Are Correlates of Physical Activity in Adolescents Similar Across Ethnicity/Race and Sex: Implications for Interventions

Abstract

Background:

This study tested for differences in personal, social, and environmental correlates of moderate to vigorous physical activity (MVPA) across ethnicity/race in male and female adolescents.

Methods:

Self-reported MVPA and 47 potential correlates of MVPA were measured in an ethnically/racially diverse cross-sectional sample of adolescents, in Minnesota, who participated in EAT-2010 (Eating and Activity in Teens). Interactions of potential correlates with ethnicity/race on MVPA were tested in linear hierarchical regression models in boys and girls.

Results:

Boys reported 1.7 more weekly hours of MVPA than girls. White adolescents reported 1.1 to 2.1 more weekly hours of MVPA than nonwhite adolescents. Among girls, neighborhood road connectivity was negatively correlated with MVPA among Hispanic and Asian participants. Among boys, sports participation was positively correlated with MVPA among all ethnicities/races, except Asians. Home media equipment was positively correlated with MVPA among Hispanic boys, but negatively correlated among white boys.

Conclusions:

A few correlates of physical activity among adolescents differed intersectionally by ethnicity/race and sex. Sports participation and home media equipment may have differing impacts on physical activity across ethnicities and races in boys, whereas neighborhood features like road connectivity may have differing impacts on physical activity across ethnicities and races in girls.

Moderate to vigorous physical activity (MVPA) is a modifiable health behavior that protects against many chronic diseases, including heart disease and cancer.¹ Adolescence is a critical window during which MVPA declines.² This decline occurs earlier among girls than among boys,² and nonwhite adolescents often report less MVPA than white adolescents.^3–5 Differences in MVPA between the sexes and between ethnicities/races invite the question of whether correlates of MVPA, organized within the social–ecological framework of personal, social, and environmental correlates,⁶ also differ intersectionally by sex and ethnicity/race. Knowing if and how correlates of physical activity differ by ethnicity/race as well as by sex will indicate how the content of interventions to increase population levels of physical activity may or may not need to be tailored.

While many studies have examined potential correlates of physical activity behavior, relatively few studies have been able to compare correlates at multiple levels of the social—ecological framework between population subgroups, such as ethnicities/ races or the sexes. Although we are aware of 15 studies^7–21 that examined differences in correlates of physical activity behavior between both sexes and among multiple ethnicities/races from the same sample, most of these studies^{7,9,12,15,17–19,21} examinedonly one correlate. Examining differences in multiple correlates within the same sample allows greater control of other contextual factors that could affect MVPA behavior differentially across ethnicity/ race by sex. More studies that undertake stratified analyses are, therefore, needed to better understand how the correlates of MVPA behavior differ between groups within a population.

This study, using the EAT-2010 (Eating and Activity in Teens) sample that included large numbers of white, African American, Hispanic, and Asian (mostly of Hmong ethnicity) participants, aims to examine whether personal, social, and neighborhood correlates of MVPA differ across ethnicity/race in adolescent boys and girls. A previous main-effects analysis of correlates of MVPA in EAT-2010 participants found that among both boys and girls, sports participation, self-efficacy, enjoyment, self-management, parent direct help, and friend support were positively associated with MVPA.²² Among girls, additionally, barriers like too little time were negatively associated with MVPA, and perceived father’s physical activity, MVPA of female friends, and distance to trails were positively associated with MVPA. The current study extends upon this earlier analysis by examining whether these correlates differ for adolescents from different ethnic/racial backgrounds. This information is important for the design of physical activity interventions for adolescents. While all interventions should be culturally sensitive, if correlates of MVPA are similar across ethnicities/races, then interventions may bridge across differentgroups.If,however,correlatesdiffer,it maybeimportant to tailor the content of interventions as well as the presentation, and interventions may not be as useful in bridging across different population groups. We hypothesize that many correlates will differ in the strength of their association with MVPA by ethnicity/race in adolescent boys and girls.

Methods

Study Sample

Data for these analyses came from 2779 adolescents participating in the EAT-2010 study and from their parents’ responses to the Project F-EAT (Families and Eating and Activity among Teens) study. These coordinated surveys aimed to assess eating behaviors, physical activity, weight status, weight control practices, and potential correlates of these outcomes in adolescents. These studies were approved by the University of Minnesota’s Institutional Review Board Human Subjects Committee and by the participating school districts. All adolescent participants provided written assent, and parents of participants provided written informed consent.

Measures

For EAT-2010, the adolescent surveys and anthropometric measures were completed by adolescents from 20 public middle and high schools in the Minneapolis/St. Paul metropolitan area of Minnesota during the 2009–2010 academic year. Test–retest reliability was assessed in the full sample, with a second survey 2 weeks following the first. For F-EAT, the parent survey, data were collected in 2009–2010 by surveying up to 2 parents/ caregivers (n = 3709) of EAT-2010 adolescents about their own eating and physical activity behaviors and the home food and activity environment. The EAT-2010 and F-EAT survey protocols have been described previously in greater detail.^23,24 The mean age of the study population was 14.4 years (SD = 2.0), and adolescents were equally divided by sex (53.1% girls) (Table 1). The majority (85.3%) of adolescent participants in EAT-2010 had at least one parent respond. Parent participants had a mean age of 42.3 years (SD = 8.6). The majority of parents were mothers or female guardians (62.0%).

