Racial and Ethnic Differences in the Relationship Between Neighborhood Environment and Physical Activity Among Middle-Aged and Older Adults

Abstract

Objectives:To examine the associations between neighborhood environment—perceived neighborhood social cohesion and perceived neighborhood physical environment—and physical activity (PA) and whether these associations differ by race/ethnicity. Methods: We analyzed data from the Health and Retirement Study, a longitudinal study of US adults aged 50+ from 2006 to 2014 (N = 17,974), using multivariate mixed-effects linear models. PA was repeatedly measured using metabolic equivalent of task estimated values accounting for the vigor and frequency of self-reported PA. Results: In multivariate models, higher levels of PA were positively associated with higher rated neighborhood social cohesion and neighborhood physical environment scores. The effects of social cohesion were stronger among non-Hispanic Whites than among non-Hispanic Black and Hispanic/Latinx participants, while race/ethnicity did not moderate the association between PA and physical environment. Discussion: Intervention strategies that address social and physical barriers of neighborhoods could promote PA in older adults. Key implications for future research are discussed.

Keywords

physical activity neighborhood social cohesion neighborhood physical environment older adults minority health

Introduction

Physical activity (PA) contributes to increased longevity, reduced morbidity, and improved health and well-being over the lifespan, including in middle age and older adulthood. In a study of older, community-dwelling women aged 70–79, women in the most physically active tertile experienced a 72% lower 5-year mortality risk compared to women in the least physically active tertile (Nicklett et al., 2012). Furthermore, even small amounts of PA have been found to be protective against health decline and mobility loss in older adulthood (Simonsick et al., 2005). Despite these health benefits, physical inactivity is the norm among middle-aged and older adults; approximately 28% adults aged 50 and older do not engage in leisure-time PA (Watson et al., 2016).

Neighborhood ecology, or the physical and social characteristics of one’s neighborhood and immediate surroundings, has long been recognized as an important factor shaping PA behaviors across the lifespan (Barnett et al., 2017; Diez Roux & Mair, 2010). Compared to other age groups, older adults spend a greater amount of time in their residential neighborhoods for social, recreational, and task-related activities; thus, the area close to one’s home is particularly impactful on the health and well-being of older adult populations (Michael & Yen, 2014). Indeed, in a study of 434 older adults aged 60 and older in Canada and the United States, participants reported that they engaged in light, moderate, and vigorous leisure-time PA most frequently at home (87.1%) or within three blocks of one’s home (76.5%) (Chaudhury et al., 2016). Therefore, the location of one’s home and community provide a context for engagement in PA, which is increasingly influential in older adulthood (Wiles et al., 2012).

Social cohesion is one aspect of the neighborhood social environment thought to influence PA (Cagney et al., 2009; Mendes de Leon et al., 2009; Quinn et al., 2019; Yi et al., 2016). Neighborhood social cohesion entails the perceived connectedness among neighbors and the extent to which individuals in that neighborhood are willing to intervene on behalf of other neighbors for the common good (McNeill et al., 2006). High neighborhood social cohesion may influence PA through the extended social network, greater social support (Chaudhury et al., 2012, 2016), enhanced communication, and social norms reinforcement (Chaudhury et al., 2016). In addition to neighborhood social cohesion, the physical conditions of a neighborhood—such as the presence or absence of hazards—also shape opportunities for PA. Previous research has identified environmental predictors of PA across the lifespan, including access to facilities or services (Gebauer et al., 2020b; Ranchod et al., 2014), access to public transportation (Gebauer et al., 2020b), walkability (Gebauer et al., 2020a; King et al., 2011; Kwarteng et al., 2014; Orstad et al., 2018) vandalism/littering (Strong et al., 2013), and traffic (Chaudhury et al., 2012). For example, Chaudhury et al. (2012) found that accessible seating and sidewalks that are even, wide, and well-lit promote PA in older adults. Perceptions of neighborhood safety have been found to be positively associated with PA (Chaudhury et al., 2012; Clark et al., 2009; Kwarteng et al., 2014; Mooney et al., 2017; Tucker-Seeley et al., 2009). Overall, findings suggest that neighborhoods with more favorable social and physical environments can provide social support and resources that promote PA.

Persistent racial and ethnic differences can be observed in PA behaviors in middle age and older adulthood. According to the Center for Disease Control and Prevention (2020), the prevalence of physical inactivity is higher for Hispanic/Latinx populations (31.7%) and non-Hispanic Blacks (30.3%) compared to non-Hispanic Whites (23.4%). While the reasons for these racial and ethnic differences in PA behaviors are not fully understood, a growing body of evidence suggests that the neighborhood environment could exacerbate racial and ethnic inequalities in health over the lifespan (Diez Roux & Mair, 2010; Ferraro et al., 2017; Franzini et al., 2010). Structural racism creates unequal access to amenities and other resources in neighborhoods that promote PA (Franzini et al., 2010; Hannon et al., 2012; Moore et al., 2008; York Cornwell & Cagney, 2014). In contrast to predominantly White neighborhoods, predominately Black and Hispanic/Latinx neighborhoods are significantly less likely to have recreational facilities (Franzini et al., 2010; Moore et al., 2008), green space (Browning & Rigolon, 2018), and favorable social processes that support outdoor PA, such as higher collective efficacy, stronger social ties, and higher satisfaction with one’s neighborhood (Franzini et al., 2010; York Cornwell & Cagney, 2014). Unlike predominantly White neighborhoods, predominantly Black and Hispanic/Latinx neighborhoods tend to have worse sidewalk conditions (Franzini et al., 2010) and higher perceived neighborhood danger, resulting in residents being more afraid to walk alone at night (York Cornwell & Cagney, 2014). Racial/ethnic disparities in accessing neighborhood-level resources that promote PA could partially explain lower levels of physical inactivity among Black and Hispanic/Latinx older adults compared to their non-Hispanic White counterparts (Diez Roux & Mair, 2010; Ferraro et al., 2017).

Neighborhood social and physical environment may have distinct effects on the health outcomes and health-promoting behaviors of older adults with different racial/ethnic backgrounds. There is mixed evidence regarding racial/ethnic differences in the association of neighborhood social cohesion with health and behavioral outcomes (Echeverria et al., 2008; Millar, 2020; Mulvaney-Day et al., 2007; Murillo et al., 2016, 2019; Quinn et al., 2019; Wong et al., 2018; Yi et al., 2016). In a study based on a nationally representative sample of US adults aged 18 and older (N = 64,754) conducted by Yi et al. (2016), higher neighborhood social cohesion was associated with higher odds of meeting PA guidelines among non-Hispanic Whites and Hispanic/Latinx adults, but not among non-Hispanic Blacks and Asian Americans. In a similar vein, in a study by Murillo et al. (2016), Hispanic/Latinx adults who reported higher social cohesion were more likely to meet the PA guidelines compared to those reporting lower levels of social cohesion (OR: 1.33; 95% CI: 1.17–1.52). In contrast, other studies found that the association between neighborhood social cohesion and PA did not differ by race/ethnicity (Echeverria et al., 2008; Quinn et al., 2019).

Evidence regarding racial/ethnic differences in the relationship between physical environment and health outcomes and health-promoting behaviors has been mixed and not well-understood (O’Brien et al., 2019; Echeverria et al., 2008; Kwarteng et al., 2014; Millar, 2020). In a study of a racially and ethnically diverse older cohort, the effects of neighborhood physical environment (e.g., exposure to trash and violence, access to recreational facilities, sidewalks) on walking behavior were not significantly different across non-Hispanic Whites, non-Hispanic Blacks, Hispanic/Latinx participants, and Chinese Americans (Echeverria et al., 2008). In contrast, Kwarteng et al. (2014) found that neighborhood physical disorder was more negatively associated with PA among non-Hispanic White participants than among non-Hispanic Black and Hispanic/Latinx participants. Neighborhood physical disorder has also been linked to poorer physical function only in non-Hispanic White respondents aged 65 and older, and not among non-Hispanic Black and Hispanic/Latinx participants (Millar, 2020). These mixed results call for further investigations on the moderating effects of race/ethnicity on the association between PA and neighborhood environment.

Of those studies that have examined the relationships between PA and the neighborhood environment (Chaudhury et al., 2016; Gebauer et al., 2020a; Gebauer et al., 2020b; Kwarteng et al., 2014; Mendes de Leon et al., 2009; Mooney et al., 2017; Murillo et al., 2016; Quinn et al., 2019; Ranchod et al., 2014; Strong et al., 2013; Whitaker et al., 2019; Yi et al., 2016), only a few utilized longitudinal designs (Mendes de Leon et al., 2009; Mooney et al., 2017). Lack of evidence from longitudinal designs challenges the ability to ascertain the direction of a relationship between the neighborhood environment and PA. To address the gaps discussed above, the present study examined the associations between perceptions of the neighborhood environment and PA among a nationally representative sample of US adults aged 50 and older. Specifically, we tested the following hypotheses: (1) respondents who perceive their neighborhood to have higher social cohesion engage in higher levels of PA; and (2) respondents who perceive their neighborhood to have a more positive physical environment engage in higher levels of PA. Further, we tested for differences in neighborhood effects by race/ethnicity. We hypothesized that (3) the effects of neighborhood—perceived social cohesion and perceived physical environment—on PA are stronger among non-Hispanic Whites than among non-Hispanic Black and Hispanic/Latinx participants. We also examined specific aspects of perceived social cohesion and perceived physical environment in relation to PA, controlling for individual-level sociodemographic and health-related characteristics.

