Associations between green/blue spaces and mental health across 18 countries

Abstract

Living near, recreating in, and feeling psychologically connected to, the natural world are all associated with better mental health, but many exposure-related questions remain. Using data from an 18-country survey (n = 16,307) we explored associations between multiple measures of mental health (positive well-being, mental distress, depression/anxiety medication use) and: (a) exposures (residential/recreational visits) to different natural settings (green/inland-blue/coastal-blue spaces); and (b) nature connectedness, across season and country. People who lived in greener/coastal neighbourhoods reported higher positive well-being, but this association largely disappeared when recreational visits were controlled for. Frequency of recreational visits to green, inland-blue, and coastal-blue spaces in the last 4 weeks were all positively associated with positive well-being and negatively associated with mental distress. Associations with green space visits were relatively consistent across seasons and countries but associations with blue space visits showed greater heterogeneity. Nature connectedness was also positively associated with positive well-being and negatively associated with mental distress and was, along with green space visits, associated with a lower likelihood of using medication for depression. By contrast inland-blue space visits were associated with a greater likelihood of using anxiety medication. Results highlight the benefits of multi-exposure, multi-response, multi-country studies in exploring complexity in nature-health associations.

Full text

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
Scientic Reports | (2021) 11:8903 | 
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Associations between green/blue
spaces and mental health across 18
countries
Mathew P. White1,2*, Lewis R. Elliott2, James Grellier2,3, Theo Economou4, Simon Bell5,
Gregory N. Bratman6, Marta Cirach7,8,9, Mireia Gascon7,8,9, Maria L. Lima10,
Mare Lõhmus11, Mark Nieuwenhuijsen7,8,9, Ann Ojala12, Anne Roiko13, P. Wesley Schultz14,
Matilda van den Bosch7,15,16 & Lora E. Fleming2
Living near, recreating in, and feeling psychologically connected to, the natural world are all
associated with better mental health, but many exposure-related questions remain. Using data
from an 18-country survey (n = 16,307) we explored associations between multiple measures of
mental health (positive well-being, mental distress, depression/anxiety medication use) and: (a)
exposures (residential/recreational visits) to dierent natural settings (green/inland-blue/coastal-
blue spaces); and (b) nature connectedness, across season and country. People who lived in greener/
coastal neighbourhoods reported higher positive well-being, but this association largely disappeared
when recreational visits were controlled for. Frequency of recreational visits to green, inland-blue,
and coastal-blue spaces in the last 4 weeks were all positively associated with positive well-being
and negatively associated with mental distress. Associations with green space visits were relatively
consistent across seasons and countries but associations with blue space visits showed greater
heterogeneity. Nature connectedness was also positively associated with positive well-being and
negatively associated with mental distress and was, along with green space visits, associated with
a lower likelihood of using medication for depression. By contrast inland-blue space visits were
associated with a greater likelihood of using anxiety medication. Results highlight the benets of
multi-exposure, multi-response, multi-country studies in exploring complexity in nature-health
associations.
Poor mental health is the leading cause of disease burden in high-income countries1. is may, at least in part, be
a consequence of rapid urbanisation2, 3 and a growing disconnection from the natural world4, 5. A growing body
of research suggests that living near and/or maintaining regular contact with nature is benecial for a range of
health and well-being outcomes6–8, but several issues remain outstanding9.
First, there is a lack of clarity about the relative importance of merely living near nature, variously referred
to as residential proximity, neighbourhood exposure or indirect contact10, compared to more direct interac-
tions including deliberate engagement through recreational visits11. Although some benets to mental health
and well-being may result from mere neighbourhood exposure, e.g. reduced noise and air pollution and lower
temperatures, others are thought to derive from voluntarily spending time in natural settings for relaxation,
meeting others, and/or undertaking physical exercise10, 12. To date, the vast majority of studies have focused on
residential proximity13 and although a positive association is sometimes reported with recreational visits14, 15,
OPEN
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there is also evidence that many people rarely visit local nature16, while others travel, sometimes quite far, outside
of their neighbourhood for exercise and nature-based recreation17, 18. Proximity is a far from perfect proxy for use.
Second, emerging evidence suggests that mental health may be non-linearly related to recreational exposure,
with diminishing marginal returns beyond a certain threshold19. As with many other ‘goods’, it may be that the
benets of nature-based recreation become less pronounced with each additional visit. Greater clarity about the
relative importance of residential exposure and recreational visits, as well as their potentially non-linear relation-
ships, is critical in designing public health interventions that not only improve availability but also support the
most appropriate levels of use, both locally and further aeld.
ird, most research has operationalised nature in terms of ‘green space’ (e.g. parks, woodlands, street trees,
vegetation cover) and under-explored the potential role of both inland-blue spaces (e.g. rivers, lakes)20, 21, and
coastal-blue spaces (e.g. beaches, promenades)22, for mental health. Although green and blue spaces share many
qualities (e.g. cooling eects, biodiversity), blue spaces also oer alternative recreational activities (e.g. swim-
ming) and have additional features (e.g. unique soundscapes)23–25. It is only through examining both in tandem
that we will get a clearer idea of their relative potential benets for mental health.
Fourth, the eld has used a wide range of mental health metrics, including indices of both positive and nega-
tive mental health6, 8, 10, 12. Rates of poor mental health tend to be lower among populations living in greener
neighbourhoods26–28, and one-o nature walks have been shown to reduce symptoms of anxiety/depression in
at-risk populations29, 30. However, there has been relatively little large-scale research exploring relationships
between voluntary, recreational time in nature and indicators of mental health11, 19, 31. is is important because
meta-analyses suggest that the benets of direct nature exposure tend to have a larger eect on promoting positive
emotions than reducing negative ones32, and thus it may be that indicators of positive mental health are more
sensitive to recreational visits than negative ones. Again, this is best explored in studies that include multiple
exposure metrics alongside multiple mental health outcomes.
Fih, research suggests that psychological connectedness to the natural world, e.g. feeling part of nature or
seeing beauty in natural things, is also positively associated with positive well-being33. Given that people high
in nature connectedness also tend to report more recreational visits34, 35, any positive association between visits
and well-being may be due to the underlying nature connectedness an individual has, rather than a product of
the environment itself. To unpack this possibility, more research is needed to explore the simultaneous relation-
ships between exposures, nature connectedness and mental health, so that their unique roles can be identied.
