Spending at least 120 minutes a week in nature is associated with good health and wellbeing

Abstract

Spending time in natural environments can benefit health and well-being, but exposure-response relationships are under-researched. We examined associations between recreational nature contact in the last seven days and self-reported health and well-being. Participants (n = 19,806) were drawn from the Monitor of Engagement with the Natural Environment Survey (2014/15–2015/16); weighted to be nationally representative. Weekly contact was categorised using 60 min blocks. Analyses controlled for residential greenspace and other neighbourhood and individual factors. Compared to no nature contact last week, the likelihood of reporting good health or high well-being became significantly greater with contact ≥120 mins (e.g. 120–179 mins: ORs [95%CIs]: Health = 1.59 [1.31–1.92]; Well-being = 1.23 [1.08–1.40]). Positive associations peaked between 200–300 mins per week with no further gain. The pattern was consistent across key groups including older adults and those with long-term health issues. It did not matter how 120 mins of contact a week was achieved (e.g. one long vs. several shorter visits/week). Prospective longitudinal and intervention studies are a critical next step in developing possible weekly nature exposure guidelines comparable to those for physical activity.

Full text

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Spending at least 120 minutes a
week in nature is associated with
good health and wellbeing
Mathew P. White1, Ian Alcock1, James Grellier
1, Benedict W. Wheeler1, Terry Hartig2,
Sara L. Warber1,3, Angie Bone1, Michael H. Depledge1 & Lora E. Fleming1
Spending time in natural environments can benet health and well-being, but exposure-response
relationships are under-researched. We examined associations between recreational nature contact in
the last seven days and self-reported health and well-being. Participants (n = 19,806) were drawn from
the Monitor of Engagement with the Natural Environment Survey (2014/15–2015/16); weighted to be
nationally representative. Weekly contact was categorised using 60 min blocks. Analyses controlled for
residential greenspace and other neighbourhood and individual factors. Compared to no nature contact
last week, the likelihood of reporting good health or high well-being became signicantly greater with
contact ≥120 mins (e.g. 120–179 mins: ORs [95%CIs]: Health = 1.59 [1.31–1.92]; Well-being = 1.23
[1.08–1.40]). Positive associations peaked between 200–300 mins per week with no further gain. The
pattern was consistent across key groups including older adults and those with long-term health issues.
It did not matter how 120 mins of contact a week was achieved (e.g. one long vs. several shorter visits/
week). Prospective longitudinal and intervention studies are a critical next step in developing possible
weekly nature exposure guidelines comparable to those for physical activity.
A growing body of epidemiological evidence indicates that greater exposure to, or ‘contact with’, natural envi-
ronments (such as parks, woodlands and beaches) is associated with better health and well-being, at least among
populations in high income, largely urbanised, societies1. While the quantity and quality of evidence varies across
outcomes, living in greener urban areas is associated with lower probabilities of cardiovascular disease2, obesity3,
diabetes4, asthma hospitalisation5, mental distress6, and ultimately mortality7, among adults; and lower risks of
obesity8 and myopia9 in children. Greater quantities of neighbourhood nature are also associated with better
self-reported health10–12, and subjective well-being13 in adults, and improved birth outcomes14, and cognitive
development15, in children.
However, the amount of greenspace in one’s neighbourhood (e.g. percent of land cover in a 1 km radius from
the home), or the distance of one’s home to the nearest publically accessible green space or park16 is only one way
of assessing an individual’s level of nature exposure. An alternative is to measure the amount of time individuals
actually spend outside in natural environments17,18, sometimes referred to as ‘direct’ exposure19. Both approaches
are potentially informative. Residential proximity to nature may be related to health promoting factors such as
reduced air and noise pollution (although the relationships are complex20); and may also provide ‘indirect’ expo-
sure via views from the property21. Residential proximity is also generally positively related to ‘direct’ exposure;
i.e. people in greener neighbourhoods tend to report visiting greenspace more oen22. Yet many nature visits
take place outside of the local neighbourhood23. Moreover, such visits may compensate for a lack of nature in the
neighbourhood24. In other words, direct exposure, or more specically in the current context, recreational time
spent in natural environments per week, cannot accurately be inferred from neighbourhood greenspace near the
home.
