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. 2021 Feb 20;13:100760. doi: 10.1016/j.ssmph.2021.100760

The Mediating role of perceived discrimination and stress in the associations between neighborhood social environment and TV Viewing among Jackson Heart Study participants

Kosuke Tamura a,∗, Stephanie L Orstad b, Ellen K Cromley c, Sam J Neally a, Sophie E Claudel a, Marcus R Andrews d, Joniqua Ceasar a, Mario Sims e, Tiffany M Powell-Wiley a,f
PMCID: PMC7930346  PMID: 33681448

Abstract

Objectives

The aim of this study was to test the mediating role of perceived discrimination and stress on associations between perceived neighborhood social environment (PNSE) and TV viewing.

Methods

Baseline data were used for 4716 participants (mean age = 55.1 y; 63.4% female) in the Jackson Heart Study (JHS), a large prospective cohort study of African Americans in Jackson, Mississippi. One binary TV viewing outcome was created: ≥4 h/day versus <4 h/day. PNSE variables included neighborhood violence, problems (higher value = more violence/problems), and social cohesion (higher value = more cohesion). Mediators included perceived lifetime discrimination, daily discrimination, and chronic stress (higher value = greater discrimination/stress). Multivariable regression was used with bootstrap-generated 95% bias-corrected confidence intervals (BC CIs) to test for mediation adjusting for demographics, health-related and psychosocial factors, and population density.

Results

Neighborhood violence, problems, and social cohesion were indirectly associated with TV viewing through lifetime discrimination (OR = 1.03, 95%BC CI = 1.00, 1.07; OR = 1.03, 95%BC CI = 0.99, 1.06 [marginal]; OR = 0.98, 95%BC CI = 0.94, 0.99, respectively) and chronic stress (OR = 0.95, 95%BC CI = 0.90, 0.99; OR = 0.96, 95%BC CI = 0.92, 0.99; OR = 1.05, 95%BC CI = 1.01, 1.10, respectively). Daily discrimination was neither directly nor indirectly associated with TV viewing.

Conclusions

Each PNSE variable was indirectly associated with TV viewing via lifetime discrimination and perceived stress, but not with daily discrimination among JHS participants. Unexpected directionality of mediating effects of lifetime discrimination and chronic stress should be replicated in future studies. Further research is also needed to pinpoint effective community efforts and physical environmental policies (e.g., installing bright street lights, community policing) to reduce adverse neighborhood conditions and psychosocial factors, and decrease TV viewing and subsequent cardiovascular disease risk.

Keywords: Sedentary time, Neighborhood social contexts, Mediation analyses, Objective built environment, African americans, Jackson heart study

Highlights

  • •

    Discrimination and chronic stress may serve as mediators in neighborhood research.

  • •

    Neighborhood violence, problems, and social cohesion were related to TV viewing.

  • •

    These associations were in part mediated by discrimination and chronic stress.

1. Introduction

Sedentary behaviors (SB) are defined as low energy expenditure behaviors with ≤1.5 metabolic equivalents (MET) for a task and include activities such as screen activities (e.g., smartphones and television [TV] viewing) (Matthews et al., 2008; Prince, Reed, McFetridge, Tremblay, & Reid, 2017). Engaging in persistent SB has been linked to greater risk of chronic diseases (Katzmarzyk, Church, Craig, & Bouchard, 2009; Patterson et al., 2018; Thorp, Owen, Neuhaus, & Dunstan, 2011), such as cardiovascular disease (CVD) (Biswas et al., 2015), cancers (Jochem, Wallmann-Sperlich, & Leitzmann, 2019), and diabetes (Wilmot et al., 2012). TV viewing is one major contributor to overall SB in the U.S. (Imran et al., 2018; Yang et al., 2019), disproportionately impacting African Americans (Bureau of Labor Statistics, 2018). Specifically, the 2017 American Time Use Survey has shown that African Americans (AAs), on average, reported watching TV 3.48 h/day, whereas whites reported 2.71 h/day, Hispanics reported 2.32 h/day, and Asians reported 1.90 h/day (Bureau of Labor Statistics, 2018).

An ecological perspective for health behaviors postulates that a key social determinant of SB is an individual's perception of the physical and societal context in which they reside (Owen et al., 2011). Adverse perceived neighborhood social environments (PNSE; e.g., crime and poverty) are thought to discourage physical activity (PA) (Tamura et al., 2019), and contribute to prolonged SB (Owen, Salmon, Koohsari, Turrell, & Giles-Corti, 2014). A few studies have examined associations between PNSE (e.g., unsafe walking (Van Dyck et al., 2012; Wallmann-Sperlich, Bucksch, Hansen, Schantz, & Froboese, 2013) and social trust/cohesion (Van Holle et al., 2014)) and sitting time among adults, but did not control for race and ethnicity.(Van Dyck et al., 2012; Van Holle et al., 2014; Wallmann-Sperlich et al., 2013). To address this limitation, only one study has investigated associations between PNSE and SB (e.g., watching TV) among AAs (Strong, Reitzel, Wetter, & McNeill, 2013). The authors stratified the analyses by sex and demonstrated that among women but not men, more perceived neighborhood problems (e.g., litter) was associated with longer TV viewing, whereas greater social cohesion (e.g., trustworthy neighbors) was associated with less TV viewing (Strong et al., 2013).

