Abstract
Background:
Active transportation (AT) increases physical activity, reducing cardiometabolic risk among non-Hispanic white adults; however, research on these linkages in racial/ethnic minority women is sparse. This study explored these associations in 327 African American and Hispanic/Latina women.
Methods:
This analysis used sociodemographics, self-reported AT via the International Physical Activity Questionnaire, accelerometer-measured moderate to vigorous physical activity (MVPA), body mass index, systolic and diastolic blood pressures, resting heart rate, and body fat percentage (BF). Unadjusted bivariate associations and associations adjusted for sociodemographic factors were examined.
Results:
AT users had higher levels of objective MVPA, but this was not statistically significant. AT was not associated with cardiometabolic risk factors in adjusted models (Ps > .05); however, systolic blood pressure was lower for AT users. MVPA was negatively associated with diastolic blood pressure and BF overall, body mass index and BF in African American women, and BF in Hispanic/Latina women (Ps < .05).
Conclusions:
MVPA was associated with improvements in body mass index, diastolic blood pressure, and BF among minority women, and these relationships may vary by race/ethnicity. Practitioners should recommend increased participation in MVPA. Future research, using longitudinal designs should investigate AT’s potential for increasing MVPA and improving cardiometabolic health along with the role of race/ethnicity in these associations.
Keywords: exercise, health disparities, health promotion, cardiovascular health, active commuting
In 2017, only 24% of adults in the United States met the 2008 physical activity recommendations.1 African American and Hispanic/Latina women were less likely (13.9% and 13.8%, respectively) to have met recommendations than non-Hispanic white women (23.7%) were. Despite the many health promoting benefits physical activity has for adults, including prevention and control of excessive weight gain, type 2 diabetes mellitus, hypertension, dyslipidemia, and/or cardiometabolic syndrome,2 low rates of physical activity participation persist among women of color.
In the United States, health care costs for an individual with cardiometabolic syndrome are approximately $4000 per year higher than costs for an individual without cardiometabolic syndrome.3 A cardiometabolic syndrome diagnosis includes having been diagnosed with at least 3 of the following risk factors: hypertension, abdominal obesity, low levels of high-density lipoproteins, hypertriglyceridemia, and prediabetes.4 Across all adult age groups, racial/ethnic minority women are disproportionately affected by cardiometabolic syndrome compared with non-Hispanic white women, with prevalence of cardiometabolic syndrome exceeding 50% among African American and Hispanic/Latina women aged 50 years or older.5
Active transportation (AT) is walking or bicycling for all or part of a commute to any destination.6 Systematic reviews conducted to evaluate AT use and health have found AT to be associated with a lower risk of diabetes and high blood pressure7–10 and lower body weight in adults.9,11 Furthermore, AT users have been found to have better cardiovascular health than those who do not use AT,9 suggesting that promotion of AT may be an effective physical activity intervention. AT may provide a sustainable method to improve health and reduce cardiometabolic syndrome in racial/ethnic minority women, because it is accessible (low cost, not requiring additional equipment) and does not require taking time away from family, which has been found to be a barrier to physical activity.12,13 Incorporating AT into a daily trip to work, school, or other destination can add numerous opportunities to be active every day and allows members of the family to participate in the activity together.
Participation in health promoting behaviors, such as physical activity and AT, is complex and determined by multiple factors. The ecological model of physical activity (EMPA) is a dynamic systems framework used to guide investigation into how factors at multiple (eg, micro and macro) environmental levels can act directly and indirectly, and interact within and across people and places to influence individual-level physical activity (including AT) and health outcomes.14–16 This framework allows for exploration of how social and economic factors, such as income, education, and cultural context may influence individual participation in health promoting behaviors and, in turn, health outcomes.
Although numerous studies have investigated the health promoting benefits of AT in samples of primarily white adults,7–9,11,17 these associations have not been well-studied in African American or Hispanic/Latina women. Furthermore, the current literature on how best to provide effective physical activity interventions and recommendations for different social groups and minority women is still sparse.2,18 Guided by the EMPA, this exploratory study addresses this gap in the literature by investigating associations among AT, physical activity, and cardiometabolic risk factors in a sample of African American and Hispanic/Latina women from 2 large cities in Texas, and examines these associations by race/ethnicity. We hypothesized that women who used AT would have lower body mass index (BMI), systolic (SBP) and diastolic blood pressure (DBP), resting heart rate (RHR), and body fat percentage (BF) than women who did not use AT. It was also expected that time spent in AT and moderate to vigorous physical activity (MVPA) would be negatively associated with BMI, SBP, DBP, RHR, and BF.
Methods
Data Source
This study was a secondary analysis of data collected as part of the Health is Power (HIP) study, a longitudinal, multisite, randomized controlled trial conducted in Houston and Austin, Texas between 2006–2008 to increase physical activity or fruit and vegetable consumption among racial/ethnic minority women (1R01CA109403). Participants completed interviewer-administered surveys at baseline health assessments.19,20 Study assessments, measures, and procedures were approved by the committee for the protection of human subjects at the University of Houston and the Institutional Review Board at the University of Texas, Austin, and informed consent was obtained by all study participants.
