bicycle for at least 10 minutes continuously to get to and from places?
      If the respondent indicates any activity for transportation, they are asked how many days in a typical week they walk or bicycle for at least 10 minutes continuously.
      We categorized individuals as reporting any versus no transportation-related physical activity (e.g., “any active transportation”, main outcome 1). Among those reporting any activity, we further assessed the number of days per week they engaged in transportation-related physical activity (e.g., “daily active transportation”, categorized as 7 days per week, vs. <7 days per week, main outcome 2).

      Independent variables

      Race/ethnicity is based on self-report and classified as non-Hispanic white, non-Hispanic black, Hispanic, or other race/ethnicity. Income is classified based on the household's income as a ratio to the Federal Poverty Level (FPL), ranging from <100% of FPL to 500% FPL or greater. Age was categorized as adolescent (12–17 years) and young adult (18–25 years) to report findings across these age groups.

      Education

      For adolescents aged 12-17 years, we use two measures of education to account for both poverty status and peer influences at the school level. The first is the highest grade level of the household reference person, who is a person 18 years or older who owns or rents the dwelling unit. For adolescents, this is most commonly a parent or guardian. This is categorized as <ninth grade, high school without diploma, high school diploma, some college, and college graduate. Grade level also is included for adolescents as not high school (e.g., middle school) or high school (grades 9-12). This is primarily to capture the differences in the psychosocial factors, such as peer influences, of middle versus high school. For young adults aged 18-25 years, education represents their own education level, categorized as <ninth grade, high school without diploma, high school diploma, some college, and college graduate.

      Weight status

      Weight status was defined using measured height and weight to calculate body mass index (BMI) in kg/m2. For 12- to 17-year-old participants, CDC growth charts [
      • Kuczmarski R.J.
      • Ogden C.L.
      • Guo S.S.
      • Grummer-Strawn L.M.
      • Flegal K.M.
      • Mei Z.
      • Wei R.
      • Curtin L.R.
      • Roche A.F.
      • Johnson C.L.
      2000 CDC Growth Charts for the United States: methods and development.
      ] based on a historical reference population were used to define five categories: healthy weight, BMI% ≥10–<85; overweight, BMI% ≥85–<95; class I obesity, BMI% ≥95–<120th percent of the 95th BMI percentile [BMI95]), class II obesity, BMI% ≥120–<140th of BMI95, and class III obesity, BMI% ≥140th of BMI95. For those 18–25 years old, five corresponding categories were constructed: healthy weight, BMI ≥18.5–24.9 kg/m2; overweight, 25.0–29.9; and obesity classes I-III, defined respectively as BMI ≥30.0–34.9, 35.0–39.9, and ≥40 kg/m2.

      Season of sampling

      Season of data collection was included to account for impact of seasonality on active transportation participation. Summer months were defined as May-October; non-summer months were defined as November-April.

      Statistical approach

      We present all results separately by age group (adolescent and young adult). We conducted bivariate analyses examining the association between the independent variables and the outcomes of any and daily active transportation, comparing differences using Pearson χ2 tests adjusted with the second-order Rao-Scott correction. We only provide the test of difference across all categories, but provide 95% confidence intervals to allow readers to consider more nuanced differences. Multivariable analyses were conducted on binary outcomes using generalized linear models with a log link to produce exponentiated coefficients (i.e., risk ratios). Model covariates included gender, race/ethnicity, weight status (i.e., healthy weight; overweight; and obesity class I, II, or III), parent education (12-17 y only), grade level (12-17 y only), participant education (18-25 y only), family income, and season of sampling. All analyses were adjusted for the complex survey design in the NHANES, including strata, primary sampling units, and probability weights, conducted using the svy commands in Stata, version 16 (StataCorp LLC). We considered 2-sided p < .05 to be statistically significant.