Finally, there may be important seasonal and societal/cultural dierences in the way nature aects mental
health9, 10, 12. For instance, most research using the Normalized Dierential Vegetation Index (NDVI) as its
measure of residential green space uses summer data, and applies it to health data for the whole year even though
relationships may be dierent when leaf cover is lower in winter months10. Similarly, blue spaces may be bet-
ter for mental health in summer/autumn when the water temperatures are higher36. Living near and spending
time in green and blue space is also likely to be quite dierent, for instance, in southern European countries
than northern European countries. Not only are temperatures and vegetation dierent, hours of daylight vary
substantially across the year potentially aecting time outdoors36, 37.
e current research used a large international survey in an attempt to begin to address these issues. We
collected data on both residential exposure, using satellite imagery of a 1000m buer around the home, and
recreational visits, using self-reported visit frequency in the last four weeks. We also explored whether individu-
als had both inland-blue and coastal-blue space within 1000m buers of their home, and how oen they had
visited each type of blue space in the last 4 weeks. We collected measures of both positive and negative mental
health. Following earlier studies in the eld38, 39 we asked participants to complete the World Health Organisa-
tion’s 5-item index of positive well-being. e aggregate 100-point WHO-5 scale has the additional benet that
low scores (i.e. < 28) are indicative of being at risk of depression/anxiety40, 41, and are thus an indicator of mental
distress. Additionally, we included two questions from the European Health Interview Survey that asked about
recent use of doctor-prescribed medication for depression and anxiety42. To explore the role of nature connected-
ness, we included the Inclusion of Nature in Self (INS) scale43, 44. Finally, our survey was conducted at four times
during a 12-month period, to explore seasonal eects, and across 18 countries/regions to explore generalisability
across locations.
We investigated four hypotheses (H). H1: Greater residential exposure to green, inland-blue and coastal-blue
spaces will be associated with (a) higher positive well-being, (b) lower probability of mental distress, and lower
probability of medication use for (c) depression and (d) anxiety. H2: More frequent recreational visits to these
three settings will show similar relations to those for residential exposure for the four outcomes. H3: e positive
association between visits and mental health in H2 will be non-linear and show diminishing marginal returns.
H4: Psychological connectedness to nature will be a signicant independent predictor of mental health outcomes
over and above residential exposure and recreational contact. Two more exploratory research questions (RQs)
focused on the consistency of any overarching relationships found between nature exposure, connectedness and
mental health across season (RQ1) and country (RQ2).
Hypotheses were tested using a series of linear mixed eects models for WHO-5 scores, and Bernoulli gener-
alised linear mixed eects models for the binary outcomes of mental distress and medication use. Main models
included: (a) residential exposure, (b) recreational visits, and (c) nature connectedness; (d) quadratic (squared)
terms for visit frequency and connectedness to test for non-linearity; and controlled for potential covariates.
Analyses were re-run using stratication on: (a) season; and (b) country, to explore RQs (see “Materials and
methods” section for more details).
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Results
Descriptive data for key predictors are presented in Table1 and data for all covariates in Supplementary TableS1.
Table2 presents core model summaries with full models including all covariates presented in Supplementary
TablesS2–S5. Due to space constraints in the text, descriptive data and covariates are only discussed in Supple-
mentary Materials, and the 95% Condence Intervals for estimates are reported in Tables and Figures. In order to
maintain model power for our more exploratory questions into seasonal and country variation we focused on the
WHO-5 positive well-being scores, rather than the dichotomous indices of mental distress and medication use.
Residential exposure (H1). ere was limited support for Hypothesis 1. e only signicant association
between residential exposure and mental health was for the WHO-5 scores for the 3rd versus 1st quartile of
greenspace (β = 1.01; p < 0.05; Table2). is was partly due to the inclusion of visit frequency in the main model.
Without visit frequency, but with socio-demographic controls (Supplementary TableS2), there were also posi-
tive associations between living in quartile 4 (vs. quartile 1) of greenspace (β = 1.78, p < 0.001) and living within
1000m of the coast (β = 1.98; p < 0.001). ere were no associations between residential exposure and mental
distress or depression/anxiety medication use in models including or excluding visit frequency (Supplementary
TablesS2, S3).
Recreational visits (H2 and H3). Supporting Hypothesis 2, the linear terms for visit frequencies were sig-
nicantly positively associated with WHO-5 scores: green space (β = 0.26; p < 0.001); inland-blue space (β = 0.12;
p < 0.001); coastal-blue space (β = 0.19; p < 0.001), and negatively associated with the likelihood of mental distress
(WHO-5 < 28; a ll three ORs = 0.97; p < 0.001). e likelihood of using depression medication was also negatively
associated with green space visit frequency (OR = 0.99, p < 0.05). In contrast, the likelihood of using anxiety
medication was positively associated with inland-blue space visits (ORs = 1.02; p < 0.05).
Partly supporting Hypothesis 3, there were also signicant quadratic terms, indicative of non-linear dimin-
ishing marginal returns, for: (a) green space and inland-blue space visits and positive well-being (WHO-5); (b)
all three visit types and mental distress (WHO-5 < 28); and (c) green space visits and depression medication use.
However, because the estimates are based on only one extra visit per 4weeks, the odds ratios are only visibly
dierent from a null result at the third decimal. To aid interpretation, Fig.1 plots the combined eects of the
linear and quadratic terms for each visit type, for each outcome (panels a–l). Taking panel (a) as an example,
the linear relationship between green space visits and WHO-5 is reected in the positive upward slope, and the
quadratic eect is reected in the rate of increase getting gradually smaller and the curve beginning to atten
out. e wider condence intervals to the right reect fewer people visiting green spaces more than 40 times in
the last four weeks and the curve ends at 56 visits due to our capping procedure at a maximum of two visits per
day (see “Methods” section). e opposite eect occurs for measures of mental distress, e.g. panel (b) shows a
decreased probability of reporting a WHO-5 score < 28 with each additional green space visit, but this decrease
gets progressively smaller as the number of visits increases. e large condence intervals for high levels of inland
visits were due to the small number of people visiting these spaces > 40 times in the last four weeks.