Participants
Physically inactive, nonpregnant women who identified as African American or Hispanic/Latina, aged 25–60 years old, and who were able to read and write in English or Spanish were recruited to participate in the study. Details of study eligibility, recruitment, and intervention have been previously published.19,20 A total of 410 participants provided written informed consent and were enrolled in the study. Only participants who completed the self-reported measure of AT at baseline were included in this secondary analysis.
Individual Measures
Sociodemographic Variables.
Sociodemographic variables included race/ethnicity, age, educational attainment, and household income adjusted for number of family members. These were collected using an adapted version of the Maternal Infant Health Assessment survey, which has been previously used with diverse populations.21
Active Transportation and Physical Activity.
Active transportation and physical activity were measured using items from the long form of the International Physical Activity Questionnaire (IPAQ). The IPAQ has established reliability (0.80) but fairly low validity (0.30), consistent with other self-reported measures of physical activity.22 The IPAQ questionnaire allows for calculation of total physical activity in metabolic equivalent (MET) minutes per week, a measure of frequency and intensity of exercise, and time spent in different domains of physical activity, including transportation, work, domestic, and leisure-time activity, as well as time spent engaging in moderate- and vigorous-intensity physical activity across all domains.22,23 AT was operationalized using 2 methods to answer our hypotheses. Time spent in AT was dichotomized into AT user (any IPAQ transportation-related physical activity) versus no AT use (no IPAQ transportation-related physical activity). In addition, IPAQ transport-related MET minutes per week were used as a continuous variable for AT. MET minutes per week spent in moderate physical activity, vigorous physical activity, combined MVPA, total physical activity, total physical activity minus AT, and MET minutes per day of MVPA were used in the analyses. Self-reported total IPAQ physical activity values of >960 MET minutes per day were excluded from the analyses based on the IPAQ scoring protocol’s data processing rules.24
Objective Moderate to Vigorous Physical Activity.
Objective MVPA was measured using unidirectional ActiGraph GT1M accelerometers (ActiGraph, Pensacola, FL),25 which has demonstrated reliability with an intraclass correlation of .87.26 During baseline health assessments, a subsample of participants from the Houston site was asked to wear the accelerometer for 7 days, taking it off for sleeping or bathing, and to record daily wear times on an accelerometer log. Accelerometers were not available at the Austin site. After 7 days, participants returned accelerometers, and all data were downloaded and processed as previously described,27 with MVPA minutes per day used in the analyses.
Cardiometabolic Risk Factors.
Body mass index, SBP, DBP, RHR, and BF were measured or derived from measures collected at baseline. Each anthropometric feature (height, weight, and BF) was measured twice using established protocols by trained research staff, and the average of the 2 measurements was computed.20 Height was measured in inches using a stadiometer for use in the calculation of BMI. Weight and BF were measured using the Tanita Body Fat 310 scale (Tanita Corp, Arlington Heights, IL) in pounds.20,28 RHR was measured by palpating the radial pulse and counting for 1 full minute, after the participant had sat quietly in a chair for 2 minutes. SBP and DBP were also measured twice and averaged using a manual aneroid sphygmomanometer on the left arm with 2 minutes between measurements, following detailed protocols previously published.29,30 SBP and DBP were analyzed as separate outcome variables to ensure the relationship between AT or MVPA on each outcome would be captured because the magnitude of the effect of exercise on each can differ.2
Statistical Analyses
Descriptive analyses were used to summarize sociodemographic characteristics (race/ethnicity, site, education, household income adjusted for family size, and age). Chi-square tests, Fisher exact tests (for expected frequencies <5), and independent samples t tests were used to examine bivariate associations of AT use (any/none) and race/ethnicity (African American and Hispanic/Latina) with sociodemographic variables, cardiometabolic risk factors (BMI, BF, RHR, SBP, and DBP), and physical activity measures (IPAQ-measured moderate, vigorous, and total physical activity and MVPA; accelerometer-measured MVPA). Bivariate correlations among AT use, physical activity measures, and cardiometabolic risk factors were computed for the full sample and separately by race/ethnicity.
Separate linear regression models were used to estimate the association between AT use and each cardiometabolic risk factor, and the relationship between MET minutes of AT and each cardiometabolic risk factor, adjusting for covariates (sociodemographic variables and total IPAQ physical activity minus AT). Associations between accelerometer-measured MVPA and cardiometabolic risk factors were also estimated, adjusting for sociodemographic variables. Finally, regression models were used to examine associations of dichotomous AT use, MET minutes of AT, and accelerometer-measured MVPA with each of the cardiometabolic risk factors separately by race/ethnicity (African American and Hispanic/Latina), adjusting for income, education, and age. Site was not included as a covariate as it was perfectly collinear with race/ethnicity. All analyses were conducted using SPSS (version 23.0; IBM Corporation, Armonk, NY) with the significance level set at .05.
Results
Of the 410 participants enrolled in the study, 367 completed the IPAQ. Of those, 40 participants reported >960 minutes per day of total physical activity and were excluded from analyses,24 yielding a total analytic sample of 327 participants. Participants excluded from analyses were significantly more likely to be African American (vs Hispanic/Latina) than those whose data were retained for analysis, χ2 (1, N = 410) = 6.49, P < .05. Exclusion (vs inclusion) was not significantly related to site, income, education level, age, or any cardiometabolic risk factor (Ps > .05). Due to accelerometry data only being collected at the Houston site, a higher proportion of participants with valid accelerometer data (n = 159) were African American (n = 103) compared with Hispanic/Latina (n = 56), but sociodemographic characteristics or outcome variables from participants with accelerometer data were not significantly different from those without (n = 168).