Methods

We examined data from the Health and Retirement Study (HRS), a nationally representative and prospective panel study of Americans aged 50 and older. The HRS has been fielded biennially since 1992 to provide data on the health, economics, and demographics of aging, including the intersection of these factors with the retirement process. Participants were sampled based on a multi-stage area probability design involving geographic stratification and clustering, including the oversampling of African Americans, Hispanic/Latinx participants, and residents from Florida (Sonnega et al., 2014).

The current study includes data from 2006 to 2014 HRS core interviews (RAND version) and the 2006 to 2014 Psychosocial and Lifestyle Questionnaire (for perceived neighborhood environment measures). Beginning in 2006, a rotating 50% of the HRS core sample participated in a psychosocial leave-behind questionnaire that involves topics on well-being, lifestyle, social relationships, personality, and self-related beliefs (Smith et al., 2013). Among those 50% selected, an alternating cohort approach was established for the Psychosocial and Lifestyle Questionnaire: Cohort 1 (assessed in 2006, 2010, 2014) and Cohort 2 (assessed in 2008 and 2012). We included the measures of the neighborhood environment from these two rotating cohorts (2006–2014) to increase statistical power and precision (as done in Chopik et al., 2018; Crowe et al., 2021). Thus, depending on the cohort to which they were assigned, each participant had 2 or 3 measures of their neighborhood environment.

Inclusion criteria for current analyses were: (1) aged 50 and older, (2) with at least one wave of available data on the perceived neighborhood environment, physical activity measure, and covariates, and (3) interviews were not answered by proxy. Proxy interviews were excluded as past studies have demonstrated lower reliability of proxy (vs. self-respondent) data for questions regarding self-reported recreational PA (Nelson et al., 1994). 20,881 individuals participated in one or more of the HRS across five survey waves (2006–2014), 2888 respondents were excluded from our analysis for the following reasons: (1) aged 49 and younger (n = 1007), (2) missing race/ethnicity measures across all waves (n = 19), and (3) interviews were answered by proxy (n = 1881), resulting in an analytic sample over five waves of 17,974 respondents. Every respondent had at least one observation for physical activity and perceived neighborhood environment measures across survey waves (no participants were excluded due to missingness for these measures). Only 19 respondents (<1%) were excluded due to missingness on race/ethnicity measures across all waves. Those who were interviewed by proxy (and excluded from the analyses) were found to be less physically active, older, and more likely to be non-Hispanic White and male. Proxy respondents reported higher numbers of chronic conditions and more difficulties in physical function but did not report significantly different perceptions of the neighborhood environment from self-respondents. At the first wave (2006), the sample size was 13,590. New participants were enrolled to supplement the loss of the previous wave in each survey year. Details on the inclusion of analytic sample across waves are illustrated in Figure 1.

Figure 1.

Diagram of analytic sample across survey waves, Health and Retirement Study Core and Psychosocial and Lifestyle Questionnaires, 2006–2014.

Variables and Measures

Physical activity. PA was measured biennially at baseline (2006) and in subsequent survey waves (2008–2014). To quantify PA level, we calculated metabolic equivalent of tasks (METs) to estimate energy expenditure based on the intensity and frequency of self-reported leisure-time PA (Ainsworth et al., 2000; Kwarteng et al., 2014; Nicklett et al., 2020). Participants were asked to rate the frequency of engagement in light, moderate, and vigorous levels of PA: (1) “how often do you take part in sports or activities that are mildly energetic, such as vacuuming, laundry, home repairs?”, (2) “how often do you take part in sports or activities that are moderately energetic, such as gardening, cleaning the car, walking at a moderate pace, dancing, floor or stretching exercises?”, and (3) “how often do you take part in sports or activities that are vigorous, such as running or jogging, swimming, cycling, aerobics or gym workout, tennis, or digging with a spade or shovel?”. The response scale ranged from 0 to4, 0 = hardly ever or never, 1 = one to three times a month, 2 = once a week, 3 = more than once/week, and 4 = every day.

Following a previous method (Kwarteng et al., 2014; Nicklett et al., 2020) motivated by Ainsworth et al. (2000), the physical activity score is a weighted sum of self-reported frequency of light/mildly energetic, moderate, and vigorous activity levels. Activity level was weighted by average METs scores: light activity (1–2.9 METS for an average 2), moderate activity (3–5.9 METs for an average of 4.5) and vigorous activity (6–10 METs for an average of 8.25) (Ainsworth et al., 2000). Correspondingly, individual responses to each physical activity question were assigned a weight (mildly energetic = 1; moderate = 2.5; vigorous = 4.25). The scores were summed across all three intensity levels of PA to give a total PA estimate (range: 0–31). Details on MET scores correspond to activities with a certain frequency and intensity level are described in the Supplementary Table.

Neighborhood environment. Perceptions of neighborhood social cohesion and neighborhood physical environment were measured at 2006/2008, 2010/2012, and 2014. Participants were asked about their perceptions regarding the area within a 20-minute walk or about a mile of their residence (Adams et al., 2009; Smith et al., 2013). Neighborhood social cohesion was measured by the extent to which the participants agreed with the following statements: (1) I feel a sense of belonging to this area, (2) I trust most people in this area, (3) I consider most people in this area to be friendly, and (4) I believe that most people in this area would help me if I were in trouble (Cagney et al., 2009; Chen-Edinboro et al., 2015; Nicklett et al., 2017; Smith et al., 2013). The level of agreement on each statement was measured using a 7-point Likert-type scale. The neighborhood social cohesion scale ranges from 1 to 7 based on the averaged response across all four statements. The final score was set to missing if there were more than two statements with missing responses (Smith et al., 2013). A higher score indicates that participants perceived their neighborhood to have higher social cohesion.

Neighborhood physical environment examined the extent to which the participants agreed that: (1) vandalism and graffiti are not a problem in the area I reside, (2) I consider the area to be relatively clean and absent of rubbish and litter, (3) I consider the area to be absent of vacant or deserted houses, (4) I feel safe walking alone in the area at night (Cagney et al., 2009; Chen-Edinboro et al., 2015; Nicklett et al., 2017; Smith et al., 2013). The level of agreement on each statement was measured using a 7-point Likert-type scale. The neighborhood physical environment scale ranges from 1 to 7 based on the averaged response across all four statements. The final score was set to missing if there were more than two statements with missing responses (Smith et al., 2013). A higher score indicates that participants perceived their neighborhood to have a more positive physical environment. The neighborhood social cohesion and neighborhood physical environment scales have a Cronbach’s alpha score of 0.84 and 0.86, respectively (Smith et al., 2013).

Health-related covariates. We included functional limitations, chronic illness comorbidities, and depressive symptoms as time-varying covariates because they could be potential confounders of the associations between PA and perceptions of one’s neighborhood environment. Functional limitations were assessed by activities of daily living (ADLs) and instrumental activities of daily living (IADLs). ADL was measured by the level of assistance needed in bathing, eating, dressing, walking across a room, and getting in or out of bed (Katz et al., 1963). IADL was measured by the level of assistance needed in using a telephone, taking medication, and handling money (Lawton & Brody, 1969). Participants rated each ADL or IADL item as 0 (without assistance) and 1 (with assistance). The final scores were summed for ADL (ranging from 0 to 5) and IADL (ranging from 0 to 3), respectively, to indicate participants’ functional limitations. Chronic illness comorbidities (ranging from 0 to 8) were the sum of self-reported diagnoses of eight chronic conditions, which include hypertension, diabetes, cancer, lung disease, heart disease, stroke, arthritis, and psychiatric problems. Depressive symptoms were measured using 8-item Center for Epidemiologic Studies Depression (CES-D) scale. The sum score range was 0–8, with a higher score indicating higher levels of depressive symptoms (Lewinsohn et al., 1997).

Sociodemographic covariates. Time-varying covariates included age in years, race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic/Latinx, and other), sex (female, male), educational attainment (less than high school, high school or equivalent, some college, college or more), total wealth (5 quintiles), and urbanicity (urban, suburban, ex-urban), which was classified by following the 2013 Beale Rural-Urban Continuum Codes. Total wealth (excluding secondary residence) was calculated by the sum of all wealth components minus all the debt¹ and was scaled in US dollars ($1000s).

Statistical Analyses

We described the baseline sample characteristics overall and by racial/ethnic group. In multivariate models, we applied the sampling weight to estimate population-weighted means, frequencies and corresponding 95% confidence intervals. To estimate the associations between PA and perceived neighborhood environments (social cohesion and physical environment), we specified multivariate mixed-effects linear models with random person-specific intercepts using maximum likelihood estimation. To examine the moderating effects of race/ethnicity on the neighborhood environment-PA associations, we incorporated two interaction terms (a. race/ethnicity x social cohesion; b. race/ethnicity x physical environment), respectively, into the main effects models. We evaluated the interaction effects based on likelihood ratio tests and p-values for the interaction terms. In addition, we compared the differential effects of specific neighborhood factors on PA by estimating the associations between PA and sub-measures of social cohesion and physical environment. We computed robust standard errors and all linear models incorporated the baseline sampling weight at level 2 (person-level) and an attrition weight at level 1 (observation-level) of the multilevel model. Using methods detailed in Heeringa et al. (2017), we created an inverse probability of retention weight for repeated observations as the level 1 weight. All analyses were conducted using Stata (17.0 StataCorp LLC, College Station, TX).