To better understand mechanisms of PNSE in relation to TV viewing, there is a need for investigating a potential pathway when examining such associations. Particularly, experiences of discrimination may increase stress, which may affect how well individuals can adhere to healthy lifestyles. These inter-relationships may help explain higher rates of chronic conditions such as CVD among AAs. Therefore, understanding the mediating mechanism of discrimination (e.g., unfair treatment) and chronic stress in the associations between PNSE and TV viewing may inform innovative ways to address the disparities in SB (Matthews, Gallo, & Taylor, 2010).

However, no study to date has tested a mediation role of perceived discrimination and perceived stress on these associations in AAs. Perceived discrimination and stress have been recognized as key factors impacting CVD risk factors, such as hypertension (Sims et al., 2012), disproportionately influencing AAs (Dunlay et al., 2017; Gebreab et al., 2012). A better understanding of PNSE that may contribute to the levels of TV viewing for AAs could help inform the design and implementation of effective environmental intervention strategies for a population at particularly high risk for poor CVD outcomes (Chen et al., 2019). Thus, the primary aim of the present study were to 1) test direct associations between neighborhood social context and TV viewing, and 2) test whether associations of PNSE with TV viewing were mediated through perceived discrimination and chronic stress among AAs (Fig. 1).

Fig. 1.

Fig. 1

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Conceptual model of indirect (Paths a x b) and direct (Path c’) relationships between neighborhood social environment and TV viewing among Jackson Heart Study participants.

Note: The figure describes the hypotheses: 1) Perceived discrimination mediate the effects of neighborhood social environment factors with TV viewing; and 2) Perceived stress mediate the effects of neighborhood social environment factors with TV viewing. Neighborhood social environment includes neighborhood violence, problems, and social cohesion. Discrimination and stress include lifetime and daily discrimination as well as chronic stress. TV viewing is defined as ≥4 hours/day.

2. Methods

2.1. Jackson Heart Study (JHS) participants

The Jackson Heart Study (JHS) is the largest cohort study designed to examine risk factors for CVD in AAs (Fuqua et al., 2005; Taylor et al., 2005). Briefly, the JHS participants were recruited from Hinds, Madison, and Rankin counties in the Jackson, Mississippi area (i.e., 17% randomly contacted, 22% volunteers, 31% secondary family members, or 30% recruited through the Atherosclerosis Risk in the Community site) (Gebreab et al., 2017). The JHS recruited 5301 African American men and women (aged 21–94 years) at baseline (Exam 1) in 2000–2004 (Campbell Jenkins et al., 2014), followed up in 2005–2008 (Exam 2 response rate = 79.2% of Exam 1 participants) and subsequently followed up in 2009–2013 (Exam 3 response rate = 90.8% of Exam 2 participants) (Forde et al., 2020). The study design and recruitment procedures have been previously described in detail (Fuqua et al., 2005; Taylor et al., 2005). Of 5301 participants, those missing data on TV viewing (n = 12), demographic and health-related factors (n = 359), lifetime and daily discrimination and perceived stress (n = 206), and PNSE (n = 8) were excluded, resulting in the final analytic sample of 4716 participants.

2.2. TV viewing outcome

Using the JHS Physical Activity Cohort (JPAC) survey, participants were asked one question on TV viewing: “During the past year, how often did you watch television?”(Diaz et al., 2016) with the following response options: Less than 1 h (h) a week; At least 1 h a week; but less than 7 h a week; At least 1 h a day but less than 2 h a day; At least 2 h a day but less than 4 h a day; and 4 or more hours a day (Diaz et al., 2016; Imran et al., 2018). Based on prior studies among U.S. adults demonstrating a greater association of over 4 h/day of TV viewing and computers, etc. with depression (Madhav, Sherchand, & Sherchan, 2017) and obesity (Hu, Li, Colditz, Willett, & Manson, 2003), one binary TV viewing outcome was created: TV viewing≥4 h/day versus <4 h/day. The item on TV viewing from the JPAC has been validated against accelerometers (ρ = 0.22) and provided a good reproducibility (Intraclass Correlation Coefficient = 0.75–0.78) (Garcia et al., 2019; Wareham et al., 2002; Wijndaele et al., 2014).

2.3. Perceived neighborhood social environment (PNSE)

The neighborhood survey items were classified into three PNSE constructs using principal component analysis with Promax oblique rotation, consistent with a previous JHS research (Mujahid, DiezRoux, Morenoff, & Raghunathan, 2007). PNSE constructs included 1) neighborhood violence, 2) neighborhood problems, and 3) neighborhood social cohesion (Gebreab et al., 2017). The survey items for neighborhood violence included five items on occurrence of neighborhood fights, gang fights, sexual assault/rape, violent arguments, and robbery with a score between 1 (never) and 4 (often). Neighborhood problems included 6 survey items with respect to neighborhood noise, lack of access to adequate food/or shopping, heavy traffic/speeding cars, lack of access to parks, and trash/litter, with a score between 1 (not really a problem) and 4 (very serious problem). Neighborhood social cohesion consisted of 5 items regarding trust in neighbors, willingness to help neighbors, shared values with neighbors, and extent to which neighbors get along, with a score ranging from 1 (strongly disagree) to 4 (strongly agree). The internal consistencies of the three constructs were relatively high, with Cronback's alpha 0.80, 0.76, and 0.77 for neighborhood violence, problems, and social cohesion, respectively (Gebreab et al., 2017). Using unconditional empirical Bayes estimation, participants' responses were aggregated to the census-tract level (Tamura et al., 2020). Higher neighborhood violence and problems indicated unfavorable neighborhood perceptions, whereas higher neighborhood social cohesion indicated favorable neighborhood perceptions.