Participant Characteristics
Demographic characteristics for the entire sample, and by both AT use and race/ethnicity are described in Table 1. The mean (SD) of cardiometabolic risk factors and physical activity measures for the total sample, AT users versus nonusers, and African American versus Hispanic/Latina women are presented in Table 2.
Table 1.
Demographic Characteristics for Sample, by AT Use and Race/Ethnicity
| Variable | Sample | AT user | Non-AT user | P value | African American | Hispanic/Latina | P value |
|---|---|---|---|---|---|---|---|
| Race/ethnicity, % | .89 | ||||||
| African American | 65.7 | 41.9 | 58.1 | 100.0 | - | ||
| Hispanic Latina | 34.3 | 41.1 | 58.9 | - | 100.0 | ||
| Site, % | .89 | <.001 | |||||
| Houston | 74.6 | 41.8 | 58.2 | 88.1 | 11.9 | ||
| Austin | 25.4 | 41.0 | 59.0 | - | 100.0 | ||
| Income, %FPL | .38 | .07 | |||||
| ≤100% | 2.3 | 2.4 | 2.2 | 1.0 | 4.7 | ||
| 101%-200% | 12.1 | 15.7 | 9.4 | 11.9 | 12.3 | ||
| 201%-300% | 19.5 | 15.7 | 22.2 | 18.4 | 21.7 | ||
| 301%-400% | 15.0 | 14.2 | 15.6 | 12.9 | 18.9 | ||
| >400% | 51.1 | 52.0 | 50.6 | 55.7 | 42.5 | ||
| Education, % | .35 | <.001 | |||||
| Less than high school | 3.1 | 1.5 | 4.3 | 0.5 | 8.1 | ||
| High school (or GED) | 6.8 | 8.9 | 5.3 | 1.4 | 17.1 | ||
| Some college | 44.6 | 44.4 | 44.7 | 43.4 | 46.8 | ||
| College graduate | 44.5 | 45.2 | 45.7 | 54.7 | 27.9 | ||
| Age, mean (SD) | 45.07 (9.47) | 45.30 (9.39) | 44.9 (9.55) | .71 | 44.64 (9.48) | 45.87 (9.44) | .27 |
Abbreviations: AT, active transportation; FPL, federal poverty level; GED, general education diploma. Note: Boldface indicates significant.
Table 2.
Outcome Variables for Sample, and by AT Use and Race/Ethnicity
| Variable | Sample, mean (SD) | AT user | Non-AT user | t | P value | African American | Hispanic/Latina | t | P value |
|---|---|---|---|---|---|---|---|---|---|
| Cardiometabolic risk factors | |||||||||
| BMI, kg/m2 | 34.43 (7.75) | 34.94 (7.80) | 34.07 (7.71) | −1.00 | .32 | 34.42 (8.08) | 34.44 (7.10) | −.02 | .99 |
| SBP, mm Hg | 125.08 (13.22) | 124.51 (12.95) | 125.48 (13.43) | .65 | .52 | 126.08 (13.43) | 123.18 (12.66) | 1.89 | .06 |
| DBP, mm Hg | 78.71 (9.58) | 78.91 (8.93) | 78.57 (10.03) | −.32 | .75 | 79.32 (9.54) | 77.54 (9.59) | 1.59 | .11 |
| RHR, bpm | 73.72 (8.92) | 74.41 (9.19) | 73.23 (8.72) | −1.18 | .24 | 73.10 (8.68) | 74.91 (9.30) | −1.74 | .08 |
| Body fat, % | 42.81 (7.14) | 42.97 (7.23) | 42.70 (7.10) | −.33 | .74 | 42.90 (7.57) | 42.65 (6.29) | .30 | .76 |
| Physical activity | |||||||||
| IPAQ transport, MET min/wk | 134.04 (317.19) | 322.29 (426.43) | - | −8.81 | <.001 | 133.54 (314.56) | 134.99 (323.61) | −.04 | .97 |
| IPAQ MVPA, MET min/wk | 1210.96 (1288.20) | 1356.71 (1325.66) | 1107.19 (1254.03) | −1.73 | .08 | 1144.22 (1292.91) | 1339.08 (1275.06) | −1.30 | .20 |
| IPAQ moderate, MET min/wk | 965.33 (1114.50) | 1046.71 (1015.61) | 907.40 (1179.08) | −1.11 | .27 | 937.71 (1136.51) | 1018.37 (1074.02) | −.62 | .54 |
| IPAQ vigorous, MET min/wk | 245.63 (581.12) | 310.00 (737.78) | 199.79 (433.12) | −1.56 | .12 | 206.51 (496.86) | 320.71 (712.19) | −1.52 | .13 |
| IPAQ nontransport MET min/wk | 1583.51 (1507.66) | 1721.57 (1549.83) | 1485.20 (1473.10) | −1.40 | .16 | 1534.35 (1526.17) | 1677.87 (1473.63) | −.82 | .42 |
| IPAQ total, MET min/wk | 1717.55 (1544.40) | 2043.86 (1588.09) | 1485.20 (1473.10) | −3.27 | .001 | 1667.89 (1572.77) | 1812.87 (1490.72) | −.81 | .42 |
| Accelerometer MVPA, min/d | 19.72 (19.78) | 21.66 (20.25) | 18.31 (19.42) | −1.06 | .29 | 25.29 (21.88) | 9.48 (8.45) | 5.20 | <.001 |
Abbreviations: AT, active transportation; bpm, beats per minute; BMI, body mass index; DBP, diastolic blood pressure; IPAQ, International Physical Activity Questionnaire; MET, metabolic equivalent; MVPA, moderate to vigorous physical activity; RHR, resting heart rate; SBP, systolic blood pressure. Note: Boldface indicates significant.