      Results

      Table 1 displays a description of the sample population for adolescents and young adults, and represents the US population of 12–25 year olds. The sample (n = 8,680) included 4,300 adolescents (12-17 y) and 4,380 young adults (18-25 y) who participated in NHANES and responded to questions about physical activity.
      Table 1Weighted population description, for adolescents and young adults
      VariableAdolescents (12-17 y) n = 4,300Young adults (18-25 y) n = 4,380
      Weighted proportion, %95% CIWeighted proportion, %95% CI
      Gender
       Male50.648.5, 52.650.248.5, 52.0
       Female49.547.4, 51.549.848.1, 51.5
      Race/ethnicity
       White58.053.6, 62.358.854.6, 62.8
       Black14.111.9, 16.713.611.4, 16.1
       Hispanic19.916.8, 23.319.216.3, 22.6
       Other
      Asian was included in Other. Asian was oversampled after 2010 surveys.
      8.16.8, 9.58.47.0, 10.0
      Weight status
      Weight status for adolescents (12-17 y) was classified based on BMI percentiles from CDC growth charts. Corresponding classifications for the young adults (18-25 y) were created from raw BMI classifications.
       Healthy weight63.961.8, 65.849.847.1, 52.4
       Overweight16.214.9, 17.625.123.3, 27.0
       Class I obesity12.711.3, 14.213.111.7, 14.6
       Class II obesity4.73.9, 5.66.45.4, 7.5
       Class III obesity2.62.0, 3.35.74.8, 6.8
      Education
      Education for adolescents (12-17 y) is described by the highest education level of a household reference person (someone 18 years or older). For young adults, education level represents their own education level.
       <9th grade7.56.4 8.95.74.8, 6.9
       HS without diploma12.110.7, 13.811.410.0, 13.0
       HS only20.618.4, 23.022.520.1, 25.1
       Some college31.929.5, 34.435.431.8, 39.1
       College graduate27.824.8, 31.22522.1, 28.1
      Family income, FPL, %
       <10020.618.3, 23.028.825.5, 32.4
       ≥100 & <20023.321.2, 25.524.322.4, 26.4
       ≥200 & <30016.614.7, 18.615.313.8, 16.9
       ≥300 & <40013.211.2, 15.410.38.7, 12.2
       ≥400 & <5008.57.0, 10.37.25.9, 8.8
       50018.015.5, 20.714.111.9, 16.5
      Season of sampling
       Non-summer (November-April)44.138.2, 50.247.040.7, 53.5
       Summer (May-October)55.949.8, 61.853.046.5, 59.3
      BMI = body mass index; CI = confidence interval; HS = high school; FPL = Federal Poverty Level.
      a Asian was included in Other. Asian was oversampled after 2010 surveys.
      b Weight status for adolescents (12-17 y) was classified based on BMI percentiles from CDC growth charts. Corresponding classifications for the young adults (18-25 y) were created from raw BMI classifications.
      c Education for adolescents (12-17 y) is described by the highest education level of a household reference person (someone 18 years or older). For young adults, education level represents their own education level.
      The percentage of individuals participating in any active transportation for both adolescents and young adults (Table 2) was higher among males, race/ethnicity minorities, and lower income participants. Among adolescents, engagement in any active transportation in a typical week was more common among Hispanics, blacks, those residing in households with lower education level, and youth not in high school. Weight status was not significantly associated with more engagement in any active transportation for adolescents. For young adults, engagement in any active transportation was more common among individuals who self-identify race/ethnicity as “other”, participants with a healthy weight BMI, and college graduates.
      Table 2Percentage of individuals reporting any
      Any active transportation is defined as whether the sampled individual walks or uses a bicycle for 10 continuous minutes to get to or from a place in a typical week.
      and daily use of active transportation by gender, race/ethnicity, weight status, education, income, and seasonality
      VariableAnyDaily
      Adolescents (12-17 y)

      n = 4,300
      Young adults (18-25 y)

      n = 3,978
      Adolescents (12-17 y)

      n = 2,010
      Young adults (18-25 y)

