Exploring How Neighborhood Environment Perceptions Moderate the Health Benefits of Movement Behaviors Among Latinos in Los Angeles

Abstract

Purpose

This study examined how the potential health benefits of movement behaviors – physical activity (PA), sedentary time, and sleep – vary by neighborhood perceptions among Latinos.

Design

Cross-sectional analysis of baseline data from churchgoing Latino adults participating in an ongoing randomized controlled trial to promote PA.

Setting

East Los Angeles, California, and surrounding neighborhoods.

Sample

Sample of 728 churchgoing Latinos (77% female, mean age 52 years).

Measures

Movement behaviors included self-reported leisure-time PA and sleep duration, and accelerometer-based sedentary time. Survey assessed perceived neighborhood crime safety, traffic safety, aesthetics, and social cohesion. Outcomes included self-reported stress, depressive symptoms, diabetes, and hypertension; and objectively-measured body mass index.

Analysis

Linear or logistic regression assessed associations of the movement behaviors with neighborhood measures, and their interactions, in relation to the health outcomes.

Results

Sleep was associated with lower perceived stress [B(SE) = −0.41 (0.09), P < .0001] and major depression [OR, 95% CI = 0.54, 0.42-0.69]. Leisure-time MVPA was associated with lower obesity [OR, 95% CI = 0.60, 0.41-0.88] and sedentary time was associated with higher diabetes [OR, 95% CI = 1.03, 1.00-1.05]. Perceived neighborhood safety from crime, social cohesion, and aesthetics had significant interactions with movement behaviors (P < 0.05) in relation to four outcomes.

Conclusion

Interaction models suggest interventions targeting neighborhood crime safety, aesthetics, and social cohesion may be key for maximizing the health benefits of movement behaviors among Latinos.

Keywords

Latinos sedentary behavior physical activity sleep mental health physical health

Purpose

Movement behaviors, including physical activity (PA), sedentary behavior, and sleep, collectively impact physical and mental health,^1,2 yet socially disadvantaged populations experience disproportionate barriers to engage in health-promoting levels of these behaviors,³ placing them at increased risk of poor health outcomes. In particular, Latinos generally report less leisure-time PA and have a higher prevalence of sleep problems compared to non-Latino White adults.^4,5 Although some studies suggest Latinos engage in more total PA and less sedentary time than non-Latino White adults,^6,7 this may be due to more physically demanding occupations, particularly among Latinos of lower income.⁸ The drivers of movement behaviors include factors across multiple levels, such as individual, social, neighborhood environment factors, as posited by socio-ecological frameworks;^9-12 yet of these correlates, there is limited understanding of the complex role of neighborhood environments. Past studies with Latino samples have largely focused on direct associations of neighborhood environmental factors with a single movement behavior.^13-15 Little is known on how environmental factors interact with movement behaviors to shape health outcomes. It is unknown how the independent associations of movement behaviors with health outcomes differ when neighborhood environments are favorable vs less favorable. In other words, do poor environments hinder the potential health benefits of movement behaviors?

Neighborhood environments are multifaceted, comprising physical (built) and social characteristics that vary significantly across communities.¹⁶ Studies suggest that, generally, Latinos reside in neighborhoods characterized by lower SES, fewer green spaces, and less favorable aesthetics (e.g., fewer well-tended yards) than non-Latino White adults.^17-19 Further, national studies show that compared to non-Latino White adults, Latino adults report higher neighborhood crime²⁰ and lower social cohesion,²¹ defined as the strength of connections and sense of solidarity with one’s neighbors.²² Reviews suggest that favorable built environments (e.g., greater walkability, accessibility, and infrastructure for walking and active transportation) and social environments (e.g., higher safety, cohesion, aesthetics) are associated with higher PA,^23,24 lower sedentary behaviors,²⁵ better sleep,²⁶ better mental health such as lower depression²⁷ and reduced stress,²⁸ and reduced risk of obesity²⁹ and chronic diseases such as diabetes²⁹ and hypertension.²⁹ To extend this research and advance health equity, studies are needed that go beyond associations at a single level (behaviors or neighborhoods) and investigate interactions across levels.

Few studies have examined interactions between neighborhood factors and movement behaviors in relation to health outcomes. One study focused on pregnant women found a significant interaction between neighborhood green space and PA in relation to mental health, with a negative association between neighborhood green space and depressive symptoms only among those who were physically active.³⁰ Those findings suggest that the benefits of green space on mental health may depend on how individuals use that green space, e.g., for PA.³⁰ Investigating interactions between neighborhood factors and movement behaviors can help identify the context in which health benefits from movement behaviors are maximized. However, to our knowledge, no such study has examined this question in a Latino sample.