There were no significant differences in cardiometabolic risk factors or physical activity measures by education. Women who reported an income of 101% to 200% of the federal poverty level self-reported significantly higher amounts of total IPAQ physical activity compared with women with an income of 201% to 300% of the federal poverty level (mean = 2173.29, SD = 1873.25 vs mean = 1203.03, SD = 1325.58, respectively; F4,302 = 2.62, P < .05). There were no differences in outcome variables by income or site.
Meeting Physical Activity Guidelines
Self-reported physical activity for the overall sample was high, with the majority (62.4%) reporting achieving the minimum recommended guidelines of at least 500 MET minutes per week of total physical activity. AT users were significantly more likely to report meeting physical activity guidelines than were nonusers (88.2% vs 69.6%; χ2 [1, n = 327] = 15.70, P < .001). There were no significant differences in meeting weekly IPAQ physical activity recommendations for African American women compared with Hispanic/Latina women (60.0% vs 67.0%; χ2 [1, n = 327] = 1.52, P > .05). Of the 159 participants with accelerometer-measured MVPA, 32.1% (n = 51) met the national MVPA recommendation of 150 minutes per week. A higher percentage of AT users also met MVPA recommendations than nonusers (35.8% vs 29.3%); however, this difference was not significant (χ2 [1, n = 159] = 0.746, P > .05). Significantly more African American women (44.7%) met the recommendation compared with Hispanic/Latina women (8.9%; χ2 [1, n = 159] = 21.26, P < .001).
Bivariate Correlations
Age was significantly correlated with SBP (r = .33; P < .001), DBP (r = .23; P < .001), BF (r = .18; P < .01), moderate IPAQ physical activity (r = .13; P < .05), and total IPAQ physical activity (r = .11; P < .05) for the sample. Table 3 presents bivariate correlations of AT use, physical activity measures, and cardiometabolic risk factors. Tables 4 and 5 present bivariate correlations of AT use, physical activity measures, and cardiometabolic risk factors for African American and Hispanic/Latina women in the sample, respectively.
Table 3.
Bivariate Correlations Among Cardiometabolic Risk Factors and Physical Activity Variables for Sample
| AT (any/none) | IPAQ transport | IPAQ moderate | IPAQ vigorous | IPAQ total | IPAQ MVPA | Accelerometer MVPA | |
|---|---|---|---|---|---|---|---|
| BMI | .056 | .019 | .014 | −.085 | −.027 | −.026 | −.199* |
| SBP | −.036 | .004 | .087 | −.021 | .031 | .066 | −.098 |
| DBP | .018 | .018 | .033 | −.116* | −.039 | −.024 | −.132 |
| RHR | .066 | .205 | .037 | −.077 | −.017 | −.003 | −.111 |
| Body fat | .019 | .036 | .053 | −.103 | .009 | −.002 | −.186* |
| Accelerometer MVPA | .084 | .096 | .098 | .061 | .136 | .112 | - |
Abbreviations: AT, active transportation; BMI, body mass index; DBP, diastolic blood pressure; IPAQ, International Physical Activity Questionnaire; MVPA, moderate to vigorous physical activity; RHR, resting heart rate; SBP, systolic blood pressure. Note: Boldface indicates significant.
P < .05.
Table 4.
Bivariate Correlations Among Cardiometabolic Risk Factors and Physical Activity Variables for African American Women
| AT (any/none) | IPAQ transport | IPAQ moderate | IPAQ vigorous | IPAQ total | IPAQ MVPA | Accelerometer MVPA | |
|---|---|---|---|---|---|---|---|
| BMI | .091 | .034 | .013 | −.054 | −.030 | −.010 | −.233* |
| SBP | −.060 | .007 | .098 | −.003 | .044 | .084 | −.154 |
| DBP | .008 | .056 | .035 | −.111 | −.033 | −.012 | −.194 |
| RHR | .062 | .095 | .050 | −.044 | .040 | .027 | −.091 |
| Body fat | .052 | .057 | .058 | −.072 | .015 | .021 | −.198* |
| Accelerometer MVPA | .126 | .087 | .150 | .164 | .202* | .201* | - |
Abbreviations: AT, active transportation; BMI, Body mass index; DBP, diastolic blood pressure; IPAQ, International Physical Activity Questionnaire; MVPA, moderate to vigorous physical activity; RHR, resting heart rate; SBP, systolic blood pressure. Note: Boldface indicates significant.
P < .05.
Table 5.
