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. 2023 Jan 18;23:127. doi: 10.1186/s12889-022-14973-1

Perceived neighborhood environment walkability and health-related quality of life among predominantly Black and Latino adults in New York City

Jiaqi Zhu 1, Hanish Kodali 1, Katarzyna E Wyka 1, Terry T-K Huang 1,
PMCID: PMC9847133  PMID: 36653809

Abstract

Background

Measures of the built environment such as neighborhood walkability have been associated with health behaviors such as physical activity, the lack of which in turn may contribute to the development of diseases such as obesity, diabetes, cardiovascular disease, and cancer. However, limited research has examined these measures in association with health-related quality of life (HR-QoL), particularly in minoritized populations. We examined the relationship between perceived neighborhood environment and HR-QoL in a sample of mostly Black and Latino residents in New York City (NYC).

Methods

This study utilized the baseline survey data from the Physical Activity and Redesigned Community Spaces (PARCS) Study among 1252 residents [34.6% Black, 54.1% Latino, 80.1% female, mean(±SD) age = 38.8 ± 12.5) in 54 park neighborhoods in NYC. Perceived built environment was measured using Neighborhood Environment and Walkability Survey, and mental and physical HR-QoL was estimated using Short Form (SF)-12. Using factor analysis, we identified two subscales of neighborhood walkability: enablers (e.g., trails, sidewalks, esthetics) vs. barriers (e.g., high crime and traffic). In addition, we included a third subscale on neighborhood satisfaction. Generalized Estimating Equation models adjusted for demographics and BMI and accounted for the clustering effect within neighborhood. Multiple imputation was used to account for missing data.

Results

Mental HR-QoL was associated with barriers of walkability (β ± SE = − 1.63 ± 0.55, p < 0.01) and neighborhood satisfaction (β ± SE = 1.55 ± 0.66, p = 0.02), after adjusting for covariates. Physical HR-QoL was associated with only barriers of walkability (β ± SE = − 1.13 ± 0.57, p < 0.05).

Conclusions

Among NYC residents living in minoritized neighborhoods, mitigating negative aspects of the neighborhood environment may be more crucial than adding positive features in terms of HR-QoL. Our study points to the need to investigate further the role of the built environment in urban, minoritized communities.

Keywords: Built environment, Walkability, Neighborhood satisfaction, Quality of life, African American, Latino, Community health

Introduction

The built environment has been increasingly recognized as an important dimension in the framework of social determinants of health [1, 2]. There is a growing body of literature linking the built environment to physical and mental health [39]. For example, the design of urban environments, such as neighborhood walkability, transportation, food density, parks and recreational facilities, and aesthetics, has been associated with health behaviors such as diet [10] and physical activity [11]. These health behaviors, in turn, contribute to the development of diseases such as obesity, type 2 diabetes, cardiovascular disease, and cancer [1215].

While objective physical attributes of the built environment are important (e.g., concrete amount of green space), perceptions of the environment, as proxies of how people experience their environment, may be just as critical. For example, built environment attributes have been found to have an indirect effect through the perception of the built environment on moderate-to-vigorous physical activity [16]. Indeed, perceptions of the built environment, such as the sense of satisfaction with one’s neighborhood, has been shown to mediate the relationship between objective measures of the environment and health [17, 18]. The perceived physical environment has also been found to be positively associated with physical health outcomes such as obesity [19] and mental health outcomes such as depression [2022].

While the conventional biomedical model emphasizes disease outcomes, quality of life, as measured by various validated survey instruments, is increasingly seen as an important health outcome in its own right [23]. Research on the relationship between the built environment and measures of health-related quality of life (HR-QoL) is limited but emerging [3, 8, 2428]. In one study, land-use heterogeneity and housing density were found to be associated with HR-QoL [28]. Perceptions of the built environment, such as perceived street noise and traffic safety, have also been related to mental and physical health components of quality of life [3, 27]. Furthermore, research has shown a positive correlation between neighborhood walkability and HR-QoL in Australia and China [26, 27]. Increased perceived diversity, safety and esthetics were found to be associated with higher physical and mental HR-QoL [27]. However, to our knowledge, little is known about these relationships in minoritized populations in the United States.

To bridge this gap, we first tested the psychometric properties of the Neighborhood Environment and Walkability Survey (NEWS) in New York City (NYC), recognizing its unique built environment compared to other major American cities. Subsequently, we examined the association between perceptions of the neighborhood environment and HR-QoL in a sample of mostly Black and Latino residents in NYC. We hypothesized that positive perceptions of the neighborhood environment would be associated with higher physical and mental components of HR-QoL.

Method

Study design

This study utilized the baseline adult survey data from the Physical Activity and Redesigned Community Spaces (PARCS) Study, a natural experiment evaluation on the impact of citywide park redesign and renovation on health and wellbeing [29]. The baseline survey data were collected from Summer 2016 – Spring 2018 (except during winter months) in 54 park neighborhoods throughout the five boroughs of NYC. A convenience sample of adult residents was invited to participate in the study survey.

Park neighborhood selection

To be included in the study, NYC Parks and Recreation identified parks in neighborhoods which met two of three selection criteria: high poverty (≥ 20% population below poverty line), high population growth (≥25% growth during 2000–2010) or high population density (≥ 110 people/acre). For the purpose of the PARCS evaluation study, study park buffer zones were defined as the area within a 0.30-mile radius from the perimeter of each park.

Sample selection

To meet the PARCS Study eligibility criteria, participants needed to live within the designated 0.3-mile buffer zone. Eligible participants were adults ≥18 years of age who owned a smart phone, spoke English, Spanish or Chinese (Mandarin or Cantonese), did not have any mobility issues and were intending to stay in the neighborhood for at least 4 years (due to the longitudinal nature of the parent study). This paper included observations from 1252 participants who provided any data on the relevant survey items described below.