Table 1.

Sample Demographics

Total sample, N (%)	2779 (100%)
Age, mean (SD), y	14.4 (2.0)
MVPA, mean (SD), h/wk	5.8 (4.7)
Demographics
Ethnicity/race and sex, n (%)
Male	1302 (46.9%)
White	277 (10.0%)
African American	378 (13.6%)
Hispanic	252 (9.1%)
Asian	260 (9.4%)
Mixed or other	135 (4.9%)
Female	1477 (53.1%)
White	248 (8.9%)
African American	428 (15.4%)
Hispanic	311 (11.2%)
Asian	293 (10.5%)
Mixed or other	197 (7.1%)
Parent education, n (%)
Less than high school	616 (23.4%)
High school	556 (21.1%)
Some college	748 (28.4%)
Bachelor’s degree	476 (18.1%)
Advanced degree	239 (9.1%)
Parent income, n (%)
Less than $20,000	846 (37.6%)
$20,000–$34,999	516 (22.9%)
$35,000–$49,999	351 (15.6%)
$50,000–$74,999	266 (11.8%)
$75,000–$99,999	137 (6.1%)
$100,000 or more	137 (6.1%)

Physical activity environments at the 20 participating schools were assessed from surveys completed by school administrators and physical education specialists regarding the availability of physical education facilities and equipment, and relevant policies and practices to promote MVPA. School staff was instructed to respond with regard to the 2009–2010 academic year and to confer with others at their school if they were unsure of current policies or practices.

ArcGIS (version 9.3.1; ESRI, Redlands, CA) was used to geocode each participant’s home and school addresses. Home and school neighborhood environment measures were collected from geographic information system (GIS) data sources, including land use and transit route data from the Twin Cities GIS data repository MetroGIS,²⁵ police reports, and commercial databases (accessed through ESRI Business Analyst).²⁶ Additional details on the protocol for neighborhood environment measures have been previously published.^22,27,28

The outcome of interest, self-reported MVPA was assessed using the 3 Godin and Shephard questions,29 which ask separately about light, moderate, and strenuous exercise and had 6 response categories: “none,” less“ than ½ hour a week,” ½“ to 2 hours a week,”“2 ½ to 4 hours a week,”“4 ½ to 6 hours a week,” or “6+ hours a week.” The 6 response categories were coded as continuous hours using midpoints of the response option ranges. As the “6+ hours a week” response option did not have an upper bound, it was coded as 8 hours for the continuous measure. This approach to modeling the Godin–Shephard questions as a continuous measure has shown a reasonable approximation of accelerometer-measured physical activity in previous Project EAT samples.³⁰ Responses for moderate and strenuous exercise were summed to calculate usual weekly hours of MVPA (test–retest correlation: r = .73). The moderate and strenuous questions did not ask specifically about destinations for activity; therefore, active transportation is not separated out from total MVPA. In all models, MVPA was treated as a continuous outcome.

Expected personal, social, and neighborhood correlates were included in analysis based on their use in a previous study that examined nonstratified correlates of MVPA in the EAT-2010 sample²² or their expected correlation with MVPA. The correlates examined in this study were organized according to the social–ecological framework constructs they were expected to reflect.^6,31

Personal inclination to activity correlates included physical activity enjoyment, barriers, self-efficacy, and self-management drawn from validated measures on the adolescent survey, as well as substance use and depression, also reported on the adolescent survey, and body mass index, measured by research staff. Environmental opportunities for team-based activity correlates included distance to the nearest gym or rec center (measured using GIS), availability of a sports bus at school, and whether there are activity fees, reported by the school physical education specialist, and participation on sports teams, reported by the adolescents. Environmental opportunities for exercise correlates included density of parks near the home, busy streets in the neighborhood, distance to the nearest trail, distance to school, access points into the neighborhood, and reported neighborhood crime (measured using GIS), as well as availability of indoor and outdoor facilities, reported by the school physical education specialist; home physical activity equipment, reported by parents; and perceived neighborhood safety, reported by adolescents. Social norms for activity correlates included perceived mother’s and father’s physical activity; whether the family supports activity or is active together; and whether friends think it is important to be active, play sports, or if they are active together, reported by the adolescents; the average-reported weekly MVPA in the adolescent’s school and among their friends; parent self-report of MVPA, being active with their adolescent, or helping or talking to their adolescent about activity; and school administrator report of school physical activity promotion. Degree of physical education correlates included the 10-year change in the school’s physical education budget, the time students spend in physical education, whether the school has a physical education requirement (reported by the school physical education specialist), and adolescent report of whether they had gym class in the last year. Passive entertainment correlates included daily screen time and whether there was a television or video games in their bedroom, reported by adolescents, and parent report of their own television viewing, how much they watch television with their adolescent, and the count of home media equipment (Appendix Table 1).