2.4. Mediators - perceived discrimination and perceived chronic stress

During JHS Exam 1, the JHS Discrimination Instrument (JHSDIS), measuring perceived daily and lifetime discrimination, was administered by trained interviewers and completed by 96% of participants. Lifetime discrimination score was derived from the surveys developed by Krieger (Krieger, 1990) and Sidney (Krieger & Sidney, 1996) with an internal consistency of α = 0.78 (Sims, Wyatt, Gutierrez, Taylor, & Williams, 2009). Participants were asked about the occurrence (yes = 1 or no = 0) of unfair treatment over the lifetime across 9 domains including: at school, getting a job, work, housing, getting services, street/public place, resources or money, medical care, or other. Lifetime discrimination score was counted the occurrence of 9 domains, ranging from 0 to 9.

Daily discrimination score was adapted from the Williams scale (Williams, Yan, Jackson, & Anderson, 1997) with a relatively high internal consistency (α = 0.88) (Sims et al., 2009). Participants were asked on frequency on 9 items, such as “You are treated with less courtesy than other people.”, “You receive poorer service than others at restaurants.”, “People act as if they are afraid of you.”, “You are threatened or harassed.” The score for the frequency ranged from 1 (“never”) to 7 (“several times a day”). Daily discrimination score was based on the average of 9 items.

The Global Perceived Stress Scale (GPSS) was adapted from the standardized stress scales (Cohen, Kamarck, & Mermelstein, 1983; Payne et al., 2005). The 8 survey items from the GPSS assessed the severity of perceived stress during the last 12 months with respect to 8 domains, including employment, racism/discrimination, meeting basic needs, caring for others, relationships, related to one's neighborhood, legal problems, and medical problems. Participants rated stress severity using a four-point scale ranging from “not stressful” to “very stressful” (0–3) with a total range from 0 to 24 (Cronbach's alpha, 0.72) (Johnson et al., 2016). All three mediators were transformed into the standard deviation (SD) from the mean (i.e., a one-SD unit increase) to ease the interpretation (Brewer et al., 2018; Sims et al., 2016).

2.5. Covariates

Demographic characteristics serving as confounders included: age, sex, educational attainment, and annual median household income. Behavioral characteristics included: current smoker and alcohol drinking. Health-related factors included: body mass index (BMI = weight (kg)/height (m2)), and prior medical conditions. Total PA is also considered to be a potential confounder and is a summary score based on the JPAC survey items (Smitherman et al., 2009) on active living, sport, and home related PA (Smitherman et al., 2009). We also controlled for walking disabilities in the models (as measured by self-reported walking limitations. An objective population density variable was also controlled as a proxy for walkable neighborhoods (Gebreab et al., 2017).

2.6. Statistical analysis

For the first objective, multivariable regression models (Hayes, 2018) were used to examine direct associations between each PNSE and TV viewing (IBM® SPSS v19), controlling for all covariates. Bootstrap resampling (k = 5000) with 95% bias-corrected confidence intervals (BC CIs) of the indirect effects was used to test for mediation by each of the three mediators separately (Hayes, 2018). An indication of statistically significant mediation was determined by a BC CI that did not include zero. Unstandardized estimates were reported for associations of each PNSE factor with odds ratios (OR) of TV viewing. Subsequently, ORs were expressed as exponents of unstandardized regression coefficients through standardized mediators (lifetime discrimination, daily discrimination, and chronic stress).

Two distinct sensitivity analyses were performed. First, multilevel modeling (i.e., PROC GLIMMIX in SAS9.4; Cary, NC) was used to control for the two-level nested data structure to examine direct associations between each PNSE and TV viewing in age- and fully-adjusted models. Second, mediation analyses were performed based on TV viewing≥2 h/day.

3. Results

All independent variables, outcomes, and covariates included in the analyses are presented in Table 1. On average, participants were 55.1 (SD ± 12.8) years. Sixty-three percent of participants were female. Approximately 80% had at least a high school education. Roughly 30% of participants earned at least $50,000. The mean BMI for participants was 31.8 (SD ± 7.3). About 13% and 45% of participants smoked cigarettes currently and drank alcohol in the past 12 months, respectively. Approximately 36% and 5% of participants had previous medical conditions (e.g., cancers) and walking limitations, respectively. Nearly 40% of participants watched TV ≥ 4 h/day in the past year.

Table 1.

Individual characteristics, health-related factors, psychosocial factors, and environmental factors in Jackson Heart Study participants (n = 4716).