Measures

Health-related quality of life (HR-QoL)

HR-QoL is a multidimensional concept that includes individuals’ perception of their physical and mental functioning, limitations due to physical or mental health problems, bodily pain, vitality, general health, and social functioning [30]. We used the well-validated Short-Form 12 (SF-12) survey [31] in this study. Eight domains were covered by the SF-12 survey questions including 1) limitations in physical activities because of health problems 2) limitations in social activities because of physical or emotional problems 3) limitations in usual role activities because of physical health problems 4) bodily pain 5) general mental health 6) limitations in usual role activities because of emotional problems 7) vitality 8) general health perceptions. For the purpose of our analysis, we used the composite scores on mental health and physical health as our two primary outcome variables, using the standard scoring protocol for SF-12 [32]. The higher the score, the better the self-reported HR-QoL in mental health and physical health (range 0–100).

Neighborhood Environment and Walkability Survey (NEWS)

NEWS was developed in 2002 to measure resident perceptions of neighborhood characteristics [33], including residential density, land use mix, street connectivity, infrastructure for walking/cycling, neighborhood aesthetics, traffic and crime safety, and neighborhood satisfaction [33]. This survey we used included: places for walking and cycling (5 items), neighborhood surroundings (6 items), safety from traffic (8 items), safety from crime (6 items), and neighborhood satisfaction (18 items) [34]. Items for the first four original subscales all had four response options (1 = strongly disagree, 2 = somewhat disagree, 3 = somewhat agree, 4 = strongly agree) while items in the neighborhood satisfaction subscale had five response options (1 = strongly dissatisfied, 2 = somewhat dissatisfied, 3 = neither satisfied nor dissatisfied, 4 = somewhat satisfied, 5 = strongly satisfied). Items were coded such that the higher the subscale score, the more positive the attitude regarding the construct.

Covariates

Demographic variables included age, gender (female vs. male as reference group), ethnicity (Black, Other vs. Latino as reference group), BMI (overweight 25–29 kg/m2, obese > = 30 kg/m2 vs. under and normal < 25 kg/m2 as reference group,), annual household income (< $20,000 vs. ≥ $20,000 as reference group), education (high school graduate, some college or higher vs. less than high school as reference group), employment (self-employed, homemaker, student, retired, unable to work, unemployed for 1 year or more, unemployed for less than 1 year, don’t know/not sure vs. employed as reference group), and marital status (divorced, widowed, separated, never married, a member of an unmarried couple living together vs. married as reference group).

Statistical analyses

NEWS was originally developed in Seattle and cross-validated in Baltimore [34]. Factor analysis has been used to adapt to countries other than the United States, such as Australia and Western Europe [35]. Due to the unique neighborhood characteristics of NYC, we first tested the psychometric properties of NEWS using exploratory factor analysis (EFA) with oblimin rotation [36]. Items with factor loading < 0.40 were dropped in subsequent analysis [37]. We used Cronbach’s alpha [38] was used to assess the internal consistency of the newly constituted subscales based on the factor analysis, considering Cronbach’s alpha > 0.7 as good and acceptable. Spearman correlation tests were performed between NEWS subscales and HR-QoL components.

To deal with missing data, we used sequential regression modelling to impute the missing values in the NEWS scales, QoL scores and covariates [39]. Twenty imputations were generated, taking into account 14% of missing cells in the original dataset. Descriptive statistics were estimated for all the NEWS subscales, HR-QoL components, and covariates with and without imputation. Then, we regressed mental and physical HR-QoL scores (in separate models) on the newly formed NEWS subscales, adjusting for covariates, with multiple imputed dataset. Generalized Estimating Equation (GEE) models [40] were used to account for the clustering effect of park neighborhoods. Sensitivity analyses were conducted using the complete cases for GEE modeling.

To test the multicollinearity between NEWS subscales, variance inflation factors (VIF) were calculated in the GEE models. The VIF for the NEWS subscales ranged from 1.1 to 1.6, showing no multicollinearity among NEWS subscales [41].

All analyses were performed using R version 4.0.4 [42]. The package of “psych” in R was utilized for the EFA [43]. We used the “MICE” package in R for multiple imputations [44]. The “geepack” package in R was utilized to test GEE models [45]. Alpha was set at p < 0.05.

Results

Factor analysis of NEWS subscales

Using the raw data, Cronbach’s alpha showed poor internal consistency for three of the original survey subscales: places for walking and cycling (0.57, 95% Confidence Interval (CI) = 0.54–0.61), safety from traffic (0.64, 95% CI = 0.61–0.67), safety from crime (0.66, 95% CI = 0.63–0.69). Neighborhood surroundings had good internal consistency with a Cronbach’s alpha of 0.79 (95% CI = 0.77–0.80) and neighborhood satisfaction had high internal consistency with a Cronbach’s alpha of 0.91 (95% CI = 0.90–0.92).

To adapt NEWS for NYC, EFA was performed for the above scales besides neighborhood satisfaction. EFA yielded two subscales: enablers (e.g., trails, sidewalks, esthetics) vs. barriers (e.g., high crime and traffic) of walkability. EFA results for the first two subscales with items demonstrating a factor loading ≥0.40 are shown in Table 1. Cronbach’s alpha was 0.84, 0.78 and 0.91 for walkability enablers, walkability barriers, and neighborhood satisfaction, indicating good internal consistency. The higher the NEWS subscale value, the stronger agreement on the statements for enablers and barriers.

Table 1.