Table 1. e Ns, percentages (%), means (Ms), standard deviations (SDs), and correlations (r / rpb) for the
four mental health outcomes as a function of residential exposure(Q = quartile), recreational visits and nature
connectedness for the analytical sample (n = 16,302). r Pearson’s correlation, rpb point bi-serial correlation (due
to binary outcome); INS= inclusion of nature in self scale. ***p < 0.001; see Supplementary TableS1 for details
of all covariates.
n % M SD
WHO-5 WHO-5 < 28 Depression Meds Anxiety Meds
M/r SD N/rpb % N/rpb % N/rpb %
Residential exposure [within 1000m]
Greenspace [Q1] 4103 25.17 1.36 1.87 58.79 21.55 381 9.29 354 8.63 366 8.92
Greenspace [Q2] 4098 25.14 19.79 9.35 59.73 21.57 352 8.59 362 8.83 389 9.49
Greenspace [Q3] 4071 24.97 62.11 14.44 61.29 21.53 333 8.18 330 8.11 374 9.19
Greenspace [Q4] 4030 24.72 96.85 4.18 60.86 22.17 352 8.73 405 10.05 416 10.32
Inland blue [no] 10,141 62.21 NA NA 60.27 22.00 897 8.85 872 8.60 962 9.49
Inland blue [yes] 6161 37.79 NA NA 59.98 21.25 521 8.46 579 9.40 583 9.46
Coastal blue [no] 14,507 88.99 NA NA 60.04 21.77 1272 8.77 1330 9.17 1410 9.72
Coastal blue [yes] 1795 11.01 NA NA 61.11 21.35 146 8.13 121 6.74 135 7.52
Recreational visits [last 4weeks]
Green NA NA 12.34 12.85 0.26*** NA − 0.12*** NA − 0.01 NA 0.03*** NA
Inland blue NA NA 6.08 8.95 0.19*** NA − 0.08*** NA 0.03* NA 0.06*** NA
Coastal blue NA NA 5.34 10.17 0.18*** NA − 0.07*** NA 0.00 NA 0.05*** NA
Nature connectedness
INS NA NA 4.14 1.65 0.24*** NA − 0.11*** NA − 0.04*** NA − 0.03*** NA
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Nature connectedness (H4). Supporting Hypothesis 4, nature connectedness was independently: (a)
positively associated with positive well-being (β = 2.35, p < 0.001); (b) negatively associated with mental distress
(OR = 0.62; p < 0.001), with diminishing marginal returns reected in a signicant quadratic term (OR = 1.05;
p < 0.001); and (c) negatively associated with depression medication use (OR = 0.83, p < 0.05). ese relationships
are shown in panels m–p in Fig.1. Note that the larger coecients for connectedness are partly a reection of
the fact this was a seven-point scale (compared to the 0–56 scale for visits).
Seasonality (RQ1). Figure2 presents visit frequency for the last 4weeks as a function of season. Despite
the drop of approximately two visits in all three settings in autumn/winter, compared to spring/summer, visits to
inland-blue and coastal-blue both remained at an average of just above 4 (i.e. once a week). e stratied results
predicting positive well-being for each season are presented in Supplementary TableS4. Residential greenspace
was only signicantly associated with positive well-being for Q3 versus Q1 in spring (β = 1.78), and there con-
tinued to be no signicant associations with either residential inland- or coastal- blue space in any season. In
terms of visits, each additional green space visit was associated with signicantly greater WHO-5 scores across
all four seasons (spring β = 0.24, summer β = 0.22, autumn β = 0.28, winter β = 0.31, all p s < 0.001). A signicant
association with coastal-blue space visits was found in summer (β = 0.23), autumn (β = 0.21) and winter (β = 0.20;
ps < 0.01), and with inland-blue space visits only in spring β = 0.14 and winter β = 0.14 (ps < 0.05). Nature con-
nectedness was also only positively associated with WHO-5 in summer (β = 2.41), autumn (β = 2.29), and w inter
(β = 3.18; all ps < 0.05).
Country-specic results (RQ2). e stratied results predicting WHO-5 positive well-being for each
country are presented in Supplementary TableS5. Results reect the country-level heterogeneity identied
through the random eect term in the main model. In terms of residential exposure, WHO-5 scores were signi-
cantly higher in both Ireland (Q2 vs. Q1: β = 4.20; Q3 vs. Q1: β = 4.15; Q4 vs. Q1: β = 3.65, ps < 0.05) and Italy (Q3
Table 2. Mental health as a function of residential exposure, recreational visits and nature connectedness
controlling for socio-demographics, season and country. Analyses used survey weights. INS inclusion
of nature in self scale. a Variance of country-level intercepts from the random eects component of the
model; Marginal R2 includes only xed eects and Conditional R2 includes the random country eect, R2
for binary outcomes = Nakawaga Pseudo R2. Models control for sex, age, household income, employment
status, education, long-term illness/disability, marital status, number of adults and children in household,
dog and car ownership, weekly physical activity, season of data collection, and use of the alternative
depression/anxiety medication for medication models only; full models in Supplementary TablesS2 and S3.
*p < 0.05,**p < 0.01,***p < 0.001.
Predictors
WHO-5 scale (0–100) WHO-5 distress (< 28) Depression medication
use Anxiety medication use
Estimates 95% CIs Odds ratios 95% CIs Odds ratios 95% CIs Odds ratios 95% CIs
(Intercept) 48.15*** 46.06, 50.24 0.19*** 0.14, 0.27 0.06*** 0.04, 0.09 0.06*** 0.04, 0.09
Residential exposure [within 1000m]
Greenspace [Q2 vs. Q1] 0.46 − 0.38, 1.30 0.94 0.80, 1.11 0.99 0.83, 1.19 1.15 0.97, 1.36
Greenspace [Q3 vs. Q1] 1.01* 0.15, 1.87 0.93 0.78, 1.10 0.85 0.71, 1.03 1.03 0.86, 1.24
Greenspace [Q4 vs. Q1] 0.37 − 0.51, 1.25 1.02 0.86, 1.21 0.99 0.82, 1.19 1.05 0.88, 1.25
Inland blue [Yes vs. No] − 0.08 − 0.74, 0.58 0.94 0.82, 1.07 0.96 0.83, 1.10 1.00 0.87, 1.14
Coastal blue [Yes vs.
