, Werner, Amburgey, & Szalay, 2007 ; Christensen, Mikkelsen, Nielsen, & Harder, 2011 ; Holt, Spence, Sehn, & Cutumisu, 2008 ; Montemurro et al., 2011 ; Spessot, 2015 ; Strath, Isaacs, & Greenwald, 2007 ), and most of those studies relied on the home-neighborhood approach. This approach seeks to
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Mika R. Moran, Perla Werner, Israel Doron, Neta HaGani, Yael Benvenisti, Abby C. King, Sandra J. Winter, Jylana L. Sheats, Randi Garber, Hadas Motro, and Shlomit Ergon
Greg Lindsey, Yuling Han, Jeffrey Wilson, and Jihui Yang
Purpose:
To model urban trail traffic as a function of neighborhood characteristics and other factors including weather and day of week.
Methods:
We used infrared monitors to measure traffic at 30 locations on five trails for periods ranging from 12 months to more than 4 y. We measured neighborhood characteristics using geographic information systems, satellite imagery, and US Census and other secondary data. We used multiple regression techniques to model daily traffic.
Results:
The statistical model explains approximately 80% of the variation in trail traffic. Trail traffic correlates positively and significantly with income, neighborhood population density, education, percent of neighborhood in commercial use, vegetative health, area of land in parking, and mean length of street segments in access networks. Trail traffic correlates negatively and significantly with the percentage of neighborhood residents in age groups greater than 64 and less than 5.
Conclusions:
Trail traffic is significantly correlated with neighborhood characteristics. Health officials can use these findings to influence the design and location of trails and to maximize opportunities for increases in physical activity.
Fuzhong Li, K. John Fisher, Adrian Bauman, Marcia G. Ory, Wojtek Chodzko-Zajko, Peter Harmer, Mark Bosworth, and Minot Cleveland
Over the past few years, attention has been drawn to the importance of neighborhood influences on physical activity behavior and the need to consider a multilevel analysis involving not only individual-level variables but also social-and physical-environment variables at the neighborhood level in explaining individual differences in physical activity outcomes. This new paradigm raises a series of issues concerning systems of influence observed at different hierarchical levels (e.g., individuals, neighborhoods) and variables that can be defined at each level. This article reviews research literature and discusses substantive, operational, and statistical issues in studies involving multilevel influences on middle-aged and older adults’ physical activity. To encourage multilevel research, the authors propose a model that focuses attention on multiple levels of influence and the interaction among variables characterizing individuals, among variables characterizing neighborhoods, and across both levels. They conclude that a multilevel perspective is needed to increase understanding of the multiple influences on physical activity.
Ka-Man Leung, Pak-Kwong Chung, Tin-Lok Yuen, Jing Dong Liu, and Donggen Wang
, encouragement, role models, and neighborhood social cohesion. Companionship refers to people walking with partners instead of walking alone. Cleland et al. ( 2010 ) found that engaging in PA with friends or colleagues increased women’s participation in both leisure-time and transport-related PA. Encouragement
Gohei Kato, Tomoyuki Arai, Yasuhiro Morita, and Hiroaki Fujita
facilitation domains of the Japanese version of Home and Community Environment ( Kato, Tamiya, Kashiwagi, & Akasaka, 2010 ; Keysor et al., 2005 ) were used to assess the subjective factors of the built environment at a neighborhood level. Objective built environmental factors Existences of built environment
Venurs H.Y. Loh, Jerome N. Rachele, Wendy J. Brown, Fatima Ghani, and Gavin Turrell
Residents of socioeconomically disadvantaged neighborhoods have significantly poorer physical function than their counterparts residing in more advantaged neighborhoods. 1 Physical function is defined as one’s ability to perform various activities that require physical capacity, ranging from
Javier Molina-García and Ana Queralt
Active commuting to school (ACS) significantly contributes to physical activity levels and health in children 1 ; however, ACS is declining due to the increasing use of motorized vehicles. 2 , 3 According to ecological models of health behavior, 4 neighborhood type is a particularly important
Kavita A. Gavand, Kelli L. Cain, Terry L. Conway, Brian E. Saelens, Lawrence D. Frank, Jacqueline Kerr, Karen Glanz, and James F. Sallis
PA in adolescence, so these potential mediators can be targeted in interventions. Neighborhood-built environment characteristics may have a particularly strong impact on health behavior in youth because adolescents spend large amounts of time in and around their homes, about 40% of waking time in one
Morgan N. Clennin and Russell R. Pate
Much is known about the individual-level characteristics (eg, genetics, age, and sex) and behaviors (eg, physical activity) that influence cardiorespiratory fitness in youth. 3 , 9 However, little is known about factors at the community level or neighborhood level that may influence youth fitness
Laura A. Dwyer, Minal Patel, Linda C. Nebeling, and April Y. Oh
promotion of mental health. 2 Understanding factors that promote PA and are potentially malleable via interventions is crucial. Variables to consider in these efforts include psychosocial and environmental attributes, such as features of one’s neighborhood. The interplay of psychosocial and environmental