Results

Table 1 shows population-weighted baseline characteristics of the total sample and by racial/ethnic group. Of 13,590 participants at the baseline, the average age was 65 years. The majority of the participants were women (56.1%) and non-Hispanic White (82.2%), followed by non-Hispanic Black (8.6%), Hispanic/Latinx (4.8%), and participants from other racial/ethnic groups (4.5%). The average neighborhood social cohesion score was 5.48 for the overall sample, 5.57 for non-Hispanic Whites, 4.96 for non-Hispanic Blacks, and 5.22 for Hispanic/Latinx participants. The average neighborhood physical environment score was 5.57 for the overall sample, 5.69 for non-Hispanic Whites, 4.85 for non-Hispanic Blacks, and 4.89 for Hispanic/Latinx participants. PA participation averaged 12.75 METs among the overall sample, 13.07 METs among non-Hispanic Whites, 10.88 METs among non-Hispanic Blacks, and 11.05 METs among Hispanic/Latinx participants.

Table 1.

Population-Weighted Characteristics of 2006 Sample, Health and Retirement Study Core Questionnaire (n = 13,590) and Psychosocial and Lifestyle Questionnaire (n = 6612).

2006 HRS core questionnaire respondents (n = 13,590)
Variables (range)	Total sample n = 13,590	Non-Hispanic White n = 10,427	Non-Hispanic Black n = 1,744	Hispanic/Latinx N = 806
	Mean/% (95%CI)	Mean/% (95%CI)	Mean/% (95%CI)	Mean/% (95%CI)
Physical activity (0–31)	12.75 (12.59, 12.91)	13.07 (12.89, 13.25)	10.88 (10.43, 11.43)	11.05 (10.39, 11.71)
Age in years (50–101)	64.91 (64.73, 65.10)	65.30 (65.08, 65.51)	63.41 (62.94, 63.88)	64.17 (63.52, 64.83)
Gender (%)
Male	43.9 (42.9, 44.9)	44.8 (43.6, 45.9)	38.0 (35.2, 40.9)	41.7 (37.5, 46.0)
Female	56.1 (55.1, 57.1)	55.2 (54.1, 56.4)	62.0 (59.1 64.8)	58.3 (54.0, 62.5)
Race/ethnicity (%)
Non-Hispanic White	82.2 (81.5, 82.9)
Non-Hispanic Black	8.6 (8.1, 9.1)
Hispanic/Latinx	4.8 (4.4, 5.2)
Other	4.5 (4.1, 4.9)
Education attainment (%)
Less than high school	15.1 (14.5, 15.8)	10.8 (10.1, 1l.4)	28.5 (26.1, 31.1	49.3 (45.1, 53.5)
High school or equivalent	35.1 (34.1, 36.0)	36.4 (35.4, 37.5)	32.6 (30.0, 35.3)	25.9 (22.4 30.0)
Some college	24.3 (23.4, 25.2)	25.0 (23.9, 25.9)	24.0 (21.6, 26.5)	15.8 (12.8, 19.4)
College or more	25.5 (24.6, 26.5)	27.9 (26.8, 29.0)	15.0 (13.0, 17.2)	9.0 (6.5, 12.2)
Total wealth (in $1K)	531.09 (507.54, 554.64)	601.06 (572.85, 629.27)	156.29 (136.45, 176.13)	239.75 (201.27, 278.24)
Total wealth (in $1K) quintile
1st Quintile (−2199, 37)	0.05 (−5.67, 5.57)	−1.46 (−10.03, 7.12)	3.89 (2.21, 5.57)	3.31 (0.78, 5.83)
2nd Quintile (37, 141)	83.76 (82.38, 85.14)	85.89 (84.26, 87.53)	77.51 (74.48, 80.53)	76.83 (72.11, 81.56)
3rd Quintile (141, 307)	215.53 (213.43, 217.63)	216.57 (214.26, 218.89)	199.95 (193.78, 206.11)	214.86 (205.35, 224.37)
4th Quintile (307, 678)	464.75 (460.03, 469.47)	466.44 (461.45, 471.43)	450.0 (427.33, 472.65)	443.37 (418.44, 468.31)
5th Quintile (678, 43512)	1792.14 (1699.71, 1884.58)	1824.35 (1725.96, 1922.75)	1412.24 (1225.01, 1599.48)	1294.20 (1133.05, 1455.34)
ADL limitations (0–5)	0.23 (0.22, 0.25)	0.20 (0.19, 0.22)	0.39 (0.34, 0.44)	0.36 (0.29, 0.43)
IADL limitations (0–3)	0.07 (0.06, 0.075)	0.06 (0.05 0.07)	0.10 (0.08, 0.12)	0.10 (0.06, 0.13)
Chronic disease comorbidities (0–8)	1.78 (1.76, 1.81)	1.78 (1.75, 1.81)	2.01 (1.92, 2.09)	1.58 (1.48, 1.69)
Depressive symptoms (0–8)	1.45 (1.40, 1.49)	1.32 (1.27, 1.36)	1.86 (1.74, 1.98)	2.26 (2.06, 2.46)
Urbanicity (%)
Urban	47.2 (46.2, 48.2)	45.2 (44.0, 46.3)	60.1 (57.2, 62.9)	53.3 (49.0, 57.6)
Suburban	23.3 (22.5, 24.2)	22.9 (22.0, 24.0)	21.1 (18.8, 23.7)	33.1 (29.2, 37.3)
Ex-urban	29.5 (28.6, 30.4)	31.9 (30.9, 33.0)	18.8 (16.6, 21.2)	13.6 (10.8, 17.0)
2006 HRS psychosocial and lifestyle questionnaire respondents (n = 6612)
Variables (range)	Total sample n = 6612	Non-Hispanic White n = 5225	Non-Hispanic Black n = 813	Hispanic/Latinx N = 327
Total social cohesion (1–7)	5.48 (5.45, 5.52)	5.57 (5.53, 5.60)	4.96 (4.84, 5.08)	5.22 (5.04, 5.40)
Sense of belonging (1–7)	5.47 (5.42, 5.52)	5.51 (5.46, 5.56)	5.12 (4.96, 5.28)	5.43 (5.16, 5.70)
Trust (1–7)	5.51 (5.46, 5.55)	5.62 (5.57, 5.67)	4.76 (4.60, 4.93)	5.01 (4.75, 5.27)
Friendliness (1–7)	5.72 (5.67, 5.76)	5.80 (5.75, 5.85)	5.22 (5.06, 5.38)	5.36 (5.10, 5.62)
Helpfulness (1–7)	5.30 (5.26, 5.35)	5.36 (5.31, 5.41)	4.89 (4.74, 5.04)	5.13 (4.91, 5.35)
Total physical environment (1–7)	5.57 (5.54, 5.61)	5.69 (5.66, 5.73)	4.85 (4.73, 4.96)	4.89 (4.70, 5.07)
Absence of vandalism/graffiti (1–7)	5.70 (5.65, 5.76)	5.82 (5.77, 5.88)	5.08 (4.90, 5.24)	4.84 (4.53, 5.14)
Absence of rubbish (1–7)	5.70 (5.65, 5.74)	5.79 (5.74, 5.84)	5.08 (4.92, 5.23)	5.21 (4.95, 5.46)
Absence of deserted houses (1–7)	5.84 (5.79, 5.90)	5.96 (5.90, 6.01)	5.02 (4.83, 5.21)	5.31 (5.02, 5.59)
Safety waking home at night (1–7)	5.17 (5.11, 5.23)	5.31 (5.25, 5.37)	4.31 (4.13, 4.50)	4.30 (4.01, 4.60)

Notes. In 2006, a random 50% of participants were selected for the Psychosocial and Lifestyle Questionnaire. Among 13,590 respondents from HRS core interviews, 6612 participants responded to neighborhood social cohesion and neighborhood physical environment measures.

Neighborhood social cohesion. The associations between PA and perceived neighborhood social cohesion are described in Table 2. In the unadjusted model, social cohesion was positively associated with a 0.46 MET equivalent score (95% CI: 0.39, 0.54). In the main effects model adjusting for sociodemographic and health-related covariates, a one-unit increase in perceived neighborhood social cohesion was associated with 0.24 higher levels of MET equivalent score (95% CI: 0.18, 0.31). As shown in the interaction model (Table 2; Figure 2), the interaction effects between race/ethnicity and social cohesion were statistically significant among non-Hispanic Black (β: −0.19, 95% CI: −0.36, −0.03) and Hispanic/Latinx participants (β: −0.22, 95% CI: −0.42, −0.03). A one-unit increase in perceived neighborhood social cohesion was associated with the highest levels of MET equivalent score among non-Hispanic Whites (β: 0.31, 95% CI: 0.23, 0.39), in contrast, an attenuated positive association between social cohesion and MET equivalent scores was observed among non-Hispanic Black and Hispanic/Latinx participants. According to the likelihood ratios test on the main effects model and the interaction model (χ² [3] = 9.93, p = 0.02), the interaction model significantly improved the overall model fit.