No] 0.74 − 0.31, 1.79 1.01 0.81, 1.25 0.90 0.71, 1.15 0.82 0.65, 1.03
Recreational visits [last 4weeks]
Green 0.26*** 0.22, 0.30 0.97*** 0.96, 0.98 0.99* 0.98, 1.00 1.00 0.99, 1.01
Green2 − 0.00* − 0.00, − 0.00 1.00* 1.00, 1.00 1.00* 1.00, 1.00 1.00 1.00, 1.00
Inland blue 0.12*** 0.05, 0.19 0.97*** 0.96, 0.99 1.01 0.99, 1.02 1.02* 1.00, 1.03
Inland blue2 − 0.00 − 0.00, 0.00 1.00** 1.00, 1.00 1.00 1.00, 1.00 1.00 1.00, 1.00
Coastal blue 0.19*** 0.12, 0.25 0.97*** 0.96, 0.99 0.99 0.98, 1.01 1.01 1.00, 1.02
Coastal blue2 − 0.00 − 0.00, 0.00 1.00* 1.00, 1.00 1.00 1.00, 1.00 1.00 1.00, 1.00
Nature connectedness
INS 2.35*** 1.45, 3.25 0.62*** 0.52, 0.72 0.83* 0.70, 1.00 0.96 0.81, 1.14
INS2 − 0.09 − 0.20, 0.01 1.05*** 1.03, 1.07 1.02 1.00, 1.04 1.00 0.98, 1.02
Random eects
18 country intercept
variancea6.34 0.07 0.18 0.13
Observations 16,302 16,302 16,302 16,302
Marginal R2/Condi-
tional R20.216/0.230 0.235/0.250 0.315/0.351 0.240/0.269
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vs. Q1: β = 4.82; Q4 vs. Q1: β = 4.54, ps < 0.05) in greener neighbourhoods. is pattern was reversed for Finland
(Q3 vs. Q1: β = − 4.20, p < 0.05), where instead, having inland water within 1000m was associated with signi-
cantly higher WHO-5 scores (β = 3.53, p < 0.01). By contrast, in Portugal, inland water was associated with sig-
nicantly lower scores (β = -3.81, p < 0.05). Ireland was the only country where living within 1000m of the coast
was associated with higher WHO-5 scores when controlling for visits and connectedness (β = 5.00, p < 0.05).
An increase of one green space visit in the last four weeks was associated with signicantly greater (at
least p < 0.05) WHO-5 scores in Australia (β = 0.41), Bulgaria (β = 0.48), California (β = 0.42), Czech Republic
(β = 0.27), Estonia (β = 0.23), Finland (β = 0.19), Greece (β = 0.54), Ireland (β = 0.39), Netherlands (β = 0.18),
Portugal (β = 0.32), and Sweden (β = 0.32). For each extra inland-blue visit, WHO-5 scores were signicantly
higher (at least p < 0.05) in Germany (β = 0.36), Hong Kong (β = 0.53) and Spain (β = 0.44), and each additional
coastal visit was associated with higher WHO-5 scores in France (β = 0.57), Portugal (β = 0.27), Spain (β = 0.24),
and Sweden (β = 0.46). Finally a one-point increase in INS scores was associated with signicantly higher (at
least p < 0.05) WHO-5 scores in Canada (β = 4.30), Czech Republic (β = 5.41), Greece (β = 4.40), Hong Kong
(β = 7.61), and UK (β = 3.59).
40
50
60
70
02040
No. of greenspace visits − last four weeks
Predicted WHO−5 score
(0−100)
a
0.0
0.2
0.4
0.6
02040
No. of greenspace visits − last four weeks
Predicted probability of
depression (WHO−5 score <28)
b
0.0
0.2
0.4
0.6
02040
No. of greenspace visits − last four weeks
Predicted probability of
taking depression medication
c
0.0
0.2
0.4
0.6
02040
No. of greenspace visits − last four weeks
Predicted probability of
taking anxiety medication
d
40
50
60
70
02040
No. of inland bluespace visits − last four weeks
Predicted WHO−5 score
(0−100)
e
0.0
0.2
0.4
0.6
02040
No. of inland bluespace visits − last four weeks
Predicted probability of
depression (WHO−5 score <28)
f
0.0
0.2
0.4
0.6
02040
No. of inland bluespace visits − last four weeks
Predicted probability of
taking depression medication
g
0.0
0.2
0.4
0.6
02040
No. of inland bluespace visits − last four weeks
Predicted probability of
taking anxiety medication
h
40
50
60
70
02040
No. of coastal bluespace visits − last four weeks
Predicted WHO−5 score
(0−100)
i
0.0
0.2
0.4
0.6
02040
No. of coastal bluespace visits − last four weeks
Predicted probability of
depression (WHO−5 score <28)
j
0.0
0.2
0.4
0.6
02040
No. of coastal bluespace visits − last four weeks
Predicted probability of
taking depression medication
k
0.0
0.2
0.4
0.6
02040
No. of coastal bluespace visits − last four weeks
Predicted probability of
taking anxiety medication
l
40
50
60
70
1234567
Nature connectedness (1−7)
Predicted WHO−5 score
(0−100)
m
0.0
0.2
0.4
0.6
1234567
Nature connectedness (1−7)
Predicted probability of
depression (WHO−5 score <28)
n
0.0
0.2
0.4
0.6
1234567
Nature connectedness (1−7)
Predicted probability of
taking depression medication
o
0.0
0.2
0.4
0.6
1234567
Nature connectedness (1−7)
Predicted probability of
taking anxiety medication
p
Figure1. Relationships between: (1) Green space visits in last 4weeks, (2) Inland-blue space visits in last
4weeks, (3) Coastal-blue space visits in last 4weeks, and (4) nature connectedness (1–7); and positive well-
being (0–100; a,e,l,m), risk of mental distress (0–1; b,f,j,n), use of depression medication (0–1; c,g,k,o), and
use of anxiety medication (0–1; d,h,l,p), averaged across 18 countries (n = 16,302). Plots are based on predicted
values from linear and logistic mixed eects regression models including linear and quadratic terms (with
95% Condence Intervals) for visit frequency and connectedness controlling for residential exposure, visit
frequencies to alternative locations, connectedness (a–l only), age, gender, employment status, relationship
status, household income, longstanding-illness, education level, household composition, dog ownership, car
ownership, physical activity, season (sample wave), and country (as a random eect). Depression models
also control for anxiety medication use and vice versa. Visit frequency was capped at n = 56 (i.e. two visits per
day over 4weeks). Covariates are held constant at their reference categories, or at their means for continuous
predictors.
