Abstract
Introduction
Physical inactivity is more prevalent among women than men and is related to poor health outcomes. Neighborhood parks constitute an important resource for physical activity (PA), however, previous studies of park users have found fewer women being physically active.
Methods
We conducted a hierarchical mixed-effect regression analysis of the independent associations between gender and park use and PA among a population-based sample in high-poverty neighborhoods in Los Angeles. Data sources included 1) structured interviews with adults (≥18 years of age) in randomly selected households within 1 mile of study parks (n = 2,973), 2) systematic observations of study parks (n = 48), and 3) neighborhood characteristics from the 2010 U.S. Census.
Results
After controlling for race/ethnicity, education, body mass index of 30 kg/m2 or greater, health status, proximity to park, having children under the age of 18, perceived park safety, estimated screen time, and park- and neighborhood-level variables, statistically significant differences were found between women and men on all outcomes. Compared with men, women reported fewer park visits in the past week (−0.28 times/week; p < .001) and shorter durations of a typical park visit (−11.11 min/visit; p < .001). Women were also less likely than men to report levels of PA that meet national guidelines (≥150 minutes of moderate to vigorous PA per week; risk difference = −0.06; p < .01) or to exercise in the park (risk difference = −0.13; p < .001) or elsewhere (risk difference = −0.13; p < .001).
Conclusions
Women living in high-poverty neighborhoods use parks less for PA than men. Improved park-level design, programming, and other policy interventions may be needed to mitigate disparities in park use and PA for all.
Physical inactivity is an important public health challenge worldwide. When measured through accelerometers, a majority of the U.S. population—58% of children, 92% of adolescents, and 95% of adults—does not meet the current physical activity (PA) recommendations (
Troiano et al., 2008
). Further, across all age groups, females are less active than males and activity decreases with advancing age (Troiano et al., 2008
). Finding ways to increase regular PA, in particular among girls and women, is imperative to addressing chronic diseases such as hypertension, diabetes, osteoporosis, particular forms of cancer, obesity, and some psychological disorders (Van Tuyckom et al., 2013
).In urban areas, parks constitute an important resource for community-based PA (
Bedimo-Rung et al., 2005
, Han et al., 2013
, Han et al., 2014
), but there are disparities in access and use across geographic settings and populations. Approximately 70% of persons in the United States live within walking distance to a park (Mowen et al., 2016
). Recent estimates among the 100 most populous cities show great variation in the percentage of their respective populations living within a 10-minute walk of a park, ranging from 26% to 99% (Harnik et al., 2017
). Further, studies using systematic observations of parks consistently find gender disparities in park use and park-based PA. A review of 24 observational studies in parks using the System for Observing Play and Recreation in Communities found that, across all age groups, on average, more males than females were observed in parks, and males were typically more physically active in parks than females (Evenson et al., 2016
). Another review including studies with a broader range of assessment methodologies reported equal numbers of men and women using parks, but did find that men engaged in more park-based moderate to vigorous PA than women (Joseph and Maddock, 2016
). Qualitative research has suggested that women may be discouraged from using parks (McCormack et al., 2010
). For example, in one study African American women were afraid to use their neighborhood parks owing to safety concerns (Wilbur et al., 2002
), and in another, Latino women reported PA barriers such as insufficient lighting and fear of crime (Cronan et al., 2008
).Neighborhood poverty level has a strong negative association with park use and park-based PA (
Cohen et al., 2012
). Parks in low-income areas may have fewer park resources and staffing, and/or residents in these areas may choose to use park-based resources less than those in more affluent areas. Parks in low-income communities can also be affected by crime, conflict, and discrimination, and sometimes have poorly maintained facilities (Stodolska et al., 2011
) and are therefore often less attractive and appealing for PA (Kaczynski et al., 2014
). Women often feel more physically vulnerable than men in such settings and have more concerns about personal safety, and thus crime-related safety may constrain their PA to a greater extent (- Kaczynski A.T.
- Besenyi G.M.
- Stanis S.A.
- Koohsari M.J.
- Oestman K.B.
- Bergstrom R.
- Reis R.S.