To address these gaps, the present study examined how movement behaviors among a sample of Latino adults are related to mental and physical health based on how they perceive their neighborhood environment (safety, social cohesion, aesthetics). Elucidating how Latino’s movement behaviors are associated with health outcomes in different environmental contexts can help inform targets for environmental interventions to promote Latino health and well-being.

Methods

Design

This study used baseline data from an ongoing cluster randomized controlled trial, Parroquias y Parques/Parishes & Parks, which links parks with churches to promote PA among churchgoing Latino adults.³¹ The intervention targets factors across multiple levels: individual, group, church, and neighborhood park. The study involves multiple partners, including a non-profit research organization, kinesiology school, the Archdiocese of Los Angeles, and the Los Angeles Department of Recreation and Parks; and is guided by a Community Advisory Board (CAB). The churches were located in areas east of downtown Los Angeles, including East Los Angeles, Boyle Heights, and Commerce, all of which are working-class communities that are over 94% Latino. Baseline data were collected from two cohorts, with data collection for cohort 1 occurring in 2019 and for cohort 2 in 2023. Across both cohorts, 728 participants completed both the baseline health survey and accelerometry measure, which capture the primary PA outcomes for the main study, and thus comprised the analytic sample for the present analysis. We obtained institutional review board approval and participant written informed consent.

Sample

Participants were recruited from 12 Catholic churches from one Deanery in East Los Angeles, California, and surrounding neighborhoods. Churches were randomized to either the intervention or control group. Churches were eligible to participate in the study if they were in a zip code with at least 80% Latino residents according to US Census data, were within 5 miles of the kinesiology school where the student instructors attended, and had a park within 1 km, i.e., where classes could take place. The study principal investigator (PI) and project manager worked with the Archdiocese of Los Angeles to identify churches and, with the study team and CAB, selected eligible churches to invite to participate. The project sent a letter of invitation, co-signed by the PI and the Bishop overseeing the parishes, to the head priests and conducted individual follow-up. Each priest who agreed to participate appointed a church coordinator, who received a stipend from the project to assist in organizing study activities, including participant recruitment. Bilingual research assistants recruited participants at the churches on Sundays by making announcements at masses and distributing flyers in English and Spanish describing the study and eligibility. Interested parishioners completed a screener to determine if they met the following eligibility criteria: they were 18 years of age or older, attended the church in the past 6 months and more than once a month, planned on attending the church for the next year and did not attend other participating churches, resided within 15 minutes driving distance of the church and planned to reside in the same neighborhood for the next year, and were able to participate in activities at the church during the week. Participants remained eligible if they had cardiovascular, metabolic, or renal health conditions, but exercised at least three days a week in the past three months, as assessed by a physical activity screener.³²

Measures

Participants completed a health survey (self- or interviewer-administered), Global Physical Activity Questionnaire³³ (GPAQ; interviewer-administered), height and weight (measured by trained research assistants), and wore an accelerometer for one week. For the accelerometer data, we only used sedentary time in these analyses. We did not use accelerometer-based moderate-to-vigorous PA (MVPA) given the data had weaker correlations with the outcomes compared to self-reported PA in preliminary models (data not shown). Further, total MVPA includes leisure-time, transportation, and occupational PA domains, the latter of which has been linked to poor health outcomes.³⁴ To disentangle domain-specific effects, we focused on self-reported leisure-time MVPA given its established benefits for both physical and mental health.³⁵

Participants could complete the survey and GPAQ in their preferred language (English or Spanish). Survey measures and the GPAQ were already available in Spanish from other studies. Participants received a $25 gift card for completing baseline measures.

Outcomes

Perceived Stress

Four items from the Perceived Stress Scale 4³⁶ assessed the frequency of experiencing four feelings/thoughts in the last month, e.g., “felt that you were unable to control the important things in your life?” (0 = never to 4 = very often). Two positively worded items were reversed coded, and a sum score was computed with the four items, where higher scores indicated higher perceived stress. Cronbach’s alpha in the sample is 0.50.

Depression

The Patient Health Questionnaire Depression Scale,³⁷ which includes eight items, assessed the frequency of being bothered by specific problems, e.g., “little interest or pleasure in doing things” (0=not at all to 3=nearly every day). A sum score was computed and dichotomized where scores of 10 or higher indicated current depression.³⁷ Cronbach’s alpha in the sample is 0.88

Obesity

Participants were asked to stand on a Tanita digital scale wearing light clothing and shoes removed to assess weight to the nearest 0.1 kg. We measured height to the nearest 0.1 cm using a fixed ruler attached to the Seca height board vertical surface; participants stood with their back flat against the vertical surface without shoes. Both weight and height were taken twice by a trained RA. We calculated body mass index (BMI) in kg/m² and dichotomized BMI: less than 30 (healthy weight) versus 30 or higher (obesity).³⁸

Physician Diagnosed Chronic Disease

Two items assessed whether (yes/no) participants had ever been diagnosed with diabetes or hypertension.³⁹ For diabetes, females were asked a follow up item if the diagnosis was when they were pregnant. Those who reported their diabetes was only during pregnancy (n = 12) were recategorized as not having diabetes as a chronic condition.

