Residential exposure to visible blue space (but not green space) associated with lower psychological distress in a capital city

Nutsford, Daniel; Pearson, Amber L.; Kingham, Simon; Reitsma, Femke

doi:10.1016/j.healthplace.2016.03.002

Cited by 290 publications

(187 citation statements)

References 47 publications

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“…Additionally, we also speculate about the importance of the presence of water and sky semantic features rather than vegetation, and bring forward this discussion in light of literature by Kellert et al (2011) that points to preferences people have to green and blue colors, and further delineate this preference with respect to an experimental study by Nutsford et al (2016). Kellert et al (2011), and other supporters of biophilia and related fields, discuss the positive feelings and relationship humans have with living systems, and by extension, with associated colors such as green and blue.…”

Section: Discussionsupporting

confidence: 72%

“…Kellert et al (2011), and other supporters of biophilia and related fields, discuss the positive feelings and relationship humans have with living systems, and by extension, with associated colors such as green and blue. In this vein, Nutsford et al (2016) examined the relationship between psychological distress and green and blues spaces. These researchers used geospatial data to quantify these spaces, and then compared those data with health, crime and demographic data of the studied regions.…”

Section: Discussionmentioning

confidence: 99%

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Image Feature Types and Their Predictions of Aesthetic Preference and Naturalness

et al. 2017

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Previous research has investigated ways to quantify visual information of a scene in terms of a visual processing hierarchy, i.e., making sense of visual environment by segmentation and integration of elementary sensory input. Guided by this research, studies have developed categories for low-level visual features (e.g., edges, colors), high-level visual features (scene-level entities that convey semantic information such as objects), and how models of those features predict aesthetic preference and naturalness. For example, in Kardan et al. (2015a), 52 participants provided aesthetic preference and naturalness ratings, which are used in the current study, for 307 images of mixed natural and urban content. Kardan et al. (2015a) then developed a model using low-level features to predict aesthetic preference and naturalness and could do so with high accuracy. What has yet to be explored is the ability of higher-level visual features (e.g., horizon line position relative to viewer, geometry of building distribution relative to visual access) to predict aesthetic preference and naturalness of scenes, and whether higher-level features mediate some of the association between the low-level features and aesthetic preference or naturalness. In this study we investigated these relationships and found that low- and high- level features explain 68.4% of the variance in aesthetic preference ratings and 88.7% of the variance in naturalness ratings. Additionally, several high-level features mediated the relationship between the low-level visual features and aaesthetic preference. In a multiple mediation analysis, the high-level feature mediators accounted for over 50% of the variance in predicting aesthetic preference. These results show that high-level visual features play a prominent role predicting aesthetic preference, but do not completely eliminate the predictive power of the low-level visual features. These strong predictors provide powerful insights for future research relating to landscape and urban design with the aim of maximizing subjective well-being, which could lead to improved health outcomes on a larger scale.

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Section: Discussionsupporting

confidence: 72%

Section: Discussionmentioning

confidence: 99%

Image Feature Types and Their Predictions of Aesthetic Preference and Naturalness

et al. 2017

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“…Considering the neighborhood variables this also comes out intuitively. Distances to the city center and waterfront increase the sales price, which is in line with previous findings such as Nutsford et al (2016). This also holds for median income areas, i.e., areas with a higher median income are associated with higher final sales prices.…”

Section: General Regression Resultssupporting

confidence: 91%

“…UGS have even been shown to influence social cohesion by, e.g., providing a meeting place for users to develop and maintain neighborhood ties (Kazmierczak 2013). Similarly, psychological benefits have also been shown to exist with access to open water (Nutsford et al 2016). …”

Section: Introductionmentioning

confidence: 99%

Capturing the value of green space in urban parks in a sustainable urban planning and design context: pros and cons of hedonic pricing

Engström¹,

Gren²

2017

E&S

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“…Other potential benefits of open water swimming include a sense of achievement and community in participating in a challenging activity,17 as well as green18 and blue19 therapies (access to and use of outdoor space which has vegetation or a body of water). At present, all potential mechanisms remain hypotheses supported by anecdotes.…”

Section: Introductionmentioning

confidence: 99%

Open water swimming as a treatment for major depressive disorder

Tulleken

Tipton

Massey³

et al. 2018

BMJ Case Reports

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A 24-year-old woman with symptoms of major depressive disorder and anxiety had been treated for the condition since the age of 17. Symptoms were resistant to fluoxetine and then citalopram. Following the birth of her daughter, she wanted to be medication-free and symptom-free. A programme of weekly open (cold) water swimming was trialled. This led to an immediate improvement in mood following each swim and a sustained and gradual reduction in symptoms of depression, and consequently a reduction in, and then cessation of, medication. On follow-up a year later, she remains medication-free.

show abstract

Residential exposure to visible blue space (but not green space) associated with lower psychological distress in a capital city

Cited by 290 publications

References 47 publications

Image Feature Types and Their Predictions of Aesthetic Preference and Naturalness

Image Feature Types and Their Predictions of Aesthetic Preference and Naturalness

Capturing the value of green space in urban parks in a sustainable urban planning and design context: pros and cons of hedonic pricing

Open water swimming as a treatment for major depressive disorder

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