Assessing opportunities for physical activity in the built environment of children: interrelation between kernel density and neighborhood scale

Built environment studies provide broad evidence that urban characteristics influence physical activity (PA). However, findings are still difficult to compare, due to inconsistent measures assessing urban point characteristics and varying definitions of spatial scale. Both were found to influence the strength of the association between the built environment and PA.MethodsWe simultaneously evaluated the effect of kernel approaches and network-distances to investigate the association between urban characteristics and physical activity depending on spatial scale and intensity measure. We assessed urban measures of point characteristics such as intersections, public transit stations, and public open spaces in ego-centered network-dependent neighborhoods based on geographical data of one German study region of the IDEFICS study. We calculated point intensities using the simple intensity and kernel approaches based on fixed bandwidths, cross-validated bandwidths including isotropic and anisotropic kernel functions and considering adaptive bandwidths that adjust for residential density. We distinguished six network-distances from 500 m up to 2 km to calculate each intensity measure. A log-gamma regression model was used to investigate the effect of each urban measure on moderate-to-vigorous physical activity (MVPA) of 400 2- to 9.9-year old children who participated in the IDEFICS study. Models were stratified by sex and age groups, i.e. pre-school children (2 to $${<}6$$<6  years) and school children (6–9.9 years), and were adjusted for age, body mass index (BMI), education and safety concerns of parents, season and valid weartime of accelerometers. Association between intensity measures and MVPA strongly differed by network-distance, with stronger effects found for larger network-distances. Simple intensity revealed smaller effect estimates and smaller goodness-of-fit compared to kernel approaches. Smallest variation in effect estimates over network-distances was found for kernel intensity measures based on isotropic and anisotropic cross-validated bandwidth selection. We found a strong variation in the association between the built environment and PA of children based on the choice of intensity measure and network-distance. Kernel intensity measures provided stable results over various scales and improved the assessment compared to the simple intensity measure. Considering different spatial scales and kernel intensity methods might reduce methodological limitations in assessing opportunities for PA in the built environment.

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title: A Framework for Using GPS Data in Physical Activity and Sedentary Behavior Studies
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title: Association between neighborhood walkability and GPS-measured walking, bicycling and vehicle time in adolescents.
abstract: To investigate relations of walking, bicycling and vehicle time to neighborhood walkability and total physical activity in youth.



Participants (N=690) were from 380 census block groups of high/low walkability and income in two US regions. Home neighborhood residential density, intersection density, retail density, entertainment density and walkability were derived using GIS. Minutes/day of walking, bicycling and vehicle time were derived from processing algorithms applied to GPS. Accelerometers estimated total daily moderate-to-vigorous physical activity (MVPA). Models were adjusted for nesting of days (N=2987) within participants within block groups.



Walking occurred on 33%, active travel on 43%, and vehicle time on 91% of the days observed. Intersection density and neighborhood walkability were positively related to walking and bicycling and negatively related to vehicle time. Residential density was positively related to walking.



Increasing walking in youth could be effective in increasing total physical activity. Built environment findings suggest potential for increasing walking in youth through improving neighborhood walkability.

title: Conceptualization and measurement of environmental exposure in epidemiology: accounting for activity space related to daily mobility.
abstract: A considerable body of literature has investigated how environmental exposures affect health through various pathways. These studies have generally adopted a common approach to define environmental exposures, focusing on the local residential environment, using census tracts or postcodes to delimit exposures. However, use of such administrative units may not be appropriate to evaluate contextual effets on health because they are generally not a 'true' representation of the environments to which individuals are exposed. Recent work has suggested that advances may be made if an activity-space approach is adopted. The present paper investigates how various disciplines may contribute to the refinement of the concept of activity space for use in health research. In particular we draw on seminal work in time geography, which provides a framework to describe individual behavior in space and time, and can help the conceptualization of activity space. In addition we review work in environmental psychology and social networks research, which provides insights on how people and places interact and offers new theories for improving the spatial definition of contextual exposures.

title: Emerging Technologies for Assessing Physical Activity Behaviors in Space and Time
abstract: Precise measurement of physical activity is important for health research, providing a better understanding of activity location, type, duration, and intensity. This article describes a novel suite of tools to measure and analyze physical activity behaviors in spatial epidemiology research. We use individual-level, high-resolution, objective data collected in a space-time framework to investigate built and social environment influences on activity. First, we collect data with accelerometers, global positioning system units, and smartphone-based digital travel and photo diaries to overcome many limitations inherent in self-reported data. Behaviors are measured continuously over the full spectrum of environmental exposures in daily life, instead of focusing exclusively on the home neighborhood. Second, data streams are integrated using common timestamps into a single data structure, the “LifeLog.” A graphic interface tool, “LifeLog View,” enables simultaneous visualization of all LifeLog data streams. Finally, we use geographic information system SmartMap rasters to measure spatially continuous environmental variables to capture exposures at the same spatial and temporal scale as in the LifeLog. These technologies enable precise measurement of behaviors in their spatial and temporal settings but also generate very large datasets; we discuss current limitations and promising methods for processing and analyzing such large datasets. Finally, we provide applications of these methods in spatially oriented research, including a natural experiment to evaluate the effects of new transportation infrastructure on activity levels, and a study of neighborhood environmental effects on activity using twins as quasi-causal controls to overcome self-selection and reverse causation problems. In summary, the integrative characteristics of large datasets contained in LifeLogs and SmartMaps hold great promise for advancing spatial epidemiologic research to promote healthy behaviors.