      n = 1,628
      Percentage, %p valuePercentage, %p valuePercentage, %p valuePercentage, %p value
      Gender
       Male48.3<.00141.2<.00125.9.00932.0.09
       Female37.833.619.226.8
      Race/ethnicity
       White39.8<.00135.0.00323.1.1428.8.38
       Black47.242.727.734.4
       Hispanic48.736.420.727.8
       Other
      Asian was included in Other. Asian was oversampled after 2010 surveys.
      46.048.820.430.8
      Weight status
      Weight status for adolescents (12-17 y) was classified based on BMI percentiles from CDC growth charts. Corresponding classifications for the young adults (18-25 y) were created from raw BMI classifications.
       Healthy weight42.5.4941.6.00125.6.0133.8.009
       Overweight42.034.319.525.9
       Class I obesity46.635.814.923.1
       Class II obesity47.430.321.917.4
       Class III obesity40.730.128.024.0
      Education
      Education for adolescents (12-17 y) is reported both as the highest education level of a household reference person (someone 18 years or older) and as the grade level of the adolescent. For young adults, the reported education level represents their own education level.
       <9th grade50.1.00837.1<.00118.9.00133.7.15
       HS without diploma49.637.230.034.0
       HS only42.525.329.121.2
       Some college42.741.322.130.6
       College graduate38.943.016.530.6
      Grade level
       Not HS46.5<.001NANA21.2.05NANA
       HS (9-12 grade)38.7NA25.8NA
      Family income, FPL, %
       <10050.2.00247.9<.00128.9.0837.1.01
       ≥100 & <20048.237.421.822.4
       ≥200 & <30041.731.524.433.8
       ≥300 & <40036.632.422.624.1
       ≥400 & <50037.925.918.121.5
       50038.230.317.523.1
      Season of sampling
       Non-Summer (November-April)40.2.07639.2.3621.6.3328.4.47
       Summer (May-October)45.435.924.031.0
      BMI = body mass index; HS = high school; FPL = Federal Poverty Level.
      N Missing, Any: Adolescents Weight Status = 40; Adolescents Household Reference Person = 102; Young Adults Weight Status = 57; Young Adults Education = 400.
      N Missing, Daily: Adolescents Weight Status = 15; Adolescents Household Reference Person = 50; Young Adults Weight Status = 15; Young Adults Education = 169.
      a Any active transportation is defined as whether the sampled individual walks or uses a bicycle for 10 continuous minutes to get to or from a place in a typical week.
      b Asian was included in Other. Asian was oversampled after 2010 surveys.
      c Weight status for adolescents (12-17 y) was classified based on BMI percentiles from CDC growth charts. Corresponding classifications for the young adults (18-25 y) were created from raw BMI classifications.
      d Education for adolescents (12-17 y) is reported both as the highest education level of a household reference person (someone 18 years or older) and as the grade level of the adolescent. For young adults, the reported education level represents their own education level.
      Daily participation in active transportation (Table 2) for adolescents was significantly higher in males and, for young adults, those with a lower family income. Daily participation in active transportation was highest among adolescent youth with class III obesity, youth in a household with a reference person education of high school without a diploma, and youth reporting a grade level of high school. Also shown in Table 2, young adult engagement in daily active transportation was highest for those with a healthy weight BMI. Education level and gender were not associated with young adult engagement in daily active transportation and family income was not associated with adolescent engagement in daily active transportation. Race/ethnicity was not associated with daily active transportation for either adolescents or young adults. Neither any nor daily active transportation in either age group differed significantly across season of sampling.

      Multivariable estimates for likelihood of adolescent (12-17 y) active transportation based on sociodemographics and weight status

