Patterns of neighborhood environment attributes in relation to children's physical activity

Kurka, Jonathan; Adams, Marc A.; Todd, Michael; Colburn, Trina; Sallis, James F.; Cain, Kelli L.; Glanz, Karen; Frank, Lawrence D.; Saelens, Brian E.

doi:10.1016/j.healthplace.2015.05.006

Cited by 54 publications

(52 citation statements)

References 21 publications

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“…Thus, it is possible and not surprising that two similar studies concerning the effects of the same environmental factor on LTPI would observe inverse associations if they are conducted in different regions of the country. This is perhaps one of the reasons why research findings were often inconsistent concerning the effects of environmental factors on health behaviors and outcomes, given that past studies on the same issue (e.g., LTPI) were often conducted in different geographic locations (e.g., [93,94]). The assessment of spatial non-stationarity and the varying environmental effects on LTPI as a function of geographic location may help us investigate and better understand how space plays a role in the prevalence of LTPI across space [95].…”

Section: Discussionmentioning

confidence: 99%

Environmental Influences on Leisure-Time Physical Inactivity in the U.S.: An Exploration of Spatial Non-Stationarity

Wang

Lee

Kwan

2018

IJGI

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Considerable research has been conducted to advance our understanding of how environmental factors influence people's health behaviors (e.g., leisure-time physical inactivity) at the neighborhood level. However, different environmental factors may operate differently at different geographic locations. This study explores the inconsistent findings regarding the associations between environmental exposures and physical inactivity. To address spatial autocorrelation and explore the impact of spatial non-stationarity on research results which may lead to biased estimators, this study uses spatial regression models to examine the associations between leisure-time physical inactivity and different social and physical environmental factors for all counties in the conterminous U.S. By comparing the results with the conventional ordinary least squares regression and spatial lag model, the geographically weighted regression model adequately addresses the problem of spatial autocorrelation (Moran's I of the residual = 0.0293) and highlights the spatial non-stationarity of the associations. The existence of spatial non-stationarity that leads to biased estimators, which were often ignored in past research, may be another reason for the inconsistent findings in previous studies besides the modifiable areal unit problem and the uncertain geographic context problem. Also, the observed associations between environmental variables and leisure-time physical inactivity are helpful for developing location-based policies and interventions to encourage people to undertake more physical activity.

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Section: Discussionmentioning

confidence: 99%

Environmental Influences on Leisure-Time Physical Inactivity in the U.S.: An Exploration of Spatial Non-Stationarity

Wang

Lee

Kwan

2018

IJGI

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“…Researchers have shown that macro-level features of the built environment, including regional land-use patterns, residential densities, and access to parks and public transportation, shape access to opportunities for physical activity (Li et al, 2008; Nagel, Carlson, Bosworth, & Michael, 2008; Troped, Wilson, Matthews, Cromley, & Melly, 2010). Diverse combinations of objectively-measured built environment features have been positively and consistently related to physical activity (Sallis et al, 2016) and walking behaviors (Adams et al, 2015; Kaczynski, 2010), and results appear robust across children (Kurka et al, 2015) and older adults (Adams et al, 2012; Kerr et al, 2014). …”

Section: Introductionmentioning

confidence: 96%

Reliability between online raters with varying familiarities of a region: Microscale Audit of Pedestrian Streetscapes (MAPS)

Zhu

Sun

Kurka

et al. 2017

Landscape and Urban Planning

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Background To test inter-rater reliability of the online Microscale Audit of Pedestrian Streetscapes (MAPS) tool between raters with varying familiarities of Phoenix, Arizona. Methods The online MAPS tool, based on the MAPS in-field audit tool and scoring system, was used for audits. Sixty route pairs, 141 segment pairs, and 92 crossing pairs in Phoenix were included. Each route, segment or crossing was audited by two independent raters: one rater in Phoenix and the other in San Diego, California, respectively. Item, subscale scores, and total scores reliability analyses were computed using Kappa or intra-class correlation coefficient (ICC). Results The route overall score had substantial reliability (ICC: 0.832). Of the route subscale and overall scores, sixteen out of twenty had moderate to substantial reliability (ICC: 0.616–0.906), and the four subscales had fair reliability (ICC: 0.409–0.563). Sixteen out of twenty scores in segment and crossing sections demonstrated fair to substantial reliability (ICC: 0.448–0.897), and the remaining four had slight reliability (ICC: 0.348–0.364). Conclusions Most of the online MAPS items, subscales, and overall scores demonstrated fair to substantial reliability between raters with varied familiarities of the Phoenix area. Results support use of online MAPS to measure microscale elements of the built environment by raters unfamiliar with a region.

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“…Latent profile analysis (LPA) addresses these limitations by yielding a discrete set of model-derived clusters of observations with distinctive patterns (profiles) of indicator (BE feature) scores in each model and allows for statistical comparison of models of differing complexity. Feature patterns derived from LPA models using perceived (vs. GIS-measured) BE features have shown predictive utility for MVPA and transportation-related physical activity for younger and older adults 22,37 and children 38 , and with GIS-measured features for adults aged 20–65. 39 Identifying feature patterns from objective measures conceptually associated with regular PA in adults over age 65 may help inform neighborhood designs that allow older adults remain relatively active and healthy.…”

Section: Introductionmentioning

confidence: 99%

GIS-measured walkability, transit, and recreation environments in relation to older Adults' physical activity: A latent profile analysis

Todd

Adams

Kurka

et al. 2016

Preventive Medicine

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An infrequently studied question is how diverse combinations of built environment (BE) features relate to physical activity (PA) for older adults. We derived patterns of Geographical Information Systems- (GIS) measured BE features and explored how they accounted for differences in objective and self-reported PA, sedentary time, and BMI in a sample of older adults. Senior Neighborhood Quality of Life Study participants (N=714, aged 66–97 years, 52.1% women, 29.7% racial/ethnic minority) were sampled in 2005–2008 from the Seattle-King County, WA and Baltimore, MD-Washington, DC regions. Participants’ home addresses were geocoded, and net residential density, land use mix, retail floor area ratio, intersection density, public transit density, and public park and private recreation facility density measures for 1-km network buffers were derived. Latent profile analyses (LPAs) were estimated from these GIS-based measures. In multilevel regression models, profiles were compared on accelerometer-measured moderate-to-vigorous PA (MVPA) and sedentary time and self-reported PA, adjusting for covariates and clustering. Analyses were conducted in 2014–2015. LPAs yielded three profiles: low walkability/transit/recreation (L-L-L); mean walkability/transit/recreation (M-M-M); and high walkability/transit/recreation (H-H-H). Three PA outcomes were more favorable in the HHH than the LLL profile group (difference of 7.2 minutes/day for MVPA, 97.8 minutes/week for walking for errands, and 79.2 minutes/week for walking for exercise; all ps < 0.02). The most and least activity-supportive BE profiles showed greater differences in older adults’ PA than did groupings based solely on a 4-component walkability index, suggesting that diverse BE features are important for healthy aging.

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Patterns of neighborhood environment attributes in relation to children's physical activity

Cited by 54 publications

References 21 publications

Environmental Influences on Leisure-Time Physical Inactivity in the U.S.: An Exploration of Spatial Non-Stationarity

Environmental Influences on Leisure-Time Physical Inactivity in the U.S.: An Exploration of Spatial Non-Stationarity

Reliability between online raters with varying familiarities of a region: Microscale Audit of Pedestrian Streetscapes (MAPS)

GIS-measured walkability, transit, and recreation environments in relation to older Adults' physical activity: A latent profile analysis

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