Movement Behaviors

Percent Sedentary Time

Participants were asked to wear an ActiGraph GT3X attached to an elastic belt over their right hip for at least 12 hours a day, every day for 7 consecutive days, and to remove it during water activities and sleep. We initialized devices at 30 Hz. For data processing using ActiLife v.6.13.4 software (ActiGraph), we defined valid wear time as 10 hours or more a day and 3 or more valid days.⁴⁰ If participants did not meet this minimum wear time, they were asked to re-wear the device for another week.⁴⁰ Non-wear time was defined as 60 consecutive minutes of zero count values.⁴¹ We downloaded data as 60-second epoch files and used the Freedson 1998 cut points⁴² to define sedentary time as 0-99 counts/minute. Sedentary hours were converted to minutes by multiplying by 60. We also converted total wear time in hours to minutes by multiplying by 60. Percent sedentary time was computed by dividing sedentary minutes by wear time minutes and multiplying by 100.

Meeting MVPA Recommendations

The Global Physical Activity Questionnaire’s (GPAQ)³³ six items for leisure-time PA (LTPA) assessed whether participants did sports, fitness, or recreational activities at moderate or vigorous intensity for at least 10 continuous minutes. If ‘yes,’ they reported the number of days they performed those activities in a typical week and the duration of those activities on a typical day. We summed the reported moderate and vigorous activity minutes per week and created a binary variable using the 2018 PA Guidelines,⁴³ where those who reported ≥150 minutes/week of moderate PA, or ≥75 minutes/week of vigorous PA, or ≥600 metabolic equivalent (MET) minutes of MVPA during leisure-time were classified as meeting PA recommendations. Else, they were classified as not meeting recommendations.

Sleep

One item from the Pittsburgh Sleep Quality Index (PSQI)⁴⁴ assessed self-reported average hours of sleep per night.

Neighborhood Aesthetics

Four items from the Neighborhood Environment Walkability Scale (NEWS)⁴⁵ assessed perceived neighborhood aesthetics, e.g., “there are trees along the streets in my neighborhood” (1 = strongly agree to 4 = strongly disagree). Response options were reverse coded and averaged, where higher scores indicated more favorable aesthetics. Cronbach’s alpha in the sample is 0.72.

Neighborhood Safety

Three items from the NEWS⁴⁵ measured safety from crime, e.g., “the crime rate in my neighborhood makes it unsafe to go on walks during the day.” One item measured safety from traffic, i.e., “there is so much traffic along nearby streets that it makes it difficult or unpleasant to walk in my neighborhood.” Response options ranged from 1 = strongly agree to 4 = strongly disagree. We computed a mean score of the three crime safety items and used the raw score for traffic safety, where higher scores indicated higher safety. Cronbach’s alpha in the sample is 0.82.

Neighborhood Social Cohesion

Two items from the Neighborhood Cohesion Scale^46,47 measured social cohesion, e.g., “I feel like I fit in with people in my neighborhood” (1 = not true at all to 3 = very true). We computed a mean score of the two items, where higher scores indicated higher cohesion. Cronbach’s alpha in the sample is 0.70.

Covariates

Socio-Demographics

Standard measures assessed age, sex, and current employment status.

Acculturation

The four-item Brief Acculturation Scale for Hispanics⁴⁸ assessed participants’ language preferences in general (for reading and speaking), to speak at home, to think, and to speak with friends (1 = only English to 5 = only Spanish; a 3 was for both equally). All items were reverse coded and summed, with higher scores indicating higher acculturation.

Health Insurance

One item⁴⁹ assessed the type of health insurance the participant uses to pay for medical bills. We created a binary (yes/no) variable for access to health insurance.

Analysis

We used linear regression for perceived stress and logistic regression for the other four binary outcomes. We performed a series of model building steps, starting with bivariate associations between each movement behavior variable and outcome (model 1), then associations with all three behaviors together (model 2), then adding the four perceived neighborhood factors (model 3), and finally adding the covariates (model 4). In model 4, we also adjusted for the cohort (1 or 2) that participants were in to adjust for any potential time differences between the samples, including pre/post COVID-19 pandemic.