Are park proximity and park features related to park use and park-based physical activity among adults? Variations by multiple socio-demographic characteristics.
International Journal of Behavioral Nutrition and Physical Activity. 2014; 11: 146
Foster and Giles-Corti, 2008
). For this reason, the physical environment's influences on meeting PA requirements are likely to be secondary to individual and social environmental determinants (Giles-Corti and Donovan, 2002
). For example, individuals' use of screen time has been shown to affect PA, including park-based PA (Cohen et al., 2012
, Derose et al., 2015
). Prior research has also found that access to recreational facilities (parks, walking trails, etc.) and neighborhood characteristics (e.g., sidewalks, streetlights) were more highly correlated with PA among women than among men (Brownson et al., 2001
).The previous literature has found that men tend to use parks more for PA than women; however, much of the evidence for gender disparities in park-based PA comes from park-based observations (i.e., among those who use the park). Few population-based samples of urban residents (including those who use the park and who do not) have examined whether there is a gender difference in park use and park-based PA among those with approximately equal access to parks and after controlling for other factors.
This paper examines the independent associations between gender and various measures of park use and PA among a population-based sample of adults in high-poverty areas within walking distance (<1 mile) to neighborhood parks in the City of Los Angeles. Our analytic approach is guided by the social ecological model, which conceptualizes multiple levels of influences on PA, including intrapersonal, interpersonal, cultural, organizational, physical environmental, and policy influences (
Sallis et al., 2006
). Our primary research question is: Among a population-based sample in high-poverty neighborhoods with equal access to parks, are there gender differences in park use and PA after controlling for other factors (individual, park, neighborhood)?Methods
Study Sample
The primary data for these analyses come from a larger study of 48 parks in high-poverty neighborhoods in Los Angeles (where >19% of households were living below the poverty line) (
Cohen et al., 2016
). The parent study was a cluster randomized controlled trial with two waves of data collection and its purpose was to examine factors associated with park use and park-based PA, and to test whether park-based interventions could increase park use and PA. Specifically, it was a four-arm study with three different interventions offered at the park being compared with a control condition: free adult exercise classes, a frequent user program, and free classes plus a frequent user program (parks were randomized to control [business as usual] or one of the three interventions). Because we found no differences among study arms in park-level use and PA between the two waves in all primary outcomes (Cohen et al., 2017
), we combined the overall study arms for the present study to increase power.For this substudy, three data sources are used from the parent study that represent three levels in our multilevel model: 1) individual factors were obtained through structured interviews with adults (≥18 years of age) in randomly selected households within 1 mile of the study parks (n = 2,973); 2) park-level factors were obtained through systematic observations of study parks (n = 48); and 3) neighborhood factors were obtained from the 2010 U.S. Census. For the interviews, we planned to survey 30 households in each park's neighborhood per wave (60 total). The 60 households were randomly selected within 1.00 mile of each park, stratified by distances of 0 to 0.25 mile, 0.25 to 0.50 mile, and 0.50 to 1.00 mile to interview 20 individuals in each stratum, where half of sampled individuals were measured in each wave. The average refusal rate across waves was 17%. Trained, bilingual community health promoters (promotoras) conducted structured interviews with one adult per household about their use of the subject park, frequency of exercise, sociodemographics, health-related factors, perceptions of park safety, and estimated screen time. These same promotoras conducted systematic observations in study parks using System for Observing Play and Recreation in Communities, a validated method using momentary time sampling to assess the characteristics of parks and their users, including their PA levels (
McKenzie et al., 2006
). Observations were conducted in each park three times on 1 day per month over a 6-month period at baseline and follow-up (12 days total, 6 weekend days, and 6 weekdays, or 36 one-hour observation periods per park). Specific measures collected through the interviews and systematic observations or obtained from the 2010 U.S. Census are listed elsewhere in this article.The RAND Human Subjects Protection Committee approved the study and an oral consent procedure for the resident survey.
