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The association of sleep with neighborhood physical and social environment.

ABSTRACT: OBJECTIVES:While sleep is critical for good health, it remains a major public health concern because millions of individuals do not obtain a sufficient amount of sleep at night to reap proper health benefits. When examining factors that contribute to deleterious sleep outcomes, few researchers to date have examined the physical and social environments together. STUDY DESIGN:This article is an analytical essay. METHODS:In the present study, 18 empirical articles on environmental factors that promote sleep loss were analyzed and synthesized according to the study type, exposure measures, outcome measures, methodology, and findings. RESULTS:Data from the literature demonstrate that neighborhood airplane, roadway, and rail noise pollution; air pollution from ozone and particulate matter (PM10); and, to some extent, ambient light, interfere with residents' ability to fall asleep, stay asleep, and wake feeling rested. There is also some evidence that neighborhood green space, walkability, safety, built environment, and other social characteristics, such as neighborhood disorder and ability to trust one's neighbors, dramatically impact residents' sleep. CONCLUSIONS:This article provides a critical assessment of the multidimensional relationship between neighborhood physical and social characteristics and sleep, addresses major methodological concerns that limit current empirical knowledge, and suggests steps to shape future research.

SUBMITTER: Hunter JC

PROVIDER: S-EPMC6269089 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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The association of sleep with neighborhood physical and social environment.

Hunter Jaimie C JC Hayden Kathleen M KM

Public health 20180721

<h4>Objectives</h4>While sleep is critical for good health, it remains a major public health concern because millions of individuals do not obtain a sufficient amount of sleep at night to reap proper health benefits. When examining factors that contribute to deleterious sleep outcomes, few researchers to date have examined the physical and social environments together.<h4>Study design</h4>This article is an analytical essay.<h4>Methods</h4>In the present study, 18 empirical articles on environme ...[more]

PMID: 30036811

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Project description:BACKGROUND:Investigating the association of the neighborhood social environment on physical activity is complex. A systematic scoping review was performed to (1) provide an inventory of studies assessing the influence of the neighborhood social environment on physical activity since 2006; (2) describe methodologies employed; and (3) formulate recommendations for the field. METHODS:Two databases were searched using terms related to 'physical activity,' 'neighborhood,' and 'social environment' in January 2017. Eligibility criteria included: 1) physical activity as an outcome; 2) neighborhood social environment as a predictor; 3) healthy population (without diagnosed clinical condition or special population); 4) observational or experimental design. Of 1352 studies identified, 181 were included. Textual data relevant to the social environment measurement and analysis were extracted from each article into qualitative software (MAXQDA) and coded to identify social environmental constructs, measurement methods, level of measurement (individual vs. aggregated to neighborhood), and whether authors explicitly recognized the construct as the social environment. The following measures were generated for each construct: number of unique measurements; % of times measured at an aggregate level; % of times authors referred to the construct as the social environment. Social environmental constructs were then grouped into larger descriptive dimensions. RESULTS/FINDINGS:Fifty-nine social environmental constructs were identified and grouped into 9 dimensions: Crime & Safety (n?=?133 studies; included in 73% of studies); Economic & Social Disadvantage (n?=?55, 33%); Social Cohesion & Capital (n?=?47, 26%); Social Relationships (n?=?22, 12%); Social Environment (n?=?16, 9%); Disorder & Incivilities (n?=?15, 8%); Sense of Place/Belonging (n?=?8, 4%); Discrimination/Segregation (n?=?3, 2%); Civic Participation & Engagement (n?=?2, 1%). Across all articles, the social environment was measured using 176 different methods, was measured at an aggregate-level 38% of the time, and referred to as the social environment 23% of the time. CONCLUSIONS:Inconsistent terminology, definitions, and measurement of the social environment and the lack of explicit language identifying constructs as the social environment make it challenging to compare results across studies and draw conclusions. Improvements are needed to increase our understanding of social environmental correlates and/or determinants of physical activity and facilitate cross-disciplinary conversations necessary to effectively intervene to promote physical activity. TRIAL REGISTRATION:PROSPERO CRD42017059580.