Using data from a representative sample of the adult population of England, we aimed to better understand
the relationships between time spent in nature per week and self-reported health and subjective well-being. Our
research builds directly on a small number of studies that have started to look at similar issues17,18,25,26, and answers
the call made in several recent reviews for more work in this area27,28. Quantication of these ‘exposure-response’
1European Centre for Environment and Human Health, University of Exeter Medical School, Exeter, UK. 2Institute
for Housing and urban Research, Uppsala University, Box 514, SE-75120, Uppsala, Sweden. 3Department of Family
Medicine, University of Michigan Medical School, Ann Arbor, MI, USA. Correspondence and requests for materials
should be addressed to M.P.W. (email: mathew.white@exeter.ac.uk)
Received: 8 May 2018
Accepted: 8 May 2019
Published: xx xx xxxx
OPEN
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relationships can contribute to the policy process, for example by providing evidence upon which to base rec-
ommendations regarding the amount of time required to be spent in nature per week to promote positive health
and well-being outcomes. A similar process was used to support development of guidelines on the amount of
recommended weekly physical activity needed for health promotion and disease prevention29.
e research advances previous work in three key ways. First, to date, researchers have examined direct nature
exposure-response relationships using either a specic visit duration17, or nature visit frequency over a prolonged
period26, or both independently18. By multiplying the duration of a representative visit within the last week by the
number of visits taken within the last week we were able to develop the rst weekly exposure metric (i.e. minutes
per week) for nature exposure, similar to those used in other health promotion contexts (e.g. physical activity29).
Second, by comparing the coecients of other, well-established, predictors of health and well-being (e.g. area
deprivation) with those for average time spent in nature per week, we were able to assess the relative strength
of any exposure-response relationship. ird, previous studies were constrained in their ability to look at the
generalisability of relationships across dierent socio-demographic groups due to relatively small, geographically
constrained samples. In this study, the current, nationally representative sample enabled us to stratify, a priori, on
socio-demographic characteristics, such as age30, gender31, ethnicity32 and area deprivation33, which appeared to
moderate the nature-health association in previous studies22.
Results
Models using duration categories. Descriptive data on the relationships between time spent in nature in
the last 7 days (in 60 min categories) and self-reported health (Good vs. poor) and subjective well-being (High vs.
low) are presented in Table1. Percentages per category are presented for both the estimation sample (n = 19,806),
and for the sample weighted to be representative of the adult population of England. Similar details for all covar-
iates can be found in Appendix B, and relationships between our key predictor, time in nature, and all other
covariates in Appendix C.
e odds ratios (ORs) and 95% condence intervals (CIs) for the survey weighted binomial logistic regres-
sions predicting health and well-being are presented in Table2 (full models in Appendix D). In the unadjusted
models the odds ratios for reporting ‘good’ health and ‘high’ well-being were signicantly higher for all nature
contact ≥60 mins per week compared to 0 mins. Contact of 1–59 mins per week was not associated with better
outcomes than 0 mins, and there was also no linear increase above 60 mins; longer durations were not associated
with better outcomes. In the adjusted models, signicance only emerged at the ≥120 mins per week category;
and again additional duration was not associated with improved outcomes. e relationship appeared somewhat
stronger for health than well-being (Fig.1).
Sensitivity analysis. We conducted three types of sensitivity analysis. First we explored exposure-response
relationships using time spent in nature as a continuous variable, and outcomes modelled as binary variables
using splines (Fig.2). e gures suggested relatively steady increases in the positive relationships for both health
and well-being up to around 120 mins, diminishing marginal returns from then until around 200 mins per week
for health and 300 mins for well-being, and then a attening out or even decrease thereon (though note the very
large CIs > 400 mins). Although Fig.2 should be treated with caution, due to hourly clustering (see Methods, and
Appendix A, Figure C), results broadly support the categorical analyses, with some suggestion that nature expo-
sure beyond 120 mins a week may have some additional benets that did not emerge when health and wellbeing
were treated as binary variables.