Table 2.

Population- and Attrition-Weighted, Multivariate Linear Mixed-Effects Models Estimating the Association between Perceived Neighborhood Social Cohesion and Physical Activity, 2006–2014 Health and Retirement Study, N = 17,974 (33,228 Observations).

	Unadjusted Model	Main Effects Model	Interaction Model
	β (95% CI)	β (95% CI)	β (95% CI)
Intercept	9.23 (8.80, 9.66)***	19.94 (19.02, 20.86)***	19.56 (18.57, 20.56)***
Social cohesion	0.46 (0.39, 0.54)***	0.24 (0.18, 0.31)***	0.31 (0.23, 0.39)***
Race (ref. Non-Hispanic White)
Non-Hispanic Black		0.24 (−0.57,-0.09)	0.73 (−0.15, 1.62)
Hispanic/Latinx		0.59 (0.03, 1.15)*	1.74 (0.58, 2.89)**
Other		0.62 (0.15, 1.08)**	1.27 (00.22, 2.32)*
Race x social cohesion (ref. Non-Hispanic White)
Non-Hispanic Black			−0.19 (−0.36, −0.03)*
Hispanic/Latinx			−0.22 (−0.42, −0.03)*
Other			−0.12 (−0.31, 0.06)
Age in years		−0.12 (−0.13, −0.11)***	−0.12 (−0.13, −0.11)***
Female		−1.15 (−1.35, −0.96)***	−1.16 (−1.35, −0.97)***
Education level (ref. Less than high school)		0.16 (−0.15, 0.46)	0.16 (−0.14, 0.47)
High school or equivalent		0.86 (0.50, 1.21)***	0.87 (0.51, 1.22)***
Some college		1.96 (1.60, 2.32)***	1.96 (1.61, 2.32)***
College or more
Wealth (ref. 1st quintile)
2nd Quintile		0.36 (0.11, 0.61)**	0.37 (0.12, 0.61)**
3rd Quintile		1.09 (0.82, 1.35)***	1.09 (0.82, 1.36)***
4th Quintile		1.77 (1.46, 2.08)***	1.76 (1.46, 2.07)***
5th Quintile		2.38 (2.05, 2.71)***	2.37 (2.03, 2.70)***
ADL limitations		−1.31 (−1.42, −1.21) ***	−1.31 (−1.42, −1.21)***
IADL limitations		−0.28 (−0.49, −0.08)**	−0.29 (−0.49, −0.08)***
Chronic disease comorbidities		−0.78 (−0.85, −0.71)***	−0.78 (−0.85, −0.71)***
Depressive symptoms		−0.40 (−0.45, −0.35)***	−0.40 (−0.45, −0.35)***
Urbanicity (ref: urban)
Suburban		0.18 (−0.12, 0.48)	0.18 (−0.12, 0.48)
Ex-urban		−0.21 (−0.55, 0.13)	−0.21 (−0.55, 0.13)

Notes. a. Levels of physical activity range from 0 to 31 METs. b. The scale of perceived neighborhood social cohesion ranges from 1 to 7.

* p < 0.05, ** p < 0.01, *** p < 0.001.

Figure 2.

Differences in the effects of perceived neighborhood social cohesion on physical activity by race/ethnicity, 2006–2014 Health and Retirement Study, N = 17,974 (33,228 observations).

Neighborhood physical environment. The associations between PA and perceived neighborhood physical environment are described in Table 3. In the unadjusted model, physical environment was positively associated with a 0.40 MET equivalent score (95% CI: 0.33, 0.48). In the main effects model adjusting for sociodemographic and health-related covariates, a one-unit increase in perceived neighborhood physical environment was associated with 0.11 higher levels of MET equivalent score (95% CI: 0.04, 0.17). As shown in the interaction model (Table 3; Figure 3), the interaction effects between race/ethnicity and physical environment were not statistically significant. According to the likelihood ratios test on the main effects model and the interaction model (χ² [3] = 3.59, p = 0.31), the interaction model did not significantly improve the overall model fit.

Table 3.

Population- and Attrition-Weighted, Multivariate Linear Mixed-Effects Models Estimating the Association between Perceived Neighborhood Physical Environment and Physical Activity, 2006–2014 Health and Retirement Study, N = 17,974 (33,228 Observations).

	Unadjusted Model	Main Effects Model	Interaction Model
	β (95% CI)	β (95% CI)	β (95% CI)
Intercept	9.53 (9.10, 9.96)***	20.49 (19.56, 21.42)***	20.30 (19.31, 21.29)***
Physical environment	0.40 (0.33, 0.48)***	0.11 (0.04, 0.17)**	0.14 (0.06, 0.22)**
Race (ref. Non-Hispanic White)
Non-Hispanic Black		−0.27 (−0.61, 0.06)	0.20 (−0.64, 1.05)
Hispanic/Latinx		0.58 (0.02, 1.14)*	1.38 (0.25, 2.52)**
Other		0.59 (0.13, 1.05)*	0.60 (−0.35, 1.54)
Race x physical environment (ref. Non-Hispanic White)
Non-Hispanic Black			−0.10 (−0.25, 0.06)
Hispanic/Latinx			−0.16 (−0.36, 0.04)
Other			0.003 (−0.17, 0.17)
Female		−1.12 (−1.32, −0.93)***	−1.12 (−1.32, −0.93)***
Age in years
Education level (ref. Less than high school)		0.17 (−0.14, 0.47)	0.17 (−0.13, 0.48)
High school or equivalent		0.87 (0.51, 1.23)***	0.88 (0.52, 1.23)***
Some college		1.96 (1.60, 2.32)***	1.96 (1.60, 2.32)***
College or more
Wealth (ref. 1^st quintile)
2nd Quintile		0.38 (0.13, 0.63)**	0.39 (0.13, 0.64)**
3rd Quintile		1.13 (0.86, 1.39)***	1.13 (0.86, 1.39)***
4th Quintile		1.83 (1.53, 2.14)***	1.83 (1.52, 2.14)***
5th Quintile		2.45 (2.11, 2.78)***	2.44 (2.10, 2.78)***
ADL limitations		−1.31 (−1.41, −1.21)***	−1.31 (−1.41, −1.21)***
IADL limitations		−0.28 (−0.49, −0.08)***	−0.28 (−0.49, −0.07)***
Chronic disease comorbidities		−0.79 (−0.86, −0.36)***	−0.79 (−0.86, 0.71)***
Depressive symptoms		−0.41 (−0.47, −0.36)***	−0.41 (−0.47, −0.36)***
Urbanicity (ref: urban)
Suburban		0.19 (−0.11, 0.49)	0.19 (−0.12, 0.49)
Ex-urban		−0.16 (−0.50, 0.17)	−0.16 (−0.50, 0.17)

Notes. a. Levels of physical activity range from 0 to 31 METs. b. The scale of perceived neighborhood physical environment ranges from 1 to 7.

* p < 0.05, ** p < 0.01, *** p < 0.001.

Figure 3.

Differences in the effects of perceived neighborhood physical environment on physical activity by race/ethnicity, 2006–2014 Health and Retirement Study, N = 17,974 (33,228 observations).

Specific neighborhood factors. The associations between the perceived neighborhood environment and PA are described in Table 4. We regressed PA on sub-measures of perceived neighborhood environments (social cohesion and physical environment scales), controlling for sociodemographic and health-related covariates. Four items of the perceived neighborhood social cohesion scale were significant predictors of PA. A one-unit increase in helpfulness was associated with the highest levels of MET equivalent score (β: 0.19, CI: 0.14, 0.25), followed by sense of belonging (β: 0.16, CI: 0.12, 0.21), trust (β: 0.15, CI: 0.09, 0.20), and friendliness (β: 0.13, CI: 0.08, 0.18). The absence of vandalism/graffiti and absence of vacant/deserted houses showed no significant associations with PA. A one-unit increase in safety walking home at night and absence of rubbish was associated with an increase of 0.11 (CI: 0.06, 0.16) and 0.08 (CI: 0.03, 0.12) in levels of MET equivalent score, respectively.

Table 4.

Population- and Attrition-Weighted, Multivariate Linear Mixed-Effects Models Estimating the Associations between Perceived Neighborhood Environment and Physical Activity, 2006–2014 Health and Retirement Study, N = 17,974 (33,228 Observations).

Neighborhood Environment	β (95% CI)
Perceived neighborhood social cohesion
Sense of belonging	0.16 (0.12, 0.21)***
Trust	0.15 (0.09, 0.20)***
Friendliness	0.13 (0.08, 0.18)***
Helpfulness	0.19 (0.14, 0.25)***
Perceived neighborhood physical environment
Absence of vandalism/graffiti	0.04 (−0.01, 0.09)
Absence of rubbish	0.08 (0.03, 0.12)**
Absence of deserted houses	0.01 (−0.02, 0.05)
Safety waking home at night	0.11 (0.06, 0.16)***

Notes. a. Models were adjusted for age, race/ethnicity, gender, education, wealth, ADL limitations, IADL limitations, chronic illness comorbidities, depressive symptoms, and urbanicity. b. Levels of physical activity range from 0 to 31 METs. c. The scale of each item ranges from 1 to 7.