Bivariate Correlations Among Cardiometabolic Risk Factors and Physical Activity Variables for Hispanic/Latina Women
| AT(any/none) | IPAQ transport | IPAQ moderate | IPAQ vigorous | IPAQ total | IPAQ MVPA | Accelerometer MVPA | |
|---|---|---|---|---|---|---|---|
| BMI | −.021 | −.011 | .017 | −.139 | −.020 | −.063 | −.297* |
| SBP | −.060 | .002 | .079 | −.023 | .017 | .054 | −.040 |
| DBP | .011 | −.049 | .037 | −.109 | −.039 | −.029 | −.161 |
| RHR | .036 | .027 | .004 | −.143 | −.038 | −.076 | .071 |
| Body fat | −.058 | −.011 | .046 | −.159 | −.001 | −.050 | −.318* |
| Accelerometer MVPA | −.156 | −.041 | .027 | −.101 | −.045 | −.034 | - |
Abbreviations: AT, active transportation; BMI, body mass index; DBP, diastolic blood pressure; IPAQ, International Physical Activity Questionnaire; MVPA, moderate to vigorous physical activity; RHR, resting heart rate; SBP, systolic blood pressure. Note: Boldface indicates significant.
P < .05.
Linear Regression Models
Active Transportation Predicting Cardiometabolic Risk.
In linear regression models adjusting for demographic variables (race/ethnicity, site, income, education, and age) and non-AT physical activity, AT use (any/none) and MET minutes of AT were not significantly associated with BMI, SBP, DBP, RHR, or BF (Ps > .05) for the full sample.
Likewise, in analyses conducted separately in African American-only and Hispanic/Latina-only subsamples, AT use and MET minutes of AT were not associated with BMI, SBP, DBP, RHR, or BF for either group (Ps > .05).
Accelerometer-Measured MVPA Predicting Cardiometabolic Risk.
In models of linear associations between accelerometer-measured MVPA and individual cardiometabolic risk factors (adjusting for race/ethnicity, site, income, education, and age), MVPA was negatively associated with DBP (b = −0.074, t = −1.98, P = .05) and BF (b = −0.094, t = −3.22, P < .01). Associations between accelerometer-measured MVPA and BMI, SBP, and RHR were not significant (Ps > .05).
For African American women, accelerometer-measured MVPA was significantly inversely associated with BMI (b = −0.097, t = −2.65, P < .05) and BF (b = −0.081, t = −2.69, P < .01), but not SBP, DBP, or RHR (Ps > .05). Among Hispanic/Latina women, BF was the only risk factor significantly related to accelerometer-measured MVPA (b = −0.292, t = −2.79, P < .01) after controlling for income, education, and age.
Discussion
The purpose of this study was to investigate relationships among AT, MVPA, and cardiometabolic risk factors among African American and Hispanic/Latina women. This is the first observational study to examine the relationship between AT use and cardiometabolic risk factors among racial/ethnic minority women, while also investigating outcomes based on race/ethnicity. The results of this secondary analysis did not provide support for the hypothesis that AT use is associated with better cardiometabolic health in this sample of women; however, SBP was found to be slightly lower, albeit not significantly so, among the women using AT compared with those who did not. Analyses showed no relationship between AT use and cardiometabolic risk factors by race/ethnicity. Accelerometer-measured MVPA was found to be inversely related to DBP and BF for the entire sample, consistent with the current literature,2 but not related to BMI, SBP, or RHR. Further analyses by race/ethnicity revealed that accelerometer-measured MVPA was only associated with lower BMI and BF among African American women, and lower BF for Hispanic/Latina women.
The findings of this study are contrary to systematic reviews, which found AT use to be inversely associated with body weight9,11 and blood pressure7,8 among adults. In addition, walking and cycling as modes of AT have been found to be related to improved cardiovascular health including lower total cholesterol, and low-density lipoproteins, increased high-density lipoproteins, and improved blood glucose levels.9 According to one review,8 those who used AT for relatively longer distances had a lower risk of type 2 diabetes mellitus and hypertension; however, an actual dose-response relationship could not be clearly ascertained due to the inconsistencies across the studies in the measurement of time spent in, and intensity of, AT. For example, studies differed in how they classified an individual as an AT user versus non-AT user, with an AT user defined as someone spending greater than 20 minutes, greater than 30 minutes, 3 MET hours, or 8 MET hours in AT per day. Furthermore, the intensity of AT, as influenced by factors such as terrain, climate, or traffic congestion, was not reported in any of the studies reviewed.8 Given that the typical intensity level of AT-related physical activity in previous studies is unknown, and that there is no way of determining intensity level in the data reported herein, directly comparing our findings to those of previous studies is impossible. This could also explain the absence of a significant relationship between AT and cardiometabolic health.
The AT users in this sample of African American and Hispanic/Latina women reported significantly more self-reported total physical activity per week compared with non-AT users, suggesting that AT may help women increase their weekly physical activity, as others have also suggested.2,31 Women reported participating in just over 300 MET minutes of AT per week, meeting over half the minimum weekly physical activity recommendations with AT alone. Furthermore, the objectively measured MVPA was somewhat higher for AT users than non-AT users, lending some support to findings from the self-reported data showing higher rates of physical activity among AT users.