Project description:BackgroundRecent obesity prevention initiatives focus on healthy neighborhood design, but most research examines neighborhood food retail and physical activity (PA) environments in isolation. We estimated joint, interactive, and cumulative impacts of neighborhood food retail and PA environment characteristics on body mass index (BMI) throughout early adulthood.Methods and findingsWe used cohort data from the Coronary Artery Risk Development in Young Adults (CARDIA) Study [n=4,092; Year 7 (24-42 years, 1992-1993) followed over 5 exams through Year 25 (2010-2011); 12,921 person-exam observations], with linked time-varying geographic information system-derived neighborhood environment measures. Using regression with fixed effects for individuals, we modeled time-lagged BMI as a function of food and PA resource density (counts per population) and neighborhood development intensity (a composite density score). We controlled for neighborhood poverty, individual-level sociodemographics, and BMI in the prior exam; and included significant interactions between neighborhood measures and by sex. Using model coefficients, we simulated BMI reductions in response to single and combined neighborhood improvements. Simulated increase in supermarket density (from 25(th) to 75(th) percentile) predicted inter-exam reduction in BMI of 0.09 kg/m(2) [estimate (95% CI): -0.09 (-0.16, -0.02)]. Increasing commercial PA facility density predicted BMI reductions up to 0.22 kg/m(2) in men, with variation across other neighborhood features [estimate (95% CI) range: -0.14 (-0.29, 0.01) to -0.22 (-0.37, -0.08)]. Simultaneous increases in supermarket and commercial PA facility density predicted inter-exam BMI reductions up to 0.31 kg/m(2) in men [estimate (95% CI) range: -0.23 (-0.39, -0.06) to -0.31 (-0.47, -0.15)] but not women. Reduced fast food restaurant and convenience store density and increased public PA facility density and neighborhood development intensity did not predict reductions in BMI.ConclusionsFindings suggest that improvements in neighborhood food retail or PA environments may accumulate to reduce BMI, but some neighborhood changes may be less beneficial to women.

Project description:BackgroundLittle is known about the associations between perceived neighborhood social environment (PNSE) and depressive symptoms among African Americans. Furthermore, the role of physical activity (PA) as a mediator of this association has not been investigated. The two-fold objectives of this study, therefore, were (1) to examine the associations between PNSE and depressive symptoms among African Americans, and (2) to test the degree to which these associations were mediated by total PA.MethodsWe used baseline data from the Jackson Heart Study (JHS), a single-site, prospective, community-based study of African-American adults (n = 2209) recruited from Jackson, Mississippi. PNSE variables included scores for neighborhood violence (i.e., higher score = more violence), problems (higher score = more problems), and social cohesion (higher score = more cohesion). Depressive symptoms were measured by the 20-item Center for Epidemiological Studies Depression (CES-D) score. First, multilevel modeling, controlling for census tract clustering effects, was used to estimate associations between each PNSE variable and CES-D score, adjusting for covariates, including demographic, health-related, and population density. Second, validated, self-reported total PA, based on active living, sport, and home indices, was tested as the mediator. Multivariable linear regressions with bootstrap-generated 95% bias-corrected confidence intervals (BC CIs) were estimated to test for significant unstandardized indirect effects, controlling for all covariates.ResultsOur participants were 64.2% female with a mean age of 52.6 (SD = 12.2) and a mean CES-D score of 10.8 (SD = 8.1). In the fully-adjusted model, neighborhood violence and problems were positively related to depressive symptoms (B = 3.59, 95%CI = 0.93, 6.26, and B = 3.06, 95%CI = 1.19, 4.93, respectively). Neighborhood violence and problems were also indirectly related to depressive symptoms via total PA (B = 0.26, 95%BC CI = 0.05, 0.55; and B = 0.15, 95%BC CI = 0.02, 0.34, respectively). Social cohesion was neither directly nor indirectly related to depressive symptoms.ConclusionsWe found that higher levels of perceived neighborhood problems and violence were directly and positively associated with depressive symptoms. These associations may be explained in part by lower total PA levels. Future interventions to reduce depressive symptoms attributed to neighborhood features should consider emphasizing built environment features that facilitate PA increases in conjunction with community efforts to reduce neighborhood violence and problems.