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To help visualise cross-country patterns we used the observed values from recreational visit frequency and
connectedness, and the predicted values of WHO-5 from our original models, averaged across all individuals
in each country (Fig.3). With lower than average visit duration and connectedness, Hong Kong, the UK, and
California, also reported the lowest positive well-being. By contrast, countries with the highest levels of posi-
tive well-being (e.g. Spain, Portugal, and Bulgaria) were among the countries with the highest nature visits and
connectedness.
Discussion
e present research provides signicant new insights into the relationships between mental health, residential
and recreational exposure to green and blue spaces, and feeling psychologically connected to the natural world.
Collecting data in four seasonal waves, across 18 dierent countries/regions allowed us to make far more nuanced
conclusions than are generally possible.
Contrary to Hypothesis 1, there was little evidence in the current sample that the amount of green, and
presence of inland- and coastal-blue space, within 1000m of the home was directly related to mental health. In
models without recreational visits, but controlling for socio-demographic confounders, residents of the greenest
and coastal areas did report higher positive well-being, but these eects disappeared when visits were added,
suggesting that visit frequency mediated these eects. In other words, the reason why residents of the greenest
and coastal neighbourhoods experienced better positive mental health might be because these neighbourhood
qualities encouraged more frequent recreational visits12, 14, 15. e only residential exposure metric that signi-
cantly predicted positive mental health controlling for visits was living in the 3rd versus 1st quartile of green
space, with the season models suggesting this was only signicant in spring.
Despite the overall picture, some residential associations did remain aer controlling for visits in the country-
specic models. Ireland showed higher WHO-5 scores for those in greener and coastal neighbourhoods, and Ital-
ians also had higher positive well-being in greener neighbourhoods even accounting for visits and connectedness.
Residents in Finland were the only sample to show signicantly lower well-being in the greenest areas, though
they did have higher well-being if they lived near rivers/lakes. Finally, those in Portugal had lower WHO-5 if
they lived near inland waters. Although tempting, we are reluctant to speculate here about possible reasons for
these cross-country dierences. Our eect sizes are small, and thus some countries may not be showing patterns
due to a lack of power. In countries where eects did emerge, we were not able to explore potential mechanisms
underlying relationships. Further cross-country research is needed with larger within-country samples and a
greater focus on potential mechanisms to address these possibilities, but the cross-country heterogeneity does
support the contention that caution is needed when trying to generalise across locations12.
Supporting Hypothesis 2, the frequency of visits to green spaces in the last 4 weeks was positively associated
with positive well-being and negatively associated with mental distress and the use of doctor-prescribed depres-
sion (though not anxiety) medication. Extending previous research, those who made more frequent visits to
both inland- and coastal- blue spaces also reported more positive well-being and lower rates of mental distress,
even controlling for the number of green space visits in the past four weeks. We recognise that despite being
signicant, these eects are, however, small in absolute terms. For instance, an extra 4 green space visits (i.e. one
0
5
10
Spring SummerAutumn Winter
Season
Average number of visits
in the last four weeks
Environment type
Greenspace
Inland bluespace
Coastal bluespace
Figure2. Average number of visits to green spaces, inland-blue spaces and coastal-blue spaces as a function of
season across the whole sample (n = 16,302). Error bars represent 95% condence intervals.
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per week) is still only associated with a 1.04% higher WHO-5 score (i.e. β = 0.26 × 4, on the 100-point WHO-5
scale). Intriguingly, visiting inland-blue spaces was positively associated with anxiety medication use. Given that
we asked about voluntary recreational visits, it seems unlikely that visits could lead to greater anxiety sucient to
require medication (or these individuals would stop going). Rather, we suspect that it reects people with anxiety
seeking these places out for the calming eects they have, and thus using them for self-management purposes45, 46.
Although visits decreased in frequency in autumn/winter, compared to spring/summer, the drop was not
substantial and was similar for both green and blue spaces. Indeed, positive well-being remained signicantly
positively associated with visiting inland and coastal waters in winter, suggesting that potential benets to men-
tal health do not only occur in the warmer months. In terms of country-level eects, a positive association was
found between at least one type of visit and WHO-5 scores in 16/18 countries, with no associations present for
Canada or the UK. Most countries (11/18) showed a positive association with green space visit frequency, and
Spain, Hong Kong and Germany showed a positive relationship with visits to inland waters. Much of the research
on inland-blue spaces has come from Germany-based researchers20, 23, 47 potentially pointing to something more
fundamental in a country with a relatively low coastline to population ratio. ree of the four countries showing
a positive association between coastal visit frequency and mental health were in the warmer European South
(Spain, Portugal and France). e fourth country to show this relationship was Sweden, which also showed a
signicant positive association with green space visits, potentially indicating the importance of overall outdoor
nature recreation among this population for mental health37.
Partially supporting Hypothesis 3 there was also tentative evidence of non-linear relationships for visits, with
diminishing marginal returns. Nevertheless, due the cross-sectional nature of the data, and small eect sizes,
we remain cautious. Further work is needed including longitudinal work that follows people’s exposure over
time and experimental work that randomly allocates people to dierent visit frequencies within a given period.