Exploratory factor analysis of the Neighborhood Environment Walkability Scale (NEWS)

Subscale Items Factor Loading
NEWS walkability enablersa (Cronbach’s α = 0.84) The sidewalks in my neighborhood are well maintained (paved, even, and not a lot of cracks). 0.515
There are bicycle or pedestrian trails in or near my neighborhood that are easy to get to 0.467
There are trees along the streets in my neighborhood 0.445
Trees give shade for the sidewalks in my neighborhood 0.486
There are many interesting things to look at while walking in my neighborhood 0.607
My neighborhood is generally free from litter 0.563
There are many attractive natural sights in my neighborhood (such as landscaping, views) 0.586
There are attractive buildings/homes in my neighborhood 0.586
The speed of traffic on the street I live on is usually slow (30 mph or less) 0.433
The speed of traffic on most nearby streets is usually slow (30 mph or less) 0.451
There are crosswalks and pedestrian signals to help walkers cross busy streets in my neighborhood 0.445
The crosswalks in my neighborhood help walkers feel safe crossing busy streets 0.541
My neighborhood streets are well lit at night 0.557
Walkers and bikers on the streets in my neighborhood can be easily seen by people in their homes 0.473
NEWS walkability barriersa (Cronbach’s α = 0.78) There is a high crime rate in my neighborhood 0.557
The crime rate in my neighborhood makes it unsafe to go on walks during the day 0.534
The crime rate in my neighborhood makes it unsafe to go on walks at night 0.625
There is so much traffic along the street I live on that it makes it difficult or unpleasant to walk in my neighborhood 0.525
There is so much traffic along nearby streets that it makes it difficult or unpleasant to walk in my neighborhood 0.524
Most drivers exceed the posted speed limits while driving in my neighborhood 0.424
When walking in my neighborhood, there are a lot of exhaust fumes (such as from cars, buses) 0.479
Neighborhood Satisfactionb (Cronbach’s α = 0.91) Highway access from your home 0.43
Access to public transportation in your neighborhood 0.42
Commuting time to work/school 0.51
Access to shopping in your neighborhood 0.57
Number of friends you have in your neighborhood 0.49
Number of people you know in your neighborhood 0.49
How easy and pleasant it is to walk in your neighborhood 0.76
How easy and pleasant it is to bicycle in your neighborhood 0.68
Quality of schools in your neighborhood 0.61
Access to entertainment in your neighborhood (restaurants, movies, clubs, etc.) 0.65
Safety from threat of crime in your neighborhood 0.70
Safety from threat of violence (or violent crime) in your neighborhood 0.71
Amount and speed of traffic in your neighborhood 0.60
Noise from traffic in my neighborhood 0.56
Number and quality of food stores in your neighborhood 0.63
Number and quality of restaurants in your neighborhood 0.63
Neighborhood as a good place to raise children 0.66
Neighborhood as a good place to live 0.67

aResponse were in 4 categories: 1 = Strongly Disagree, 2 = Somewhat disagree, 3 = Somewhat agree, 4 = Strongly agree

bResponse were in 5 categories: 1 = Strongly dissatisfied, 2 = Somewhat dissatisfied, 3 = Neither satisfied nor dissatisfied, 4 = Somewhat satisfied, 5 = Strongly satisfied

Participant characteristics and bivariate associations of NEWS and HR-QoL

Table 2 provides the descriptive statistics of the sample with and without imputation. The imputed results closely matched those of the non-imputed data. The sample had a mean (±standard error) age of 38.8 ± 12.5 years and was 80.1% female. The vast majority of the participants were Latino (54.1%) or Black (34.6%). Just over half of the participants were in a household with an annual income lower than $20,000 while almost half had some college or a higher degree. In terms of employment, 37.5% of the sample was employed and 11.4% was self-employed). Just over one-third of the participants were married or living with a partner. Table 3 shows significant crude associations between NEWS scales and HR-QoL. The correlation coefficients of NEWS walkability enablers, NEWS walkability barriers, and NEWS neighborhood satisfaction with physical HR-QoL are 0.086, − 0.151 and 0.098 (p < 0.05). The correlation coefficients of NEWS walkability enablers, NEWS walkability barriers, and NEWS neighborhood satisfaction with mental HR-QoL are 0.180, − 0.143 and 0.218 (p < 0.05).

Table 2.

Descriptive statistics

Without MI With MI
N % N %
Gender
 Male 249 19.9 249 19.9
 Female 1000 80.1 1003 80.1
BMI
 Under and Normal (< 25 kg/m2) 308 26.1 330 26.4
 Overweight (25–29.9 kg/m2) 378 32.0 399 31.9
 Obese (≥30 kg/m2) 495 41.9 523 41.8
Income
 $20,000 or more 496 45.4 566 45.2
 Less than $20,000 596 54.6 686 54.8
Education
 Less than HS 226 18.7 234 18.7
 HS graduate 405 33.4 418 34.6
 Some college or college graduate 580 47.9 600 47.9
Employment
 Employed 455 37.9 469 37.5
 Self-employed 133 11.1 143 11.4
 Homemaker 137 11.4 144 11.5
 Student 90 7.5 92 7.3
 Retired 63 5.3 66 5.3
 Unable to work 131 10.9 136 10.9
 Unemployed for 1 year or more 81 6.8 84 6.7
 Unemployed for less than 1 year 72 6.0 78 6.2
 Don’t know/not sure 38 3.2 40 3.2
Marital status
 Married 333 27.3 340 27.2
 Divorced 83 6.8 84 6.7
 Widowed 34 2.8 37 3
 Separated 120 9.9 123 9.8
 Never married 537 44.1 553 44.2
 A member of an unmarried couple living together 111 9.1 115 9.2
Ethnicity
 Hispanic 563 53.9 678 54.1
 Black 372 35.6 433 34.6
 Other 110 10.5 141 11.3
Mean SD Mean SD
Age 38.8 12.5 38.8 12.5
NEWS walkability enablers (range = 1–4) 2.7 0.5 2.7 0.5
NEWS walkability barriers (range = 1–4) 2.6 0.6 2.6 0.6
NEWS neighborhood satisfaction (range = 1–5) 3.4 0.8 3.4 0.8
Physical HR-QoL (0–100) 46.0 9.7 45.8 9.5
Mental HR-QoL (0–100) 48.7 11.3 48.9 11.1

NEWS Neighborhood Environmental Walkability Survey, HR-QoL health-related quality of life

Table 3.