* p < 0.05, ** p < 0.01, *** p < 0.001.

Discussion

This study suggests that the social and physical characteristics of one’s neighborhood environment do, in fact, relate to individual-level behaviors among middle-aged and older adults. First, our results supported our first hypothesis that individuals who perceived their neighborhood to have higher social cohesion engaged in higher levels of PA. Items of the perceived neighborhood social cohesion scale that were positively associated with higher levels of PA included sense of belonging to the neighborhood, sense of trust on neighbors, friendliness, and helpfulness of neighbors. Second, as we hypothesized, individuals who perceived their neighborhood to have a more positive physical environment engaged in higher levels of PA. Items of the perceived neighborhood physical environment scale that were positively associated with higher levels of PA included the absence of rubbish in the neighborhood and feeling safe walking alone at night. Finally, race/ethnicity moderated the PA-social cohesion association but not the PA-physical environment association. The effects of neighborhood social cohesion on PA were stronger among non-Hispanic Whites than among non-Hispanic Black and Hispanic/Latinx participants.

In prior studies, neighborhood social cohesion has been linked to more favorable health outcomes in middle-aged and older adults, including lower prevalence of frailty (Cramm & Nieboer, 2013), lower odds of falls (Nicklett et al., 2017), and lower odds of insomnia symptoms (Chen-Edinboro et al., 2015). In this study, higher levels of neighborhood social cohesion were associated with higher levels of PA in middle-aged and older adults, which was consistent with previous literature (Echeverria et al., 2008; Murillo et al., 2016; Quinn et al., 2019; Yi et al., 2016). Consistent with Quinn and colleagues’ (2019) study, our findings showed that PA was positively associated with the sense of belonging, sense of trust, neighbors’ friendliness, and willingness to help. Individuals’ sense of belonging in their neighborhood and trust in neighbors may encourage them to foster healthy social contacts, seek social support, and enhance their social networks. At the neighborhood level, social cohesion can provide resources for individuals to cope with and adapt to environmental stressors, such as limited access to recreational facilities or green space, lower neighborhood walkability, traffic, and vandalism/littering issues (Diez Roux & Mair, 2010; Kawachi & Berkman, 2014). The buffering effects of social cohesion may facilitate residents to initiate and maintain PA, enforce norms and social control inducing PA, and reduce stress that constrains on PA.

The presence of environmental hazards and perceived neighborhood danger have been recognized as risk factors for falls (Nicklett et al., 2017), physical inactivity (Chaudhury et al., 2012; Mooney et al., 2017; Strong et al., 2013), and poorer physical function (Millar, 2020) in middle-aged and older adults. Our study found that a more positive neighborhood physical environment was significantly associated with higher levels of PA. Consistent with the study by Strong et al. (2013), we found that two specific neighborhood physical features—the presence of rubbish in the neighborhood and perceived safety walking at night—were independently related to PA. These findings suggest that reducing environmental hazards at the neighborhood level is an important element to consider for PA-promoting interventions.

Of note, race/ethnicity was found to moderate the association of PA-neighborhood social cohesion, consistent with the direction we hypothesized. An attenuated association between social cohesion and MET equivalent scores was observed among non-Hispanic Black (0.19 lower MET unit) and Hispanic/Latinx participants (0.22 lower MET unit) relative to non-Hispanic White participants. The 0.19 to 0.22 difference in MET units can be interpreted as an average change in mildly energetic activity from “hardly ever or never” to “one to three times per month” and vice versa (Ainsworth et al., 2000). While this effect is modest, it should be noted that average physical activity participation levels remain below recommended levels (Physical Activity Guideline Advisory Committee, 2018) across all racial/ethnic groups examined. Prior research has found that even small increases in physical activity can contribute to the health, mobility, and longevity of older adults (Hupin et al., 2015; Simonsick et al., 2005). Therefore, multilevel strategies are needed to encourage regular physical activity, including investments in neighborhood environments that facilitate and promote opportunities for older adults to engage in regular physical activity.

As a protective factor, social cohesion showed a greater slope for non-Hispanic Whites than for non-Hispanic Black and Hispanic/Latinx individuals. The greater slope can be interpreted as an indicator of relative advantage; greater perceived social cohesion is associated with higher levels of PA among non-Hispanic Whites but not among Hispanic/Latinx and non-Hispanic Black participants in this study. On the other hand, the greater slope could also reflect potential vulnerability of non-Hispanic Whites—greater loss of PA—when exposed to a neighborhood with low social cohesion. Previous studies share similar findings that non-Hispanic Whites had fared worse than other groups in face of stressful life events or environmental stressors (Assari, 2016; Kwarteng et al., 2014; Millar, 2020; Nicklett et al., 2020). For example, Assari (2016) found that the association between perceived neighborhood safety and all-cause mortality was stronger for Whites compared to Blacks, which is comparable to our findings that a stronger association of between PA and neighborhood social cohesion was observed among non-Hispanic Whites than among Hispanic/Latinx and non-Hispanic Black individuals.

While non-Hispanic Whites have greater than average access to amenities and other resources in neighborhoods that promote PA (Viruell-Fuentes et al., 2012; Williams & Mohammed, 2013; York Cornwell & Cagney, 2014), they may be less resilient and have fewer psychosocial assets to positively respond to the stress caused by adverse social and physical environments (Assari, 2018; DiAngelo, 2011; Keyes, 2009; Malat et al., 2018). Malat et al. (2018) proposed that Whites’ greater vulnerability to stressors may be due to the cost of their privileges. Compared to Blacks who have endured a wide range of social and economic stressors for centuries, Whites may be less prepared and adaptable to adversity (Assari, 2018; Keyes, 2009). Communities disproportionately burdened by structural racism may equip some minoritized seniors with individual and collective strategies to cope with and overcome environmental stressors (Eschbach et al., 2004; Ferraro et al., 2017; Gilbert et al., 2022; Meyer, 1995, 2003). For example, the greater sense of connection across generations among Hispanic/Latinx older adults (Burr & Mutchler, 1999) or the active engagement in faith-based organizations among Black individuals (Chatters et al., 2009; Assari, 2018: ; Gilbert et al., 2022) could contribute to building greater social cohesion, social support, and social norms that promote PA, as well as utilizing psychosocial and institutional resources to buffer the effects of adverse neighborhood environments. Future qualitative studies are warranted to promote a better understanding of how social cohesion operationalizes differently to affect health-promoting behaviors among non-Hispanic White, non-Hispanic Black, and Hispanic/Latinx communities.

Strengths and Limitations

The interest in the impact of the neighborhood environment on PA has become salient in public health research, however, previous studies have primarily relied on cross-sectional data. We addressed this gap by examining the associations between the neighborhood environment (social cohesion/physical environment), PA, and race/ethnicity using a population-based cohort study of racially and ethnically diverse older adults. One major strength is the utilization of a large nationally representative sample (17,974 participants), which allows our findings to be generalizable to community-dwelling adults aged 50 and older in the United States. We incorporated repeated measures of PA, perceptions of neighborhood environment, and selected covariates over an 8-year period. Multilevel modeling is a suitable and robust approach to model clustered data accounting for repeated observations across waves (level 1) nested within respondents (level 2) (Chopik et al., 2018; Crowe et al., 2021; Snijders & Bosker, 2011). Our metric of PA included both intensity and frequency measures across a broad range of activities with various levels, allowing us to capture the dynamic nature of PA.

Although our findings are robust and noteworthy, several limitations must be considered. First, the present study only examined recreational PA and excluded occupation-associated PA, which may lead to an underestimate of PA among respondents with physically demanding occupations. The measure of PA used in this study was based on self-reported data. Responses could have been affected by social desirability bias and the degree of over-reporting may be influenced by individual perceived and measured weight status (Ferrari et al., 2007; Warner et al., 2012). Our PA measure is an estimate of METs based on the frequency of engaging in light, moderate, and vigorous activities (Latham & Williams, 2015; Li et al., 2019; Kraal et al., 2021; Nicklett et al., 2020). Although no studies have yet validated this measure, prior studies have assessed the validity and reliability of estimating METs based on an established self-reported PA questionnaire (Craig et al., 2003; Sember et al., 2020). These studies demonstrated the moderately high test-retest reliability and concurrent validity of self-reported PA measures. Despite the large and racially/ethnically diverse sample, it is possible that the differential sample sizes represented among racial/ethnic groups lead to differential ability of our model to detect PA differences in relation to social cohesion between groups. Further, the mean and range of social cohesion scores differed by racial and ethnic group, with the highest scores among non-Hispanic Whites (5.57, range: 5.53–5.60), followed by Hispanics/Latinos (5.22, range: 5.04–5.40) and non-Hispanic Blacks (4.96, range: 4.84, 5.08). It is also possible that race/ethnicity is a significant moderator of PA-social cohesion association only when a threshold is reached or when present at higher levels (Hobson-Prater & Leech, 2012).