Measures
Dependent variables
Park use was defined as the number of times residents stated visiting their neighborhood park in the previous 7 days, which has been validated with global positioning system monitoring in a racially and ethnically diverse sample (
Evenson et al., 2013
). Typical duration of a park visit was determined by asking residents, “On a typical day when you go to the park, how long do you stay there?” with response options: 0 to 30 minutes, 31 to 60 minutes, more than 1 hour but less than 2 hours, 2 to 3 hours, or more than 3 hours. This measure has also been validated with global positioning system monitoring in a racially and ethnically diverse sample (Evenson et al., 2013
). Estimates were derived by taking the midpoints of the ranges (and 3 hours for the last category).Meets PA recommendations (based on national guidelines for adults [
Physical Activity Guidelines Advisory Committee, 2009
]) was defined as reporting 150 or more minutes of PA per week and were computed for each resident based on their responses to questions on 1) how many times per week they usually exercise, and 2) how long on average each exercise sessions lasts. Together these two measures constitute an “exercise vital sign,” which has demonstrated face and discriminant validity among a racially and ethnically diverse sample of health plan members in Southern California (Coleman et al., 2012
).The use of parks for exercise was determined by classifying residents into one of the following three groups: 1) does not exercise, 2) exercises but not in a park, and 3) exercises in a park. These classifications were based on answers to a question: “Where do you usually exercise?” with response options of “I do not usually exercise, park, home, private health club, streets or sidewalks, or other.” For analysis, we conducted separate comparisons: a) those who exercise in a park versus those who do not exercise; and b) those who exercise elsewhere (home, private health club, streets or sidewalks, or other) versus those who do not exercise.
Independent Variables
Our primary independent variable of interest was gender (male, female) as self-reported by participants. We included as covariates other individual characteristics that have been associated with park use and PA in previous studies (
Cohen et al., 2012
, Derose et al., 2015
, Paxton et al., 2005
): age, race-ethnicity (African American, Asian/Pacific Islander, Latino, White, or other), having a child, proximity to park (within 0.25 mile, 0.50 mile, and 1 mile), health status (fair/poor vs. good to excellent), body mass index of 30 kg/m2 or greater (obese) based on self-reported height and weight, perceptions of park safety (safe or very safe vs. not very safe or not at all safe), time spent watching television, using computers, and other screen-time, and educational status (<less than high school, high school graduate or GED, some college or college graduate).We also included park-level factors that have been found to correlate with park use and PA (
Cohen et al., 2012
; e.g., park size [acres]; number of observed organized activity sessions). We included two park-level variables that we hypothesized might influence individual's park use, in particular women: number of observed park users and percent of park users that are male.Finally, we included neighborhood-level factors that have been found to correlate with park use and PA (
Cohen et al., 2012
; total population and percent of households in poverty, both within a 1-mile radius of park addresses).Statistical Analysis
We first calculated one-sample descriptive statistics of all park-level factors and a simple bivariate analysis of all variables by gender. Next, we fitted a set of hierarchical mixed-effect regression models to estimate relationships between gender and the park use and PA outcomes, controlling for other individual, park-level, and neighborhood-level factors. We also included park-level random effects to account for potential park-level clustering among survey respondents. We also included a fixed-effect for survey waves to account for secular trends during the study period (e.g., changes in city-level budget and management policies). All outcomes were modeled on their original scales without transformation for easy and meaningful interpretation of regression coefficients. The estimated gender effects were differences in means for continuous outcomes (number of days of park use, duration of park use), and differences in probabilities (i.e., risk differences) for a binary outcome (meets PA guideline, exercise in park vs. other location). Robust standard errors were applied to account for different distribution types in the outcomes. All models were fitted by PROC MIXED in SAS 9.4 (SAS Institute Inc., Cary, NC). In discussing results, we use the term association when talking about the relationship between a specific variable (e.g., gender) and any of the outcomes. We use the term differences when translating what these associations mean in terms of the outcomes (means or probabilities) for the subgroup being compared (e.g., men vs. women).
Results
Park Characteristics
Table 1 provides an overview of the park-level and neighborhood-level predictors for the 48 study parks. The populations within 1 mile of each park averaged 52,310 individuals and 27% of households in poverty. Parks averaged 8 acres in size and we observed an average of 20 organized activity sessions and 3,079 park users per park, of which 65% were male.