Spearman correlation of NEWS subscale and HR-QoL

Mental HR-QoL Physical HR-QoL
NEWS walkability enablers 0.180* 0.086*
NEWS walkability barriers −0.143* −0.151*
NEWS neighborhood satisfaction 0.218* 0.098*

*p-value < 0.05

Regression analysis

Using separate GEE models with multiple imputations, mental and physical HR- QoL scores were regressed on NEWS subscales adjusting for covariates (Table 4). Results (with multiple imputations) showed mental HR-QoL was negatively associated with barriers of walkability (β ± SE = − 1.63 ± 0.55, p = 0.003) but positively associated with neighborhood satisfaction (β ± SE = 1.55 ± 0.66, p = 0.02), after adjusting for covariates. Physical HR-QoL was associated with only barriers of walkability (β ± SE = − 1.13 ± 0.57, p < 0.05). Sensitivity analyses showed the regression results were similar in imputed vs. non-imputed data (data not shown).

Table 4.

GEE model results for mental and physical health-related quality of life (HR-QoL)

Outcome variable
Mental HR-QoL Physical HR-QoL
With MI With MI
β SE p β SE p
NEWS walkability barriers −1.63 0.55 0.003 −1.13 0.57 0.05
NEWS walkability enablers 1.55 0.88 0.08 0.71 0.71 0.32
NEWS neighborhood satisfaction 1.55 0.655 0.02 0.24 0.51 0.64

Adjusted for age, BMI (underweight and normal < 25; overweight 25–29.9; obese > = 30), gender (male or female), income ($20,000 or more or less than $20,000), education (less than HS; HS graduate; some college or college graduate; employed; self-employed; homemaker; student; retired; unable to work; unemployed for 1 year or more; unemployed for less than 1 year; don’t know/not sure), marital status (married; divorced; widowed; separated; never married; a member of an unmarried couple living together), and ethnicity (Latino, Black, other)

SE standard error, MI multiple imputation

Discussion

This is one of the first studies to examine HR-QoL in relation to perceived neighborhood environment. In particular, our study adds to the emerging literature with a specific focus on Latino and Black residents in lower income neighborhoods. We found that high perceived barriers of walkability was associated with both lower physical and mental HR-QoL. In addition, a global scale of neighborhood satisfaction was positively related to mental, but not physical, HR-QoL.

Despite the ubiquity of NEWS and SF-12 (or SF-36) in the literature, there have been surprisingly few studies investigating the relationship between these two sets of measures. In prior research, an Australian study on people aged 75 or greater showed a positive correlation between the original NEWS subscales and physical and mental HR-QoL using SF-36, but the study did not consider covariates [26]. Another study conducted in adults of different ages in 6 urban centers of China used multivariable models to examine each original NEWS subscale in relation to mental and physical HR-QoL (measured by SF-12) and found that higher perceived land use diversity, safety and esthetics were associated with higher physical and mental well-being [27]. Our study is the first to examine the association of the NEWS in relation to HR-QoL in the United States, especially among minority populations.

HR-QoL is an important public health outcome given the growing body of literature showing it to be an independent predictor of diverse clinical outcomes [4654]. Physical HR-QoL as measured by SF-12 or SF-36 has been associated with the mortality of patients with hemodialysis [48], after coronary artery bypass graft surgery [54], and within 48 hours of admission to the ICU [52], as well as the development of obesity [55], diabetes [56], cardiovascular disease [57], and several cancers (e.g., oral [53] and advanced breast cancer [51]). In addition, patients with better mental HR-QoL scores were shown to be more likely to improve after lumbar fusion [49]. Mental HR-QoL has also been associated with mental health outcomes, such as anxiety [58], depression [59], and relapse of schizophrenia at 24-month follow up [47]. Therefore, HR-QoL can be considered a proxy of global well-being but more research is warranted on how to intervene on HR-QoL, including potentially via environmental strategies such as improving neighborhood walkability.

The items for the barriers for walkability subscale were a function of perceived traffic and crime. Our results are corroborated by prior research linking these environmental factors to health. For example, a previous study on children found that children who were exposed to high traffic volumes had significantly higher odds of asthma [60]. In addition, exposure to traffic congestion has been associated with on-the-job elevations of urinary catecholamines (a marker of stress) among bus drivers [61]. Elsewhere, the safer or less crime individuals feel in their neighborhood, the better mental health outcomes they have such as lower distress [62, 63] and better physical health [64]. Collectively, these and our findings point to the potential importance of transportation and neighborhood design in urban areas where the alleviation of traffic [65] and improvement in perceived safety [62, 6668] may contribute to population well-being, which in turn may have a downstream impact on reducing health disparities.

It was notable that neighborhood satisfaction was significantly associated with mental but not physical HR-QoL. Neighborhood satisfaction has previously been found to be a predictor of mental health outcomes [69]. Neighborhood satisfaction has also been studied as a significant mediator between the quality of green space in a neighborhood and general health outcomes [18]. It is possible that mental HR-QoL serves as a mediator between neighborhood satisfaction and physical HR-QoL over time; future longitudinal studies are needed to further examine this hypothesis.