Second, we explored exposure-response relationships using time spent in nature as a categorical variable and
health and wellbeing modelled as ordinal variables. Results were again very similar (Appendix E). e only slight
change was signicance at the 60–119 min category for both outcomes, but this nding is not easily comparable
to the binary logistic results for reasons explained in more detail in Appendix E.
Self-reported health Subjective well-being (Life satisfaction)
Raw Ns and %s (Weighted %s) Raw Ns and %s (Weighted %s)
Not good Good Total
N
Not
good Good Low High Total
N
Low High
N % N % % % N % N % % %
Nature visit exposure
Weekly visit duration
≥300 mins 700 20.1 2784 79.9 3484 (18.1 81.9) 1228 35.2 2256 64.8 3484 (34.5 65.5)
240–299 mins 159 18.0 723 82.0 882 (15.5 84.5) 309 35.0 537 65.0 882 (34.1 65.9)
180–239 mins 207 20.4 807 79.6 1014 (18.1 81.9) 374 36.9 640 63.1 1014 (36.0 64.0)
120–179 mins 232 18.0 1058 82.0 1290 (15.5 84.5) 465 36.0 825 64.0 1290 (35.3 64.7)
60–119 mins 253 22.7 860 77.3 1113 (19.7 80.3) 439 39.4 674 60.6 1113 (38.2 61.8)
1–59 mins 97 27.3 258 72.7 355 (25.2 74.8) 155 43.7 200 56.3 355 (41.7 58.3)
0 mins 3678 31.5 7990 68.5 11668 (27.7 72.3) 5173 44.3 6495 55.7 11668 (42.8 57.2)
Tot a l s 5326 26.914480 73.119806 (23.5 76.5) 8143 41.111663 58.919806 (39.8 60.2)
Table 1. e frequency and percent of respondents in each category of each predictor who reported good/very
good health and high well-being. Notes. Weighted %s (in brackets) take into account sample weights. Similar
details for all covariates available in Supplementary Table1.
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Our nal sensitivity analysis modelled both time and well-being as continuous variables (Appendix E, Figure
D). Again the results were very similar to the original model (Fig.2b). Due to the inherently ordinal structure of
the general health variable, we were unable to conduct a comparable sensitivity model for health.
Contextualisation of results. To contextualise the magnitude of the relationship between weekly nature
contact and health and well-being, Fig.3 presents the relevant ORs (CIs) alongside those for selected predictors
including: neighbourhood greenspace and deprivation; physical exercise; individual SES; and relationship status
(see Appendix D for details on all covariates). e gure highlights that 120–179 mins vs. 0 mins of nature contact
per week was associated with: (a) a similar likelihood of reporting good health as, living in an area of low vs. high
deprivation; meeting vs. not meeting physical activity guidelines, and (c) being in a high vs. low SES occupation.
Although the association between nature contact at this level and wellbeing was similar to that between high vs.
low: greenspace, deprivation and physical activity; it was less important than SES and relationship status.