Supporting Hypothesis 4, greater nature connectedness was positively associated with positive well-being and
negatively associated with both mental distress and depression medication use. When stratied by season and
country (for WHO-5) a more complicated picture emerged. Although the association between connectedness
and positive well-being was evident in summer, autumn and winter, it was non-signicant in spring. By contrast,
Queensland, AU
Bulgaria
California, US
Canada Czech Republic
Estonia
Finland
France
Germany
Greece
Hong Kong, CN
Ireland
Italy
Netherlands
Portugal
Spain
Sweden
United Kingdom
50
55
60
65
70
7.510.012.515.017.5
Average number of greenspace visits − last four weeks
Average predicted WHO5
wellbeing index score (0−100)
Queensland, AU
Bulgaria
California, US
Canada
Czech Republic
Estonia
Finland
France
Germany
Greece
Hong Kong, CN
Ireland
Italy
Netherlands
Portugal
Spain
Sweden
United Kingdom
50
55
60
65
70
46 18 0
Average number of inland bluespace visits − last four weeks
Average predicted WHO5
wellbeing index score (0−100)
Queensland, AU
Bulgaria
California, US
Canada
Czech Republic
Estonia
Finland
France
Germany
Greece
Hong Kong, CN
Ireland
Italy
Netherlands
Portugal
Spain
Sweden
United Kingdom
50
55
60
65
70
4812
Average number of coastal bluespace visits − last four weeks
Average predicted WHO5
wellbeing index score (0−100)
Queensland, AU
Bulgaria
California, US
Canada
Czech Republic
Estonia
Finland
France
Germany
Greece
Hong Kong, CN
Ireland
Italy
Netherlands
Portugal
Spain
Sweden
United Kingdom
50
55
60
65
70
345
Average nature connectedness (1−7)
Average predicted WHO5
wellbeing index score (0−100)
Figure3. Country level relationships between positive well-being (0–100) and: (a) Green space visits in last
4weeks; (b) Inland-blue space visits in last 4weeks; (c) Coastal-blue space visits in last 4weeks and (d) nature
connectedness (1–7). Plots are based on aggregated predicted values across countries from our original mixed
models controlling for residential exposure visit frequencies to alternative locations, connectedness (a–c),
age, gender, employment status, household income, longstanding-illness, relationship status, education level,
household composition, dog ownership, car ownership, physical activity, and season (sample wave).
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we note that residential greenness was only related to WHO-5 in spring and it may be that these are interacting in
some way but we were unable to explore this further here. Combined, the ndings suggest that researchers with
quite a broad spectrum of interests in the nature-health eld (inc. residential exposure, visits, connectedness)
might want to be more sensitive to issues of seasonality in future work.
Further, only four countries showed signicant associations between positive well-being and nature con-
nectedness in the stratied models, two of which, Canada and the UK, were the only countries to not show
signicant associations between positive well-being and at least one sort of visit. While recognising the potential
for statistical artefacts (due to shared variance), as with Germany and inland-blue space research, we note a strong
research tradition in nature connectedness in both Canada34 and the UK48. Again we wonder whether this is
merely coincidence or whether it reects a higher level of importance of nature connectedness in these countries
that has ltered through to research priorities, perhaps because of the relatively low levels of connectedness at
the population level.
Intriguingly, the other countries/regions with low levels of connectedness (and visit frequencies), Hong Kong,
California, Queensland, and Ireland, have certain commonalities in terms of language and cultural heritage with
UK/Canada. Although there are very few international studies with which to compare our ndings, Kruize etal.49
also found the lowest amount of regular time in nature in the UK city (Stoke on Trent) of their four city study
(Barcelona [Spain], Kaunas [Lithuania], and Doetinchem [Netherlands]), supporting the current visit results.
Further research is needed to explore what other commonalities these countries might have (e.g. economic
models of growth or attitudes towards the natural environment) that could explain these ndings.
Despite the robust sample and use of multiple, internationally recognised measures of well-being and mental
health, we recognise several limitations with the current work. First, we acknowledge that multiple residen-
tial buers have been used in past research, and it may be that the relatively little evidence of an association
between residential exposure and mental health here is in part a consequence of our 1000m selection based on
a 10–15min walk50. Further there may be limitations in the methods we used to establish common green/blue
space residential metrics across European and non-European countries, or the way in which we operationalised
green and blue spaces with these metrics (e.g. the landcovers we included in green space)51. Future international
studies may want to select alternative buers and/or methods of assessing residential exposure.
Second, much of the data were self-reported and we were unable to validate, for instance, people’s nature
experiences or medication use. For current purposes, we applied approximate numerical values to verbal visit
frequency response categories and it is also possible that some respondents ‘double-counted’ some visit loca-
tions (e.g. saying they had visited woodlands and a lake in the last 4weeks when in fact they only did one visit
that included both features). Similarly, although our prescription item is widely used42, it also does not account
for length of use or dosage. Although challenging to collect on a similar scale as our multi-country study, more
objective data on time in nature, e.g. using experience sampling approaches22, and mental health status should
be a goal of future research.
ird, as already noted, the data is cross-sectional and thus can only speak to associations rather than cau-
sation. is was perhaps most evident in the positive association between inland-blue space visits and anxiety
medication, which we took to suggest reverse causality. Nonetheless, many of our results are consistent with a
growing body of experimental and longitudinal research, and used the sort of sample that would not be easily
possible with these approaches.
Fourth, our results focus on averages and we recognise that individuals may vary widely in terms of the
amount of nature that may benet them personally, and that this too is likely to change over time as a function
of need45.
Fih, although our sample was collected by an international polling company and was weighted to be repre-
sentative by age, gender and region within each country, it was not fully representative of the respective countries,
in part due to limitations of online panels52. Our country-level observations therefore remain tentative at this
stage.
Finally, our sample was limited to a selection of high-income countries/regions, and further research is
needed in low-middle income nations where contact with the natural world, and consequent relationships, may
be dierent. At this stage, our ndings only speak to relatively developed settings where, typically, the natural
world presents few threats and challenges. Conclusions about whether contact with, and connectedness to, the
natural world is a universal good for human mental health and well-being will depend on the results of similar
research across a far broader range of contexts.
ese limitations notwithstanding, our ndings have a number of implications. Results suggest the associa-
tions between recreational nature contact and clinical levels of mental distress are complicated. People may be
using these environments to manage symptoms46 and perhaps we should not necessarily expect higher levels
of recreational contact to be associated with incidence of depression and/or anxiety at a population level. More
research is needed into how people with poor mental health spontaneously use nature to help with self-man-
agement, alongside more traditional research trying to support them to access these places through things such
as ‘green prescriptions’53.