The overall sample self-reported participating in large amounts of weekly total physical activity (1718 MET min) and MVPA (1211 MET min), which is higher than national trends for this population.1 This discrepancy may reflect overestimation of physical activity, a bias commonly found in self-reported physical activity data. Interestingly, Hispanic/Latina women self-reported higher levels of moderate and vigorous physical activity than African American; however, accelerometry demonstrated African American women actually had significantly higher levels of MVPA. When comparing self-report and objective physical activity measures, objectively measured MVPA was not associated with self-reported total physical activity or MVPA for the sample; however, measured MVPA was positively associated with both self-reported measures for African American women. This may suggest that IPAQ more accurately captures total physical activity and MVPA participation in African American women compared with Hispanic/Latina women, which is consistent with findings suggesting that Hispanic/Latina women may overestimate their MVPA on self-report measures.32
This sample engaged in approximately 133 minutes of accelerometer-measured MVPA per week, which is just below the weekly aerobic recommendation of 150 minutes of MVPA to maintain and/or improve health2; however, greater MVPA minutes were only related to improvements in DBP and BF in this sample. When examining associations by race/ethnicity, accelerometer-measured MVPA and DBP were no longer significantly related, but associations with BMI became significant. African American women achieved a mean of 177 minutes of objective MVPA per week, surpassing the recommended weekly MVPA guidelines. This was associated with BMI and BF, but not SBP, DBP, or RHR. Hispanic/Latina women participated in only approximately 66 minutes of MVPA per week, and for them, MVPA was negatively related to BF.
Contrary to our hypotheses, higher MET minutes of self-reported total physical activity and MVPA were not associated with better cardiometabolic health outcomes in this sample of women. Moreover, the sample of African American women had poor cardiometabolic health, even though they met weekly MVPA guidelines per accelerometry and self-report. One possible explanation is that the observed total physical activity and MVPA participation may not have been sufficient to overcome caloric intake, an important determinant of cardiometabolic health33 that was not assessed in this investigation. Prolonged sitting and sleep duration, 2 other variables not measured in this study, may have attenuated the associations of interest. It has been suggested the interaction of these variables may mediate the relationship between physical activity and cardiometabolic health necessitating adjustments to MVPA recommendations.34 Future research should investigate the relationship between physical activity, dietary factors, prolonged sitting, and sleep duration on cardiometabolic health among ethnic minority women.
When considering the findings of the study through the lens of the EMPA, the linkages between race/ethnicity, education, MVPA participation, and cardiometabolic outcomes are complex. The African American women in this sample had significantly more education than the Hispanic/Latina women and, as expected, the African American women participated in significantly more accelerometer-measured MVPA. It was also expected that the higher levels of MVPA would have translated to better health outcomes, though this was only consistent with BMI and BF. Furthermore, African American women actually had higher SBP, DBP, and BF than the Hispanic/Latina women in this sample, and, although this difference was not statistically significant, it is clinically significant, especially related to SBP. The sample mean for SBP of the African American women was 126.08 mm Hg compared with 123.18 for Hispanic/Latina women, increasing their risks for all-cause and cardiovascular disease mortality, coronary heart disease, and cardiovascular disease.35 Consistent with the EMPA, this may suggest there are other cultural and/or biological factors that moderate the effects of MVPA on cardiometabolic health in racial/ethnic minority women necessitating further investigation.14–16
Strengths and Limitations
This is the first study investigating the associations between AT use and cardiometabolic risk factors in a large sample of racial/ethnic minority women, a population that has been underrepresented in AT and physical activity research.2,7–9,11 The IPAQ has been shown to be reliable in diverse populations,22 and the addition of accelerometer-measured physical activity is a strength of this study. All cardiometabolic measures were objectively measured by trained research staff.
Despite these strengths, some features of the study limit the conclusions that can be drawn. History of hypertension or antihypertensive medication status was not recorded at the time of measurement; however, women with untreated hypertension were excluded from the study following the Physical Activity Readiness Questionnaire (PAR-Q) protocol. If participants’ blood pressure were lowered due to antihypertension medication, this would limit the ability to detect differences in blood pressure between AT users and nonusers. One caveat of this study is that the education and income levels of the study participants were quite high, limiting generalizability and negatively influencing AT participation rates.36 A majority of the sample was obese with elevated SBP, which could potentially limit the ability to detect differences and associations related to those outcomes. Self-report measures may have been biased by overestimation or social desirability introducing error and limiting the ability to detect associations between MET minutes of AT and health outcomes. As these data were collected using correlational cross-sectional design, causation may not be inferred.
Future research should continue to examine this relationship using objectively measured AT and physical activity, as well as AT intensity, using longitudinal designs to determine the presence and magnitude of a potential dose-response relationship between AT use and MVPA on cardiometabolic risk factors among racial/ethnic minority women. Further investigation into the relationship between AT use and additional cardiometabolic risk outcomes, including fasting blood glucose, triglycerides, and high-density lipoproteins is recommended. Health practitioners should, when appropriate, encourage patients to substitute AT for any daily motorized transport to increase levels of MVPA. Policymakers should develop and pass policies that increase safe and pleasant routes to destinations for active commuters and walkability of the built environment to promote AT use to potentially increase overall MVPA levels.