      Multivariable estimates for likelihood of adolescent active transportation (accounting for gender, race/ethnicity, weight status, parent education, grade level, family income, and season of sampling) are presented in Table 3. For adolescent participants, males were more likely than females to participate in any (risk ratio [RR] = 1.3; 95% confidence interval [CI], 1.16-1.40) and daily (RR = 1.3; 95% CI, 1.06-1.63) active transportation. Black (RR = 1.1; 95% CI, 1.01-1.31) and Hispanic (RR = 1.2; 95% CI, 1.05-1.31) adolescents were more likely to engage in any active transportation compared to whites. Weight status and household education level were not associated with any active transportation. However, adolescents enrolled in high school (RR = .8; 95% CI, .76-.95) versus not in high school were less likely to engage in any active transportation. Also, adolescents sampled in the period May-October were more likely to engage in any active transportation than those sampled during November-April (RR = 1.2; 95% CI, 1.06-1.42).
      Table 3Generalized linear model of likelihood of any
      Any active transportation is defined as whether the sampled individual walks or uses a bicycle for 10 continuous minutes to get to or from a place in a typical week.
      and daily active transportation for adolescents (12-17 y) based on gender, race/ethnicity, weight status, education, income, and seasonality
      VariableAnyDaily
      RR95% CIp valueRR95% CIp value
      Gender
       Female1[Reference]NA1[Reference]NA
       Male1.271.161.40.001.321.061.63.01
      Race/ethnicity
       White1[Reference]NA1[Reference]NA
       Black1.151.011.31.041.02.821.28.84
       Hispanic1.171.051.31.01.81.631.04.10
       Other
      Asian was included in Other. Asian was oversampled after 2010 surveys.
      1.13.961.32.13.87.621.23.44
      Weight status
      Weight status for adolescents (12-17 y) was classified based on BMI percentiles from CDC growth charts. Corresponding classifications for the young adults (18-25 y) were created from raw BMI classifications.
       Healthy weight1[Reference]NA1[Reference]NA
       Overweight.97.881.08.62.72.53.97.03
       Class I obesity1.07.911.25.40.59.43.81.00
       Class II obesity1.07.901.27.45.79.511.22.27
       Class III obesity.88.671.16.36.85.461.55.59
      Parent education
      Education for adolescents (12-17 y) is reported both as the highest education level of a household reference person (someone 18 years or older) and as the grade level of the adolescent.
       HS only1[Reference]NA1[Reference]NA
       <9th grade1.09.931.27.30.65.45.92.02
       HS without diploma1.11.971.27.12.98.741.29.86
       Some college1.05.911.21.52.77.581.03.08
       College graduate1.00.851.181.00.60.38.95.03
      Grade level
       Not HS1[Reference]NA1[Reference]NA
       HS (9-12 grade).85.76.95.001.17.961.14.12
      Family income, FPL, %
       5001[Reference]NA1[Reference]NA
       <1001.18.961.45.111.42.942.15.09
       ≥100 & <2001.15.941.41.181.08.711.66.71
       ≥200 & <3001.06.861.32.581.26.752.10.38
       ≥300 & <400.91.721.14.411.07.621.85.81
       ≥400 & <500.96.741.25.77.89.451.74.73
      Season of sampling
       Non-summer (November-April)1[Reference]NA1[Reference]NA
       Summer (May-October)1.221.061.42.011.10.891.35.38
      BMI = body mass index; CI = confidence interval; HS = high school; FPL = Federal Poverty Level; NA = not applicable; RR = risk ratio.
      a Any active transportation is defined as whether the sampled individual walks or uses a bicycle for 10 continuous minutes to get to or from a place in a typical week.
      b Asian was included in Other. Asian was oversampled after 2010 surveys.
      c Weight status for adolescents (12-17 y) was classified based on BMI percentiles from CDC growth charts. Corresponding classifications for the young adults (18-25 y) were created from raw BMI classifications.
      d Education for adolescents (12-17 y) is reported both as the highest education level of a household reference person (someone 18 years or older) and as the grade level of the adolescent.
      Race/ethnicity was not associated with daily active transportation among adolescents. Adolescents with overweight (RR = .7; 95% CI, .53-.97) or class I obesity (RR = .6; 95% CI, .43-.81) were less likely to participate in daily active transportation compared to healthy weight adolescents. Youth with a highest household education level of less than ninth grade (RR = .7; 95% CI, .45-.92), and college graduates (RR = .6; 95% CI, .38-.95) were less likely than households with high school only to engage in daily active transportation. Grade level was not associated with daily active transportation and family income was not associated with any or daily active transportation among adolescents.

      Multivariable estimates for likelihood of young adult (18-25 y) active transportation based on sociodemographics and weight status