Overall, n (%) n = 4716 TV viewing ≥ 4 h/day, n (%), n = 1766 TV viewing < 4 h/day, n (%), n = 2950 Pd
Individual characteristics
Age (years), M (±SD) 55.1 (±12.8) 56.6 (±12.9) 54.2 (±12.7) <.0001
Female 2990 (63.4) 1123 (63.59) 1867 (63.3) 0.835
Education <.0001
High school graduate or more 3772 (80.0) 1288 (72.9) 2484 (84.2)
Less than high school 944 (20.0) 478 (27.1) 466 (15.8)
Annual median household income <.0001
≥$50,000 1390 (29.5) 384 (21.7) 1006 (34.1)
<$50,000 2617 (55.5) 1156 (65.5) 1461 (49.5)
Not reported 709 (15.03) 226 (12.80) 483 (16.37)
Health-related factors
Body Mass Index, M (±SD) 31.8 (±7.3) 32.1 (±7.6) 31.6 (±7.1) 0.0196
Total physical activity, M (±SD) 6.5 (±2.0) 5.8 (±1.9) 6.9 (±1.9) <.0001
Current smoker 601 (12.7) 295 (16.7) 306 (10.4) <.0001
Drinking alcohol in the past 12 monthsa 2164 (45.9) 778 (44.1) 1386 (47.0) 0.0508
Disable from walking 232 (4.9) 118 (6.7) 114 (3.9) <.0001
Previous medical conditions 1716 (36.4) 707 (40.0) 1009 (34.2) <.0001
TV viewing
≥4 h/day 1766 (37.4)
<4 h/day 2950 (62.6)
Psychosocial factors, M (±SD)
Lifetime discrimination 3.0 (±2.1) 2.7 (±2.1) 3.1 (±2.1) <.0001
Daily discrimination 2.1 (±1.0) 2.0 (±1.1) 2.1 (±1.0) 0.003
Chronic stress 5.2 (±4.4) 4.6 (±4.4) 5.5 (±4.4) <.0001
Environmental factors, M (±SD)
Perceived neighborhood social environmentb
Violence 1.3 (±0.1) 1.3 (±0.1) 1.3 (±0.1) <.0001
Problems 1.6 (±0.2) 1.6 (±0.2) 1.6 (±0.2) <.0001
Social cohesion 3.0 (±0.1) 3.0 (±0.1) 3.0 (±0.1) <.0001
Objective built environment
Population density (people/km2)c 816.4 (±489.3) 885.4 (±475.3) 775.0 (±493.0) <.0001
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Note: aAlcohol consumption in the past 12 months. bEach perceived social environment variable was based on unconditional empirical Bayes estimation adjusting for age and sex. cPopulation density was measured around 1 mile from participant's residence. dP-values were based on t-tests for continuous variables and chi-square tests for categorical variables.

Those who reported watching TV ≥ 4 h/day were older, more likely to be current smokers and less likely to drink alcohol, with lower educational attainment, higher BMI, lower PA levels, annual median household income <$50,000 and disability limiting walking, and more medical conditions (all p < .05), compared to those who reported TV < 4 h/day (Table 1). Participants who watched TV ≥ 4 h/day also reported lower perceived lifetime discrimination, daily discrimination, and stress, lower scores in neighborhood violence, problems, and higher social cohesion, and resided in more densely populated areas compared to those watching TV < 4 h/day (all p < .05).

Participants reporting higher neighborhood violence tended to be older, female, had less than a high school education and an annual median household income <$50,000 (all p < .01) (Table 2). Participants with higher neighborhood violence had a higher BMI, were less physically active, currently smoked, drank less alcohol, and had previous medical conditions, compared to individuals perceiving lower neighborhood violence (all p < .05).

Table 2.

Individual characteristics, health-related factors, psychosocial factors, and environmental factors by high/low perceived neighborhood social environment factor (n = 4716).