Table 1Park- and Neighborhood-Level Characteristics of Study Parks across Low-Income Neighborhoods in Los Angeles (n = 48)
| Mean | Range | |
|---|---|---|
| Park neighborhood characteristics | ||
| Percent of households in poverty | 27 | 14–41 |
| Population within 1 mile of park | 52,310 | 25,530–133,123 |
| Park characteristics | ||
| Size (in acres) | 8 | 2–26 |
| Park observations (over 12 days, 3 observation periods per day) | ||
| Average number of park users observed per park | 3,079 | 368–7,566 |
| Percent male park users | 65 | 57–76 |
| Number of observed organized activity sessions | 20 | 4–65 |
∗ Derived from 2010 Census data; based on a 1-mile radius from the park recreation center address.
Bivariate Analyses
Table 2 shows the associations between gender and all study variables, including individual-level predictors (participant sociodemographics) and the park use and PA outcomes: frequency and duration of park use, level of PA, and exercising in park and other places. Statistically significant differences between men and women were found for the typical duration of a park visit (95 minutes for men vs. 84 minutes for women; p < .0001), percent who meet PA recommendations (30.8% of men vs. 23.6% of women; p < .001), percent who usually exercise at a health club (9.4% of men vs. 5.5% of women; p < .0001), and percent who do not exercise (36.8% of men vs. 47.2% of women; p < .0001). On other outcomes (average number of park visits in past 7 days and the percent who usually exercise at park and at home), there were no differences between men and women. Among covariates, the only factors not associated with gender were obesity status and screen time (lack of differences between men and women on proximity to park was likely due to our sampling households equally across three strata).
Table 2Bivariate Associations between Gender and Study Variables Among Neighborhood Residents Surveyed (n = 2,973)
| Participant Characteristic | Total (n = 2,973) | Women (n = 1,763) | Men (n = 1,210) | p Value |
|---|---|---|---|---|
| Average age (y) | 43.06 | 42.13 | 44.41 | <.0001 |
| Race/ethnicity (%) | ||||
| Latino | 73.43 | 77.51 | 67.47 | <.0001 |
| African American | 9.78 | 7.78 | 12.70 | <.0001 |
| White | 10.32 | 9.03 | 12.20 | .0053 |
| Asian | 1.99 | 1.99 | 1.99 | .9936 |
| Other race/ethnicity | 0.10 | 0.11 | 0.08 | .7969 |
| Education level (%) | ||||
| <High school | 32.69 | 29.83 | 36.88 | <.0001 |
| High school graduate | 32.82 | 37.54 | 25.90 | <.0001 |
| Some college | 16.11 | 18.11 | 13.16 | .0003 |
| College graduate | 18.38 | 14.51 | 24.06 | <.0001 |
| Has child under the age of 18 (%) | 46.04 | 52.82 | 36.16 | <.0001 |
| Distance living from park (%) | ||||
| Within 0.25 mile of park | 33.37 | 34.43 | 31.82 | .1379 |
| Within 0.25–0.50 mile of park | 33.74 | 33.24 | 34.46 | .4880 |
| Within 0.50–1 mile of park | 32.90 | 32.33 | 33.72 | .4288 |
| Perceive park safe/very safe (%) | 75.10 | 73.56 | 77.42 | .0367 |
| Poor or fair self-rated health (%) | 22.77 | 20.07 | 26.70 | <.0001 |
| Obese (BMI ≥ 30 kg,m2) (%) | 18.97 | 18.92 | 19.06 | .9258 |
| Mean screen time (minutes per week) | 162.25 | 164.48 | 158.95 | .0738 |
| Park use and PA | ||||
| Average no. of visits in past 7 days | 0.94 | 0.90 | 1.02 | .0538 |
| Typical duration of park visit (min) | 88 | 84 | 95 | <.0001 |
| Meet PA recommendations (≥150 minutes per week) (%) | 26.54 | 23.57 | 30.83 | <.0001 |
| Usually exercise at park (%) | 18.44 | 17.68 | 19.53 | .2025 |
| Usually exercise at home (%) | 18.03 | 17.46 | 18.87 | .3260 |
| Usually exercise at health club (%) | 7.10 | 5.53 | 9.39 | <.0001 |
| Do not exercise (%) | 43.0 | 47.23 | 36.82 | <.0001 |
Abbreviations: BMI, body mass index; PA, physical activity.