To test the moderating effects of the neighborhood factors, we tested two-way interactions between each movement behavior and perceived neighborhood factor in relation to each outcome. For each outcome, we first conducted four separate models, each testing three interactions between each of the three movement behaviors and the same neighborhood factor. Interactions from these models that were significant at P < 0.05 were then tested together in a fifth model. Interactions that were no longer significant at P < 0.05 from this model were dropped. The remaining significant interactions were then probed to compute the association between the movement behavior (from the interaction) and health outcome at ‘low’ (−1 SD from the mean score) and ‘high’ (+1 SD from the mean score) levels of the perceived neighborhood factor. All analyses were performed in SAS v. 9.4 (SAS Institute Inc., Cary, NC).

Results

The overall sample had a mean age of 52 years, was predominantly female (77.3%) and had health insurance (81.7%) (Table 1). The mean perceived stress score was 4.7 and 9.5% met the clinical cut off for major depression (Table 1). About half the sample had obesity, 16.5% reported a diabetes diagnosis, and 23.4% reported a hypertension diagnosis; cohort 1 had slightly higher prevalence of all three outcomes than cohort 2 (Table 1). Overall, the sample spent about half their accelerometer-measured time in sedentary behaviors, only 30.5% met MVPA recommendations based on LTPA, and the average self-reported sleep duration was 6.8 hours a night (Table 1). Neighborhood perception mean scores ranged from 2.4 (social cohesion) to 2.6 (aesthetics) (Table 1).

Table 1.

Characteristics of Latino Participants in Parishes & Parks (n = 728), Overall and by Cohort.

		Total	Cohort 1 (n = 408)	Cohort 2 (n = 320)
Variable	n	Mean (SD) or % (n)	Mean (SD) or % (n)	Mean (SD) or % (n)
Health outcome
Perceived stress, mean (SD)	708	4.66 (3.04)	4.35 (3.10)	5.05 (2.93)
PHQ-8 score, mean (SD)	674	3.43 (4.32)	3.52 (4.47)	3.31 (4.14)
Major depression (PHQ-8 score ≥ 10), % (n)	674	9.50% (64)	9.86% (36)	9.06% (28)
BMI (kg/m2), mean (SD)	720	31.11 (6.35)	31.79 (7.05)	30.23 (5.20)
Obesity (BMI ≥ 30), % (n)	720	53.33% (384)	57.64% (234)	47.77% (150)
Physician-diagnosed diabetes (yes), % (n)	703	16.50% (116)	18.78% (74)	13.59% (42)
Physician-diagnosed hypertension (yes), % (n)	717	23.43% (168)	24.50% (98)	22.08% (70)
Movement behavior
Accelerometer wear time (total minutes), mean (SD)	728	4388.79 (1593.12)	4504.88 (1533.69)	4240.78 (1656.46)
Accelerometer-based sedentary time (total minutes), mean (SD)	728	2552.15 (1001.19)	2629.98 (978.50)	2452.93 (1022.35)
Accelerometer-based % sedentary time, mean (SD)	728	58.46 (11.26)	58.71 (11.13)	58.14 (11.43)
Self-reported LTPA (min/week), mean (SD)	719	136.56 (330.56)	139.49 (376.45)	132.88 (262.02)
Meets MVPA recommendations via LTPA (yes), % (n)	719	30.46% (219)	30.42% (122)	30.50% (97)
Self-reported sleep (hours/night), mean (SD)	713	6.76 (1.28)	6.74 (1.30)	6.78 (1.26)
Perceived neighborhood factor^a
Aesthetics, mean (SD)	670	2.57 (0.59)	2.51 (0.62)	2.63 (0.53)
Safety from crime, mean (SD)	689	2.52 (0.71)	2.50 (0.71)	2.54 (0.71)
Safety from traffic, mean (SD)	706	2.43 (0.88)	2.41 (0.89)	2.46 (0.87)
Social cohesion, mean (SD)	711	2.36 (0.57)	2.39 (0.58)	2.33 (0.56)
Socio-demographic
Age (years), mean (SD)	721	52.31 (13.78)	52.13 (13.48)	52.56 (14.17)
Gender, % (n)	719
Female		77.33% (556)	79.06% (321)	75.08% (235)
Male		22.67% (163)	20.94% (85)	24.92% (78)
Employment, % (n)^b	716
Not employed		47.49% (340)	50.50% (203)	43.63% (137)
Employed		52.51% (376)	49.50% (199)	56.37% (177)
Health insurance, % (n)	717
Uninsured		18.27% (131)	20.10% (80)	15.99% (51)
Has insurance		81.73% (586)	79.90% (318)	84.01% (268)
Acculturation score, mean (SD)	703	7.90 (4.58)	8.07 (4.63)	7.68 (4.52)

Notes: BMI = body mass index; LTPA = leisure time physical activity; MVPA = moderate-to vigorous-physical activity; PHQ = Patient Health Questionnaire; SD = standard deviation.