Overall (n = 4716)
 Neighborhood Violencea
 Pb Neighborhood Problemsa
 Pb Neighborhood Social Cohesiona
 Pb
n (%) High n = 2371 Low n = 2345 High n = 2364 Low n = 2352 High n = 2361 Low n = 2355
Individual characteristics
Age (years), M (±SD) 55.1 (±12.8) 57.8 (±12.9) 52.3 (±12.2) <.0001 57.3 (±12.8) 52.9 (±12.5) <.0001 54.3 (±12.5) 55.9 (±13.1) <.0001
Female 2990 (63.4) 1545 (65.2) 1445 (61.6) 0.0116 1539 (65.1) 1451 (61.7) 0.0151 1472 (62.4) 1518 (64.5) 0.1322
Education <.0001 <.0001 <.0001
High school graduate or more 3772 (79.9) 1713 (72.3) 2059 (87.8) 1713 (72.5) 2059 (87.5) 2005 (84.9) 1767 (75.0)
Less than high school 944 (20.0) 658 (27.8) 286 (12.2) 651 (27.5) 293 (12.5) 356 (15.1) 588 (25.0)
Annual median household income <.0001 <.0001 <.0001
≥$50,000 1390 (29.5) 441 (18.6) 949 (40.5) 428 (18.1) 962 (40.9) 907 (38.4) 483 (20.5)
<$50,000 2617 (55.5) 1592 (67.1) 1025 (43.7) 1587 (67.1) 1030 (43.8) 1103 (46.7) 1514 (64.3)
Not reported 709 (15.0) 338 (14.3) 371 (15.8) 349 (14.8) 360 (15.3) 351 (14.9) 358 (15.2)
Health-related factors
Body Mass Index, M (±SD) 31.8 (±7.3) 32.0 (±7.4) 31.5 (±7.1) 0.0169 31.9 (±7.3) 31.6 (±7.2) 0.2204 31.5 (±7.1) 32.0 (±7.4) 0.0122
Total physical activity, M (±SD) 6.5 (±2.0) 6.2 (±2.0) 6.8 (±2.0) <.0001 6.3 (±2.0) 6.8 (±2.0) <.0001 6.7 (±2.0) 6.32 (±2.0) <.0001
Current smoker 601 (12.7) 351 (14.8) 250 (10.7) <.0001 346 (14.6) 255 (10.8) <.0001 260 (11.0) 341 (14.5) 0.0004
Drinking alcohol in the past 12 monthsc 2164 (45.9) 967 (41.2) 1188 (50.7) <.0001 993 (42.0) 1171 (49.8) <.0001 1149 (48.7) 1015 (43.1) 0.0001
Disable from walking 232 (4.9) 130 (5.5) 102 (4.4) 0.072 127 (5.5) 103 (4.4) 0.0871 115 (4.9) 117 (5.0) 0.8772
Previous medical conditions 1716 (36.4) 991 (41.8) 725 (30.9) <.0001 974 (41.2) 742 (31.6) <.0001 774 (32.8) 942 (40.0) <.0001
TV viewing <.0001 <.0001 <.0001
≥4 h/day 1766 (37.5) 1028 (43.4) 738 (31.5) 998 (42.2) 768 (32.7) 816 (34.6) 950 (40.3)
<4 h/day 2950 (47.5) 1343 (56.6) 1607 (68.5) 1366 (57.8) 1584 (67.4) 1545 (65.4) 1405 (59.7)
Psychosocial factors, M (±SD)
Lifetime discrimination 3.0 (±2.1) 2.8 (±2.1) 3.2 (±2.1) <.0001 2.8 (±2.1) 3.1 (±2.1) <.0001 3.1 (±2.1) 2.9 (±2.1) 0.0004
Daily discrimination 2.1 (±1.0) 2.0 (±1.0) 2.1 (±1.0) 0.0052 2.1 (±1.1) 2.1 (±1.0) 0.3386 2.1 (±1.0) 2.1 (±1.1) 0.9496
Chronic stress 5.2 (±4.4) 5.2 (±4.6) 5.1 (±4.2) 0.5834 5.2 (±4.6) 5.1 (±4.2) 0.928 5.0 (±4.2) 5.3 (±4.6) <.0001
Environmental factors, M (±SD)
Perceived neighborhood social environmentd
Violence 1.3 (±0.1) 1.4 (±0.1) 1.2 (±0.1) <.0001 1.4 (±0.1) 1.2 (±0.1) <.0001 1.2 (±0.1) 1.3 (±0.1) <.0001
Problems 1.6 (±0.2) 1.7 (±0.1) 1.4 (±0.1) <.0001 1.7 (±0.1) 1.4 (±0.1) <.0001 1.5 (±0.1) 1.7 (±0.2) <.0001
Social cohesion 3.0 (±0.1) 2.9 (±0.1) 3.1 (±0.1) <.0001 2.9 (±0.1) 3.1 (±0.1) <.0001 3.1 (±0.1) 2.9 (±0.1) <.0001
Objective built environment
Population density (people/km2)e 816.4 (±489.3) 1081.6 (±373.3) 548.2 (±444.5) <.0001 1085.8 (±387.0) 545.6 (±428.2) <.0001 684.5 (±483.3) 998.7 (±423.1) <.0001
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Note: aBased on the median. bP-values were based on t-tests for continuous variables and chi-square tests for categorical variables. cAlcohol consumption in the past 12 months. dEach perceived social environment variable was based on unconditional empirical Bayes estimation adjusting for age and sex. ePopulation density (people/km2) was calculated with the total population divided by total land area around 1 mile from participant's residence.

Additionally, participants reporting higher levels of neighborhood violence had a lower score for lifetime discrimination and daily discrimination (Table 2). Also, areas with higher perceived neighborhood violence had a higher population density than areas with lower neighborhood violence (all p < .01). The patterns of associations of socio-demographics, health-related factors, and psychosocial factors with higher neighborhood problems and lower neighborhood social cohesion appeared similar to the associations with higher neighborhood violence.

Less favorable perceptions of neighborhood violence and problems were indirectly and positively related to TV viewing (≥4 h/day) via lifetime discrimination (OR [Paths ab] = 1.031, 95% BC CI = 1.001, 1.074; OR = 1.027, 95% BC CI = 0.999, 1.062 [marginal], respectively) (Table 3). Higher neighborhood violence and problems were associated with lower lifetime discrimination. In turn, higher lifetime discrimination was negatively associated with TV viewing. Neighborhood violence and problems were not directly associated with TV viewing (Path c’).

Table 3.

Indirect and direct associations between neighborhood social environment (IV) and TV viewing ≥4 h/day (DV) via mediators (M) in JHS participants (n = 4716).

Path a: Effect of IV on M
 Path b: Effect of M on DV
 Path c': Direct Effect
 Paths a x b: Indirect Effect
beta SE 95% CI OR 95% CI OR 95% CI OR 95% BC CI
M: Lifetime Discrimination
Neighborhood violence −0.412*** 0.116 −0.640, −0.184 0.929* 0.863, 0.999 1.589 0.891, 2.833 1.031 1.001, 1.074⁑
Neighborhood problems −0.364*** 0.084 −0.528, −0.199 0.930 0.863, 1.000 1.446 0.950, 2.201 1.027 0.999, 1.062
Neighborhood social cohesion 0.303** 0.113 0.082, 0.525 0.925* 0.859, 0.996 1.200 0.683, 2.109 0.977 0.944, 0.999⁑
M: Daily Discrimination
Neighborhood violence 0.034 0.116 −0.193, 0.261 1.005 0.934, 1.081 1.589 0.891, 2.833 1.000 0.990, 1.010
Neighborhood problems 0.114 0.084 −0.050, 0.278 1.004 0.933, 1.080 1.446 0.950, 2.201 1.001 0.989, 1.012
Neighborhood social cohesion −0.168 0.113 −0.389, 0.053 1.006 0.935, 1.082 1.200 0.683, 2.109 0.999 0.982, 1.015
M: Perceived Stress
Neighborhood violence 0.333** 0.118 0.103, 0.563 0.847*** 0.787, 0.911 1.589 0.891, 2.833 0.946 0.898, 0.986⁑
Neighborhood problems 0.240** 0.085 0.074, 0.407 0.846*** 0.786, 0.911 1.446 0.950, 2.201 0.961 0.924, 0.989⁑
Neighborhood social cohesion −0.276* 0.114 −0.501, −0.052 0.849*** 0.789, 0.914 1.200 0.683, 2.109 1.046 1.008, 1.101⁑
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Note: P-values: *p < .05; **p < .01; ***p < .001, ⁑95% BC CI. IV: Independent variables. DV: Dependent variable. M: Mediators. All models were adjusted for covariates.