Generalisability of results. Table3 shows results of analyses stratied on key area and individual level
factors (see Appendix F for full details). For these analyses, nature contact was recongured into three duration
levels reecting: (a) ‘no exposure’ (0 minutes, ref); (b) ‘low exposure’, not associated with signicantly greater
likelihood of good health and high wellbeing (1–119 mins); and (c) ‘high exposure’, i.e. all durations associated
with signicantly higher likelihood of good health and high well-being combined (≥120 mins). Estimates from
Self-reported health (Good vs. poor) Subjective well-being (High vs. low)
Unadjusted AdjustedaUnadjusted Adjusteda
OR
95% CIs
OR
95% CIs
OR
95% CIs
OR
95% CIs
Low High Low High Low High Low High
Nature visit exposure
Weekly visit duration
≥300 mins 1.73*** 1.57 1.91 1.33*** 1.18 1.50 1.42*** 1.31 1.54 1.20*** 1.09 1.31
240–299 mins 2.10*** 1.74 2.53 1.55*** 1.25 1.93 1.45*** 1.24 1.68 1.25** 1.07 1.46
180–239 mins 1.74*** 1.47 2.06 1.44*** 1.18 1.76 1.33*** 1.16 1.53 1.16*1.00 1.34
120–179 mins 2.09*** 1.79 2.44 1.59*** 1.31 1.92 1.37*** 1.21 1.55 1.23** 1.08 1.40
60–119 mins 1.56*** 1.34 1.83 1.13 0.94 1.37 1.21** 1.06 1.39 1.10 0.96 1.27
1–59 mins 1.14 0.88 1.46 1.04 0.76 1.41 1.05 0.83 1.31 0.99 0.78 1.26
0 mins ref ref ref ref ref ref ref ref ref ref ref ref
Covariates
Area NO YES NO YES
Individual NO YES NO YES
Constant 2.61 2.50 2.72 0.28 0.24 0.33 1.34 1.29 1.39 0.36 0.31 0.41
Pseudo R20.01 0.23 0.01 0.05
Valid N 20,264 19,806 20,264 19,806
Table 2. e odds ratios (OR) and 95% condence intervals (CIs) of reporting good health and high well-being
as a function of nature visit duration in the last 7 days. Notes. aFull models including covariates (urbanicity,
neighbourhood greenspace, area deprivation, background PM10, sex, age, SES, restricted functioning, physical
activity, employment status, relationship status, ethnicity, children in household, dog ownership and year)
available in Supplementary Table2. *p < 0.05; **p < 0.01; ***p < 0.001.
0.60
0.80
1.00
1.20
1.40
1.60
1.80
2.00
0 mins 1-59 mins 60-119 mins 120-179 mins 180-239 mins 240-299 mins ≥ 300 mins
gnitroperrofsoitarsddO
htlaehdoogyreV/dooG
0.60
0.80
1.00
1.20
1.40
1.60
1.80
2.00
0 mins 1-59 mins 60-119 mins 120-179 mins 180-239 mins 240-299 mins ≥ 300 mins
hgiHgnitroperrofsoitarsddO
well-being
Time spent visiting natural environments in last 7 days
Figure 1. e odds ratios (OR) and 95% condence intervals of reporting good health and high well-being as a
function of nature visit duration in the last 7 days (0 mins = reference category). Note: Adjusted for urbanicity,
neighbourhood greenspace, area deprivation, background PM10, sex, age, SES, restricted functioning, physical
activity, employment status, relationship status, ethnicity, children in household, dog ownership and year.
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the models of health showed that the positive relationship found for ‘high’ but not ‘low’ exposure, compared to
‘no exposure’, in the overall model was consistent across those living in urban and rural, and high and low dep-
rivation, areas. It was also consistent for: both males/females; those above/below 65years old; those of high/low
occupational social grade; those with/without a long-term illness/disability; and for those who did vs. did not
meet physical activity recommendations. Stratication on neighbourhood greenspace suggested those in areas
Figure 2. e probability of reporting (a) good health and (b) high well-being (with 95% condence intervals)
as a function of time spent in nature in the last 7 days using a generalised additive model (GAM) with a
penalized cubic spline for nature contact. Note. e GAM is adjusted for urbanicity, neighbourhood greenspace,
area deprivation, background PM10, sex, age, SES, restricted functioning, physical activity, employment status,
relationship status, ethnicity, children in household, dog ownership and year.
0.60
0.80
1.00
1.20
1.40
1.60
1.80
2.00
120-179 vs. 0 mins
nature
High vs. low greenLow vs. high
deprivation
Meets activity
guidelines vs. not
Top vs. bottom SESCouple vs. single
gnitroperrofsoitarsddO
htlaehdoogyreV/dooG
0.60
0.80
1.00
1.20
1.40
1.60
1.80
2.00
120-179 vs. 0 mins
nature
High vs. low greenLow vs. high
deprivation
Meets activity
guidelines vs. not
Top vs. bottom SESCouple vs. single
hgiHgnitroperrofsoitarsddO
well-being
Selected comparators
Figure 3. e odds ratios (OR) and 95% condence intervals of reporting good health and high well-being as a
function of nature visits and selected covariates (controlling for all other covariates).