Sex was reported by participants as male or female. Ethnicity/ race was reported by participants as any combination of: white, black, or African American; Hispanic or Latino; Asian American, Hawaiian, or Pacific Islander; or American Indian or Native American. Participants who reported Hispanic or Latino ethnicity were classified as Hispanic or Latino regardless of racial identity. Non-Hispanic participants who reported 2 races with 1 race being “white” were classified as the nonwhite race they reported, under the assumption that the social experience of mixed-race participants would more closely align with the nonwhite race that they reported. Because of small sample sizes of Hawaiian or Pacific Islander and American Indian or Native American, these groups were included in the mixed/other race category. Age (in years) was calculated by subtracting the participants’ birthdates from the survey date. Parent education attainment and household income were reported by the participants’ parents. Nativity was reported by participants as whether or not they were born in the United States. Neighborhood median income was assigned as the median income for the Census tract in which the participant’s reported address was located.

The range of missing data among correlates was 0% to 24.2%. To address missing data, all analyses were performed on a multiply imputed data set using SAS (version 9.4; SAS Institute, Cary, NC) Proc MI and MIANALYZE. Twenty imputed data sets were created using a Markov Chain Monte Carlo algorithm and all correlates and demographics. Multiple imputation methods for EAT-2010 have been previously described in greater detail.²²

Statistical Analyses

All statistical analyses were performed in 2016. Estimates of mean weekly hours of MVPA were calculated for each ethnic/racial subgroup by sex. Tests for heterogeneity of associations (additive effect measure modification) with MVPA for each potential correlate by ethnicity/race were conducted separately for each sex using hierarchical linear regression models. Models were adjusted for age, parent education, and parent income, with a random effect for school to account for correlation in MVPA among students from the same school. The test statistics for the interaction terms with categorical ethnicity/race were derived from analysis of variance type III f statistics. The method of Raghunathan and Dong³² to pool analysis of variance statistics in multiply imputed data sets was used to generate correct P value estimates. To account for multiple tests, we calculated the false discovery rate (FDR) for each test from the table of raw P values for the interactions of each possible correlate with ethnicity/race.³³ The FDR applies a Bonferroni correction to the smallest P value obtained, and then applies progressively less stringent correction to each subsequent P value in the ordered list of P values: (i/m) × α, where i is the position of the P value in the ordered list, m is the total number of tests, and α is the FDR cut point. The FDR is more statistically powerful than Bonferroni correction and, therefore, is more appropriate for exploratory studies. A lower value of FDR indicates a lower probability that the discovery of heterogeneity of associations is in fact false. An FDR of 0.10, for example, indicates that 1 in every 10 positive tests would be a false positive: a 10% error in discovery. Interaction terms with an FDR of 10% or less were considered strong evidence of an interaction; an FDR of 10% to 20% was considered moderate evidence of an interaction.

Correlates with strong or moderate evidence of heterogeneity in association with MVPA by ethnicity/race were examined using hierarchical linear regression models stratified to 4 ethnicities/races within each sex: white, African American, Hispanic, and Asian. Models were adjusted for age, parent education and parent income, and a random effect for school. Post hoc sensitivity analyses for stratified models were conducted, adjusting for other potential confounders: nativity and neighborhood median income. An additional sensitivity analysis was conducted with models adjusted for age only, age and parent education only, and age and parent income only. Correlates in all stratified models were treated as continuous and linear in their association with MVPA except for perceived mother’s physical activity level among girls, which was treated categorically due to a limited number of response options. It is possible that sports participation could have a nonlinear association with MVPA; for example, the difference in MVPA between students competing in 3 sports compared with students competing in 2 sports may not be as great as the difference in MVPA between students competing in 1 sport compared with students competing in no sports. To test for a nonlinear association, a quadratic term for sports participation was tested in the stratified models and retained if it achieved statistical significance at P < .05.

Results

Boys reported significantly more hours per week of MVPA (6.7 h/wk; 95% confidence interval [CI], 6.4–6.9, P < .001) than girls (5.0 h/wk; 95% CI, 4.8–5.2). Mean age–adjusted levels of MVPA differed significantly by ethnicity/race within each sex. White boys reported significantly more hours per week of MVPA than other ethnicities/ races (P < .001), and white girls reported significantly more hours per week MVPA than other ethnicities/races (P < .001) (Table 2). Eight of the 10 correlates previously identified in the whole sample,²² self-efficacy, enjoyment, self-management, parent direct help, friend support, barriers, perceived father’s physical activity, and MVPA of female friends, were similar across ethnicity/race in boys and girls. Only 3 of the 47 potential correlates examined in this study, neighborhood road connectivity, distance to the nearest trail, and perceived mothers’ physical activity, differed across ethnicity/race in their associations with MVPA among girls. Only 2 of the 47 correlates, home media equipment and sports participation, differed across ethnicity/race among boys.

Table 2.

Age-Adjusted Mean Weekly Hours of Self-Reported MVPA by Ethnicity/Race and Sex

	Mean MVPA (95% CI)
Female	5.0 (4.8 to 5.2)
White	6.3 (5.7 to 6.8)
African American	4.9 (4.4 to 5.3)*
Hispanic	4.3 (3.8 to 4.8)*
Asian	4.4 (3.9 to 4.9)*
Male	6.7 (6.4 to 6.9)
White	7.9 (7.3 to 8.4)
African American	6.8 (6.3 to 7.2)**
Hispanic	6 (5.4 to 6.6)**
Asian	5.8 (5.2 to 6.4)**

Abbreviations: CI, confidence interval; MVPA, moderate to vigorous physical activity.

