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INTRODUCTION: Built and natural environments may provide opportunities for physical activity. However, studies are limited by primarily using residential addresses to define exposure and self-report to measure physical activity. We quantified associations between global positioning systems (GPS)-based activity space measures of environmental exposure and accelerometer-based physical activity. METHODS: Using a nationwide sample of working female adults (N = 354), we obtained seven days of GPS and accelerometry data. We created Daily Path Area activity spaces using GPS data and linked these activity spaces to spatial datasets on walkability (EPA Smart Location Database at the Census block group level) and greenness (satellite vegetation at 250 m resolution). We utilized generalized additive models to examine nonlinear associations between activity space exposures and accelerometer-derived physical activity outcomes adjusted for demographic characteristics, socioeconomic factors, and self-rated health. RESULTS: Higher activity space walkability was associated with higher levels of moderate-vigorous physical activity, and higher activity space greenness was associated with greater numbers of steps per week. No strong relationships were observed for sedentary behavior or light physical activity. Highest levels of moderate-vigorous physical activity were observed for participants with both high walkability and high greenness in their activity spaces. CONCLUSION: This study contributes evidence that higher levels of physical activity occur in environments with more dense, diverse, and well-connected built environments, and with higher amounts of vegetation. These data suggest that urban planners, landscape architects, and policy makers should implement and evaluate environmental interventions to encourage higher levels of physical activity.

While many studies suggest evidence for the health benefits of nature, there is currently no standardized method to measure time spent in nature or nature contact, nor agreement on how best to define nature contact in research. The purpose of this review is to summarize how nature contact has been measured in recent health research and provide insight into current metrics of exposure to nature at individual and population scales. The most common methods include surrounding greenness, questionnaires, and global positioning systems (GPS) tracking. Several national-level surveys exist, though these are limited by their cross-sectional design, often measuring only a single component of time spent in nature, and poor links to measures of health. In future research, exposure assessment combining the quantifying (e.g., time spent in nature and frequency of visits to nature) and qualifying (e.g., greenness by the normalized difference of vegetation index (NDVI) and ratings on perception by individuals) aspects of current methods and leveraging innovative methods (e.g., experience sampling methods, ecological momentary assessment) will provide a more comprehensive understanding of the health effects of nature exposure and inform health policy and urban planning.

Since Putnam introduced his constrict thesis in 2007, many researchers have established that ethnic diversity lowers perceptions of social cohesion, at least in the short term. The connection between ethnic diversity and social behavior, however, is less certain. In this paper we draw on social distance and social identity theories to empirically test if ethnic diversity encourages behaviors linked to social withdrawal. Using data from a longitudinal panel study of urban communities in Australia, we examine the influence of social distance on neighborhood ties, neighborly exchange, and civic engagement and assess if an individual’s social identity (ethnic or civic) strengthens or weakens these relationships. We find individuals that endorse an ethnic identity are more likely to engage in social withdrawal behaviors. Withdrawal is also more likely in neighborhoods where individuals distort the presence of minorities.

PurposeTo test the validity of the Ecological Video Identification of Physical Activity (EVIP) computer vision algorithms for automated video-based ecological assessment of physical activity in settings such as parks and schoolyards.MethodsTwenty-seven hours of video were collected from stationary overhead video cameras across 22 visits in nine sites capturing organized activities. Each person in the setting wore an accelerometer, and each second was classified as moderate-to-vigorous physical activity or sedentary/light activity. Data with 57,987 s were used to train and test computer vision algorithms for estimating the total number of people in the video and number of people active (in moderate-to-vigorous physical activity) each second. In the testing data set (38,658 s), video-based System for Observing Play and Recreation in Communities (SOPARC) observations were conducted every 5 min (130 observations). Concordance correlation coefficients (CCC) and mean absolute errors (MAE) assessed agreement between (1) EVIP and ground truth (people counts+accelerometry) and (2) SOPARC observation and ground truth. Site and scene-level correlates of error were investigated.ResultsAgreement between EVIP and ground truth was high for number of people in the scene (CCC = 0.88; MAE = 2.70) and moderate for number of people active (CCC = 0.55; MAE = 2.57). The EVIP error was uncorrelated with camera placement, presence of obstructions or shadows, and setting type. For both number in scene and number active, EVIP outperformed SOPARC observations in estimating ground truth values (CCC were larger by 0.11-0.12 and MAE smaller by 41%-48%).ConclusionsComputer vision algorithms are promising for automated assessment of setting-based physical activity. Such tools would require less manpower than human observation, produce more and potentially more accurate data, and allow for ongoing monitoring and feedback to inform interventions.

