Association between perceived urban built environment attributes and leisure-time physical activity among adults in Hangzhou, China

Su, Meng; Tan, Yin; Liu, Qingmin; Ren, Yanjun; Kawachi, Ichiro; Li, Liming; Lv, Jun

doi:10.1016/j.ypmed.2014.06.001

Cited by 91 publications

(65 citation statements)

References 28 publications

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“…Researchers hitherto have not often treated these factors of the built environment, partly because such aesthetic features are relatively hard to quantify. Yet, in a handful of existing studies, scenic routes are found to be preferred by bicyclists (Pikora, Giles-Corti, Bull, Jamrozik, & Rob, 2003;Stefansdottir, 2014;Su et al, 2014). The majority of bicycle trips is motivated by recreational purposes (Goetzke & Rave, 2010;Su et al, 2014), and the attractiveness of the built environment acts as a key factor in bicycle routing (Lovasi et al, 2013;Stefansdottir, 2014).…”

Section: 2mentioning

confidence: 99%

Built environment determinants of bicycle volume: A longitudinal analysis

Chen

Zhou

Sun

2017

JTLU

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Abstract:This study examines determinants of bicycle volume in the built environment with a five-year bicycle count dataset from Seattle, Washington. A generalized linear mixed model (GLMM) is used to capture the bicycle volume change over time while controlling for temporal autocorrelations. The GLMM assumes that bicycle count follows a Poisson distribution. The model results show that (1) the variables of non-winter seasons, peak hours, and weekends are positively associated with the increase of bicycle counts over time; (2) bicycle counts are fewer in steep areas; (3) bicycle counts are greater in zones with more mixed land use, a higher percentage of water bodies, or a greater percentage of workplaces; (4) the increment of bicycle infrastructure is positively associated with the increase of bicycle volume; and (5) bicycling is more popular in neighborhoods with a greater percentage of whites and younger adults. It concludes that areas with a smaller slope variation, a higher employment density, and a shorter distance to water bodies encourage bicycling. This conclusion suggests that to best boost bicycling, decision-makers should consider building more bicycle facilities in flat areas and integrating the facilities with employment densification and open-space creation and planning.

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Section: 2mentioning

confidence: 99%

Built environment determinants of bicycle volume: A longitudinal analysis

Chen

Zhou

Sun

2017

JTLU

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“…Physical inactivity includes leisure-time physical inactivity (LTPI) and non-leisure-time physical inactivity (NLTPI) [14]. Previous studies have found that both LTPI and NLTPI are affected by environmental contexts [15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

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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“…However, in order to reduce the burden of CVD risk factors, environmental interventions have become 2 Advances in Epidemiology increasingly recognized as necessary approaches to support behavior change [12]. The fundamental attributes of built environments and community design [13,14] have been linked with increased risk of hypertension and cardiovascular events [15], but only a few studies have evaluated these relationships in an African context [16]. Thus, this study evaluates the association between perceived built environment attributes and the prevalence of hypertension in South African adults in both an urban (Cape Town) and a rural (Mount Frere) context.…”

Section: Introductionmentioning

confidence: 99%

Association between Perceived Built Environment and Prevalent Hypertension among South African Adults

Malambo

Kengne

Lambert

et al. 2016

Advances in Epidemiology

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Introduction. The association between perceived built environmental attributes and hypertension among adults has received little attention in an African context. We investigated the association between the perceived built environment and prevalent hypertension in adult South Africans. Method. A cross-sectional study was conducted using [2008][2009] Prospective Urban Rural Epidemiology data among South African ( = 671) adults aged ≥35 years. Perceived built environment was assessed using the neighborhood environment walkability scale questionnaire. Prevalent hypertension was defined as previously diagnosed by a physician, screen-detected hypertension as ≥140/90 mmHg, and a combination of both as any hypertension. Logistic regressions were applied for analyses. Results. In crude logistic regressions, self-reported hypertension was associated with land use mixdiversity, street connectivity, infrastructure for walking/cycling, aesthetics, traffic, and crime. In adjusted model, land use mixdiversity was significantly associated with self-reported hypertension. In similar multivariable models, the direction and magnitude of the effects were mostly similar to the outcomes of "screen-detected hypertension" which was further predicted by perceived lack of safety from traffic. Conclusion. Perceived built environment attributes were significantly associated with hypertension. This has relevance to population-based approaches to hypertension prevention and control.

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Association between perceived urban built environment attributes and leisure-time physical activity among adults in Hangzhou, China

Abstract: The present study demonstrated that perceived urban built environment attributes significantly correlate with leisure-time physical activity in Hangzhou, China.

Cited by 91 publications

References 28 publications

Built environment determinants of bicycle volume: A longitudinal analysis

Built environment determinants of bicycle volume: A longitudinal analysis

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

Association between Perceived Built Environment and Prevalent Hypertension among South African Adults

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