Due to the limitation of race/ethnicity groupings in the survey design, we were unable to estimate the differential influence of the neighborhood environment on PA across specific subgroups within non-Hispanic Black and Hispanic/Latinx participants, as well as among Asian American and American Indian/Alaska native groups and subgroups. The groups examined here, such as Hispanic/Latinx participants, are heterogeneous and have diverse language and cultural backgrounds. Perceptions of one’s neighborhood environment and the subsequent effects on PA may vary significantly across racial/ethnic and cultural groups.

In addition, this study did not examine other aspects of neighborhood environments that could be explanatory factors for individual-level variations in PA, namely social capital, social support, infrastructure/built environment, and toxic environmental exposures. Lastly, the present study merely relied on a self-rated neighborhood physical environment scale which is likely to be dependent on individual characteristics (Boslaugh et al., 2004). This subjective measure warrants caution when interpreting findings. More objective measures of neighborhood physical characteristics have been commonly utilized in prior studies, such as the photovoice method (Chaudhury et al., 2012), systematic social observation (Kwarteng et al., 2014), and Walk Score^TM (Towne et al., 2016). Results regarding associations of neighborhood physical environment and PA has been mixed, potentially given the variation in measuring neighborhood physical environment (Orstad et al., 2017). Future research is warranted to disentangle the extent to which objective and subjective measures of neighborhood physical environment overlap and complement each other in affecting PA levels.

Conclusion

Strategies to enhance neighborhood physical and social environment could encourage PA among middle-aged and older adults. For example, intervention strategies that improve neighborhood safety and reduce the presence of litter—as well as those enhancing community social engagement and trust, communications, and social connections between neighborhoods—could promote the initiation and maintenance of PA. Community and government institutions should focus on building and maintaining a variety of social ties through, for example, hosting social events and interest group meetings, including those involving and promoting physical activity. Our findings also shed light on a potential difference in the association between PA and the neighborhood environment among middle-aged and older adults from different racial/ethnic backgrounds. We must acknowledge that “one size does not fit all.” The effectiveness of community intervention strategies may differ across and within neighborhoods. Future research focusing on racial/ethnic minority groups and subgroups is needed to fully characterize the pathways operating racial/ethnic differences in the relationships between the neighborhood environment and PA or other health behaviors.

Supplemental Material

Supplemental Material - Racial and Ethnic Differences in the Relationship Between Neighborhood Environment and Physical Activity Among Middle-Aged and Older Adults

Supplemental Material for Racial and Ethnic Differences in the Relationship Between Neighborhood Environment and Physical Activity Among Middle-Aged and Older Adults by Greta J. Cheng, and Emily J. Nicklett in Journal of Aging and Health

Footnotes

Acknowledgments

The authors are gratefully to Ann Reiter, MSW for her contribution to the literature review.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Greta Jianjia Cheng:

Supplemental Material

Supplemental material for this article is available online.

Note

References

Adams

M. A.

Ryan

Kerr

Sallis

J. F.

Patrick

Frank

L. D.

Norman

G. J.

(2009). Validation of the neighborhood environment walkability scale (NEWS) items using Geographic Information Systems. Journal of Physical Activity and Health, 6(s1), S113–S123. https://doi.org/10.1123/jpah.6.s1.s113

Ainsworth

B. E.

Haskell

W. L.

Whitt

M. C.

Irwin

M. L.

Swartz

A. M.

Strath

S. J.

O’Brien

W. L.

Bassett

D. R.

Schmitz

K. H.

Emplaincourt

P. O.

Jacobs

D. R.

Leon

A. S.

(2000). Compendium of physical activities: An update of activity codes and MET intensities: Medicine & Science in Sports & Exercise, 32(Suppl), S498–S516. https://doi.org/10.1097/00005768-200009001-00009

Assari

(2016). Perceived neighborhood safety better predicts risk of mortality for Whites than Blacks. Journal of Racial and Ethnic Health Disparities, 4(5), 937–948. https://doi.org/10.1007/s40615-016-0297-x

Assari

(2018). Health disparities due to diminished return among Black Americans: Public policy solutions. Social Issues and Policy Review, 12(1), 112–145. https://doi.org/10.1111/sipr.12042

Barnett

D. W.

Barnett

Nathan

Van Cauwenberg

Cerin

(2017). Built environmental correlates of older adults’ total physical activity and walking: A systematic review and meta-analysis. International Journal of Behavioral Nutrition and Physical Activity, 14(1), 103. https://doi.org/10.1186/s12966-017-0558-z

Boslaugh

S. E.

Luke

D. A.

Brownson

R. C.

Naleid

K. S.

Kreuter

M. W.

(2004). Perceptions of neighborhood environment for physical activity: Is it “Who you are” or “Where you live”? Journal of Urban Health: Bulletin of the New York Academy of Medicine, 81(4), 671–681. https://doi.org/10.1093/jurban/jth150

Browning

Rigolon

(2018). Do income, race and ethnicity, and sprawl influence the greenspace-human health link in city-level analyses? Findings from 496 cities in the United States. International Journal of Environmental Research and Public Health, 15(7), 1541. https://doi.org/10.3390/ijerph15071541

Burr

J. A.

Mutchler

J. E.

(1999). Race and ethnic variation in norms of filial responsibility among older persons. Journal of Marriage and the Family, 61(3), 674. https://doi.org/10.2307/353569

Cagney

K. A.

Glass

T. A.

Skarupski

K. A.

Barnes

L. L.

Schwartz

B. S.

Mendes de Leon

C. F.

(2009). Neighborhood-level cohesion and disorder: Measurement and validation in two older adult urban populations. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 64B(3), 415–424. https://doi.org/10.1093/geronb/gbn041

10.

Centers for Disease Control and Prevention . (2020). Adult physical inactivity prevalence maps by race/ethnicity [Internet]. 2020 Jan [cited 2021 Mar 11]. https://www.cdc.gov/physicalactivity/data/inactivity-prevalence-maps/index.html

11.

Chatters

L. M.

Taylor

R. J.

Bullard

K. M.

Jackson

J. S.

(2009). Race and ethnic differences in religious involvement: African Americans, Caribbean blacks and non-Hispanic whites. Ethnic and Racial Studies, 32(7), 1143–1163. https://doi.org/10.1080/01419870802334531

12.

Chaudhury

Campo

Michael

Mahmood

(2016). Neighborhood environment and physical activity in older adults. Social Science & Medicine, 149, 104–113. https://doi.org/10.1016/j.socscimed.2015.12.011

13.

Chaudhury

Mahmood

Michael

Y. L.

Campo

Hay

(2012). The influence of neighborhood residential density, physical and social environments on older adults’ physical activity: An exploratory study in two metropolitan areas. Journal of Aging Studies, 26(1), 35–43. https://doi.org/10.1016/j.jaging.2011.07.001

14.

Chen-Edinboro

L. P.

Kaufmann

C. N.

Augustinavicius

J. L.

Mojtabai

Parisi

J. M.

Wennberg

A. M. V.

Smith

M. T.

Spira

A. P.

(2015). Neighborhood physical disorder, social cohesion, and insomnia: Results from participants over age 50 in the Health and Retirement Study. International Psychogeriatrics, 27(2), 289–296. https://doi.org/10.1017/S1041610214001823

15.

Chopik

W. J.

Kim

E. S.

Smith

(2018). An examination of dyadic changes in optimism and physical health over time. Health Psychology, 37(1), 42–50. https://doi.org/10.1037/hea0000549

16.

Clark

C. R.

Kawachi

Ryan

Ertel

Fay

M. E.

Berkman

L. F.

(2009). Perceived neighborhood safety and incident mobility disability among elders: The hazards of poverty. BMC Public Health, 9(1), 162. https://doi.org/10.1186/1471-2458-9-162

17.

Craig

C. L.

Marshall

A. L.

Sjöström

Bauman

A. E.

Booth

M. L.

Ainsworth

B. E.

Pratt

Ekelund

Yngve

Sallis

J. F.

Oja

(2003). International physical activity questionnaire: 12-country reliability and validity. Medicine & Science in Sports & Exercise, 35(8), 1381-1395. https://doi.org/10.1249/01.MSS.0000078924.61453.FB

18.

Cramm

J. M.

Nieboer

A. P.

(2013). Relationships between frailty, neighborhood security, social cohesion and sense of belonging among community-dwelling older people: Frailty among community-dwelling older people. Geriatrics & Gerontology International, 13(3), 759–763. https://doi.org/10.1111/j.1447-0594.2012.00967.x

19.

Crowe

C. L.

Domingue

B. W.

Graf

G. H.

Keyes

K. M.

Kwon

Belsky

D. W.

(2021). Associations of loneliness and social isolation with health span and life span in the U.S. Health and Retirement Study. The Journals of Gerontology: Series A, 76(11), 1997–2006. https://doi.org/10.1093/gerona/glab128

20.

DiAngelo

(2011). White fragility. The International Journal of Critical Pedagogy, 3(3). https://libjournal.uncg.edu/ijcp/article/viewFile/249/116

21.