      Multivariable estimates for likelihood of young adult active transportation (accounting for gender, race/ethnicity, weight status, participant education, family income, and season of sampling) are presented in Table 4. For young adults, males were more likely to participate in any (RR = 1.3; 95% CI, 1.20-1.50) and daily (RR = 1.3; 95% CI, 1.08-1.55) active transportation than females. Young adults who identified as “Other” race/ethnicity (RR = 1.3; 95% CI, 1.05-1.49) were more likely to engage in any active transportation compared with whites. Young adults with overweight (RR = .8; 95% CI, .74-.97), class I obesity (RR = .9; 95% CI, .74-1.00) or class III obesity (RR = .7; 95% CI, .58-.95) were less likely to engage in any active transportation compared to those with a healthy weight BMI. Any level of education was associated with a higher likelihood of participating in any active transportation (RR = 1.3; 95% CI, .97-1.76 for <ninth grade, RR = 1.4; 95% CI, 1.11-1.66 for high school without a diploma, RR = 1.6; 95% CI 1.35-1.94 for some college, and RR = 1.8; 95% CI, 1.49-1.26 for college graduate) when compared to young adults reporting only high school education. Young adults with a lower family income (RR = 1.7; 95% CI, 1.21-2.26 for <100 and RR = 1.4; 95% CI, 1.07-1.70 for ≥100 and <200) were more likely engage in any active transportation compared to those with a family income of 500% FPL.
      Table 4Generalized linear model of likelihood of any
      Any active transportation is defined as whether the sampled individual walks or uses a bicycle for 10 continuous minutes to get to or from a place in a typical week.
      and daily active transportation for young adults (18-25 y) based on gender, race/ethnicity, weight status, education, income, and seasonality
      VariableAnyDaily
      RR95% CIp valueRR95% CIp value
      Gender
       Female1[Reference]NA1[Reference]NA
       Male1.341.201.50.001.301.081.55.01
      Race/ethnicity
       White1[Reference]NA1[Reference]NA
       Black1.12.951.32.171.23.981.55.07
       Hispanic1.02.841.24.851.02.791.32.89
       Other
      Asian was included in Other. Asian was oversampled after 2010 surveys.
      1.251.051.49.01.98.751.30.91
      Weight status
      Weight status for adolescents (12-17 y) was classified based on BMI percentiles from CDC growth charts. Corresponding classifications for the young adults (18-25 y) were created from raw BMI classifications.
       Healthy weight1[Reference]NA1[Reference]NA
       Overweight.85.74.97.02.77.60.99.04
       Class I obesity.86.741.00.06.65.46.93.02
       Class II obesity.82.631.05.11.52.33.84.01
       Class III obesity.74.58.95.02.56.33.96.04
      Education
      For young adults (18-25 y), the reported education level represents their own education level.
       HS only1[Reference]NA1[Reference]NA
       <9th grade1.31.971.76.081.661.042.65.04
       HS without diploma1.361.111.66.001.27.901.80.18
       Some college1.621.351.94.001.29.971.71.08
       College grad1.831.492.26.001.29.911.83.15
      Family income, FPL, %
       5001[Reference]NA1[Reference]NA
       <1001.721.212.26.001.50.952.37.09
       ≥100 & <2001.351.071.70.01.90.541.49.67
       ≥200 & <3001.16.901.49.241.38.812.34.23
       ≥300 & <4001.12.841.48.431.06.562.03.85
       ≥400 & <500.80.581.10.171.20.592.45.61
      Season of sampling
       Non-Summer (November-April)1[Reference]NA1[Reference]NA
       Summer (May-October).96.811.14.641.15.931.42.19
      BMI = body mass index; CI = confidence interval; HS = high school; FPL = Federal Poverty Level; NA = not applicable; RR = risk ratio.
      a Any active transportation is defined as whether the sampled individual walks or uses a bicycle for 10 continuous minutes to get to or from a place in a typical week.
      b Asian was included in Other. Asian was oversampled after 2010 surveys.
      c Weight status for adolescents (12-17 y) was classified based on BMI percentiles from CDC growth charts. Corresponding classifications for the young adults (18-25 y) were created from raw BMI classifications.
      d For young adults (18-25 y), the reported education level represents their own education level.
      Black young adults (RR = 1.2; 95% CI, .98-1.55, p = .07; Table 4) were more likely to engage in daily active transportation compared with white young adults. Also, young adults with any level of overweight or obesity were significantly less likely to engage in daily active transportation compared to their healthy weight counterparts (RR = .77; 95% CI, .60-.99 for overweight, RR = .65; 95% CI, .46-.93 for class I obesity, RR = .52; 95% CI, .33-.84 for class 2 obesity, and RR = .56; 95% CI, .33-.96 for class III obesity). For daily active transportation, young adults reporting less than a ninth grade education level (RR = 1.7; 95% CI, 1.04-2.65) were more likely to participate than those with only high school. Family income was not associated with daily participation in active transportation and seasonality of sampling was not associated with any or daily active transportation participation in young adults.

