title: Assessing opportunities for physical activity in the built environment of children: interrelation between kernel density and neighborhood scale.
abstract: BACKGROUND
  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.
  
  
  METHODS
  We 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 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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Leibniz Institute for Prevention Research and Epidemiology, BIPS, Bremen, Germany. buck@bips.uni-bremen.de.
authors.forename: Christoph
authors.initials: C
authors.lastname: Buck
authors.affiliation: Faculty of Economic Sciences, University of Göttingen, Göttingen, Germany.
authors.forename: Thomas
authors.initials: T
authors.lastname: Kneib
authors.affiliation: Institute of Geography, University of Bremen, Bremen, Germany.
authors.forename: Tobias
authors.initials: T
authors.lastname: Tkaczick
authors.affiliation: National Institute for Health Development, Tallinn, Estonia.|Institute of Psychology, University of Tartu, Tartu, Estonia.
authors.forename: Kenn
authors.initials: K
authors.lastname: Konstabel
authors.affiliation: Leibniz Institute for Prevention Research and Epidemiology, BIPS, Bremen, Germany.|Faculty of Mathematics and Computer Science, University of Bremen, Bremen, Germany.
authors.forename: Iris
authors.initials: I
authors.lastname: Pigeot
mesh_terms: D061725:Accelerometry / Q000295:instrumentation / Q000379:methods; D015992:Body Mass Index; D002648:Child; D002675:Child, Preschool; D004779:Environment Design; D005260:Female; D005858:Germany; D006801:Humans; D016018:Least-Squares Analysis; D008297:Male; D008991:Monitoring, Physiologic / Q000295:instrumentation / Q000379:methods; D009043:Motor Activity; D011156:Population Density; D012044:Regression Analysis; D012111:Residence Characteristics; D012449:Safety; D062206:Spatial Analysis; D014505:Urban Population; D016138:Walking / Q000706:statistics & numerical data