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of high (but not low) greenspace also had greater odds of good health if they spent any time in nature per week
compared to 0 mins, possibly reecting the importance of indirect exposure among this cohort. Stratication
on ethnicity showed the threshold was maintained amongst white British, but not ‘other’ respondents. Stratied
models of well-being showed that ‘high’ but not ‘low’ exposure was associated with signicantly greater odds of
high wellbeing in all cases.
Additional analyses found no dierences in health and well-being as a function of how ‘high’ exposure was
achieved (a) one 120+ min visit; (b) two 60+ min visits; or (c) or three/more ≤ 40 min visits (see Appendix G for
details).
Discussion
Growing evidence of a positive association between contact with natural environments and health and well-being
has led to calls for improved understanding of any exposure-response relationships27,28. e aim of the current
study was to assess these relationships with a measure based on direct exposure to natural environments, rather
than residential proximity, using data from a large nationally representative sample in England. Exposure was
dened in terms of the self-reported minutes spent in natural environments for recreation in the last seven days;
and outcomes were self-reported health and subjective well-being.
Self-reported health (Good vs. poor) Subjective well-being (High vs. low)
1–119 mins ≥120 mins 1–119 mins ≥120 mins
OR
95% CIs
OR
95% CIs
OR
95% CIs
OR
95% CIs
Low High Low High Low High Low High
Area level stratication
Urbanicity
Urban
(n = 18,694) 1.08 0.91 1.27 1.38*** 1.25 1.52 1.07 0.94 1.21 1.19*** 1.11 1.28
Rural (n = 1,112) 1.94 0.96 3.95 2.27*** 1.49 3.47 1.37 0.79 2.39 1.59** 1.17 2.18
Area greenspace
High (n = 8,510) 1.48** 1.16 1.90 1.54*** 1.34 1.78 1.05 0.87 1.27 1.28*** 1.14 1.42
Low (n = 11,966) 0.88 0.71 1.09 1.33*** 1.17 1.52 1.11 0.94 1.30 1.16** 1.06 1.27
Area deprivation
High (n = 11,796) 1.03 0.84 1.27 1.36*** 1.20 1.54 1.10 0.94 1.29 1.29** 1.17 1.41
Low (n = 8,010) 1.23 0.95 1.59 1.53*** 1.32 1.78 1.13 0.93 1.36 1.23** 1.10 1.37
Individual level stratication
Sex
Female
(n = 10,419) 0.99 0.79 1.23 1.38*** 1.21 1.58 1.03 0.88 1.22 1.22*** 1.11 1.35
Male (n = 9,387) 1.25 0.98 1.58 1.46*** 1.27 1.67 1.12 0.94 1.34 1.20** 1.08 1.32
Age
16–64 yrs
(n = 14,667) 1.05 0.87 1.27 1.28*** 1.14 1.44 1.07 0.94 1.29 1.18*** 1.09 1.28
65+yrs
(n = 5,193) 1.27 0.91 1.76 1.87*** 1.57 2.22 1.10 0.84 1.43 1.35*** 1.16 1.57
Restricted f unctioning
Yes (n = 4,545) 1.31 0.97 1.77 1.42*** 1.20 1.68 1.07 0.80 1.43 1.31*** 1.12 1.53
No (n = 15,261) 1.05 0.87 1.26 1.42*** 1.27 1.60 1.08 0.94 1.23 1.19*** 1.10 1.29
SES
AB/C1 (highest)
(n = 8,624) 1.09 0.86 1.39 1.43*** 1.24 1.65 1.01 0.85 1.20 1.16** 1.05 1.28
C2/DE (lowest)
(n = 11,182) 1.16 0.94 1.44 1.43*** 1.26 1.62 1.18 1.00 1.40 1.29** 1.17 1.43
Ethnicity
White British
(n = 15,198) 1.14 0.96 1.36 1.47*** 1.33 1.63 1.12 0.97 1.29 1.21*** 1.11 1.31
Other (n = 4,608) 1.01 0.69 1.48 1.21 0.95 1.53 0.96 0.75 1.21 1.22*1.05 1.42
Meets activity guidelines
No (n = 15,008) 1.13 0.95 1.36 1.48*** 1.32 1.65 1.05 0.92 1.20 1.17*** 1.08 1.27
Yes (n = 4,798) 0.98 0.67 1.43 1.22*1.01 1.47 1.21 0.92 1.60 1.32*** 1.14 1.51
Table 3. e odds ratios (OR) and 95% condence intervals (CIs) of reporting good health and high well-being
as a function of the three main categories of nature visit duration in the last 7 days, stratied on key area and
individual covariates. Notes. In all analyses the reference duration is 0 minutes per week; All analyses control
for area and individual covariates not being stratied. Full models available on request. *p < 0.05; **p < 0.01;