The recent popularity of models that capture the dynamic coevolution of both network structure and behavior has driven the need for summary indices to assess the adequacy of these models to reproduce dynamic properties of scientific or practical importance. Whereas there are several existing indices for assessing the ability of the model to reproduce network structure over time, to date there are few indices for assessing the ability of the model to reproduce individuals’ behavior patterns. Drawing on the widely used strategy of assessing model adequacy by comparing index values summarizing features of the observed data to the distribution of those index values on simulated data from the fitted model, we propose four goals that a researcher could reasonably expect of a joint structure/behavior model regarding how well it captures behavior and describe indices for assessing each of these. These reasonably simple and easily implemented indices can be used for assessing model adequacy with any dynamic network models jointly working with networks and behavior, including the stochastic actor-based models implemented within software packages such as RSien version 1.2-24. We demonstrate the use of our indices with an empirical example to show how they can be employed in practical settings, with an additional extension to modeling affiliation dynamics in two-mode networks. Key scripts are provided in the Supplemental Document (which can be found at http://smr.sagepub.com/supplemental/).

Few studies assess built environment correlates of active commuting in low-and-middle-income countries (LMICs), but the different context could yield distinct findings. Policies and investments to promote active commuting remain under-developed in LMICs like India, which grapples with traffic congestion, lack of activity-supportive infrastructure, poor enforcement of traffic rules and regulations, air pollution, and overcrowding. This cross-sectional study investigated associations between home neighborhood environment characteristics and active commuting in Chennai, India. Adults (N = 370, 47.2% female, mean age =37.9 years) were recruited from 155 wards in the metropolitan area of Chennai in southern India between January and June 2015. Participants self-reported their usual mode of commute to work, with responses recoded into three categories: (1) multi-modal or active commuting (walking and bicycling; n = 56); (2) public transit (n = 52); and (3) private transport (n = 111). Environmental attributes around participants' homes were assessed using the Neighborhood Environment Walkability Scale for India (NEWS-India). Associations between environmental characteristics and likelihood of active commuting and public transit use were modeled using logistic regression with private transport (driving alone or carpool) as the reference category, adjusting for age, gender, and household car ownership. Consistent with other international studies, participants living in neighborhoods with a mix of land uses and a transit stop within a 10-minute walk from home were more likely to use active commuting (both p < 0.01). Land-use mix was significantly associated with the use of public transit compared to private transport (adjusted odds ratio (aOR) =5.2, p = 0.002). Contrary to findings in high-income countries, the odds of active commuting were reduced with improved safety from crime (aOR =0.2, p = 0.003), aesthetics (aOR =0.2, p = 0.05), and street connectivity (aOR =0.2, p = 0.003). Different environmental attributes were associated with active commuting, suggesting that these relationships are complex and may distinctly differ from those in high-income countries. Unexpected inverse associations of perceived safety from crime and aesthetics with active commuting emphasize the need for high-quality epidemiologic studies with greater context specificity in the study of physical activity in LMICs. Findings have public health implications for India and suggest that caution should be taken when translating evidence across countries.

This paper explores the association between neighbourhood land use features and informal social control. More specifically, we examine the extent to which such features in combination with the socio-demographic context of the neighbourhood facilitate or impede collective efficacy and local civic actions. We achieve this through spatially integrating data from the census, topographic databases and a 2012 survey of 4132 residents from 148 neighbourhoods in Brisbane, Australia. The study creates a new classification of a neighbourhood’s physical environment by creating novel categories of land use features that depict social conduits, social holes and social wedges. Social conduits are features of the neighbourhood that facilitate interaction between individuals, social holes are land uses that create situations where there is no occupancy, and social wedges are features that carve up neighbourhoods. We find some evidence to suggest that residents’ reports of collective efficacy are higher in neighbourhoods with a greater density of social conduits. Density of social conduits is also positively associated with local civic action. However, in neighbourhoods with more greenspace, residents are less likely to engage in local civic actions.

STUDY OBJECTIVES: To examine associations between 24-hour rest-activity patterns and body mass index (BMI) among community-dwelling US adults. Rest-activity patterns provide a field method to study exposures related to circadian rhythms. METHODS: Adults (N = 578) wore an actigraph on their nondominant wrist for 7 days. Intradaily variability and interdaily stability (IS), M10 (most active 10-hours), L5 (least active 5-hours), and relative amplitude (RA) were derived using nonparametric rhythm analysis. Mesor, acrophase, and amplitude were calculated from log-transformed count data using the parametric cosinor approach. RESULTS: Participants were 80% female and mean (standard deviation) age was 52 (15) years. Participants with higher BMI had lower values for magnitude, RA, IS, total sleep time (TST), and sleep efficiency. In multivariable analyses, less robust 24-hour rest-activity patterns as represented by lower RA were consistently associated with higher BMI: comparing the bottom quintile (least robust) to the top quintile (most robust 24-hour rest-activity pattern) of RA, BMI was 3-kg/m2 higher (p = .02). Associations were similar in magnitude to an hour less of TST (1-kg/m2 higher BMI) or a 10% decrease in sleep efficiency (2-kg/m2 higher BMI), and independent of age, sex, race, education, and the duration of rest and/or activity. CONCLUSIONS: Lower RA, reflecting both higher night activity and lower daytime activity, was associated with higher BMI. Independent of the duration of rest or activity during the day or night, 24-hour rest, and activity patterns from actigraphy provide aggregated measures of activity that associate with BMI in community-dwelling adults.