^*Statistically significant difference from white females at P < .01 (Bonferroni corrected).

^**Statistically significant difference from white males at P < .01 (Bonferroni corrected).

Correlates of Physical Activity Among Girls

Among girls, one variable showed strong evidence of differing by ethnicity/race in its association with MVPA: neighborhood road connectivity, which is the count of local roads that intersect a 1600-m perimeter around the participant’s house (raw P value = 0.0004, FDR = 0.04). Neighborhood road connectivity is an indicator of both the walkability of the neighborhood and the road network density. Among white and African American girls, neighborhood road connectivity was not associated with MVPA. Among Hispanic and Asian girls, increased road network density, represented by 10 additional road crossings in the 1600-m perimeter around the participant’s home, was associated with, respectively, 0.68 (95% CI, -0.43 to -0.93) and 0.33 (95% CI, -0.004 to -0.66) fewer hours per week of MVPA (Figure 1A).

Figure 1 —

Stratified hierarchical linear regression coefficients for predictors of physical activity that showed heterogeneity by ethnicity/race. (A) Association of neighborhood road connectivity with MVPA by race among females. (B) Association of distance from home to trail with MVPA by race among females. (C) Association of sport participation with MVPA by race among males. (D) Association of home sedentary equipment with MVPA by race among males. MVPA indicates moderate to vigorous physical activity.

Two variables among girls showed moderate evidence of differing by ethnicity/race in their association with MVPA: perceived mother’s level of physical activity (raw P value = .004, FDR = 0.14) and distance to the nearest trail (raw P value = .009, FDR = 0.18).

Although associations between distance to the nearest trail and MVPA were not statistically significant in any of the strata of ethnicity/race among girls, the association estimates did show moderate evidence of heterogeneity by ethnicity/race. White and African American girls who lived nearer to trails trended toward more MVPA, whereas Asian girls who lived nearer to trails trended toward less MVPA (Figure 1B).

Among nonwhite girls, weekly hours of MVPA were higher at higher perceived levels of mothers’ physical activity. These differences were statistically significant among African American and Hispanic girls. White girls who reported that their mothers sometimes or rarely were active reported significantly fewer hours per week of MVPA than girls who reported their mothers were never active (Table 3).

Table 3.

Ethnicity/Race Stratified Differences in MVPA (in Hours/Week) Among Female Adolescents by Perceived Level of Mother’s PA^a

	White females	African American females	Hispanic females	Asian females
Perceived mother’s PA Regular	−1.25 (−3.15 to 0.64)	2.21 (0.88 to 3.55)*	2.38 (1.02 to 3.75)*	0.55 (−1.15 to 2.24)
Sometimes	−2.04 (−3.78 to −0.30)*	1.74 (0.59 to 2.88)*	1.10 (−0.11 to 2.31)	1.01 (−0.35 to 2.37)
Rarely	−1.77 (−3.47 to 0.07)*	0.13 (−1.06 to 1.31)	1.01 (−0.16 to 2.18)	1.17 (−0.18 to 2.51)
Never	0 (ref)	0 (ref)	0 (ref)	0 (ref)

Abbreviations: CI, confidence interval; PA, physical activity; ref, reference level; MVPA, moderate to vigorous PA.

^aModel adjusted for age, parent education, and parent income. Values reported are regression coefficients (95% CI) from hierarchical linear regressions.

^*Statistically significant difference from the reference level at P < .05.

Correlates of Physical Activity Among Boys

Among boys, most correlates of physical activity were similar across ethnicity/race. Two variables showed moderate evidence of differing by ethnicity/race in their association with MVPA: sports participation (raw P value = .009, FDR = 0.18) and count of home media equipment (raw P value = .005, FDR = 0.14).

Sports participation was associated with significantly more hours per week of MVPA among white, African American, and Hispanic boys. Among Asian boys, the association estimate for sports participation with MVPA was far smaller and did not achieve statistical significance (Figure 1C). Quadratic terms tested for sports participation did not achieve statistical significance in any of the ethnicities/races, indicating that it was appropriate in this sample to model sports participation as a linear predictor of physical activity.

The availability of home media equipment was not associated with weekly hours of MVPA among African American and Asian boys. By contrast, each additional home media device was associated with 0.24; 95% CI, -0.07 to -0.42 fewer hours per week MVPA among white boys. This association was reversed among Hispanic boys, with each additional home media device associated with 0.26; 95% CI, 0.05 to 0.48 more hours per week MVPA (Figure 1D).

None of the associations modeled differed substantially when adjusting for age alone; age and parent education; age and parent income; or age, parent education, and parent income (Appendix Table 2).

Discussion

This study aimed to identify whether correlates of physical activity that may be important to planning interventions differ across ethnicities/races among adolescent boys and girls. We found that most correlates of MVPA were similar across ethnicity/ race. Encouragingly, of 47 potential correlates of MVPA modeled, only 5 showed different associations with MVPA by ethnicity/race in boys and girls. We found only a few notable differences in correlates of MVPA by ethnicity/race. Differences in association with MVPA by ethnicity/race among adolescent girls emerged for neighborhood environment correlates, neighborhood road connectivity, and distance to trails, and for perceived mothers’ physical activity. Differences in association with MVPA by ethnicity/race among boys emerged for home media equipment and sports participation. These findings suggest that many aspects of interventions to increase MVPA in adolescents may bridge across ethnicity/race and sex.