Greater perceived neighborhood social cohesion was indirectly and negatively related to TV viewing via lifetime discrimination (OR [paths ab] = 0.977, 95% BC CI = 0.944, 0.999) (Table 3). Higher neighborhood social cohesion was associated with higher lifetime discrimination. In turn, higher lifetime discrimination was negatively associated with TV viewing. Neighborhood social cohesion was not directly associated with TV viewing (path c’).

None of the direct and indirect associations between neighborhood social environment and TV viewing were significantly mediated by daily discrimination (Table 3). However, the directionality of the associations generally support the hypothesized associations. For example, higher neighborhood violence and problems were related to higher daily discrimination. In turn, higher daily discrimination was positively related to TV viewing.

Higher perceived neighborhood violence and problems were indirectly and negatively related to TV viewing via chronic stress (OR [paths ab] = 0.946, 95% BC CI = 0.898, 0.986; OR [paths ab] = 0.961, 95% BC CI = 0.924, 0.989, respectively) (Table 3). Higher neighborhood violence and problems were associated with higher chronic stress. In turn, higher chronic stress was negatively associated with TV viewing. Both neighborhood violence and problems were not directly associated with TV viewing (Path c’).

Favorable perceptions of neighborhood social cohesion were indirectly and positively related to TV viewing via chronic stress (OR [paths ab] = 1.046, 95% BC CI = 1.008, 1.101) (Table 3). Higher neighborhood social cohesion was associated with lower chronic stress. In turn, higher chronic stress was negatively associated with TV viewing. Neighborhood social cohesion was not directly associated with TV viewing (Path c’).

A sensitivity analysis was performed for direct effects (similar to path c’) in the fully-adjusted models (Table 4). All estimates were attenuated relative to age-adjusted models, and none of them were statistically significant, which is similar results compared to those of path c’. Only neighborhood social cohesion was marginally significant (p = .06). A sensitivity analysis based on TV viewing≥2 h/day indicated that there were statistically significant associations between each PNSE factor and TV viewing, only through perceived stress, but not lifetime and daily discrimination (Table 5).

Table 4.

Sensitivity analysis of associations between perceived neighborhood social environment and TV viewing among the JHS participants (n = 4716).

Age-adjusted
 Fully-adjusteda
OR 95% C.I. OR 95% C.I.
Neighborhood Violence 5.778*** 3.156, 10.579 1.678 0.881, 3.195
Neighborhood Problems 3.661*** 2.430, 5.515 1.557+ 0.969, 2.502
Neighborhood Social Cohesion 0.257*** 0.125, 0.527 1.062 0.548, 2.057
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Note: OR: Odds ratios. C.I.: Confidence interval. aFully-adjusted models controlled for all covariates. P-values: +p < .1; *p < .05; **p < .01; ***p < .001.

Table 5.

Sensitivity analysis of indirect and direct associations between neighborhood social environment (IV) and TV viewing ≥2 h/day (DV) via mediators (M) in JHS participants (n = 4716).

Path a: Effect of IV on M
 Path b: Effect of M on DV
 Path c': Direct Effect
 Paths a x b: Indirect Effect
beta SE 95% CI OR 95% CI OR 95% CI OR 95% BC CI
M: Lifetime Discrimination
Neighborhood violence −0.412*** 0.116 −0.640, −0.184 1.017 0.941, 1.098 1.103 0.589, 2.064 0.993 0.958, 1.026
Neighborhood problems −0.364*** 0.084 −0.528, −0.199 1.018 0.943, 1.100 1.244 0.795, 1.949 0.993 0.963, 1.022
Neighborhood social cohesion 0.303** 0.113 0.082, 0.525 1.014 0.938, 1.095 1.577 0.859, 2.894 1.004 0.979, 1.031
M: Daily Discrimination
Neighborhood violence 0.034 0.116 −0.193, 0.261 0.927+ 0.858, 1.001 1.103 0.589, 2.064 0.997 0.975, 1.016
Neighborhood problems 0.114 0.084 −0.050, 0.278 0.926+ 0.857, 1.000 1.244 0.795, 1.949 0.991 0.972, 1.005
Neighborhood social cohesion −0.168 0.113 −0.389, 0.053 0.928+ 0.858, 1.002 1.577 0.589, 2.894 1.013 0.996, 1.041
M: Perceived Stress
Neighborhood violence 0.333** 0.118 0.103, 0.563 0.876*** 0.812, 0.945 1.103 0.589, 2.064 0.957 0.913, 0.990⁑
Neighborhood problems 0.240** 0.085 0.074, 0.407 0.875*** 0.811, 0.944 1.244 0.795, 1.949 0.968 0.937, 0.993⁑
Neighborhood social cohesion −0.276* 0.114 −0.501, −0.052 0.878*** 0.814, 0.947 1.577 0.859, 2.894 1.037 1.005, 1.082⁑
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Note: P-values: +p < .1; *p < .05; **p < .01; ***p < .001, ⁑95% BC CI. IV: Independent variables. DV: Dependent variable. M: Mediators. All models were adjusted for covariates.