^aHigher scores indicate more favorable perceptions of the neighborhood factor.

^bNot employed includes unemployed, retired, disabled, student, homemaker or stay at home parent; employed includes employed full or part time and self-employed.

Table 2 shows the associations of the movement behaviors and perceived neighborhood factors with the mental health outcomes. Lower perceived stress scores were significantly associated with higher self-reported sleep (b (SE) = −0.41 (0.09), P < .0001), perceived safety from traffic (b (SE) = −0.34 (0.15), P = 0.02), and perceived social cohesion (b (SE) = −0.66 (0.22), P = 0.004). There was a significantly lower odds of major depression with higher self-reported sleep (OR, 95% CI = 0.54, 0.42-0.69) and perceived safety from crime (OR, 95% CI = 0.58, 0.35-0.98).

Table 2.

Associations of Activity Behaviors and Neighborhood Perceptions With Mental Health Outcomes.

Model	Perceived stress score		Major depression
Model	B (SE)	p	OR (95% CI)
M1: Bivariate
% sedentary time	0.01 (0.01)	0.47	1.03 (1.01-1.06)
Meets MVPA recommendations via LTPA	0.11 (0.25)	0.65	0.71 (0.38-1.29)
Sleep	−0.36 (0.09)	<.0001	0.58 (0.47-0.71)
M2: All activity behaviors
% sedentary time	0.01 (0.01)	0.59	1.03 (1.00-1.06)
Meets MVPA recommendations via LTPA	0.17 (0.25)	0.51	0.85 (0.46-1.59)
Sleep	−0.36 (0.09)	<.0001	0.60 (0.49-0.73)
M3: M2 + perceived neighborhood factors^a
% sedentary time	0.001 (0.01)	0.92	1.03 (1.00-1.06)
Meets MVPA recommendations via LTPA	0.17 (0.26)	0.51	0.78 (0.37-1.61)
Sleep	−0.35 (0.09)	0.0002	0.57 (0.45-0.71)
Aesthetics	0.24 (0.21)	0.26	0.75 (0.44-1.27)
Safety from crime	−0.34 (0.19)	0.07	0.56 (0.34-0.92)
Safety from traffic	−0.26 (0.15)	0.07	0.71 (0.48-1.04)
Social cohesion	−0.82 (0.22)	0.0002	0.82 (0.47-1.42)
M4: M3 + covariates^b
% sedentary time	−0.0003 (0.01)	0.98	1.03 (0.995-1.06)
Meets MVPA recommendations via LTPA	0.06 (0.28)	0.83	0.63 (0.29-1.35)
Sleep	−0.41 (0.09)	<.0001	0.54 (0.42-0.69)
Aesthetics	0.20 (0.22)	0.35	0.70 (0.41-1.21)
Safety from crime	−0.23 (0.20)	0.24	0.58 (0.35-0.98)
Safety from traffic	−0.34 (0.15)	0.02	0.69 (0.46-1.02)
Social cohesion	−0.66 (0.22)	0.0035	0.78 (0.44-1.40)

Notes: LTPA = leisure time physical activity; MVPA = moderate-to vigorous-physical activity. Bolded values indicate significance at P < 0.05.

^aHigher scores indicate more favorable perceptions of the neighborhood factor.

^bAdjusted for cohort, age, gender, employment, health insurance, and acculturation.

Table 3 shows the associations of the movement behaviors and perceived neighborhood factors with the physical health outcomes. The odds of obesity were significantly lower among those who met MVPA recommendations via LTPA (OR, 95% CI = 0.60, 0.41-0.88) but higher among those with greater perceived social cohesion (OR, 95% CI = 1.38, 1.01-1.87). Higher percent sedentary time was associated with a significantly higher odds of diabetes (OR, 95% CI = 1.03, 1.00-1.05). Higher perceived safety from crime was associated with a significantly lower odds of hypertension (OR, 95% CI = 0.69, 0.49-0.98).

Table 3.

Associations of Activity Behaviors and Neighborhood Perceptions With Physical Health Outcomes.