Of the main-effects correlates among EAT-2010 participants previously identified by Graham et al,²² we found only 2, sports participation and distance to trails, that differed by ethnicity/race, giving further evidence that content of physical activity interventions can be constructed in a way that appeals to many population groups at once. We believe that this is an important finding because physical activity programs offer not only health benefits, but also opportunities to connect socially. Targeting physical activity interventions to stratified population groups may result in missed opportunities for people to connect across racial and ethnic lines. Further study in a variety of geographic locations will be needed to confirm or refute this conclusion, especially as a recent study by Barr-Anderson et al,³⁴ among middle school students in South Carolina, concluded that tailoring by race/ethnicity would be beneficial. Sample differences could explain part of the differences in our findings from those of Barr-Anderson et al: our sample included Asian participants in addition to white, African American, and Hispanic; Asian participants were not included in the analysis by Barr-Anderson et al. The differences between our findings and those of Barr-Anderson et al may also reflect geographic differences between a South Carolina population and a Minnesota population.

Our results help to clarify the previously published unexpected finding that greater distance from the nearest trail predicted more MVPA among female participants in EAT-2010.²² Our results show that white and African American girls show the association in the expected direction: living closer to a trail was correlated with more MVPA, though these are not statistically significant. The association in the unexpected direction is among Asian girls. As the Asian participants in Project EAT-2010 were primarily of Hmong ethnicity, a recent immigrant group that began arriving in America in the 1970s, we hypothesized that acculturation or neighborhood poverty may confound these associations. However, post hoc adjustment for participants’ country of birth and for neighborhood poverty proportion did not change the associations. Previous work has shown that Hmong immigrants to the United States feel out of balance with the environment and are unable to be physically active as they were in rural Laos, and that Hmong parental concerns for safety are higher for female children.^35,36 Taken together with our finding of more MVPA with greater distance to trails and with lower neighborhood road connectivity, this feeling of being out of balance suggests that a less dense urban form may be conducive to physical activity among Hmong girls. Further study will be needed to determine if interventions to increase MVPA in Hmong girls should be targeted to neighborhoods of denser form.

We also found that sports participation does not correlate as strongly with MVPA among Asian boys compared with boys of other ethnicities/races. This is an important finding as previous analysis showed sports participation to be among the strongest main effect correlates of MVPA in the EAT-2010,²² and previous reviews³ have shown that sports participation is positively related to physical activity. Our findings suggest that Asian boys in EAT-2010, primarily Hmong ethnicity, are getting less MVPA through sports participation. This finding has many possible implications for interventions: first, interventions to increase MVPA in Hmong boys might focus on nonsport activity; in addition, community sport offerings could be expanded to match the preferences of the Hmong boys. Further studies will be needed to determine if expanding sports offerings at schools, or offering different nonsport physical activities, may help engage groups that are getting less activity through sports, like Asian boys.

Our findings additionally show that home media equipment, which was not correlated with physical activity in the whole EAT-2010 sample,²² may be a relevant correlate in some population groups. We are aware of only 2 studies that considered the association of home media equipment with physical activity in groups of ethnicity/race and sex.^37,38 As far as we are aware, the present study is the first to show that there may be differences in the association of media equipment availability with MVPA by ethnicity/race and sex. Further studies will be needed to examine potential mechanisms for our finding that greater media equipment availability predicts less MVPA in white adolescents and predicts more MVPA in Hispanic adolescents. Specifically, it will be important to determine if home media equipment mediates a relationship between sedentary behavior and MVPA, and if there are any other moderators or confounders, particularly more refined and precise measures of socioeconomic status, that can further explain this difference.

The major strength of this study is its diversity of participants and of correlates. With over 200 participants in each of the 8 groups of ethnicity/race and sex, we were able to examine differences in MVPA and its correlates by ethnicity/race and sex in ways that are rarely possible. While drawing all participants from one metropolitan area may limit generalizability, it also ensures that some potential confounders, like weather, are balanced across the subgroups, which strengthens our comparisons across the ethnicities/ races. A further strength of this study was that multiple potential correlates of MVPA at the environmental, social, and personal levels were measured, allowing for an analysis of differences in correlates at multiple levels of the social–ecological framework.

A limitation of this study is the possibility of reverse causality. Data for this study were cross-sectional, and the temporality of the association between the correlates and MVPA cannot be established. Future studies will need to be conducted to test these associations longitudinally. Another limitation of this study is the possibility of measurement error in self-reported MVPA. While self-report measures of MVPA may capture some modes of physical activity, like biking or swimming, which are commonly missed by accelerometer measures, self-report measures are more subject to social desirability bias than accelerometry. In addition, biological sex was reported as male or female, creating a limitation on the generalizability of results to genders that may be nonbinary. While this study used sensitivity analyses to assess the impact of nativity, education, and income on these results, it was beyond the scope of this study to employ a more rigorous assessment of the intersection of these factors with ethnicity/race and sex on predicting physical activity. In presenting results adjusted for education and income, this analysis estimates a “direct” impact of race on predictors of MVPA. The sensitivity analyses showed that results were not substantially impacted by removing education or income, or both, as covariates in the models. However, the intersection of these demographics may be complex. For example, income and education attainment may mediate impacts of ethnicity/race on differences in predictors of MVPA, and future studies should empirically test such complex possible intersections.