∗ p < .05.
† Owing to our sampling approach, which selected random samples of households within each of these distance strata, these groups are expected to be distributed approximately equally.
Multivariate Analyses
Table 3 provides multivariate associations between gender and other covariates and outcomes. Controlling for race/ethnicity, education, body mass index, health status, proximity to park, having children under the age of 18, perceived park safety, estimated screen time, and park- and neighborhood-level independent variables, statistically significant differences were found between women and men on all outcomes.
Table 3Multivariate Associations between Gender and Other Covariates and Park Use and PA among Neighborhood Residents (n = 2,973)
| Characteristic | No. of Visits to Parks in Last 7 Days | Typical Duration of Park Visit (min) | Meets PA Recommendations (≥150 min/week) | Exercises in Parks (vs. No Exercise) | Exercises in Other Places (vs. No Exercise) |
|---|---|---|---|---|---|
| Mean (95% CI) | Probability (95% CI) | ||||
| Individual | |||||
| Female vs. male gender | −0.28 (−0.43, −0.12)*** | −11.11 (−15.52, −6.69)*** | −0.06 (−0.10, −0.02)** | −0.13 (−0.19, −0.08)*** | −0.13 (−0.18, −0.08)*** |
| Age (y) | −0.01 (−0.02, −0.003)** | −0.519 (−0.71, −0.32)*** | −0.004 (−0.006, −0.002)*** | −0.005 (−0.007, −0.002)*** | −0.002 (−0.004, 0.000)* |
| Black vs. Whites/others | 0.04 (−0.30, 0.38) | 9.36 (−0.04, 18.77) | −0.08 (−0.17, 0.00) | −0.23 (−0.35, −0.11)*** | −0.26 (−0.36, −0.16)*** |
| Latino vs. Whites/others | −0.32 (−0.59, −0.05)* | −4.09 (−11.32, 3.13) | −0.10 (−0.16, −0.03)** | −0.21 (−0.31, −0.11)*** | −0.22 (−0.31, −0.14)*** |
| HS graduate (vs. <HS) | 0.01 (−0.19, 0.22) | 2.05 (−3.70, 7.81) | 0.01 (−0.04, 0.06) | 0.03 (−0.04, 0.09) | −0.04 (−0.11, 0.03) |
| Some college (vs. <HS) | 0.28 (0.03, 0.54)* | −1.96 (−8.84, 4.93) | −0.01 (−0.08, 0.06) | −0.01 (−0.10, 0.07) | 0.00 (−0.08, 0.09) |
| College graduate (vs. <HS) | 0.29 (0.01, 0.57)* | −7.07 (−14.61, 0.48) | 0.17 (0.10, 0.25)*** | 0.10 (0.00, 0.20) | 0.13 (0.04, 0.22)** |
| Obese (BMI ≥ 30 kg/m2) | 0.16 (−0.05, 0.36) | 5.02 (−0.63, 10.67) | −0.02 (−0.08, 0.03) | 0.02 (−0.05, 0.09) | −0.02 (−0.08, 0.05) |
| Fair or poor health status | 0.33 (0.11, 0.55)* | −0.13 (−6.40, 6.14) | −0.09 (−0.15, −0.03)** | 0.09 (0.02, 0.16)* | −0.04 (−0.11, 0.03) |
| Lives 0–0.25 mile from park (vs. > 0.50 mile) | 0.79 (0.60, 0.98)*** | −4.22 (−9.39, 0.94) | −0.02 (−0.06, 0.03) | 0.14 (0.07, 0.20)*** | −0.04 (−0.10, 0.02) |
| Lives 0.25–0.50 mile from park (vs. > 0.50 mile) | 0.29 (0.09, 0.48)** | 1.70 (−3.74, 7.14) | −0.02 (−0.07, 0.02) | 0.04 (−0.02, 0.11) | −0.05 (−0.11, 0.01) |
| Has child <18 years old | 0.19 (0.03, 0.36)* | −2.05 (−6.58, 2.48) | −0.03 (−0.07, 0.01) | 0.04 (−0.01, 0.10) | −0.01 (−0.06, 0.04) |
| Perceives park as safe | 0.31 (0.12, 0.51)** | 9.85 (3.77, 15.93)** | 0.05 (0.00, 0.10)* | 0.08 (0.01, 0.15)* | −0.05 (−0.11, 0.00) |
| Screen time (h) | 0.04 (−0.02, 0.10) | 1.55 (−0.08, 3.18) | 0.00 (−0.02, 0.01) | −0.02 (−0.04, 0.00)* | −0.03 (−0.05, −0.01)** |