This study highlights a caveat for the generalizability of well published psychosocial scales such as NEWS without further psychometric testing. NEWS has been widely used in research globally [35, 7072]. However, items in NEWS may be context-specific and may need to be customized to specific study populations [26, 70]. An important contribution of this paper is the application of EFA to reconstruct the factors, an approach that could be considered for future studies using surveys of perceptions of the environment.

This study and its findings add to the literature on urban livability. Urban livability is a multifaceted concept that incorporates diverse aspects of the neighborhood environment, including physical, biological and socioeconomic characteristics and their interactions [73, 74]. HR-QoL in this literature may be conceptualized, for instance, in terms of the number or density of health-related facilities and services [73]. Our study shows that the lived experience of community residents is an additional important dimension to consider. As such, this study has important research and policy implications that require the convergence of public health, urban planning and design, and other fields in a more holistic approach to urban livability.

Several limitations are inherent in this study. The study was cross-sectional; thus, no causality could be inferred. The target population included mostly residents of low-income, minority neighborhoods, limiting the generalizability of our findings to all of NYC or elsewhere. In addition, we cannot rule out the possibility of selection bias among those who chose to participate in the survey study, thus study findings may not be representative of the entirety of the underlying communities. The population focus, however, was also a strength of the study given the heightened health disparities experienced by Latino and Black communities in the United States.

In conclusion, perceptions of the built environment appear to be important factors in the HR-QoL of low-income residents in NYC. Further research is warranted to investigate the pathways by which such perceptions influence HR-QoL, including potential stress mechanisms. The current paper adds to the literature on urban health and urban planning and shows the potential value of incorporating community members’ experiences of the built environment and robust HR-QoL measures in studies of population well-being and environmental livability.

Acknowledgements

None.

Authors’ contributions

JZ led the drafting and analysis of the paper. HK and KW assisted with data analysis. TH provided scientific direction of the study and overall conceptualization of the paper. All authors provided critical review of and edits to the manuscript. The author(s) read and approved the final manuscript.

Funding

This study was supported by the National Cancer Institute (R01CA206877), New York State Health Foundation (#16–04236), Robert Wood Johnson Foundation (E4A Program Grant #76473), and Bryant Park Corporation. Additional funding support for TH, KW, and LT was provided by a grant U48DP006396 from the Centers for Disease Control and Prevention.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to the fact that this is an ongoing study but are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

The PARCS Study was approved by the City University of New York IRB (#2016–0248) and participants provided written consent prior to study enrollment. Informed consent was obtained from all subjects and/or their legal guardian(s). All methods were performed in accordance with the relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