***p < 0.001.
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Aer a range of covariates had been taken into account, individuals who spent between 1 and 119 mins in
nature in the last week were no more likely to report good health or high well-being than those who reported
0 mins. However, individuals who reported spending ≥120 mins in nature last week had consistently higher lev-
els of both health and well-being than those who reported no exposure. Sensitivity analyses using splines to
allow duration to be modelled as a continuous variable suggested that beyond 120 mins there were decreasing
marginal returns until around 200–300 mins when the relationship attened or even dropped. We tentatively
suggest, therefore, that 120 mins contact with nature per week may reect a kind of “threshold”, below which
there is insucient contact to produce signicant benets to health and well-being, but above which such benets
become manifest.
In terms of magnitude, the association between health, well-being and ≥120 mins spent in nature a week, was
similar to associations between health, well-being and: (a) living in an area of low vs. high deprivation; (b) being
employed in a high vs. low social grade occupation; and (c) achieving vs. not achieving recommended levels of
physical activity in the last week. Given the widely stated importance of all these factors for health and well-being,
we interpret the size of the nature relationship to be meaningful in terms of potential public health implications.
at the ≥120 mins “threshold” was present even for those who lived in low greenspace areas reects the
importance of measuring recreational nature contact directly when possible, rather than simply using residential
proximity as a proxy for all types of nature exposure. People travel beyond their local neighbourhoods to access
recreational nature experiences, and indeed in our own data those who lived in the least green areas had higher
odds of spending ≥120 mins in nature than those living in greener neighbourhoods (Appendix C). Impoverished
local opportunities need not be a barrier to nature exposure23,24. at the “threshold” was also present for those
with long-term illnesses/disability, suggests that the positive overall association in the data was not simply due to
healthier people visiting nature more oen.
One explanation for our ndings might be that time spent in nature is a proxy for physical activity, and it is
this which is driving the relationship, not nature contact per se. In England, for instance, over 3 million adults
achieve recommended activity levels fully, or in part, in natural settings34. Although: (a) we tried to control for
this by including physical activity over the last 7 days in our models; and (b) the threshold applied to individuals
who did not meet activity guidelines; we were unable to fully untangle these issues. Experimental research, how-
ever, indicates that some benets cannot be due solely to physical activity. Research into shinrin-yoku (Japanese
“forest bathing”)35, for instance, suggested that various psycho-physiological benets can be gained from merely
sitting passively in natural vs. urban settings. Moreover, physical activity conducted in nature may be more psy-
chologically benecial than in other locations36, suggesting a complex interaction between the two which requires
further research to fully understand20.
e current results also suggested that it did not matter how the “threshold” was achieved. is may be
because individuals selected exposures to t their personal preferences and circumstances. For instance, some
may prefer long walks on the weekend in locations further from home; while others may prefer regular shorter
visits to parks in the local area. To recommend the former type of person stops their long weekly visit in favour of
several shorter trips or vice versa may be misguided.