Model	Obesity	Diabetes	Hypertension
Model	OR (95% CI)	OR (95% CI)	OR (95% CI)
M1: Bivariate
% sedentary time	0.99 (0.98-1.01)	1.04 (1.02-1.06)	1.02 (1.01-1.04)
Meets MVPA recommendations via LTPA	0.53 (0.39-0.73)	0.59 (0.37-0.95)	0.66 (0.44-0.98)
Sleep	0.95 (0.85-1.07)	0.91 (0.78-1.06)	0.90 (0.79-1.04)
M2: All activity behaviors
% sedentary time	0.99 (0.98-1.01)	1.04 (1.02-1.06)	1.02 (1.01-1.04)
Meets MVPA recommendations via LTPA	0.54 (0.39-0.75)	0.62 (0.38-1.01)	0.70 (0.47-1.05)
Sleep	0.97 (0.86-1.09)	0.92 (0.79-1.08)	0.92 (0.80-1.05)
M3: M2 + perceived neighborhood factors^a
% sedentary time	0.99 (0.98-1.01)	1.04 (1.01-1.06)	1.02 (1.01-1.04)
Meets MVPA recommendations via LTPA	0.60 (0.42-0.86)	0.63 (0.38-1.06)	0.75 (0.49-1.16)
Sleep	0.97 (0.86-1.10)	0.95 (0.80-1.12)	0.96 (0.83-1.12)
Aesthetics	1.06 (0.80-1.40)	1.35 (0.91-2.01)	1.02 (0.73-1.43)
Safety from crime	0.86 (0.67-1.12)	0.86 (0.61-1.22)	0.82 (0.61-1.11)
Safety from traffic	0.95 (0.78-1.16)	1.02 (0.77-1.33)	1.07 (0.85-1.35)
Social cohesion	1.43 (1.07-1.92)	1.12 (0.75-1.66)	0.84 (0.60-1.18)
M4: M3 + covariates^b
% sedentary time	0.99 (0.98-1.01)	1.03 (1.00-1.05)	1.01 (0.99-1.03)
Meets MVPA recommendations via LTPA	0.60 (0.41-0.88)	0.75 (0.43-1.30)	0.91 (0.56-1.49)
Sleep	0.96 (0.84-1.09)	0.97 (0.81-1.15)	1.01 (0.87-1.19)
Aesthetics	1.08 (0.80-1.45)	1.45 (0.95-2.22)	1.06 (0.73-1.55)
Safety from crime	0.88 (0.67-1.15)	0.85 (0.58-1.24)	0.69 (0.49-0.98)
Safety from traffic	0.95 (0.77-1.17)	1.03 (0.77-1.38)	1.14 (0.88-1.49)
Social cohesion	1.38 (1.01-1.87)	1.06 (0.71-1.61)	0.69 (0.48-1.01)

Notes: LTPA = leisure time physical activity; MVPA = moderate-to vigorous-physical activity. Bolded values indicate significance at P < 0.05.

^aHigher scores indicate more favorable perceptions of the neighborhood factor.

^bAdjusted for cohort, age, gender, employment, health insurance, and acculturation.

There were five significant interactions between movement behaviors and perceived neighborhood factors in relation to the health outcomes (Table 4). For major depression, there was one significant interaction between self-reported sleep and perceived safety from crime (P = 0.04). Specifically, higher sleep was significantly associated with a lower odds of major depression only among those who reported lower perceived safety from crime (OR, 95% CI = 0.45, 0.32-0.61).

Table 4.

Significant Interactions Between Movement Behaviors and Perceived Neighborhood Factors in Relation to Health Outcomes.

			Interaction between behavior and moderator	Association between movement behavior and outcome at low/high levels of neighborhood moderator^a
			Interaction between behavior and moderator	Low (−1 SD)	High (+1 SD)
Outcome	Movement behavior	Neighborhood moderator	b (SE), p	OR (95%CI)	OR (95%CI)
Major depression	Sleep	Safety from crime	0.40 (0.19), P = 0.0366	0.45 (0.32-0.61)	0.79 (0.52-1.19)
Obesity	Sleep	Social cohesion	0.24 (0.12), P = 0.0397	0.83 (0.68-1.01)	1.09 (0.91-1.30)
Diabetes	% sedentary time	Social cohesion	0.04 (0.02), P = 0.0439	1.01 (0.98-1.04)	1.05 (1.02-1.09)
Hypertension	% sedentary time	Aesthetics	−0.04 (0.02), P = 0.0126	1.04 (1.00-1.07)	0.99 (0.97-1.02)
	Meets MVPA recommendation via LTPA (ref: No)	Social cohesion	0.55 (0.23), P = 0.0172	0.46 (0.21-1.04)	1.62 (0.85-3.07)

Notes: LTPA = leisure time physical activity; MVPA = moderate-to vigorous-physical activity. Bolded values indicate significance at P < 0.05.