Conclusions

Physical activity behavior and its correlates are not likely to be completely homogenous within any population. Failing to consider how correlates differ among groups within populations may be one reason that the modifiable correlates of MVPA remain poorly understood. However, our finding that relatively few physical activity correlates in this study differed by ethnicity/race will be useful when planning physical activity interventions. We provide evidence that, while the design of interventions should always be culturally sensitive, many aspects of the content of interventions may appeal to many population groups at once, allowing simpler program planning and more opportunity to leverage physical activity interventions to create connections across ethnic and racial lines.

Appendix Table 1:

Description of Measures Examined as Potential Correlates of MVPA by Expected Socio-Ecological Framework Domain

Variable (unit)	Source	Mean (SD)	Description
Personal: inclination to activity
Physical Activity Enjoyment (Range: 3–12)	Adolescent Report (EAT-2010)	5.26 (2.25)	3-Item scale (Cronbach a = .82)
Physical Activity Barriers (Range: 4–20)	Adolescent Report (EAT-2010)	9.73 (3.22)	4-Item scale (Cronbach a = .49)
Physical Activity Self-Efficacy (Range: 3–12)	Adolescent Report (EAT-2010)	7.86 (2.38)	3-Item scale (Cronbach a = .76)
Physical Activity Self-Management (Range: 5–15)	Adolescent Report (EAT-2010)	8.82 (3.14)	3-Item scale (Cronbach a = .82)
BMI (z score)	Measured by Research Staff	0.71 (1.07)	z score of BMI measured as kg/m2
Past Year Substance Use (yes/no)	Adolescent Report (EAT-2010)	3.85 (1.88)	Participant used alcohol, cigarettes, or marijuana in the past year (test-retest reliability: r = .83)
Depression (Range: 6–18)	Adolescent Report (EAT-2010)	10.21 (3.01)	6-Item scale (Cronbach a = .83)
Environmental: opportunities for team-based activity
Activity Fee (Range: 1–3)	PE Specialist Survey	2.23 (0.52)	Students must pay an activity fee to participate in any sport, intramural, or physical activity clubs? (No/Waiver Available/Yes)
Availability of a Sport Bus (yes/no)	PE Specialist Survey	61% Yes	School has an afterschool bus for sports, academic, club, or discipline reasons (yes/no)
Distance to the Nearest Gym (kilometers)	GIS	1.26 (0.73)	Distance (km) to nearest gym
Distance to the nearest Rec Center (kilometers)	GIS	0.52 (0.37)	Distance (km) to nearest rec center
Participation on Sports Teams (count)	Adolescent Report (EAT-2010)	2.03 (1.06)	“During the past 12 months, on how many sports teams did you play?” (test-retest reliability: r = .86)
Environmental: opportunities for exercise
Density of Parks near Home (percent)	GIS	9.5 (7.4)	Percent of 1600-m buffer around home that is Greenspace
Indoor Physical Education Facilities at School (count)	PE Specialist Survey	4.55 (1.96)	Count of indoor activity facilities reported by PE Specialist
Outdoor Physical Education Facilities at School (count)	PE Specialist Survey	3.74 (1.36)	Count of outdoor activity facilities reported by PE Specialist
Perceived Neighborhood daytime safety (unsafe_day_10)	Adolescent Report (EAT-2010)	1.72 (0.9)	“The crime rate in my neighborhood makes it unsafe to go on walks during the day.” (test-retest reliability: r = .57)
Perceived Neighborhood nighttime safety (unsafe_night_10)	Adolescent Report (EAT-2010)	2.29 (1.14)	“The crime rate in my neighborhood makes it unsafe to go on walks at night.” (test-retest reliability: r = .65)
Busy Streets in Neighborhood (count)	GIS	1.96 (0.95)	Percentage of streets in 1600-m buffer around home that are busy
Distance to the nearest Trail (kilometers)	GIS	0.64 (0.47)	Street network distance (km) to nearest bike or walking trail
Access Points (10 count)	GIS	6.62 (1.67)	Count of streets crossing into 1600-m buffer around home (unit = 10 crossings)
Reported Neighborhood Crime (count)	GIS	1.72 (0.97)	Count of total crimes per hectare (2.47 acres) near home in 2010
Distance to School (kilometers)	GIS	5.92 (4.58)	Street network distance (km) to school
Home Physical Activity Equipment (Range 0:5)	Parent Report (F-EAT)	2.28 (1.37)	Parent-reported count of exercise equipment available at home. (test-retest reliability: percent concordance = 80%-89%)
Social: social norms
Perceived Mother’s PA (mom_pa_10)	Adolescent Report (EAT-2010)	2.48 (0.99)	“My mother is physically active in her free time.” (test-retest reliability: r = .70)