Technological advances provide opportunities for automating direct observations of physical activity, which allow for continuous monitoring and feedback. This pilot study evaluated the initial validity of computer vision algorithms for ecological assessment of physical activity. The sample comprised 6630 seconds per camera (three cameras in total) of video capturing up to nine participants engaged in sitting, standing, walking, and jogging in an open outdoor space while wearing accelerometers. Computer vision algorithms were developed to assess the number and proportion of people in sedentary, light, moderate, and vigorous activity, and group-based metabolic equivalents of tasks (MET)-minutes. Means and standard deviations (SD) of bias/difference values, and intraclass correlation coefficients (ICC) assessed the criterion validity compared to accelerometry separately for each camera. The number and proportion of participants sedentary and in moderate-to-vigorous physical activity (MVPA) had small biases (within 20% of the criterion mean) and the ICCs were excellent (0.82-0.98). Total MET-minutes were slightly underestimated by 9.3-17.1% and the ICCs were good (0.68-0.79). The standard deviations of the bias estimates were moderate-to-large relative to the means. The computer vision algorithms appeared to have acceptable sample-level validity (i.e., across a sample of time intervals) and are promising for automated ecological assessment of activity in open outdoor settings, but further development and testing is needed before such tools can be used in a diverse range of settings.

Background: Physical inactivity is a risk factor for cancer that may be influenced by environmental factors. Indeed, dense and well-connected built environments and environments with natural vegetation may create opportunities for higher routine physical activity. However, studies have focused primarily on residential environments to define exposure and self-reported methods to estimate physical activity. This study explores the momentary association between minute-level global positioning systems (GPS)-based greenness exposure and time-matched objectively measured physical activity.Methods: Adult women were recruited from sites across the United States. Participants wore a GPS device and accelerometer on the hip for 7 days to assess location and physical activity at minute-level epochs. GPS records were linked to 250 m resolution satellite-based vegetation data and Census Block Group-level U.S. Environmental Protection Agency (EPA) Smart Location Database walkability data. Minute-level generalized additive mixed models were conducted to test for associations between GPS measures and accelerometer count data, accounting for repeated measures within participant and allowing for deviations from linearity using splines.Results: Among 360 adult women (mean age of 55.3 ± 10.2 years), we observed positive nonlinear relationships between physical activity and both greenness and walkability. In exploratory analyses, the relationships between environmental factors and physical activity were strongest among those who were white, had higher incomes, and who were middle-aged.Conclusions: Our results indicate that higher levels of physical activity occurred in areas with higher greenness and higher walkability.Impact: Findings suggest that planning and design policies should focus on these environments to optimize opportunities for physical activity. Cancer Epidemiol Biomarkers Prev; 26(4); 525-32. ©2017 AACRSee all the articles in this CEBP Focus section, Geospatial Approaches to Cancer Control and Population Sciences.

Rest-activity patterns provide an indication of circadian rhythmicity in the free-living setting. We aimed to describe the distributions of rest-activity patterns in a sample of adults and children across demographic variables. A sample of adults (N = 590) and children (N = 58) wore an actigraph on their nondominant wrist for 7 days and nights. We generated rest-activity patterns from cosinor analysis (MESOR, acrophase and magnitude) and nonparametric circadian rhythm analysis (IS: interdaily stability; IV: intradaily variability; L5: least active 5-hour period; M10: most active 10-hour period; and RA: relative amplitude). Demographic variables included age, sex, race, education, marital status, and income. Linear mixed-effects models were used to test for demographic differences in rest-activity patterns. Adolescents, compared to younger children, had (1) later M10 midpoints (β = 1.12 hours [95% CI: 0.43, 1.18] and lower M10 activity levels; (2) later L5 midpoints (β = 1.6 hours [95% CI: 0.9, 2.3]) and lower L5 activity levels; (3) less regular rest-activity patterns (lower IS and higher IV); and 4) lower magnitudes (β = -0.95 [95% CI: -1.28, -0.63]) and relative amplitudes (β = -0.1 [95% CI: -0.14, -0.06]). Mid-to-older adults, compared to younger adults (aged 18-29 years), had (1) earlier M10 midpoints (β = -1.0 hours [95% CI: -1.6, -0.4]; (2) earlier L5 midpoints (β = -0.7 hours [95% CI: -1.2, -0.2]); and (3) more regular rest-activity patterns (higher IS and lower IV). The magnitudes and relative amplitudes were similar across the adult age categories. Sex, race and education level rest-activity differences were also observed. Rest-activity patterns vary across the lifespan, and differ by race, sex and education. Understanding population variation in these patterns provides a foundation for further elucidating the health implications of rest-activity patterns across the lifespan.