      Discussion

      Consistent with prior research on youth transportation to school [
      • McDonald N.C.
      Active transportation to school: Trends among US schoolchildren, 1969–2001.
      ], this study of US adolescents and young adults found a higher proportion of minority and low-income youth participate in active transportation compared with their nonminority and higher income peers. However, physical activity levels are shown in prior research studies to be lower in minority/low-income subgroups [
      • Armstrong S.
      • Wong C.A.
      • Perrin E.
      • et al.
      Association of physical activity with income, race/ethnicity, and sex among adolescents and young adults in the United States: Findings from the national health and Nutrition examination survey, 2007-2016.
      ]. Prior studies also show that for disadvantaged populations, physical activity is a particularly important driver of cardiovascular health disparities [
      • Saghapour T.
      • Giles-Corti B.
      • Rachele J.
      • Turrell G.
      A cross-sectional and longitudinal study of neighbourhood disadvantage and cardiovascular disease and the mediating role of physical activity.
      ], and that lower neighborhood support of physical activity is associated with higher youth obesity [
      • Saelens B.E.
      • Sallis J.F.
      • Frank L.D.
      • et al.
      Obesogenic neighborhood environments, child and parent obesity: The neighborhood impact on Kids study.
      ]. Given that social and structural drivers of health are critical determinants of youth physical activity participation [
      CSDH
      Closing the gap in a generation: Health equity through action on the social determinants of health. Final report of the Commission on social determinants of health.
      ,
      • Gordon-Larsen P.
      • Nelson M.C.
      • Page P.
      • Popkin B.M.
      Inequality in the built environment underlies key health disparities in physical activity and obesity.
      ], promoting active transportation may present an opportunity to increase physical activity and reduce youth health disparities in under resourced settings [
      • Zimmerman S.
      • Lieberman M.
      • Kramer K.
      • Sadler B.
      At the Intersection of active transportation and equity: Joining Forces to Make Communities Healthier and Fairer.
      ]. Our study findings provide key information suggesting that active transportation is a feasible target for low-income and minority youth, particularly in light of social and structural barriers to physical activity, to reduce disparities and support optimal health.
      Consistent with prior reports on physical activity [
      • Armstrong S.
      • Wong C.A.
      • Perrin E.
      • et al.
      Association of physical activity with income, race/ethnicity, and sex among adolescents and young adults in the United States: Findings from the national health and Nutrition examination survey, 2007-2016.
      ], young adults and females in this study were less likely to participate in any active transportation; however, young adults who did participate in any active transportation were more likely to engage in daily active transportation compared with their adolescent counterparts. In this sense, our data show that while young adults report less active transportation overall, when they do, they use it more frequently than adolescents. We hypothesize that young adults may be using active transportation to commute to work (vs. owning a personal vehicle), although this data set does not include information specific to this possibility. Other studies have shown that minority race/ethnicity, poverty, household vehicle ownership, safety, parental active transport, parental safety concerns, social connectedness to peers, and built environment factors (e.g. number of accessible recreation destinations, walkability, mixed land use, street connectivity) are important predictors of youth active transportation [
      • McDonald N.C.
      Active transportation to school: Trends among US schoolchildren, 1969–2001.
      ,
      • Hinckson E.
      Perceived challenges and facilitators of active travel following implementation of the School Travel-Plan programme in New Zealand children and adolescents.
      ,
      • Tewahade S.
      • Li K.
      • Goldstein R.B.
      • et al.
      Association between the built environment and active transportation among US adolescents.
      ]. Additional follow-up studies are needed to address drivers of youth active transportation specifically among minority youth to determine whether particular factors can be leveraged to promote active transportation in this subgroup.
      We also observed an inverse relationship between weight class and active transportation participation, although this may be reflective of bidirectionality (high active transportation levels would increase overall physical activity, which is associated with lower weight class). However, these findings are supported by prior literature showing a similar relationship between youth obesity and physical activity [
      • Poitras V.J.
      • Gray C.E.
      • Borghese M.M.
      • et al.
      Systematic review of the relationships between objectively measured physical activity and health indicators in school-aged children and youth.
      ,
      • Guinhouya B.C.
      • Samouda H.
      • De Beaufort C.
      Level of physical activity among children and adolescents in Europe: A review of physical activity assessed objectively by accelerometry.
      ] and may suggest that active transportation interventions may help to reduce overweight and obesity among youth. There may also be confounding by participant characteristics—minority and lower income participants were more likely to engage in active transportation, yet are also more likely to have obesity. Specifically, we observed that black and Hispanic adolescents were 10% and 20%, respectively, more likely to engage in any active transportation compared to whites. Black young adults were 23% more likely to engage in daily active transportation compared to whites. Young adults with a low family income <100 and <100 and <200 of the federal poverty level also were 73% and 35%, respectively, more likely engage in any active transportation compared to those with a high family income (>500). Although prior literature shows disparities in physical activity for minority/low-income populations [