Diez Roux

A. V.

Mair

(2010). Neighborhoods and health. Annals of the New York Academy of Sciences, 1186(1), 125–145. https://doi.org/10.1111/j.1749-6632.2009.05333.x

22.

Echeverria

Diez-Roux

A. V.

Shea

Borrell

L. N.

Jackson

(2008). Associations of neighborhood problems and neighborhood social cohesion with mental health and health behaviors: The Multi-Ethnic Study of Atherosclerosis. Health & Place, 14(4), 853–865. https://doi.org/10.1016/j.healthplace.2008.01.004

23.

Eschbach

Ostir

G. V.

Patel

K. V.

Markides

K. S.

Goodwin

J. S.

(2004). Neighborhood context and mortality among older Mexican Americans: Is there a barrio advantage? American Journal of Public Health, 94(10), 1807–1812. https://doi.org/10.2105/AJPH.94.10.1807

24.

Ferrari

Friedenreich

Matthews

C. E.

(2007). The role of measurement error in estimating levels of physical activity. American Journal of Epidemiology, 166(7), 832–840. https://doi.org/10.1093/aje/kwm148

25.

Ferraro

K. F.

Kemp

B. R.

Williams

M. M.

(2017). Diverse aging and health inequality by race and ethnicity. Innovation in Aging, 1(1), igx002. https://doi.org/10.1093/geroni/igx002

26.

Franzini

Taylor

Elliott

M. N.

Cuccaro

Tortolero

S. R.

Janice Gilliland

Grunbaum

Schuster

M. A.

(2010). Neighborhood characteristics favorable to outdoor physical activity: Disparities by socioeconomic and racial/ethnic composition. Health & Place, 16(2), 267–274. https://doi.org/10.1016/j.healthplace.2009.10.009

27.

Gebauer

Schootman

Xian

Xaverius

(2020a). Neighborhood built and social environment and meeting physical activity recommendations among mid to older adults with joint pain. Preventive Medicine Reports, 18, 101063. https://doi.org/10.1016/j.pmedr.2020.101063

28.

Gebauer

S. C.

Salas

Scherrer

Callahan

L. F.

(2020b). Which aspects of neighborhood environment are most associated with meeting physical activity recommendations in American adults: An NHIS study. BMJ Open, 10(9), Article e038473. https://doi.org/10.1136/bmjopen-2020-038473

29.

Gilbert

K. L.

Ransome

Dean

L. T.

DeCaille

Kawachi

(2022). Social capital, Black social mobility, and health disparities. Annual Review of Public Health, 43(1). 191, https://doi.org/10.1146/annurev-publhealth-052020-112623

30.

Hannon

Sawyer

Allman

R. M.

(2012). Housing, the neighborhood environment, and physical activity among older African Americans. Journal of Health Disparities Research and Practice, 5(3), 27–41. https://europepmc.org/article/med/23745172

31.

Heeringa

S. G.

West

B. T.

Berglund

P. A.

(2017). Applied survey data analysis. CRC Press.

32.

Hobson-Prater

Leech

T. G.

(2012). The significance of race for neighborhood social cohesion: Perceived difficulty of collective action in majority black neighborhoods. Journal of Sociology and Social Welfare, 39(1), 89–109. https://heinonline.org/HOL/P?h=hein.journals/jrlsasw39&i=89

33.

Hupin

Roche

Gremeaux

Chatard

J.-C.

Oriol

Gaspoz

J.-M.

Barthélémy

J.-C.

Edouard

(2015). Even a low-dose of moderate-to-vigorous physical activity reduces mortality by 22% in adults aged ≥60 years: A systematic review and meta-analysis. British Journal of Sports Medicine, 49(19), 1262–1267. https://doi.org/10.1136/bjsports-2014-094306

34.

Katz

Ford

A. B.

Moskowitz

R. W.

Jackson

B. A.

Jaffe

M. W.

(1963). Studies of illness in the aged: The index of ADL: A standardized measure of biological and psychosocial function. JAMA, 185(12), 914–919. https://doi.org/10.1001/jama.1963.03060120024016

35.

Kawachi

Berkman

L. F.

(2014). Social capital, social cohesion, and health. In Berkman

L. F.

Kawachi

Glymour

M. M.

(Eds.), Social epidemiology (pp. 290–319). Oxford University Press. https://doi.org/10.1093/med/9780195377903.003.0008

36.

Keyes

C. L. M.

(2009). The Black-White paradox in health: Flourishing in the face of social inequality and discrimination: The Black-White paradox in health. Journal of Personality, 77(6), 1677–1706. https://doi.org/10.1111/j.1467-6494.2009.00597.x

37.

King

A. C.

Sallis

J. F.

Frank

L. D.

Saelens

B. E.

Cain

Conway

T. L.

Chapman

J. E.

Ahn

D. K.

Kerr

(2011). Aging in neighborhoods differing in walkability and income: Associations with physical activity and obesity in older adults. Social Science & Medicine, 73(10), 1525–1533. https://doi.org/10.1016/j.socscimed.2011.08.032

38.

Kraal

A. Z.

Dotterer

H. L.

Sharifian

Morris

E. P.

Sol

Zaheed

A. B.

Smith

Zahodne

L. B.

(2021). Physical activity in early- and mid-adulthood are independently associated with longitudinal memory trajectories in later life. The Journals of Gerontology: Series A, 76(8), 1495–1503. https://doi.org/10.1093/gerona/glaa252

39.

Kwarteng

J. L.

Schulz

A. J.

Mentz

G. B.

Zenk

S. N.

Opperman

A. A.

(2014). Associations between observed neighborhood characteristics and physical activity: Findings from a multiethnic urban community. Journal of Public Health, 36(3), 358–367. https://doi.org/10.1093/pubmed/fdt099

40.

Latham

Williams

M. M.

(2015). Does neighborhood disorder predict recovery from mobility limitation? Findings from the health and retirement study. Journal of Aging and Health, 27(8), 1415–1442. https://doi.org/10.1177/0898264315584328

41.

Lawton

M. P.

Brody

E. M.

(1969). Assessment of older people: Self-maintaining and instrumental activities of daily living. The Gerontologist, 9(3), 179–186. http://www.eurohex.eu/bibliography/pdf/Lawton_Gerontol_1969-1502121986/Lawton_Gerontol_1969.pdf

42.

Lewinsohn

P. M.

Seeley

J. R.

Roberts

R. E.

Allen

N. B.

(1997). Center for Epidemiologic Studies Depression Scale (CES-D) as a screening instrument for depression among community-residing older adults. Psychology and Aging, 12(2), 277–287. https://doi.org/10.1037/0882-7974.12.2.277

43.

White

O’Shields

K. R.

McLain

A. C.

Merchant

A. T.

(2019). Light-intensity physical activity and cardiometabolic risk among older adults with multiple chronic conditions. American Journal of Health Promotion, 33(4), 507–515. https://doi.org/10.1177/0890117118796459

44.

Malat

Mayorga-Gallo

Williams

D. R.

(2018). The effects of whiteness on the health of whites in the USA. Social Science & Medicine, 199, 148–156. https://doi.org/10.1016/j.socscimed.2017.06.034

45.

McNeill

L. H.

Kreuter

M. W.

Subramanian

S. V.

(2006). Social environment and physical activity: A review of concepts and evidence. Social Science & Medicine, 63(4), 1011–1022. https://doi.org/10.1016/j.socscimed.2006.03.012

46.

Mendes de Leon

C. F.

Cagney

K. A.

Bienias

J. L.

Barnes

L. L.

Skarupski

K. A.

Scherr

P. A.

Evans

D. A.

(2009). Neighborhood social cohesion and disorder in relation to walking in community-dwelling older adults: A multilevel analysis. Journal of Aging and Health, 21(1), 155–171. https://doi.org/10.1177/0898264308328650

47.

Meyer

I. H.

(1995). Minority stress and mental health in gay men. Journal of Health and Social Behavior, 36(1), 38. https://doi.org/10.2307/2137286

48.

Meyer

I. H.

(2003). Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations: Conceptual issues and research evidence. Psychological Bulletin, 129(5), 674–697. https://doi.org/10.1037/0033-2909.129.5.674

49.

Michael

Y. L.

Yen

I. H.

(2014). Aging and place—neighborhoods and health in a world growing older. Journal of Aging and Health, 26(8), 1251–1260. https://doi.org/10.1177/0898264314562148

50.

Millar

R. J.

(2020). Neighborhood cohesion, disorder, and physical function in older adults: An examination of racial/ethnic differences. Journal of Aging and Health, 32(9), 1133–1144. https://doi.org/10.1177/0898264319890944

51.

Mooney

S. J.

Joshi

Cerdá

Kennedy

G. J.

Beard

J. R.

Rundle

A. G.

(2017). Neighborhood disorder and physical activity among older adults: A longitudinal study. Journal of Urban Health, 94(1), 30–42. https://doi.org/10.1007/s11524-016-0125-y

52.

Moore

L. V.

Diez Roux

A. V.

Evenson

K. R.

McGinn

A. P.

Brines

S. J.

(2008). Availability of recreational resources in minority and low socioeconomic status areas. American Journal of Preventive Medicine, 34(1), 16–22. https://doi.org/10.1016/j.amepre.2007.09.021

53.