Perceived Father’s PA (dad_pa_10)	Adolescent Report (EAT-2010)	2.54 (1.08)	“My father is physically active in his free time.” (test-retest reliability: r = .69)
Parent Self-Reported PA (hours/week)	Parent Report (F-EAT)	4.08 (3.7)	Parent-reported moderate and vigorous physical activity (Godin-Shephard, test-retest reliability: r = .56 to .75)
Parent Active with Child (hours/week)	Parent Report (F-EAT)	1.22 (1.53)	Hours per week parent is active with their adolescent (test-retest reliability: r = .58)
Family does Active Things (Range: 1–4)	Adolescent Report (EAT-2010)	2.59 (0.98)	“My family and I do active things together.” (test-retest reliability: r = .73)
Family Support for Physical Activity (Range: 1–4)	Adolescent Report (EAT-2010)	3.14 (0.92)	“My family supports me in being physically active.” (test-retest reliability: r = .60)
Parent helps Child be Active (hours/week)	Parent Report (F-EAT)	1.52 (1.86)	Hours per week parent helps their adolescent be physically active (test-retest reliability: r = .62)
Parent Talks to Child about being Active (Range: 1–5)	Parent Report (F-EAT)	2.97 (1.2)	“Have you had a conversation with your child about being physically active?” (test-retest reliability: r = .64)
Friends Play Sports (Range: 1–4)	Adolescent Report (EAT-2010)	3.18 (0.81)	“My friends often play sports or do some-thing active.” (test-retest reliability: r = .54)
Friends think it is Important to be Active (Range: 1–4)	Adolescent Report (EAT-2010)	2.91 (0.83)	“My friends think it is important to be physically active.” (test-retest reliability: r = .43)
Friends are Active Together (Range: 1–4)	Adolescent Report (EAT-2010)	3.1 (0.87)	“My friends and I like to do active things together.” (test-retest reliability: r = .49)
Friend Self-Reported Physical Activity (hours/week)	Adolescent Report (EAT-2010)	6.19 (3.61)	Mean of Godin-Shephard self-reported MVPA among nominated friends.
School Physical Activity Promotion (Range: 1–5)	School Administrator Survey	3.49 (0.77)	“To what extent has your school made a serious effort to promote increased physical activity among students?”
Average Physical Activity of Students in School (hours/week)	Adolescent Report (EAT-2010)	5.78 (0.72)	Mean of Godin-Shephard self-reported MVPA among students within the school.
Environmental: degree of PE
10-Year change in PE budget (yes/no)	PE Specialist Survey	0.37 (0.48)	“Over the past 10 years, has there been a change in the budget for physical education equipment and supplies at this school?”
Time spent in PA in an average week (hours/week)	PE Specialist Survey	3.91 (1.5)	“On average, how many minutes per week do students at your school participate in physical education?”
School’s Required Physical Education Credits (credits/year)	PE Specialist Survey	0.65 (0.58)	“What is the minimum physical education requirement for students at your school?”
Had gym class in the last year (yes/no)	Adolescent Report (EAT-2010)	47% Yes	Adolescent participated in gym class in the past year.
Personal: passive entertainment
Video Games in Bedroom (yes/no)	Adolescent Report (EAT-2010)	64% Yes	In the room where you sleep, do you have an electronic games console?” (test-retest reliability: percent concordance = 94%)
TV in Bedroom (yes/no)	Adolescent Report (EAT-2010)	39% Yes	In the room where you sleep, do you have a television?” (test-retest reliability: percent concordance = 97%)
Screen Time (hours/day)	Adolescent Report (EAT-2010)	5.74 (3.82)	Hours per day watching TV, using a computer, or playing video-games. (test-retest reliability: r = .86)
Parent watches TV with Child (hours/week)	Parent Report (F-EAT)	2.38 (1.92)	Hours per week parent watches TV with their adolescent (test-retest reliability: r = .53)
Parent TV hours (hours/week)	Parent Report (F-EAT)	14.35 (9.14)	“On an average day, how many hours do you spend watching TV, DVDs, or videos?” (test-retest reliability: r = .78)
Home Media Equipment (count)	Parent Report (F-EAT)	7.75 (3.33)	Parent-reported count of exercise media equipment available at home. (test-retest reliability: r = .73–.90)

Abbreviations: BMI, body mass Index; EAT-2010, Eating and Activity in Teens; F-EAT, Families and Eating and Activity among Teens; GIS, geographic Information system.

Appendix Table 2:

A Sensitivity Analyses of Different Combinations of Covariate Adjustments in Models of Correlates of MVPA (Hours/Week) That Differed Significantly by Ethnicity/Race and Sex