^aAdjusted for the individual variables in the interaction term, cohort, age, gender, employment, health insurane, and acculturation.

For obesity, we found a significant interaction between sleep and perceived social cohesion (P = 0.04). However, associations in the low and high perceived social cohesion groups were not statistically significant.

For diabetes, we found a significant interaction between accelerometer-based percent sedentary time and perceived social cohesion. Higher percent sedentary time was significantly associated with higher odds of diabetes only among those who reported higher perceived social cohesion (OR, 95% CI = 1.05, 1.02-1.09).

For hypertension, we found two significant interactions, one between accelerometer-based percent sedentary time and perceived aesthetics (P = 0.01) and another between meeting MVPA recommendations via LTPA and perceived social cohesion (P = 0.02). Higher percent sedentary time was significantly associated with higher odds of hypertension only among those who reported lower perceived neighborhood aesthetics (OR, 95% CI = 1.04, 1.00-1.07). For the association between meeting MVPA recommendations via LTPA and hypertension, neither association in the low/high social cohesion groups were statistically significant.

Discussion

This is one of the first studies to investigate how the potential health benefits of movement behaviors might vary by neighborhood perceptions among Latinos. Findings from the main effects models suggest that when examined collectively, different movement behaviors may have varying health benefits. In this sample of churchgoing Latinos, sleep appeared to be more strongly associated with mental health, whereas activity and sedentary behaviors were more strongly associated with physical health. Perceptions of different neighborhood factors had varying associations with health, independent of movement behaviors and socio-demographic factors. Further, results from the interaction models showed that perceived neighborhood safety from crime, social cohesion, and aesthetics were significant moderators of associations between movement behaviors and health outcomes. Of importance, the moderating effects of safety from crime and social cohesion were in the unexpected directions, pointing to their complex relationship with health and movement behaviors.

Findings from the main effects models suggest that sleep is a stronger correlate of favorable mental health whereas PA and sedentary time were only associated with physical health outcomes. The finding with sleep and mental health is consistent with another study with Latinos,⁵⁰ however, that study did not control for PA and sedentary time. Another study found that poor sleep quality was positively associated with PTSD, independent of PA and sedentary time, among trauma-exposed individuals.⁵¹ Limited research has examined all three movement behaviors – PA, sedentary time, and sleep – in relation to physical health outcomes among Latinos. One U.S. study using a nationally representative survey showed that higher PA and lower sedentary time were significantly associated with lower BMI and better cardiometabolic markers, controlling for sleep.⁵² The present study fills an important gap in understanding how the collection of movement behaviors are associated with multiple health outcomes among Latinos.

The main effects models also showed significant associations of neighborhood perceptions with health outcomes, independent of movement behaviors. Higher perceived safety from crime was associated with lower depression and hypertension, which aligns with past studies with Latino samples.^53,54 Higher safety from traffic was associated with lower perceived stress, which also aligns with another study.²⁸ However, a different study with a Latino sample on the Texas-Mexico border found slightly different results, where perceived crime and traffic were related to higher anxiety; perceived traffic was related to higher depression; and none of the examined neighborhood factors were related to diabetes or metabolic syndrome.⁵⁵ A potential reason for the differences in results is that the socio-demographic and environmental characteristics along the Texas-Mexico border are different from the East Los Angeles area. This points to the importance of understanding neighborhood-health associations in a variety of contexts.

The present study also found mixed associations for higher social cohesion, including a negative association with perceived stress but positive association with obesity. A nationally representative study also reported an inverse association between social cohesion and perceived stress.⁵⁶ A study using data from the Hispanic Community Health Study found no association between social cohesion and obesity,⁵⁷ but that study had a more diverse sample of Latino adults from four cities and used a 5-item cohesion scale, whereas the present study focused on a predominantly Mexican sample and only used two items. A 2018 review points to inconsistent evidence linking social cohesion and obesity.⁵⁸ The potential adverse role of social cohesion has also been cited in publications on ‘behavioral social contagion,’ which posits that unhealthy behaviors, including obesity-related behaviors, can be spread through social networks.⁵⁹ Thus, social cohesion may be beneficial for some health outcomes but not others.

Findings from the interaction models provide evidence for the moderating effects of neighborhood perceptions on the associations of movement behaviors and health outcomes. We found that among those who reported lower perceived safety from crime, there was a 55% decrease in the odds of major depression with each 1-hour increase in sleep. This finding is unexpected and suggests that sleep may be more protective against depressive symptoms among those who reside in less safe neighborhoods. One study from Texas found that neighborhood disorder was associated with lower distress only among those who reported excellent sleep.⁶⁰ Similar to our study, that Texas study suggests that sleep’s mental health benefits may be more pronounced in less favorable social environmental conditions. This sounds counterintuitive given unfavorable environments have been linked to both poor sleep²⁶ and poor mental health.^27,28 Interventions that help individuals navigate poor environmental conditions may help support mental health. However, for long term benefits, it is necessary to address the underlying environmental barriers to health.