Results also oer support for initiatives e.g. education programs, aimed at increasing levels of psychologi-
cal connectedness to the natural world, irrespective of direct exposure, for mental health as well as ecological
reasons54. Given how relatively disconnected from the natural world our UK sample was, alongside low levels
of well-being, it is promising that the UK government is prioritising the building of nature connectedness in
the population55. Other countries in the English speaking world with low nature connectedness and well-being
might consider a similar approach.
Finally, the results suggest that spending recreational time in both green and blue settings may be more
important than merely living near nature, at least in terms of mental health. Although social inequalities in access
and quality remain56, over 90% of people living in urban areas of Europe already have access to a public green
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space > 0.25 hectares within a 10-min walk of their home57. Promoting greater use of these green (and blue) spaces
may be a policy objective to go alongside structural changes in the amount of green and blue spaces in people’s
neighbourhoods. For instance, the United Nations (UN) Sustainable Development Goal [SDG] 11.7 proposes
that “by 2030, [states should] provide universal access to safe, inclusive and accessible, green and public spaces,
particularly for women and children, older persons and persons with disabilities”58. Future SDGs, or similar
programs, might consider expressing targets in terms of use of, as well as access to, green/blue spaces, analogous
to how SDG 12: ‘Ensure sustainable consumption and production patterns’, has sub-goals for both policies and
infrastructure (12.1), and citizen actions and behaviors (12.5).
Materials and methods
Sample and survey. Data came from an 18-country self-report survey conducted as part of the BlueHealth
project59, exploring recreational use of the natural environment with a particular focus on aquatic, or blue space,
environments such as rivers, lakes and seas. It was administered by an international polling company using
established online panels in four seasonal waves between June 2017 and April 2018. Stratied samples of ≈
1000 respondents were collected in 14 European countries (Bulgaria, Czech Republic, Estonia, Finland, France,
Germany, Greece, Ireland, Italy, Netherlands, Portugal, Spain, Sweden, and the United Kingdom) and four other
countries/regions (California [USA], Canada, Hong Kong [China], and Queensland [Australia]). Stratied sam-
pling by sex, age, and region of residence was undertaken to achieve broad national representativeness. e
full sample consisted of 18,838 respondents, and survey weights were provided by data collectors to adjust for
representativeness in analyses. Due to missing data (e.g. ‘don’t know’ responses on the INS scale and elsewhere)
the analytical sample was n = 16,307. Full methodological details are available on the Open Science Framework
website: https:// doi. org/ 10. 17605/ OSF. IO/ 7AZU251. Data collection was carried out in accordance with rele-
vant guidelines and regulations, and informed consent was obtained from all participants. Ethical approval was
granted by the University of Exeter Medical School’s Research Ethics Committee (Ref: Aug16/B/099).
Mental health. Following previous research in the eld38, 39, our measure of positive well-being was the
World Health Organisation 5-item wellbeing index (WHO-5). Participants responded to ve statements about
their emotional state during the past two weeks e.g. “I have felt calm and relaxed”, on scales from ‘At no time’ (0)
to ‘All of the time’ (5). Values were summed and multiplied by 4 to give a score out of 100, with higher scores
reecting higher well-being. An advantage of the WHO-5 is that scores < 28 have shown concurrent validity with
structured clinical interviews for diagnosing depression/anxiety40, 41, and thus this threshold provided our rst
indicator of poor mental health, i.e. mental distress.
Our second and third indicators of poor mental health were self-reported use of doctor-prescribed medica-
tion for: (a) depression, and (b) anxiety. Respondents were asked: “During the past two weeks, have you used
any medicines for any of the following conditions that were prescribed for you by a doctor? Please select all that
apply”, with ‘yes’/‘no’ response options. Alongside physical health conditions, e.g. high blood pressure, were the
conditions of current interest: ‘depression’ and ‘tension and anxiety’. e question was taken from the European
Health Interview Survey42. As 4.0% (n = 740) reported taking both medications, our regressions predicting either
outcome, controlled for concurrent use of the alternative medication type to identify the unique associations
with contact and connectedness with use of each medication.
Residential exposure. Participants were asked to input their home location via a Google Maps applica-
tion programming interface. For condentiality reasons, recorded coordinates were rounded to three decimal
degrees on both the longitude and latitude scale. Residential natural environment exposure indicators were
assigned to these coordinates using the Global Land Cover dataset (GlobeLand30), which is a globally-consist-
ent 30m resolution raster data set based on classication of remotely-sensed data. Full details of our processing
of this data and references to relevant earlier work can be found in the technical report51. e data feature ten
land cover classes which have demonstrated satisfactory congruence with more localised land use maps (general
accuracy level of > 80%). Land classied as “forests”, “grassland”, “shrubland” and “cultivated land” was collapsed
into a ‘green space’ measure and land classied as “water bodies” or “wetlands” into an ‘inland-blue space’ meas-
ure. Radial buers of 1000m around residential locations, representing a 10–15min walk50 were established
and the percentage of green and inland-blue spaces within these buers assigned. Residential green space was
divided into four quartiles, and due to a highly skewed distribution15, inland-blue space was categorised into
just “none” = 0% (reference) and “some” > 0% to 100%. Residential exposure to coastal-blue space within 1000m
was calculated using a Euclidean (crow-ies) distance metric. Distance from the home coordinate to the nearest
coastline was dened by the highest resolution version of the Global Self-consistent Hierarchical High-resolu-
tion Geography shoreline database from the National Oceanic and Atmospheric Administration51. is dataset
provides a balance between renement in capturing a good representation of the land-sea interface, but enough
granularity that smaller rivers and other inland waterways are rarely miss-classied as coastline.
Recreational contact with green/blue spaces. Participants were presented with a list, and archetypical
pictures of, 12 types of green spaces (e.g. local park, woodlands, meadows), 9 inland-blue spaces (e.g. lake, rural
river, canal) and 8 coastal-blue spaces (e.g. esplanades, rocky shores, beaches) and asked how oen in the last 4
weeks they had visited each type of location. e last 4 weeks was chosen as an appropriate recall period due to
its use in previous leisure visit surveys51. Response options, were: “Not at all in the last 4 weeks”, “Once or twice
in the last 4 weeks”, ” Once a week” and “Several times a week”. For current purposes we estimated a numerical
equivalent of these response options to be zero, one, four and eight visits in the last 4 weeks respectively.