      • McDonald N.C.
      Critical factors for active transportation to school among low-income and minority students: Evidence from the 2001 national household travel survey.
      ,
      American Public Health Association
      Public health and equity: Promote environmental justice and equity principles in transportation policy.
      ], our findings suggest that if active transportation neighborhood resources are made available, accessible and safe, physical activity disparities among disadvantaged subgroups may be reduced.
      Based on findings from this study, minority and low-income youth who participate in active transportation do so more than their nonminority and higher-income peers. Prior research, however, has demonstrated that higher participation in active transportation by minority and low-income youth attenuates after accounting for potential confounding and mediating variables including household vehicle ownership, distance to destinations, safety, and residential density [
      • McDonald N.C.
      Active transportation to school: Trends among US schoolchildren, 1969–2001.
      ]. It is possible that minority and low-income youth who are actively traveling are doing so out of necessity rather than by choice. Future research should address the need for interventions to improve youth access and safety to reach and use public transportation, programs to promote youth self-efficacy pertaining to transportation system use, and expanding afterschool/community programs of interest to youth in order to incentivize transportation system use to attend these programs. Although findings in this study included many individual/household factors potential confounders, area-level built environment and psychosocial variables were not addressed in this analysis and may be driving the associations observed. Nevertheless, given that active transportation accounts for a small proportion of total physical activity in adolescents (18% and 16% for adolescent and young adults, respectively) [
      • Moseley C.A.
      • Skinner A.C.
      • Perrin E.M.
      • et al.
      Adolescent and young adult recreational, occupational, and transportation activity: Activity recommendation and weight status relationships.
      ], our results suggest active transportation can be leveraged to promote overall physical activity. Moreover, active transportation can be an accessible/affordable method of physical activity engagement, and therefore may represent a feasible and acceptable method to reduce youth physical activity disparities across race/ethnicity and income [
      • Saelens B.E.
      • Sallis J.F.
      • Frank L.D.
      • et al.
      Obesogenic neighborhood environments, child and parent obesity: The neighborhood impact on Kids study.
      ]. In this sense, active transportation may serve as an important contributor to overall physical activity particularly for high-need youth populations, and possibly an important target for activity promotion.
      Novel frameworks proposed in the literature suggest that funding be directed towards cross-sector collaboration and policy to improve youth physical activity with transportation measures [
      • Piccone C.
      • D’Agostino E.M.
      Targeting youth transportation equity and self-efficacy to reduce youth physical activity disparities: A novel approach for a public health 3.0 agenda.
      ]. Policy can be targeted to improve pedestrian and biking safety including bike racks at schools, traffic calming on school properties and promoting biking and walking to school [
      • Nicholas W.V.I.
      • Caesar E.
      • Maizlish N.
      Routine assessment of health impacts of local transportation Plans: A Case study from the city of Los Angeles.
      ]. In light of national reports indicating that less than 10% of students at >60% of schools actively commute to school [
      • Jones S.E.
      • Sliwa S.
      Peer Reviewed: School factors associated with the percentage of students who walk or bike to school, school health policies and Practices study, 2014.
      ], and dramatic declines in US youth active transportation to schools from 41% in 1,969% to 13% in 2001 [
      • McDonald N.C.
      Active transportation to school: Trends among US schoolchildren, 1969–2001.
      ], calls have been issued also to “re-normalize active transportation to school” [
      • Pate R.R.
      • Flynn J.I.
      • Dowda M.
      Policies for promotion of physical activity and prevention of obesity in adolescence.
      ]. Federally funded initiatives, including Healthy People 2030, similarly include the Safe Routes to Schools program [
      • Moudon A.V.
      • Stewart O.
      • Lin L.
      Safe routes to school (SRTS) statewide mobility assessment study: phase I report.
      ] dedicated to promoting US students walking and biking to school while emphasizing the social benefits of such active transportation and addressing parental safety concerns. Improving access to well-maintained/well-lit sidewalks and bike lanes, and providing free bus passes and connectivity between sidewalks and city busses, will also facilitate more physical activity from transportation among youth [
      • Meyer M.D.
      • Elrahman O.A.
      Transportation and public health: An Integrated Approach to policy, Planning, and Implementation.
      ]. Moreover, partnerships among public health professionals, transportation specialists, and local youth organizations can improve youths' and parents' awareness of safe active transportation options [
      • Zimmerman S.
      • Lieberman M.
      • Kramer K.
      • Sadler B.
      At the Intersection of active transportation and equity: Joining Forces to Make Communities Healthier and Fairer.
      ,
      • Carlson J.A.
      • Saelens B.E.
      • Kerr J.
      • et al.
      Association between neighborhood walkability and GPS-measured walking, bicycling and vehicle time in adolescents.
      ].
      US youth active transportation may also be promoted through community-based and built environment solutions. Active transportation is not universally accessible, feasible, or amenable, particularly for high need youth, girls, and youth with obesity. [
      • Olds T.S.
      • Ferrar K.E.
      • Schranz N.K.
      • Maher C.A.
      Obese adolescents are less active than their normal-weight peers, but wherein lies the difference?.