Project description:Background: Few older adults achieve recommended physical activity levels. We conducted a "neighborhood environment-wide association study (NE-WAS)" of neighborhood influences on physical activity among older adults, analogous, in a genetic context, to a genome-wide association study.Methods: Physical Activity Scale for the Elderly (PASE) and sociodemographic data were collected via telephone survey of 3,497 residents of New York City aged 65 to 75 years. Using Geographic Information Systems, we created 337 variables describing each participant's residential neighborhood's built, social, and economic context. We used survey-weighted regression models adjusting for individual-level covariates to test for associations between each neighborhood variable and (i) total PASE score, (ii) gardening activity, (iii) walking, and (iv) housework (as a negative control). We also applied two "Big Data" analytic techniques, LASSO regression, and Random Forests, to algorithmically select neighborhood variables predictive of these four physical activity measures.Results: Of all 337 measures, proportion of residents living in extreme poverty was most strongly associated with total physical activity [-0.85; (95% confidence interval, -1.14 to -0.56) PASE units per 1% increase in proportion of residents living with household incomes less than half the federal poverty line]. Only neighborhood socioeconomic status and disorder measures were associated with total activity and gardening, whereas a broader range of measures was associated with walking. As expected, no neighborhood meaZsures were associated with housework after accounting for multiple comparisons.Conclusions: This systematic approach revealed patterns in the domains of neighborhood measures associated with physical activity.Impact: The NE-WAS approach appears to be a promising exploratory technique. Cancer Epidemiol Biomarkers Prev; 26(4); 495-504. ©2017 AACRSee all the articles in this CEBP Focus section, "Geospatial Approaches to Cancer Control and Population Sciences."

Project description:Quorum-sensing (QS) is a cell-cell communication system that controls gene expression in many bacterial species, mediated by diffusible signal molecules. While the intracellular regulatory mechanisms of QS are often well-understood, the functional roles of QS remain controversial. In particular, the use of multiple signals by many bacterial species poses a serious challenge to current functional theories. Here we address this challenge by showing that bacteria can use multiple QS signals to infer both their social (density) and physical (mass-transfer) environment. Analytical and evolutionary simulation models show that the detection of and response to complex social/physical contrasts requires multiple signals with distinct half-lives and combinatorial (non-additive) responses to signal concentrations. We test these predictions using the opportunistic pathogen Pseudomonas aeruginosa, and demonstrate significant differences in signal decay between its two primary signal molecules as well as diverse combinatorial responses to dual signal inputs. QS is associated with the control of secreted factors, and we show that secretome genes are preferentially controlled by synergistic M-bM-^@M-^XAND-gateM-bM-^@M-^Y responses to multiple signal inputs, ensuring the effective expression of secreted factors in high density and low mass-transfer environments. Our results support a novel functional hypothesis for the use of multiple signals and, more generally, show that bacteria are capable of combinatorial communication. The two primary signal molecules of P. aeruginosa are the homoserine lactones N-(3-oxododecanoyl)-L-homoserine lactone (3-oxo-C12-HSL) and N-butyryl-homoserine lactone (C4-HSL). Effects of the different signal molecules was assessed using a double QS synthase mutant of Pseudomonas aeruginosa PAO1 lasI/rhlI grown at 37M-BM-0C in 25 ml LB broth and 250 ml flasks with shaking at 200 r.p.m. in four treatments, each with a replicate: (a) no addition; (b) 3-oxo- C12-HSL; (c) C4-HSL; and (d) both 3-oxo-C12-HSL and C4-HSL.

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The association of sleep with neighborhood physical and social environment.

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The association of sleep with neighborhood physical and social environment.

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