Mulvaney-Day

N. E.

Alegría

Sribney

(2007). Social cohesion, social support, and health among Latinos in the United States. Social Science & Medicine, 64(2), 477–495. https://doi.org/10.1016/j.socscimed.2006.08.030

54.

Murillo

Ayalew

Hernandez

D. C.

(2019). The association between neighborhood social cohesion and sleep duration in Latinos. Ethnicity & Health, 26(7), 1000–1011. https://doi.org/10.1080/13557858.2019.1659233

55.

Murillo

Echeverria

Vasquez

(2016). Differences in neighborhood social cohesion and aerobic physical activity by Latino subgroup. SSM - Population Health, 2, 536–541. https://doi.org/10.1016/j.ssmph.2016.08.003

56.

Nelson

L. M.

Longstreth

W. T.

Jr Koepsell

T. D.

Checkoway

van Belle

(1994). Completeness and accuracy of interview data from proxy respondents: Demographic, medical, and life-style factors. Epidemiology, 5(2), 204–217. http://doi.org/10.1097/00001648-199403000-00011

57.

Nicklett

Lohman

Smith

(2017). Neighborhood environment and falls among community-dwelling older adults. International Journal of Environmental Research and Public Health, 14(2), 175. http://doi.org/10.3390/ijerph14020175

58.

Nicklett

E. J.

Chen

Xiang

Abrams

L. R.

Sonnega

A. J.

Johnson

K. E.

Cheng

Assari

(2020). Associations between diagnosis with type 2 diabetes and changes in physical activity among middle-aged and older adults in the United States. Innovation in Aging, 4(1), igz048. http://doi.org/10.1093/geroni/igz048

59.

Nicklett

E. J.

Semba

R. D.

Xue

Q.-L.

Tian

Sun

Cappola

A. R.

Simonsick

E. M.

Ferrucci

Fried

L. P.

(2012). Fruit and vegetable intake, physical activity, and mortality in older community-dwelling women. Journal of the American Geriatrics Society, 60(5), 862–868. http://doi.org/10.1111/j.1532-5415.2012.03924.x

60.

O’Brien

D. T.

Farrell

Welsh

B. C.

(2019). Broken (windows) theory: A meta-analysis of the evidence for the pathways from neighborhood disorder to resident health outcomes and behaviors. Social Science & Medicine, 228, 272–292. http://doi.org/10.1016/j.socscimed.2018.11.015

61.

Orstad

S. L.

McDonough

M. H.

James

Klenosky

D. B.

Laden

Mattson

Troped

P. J.

(2018). Neighborhood walkability and physical activity among older women: Tests of mediation by environmental perceptions and moderation by depressive symptoms. Preventive Medicine, 116, 60–67. http://doi.org/10.1016/j.ypmed.2018.08.008

62.

Orstad

S. L.

McDonough

M. H.

Stapleton

Altincekic

Troped

P. J.

(2017). A systematic review of agreement between perceived and objective neighborhood environment measures and associations with physical activity outcomes. Environment and Behavior, 49(8), 904–932. http://doi.org/10.1177/0013916516670982

63.

Physical Activity Guidelines Advisory Committee . (2018). 2018 Physical Activity Guidelines Advisory Committee Scientific Report. Washington, DC: U.S. Department of Health and Human Services, 2018. Available: U.S. Department of Health and Human Services. https://health.gov/sites/default/files/2019-09/PAG_Advisory_Committee_Report.pdf

64.

Quinn

T. D.

Mody

Bushover

Mendez

D. D.

Schiff

Fabio

(2019). Associations between neighborhood social cohesion and physical activity in the United States, National Health Interview Survey, 2017. Preventing Chronic Disease, 16. E163, http://doi.org/10.5888/pcd16.190085

65.

Ranchod

Y. K.

Diez Roux

A. V.

Evenson

K. R.

Sanchez

B. N.

Moore

(2014). Longitudinal associations between neighborhood recreational facilities and change in recreational physical activity in the Multi-Ethnic Study of Atherosclerosis, 2000-2007. American Journal of Epidemiology, 179(3), 335–343. http://doi.org/10.1093/aje/kwt263

66.

Sember

Meh

Sorić

Starc

Rocha

Jurak

(2020). Validity and reliability of international physical activity questionnaires for adults across EU countries: Systematic review and meta analysis. International Journal of Environmental Research and Public Health, 17(19), 7161. http://doi.org/10.3390/ijerph17197161

67.

Simonsick

E. M.

Guralnik

J. M.

Volpato

Balfour

Fried

L. P.

(2005). Just get out the door! Importance of walking outside the home for maintaining mobility: Findings from the Women’s Health and Aging Study. Journal of the American Geriatrics Society, 53(2), 198–203. http://doi.org/10.1111/j.1532-5415.2005.53103.x

68.

Smith

Fisher

Ryan

Clarke

House

Weir

(2013). Psychosocial and lifestyle questionnaire. Survey Research Center, Institute for Social Research.

69.

Snijders

T. A.

Bosker

R. J.

(2011). Multilevel analysis: An introduction to basic and advanced multilevel modeling.

70.

Sonnega

Faul

J. D.

Ofstedal

M. B.

Langa

K. M.

Phillips

J. W.

Weir

D. R.

(2014). Cohort profile: The health and retirement study (HRS). International Journal of Epidemiology, 43(2), 576–585, https://doi.org/10.1093/ije/dyu067

71.

Strong

L. L.

Reitzel

L. R.

Wetter

D. W.

McNeill

L. H.

(2013). Associations of perceived neighborhood physical and social environments with physical activity and television viewing in African-American men and women. American Journal of Health Promotion, 27(6), 401–409. http://doi.org/10.4278/ajhp.120306-QUAN-127

72.

Towne

S. D.

Won

Lee

Ory

M. G.

Forjuoh

S. N.

Wang

Lee

(2016). Using Walk Score^TM and neighborhood perceptions to assess walking among middle-aged and older adults. Journal of Community Health, 41(5), 977–988. http://doi.org/10.1007/s10900-016-0180-z

73.

Tucker-Seeley

R. D.

Subramanian

S. V.

Sorensen

(2009). Neighborhood safety, socioeconomic status, and physical activity in older adults. American Journal of Preventive Medicine, 37(3), 207–213. http://doi.org/10.1016/j.amepre.2009.06.005

74.

Viruell-Fuentes

E. A.

Miranda

P. Y.

Abdulrahim

(2012). More than culture: Structural racism, intersectionality theory, and immigrant health. Social Science and Medicine, 75(12), 2099–2106. http://doi.org/10.1016/j.socscimed.2011.12.037

75.

Warner

E. T.

Wolin

K. Y.

Duncan

D. T.

Heil

D. P.

Askew

Bennett

G. G.

(2012). Differential accuracy of physical activity self-report by Body Mass Index. American Journal of Health Behavior, 36(2), 168–178. http://doi.org/10.5993/AJHB.36.2.3

76.

Watson

K. B.

Carlson

S. A.

Gunn

J. P.

Galuska

D. A.

O’Connor

Greenlund

K. J.

Fulton

J. E.

(2016). Physical inactivity among adults aged 50 years and older—United States, 2014. MMWR. Morbidity and Mortality Weekly Report, 65(36), 954–958. http://doi.org/10.15585/mmwr.mm6536a3

77.

Whitaker

K. M.

Xiao

Pettee Gabriel

Gordon Larsen

Jacobs

D. R.

Sidney

Reis

J. P.

Barone Gibbs

Sternfeld

Kershaw

(2019). Perceived and objective characteristics of the neighborhood environment are associated with accelerometer-measured sedentary time and physical activity, the CARDIA Study. Preventive Medicine, 123, 242–249. http://doi.org/10.1016/j.ypmed.2019.03.039

78.

Wiles

J. L.

Leibing

Guberman

Reeve

Allen

R. E. S.

(2012). The meaning of “Aging in Place” to older people. The Gerontologist, 52(3), 357–366. http://doi.org/10.1093/geront/gnr098

79.

Williams

D. R.

Mohammed

S. A.

(2013). Racism and health: Pathways and scientific evidence. American Behavioral Scientist, 57(8), 1199–1216. http://doi.org/10.1177/0002764213487340

80.

Wong

M. S.

Chan

K. S.

Jones-Smith

J. C.

Colantuoni

Thorpe

R. J.

Bleich

S. N.

(2018). The neighborhood environment and obesity: Understanding variation by race/ethnicity. Preventive Medicine, 111, 371–377. http://doi.org/10.1016/j.ypmed.2017.11.029

81.

S. S.

Trinh-Shevrin

Yen

I. H.

Kwon

S. C.

(2016). Racial/Ethnic differences in associations between neighborhood social cohesion and meeting physical activity guidelines, United States, 2013–2014. Preventing Chronic Disease, 13, 160261. http://doi.org/10.5888/pcd13.160261

82.

York Cornwell

Cagney

K. A.

(2014). Assessment of neighborhood context in a nationally representative study. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 69(Suppl 2), S51–S63. http://doi.org/10.1093/geronb/gbu052

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