Conclusions
Active transportation use has been found to be associated with improved cardiometabolic health in the literature; however, this investigation did not show a significant relationship between AT and cardiometabolic risk factors in this sample of women. Objectively measured MVPA was associated with more favorable DBP, BMI, and BF values; therefore, health practitioners should recommend to their African American and Hispanic/Latina women patients that they should increase overall MVPA levels to improve cardiometabolic risk factors and promote health. AT appears to be a good strategy for racial/ethnic minority women to increase their daily physical activity levels; however, more research is needed to determine the cardiometabolic health benefits of AT, including at different intensity levels. African American women who meet national weekly aerobic physical activity recommendations may not see improvements in health outcomes, necessitating research to determine other factors that may influence the effects of physical activity on cardiometabolic health, as well as the amount and types (eg, aerobic, strength training, flexibility) of physical activity needed to result in improved health outcomes in this population.
Contributor Information
Elizabeth Lorenzo, Center for Health Promotion and Disease Prevention, Edson College of Nursing and Health Innovation, Arizona State University, Phoenix, AZ, USA..
Jacob Szeszulski, Center for Health Promotion and Prevention Research, Michael Susan Dell Center for Healthy Living, University of Texas Health Science Center at Houston, Houston, TX, USA..
Michael Todd, Edson College of Nursing and Health Innovation, Arizona State University, Phoenix, AZ, USA..
Scherezade K. Mama, Department of Kinesiology, The Pennsylvania State University, University Park, PA, USA.
Rebecca E. Lee, Center for Health Promotion and Disease Prevention, Edson College of Nursing and Health Innovation, Arizona State University, Phoenix, AZ, USA.
References
- 1.Blackwell DL, Villarroel MA. Tables of Summary Health Statistics for U.S. adults: 2017 National Health Interview Survey. National Center for Health Statistics; 2018. https://ftp.cdc.gov/pub/Health_Statistics/NCHS/NHIS/SHS/2017_SHS_Table_A-14.pdf. Accessed February 14, 2019. [Google Scholar]
- 2.Physical Activity Guidelines Advisory Committee. Physical Activity Guidelines Advisory Committee Scientific Report. Washington, DC: US Department of Health and Human Services; 2018. https://health.gov/paguidelines/second-edition/report/pdf/PAG_Advisory_Committee_Report.pdf. Accessed February 14, 2019. [Google Scholar]
- 3.McQueen RB, Ghushchyan V, Olufade T, Sheehan JJ, Nair KV, Saseen JJ. Incremental increases in economic burden parallels cardiometabolic risk factors in the US. Diabetes Metab Syndr Obes. 2016;9:233–241. doi: 10.2147/DMSO.S106809 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.National Heart Lung and Blood Institute. Metabolic syndrome. https://www.nhlbi.nih.gov/health-topics/metabolic-syndrome. Accessed January 3, 2019.
- 5.Moore JX, Chaudhary N, Akinyemiju T. Metabolic syndrome prevalence by race/ethnicity and sex in the United States, National Health and Nutrition Examination Survey, 1988–2012. Prev Chronic Dis. 2017;14:E24. doi: 10.5888/pcd14.160287 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.US Department of Health Health & Human Services C for DC and P. Healthy places: transportation health impact assessment toolkit. 2011. https://www.cdc.gov/healthyplaces/transportation/promote_strategy.htm. Accessed February 14, 2019.
- 7.Furie GL, Desai MM. Active transportation and cardiovascular disease risk factors in US adults. Am J Prev Med. 2012;43(6): 621–628. doi: 10.1016/j.amepre.2012.06.034 [DOI] [PubMed] [Google Scholar]
- 8.Saunders LE, Green JM, Petticrew MP, Steinbach R, Roberts H. What are the health benefits of active travel? A systematic review of trials and cohort studies. PLoS One. 2013;8(8):e69912. doi: 10.1371/journal.pone.0069912 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Xu H, Wen LM, Rissel C. The relationships between active transport to work or school and cardiovascular health or body weight: a systematic review. Asia Pac J Public Health. 2013;25(4):298–315. doi: 10.1177/1010539513482965 [DOI] [PubMed] [Google Scholar]
- 10.Berger AT, Qian X, Pereira MA. Associations between bicycling for transportation and cardiometabolic risk factors among Minneapolis-Saint Paul area commuters: a cross-sectional study in working-age adults. Am J Health Promot. 2017;32(3):631–637. doi: 10.1177/0890117117710735 [DOI] [PubMed] [Google Scholar]
- 11.Wanner M, Götschi T, Martin-Diener E, Kahlmeier S, Martin BW. Active transport, physical activity, and body weight in adults: a systematic review. Am J Prev Med. 2012;42(5):493–502. doi: 10.1016/j.amepre.2012.01.030 [DOI] [PubMed] [Google Scholar]
- 12.Bautista L, Reininger B, Gay JL, Barroso CS, McCormick JB. Perceived barriers to exercise in Hispanic adults by level of activity. J Phys Act Health. 2011;8(7):916–925. doi: 10.1123/jpah.8.7.916 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Im E-O, Lee B, Hwang H, et al. “A waste of time:” Hispanic women’s attitudes toward physical activity. Women Health. 2010;50(6):563–579. doi: 10.1080/03630242.2010.510387 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Spence JC, Lee RE. Toward a comprehensive model of physical activity. Psychol Sport Exerc. 2003;4(1):7–24. doi: 10.1016/S1469-0292(02)00014-6 [DOI] [Google Scholar]