Physical activity and time spent outdoors may be important non-pharmacological approaches to improve sleep quality and duration (or sleep patterns) but there is little empirical research evaluating the two simultaneously. The current study assesses the role of physical activity and time outdoors in predicting sleep health by using objective measurement of the three variables. A convenience sample of 360 adult women (mean age = 55.38 ±9.89 years; mean body mass index = 27.74 ±6.12) was recruited from different regions of the U.S. Participants wore a Global Positioning System device and ActiGraph GT3X+ accelerometers on the hip for 7 days and on the wrist for 7 days and 7 nights to assess total time and time of day spent outdoors, total minutes in moderate-to-vigorous physical activity per day, and 4 measures of sleep health, respectively. A generalized mixed-effects model was used to assess temporal associations between moderate-to-vigorous physical activity, outdoor time, and sleep at the daily level (days = 1931) within individuals. There was a significant interaction (p = 0.04) between moderate-to-vigorous physical activity and time spent outdoors in predicting total sleep time but not for predicting sleep efficiency. Increasing time outdoors in the afternoon (versus morning) predicted lower sleep efficiency, but had no effect on total sleep time. Time spent outdoors and the time of day spent outdoors may be important moderators in assessing the relation between physical activity and sleep. More research is needed in larger populations using experimental designs.

Based on prior research, the presence of more residents diagnosed with mental illness in a neighborhood may indicate the presence of more offenders or more targets. We suggest that it may also indicate reduced guardianship and lower informal social control. Using data from blocks in 108 cities in Southern California, we examined the association between two types of mental disorders (affective disorders and substance use disorders) and crime rates. Even after controlling for more traditional predictors of neighborhood crime, we found a strong positive relationship between certain mental disorders and aggravated assault and motor vehicle theft rates. We also tested nonlinear interactions with economic disadvantage and found that a higher proportion of mentally ill residents in a zip code was associated with higher block-level crime rates in zip codes with lower levels of concentrated economic disadvantage. The findings suggest that mental illness is a unique ecological factor by which to understand neighborhood crime.

In the past 15 years, a major research enterprise has emerged that is aimed at understanding associations between geographic and contextual features of the environment (especially the built environment) and elements of human energy balance, including diet, weight, and physical activity. Here we highlight aspects of this research area with a particular focus on research and opportunities in the United States as an example. We address four main areas: 1) The importance of valid and comparable data concerning behavior across geographies, 2) The ongoing need to identify and explore new environmental variables, 3) The challenge of identifying the causally relevant context, and 4) The pressing need for stronger study designs and analytical methods. Additionally, we discuss existing sources of geo-referenced health data which might be exploited by interdisciplinary research teams, personnel challenges and some aspects of funding for geospatial research by the US National Institutes of Health in the past decade, including funding for international collaboration and training opportunities.

Recent technological, geophysical, and societal forces have fundamentally altered the structure and functioning of human environments. Prominent among these forces are the rise of the Internet; rapid rates of global environmental change; and widening rifts among different socioeconomic, racial, religious, and ethnic groups. The present article traces the influence of these conditions on individuals' cognition, behavior, and well-being. New theoretical questions are raised and conceptual frameworks proposed to understand how global conditions are restructuring people's relationships with their everyday environments. New directions for psychological research and practice aimed at reducing global threats to personal and societal well-being are discussed.

Much of the current neighborhood-based research uses variables aggregated on administrative boundaries such as zip codes, census tracts, and block groups. However, other methods using current technological advances in geographic sciences may broaden our ability to explore the spatial concentration of neighborhood factors affecting individuals and groups. This article builds on extant neighborhood-based research methods and proposes using a geographic information system (GIS) to explore a conceptually new method of defining neighborhood boundaries for social measurement. Geospatial factors are important in the formation of neighborhoods, and a GIS can be used to define neighborhood boundaries that account for the spatial factors influencing the natural flow and pattern of neighbor interactions. Georeferenced data from the 2000 U.S. census for a midwestern city are presented to elucidate the potential aggregation problems and theoretical arguments. Moran's I statistics show that administrative boundaries may not be valid aggregate measures of neighborhoods or their effects.