| Park | |||||
| Acres | 0.00 (−0.06, 0.05) | −0.03 (−1.93, 1.87) | 0.00 (−0.01, 0.01) | 0.00 (−0.02, 0.02) | 0.01 (−0.01, 0.02) |
| Observed no. of park users | 0.00 (0.00, 0.01) | 0.28 (0.10, 0.45)** | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.00) |
| Male park users (%) | 0.00 (−0.03, 0.02) | 0.18 (−0.60, 0.96) | 0.00 (−0.01, 0.00) | 0.00 (−0.01, 0.01) | 0.00 (0.00, 0.01) |
| No. of organized activity sessions | 0.00 (−0.02, 0.01) | −0.22 (−0.63, 0.19) | 0.00 (−0.01, 0.00) | 0.00 (−0.01, 0.00) | 0.00 (0.00, 0.00) |
| Neighborhood | |||||
| Households in poverty within 1 mile (%) | 0.00 (−0.02, 0.02) | −0.14 (−0.68, 0.40) | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.01) | 0.00 (0.00, 0.00) |
| Population within 1 mile | 0.02 (0.00, 0.05) | 0.00 (−0.82, 0.81) | 0.00 (−0.01, 0.00) | 0.00 (0.00, 0.01) | 0.00 (−0.01, 0.00) |
Abbreviations: BMI, body mass index; HS, high school; PA, physical activity.
*p < .05; **p < .01; ***p < .001
Park Use
Compared with men, women reported fewer park visits in the past week (−0.28 times/week; p < .001) and shorter durations of a typical park visit (−11.11 min/visit; p < .001). (The fact that women did not have a statistically significant fewer number of visits in bivariate analysis [p = .0538] is likely due to the increased precision and power of the multivariate analyses). The number and duration of park visits were negatively associated with age in years (−0.01 times/week [p < .01] and −0.52 min/visit [p < .001], respectively) and positively associated with a perception that the park is safe (0.31 times/week [p < .01] and 9.85 min/visit [p < .01], respectively). Several additional covariates were associated with the number of visits, namely Latino ethnicity (compared with Whites and others, −0.32 times/week; p < .05); some college or college graduate (vs. less than high school, 0.28 and 0.29 times/week, respectively; p < .05); fair or poor health status (compared with excellent, very good, or good, 0.33 times/week; p < .01), proximity to park (0–0.25 and 0.25–0.50 mile vs. 0.50–1 mile, 0.79 times/week [p < .001] and 0.29 times/week [p < .01], respectively), and having a child under 18 years of age at home (0.19 times/week; p < .05). The number of observed park users was significantly associated with visit duration (0.28 min/visit; p < .01).
Meets PA Recommendations
Women were also less likely than men (risk difference = −.06 or 6 percentage points lower probability; p < .01) to report levels of PA that meet national guidelines (≥150 minutes of moderate to vigorous PA per week). Age was inversely associated with meeting PA guidelines (each addition year was associated with an 0.4-percentage point lower probability; p < .001). Latinos and those with fair or poor health status had a 10- and 9-percentage point lower probability, respectively (both p < .01), to meet PA guidelines than other groups. College graduates had a 17-percentage point higher probability to meet recommendations than those with less than a high school education (p < .001).
