Walk Score and Australian adults' home-based walking for transport

Cole, Rachel; Dunn, Peter J.; Hunter, Ian W.; Owen, Neville; Sugiyama, Takemi

doi:10.1016/j.healthplace.2015.06.011

Cited by 57 publications

(49 citation statements)

References 15 publications

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“…Walk Score, a commercially generated index that considers proximities to diverse destinations (e.g., retail, services), provides an estimate of walkability specific to individual addresses and can be obtained for free by entering addresses into a Web site or at cost through arrangements with the company (http://www.walkscore.com). Several studies have demonstrated associations between Walk Score and physical activity obtained by walking, particularly walking for transportation . If submission of addresses to a Web‐based data source is a concern, linkage to precalculated area‐based measures of walkability is an alternative measure.…”

Section: Environmental Constructs and Corresponding Measures Recommenmentioning

confidence: 99%

Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures: Environmental Domain

Saelens

Arteaga

Berrigan

et al. 2018

Obesity

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Starting with the home address, the environmental domain subgroup recommended for inclusion in future weight-loss or maintenance studies constructs and measures related to walkability, perceived land use mix, food outlet accessibility (perceived and objective), perceived food availability, socioeconomics, and crime-related safety (perceived and objective) to characterize the home neighborhood environment. The subgroup also recommended constructs and measures related to social norms (perceived and objective) and perceived support to characterize an individual's social environment. The 12 neighborhood and social environment constructs and corresponding measures provide a succinct and comprehensive set to allow for more systematic examination of the impact of environment on adults' weight loss and maintenance.

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Section: Environmental Constructs and Corresponding Measures Recommenmentioning

confidence: 99%

Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures: Environmental Domain

Saelens

Arteaga

Berrigan

et al. 2018

Obesity

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“…The highest risks of neoplasms and MI admission rates were found in Kambah (Walk Score 28) and Kaleen (Walk Score 39), which were classified as ‘car-dependent’ by Walk Score. While a number of studies have shown that Walk Score is related to walking for recreation and transportation14–16 37 ours is one of the few studies23 24 that showed a significant relationship between Walk Score and hospital admissions.…”

Section: Discussionmentioning

confidence: 76%

“…Walk Score is a measure of walkability produced by a United States (US)-based company that has been validated37 and has been used in a number of public health studies in the US. In the Australian context, it has been found to have strong relationships with walking for transport in a recent study,14 though relationships with health outcomes have not previously been found 23. Walk Score is a composite measure of destination density.…”

Section: Methodsmentioning

confidence: 92%

“…We summarise this in figure 1. In Australia and elsewhere, a number of research papers have established the relationships between environment and behaviours (link A—see figure 1)14–18 or behaviours and health outcomes (link B—see figure 1). 19 20 It logically follows that the environment is related to health outcomes through the individual lifestyle behavioural pathway.…”

Section: Methodsmentioning

confidence: 99%

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Is Walk Score associated with hospital admissions from chronic diseases? Evidence from a cross-sectional study in a high socioeconomic status Australian city-state

et al. 2016

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ObjectivesTo explore patterns of non-communicable diseases (NCDs) in the Australian Capital Territory (ACT).To ascertain the effect of the neighbourhood built environmental features and especially walkability on health outcomes, specifically for hospital admissions from NCDs.DesignA cross-sectional analysis of public hospital episode data (2007–2013).SettingHospitalisations from the ACT, Australia at very small geographic areas.ParticipantsSecondary data on 75 290 unique hospital episodes representing 39 851 patients who were admitted to ACT hospitals from 2007 to 2013. No restrictions on age, sex or ethnicity.Main exposure measuresGeographic Information System derived or compatible measures of general practitioner access, neighbourhood socioeconomic status, alcohol access, exposure to traffic and Walk Score walkability.Main outcome measuresHospitalisations of circulatory diseases, specific endocrine, nutritional and metabolic diseases, respiratory diseases and specific cancers.ResultsGeographic clusters with significant high and low risks of NCDs were found that displayed an overall geographic pattern of high risk in the outlying suburbs of the territory. Significant relationships between neighbourhood walkability as measured by Walk Score and the likelihood of hospitalisation with a primary diagnosis of myocardial infarction (heart attack) were found. A possible relationship was also found with the likelihood of being hospitalised with 4 major lifestyle-related cancers.ConclusionsOur research augments the growing literature underscoring the relationships between the built environment and health outcomes. In addition, it supports the importance of walkable neighbourhoods, as measured by Walk Score, for improved health.

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“…The built environment around each participant's home was measured using Walk Score. Walk Score has been previously demonstrated as a valid and reliable measure of neighbourhood walkability (Carr et al 2010;Duncan et al 2011;Manaugh and El-Geneidy 2011) and has been used in Australian context (Cole et al 2015). Each participant was assigned a walkability score based on their home address.…”

Section: Data Processingmentioning

confidence: 99%

Social marketing and the built environment: What matters for travel behaviour change?

Mulley

Liu

2016

Transportation

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Social marketing and the built environment are two important 'tools' to manage travel demand which have had significant attention in the literature separately. Most previous studies evaluating the effects of social marketing programs have relied on preand post-surveys, using self-reported measures without any objective measures of travel behaviour change. Further, there is a lack of evidence on whether the effects of the built environment are synergistic when combined with other intervention programs, such as social marketing programs. This study contributes by quantitatively evaluating the relative and combined effects of the TravelSmart and the built environment on travel behaviour using objective GPS measurements. Between 2012 and 2014, daily travel data were collected using GPS equipment in suburbs of inner northern Adelaide, South Australia. Individuals in the households aged over 14 carried a portable GPS device everywhere for a period of 15 days during March-May in each year from 2012 to 2014, providing a total of three waves of panel data. The empirical analysis suggests that the TravelSmart program as a 'treatment' significantly reduced the car trips soon after implementation with longer term effects on reducing car trips in high-walkable neighbourhoods. For walking and bus trips, the TravelSmart program increased these 1 year after the 'treatment' with stronger effects on travel behaviour change for the participants living in high-walkable neighbourhoods than for those living in low-walkable neighbourhoods.

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Walk Score and Australian adults' home-based walking for transport

Cited by 57 publications

References 15 publications

Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures: Environmental Domain

Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures: Environmental Domain

Is Walk Score associated with hospital admissions from chronic diseases? Evidence from a cross-sectional study in a high socioeconomic status Australian city-state

Social marketing and the built environment: What matters for travel behaviour change?

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