      ,
      • Schofield L.
      • Mummery K.
      • Schofield G.
      Active transportation: An important part of adolescent physical activity.
      ] The American Academy of Pediatrics has issued policy statements calling for community designs that facilitate active transportation opportunities for youth [
      Committee on Environmental Health
      The built environment: Designing communities to promote physical activity in children.
      ]. Infrastructure spending, such as in the American Recovery and Reinvestment Act and the Surface Transportation Bill, were implemented in part to advance the nation's physical activity environment for current and future generations. Reducing the convenience of a car, and also encouraging active commuting with safer routes, are shown to be most effective for promoting uptake and maintenance of youth active commuting programs [
      • Panter J.
      • Corder K.
      • Griffin S.J.
      • et al.
      Individual, socio-cultural and environmental predictors of uptake and maintenance of active commuting in children: Longitudinal results from the SPEEDY study.
      ]. Facilitating safe active transportation to physical activity recreation sites, including addressing perceived traffic safety and crime, likewise will support this effort [
      • Carver A.
      • Timperio A.
      • Hesketh K.
      • Crawford D.
      Are safety-related features of the road environment associated with smaller declines in physical activity among youth?.
      ]. It is also critical to provide equitable distribution of environmental features, including neighborhood walkability, quality and accessibility of parks and playgrounds, and active transportation programs and infrastructure in order to address widening racial/ethnic and income health disparities [
      • Pate R.R.
      • Frongillo E.A.
      • McIver K.L.
      • et al.
      Associations between community programmes and policies and children's physical activity: The Healthy Communities Study.
      ]. Such efforts can offer solutions to supporting high-need youth subgroups, and particularly minority and low-income youth, to address health disparities through physical activity promotion. Of note, policy and program initiatives may inadvertently further widen youth physical activity disparities given disparate health benefits experienced by nonminority versus minority youth from neighborhood amenities (e.g. sidewalks, bike paths, trails) [
      • Gayman M.D.
      • Wilkin H.A.
      • Stover S.
      • et al.
      Perceived built environment and physical Limitations: Race Contrasts in historically lower-income African American neighborhoods.
      ]. Future measures to reduce physical activity disparities should consider not only the presence of activity–promoting neighborhood amenities but also safety and accessibility (e.g. lighting, well maintained) of those amenities [
      • Gayman M.D.
      • Wilkin H.A.
      • Stover S.
      • et al.
      Perceived built environment and physical Limitations: Race Contrasts in historically lower-income African American neighborhoods.
      ].
      This study presents nationally representative data on US youth active transportation prevalence and patterns at a population level, drawing from 9 years of data and representing more than 57 million youth. This work reports data from NHANES, a multiwave cross-sectional study. Temporality and causality may not be inferred from findings reported here. Also, data are based on self-report, and findings may be limited due to social desirability bias. The validity of the NHANES active transportation questions has not been reported, and no information was available from participants on their purpose for using active transportation use or home address that would permit the inclusion of neighborhood factors (distance to school or work, proximity to high-density crime, public transportation, and recreational amenities), or additional modes of active transportation (e.g., nonmotorized scooters). In addition, although the sample is nationally representative, information was not available pertaining to the urban/rural context of participants' home residences, limiting the ability to determine how this sample generalizes from an urban-rural perspective, particularly given lower rates of youth active transportation in rural settings [
      • McDonald N.C.
      Active transportation to school: Trends among US schoolchildren, 1969–2001.
      ].
      In conclusion, just 24% of US youth ages 6–17 years old meet physical activity recommendations [
      • Katzmarzyk P.T.
      • Denstel K.D.
      • Beals K.
      • et al.
      Results from the United States 2018 report card on physical activity for children and youth.
      ]. Findings from this study of US adolescents and young adults indicate that among youth who engage in active transportation, participation was higher in males, minorities and lower income populations, suggesting that active transportation is a feasible target for reducing health disparities and promoting optimal health. In light of inadequate physical activity among our nation's youth, designing and implementing physical activity interventions targeting active transportation aligns with Healthy People 2030 priorities and American Academy of Pediatrics objectives in support of reducing chronic disease disparities across the lifespan.

      Funding Sources

      No funding was received for this work.

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      Linked Article

      • Physical Activity From Transportation: New Insights and Lingering Questions
        Journal of Adolescent HealthVol. 69Issue 2
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          In 2009, the American Academy of Pediatrics issued a policy statement promoting active transportation, such as walking or cycling to work and school, as a way to increase physical activity (PA) among youths in the United States (US) [1]. As few as 42.5% of 6- to 11-year-old individuals, 7.5% of 12- to 15-year-old individuals, and 5.1% of 16- to 19-year-old individuals meet PA recommendations based on accelerometry-derived estimates of PA [2], and there is some evidence that active transportation is associated with higher moderate-to-vigorous physical activity (MVPA) in school-age children [3].
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