None.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Stokols D. Establishing and maintaining healthy environments: toward a social ecology of health promotion. Am Psychol. 1992;47(1):6. doi: 10.1037/0003-066X.47.1.6. [DOI] [PubMed] [Google Scholar]
  • 2.Ward Thompson C, Aspinall PA. Natural environments and their impact on activity, health, and quality of life. Appl Psychol. 2011;3(3):230–260. [Google Scholar]
  • 3.Parra DC, Gomez LF, Sarmiento OL, Buchner D, Brownson R, Schimd T, et al. Perceived and objective neighborhood environment attributes and health related quality of life among the elderly in Bogota, Colombia. Soc Sci Med. 2010;70(7):1070–1076. doi: 10.1016/j.socscimed.2009.12.024. [DOI] [PubMed] [Google Scholar]
  • 4.Evans GW. The built environment and mental health. J Urban Health. 2003;80(4):536–555. doi: 10.1093/jurban/jtg063. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Jackson RJ, Dannenberg AL, Frumkin H. Health and the built environment: 10 years after. Am J Public Health. 2013;103(9):1542–1544. doi: 10.2105/AJPH.2013.301482. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Rachele JN, Sugiyama T, Davies S, Loh VH, Turrell G, Carver A, et al. Neighbourhood built environment and physical function among mid-to-older aged adults: a systematic review. Health Place. 2019;58:102137. doi: 10.1016/j.healthplace.2019.05.015. [DOI] [PubMed] [Google Scholar]
  • 7.Fleming R, Goodenough B, Low L-F, Chenoweth L, Brodaty H. The relationship between the quality of the built environment and the quality of life of people with dementia in residential care. Dementia. 2016;15(4):663–680. doi: 10.1177/1471301214532460. [DOI] [PubMed] [Google Scholar]
  • 8.Moore T, Kesten J, López-López JA, Ijaz S, McAleenan A, Richards A, et al. The effects of changes to the built environment on the mental health and well-being of adults: systematic review. Health Place. 2018;53:237–257. doi: 10.1016/j.healthplace.2018.07.012. [DOI] [PubMed] [Google Scholar]
  • 9.Schulz M, Romppel M, Grande G. Built environment and health: a systematic review of studies in Germany. J Public Health. 2018;40(1):8–15. doi: 10.1093/pubmed/fdw141. [DOI] [PubMed] [Google Scholar]
  • 10.Patel O, Shahulhameed S, Shivashankar R, Tayyab M, Rahman A, Prabhakaran D, et al. Association between full service and fast food restaurant density, dietary intake and overweight/obesity among adults in Delhi, India. BMC Public Health. 2018;18(1):1–11. doi: 10.1186/s12889-017-4598-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Nicosia N, Datar A. Neighborhood environments and physical activity: a longitudinal study of adolescents in a natural experiment. Am J Prev Med. 2018;54(5):671–678. doi: 10.1016/j.amepre.2018.01.030. [DOI] [PubMed] [Google Scholar]
  • 12.Hruby A, Manson JE, Qi L, Malik VS, Rimm EB, Sun Q, et al. Determinants and consequences of obesity. Am J Public Health. 2016;106(9):1656–1662. doi: 10.2105/AJPH.2016.303326. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Wray AJD, Minaker LM. Is cancer prevention influenced by the built environment? A multidisciplinary scoping review. Cancer. 2019;125(19):3299–3311. doi: 10.1002/cncr.32376. [DOI] [PubMed] [Google Scholar]
  • 14.Malambo P, Kengne AP, De Villiers A, Lambert EV, Puoane T. Built environment, selected risk factors and major cardiovascular disease outcomes: a systematic review. PLoS One. 2016;11(11):e0166846. doi: 10.1371/journal.pone.0166846. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Amuda AT, Berkowitz SA. Diabetes and the built environment: evidence and policies. Curr Diabetes Rep. 2019;19(7):1–8. doi: 10.1007/s11892-019-1162-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Zhao L, Shen Z, Zhang Y, Sheng F. Study on the impact of the objective characteristics and subjective perception of the built environment on residents’ physical activities in Fuzhou, China. Sustainability. 2019;12(1):329. doi: 10.3390/su12010329. [DOI] [Google Scholar]
  • 17.Xiao Y, Miao S, Sarkar C, Geng H, Lu Y. Exploring the impacts of housing condition on migrants’ mental health in nanxiang, shanghai: a structural equation modelling approach. Int J Environ Res Public Health. 2018;15(2):225. doi: 10.3390/ijerph15020225. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.De Jong K, Albin M, Skärbäck E, Grahn P, Björk J. Perceived green qualities were associated with neighborhood satisfaction, physical activity, and general health: results from a cross-sectional study in suburban and rural Scania, southern Sweden. Health Place. 2012;18(6):1374–1380. doi: 10.1016/j.healthplace.2012.07.001. [DOI] [PubMed] [Google Scholar]
  • 19.Powell-Wiley TM, Ayers CR, De Lemos JA, Lakoski SG, Vega GL, Grundy S, et al. Relationship between perceptions about neighborhood environment and prevalent obesity: data from the Dallas heart study. Obesity. 2013;21(1):E14–E21. doi: 10.1002/oby.20012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Van Dyck D, Teychenne M, McNaughton SA, De Bourdeaudhuij I, Salmon J. Relationship of the perceived social and physical environment with mental health-related quality of life in middle-aged and older adults: mediating effects of physical activity. PLoS One. 2015;10(3):e0120475. doi: 10.1371/journal.pone.0120475. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Roh S, Jang Y, Chiriboga DA, Kwag KH, Cho S, Bernstein K. Perceived neighborhood environment affecting physical and mental health: a study with Korean American older adults in New York City. J Immigr Minor Health. 2011;13(6):1005. doi: 10.1007/s10903-011-9492-3. [DOI] [PubMed] [Google Scholar]