	Age-adjusted regression coefficient (95% CI)	Age, education-adjusted regression coefficient (95% CI)	Age, income-adjusted regression coefficient (95% CI)	Age, income, education-adjusted regression coefficient (95% CI)
Association of home sedentary equipment with MVPA by race among males
White males	−0.23 (−0.39 to −0.06)*	−0.23 (−0.40 to −0.07)*	−0.22 (−0.39 to −0.05)*	−0.24 (−0.41 to −0.07)*
African American males	0.05 (−0.11 to 0.21)	0.01 (−0.15 to 0.17)	0.01 (−0.15 to 0.17)	−0.005 (−0.16 to 0.16)
Hispanic males	0.29 (0.07 to 0.50)*	0.26 (0.04 to 0.49)*	0.27 (0.05 to 0.50)*	0.26 (0.03 to 0.49)*
Asian males	0.03 (−0.18 to 0.23)	0.02 (−0.19 to 0.23)	0.02 (−0.19 to 0.23)	0.02 (−0.19 to 0.23)
Association of sport participation with MVPA by race among males
White males	1.45 (0.96 to 1.94)*	1.47 (0.98 to 1.97)*	1.53 (1.03 to 2.03)*	1.52 (1.02 to 2.02)*
African American males	1.17 (0.72 to 1.62)*	1.14 (0.70 to 1.58)*	1.14 (0.70 to 1.59)*	1.13 (0.68 to 1.57)*
Hispanic males	1.84 (1.34 to 2.33)*	1.82 (1.33 to 2.31)*	1.82 (1.33 to 2.32)*	1.82 (1.32 to 2.31)*
Asian males	0.38 (−0.23 to 1.00)	0.37 (−0.24 to 0.99)	0.37 (−0.24 to 0.99)	0.37 (−0.25 to 0.99)
Association of neighborhood road connectivity with MVPA by race among females
White females	0.2 (−0.13 to 0.54)	0.20 (−0.13 to 0.52)	0.21 (−0.12 to 0.54)	0.19 (−0.13 to 0.52)
African American females	0.03 (−0.25 to 0.30)	0.05 (−0.23 to 0.32)	0.06 (−0.22 to 0.33)	0.06 (−0.21 to 0.34)
Hispanic females	−0.71 (−0.96 to −0.46)*	−0.69 (−0.93 to −0.44)*	−0.67 (−0.93 to −0.42)*	−0.67 (−0.92 to −0.43)*
Asian females	−0.34 (−0.66 to −0.02)*	−0.33 (−0.65 to −0.01)*	−0.32 (−0.65 to 0.002)	−0.33 (−0.66 to −0.005)*
Association of distance from home to trail with MVPA by race among females
White females	−0.61 (−2.02 to 0.80)	−0.57 (−1.96 to 0.83)	−0.73 (−2.15 to 0.69)	−0.56 (−1.97 to 0.85)
African American females	−0.33 (−1.16 to 0.50)	−0.37 (−1.20 to 0.46)	−0.36 (−1.18 to 0.47)	−0.38 (−1.21 to 0.45)
Hispanic females	0.01 (−1.01 to 1.03)	0.05 (−0.96 to 1.06)	−0.06 (−1.07 to 0.95)	0.003 (−1.01 to 1.01)
Asian females	1.16 (−0.15 to 2.46)	1.16 (−0.14 to 2.46)	1.19 (−0.12 to 2.49)	1.18 (−0.13 to 2.49)
Association of perceived mother’s physical activity level with daughters’ MVPA (reference level = “never”)
White females
Regular	−0.69 (−2.53 to 1.15)	−1.19 (−3.03 to 0.64)	−1.13 (−3.05 to 0.79)	−1.25 (−3.15 to 0.64)
Sometimes	−1.53 (−3.24 to 0.19)	−1.99 (−3.70 to −0.29)*	−1.87 (−3.63 to −0.11)*	−2.04 (−3.78 to −0.30)*
Rarely	−1.50 (−3.21 to 0.21)	−1.74 (−3.43 to −0.05)*	−1.67 (−3.40 to 0.05)	−1.77 (−3.47 to −0.07)*
African American females
Regular	2.31 (0.97 to 3.64)*	2.23 (0.89 to 3.57)*	2.25 (0.92 to 3.59)*	2.21 (0.88 to 3.55)*
Sometimes	1.76 (0.62 to 2.91)*	1.72 (0.58 to 2.87)*	1.76 (0.62 to 2.91)*	1.74 (0.59 to 2.88)*
Rarely	0.16 (−1.02 to 1.34)	0.09 (−1.09 to 1.27)	0.19 (−0.99 to 1.36)	0.13 (−1.06 to 1.31)
Hispanic females
Regular	2.50 (1.13 to 3.87)*	2.40 (1.03 to 3.76)*	2.44 (1.08 to 3.81)*	2.38 (1.02 to 3.75)*
Sometimes	1.04 (−0.18 to 2.26)	1.04 (−0.17 to 2.25)	1.12 (−0.09 to 2.34)	1.1 (−0.11 to 2.31)
Rarely	0.97 (−0.20 to 2.14)	0.94 (−0.22 to 2.11)	1.06 (−0.11 to 2.23)	1.01 (−0.16 to 2.18)
Asian females
Regular	0.72 (−0.94 to 2.37)	0.56 (−1.13 to 2.25)	0.63 (−1.05 to 2.31)	0.55 (−1.15 to 2.24)
Sometimes	1.09 (−0.24 to 2.43)	1.03 (−0.32 to 2.37)	1.03 (−0.33 to 2.38)	1.01 (−0.35 to 2.37)
Rarely	1.21 (−0.12 to 2.53)	1.18 (−0.15 to 2.51)	1.16 (−0.18 to 2.49)	1.17 (−0.18 to 2.51)

^*Statistically significant difference at P < .05.