- 15.Lee RE, Cubbin C. Striding toward social justice: the ecologic milieu of physical activity. Exerc Sport Sci Rev. 2009;37(1):10–17. doi: 10.1097/JES.0b013e318190eb2e [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Lee RE, Adamus-Leach HJ, Soltero GE, et al. Obesity: an ecologic perspective on challenges and solutions In: Stein N, ed. Public health nutrition: principles and practice for community and global health. Burlington, MA: Jones & Bartlett Learning; 2015:363–386. ISBN-13: 9781449692049. [Google Scholar]
- 17.Laverty AA, Mindell JS, Webb EA, Millett C. Active travel to work and cardiovascular risk factors in the United Kingdom. Am J Prev Med. 2013;45(3):282–288. doi: 10.1016/j.amepre.2013.04.012 [DOI] [PubMed] [Google Scholar]
- 18.Ball K, Carver A, Downing K, Jackson M, O’Rourke K. Addressing the social determinants of inequities in physical activity and sedentary behaviours. Health Promot Int. 2015;30(suppl 2):ii8–ii19. doi: 10.1093/heapro/dav022 [DOI] [PubMed] [Google Scholar]
- 19.Lee RE, Medina AV, Mama SK, et al. Health is power: an ecological, theory-based health intervention for women of color. Contemp Clin Trials. 2011;32(6):916–923. doi: 10.1016/j.cct.2011.07.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Lee RE, Mama SK, Medina AV, et al. Multiple measures of physical activity, dietary habits and weight status in African American and Hispanic or Latina women. J Community Health. 2011;36(6):1011. doi: 10.1007/s10900-011-9403-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.California Department of Public Health. Maternal and Infant Health Assessment (MIHA) survey. 2010. https://www.cdph.ca.gov/Programs/CFH/DMCAH/MIHA/Pages/Questionnaire.aspx. Accessed February 14, 2019.
- 22.Craig CL, Marshall AL, Sjostrom M, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–1395. doi: 10.1249/01.MSS.0000078924.61453.FB [DOI] [PubMed] [Google Scholar]
- 23.Sjöström M, Bull F, Craig C. Towards standardized global assessment of health-related physical activity-the International Physical Activity Questionnaires (IPAQ). Med Sci Sports Exerc. 2002;34(5): S202. [Google Scholar]
- 24.IPAQ Research Committee. Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ)-short and long forms. 2005. https://bit.ly/2CMBOw0
- 25.ActiGraph. ActiGraph. 2020. http://www.actigraphcorp.com/. Accessed February 14, 2019.
- 26.Welk GJ. Principles of design and analyses for the calibration of accelerometry-based activity monitors. Med Sci Sports Exerc. 2005; 37(11 suppl):S501–S511. doi: 10.1249/01.mss.0000185660.38335.de [DOI] [PubMed] [Google Scholar]
- 27.Layne CS, Mama SK, Banda JA, Lee RE. Development of an ecologically valid approach to assess moderate physical activity using accelerometry in community dwelling women of color: a cross-sectional study. Int J Behav Nutr Phys Act. 2011;8(1):21. doi: 10.1186/1479-5868-8-21 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Kueht ML, McFarlin BK, Lee RE. Severely obese have greater LPS-stimulated TNF-α production than normal weight African-American women. Obesity. 2009;17(3):447–451. doi: 10.1038/oby.2008.552 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Lee RE, Mama SK, Adamus-Leach HJ. Neighborhood street scale elements, sedentary time and cardiometabolic risk factors in inactive ethnic minority women. PLoS One. 2012;7(12):e51081. doi: 10.1371/journal.pone.0051081 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Lee RE, Mama SK, Lopez Y III. Sitting time and cardiometabolic risk factors in African American overweight women. J Obes. 2012; 2012:803467. doi: 10.1155/2012/803467 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Whitfield GP, Paul P, Wendel AM. Active transportation surveillance-United States, 1999–2012. MMWR Morb Mortal Wkly Rep. 2015;64(SS07):1–17. https://www.cdc.gov/mmWR/preview/mmwrhtml/ss6407a1.htm [PubMed] [Google Scholar]
- 32.Nicaise V, Marshall S, Ainsworth BE. Domain-specific physical activity and self-report bias among low-income Latinas living in San Diego County. J Phys Act Health. 2011;8(7):881–890. doi: 10.1123/jpah.8.7.881 [DOI] [PubMed] [Google Scholar]
- 33.Mozaffarian D. Dietary and policy priorities for cardiovascular disease, diabetes, and obesity. Circulation. 2016;133(2):187–225. doi: 10.1161/CIRCULATIONAHA.115.018585 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Vincent GE, Jay SM, Sargent C, Vandelanotte C, Ridgers ND, Ferguson SA. Improving cardiometabolic health with diet, physical activity, and breaking up sitting: what about sleep? Front Physiol. 2017;8: 865. doi: 10.3389/fphys.2017.00865 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Bundy JD, Li C, Stuchlik P, et al. Systolic blood pressure reduction and risk of cardiovascular disease and mortality. JAMA Cardiol. 2017;2(7):775. doi: 10.1001/jamacardio.2017.1421 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Lee RE, Lorenzo E, Heck K, Kohl HW III, Cubbin C. Interrelationships of physical activity in different domains: evidence from the Geographic Research on Wellbeing (GROW) study. J Transp Heal. 2017;6:538–547. doi: 10.1016/j.jth.2017.02.004 [DOI] [PMC free article] [PubMed] [Google Scholar]