4. Discussion

Using a large community-based, cohort of AAs, indirect and direct relationships between each PNSE variable and TV viewing (≥4 h/day) through perceived discrimination and stress were tested. Our findings indicated the complex ways in which perceived discrimination and stress mediated such associations, which is inconsistent with previous studies (Strong et al., 2013; Van Dyck et al., 2012; Van Holle et al., 2014; Wallmann-Sperlich et al., 2013). In our study, greater perceived violence and problems were positively associated with TV viewing, while more social cohesion was negatively associated with TV viewing when lifetime discrimination mediated the relationship. In contrast, our findings suggest that greater perceived violence and problems unexpectedly were negatively associated with TV viewing, while higher social cohesion was positively related to TV viewing when chronic stress was a mediator. There were no indirect associations between each PNSE variable and TV viewing through daily discrimination and no direct associations with TV viewing when accounting for mediators.

Our findings indicated opposing directions of the associations between PNSE and TV viewing when examining lifetime discrimination and stress as possible mediators. Specifically, path a (Table 3) for both mediators indicated inconsistent directionality of associations. In the case of lifetime discrimination, higher neighborhood violence and problems were associated with lower lifetime discrimination. This finding may relate to the racial composition of the neighborhoods in which participants live (English, Lambert, Evans, & Zonderman, 2014; Hunt, Wise, Jipguep, Cozier, & Rosenberg, 2007). For instance, one study found that AAs living in neighborhoods with a higher percentage of Whites reported experiencing greater discrimination than those living in neighborhoods with a lower percentage of Whites (English et al., 2014). Another study demonstrated that, for African American women, living in neighborhoods with a greater percentage of AAs was associated with lower lifetime discrimination (i.e., occurrences of discrimination by the police due to race) (Hunt et al., 2007). However, measures of neighborhood racial composition like those used in these two previous studies (English et al., 2014; Hunt et al., 2007) were not available for the present study (Hunt et al., 2007). In future research, researchers should consider investigating how neighborhood racial composition might explain the associations of neighborhood violence and problems with lifetime discrimination to further elucidate this mechanism between neighborhood conditions and discrimination.

In contrast, our finding of associations of higher perceived neighborhood violence and problems with greater chronic stress were to some extent consistent with the previous studies (Giurgescu et al., 2012, 2015). For instance, two studies among African American women indicated that individuals who reported higher perceived physical and social disorder (Giurgescu et al., 2012) and lower neighborhood quality (Giurgescu et al., 2015) had higher perceived stress. Opposing directionality of associations when lifetime discrimination was examined as a mediator, as opposed to stress may be in part due to differences in the derived measures (i.e., lifetime discrimination based on instances of unfair treatment in specific domains [e.g., at work, getting medical care] versus chronic stress based on perceptions within more generalized domains [e.g. racism, and meeting basic needs, etc.]). Therefore, more research is needed to better clarify how perceived discrimination and stress operate differently, depending on certain health behaviors.

Unexpectedly, path b, which examined associations of lifetime discrimination and chronic stress with TV viewing, demonstrated that both mediators were negatively associated with TV viewing. This finding is not directly comparable to prior work, but might be somewhat consistent with previous studies from the JHS (Sims et al., 2016, 2017). Two prior JHS studies found an unexpected direction of associations where lifetime discrimination (for women only) (Sims et al., 2016) and chronic stress (Sims et al., 2016) were positively associated with PA. Findings from these studies suggested that someone who is chronically stressed may engage in more PA to cope with psychosocial stressors (e.g., chronic stress, discrimination). Thus, individuals who perceived greater discrimination and chronic stress may engage in more PA to relieve various types of stress, thereby, reducing the time spent in SB, such as TV viewing. Alternatively, those individuals with more stress may have less time for SB leisure time activities such as TV viewing, due to employment, or caretaking activities. However, another study investigating screen time as an outcome stratified by sex and race showed that more experiences of discrimination was related to longer screen time for black men and white women only (Womack et al., 2014). Further research is needed to elucidate the mechanisms by which psychosocial factors link to SB, specifically in AAs, and how TV viewing is seen as a social activity among them (i.e., family time, stress relieving) versus a solitary activity that might promote social isolation and worsen depressive symptoms.

To our knowledge, this study may be the first to test for mediating effects of psychosocial factors on the associations between PNSE and TV viewing among AAs. When considering lifetime discrimination as a mediator (paths a x b), our findings were consistent with previous studies (Claudel et al., 2019; Strong et al., 2013; Xiao, Keadle, Berrigan, & Matthews, 2018), which did not test for mediators. For instance, previous studies demonstrated that worsening perceptions of neighborhood features (Claudel et al., 2019; Strong et al., 2013) and neighborhood deprivation (Xiao et al., 2018) are related to prolonged SB. Furthermore, our study demonstrated that higher neighborhood social cohesion was associated with less TV viewing indirectly through lifetime discrimination, which is consistent with previous studies (Claudel et al., 2019; Strong et al., 2013).