Results also showed that higher sedentary time was related to an increased odds of diabetes only among those who reported higher social cohesion. Although this is unexpected, there is evidence of social cohesion’s mixed effects on health.⁵⁹ Participants in this study who perceived higher social cohesion may have had higher contact with neighbors who engaged in more sedentary activities and participants likely took part in those behaviors when interacting with them, which may have also included consumption of unhealthy foods or drinks. Interventions are needed targeting behavior change across social networks along with complementary strategies that help individuals navigate unhealthy norms that promote high sedentary time.

We also found that higher sedentary time was associated with higher odds of hypertension only among those who reported worse aesthetics. A potential explanation for this finding is that unfavorable neighborhood aesthetics may reduce one’s motivation to engage in activity outdoors, prompting one to spend more time indoors instead, likely engaging in sedentary activities. This finding is in line with prior research showing that less favorable aesthetics can be a barrier for PA.⁶¹ One study also showed that Latina women who perceived more favorable neighborhood aesthetics were more likely to benefit from a PA intervention than those who reported less favorable aesthetics.⁶² Few studies have examined how neighborhood factors interact with sedentary time in relation to health. For example, one study using data from older adults found that higher sedentary time was related to higher odds of overweight/obesity in neighborhoods with lower access to food outlets.⁶³ Interventions improving neighborhood aesthetics, such as through place-making art or greening initiatives, complemented with community activities that promote use of these spaces for PA, may help reduce sedentary behaviors and benefit health.

Finally, we found two more interactions, one for obesity and one for hypertension, but given the associations at different levels of perceived neighborhood social cohesion were not significant, we will not provide interpretations. Instead, we recommend that additional research with larger samples and power replicate these interactions to understand their moderating effects. Given the dearth of research on social cohesion in predominantly-Spanish-speaking samples, studies are needed investigating how Latino immigrants understand social cohesion and how it influences behaviors and health.

Although the present study focused only on neighborhood social factors (not the built environment), few studies have examined their role on Latino health, with no prior study testing interactions between social neighborhood factors and Latinos’ movement behaviors; thus, this study addresses an important gap. Perceptions can also introduce biases (e.g., recall, social desirability) and vary among individuals, even among those nested in the same neighborhood. However, perceived neighborhood measures are valuable in the study of place and health. Studies point to the effects of perceived neighborhood factors with health outcomes independent of objective measures, e.g., census measures.⁶⁴ Data for this study came from a sample residing in southern California; thus, findings are not generalizable. Finally, longitudinal studies are needed to test for causal relationships and the mechanisms by which movement behaviors and neighborhood perceptions influence health among Latinos.

The combination of engaging in recommended levels of PA, low sedentary time, and sufficient sleep can provide many mental and physical health benefits, yet these benefits might be altered by unfavorable environmental conditions. Findings from the interaction models suggest that interventions aimed to improve neighborhood safety from crime, aesthetics, and social cohesion may be key for maximizing the health benefits of movement behaviors among Latinos. Given social environmental factors are rarely targeted by neighborhood initiatives, our findings provide support for addressing social factors as part of comprehensive urban planning initiatives. Such efforts must be complemented by individual and social strategies to enhance education and support for healthy movement behaviors, as well as policies to sustain health-promoting environments and health equity.

So What?

What is already known on this topic?

Individual movement behaviors and neighborhood environmental factors are key determinants of physical and mental health.

What does this article add?

This is one of the first studies to test how the potential health benefits of movement behaviors vary by neighborhood environment perceptions among Latinos.

What are the implications for health promotion practice or research?

Interventions aimed to improve neighborhood safety from crime, aesthetics, and social cohesion may be key for maximizing the health benefits of movement behaviors among Latinos, and should be included in comprehensive urban planning initiatives to advance health equity.

Footnotes

Acknowledgements

We would like to thank the Parishes & Parks participants, partners, and study team for their important contributions to the project.

Author contributions

Lilian Perez led the study conception and design, wrote the first and final drafts of the manuscript. All authors commented on previous versions of the manuscript. Data analyses were performed by Rachana Seelam. All authors read and approved the final manuscript.

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by the National Cancer Institute (R01CA218188). The lead author was funded by a Diversity Supplement from the National Cancer Institute (3R01CA218188-03S2).

Ethical Statement

ORCID iDs

Lilian G. Perez

Kathryn P. Derose

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