  • 22.Andrews MR, Ceasar J, Tamura K, Langerman SD, Mitchell VM, Collins BS, et al. Neighborhood environment perceptions associate with depression levels and cardiovascular risk among middle-aged and older adults: data from the Washington, DC cardiovascular health and needs assessment. Aging Ment Health. 2021;25(11):2078–2089. doi: 10.1080/13607863.2020.1793898. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Haraldstad K, Wahl A, Andenæs R, Andersen JR, Andersen MH, Beisland E, et al. A systematic review of quality of life research in medicine and health sciences. Qual Life Res. 2019;28(10):2641–2650. doi: 10.1007/s11136-019-02214-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Brennan P, McKay J, Moore L, Zaridze D, Mukeria A, Szeszenia-Dabrowska N, et al. Obesity and cancer: Mendelian randomization approach utilizing the FTO genotype. Int J Epidemiol. 2009;38(4):971–975. doi: 10.1093/ije/dyp162. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Sallis JF, Saelens BE, Frank LD, Conway TL, Slymen DJ, Cain KL, et al. Neighborhood built environment and income: examining multiple health outcomes. Soc Sci Med. 2009;68(7):1285–1293. doi: 10.1016/j.socscimed.2009.01.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Byles JE, Mackenzie L, Redman S, Parkinson L, Leigh L, Curryer C. Supporting housing and neighbourhoods for healthy ageing: findings from the H ousing and I ndependent L iving S tudy (HAIL) Australas J Ageing. 2014;33(1):29–35. doi: 10.1111/j.1741-6612.2012.00646.x. [DOI] [PubMed] [Google Scholar]
  • 27.Gao M, Ahern J, Koshland CP. Perceived built environment and health-related quality of life in four types of neighborhoods in Xi’an, China. Health Place. 2016;39:110–115. doi: 10.1016/j.healthplace.2016.03.008. [DOI] [PubMed] [Google Scholar]
  • 28.Sarmiento OL, Schmid TL, Parra DC, Díaz-del-Castillo A, Gómez LF, Pratt M, et al. Quality of life, physical activity, and built environment characteristics among colombian adults. J Phys Act Health. 2010;7(s2):S181–SS95. doi: 10.1123/jpah.7.s2.s181. [DOI] [PubMed] [Google Scholar]
  • 29.Huang TT, Wyka KE, Ferris EB, Gardner J, Evenson KR, Tripathi D, et al. The physical activity and redesigned community spaces (PARCS) study: protocol of a natural experiment to investigate the impact of citywide park redesign and renovation. BMC Public Health. 2016;16(1):1–12. doi: 10.1186/s12889-016-3822-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Rissanen R, Berg H-Y, Hasselberg M. Quality of life following road traffic injury: a systematic literature review. Accid Anal Prev. 2017;108:308–320. doi: 10.1016/j.aap.2017.09.013. [DOI] [PubMed] [Google Scholar]
  • 31.Schofield MJ, Mishra G. Validity of the SF-12 compared with the SF-36 health survey in pilot studies of the Australian longitudinal study on Women's health. J Health Psychol. 1998;3(2):259–271. doi: 10.1177/135910539800300209. [DOI] [PubMed] [Google Scholar]
  • 32.Ware JE, Jr, Kosinski M, Keller SD. A 12-item short-form health survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34:220–233. doi: 10.1097/00005650-199603000-00003. [DOI] [PubMed] [Google Scholar]
  • 33.Cerin E, Saelens BE, Sallis JF, Frank LD. Neighborhood environment walkability scale: validity and development of a short form. Med Sci Sports Exerc. 2006;38(9):1682. doi: 10.1249/01.mss.0000227639.83607.4d. [DOI] [PubMed] [Google Scholar]
  • 34.Cerin E, Conway TL, Saelens BE, Frank LD, Sallis JF. Cross-validation of the factorial structure of the neighborhood environment walkability scale (NEWS) and its abbreviated form (NEWS-A) Int J Behav Nutr Phys Act. 2009;6(1):1–10. doi: 10.1186/1479-5868-6-32. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Cerin E, Conway TL, Cain KL, Kerr J, De Bourdeaudhuij I, Owen N, et al. Sharing good NEWS across the world: developing comparable scores across 12 countries for the neighborhood environment walkability scale (NEWS) BMC Public Health. 2013;13(1):1–14. doi: 10.1186/1471-2458-13-309. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Watkins MW. Exploratory factor analysis: a guide to best practice. J Black Psychol. 2018;44(3):219–246. doi: 10.1177/0095798418771807. [DOI] [Google Scholar]
  • 37.Stevens JP. Applied multivariate statistics for the social sciences. 5th ed. New York: Routledge; 2012.
  • 38.Cronbach LJ. Coefficient alpha and the internal structure of tests. Psychometrika. 1951;16(3):297–334. doi: 10.1007/BF02310555. [DOI] [Google Scholar]
  • 39.Van Buuren S, Groothuis-Oudshoorn K. Mice: multivariate imputation by chained equations in R. J Stat Softw. 2011;45(1):1–67. [Google Scholar]
  • 40.Liang K-Y, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika. 1986;73(1):13–22. doi: 10.1093/biomet/73.1.13. [DOI] [Google Scholar]
  • 41.Alin A. Multicollinearity. Wiley Interdiscip Rev. 2010;2(3):370–374. doi: 10.1002/wics.84. [DOI] [Google Scholar]
  • 42.Chambers JM. Software for data analysis: programming with R. New York: Springer; 2008. [Google Scholar]
  • 43.Revelle W, Revelle MW. Package ‘psych’. The comprehensive R archive network. 2015;337:338.
  • 44.Azur MJ, Stuart EA, Frangakis C, Leaf PJ. Multiple imputation by chained equations: what is it and how does it work? Int J Methods Psychiatr Res. 2011;20(1):40–49. doi: 10.1002/mpr.329. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Højsgaard S, Halekoh U, Yan J. The R package geepack for generalized estimating equations. J Stat Softw. 2006;15:1–11. [Google Scholar]
  • 46.Fang FM, Liu YT, Tang Y, Wang CJ, Ko SF. Quality of life as a survival predictor for patients with advanced head and neck carcinoma treated with radiotherapy. Cancer. 2004;100(2):425–432. doi: 10.1002/cncr.20010. [DOI] [PubMed] [Google Scholar]
  • 47.Boyer L, Millier A, Perthame E, Aballea S, Auquier P, Toumi M. Quality of life is predictive of relapse in schizophrenia. BMC Psychiatry. 2013;13(1):1–8. doi: 10.1186/1471-244X-13-15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Mapes DL, Lopes AA, Satayathum S, Mccullough KP, Goodkin DA, Locatelli F, et al. Health-related quality of life as a predictor of mortality and hospitalization: the Dialysis outcomes and practice patterns study (DOPPS) Kidney Int. 2003;64(1):339–349. doi: 10.1046/j.1523-1755.2003.00072.x. [DOI] [PubMed] [Google Scholar]
  • 49.Carreon LY, Glassman SD, Djurasovic M, Dimar JR, Johnson JR, Puno RM, et al. Are preoperative health-related quality of life scores predictive of clinical outcomes after lumbar fusion? Spine. 2009;34(7):725–730. doi: 10.1097/BRS.0b013e318198cae4. [DOI] [PubMed] [Google Scholar]