Contrary to our expectations, higher neighborhood violence and problems were indirectly and negatively associated with TV viewing while considering chronic stress (e.g., employment) as a mediator (paths a x b). Furthermore, higher social cohesion was positively associated with TV viewing when mediated through chronic stress. It might be that individuals living in poor neighborhood conditions may report greater chronic stress (responding to different domains of chronic stress), whereas those residing in neighborhoods with higher social cohesion may have greater social capital (Sartorius, 2003), allowing individuals to use available resources to manage stress, and thus reduce their stress (Henderson, Child, Moore, Moore, & Kaczynski, 2016). Further interventions might be that health practitioners could circulate information for available community resources to learn skills/tools to cope with stress levels, when individuals perceive greater neighborhood issues. Our findings and those from prior studies cannot be directly compared because previous studies neither tested for mediating roles of lifetime discrimination or chronic stress nor controlled for these psychosocial factors in modeling, which highlights a unique aspect of our work.

We found no direct or indirect associations between PNSE and TV viewing with daily discrimination (e.g., disrespect) as a mediator. A possible explanation for null findings for daily discrimination as a mediator could be that the chronicity of the psychosocial stressor, which may relate to the length of neighborhood residence and neighborhood racial composition, may make the stressor a more likely mediator of the relationship between neighborhood perceptions and SB. Prior work has demonstrated that adverse neighborhood conditions, as measured by neighborhood socioeconomic deprivation, can have differential associations with cardiometabolic health outcomes based on length of neighborhood residence (Powell-Wiley et al., 2015).

The strength of this study is that this study contributes new knowledge to current literature by testing mediating roles of discrimination and stress on the links between each PNSE and TV viewing. This study also utilizes a community-based large cohort for the mediation analyses and uses validated and standardized scales for data collection on PNSE, discrimination, and stress with high internal consistencies (Sims et al., 2009). This study has several limitations to consider. First, the findings from this study might not be generalizable to other African American adults given that the study population is middle-aged living in a single city, and not a nationally representative sample. TV viewing was self-reported, with a pre-defined duration of hours/day or a week (Diaz et al., 2016), which may lead to underestimation of TV viewing (i.e., social desirability bias) (Peiser & Peter, 2000). Additionally, this is a cross-sectional design and causality cannot be made; therefore, it cannot be determined whether experiences of perceived discrimination and stress occurred before or after TV viewing, as in many epidemiologic studies (Johnson et al., 2016). Although individuals were nested within census tracts, mediation analyses did not control for such data structure. Due to non-availability of recent data, this study has been performed, which were limited to the baseline data (2000–2004). Lastly, relevant covariates (e.g., population density as a proxy for neighborhood walkability), have been adjusted in the mediation analyses. However, unmeasured variables (e.g., neighborhood level SES) could confound the associations between each PNSE variable and TV viewing via a mediator. As each PNSE was highly correlated with neighborhood level SES, it was not controlled in the analyses.

5. Conclusions

Using a large, community-based sample of AAs, this study examined the mediating role of discrimination and chronic stress in the association of neighborhood social context with TV viewing as an indicator of SB. Greater perceived neighborhood violence and problems are indirectly associated with greater TV viewing through lifetime discrimination. Future interventions may focus on community efforts reducing adverse neighborhood issues and discrimination, which in turn could reduce TV viewing, thereby, reducing CVD risk.

Ethics approval and consent to participate

The study was approved by the institutional review boards of the University of Mississippi Medical Center, Jackson State University, and Tougaloo College. All participants provided written informed consent. The National Institutes of Health Office of Human Subjects Research Protections approved our current study.

Authors’ contributions

KT and TPW made substantial contributions to the conceptualization of the work, drafted the work, and revised the work. KT and SO performed all the analysis and interpretation of the data. SJN made all tables and figures. EKC, SJN, SC, MA, JC, SO, and MS made substantial contributions to the interpretation of the data. All authors read and approved the final manuscript.

Data sharing

The data in the present study came from the Jackson Heart Study (https://www.jacksonheartstudy.org/). These data are not publicly available, and its use is restricted.

Funding

Funding for the Social Determinants of Obesity and Cardiovascular Risk Laboratory is provided through the Division of Intramural Research (DIR) of the NHLBI of the NIH, and through the Intramural Research Program of the NIMHD of the NIH. Kosuke Tamura is supported by the 2019 Lenfant Biomedical Fellowship Award from the NHLBI. This research was made possible through the NIH Medical Research Scholars Program, a public-private partnership supported jointly by the NIH and generous contributions to the Foundation for the NIH from the Doris Duke Charitable Foundation, Genentech, the American Association for Dental Research, the Colgate-Palmolive Company, Elsevier, alumni of student research programs, and other individual supporters via contributions to the Foundation for the National Institutes of Health. The Jackson Heart Study (JHS) is supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I), and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I and HHSN268201800012I) contracts from the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute on Minority Health and Health Disparities (NIMHD). The views of the present study are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute (NHLBI), the National Institute on Minority Health and Health Disparities (NIMHD), the National Institutes of Health (NIH), or the U.S. Department of Health and Human Services.

Declaration of competing interest

None.

Acknowledgements

We thank the Jackson Heart Study participants and research teams at University of Mississippi Medical Center and Jackson State University for their long-term commitment to expanding our knowledge of cardiovascular risk factors toward the eradication of cardiovascular health disparities.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ssmph.2021.100760.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

Multimedia component 1
mmc1.docx (15KB, docx)

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