Doses of Nearby Nature Simultaneously Associated with Multiple Health Benefits

Exposure to nature provides a wide range of health benefits. A significant proportion of these are delivered close to home, because this offers an immediate and easily accessible opportunity for people to experience nature. However, there is limited information to guide recommendations on its management and appropriate use. We apply a nature dose-response framework to quantify the simultaneous association between exposure to nearby nature and multiple health benefits. We surveyed ca. 1000 respondents in Southern England, UK, to determine relationships between (a) nature dose type, that is the frequency and duration (time spent in private green space) and intensity (quantity of neighbourhood vegetation cover) of nature exposure and (b) health outcomes, including mental, physical and social health, physical behaviour and nature orientation. We then modelled dose-response relationships between dose type and self-reported depression. We demonstrate positive relationships between nature dose and mental and social health, increased physical activity and nature orientation. Dose-response analysis showed that lower levels of depression were associated with minimum thresholds of weekly nature dose. Nearby nature is associated with quantifiable health benefits, with potential for lowering the human and financial costs of ill health. Dose-response analysis has the potential to guide minimum and optimum recommendations on the management and use of nearby nature for preventative healthcare.


Development of Depression Measure
We used the depression component of the Depression, Anxiety and Stress Scale (DASS 21) included in the urban lifestyle questionnaire (taken from [1] and reproduced here for ease of reference). Respondents were asked to rate how much each of seven statements applied to them over the past week. Answers were given on a four-point scale from: did not apply to me at all; applied to me to some degree, or some of the time; applied to me to a considerable degree, or a good part of the time; applied to me very much, or most of the time; "I couldn't seem to experience any positive feelings at all", "I found it difficult to work up the initiative to do things", "I felt that I had nothing to look forward to", I felt down-hearted and blue", "I was unable to become enthusiastic about anything", "I felt I wasn't worth much as a person", "I felt life was meaningless". The severity of depression was then rated by summing the scores (normal, 0-4; mild, 5-6; moderate, 7-10; severe, 11-13; extremely severe, 14+). Finally, in the nature dose, analysis predictors are treated as "risk factors", an established practice in population epidemiology [2,3]. We therefore converted the score to a binary measure, as those without depression and those with mild or worse depression.

Development of Social Cohesion Measure
We generated estimates of each respondent's perception of social cohesion using three sets of questions that provided an indication of trust, reciprocal exchange within communities and general community cohesion. The first set was a social cohesion and trust scale developed by [4]. Respondents were asked how strongly they agreed (selecting from "don't know", "disagree strongly", "disagree", "agree", "agree strongly") that "People in this community are willing to help their neighbours", "This is a close-knit community", "People in this community can be trusted", "People in this community generally don't get along with each other" and "People in this community do not share the same values". Items were scored from 0-4; low scores indicated poor social cohesion with "don't know" scoring zero (as it indicated no knowledge of the community in which a person lived), through to "agree strongly", which was coded the highest at four. The last two statements were reverse coded. The second set was adapted from the reciprocated exchange scale developed by [5]. Respondents answered "don't know", "never", "rarely", "sometimes", "often" (scored 0-4 respectively) to six items, specifically "About how often do you and people in your community do favours for each other?", "When a neighbour is not at home how often do you and other neighbours watch over their property?", "About how often do you and people in your community ask each other advice about things such as child rearing or job openings?", "About how often do you and people in your community visit in each other's homes or on the street?", "About how often do you and people in your community have parties or other get-togethers?", "About how often do you and people in your community spend leisure time together going out for dinner, to the movies, to a sporting event etc.?" The third set provided a general measure of social capital using components from [6], with respondents answering "don't know", "not at all", "not often", "sometimes" or "yes, definitely" (scaled 0-4, respectively) to six questions. These were "Do you feel safe walking alone down your street after dark?", "Do you feel valued by society?", "Do you feel there are opportunities to have a real say on issues that are important to you?", "Can you get help from friends, family and neighbours when needed?" "Do you help out a local group as a volunteer?", "Do you think multiculturalism makes life in your area better?" For all three sets of questions, an average score was generated, and higher scores indicated greater natural capital. Finally, to provide an overall estimate of social cohesion, the scores from the three scales were averaged for inclusion in the analysis.

Characterisation of Neighbourhood Urban Form
The urban form of the neighbourhood of each respondent who provided a postcode was characterised using airborne hyperspectral data (Eagle spectrometer) and LiDAR (Leica ALS50-II) data collected by the Natural Environment Research Council (NERC) Airborne Research and Survey Facility (ARSF) aircraft in July and September 2012. The Normalized Difference Vegetation Index (NDVI) was calculated from the hyperspectral data using a red band focused at 570 nm and a near infrared band focused at 860 nm with a spatial resolution of 2 m. Histograms of NDVI were examined and a threshold of 0.2 identified as being suitable to separate vegetated (NDVI ≥ 0.2) from nonvegetated (NDVI < 0.2) pixels [7]. The LiDAR data were used in discrete return mode, with up to four returns per laser pulse. The laser point density was between one point per 25 cm 2 and one point per 2 m 2 , depending on the flight line overlap. The lastools software [8] "lasground" function was used to find ground returns within the LiDAR point cloud. Pixels (2-m resolution) with an NDVI greater than 0.2 and a mean height of first return more than 0.7 m above the ground were marked as tall vegetation. Heights from discrete return LiDAR are well known to produce biased results over vegetation [9], and so, this 0.7-m threshold may have represented a more variable vegetation threshold height; since that bias is most usually an underestimation, it could correspond to taller vegetation (up to 1.7 m tall). All data extraction and analysis were performed in QGIS (v2.6; [10]) in R (v3.2; [11]). Respondents provided a self-reported indication of the number of days a week they work. The resulting count variable was between 0 and 7   Table 1, we transformed each into a binary risk factor conveying "high" (1) versus "low" (0) risk. We used existing evidence where possible. We also transformed each of the nature dose variables into binary risk factors by setting incrementally higher thresholds of exposure.

Age
The prevalence of mood disorders begins to decline around 45 years [12]. We therefore created a binary risk factor, at which above 45 years, the risk of having poor mental health was (0) and below was (1).

Self-assessment of health
There is a higher prevalence of poor mental health in people with poor physical health (e.g., [13]). We created a binary risk factor at which the risk of having poor mental health was (0) in people with average to very good health and (1) in people with poor to very poor health.

Relative time outdoors
Respondents were considered at higher risk of poor mental health if they spent less time out of doors than usual in the previous week (1). If respondents spent the same, or more time out of doors than usual, they were considered at low risk of poor mental health (0).

Frequency of exposure
Respondents were considered to be at a higher risk of poor mental health if the frequency of visits were not met: less than (1) or ≥ once per week (0); less than (1) or ≥2-4 times per week (0); less than (1) or ≥4-5 times per week (0); less than (1) or ≥6-7 times per week (0).