title: Variations in active transport behavior among different neighborhoods and across adult lifestages.
abstract: Built environment characteristics are closely related to transport behavior, but observed variations could be due to residents own choice of neighborhood called residential self-selection. The aim of this study was to investigate differences in neighborhood walkability and residential self-selection across life stages in relation to active transport behavior.



The IPEN walkability index, which consists of four built environment characteristics, was used to define 16 high and low walkable neighborhoods in Aarhus, Denmark (250.000 inhabitants). Transport behavior was assessed using the IPAQ questionnaire. Life stages were categorized in three groups according to age and parental status. A factor analysis was conducted to investigate patterns of self-selection. Multivariable logistic regression analyses were carried out to evaluate the association between walkability and transport behavior i.e. walking, cycling and motorized transport adjusted for residential self-selection and life stages.



A total of 642 adults aged 20-65 years completed the questionnaire. The highest rated self-selection preference across all groups was a safe and secure neighborhood followed by getting around easily on foot and by bicycle. Three self-selection factors were detected, and varied across the life stages. In the multivariable models high neighborhood walkability was associated with less motorized transport (OR 0.33 95%CI 0.18-0.58), more walking (OR 1.65 95%CI 1.03-2.65) and cycling (OR 1.50 95% CI 1.01-2.23). Self-selection and life stage were also associated with transport behavior, and attenuated the association with walkability.



This study supports the hypothesis that some variation in transport behavior can be explained by life stages and self-selection, but the association between living in a more walkable neighborhood and active transport is still significant after adjusting for these factors. Life stage significantly moderated the association between neighborhood walkability and cycling for transport, and household income significantly moderated the association between neighborhood walkability and walking for transport. Getting around easily by bicycle and on foot was the highest rated self-selection factor second only to perceived neighborhood safety.

title: Patterns of neighborhood environment attributes related to physical activity across 11 countries: a latent class analysis
abstract: BackgroundNeighborhood environment studies of physical activity (PA) have been mainly single-country focused. The International Prevalence Study (IPS) presented a rare opportunity to examine neighborhood features across countries. The purpose of this analysis was to: 1) detect international neighborhood typologies based on participants’ response patterns to an environment survey and 2) to estimate associations between neighborhood environment patterns and PA.MethodsA Latent Class Analysis (LCA) was conducted on pooled IPS adults (N=11,541) aged 18 to 64 years old (mean=37.5 ±12.8 yrs; 55.6% women) from 11 countries including Belgium, Brazil, Canada, Colombia, Hong Kong, Japan, Lithuania, New Zealand, Norway, Sweden, and the U.S. This subset used the Physical Activity Neighborhood Environment Survey (PANES) that briefly assessed 7 attributes within 10–15 minutes walk of participants’ residences, including residential density, access to shops/services, recreational facilities, public transit facilities, presence of sidewalks and bike paths, and personal safety. LCA derived meaningful subgroups from participants’ response patterns to PANES items, and participants were assigned to neighborhood types. The validated short-form International Physical Activity Questionnaire (IPAQ) measured likelihood of meeting the 150 minutes/week PA guideline. To validate derived classes, meeting the guideline either by walking or total PA was regressed on neighborhood types using a weighted generalized linear regression model, adjusting for gender, age and country. 5-subgroup solution fitted the dataset and was interpretable. Neighborhood types were labeled, “Overall Activity Supportive (52% of sample)”, “High Walkable and Unsafe with Few Recreation Facilities (16%)”, “Safe with Active Transport Facilities (12%)”, “Transit and Shops Dense with Few Amenities (15%)”, and “Safe but Activity Unsupportive (5%)”. Country representation differed by type (e.g., U.S. disproportionally represented “Safe but Activity Unsupportive”). Compared to the Safe but Activity Unsupportive, two types showed greater odds of meeting PA guideline for walking outcome (High Walkable and Unsafe with Few Recreation Facilities, OR= 2.26 (95% CI 1.18-4.31); Overall Activity Supportive, OR= 1.90 (95% CI 1.13-3.21). Significant but smaller odds ratios were also found for total PA.ConclusionsMeaningful neighborhood patterns generalized across countries and explained practical differences in PA. These observational results support WHO/UN recommendations for programs and policies targeted to improve features of the neighborhood environment for PA.