Exercising in Park and Elsewhere
Women had a 13-percentage point lower probability than men to exercise in the park and elsewhere (both p < .001). (The fact that women did not have lower probability of exercising in the park and elsewhere in bivariate analyses [p = .2025 and p = .3260, respectively] is due to how we defined these variables differently in Table 2 [for descriptive purposes] vs. Table 3 [for ease of interpretation]). Each additional year of age was associated with an 0.5-percentage point lower probability of exercising in the park and an 0.2-percentage point lower probability of exercising other places versus no exercise (p < .001 and p < .05, respectively). African Americans had a 23-percentage point lower probability of exercising in the park (p < .001) and a 26-percentage point lower probability of exercising elsewhere than Whites, Asians, and those of other races/ethnicities (p < .001). Latinos had a 21- and 22-percentage point lower probability of exercising in the park and elsewhere, respectively, than Whites, Asians, and those of other races/ethnicities (p < .001). Each additional hour of screen time had a 2- and 3-percentage point lower probability of exercising in the park and elsewhere, respectively (p < .05 and p < .01). Additional covariates associated only with exercising in the park were fair or poor health status (a 9-percentage point lower probability; p < .05), living 0 to 0.25 mile from the park compared with 0.50 to 1.00 mile (a 14-percentage point higher probability; p < .001), and perceiving the park to be safe or very safe (an 8-percentage point higher probability; p < .05). Finally, college graduates had a 13-percentage point higher probability of exercising outside the park compared with those with less than a high school education (p < .01).
Discussion
In this population-based sample of households in high-poverty neighborhoods within 1 mile of a Los Angeles park, we found consistent gender disparities in terms of park use and PA. Specifically, women had fewer visits and shorter durations in visits to their neighborhood park than men, and women were less likely than men to report 150 minutes or more of PA per week and exercising in the park or elsewhere. Women are, thus, not getting the same levels of PA from parks in high-poverty Los Angeles neighborhoods as men are. This is concerning, particularly because research has found that physical inactivity contributes substantially to mortality in later life and is partially responsible for socioeconomic inequalities in the risk of disability onset, especially among women (
Shaw et al., 2014
).Given that nearly three-quarters of those surveyed were Latinos and the fact that Latinos comprise a near-majority in Los Angeles, it is also worth reflecting on the disparities among Latinos. Latinos’ reduced use of parks and lower levels of PA suggests that Latinos are particularly disadvantaged when it comes to park use and PA. Interestingly, having one or more children at home was associated with more frequent park use—suggesting that parents have more of a reason to go to the park. Further, it should be noted the only outcome where Latinos were not significantly different from Whites or others was in typical duration of park visit. Parks can be important venues for family gatherings and socialization among Latinos (
Derose et al., 2015
, Gobster and Delgado, 1993
, Sasidharan et al., 2005
).Perceiving the park as safe was also consistently and positively associated with park use, meeting PA recommendations, and exercising in the park. Park safety is likely influenced by multiple factors, including the overall level of crime within the surrounding community. However, research among Latinos has found that perceived safety is more important than objective crime in predicting objectively measured PA (
van Bakergem et al., 2017
). Research conducted across multiple U.S. cities among residents living near parks has found that perceived safety was the strongest predictor of park use and PA and completely mediates the effect of neighborhood physical incivilities (Lapham et al., 2016
). Further, perceived safety has been found to mediate the relationship between all social environmental variables and leisure time PA and walking, especially among urban women (Timperio et al., 2015
). Research on perceptions of safety regarding park settings has also found that environmental cues (e.g., low lighting, litter, blocked views) and social cues (e.g., presence of other people in the park) also play a role and interact significantly with gender (Jorgensen et al., 2013
).Fair or poor health status was positively associated with park use and exercising in a park, although negatively with meeting PA recommendations. This finding contrasts somewhat with previous findings among a broader range of Los Angeles parks (not just high poverty)—where good to excellent health was consistently and positively related to park use, PA, and exercising in the park and elsewhere (
Derose et al., 2015
). In high-poverty neighborhoods, parks seem to be an important source of PA for individuals reporting fair to poor health status. However, screen time was negatively associated with exercising in a park and elsewhere, reflecting the stiff competition for residents’ leisure time posed by increasing use of technology devices.In terms of park and neighborhood characteristics, only one was significantly associated with a study outcome: the average number of park users observed at baseline was associated with slightly longer visits on average reported by neighborhood residents. More people using the park could enhance residents’ perceptions of park safety and encourage longer visits.