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Total green space visits in the last 4weeks were derived by summing the visit frequency estimates for each of
the 12 green space types. Due to a small number of people reporting very high visit frequencies, and introduc-
ing considerable skew, we capped the total number of visits to 56, which would be consistent with someone, for
instance, walking their dog twice a day over a 4-week period. Only 1.5% of respondents were capped in this way.
Four weekly inland- and coastal-blue space visit frequencies were derived in a similar way with only 0.5% and
0.6% of respondents requiring a cap for inland and coastal visits respectively.
Nature connectedness. Psychological connectedness to the natural world was measured using the Inclu-
sion of Nature in Self (INS) scale43, 44. Seven images were presented with two circles, one labelled ‘Self’ and
one labelled ‘Nature’, which increasingly overlapped with each image to indicate greater nature connectedness.
Participants were asked to select the picture “that best describes your relationship with the natural environment.
How interconnected are you with nature?” with the lowest connectedness reecting no overlap between the
circles (1), and highest connectedness reecting almost totally overlapping circles (7).
Covariates. Sociodemographic controls, comparable to related studies, included: gender (female = ref;
male); age (16–29years = ref; 30–39years; 40–49years; 50–59years; ≥ 60years); highest educational achieve-
ment (degree; below degree = ref); employment status (in paid employment, in education, retired, homemaker;
not working/unemployed = ref); disposable household income quintiles (lowest quintile = ref); longstanding ill-
ness or disability (i.e. underlying health condition, yes, no = ref); relationship status (married/cohabiting; single/
separated/divorced/widowed = ref); number of adults in the household (1 = ref; 2, ≥ 3); number of children in the
household (0 = ref; 1, ≥ 2); dog ownership (yes, no = ref); car ownership (yes, no = ref); weekly days of physical
activity ≥ 30min (0 = ref, 1–4, ≥ 5); and survey wave (spring = ref, summer, autumn, winter). Of note seasons were
approximate since ‘Spring’ data were collected in June and referred to the ‘last 4weeks’ (i.e. May–June), ‘Summer’
in September (i.e. August–September), ‘Autumn’ in December (November to December), and ‘Winter’ in March
(i.e. February–March), seasons were reversed for Australia. Again, full details are available in the technical report
online51.
Analyses. Hypotheses were tested using a series of linear mixed eects models for WHO-5 scores, and
Bernoulli generalised linear mixed eects models for the binary outcome variables of mental distress and use
of medication for depression and anxiety. Models included quadratic (squared) terms for visit frequency and
connectedness to test for non-linearity (diminishing marginal returns)19. Country of residence was included
as a random intercept term to account for national-level respondent clustering. Models were tted by maxi-
mum likelihood with Laplace approximation (to integrate the random eects), and survey weights were applied
to improve national representativeness with regards to the sampling strata within each country (sex, age, and
region of residence). Analyses controlled for covariates listed above, with models for depression medication also
controlling for anxiety medication and vice versa. Each dependent variable was analysed using three models: (a)
residential exposure and covariates only, (b) residential, covariates plus recreational contact; and (c) residential,
covariates, recreational plus connectedness. is allowed us to see how the addition of recreation and connect-
edness aected residential relationships. e largest generalized variance ination factor (VIF) of any term in
any of the fully-adjusted models was VIF = 1.81, suggesting there was no substantive multi-collinearity in any
of the models. All models are presented in Supplementary TablesS2 and S3 and only the nal models includ-
ing all exposure measures are in the main text due to space constraints (Table2). e full WHO-5 model was
subsequently stratied by season and country to explore potential variation across the year and location. We did
not perform similar stratications for mental distress or medication use due to lack of power in predicting these
binary outcomes in stratied models. Analyses were performed in R v3.6.0 (R Core Team, 2019) using the ‘lme4’
package for statistical modelling60.
Data availability
All data for the BlueHealth International Survey will be made open access in 2025 in accordance with an embargo
agreement by research partners. For queries about the specic data and analysis, including r script, used in the
present manuscript please contact the corresponding author.
Received: 17 July 2020; Accepted: 31 March 2021
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Acknowledgements
We thank Ben Butler, Gavin Ellison, and Tom Powell at YouGov for managing data collection and Joanne Garrett,
Michelle Tester-Jones, Leanne Martin, Sabine Pahl, Beth Roberts, Emma Squire, Tim Taylor, and Ben Wheeler
for comments and advice on this research.
Author contributions
M.P.W.: Conceptualization, Methodology, Formal analysis, Writing—original dra, Writing—review & editing,
Project administration, Funding acquisition. L.R.E.: Conceptualization, Methodology, Formal analysis, Data
curation, Writing—review & editing, Visualization. J.G.: Methodology, Formal analysis, Writing—review &
editing, Visualization, Project administration. T.E.: Formal analysis, Writing—review & editing. S.B., G.N.B.,
M.N., A.O., A.R., M.L.M. and M.v.d.B.: Writing—review & editing, Funding acquisition. M.C.: Methodology
(Residential exposure). M.G.: Conceptualization, Writing—review & editing. M.L.S., S.P. and W.S.: Writing—
review & editing. L.E.F.: Writing—review & editing, Supervision, Project administration, Funding acquisition.
Funding
is project has received funding from the European Union’s Horizon 2020 research and innovation programme
under grant agreement No 666773 (BlueHealth). Data collection in California was supported by the Center for
Conservation Biology, Stanford University. Data collection in Canada was supported by the Faculty of Forestry,
University of British Columbia. Data collection in Finland was supported by the Natural Resources Institute
Finland (Luke). Data collection in Australia was supported by Grith University and the University of the Sun-
shine Coast. Data collection in Portugal was supported by ISCTE—University Institute of Lisbon. Data collection
in Ireland was supported by the Environmental Protection Agency, Ireland. Data collection in Hong Kong was
supported by an internal University of Exeter—Chinese University of Hong Kong international collaboration
fund. e funders had no role in the conceptualisation, design, analysis, decision to publish or preparation of
the manuscript.
Competing interests
e authors declare no competing interests.
Additional information
Supplementary Information e online version contains supplementary material available at https:// doi. org/
10. 1038/ s41598- 021- 87675-0.
Correspondence and requests for materials should be addressed to M.P.W.
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