  • 50.Seid M, Varni JW, Segall D, Kurtin PS. Health-related quality of life as a predictor of pediatric healthcare costs: a two-year prospective cohort analysis. Health Qual Life Outcomes. 2004;2(1):1–10. doi: 10.1186/1477-7525-2-48. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Lee C, Stockler M, Coates A, Gebski V, Lord S, Simes R. Self-reported health-related quality of life is an independent predictor of chemotherapy treatment benefit and toxicity in women with advanced breast cancer. Br J Cancer. 2010;102(9):1341–1347. doi: 10.1038/sj.bjc.6605649. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Hofhuis JG, Spronk PE, Van Stel HF, Schrijvers AJ, Bakker J. Quality of life before intensive care unit admission is a predictor of survival. Crit Care. 2007;11(4):1–7. doi: 10.1186/cc5970. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Tarsitano A, Pizzigallo A, Ballone E, Marchetti C. Health-related quality of life as a survival predictor for patients with oral cancer: is quality of life associated with long-term overall survival? Oral Surg Oral Med Oral Pathol Oral Radiol. 2012;114(6):756–763. doi: 10.1016/j.oooo.2012.06.022. [DOI] [PubMed] [Google Scholar]
  • 54.Rumsfeld JS, MaWhinney S, McCarthy M, Jr, Shroyer ALW, VillaNueva CB, O'Brien M, et al. Health-related quality of life as a predictor of mortality following coronary artery bypass graft surgery. JAMA. 1999;281(14):1298–1303. doi: 10.1001/jama.281.14.1298. [DOI] [PubMed] [Google Scholar]
  • 55.Kolotkin R, Meter K, Williams G. Quality of life and obesity. Obes Rev. 2001;2(4):219–229. doi: 10.1046/j.1467-789X.2001.00040.x. [DOI] [PubMed] [Google Scholar]
  • 56.Rubin RR, Peyrot M. Quality of life and diabetes. Diabetes Metab Res Rev. 1999;15(3):205–218. doi: 10.1002/(SICI)1520-7560(199905/06)15:3&#x0003c;205::AID-DMRR29&#x0003e;3.0.CO;2-O. [DOI] [PubMed] [Google Scholar]
  • 57.Li C, Ford ES, Mokdad AH, Balluz LS, Brown DW, Giles WH. Clustering of cardiovascular disease risk factors and health-related quality of life among US adults. Value Health. 2008;11(4):689–699. doi: 10.1111/j.1524-4733.2007.00307.x. [DOI] [PubMed] [Google Scholar]
  • 58.Conn WS, Taylor SG, Wiman P. Anxiety, depression, quality of life, and self-care among survivors of myocardial infarction. Issues Ment Health Nurs. 1991;12(4):321–331. doi: 10.3109/01612849109010014. [DOI] [PubMed] [Google Scholar]
  • 59.Rothrock NE, Lutgendorf SK, Kreder KJ. Coping strategies in patients with interstitial cystitis: relationships with quality of life and depression. J Urol. 2003;169(1):233–236. doi: 10.1016/S0022-5347(05)64075-X. [DOI] [PubMed] [Google Scholar]
  • 60.Feng X, Astell-Burt T. Is neighborhood green space protective against associations between child asthma, neighborhood traffic volume and perceived lack of area safety? Multilevel analysis of 4447 Australian children. Int J Environ Res Public Health. 2017;14(5):543. doi: 10.3390/ijerph14050543. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Evans GW, Carrère S. Traffic congestion, perceived control, and psychophysiological stress among urban bus drivers. J Appl Psychol. 1991;76(5):658. doi: 10.1037/0021-9010.76.5.658. [DOI] [PubMed] [Google Scholar]
  • 62.White M, Kasl SV, Zahner GE, Will JC. Perceived crime in the neighborhood and mental health of women and children. Environ Behav. 1987;19(5):588–613. doi: 10.1177/0013916587195003. [DOI] [Google Scholar]
  • 63.Booth J, Ayers SL, Marsiglia FF. Perceived neighborhood safety and psychological distress: exploring protective factors. J Soc Soc Welfare. 2012;39:137. [Google Scholar]
  • 64.Lovasi GS, Goh CE, Pearson AL, Breetzke G. The independent associations of recorded crime and perceived safety with physical health in a nationally representative cross-sectional survey of men and women in New Zealand. BMJ Open. 2014;4(3):e004058. doi: 10.1136/bmjopen-2013-004058. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Habib N, Mohammed K. Evaluation of planning options to alleviate traffic congestion and resulting air pollution in Dhaka City. 2002. [Google Scholar]
  • 66.Marzbali MH, Abdullah A, Tilaki MJM. The effectiveness of interventions in the built environment for improving health by addressing fear of crime. Int J Law Crime Justice. 2016;45:120–140. doi: 10.1016/j.ijlcj.2015.12.002. [DOI] [Google Scholar]
  • 67.Tamayo A, Karter AJ, Mujahid MS, Warton EM, Moffet HH, Adler N, et al. Associations of perceived neighborhood safety and crime with cardiometabolic risk factors among a population with type 2 diabetes. Health Place. 2016;39:116–121. doi: 10.1016/j.healthplace.2016.03.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Won J, Lee C, Forjuoh SN, Ory MG. Neighborhood safety factors associated with older adults' health-related outcomes: a systematic literature review. Soc Sci Med. 2016;165:177–186. doi: 10.1016/j.socscimed.2016.07.024. [DOI] [PubMed] [Google Scholar]
  • 69.Adams RE. Is happiness a home in the suburbs?: the influence of urban versus suburban neighborhoods on psychological health. J Community Psychol. 1992;20(4):353–372. doi: 10.1002/1520-6629(199210)20:4&#x0003c;353::AID-JCOP2290200409&#x0003e;3.0.CO;2-Z. [DOI] [Google Scholar]
  • 70.Van Dyck D, Cerin E, Conway TL, De Bourdeaudhuij I, Owen N, Kerr J, et al. Perceived neighborhood environmental attributes associated with adults’ transport-related walking and cycling: findings from the USA, Australia and Belgium. Int J Behav Nutr Phys Act. 2012;9(1):1–14. doi: 10.1186/1479-5868-9-70. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Frank LD, Sallis JF, Saelens BE, Leary L, Cain K, Conway TL, et al. The development of a walkability index: application to the neighborhood quality of life study. Br J Sports Med. 2010;44(13):924–933. doi: 10.1136/bjsm.2009.058701. [DOI] [PubMed] [Google Scholar]
  • 72.Cerin E, Sit CH, Cheung M-c, Ho S-y, L-cJ L, Chan W-m. Reliable and valid NEWS for Chinese seniors: measuring perceived neighborhood attributes related to walking. Int J Behav Nutr Phys Act. 2010;7(1):1–14. doi: 10.1186/1479-5868-7-84. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Kashef M. Urban livability across disciplinary and professional boundaries. Front Architect Res. 2016;5(2):239–253. doi: 10.1016/j.foar.2016.03.003. [DOI] [Google Scholar]
  • 74.Pacione M. Urban liveability: a review. Urban Geogr. 1990;11(1):1–30. doi: 10.2747/0272-3638.11.1.1. [DOI] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets generated and/or analyzed during the current study are not publicly available due to the fact that this is an ongoing study but are available from the corresponding author on reasonable request.


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