Limitations
Our data come from two cross-sectional surveys, and thus the directions of the relationships are unclear, and causality cannot be inferred. In addition, most of our measures, at least the individual-level and outcome measures, are based on self-report and therefore subject to various kinds of bias including recall and social desirability. Finally, the surveys were conducted in one metropolitan area, which may limit generalizability.
Implications for Practice and/or Policy
Parks offer a sustainable way to promote PA among diverse populations, but for women to enjoy these benefits, attention may need to be paid to various types of programming and park design issues that can facilitate PA among women. For example, previous research has suggested that programs that provide child care may be necessary to facilitate Latino women engaging in park-based PA (
Casper et al., 2013
, Cronan et al., 2008
). Also, park programming can be arranged so that women can drop off kids at sports or other activities, and then attend adult fitness classes that start a few minutes after and end a few minutes before children's activities. Issues related to park design could facilitate PA among women, such as creating walking paths or placing exercise equipment around playgrounds. Further, given the important role that parks have in providing play areas for children and venues for families and friends to socialize, having park staff available to facilitate group PA activities around the playground and at group events (e.g., sack races, dance) could reach large numbers of individuals already in the park, but engaging in mostly sedentary activities. Addressing environmental and social cues through park design and maintenance can address some of the safety issues of most concern to women. Further, because we also found that living closer to the park was associated with an increased number of park visits and the likelihood of exercising in the park, municipalities should consider ways to meet the standard being promoted by park advocates that all residents have a park within an 0.50-mile or 10-minute walk (Harnik and Martin, nd
).Harnik, P., & Martin, A. (N.D.). Close-to-home parks: A half-mile or less. Available: http://parkscore.tpl.org/Methodology/TPL_10MinWalk.pdf. Accessed: November 30, 2017.
Conclusions
Despite similar proximity to parks and controlling for a range of individual, park- and neighborhood-level factors, women in high-poverty neighborhoods experience consistent disparities in park use and PA as compared with men. Park-level design and programming and policy interventions to address these disparities are needed to fully realize parks’ potential for promoting PA among all residents of high-poverty communities.
Acknowledgments
The authors thank collaborators at the Los Angeles Department of Recreation and Parks and in particular: Mark Mariscal, Sophia Pina-Cortez, and Kevin Regan. We also acknowledge the important role played by the promotoras (bilingual community health promoters) from AltaMed Health Services in helping to collect the data, and Terry Marsh and Laura Raaen of RAND in managing fieldwork.
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Biography
Kathryn P. Derose, PhD, MPH, is Senior Policy Researcher, RAND, and focuses on understanding health inequalities and developing community-based and policy solutions to address them. She has extensive experience with Latinos, gender issues, and community partnerships to improve health among the underserved.
Bing Han, PhD, is Statistician at RAND. His research interest includes biostatistics and its application in public health and other public policy areas.
Stephanie Williamson, BA, is Research Programmer at RAND. She has experience in developing analytic databases and analyzing data on a range of studies including parks and physical activity, teacher effectiveness, and workers compensation.
Deborah A. Cohen, MD, MPH, is Senior Scientist, RAND, and the principal investigator of studies on parks and physical activity. She is the author of A Big Fat Crisis—The Hidden Forces Behind the Obesity Epidemic and How We Can End It.
Article info
Publication history
Published online: December 11, 2017
Accepted:
November 7,
2017
Received in revised form:
November 5,
2017
Received:
March 15,
2017
Footnotes
Research reported in this publication was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health (NIH) under Award Number R01HL114283 (ClinicalTrials.gov Identifier: NCT01925404). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The authors declare no financial conflicts of interest.
Identification
Copyright
© 2017 Jacobs Institute of Women's Health. Published by Elsevier Inc.