Whilst this study deepens our understanding of the potential value of spending time outdoors in nature to
health and well-being, it is too early to make specic guidance due to several limitations. First, the data are obser-
vational and cross-sectional; and thus, notwithstanding the same pattern holding for those with a long-term
illness/disability, we are unable to rule out the possibility that the association is, at least in part, due to healthier,
happier people spending more time in nature. Prospective longitudinal studies of the kind used to help develop
physical activity guidelines29, and nature-based intervention studies are needed to better understand causality.
Cimprich and Ronis37, for instance, found that women recently diagnosed with breast cancer scored higher on
several attention tasks, compared to standard care controls, following a ve-week period of spending 120 mins
per week in ‘natural restorative environments’. e authors argued that the 120 mins per week of nature expo-
sure helped the women restore cognitive resources depleted by the stress of their diagnoses and early treatment.
Although our sample was more heterogeneous, weekly nature exposure may work in a similar fashion by reducing
generally high levels of stress38. Similar studies are needed to see how generalizable any potential “threshold” is
across a range of situations, and to see how long an individual needs to maintain a certain amount of weekly expo-
sure to achieve health and well-being gains. Although eects on attentional processes were observed aer just 5
weeks in Cimprich and Ronis37, health eects may need longer; and it is also important to see whether dierent
types of nature contact might confer dierent benets.
We also note that, although signicant, time in nature explained relatively little variance in either health or
wellbeing in these models based on cross-sectional data (approx. 1% in unadjusted models in both cases). It will
therefore be important to explore eect sizes in prospective/experimental studies to better understand the cost/
benet implications of any potentials interventions.
Another limitation concerned our estimate of weekly exposure. As duration was asked about only a single
randomly selected visit in the last week, we assumed that at the population level this was representative of all vis-
its. Although rigorous collection protocols meant that the eects of a typical visit selection are likely to cancel out
over a sample of nearly 20,000, we recognise that accuracy at the individual level would be improved if duration
were asked about all visits in the last week. We also acknowledge that our data rely on self-reports and thus results
needed to be treated with caution. For instance, self-reported duration is likely to be less accurate than measures
obtained from geo-tracking individuals during specic visits39, or over several days40, and individuals may have
been unsure about, or reluctant to discuss, certain issues which were included as covariates (e.g. long standing
illness/disability). Future studies would ideally collate as much data via non self-report measures as possible. We
note, moreover, that unlike exposure to oen invisible environmental factors such as air pollution, we can poten-
tially ‘re-live’ our experiences of the natural world in memory, for instance during periods of ‘mind wandering’,
and derive benets from these recollections independent of those experienced in situ41. us, an exposure in this
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context may be considered as the time in situ plus all subsequent time spent thinking about the experience42. In
short, we believe further work is needed to think more critically and creatively about what the term ‘exposure’
means in the current context.
We also remain cautious about any potential ≥120 mins “threshold”. In part its emergence may be a conse-
quence of the clustering of duration responses around the hour mark and subsequent stratication, rather than
anything materially dierent occurring at this level of exposure. e spline models, for instance, suggested a more
nuanced pattern. However, this smoothing of the data was still reliant on a highly non-normal distribution, sug-
gesting that we need to be cautious about these analyses as well. Further work is also needed to explore the ‘peak’
of returns at around 200–300 mins, to better understand why spending more time in nature is associated with
little marginal gain. us, we see the tentative “threshold” and “peak” discussed here more as a starting points for
discussion and further investigation, than clearly established ndings.
Finally, our results say little about exposure ‘quality’. Research considering the quality of the natural environ-
ment in terms of plant and/or animal species richness suggests that experiences may be better in more biodiverse
settings25,43. Contact with nature is more than just a complex multi-sensory experience, to varying degrees per-
sonal histories and meanings, longstanding cultural practices, and a sense of place play some role in the benets
realised44–46, factors which may account for why we did not nd the same pattern for health individuals not iden-
tifying as White British. In the current research, for instance, exposure estimates relied upon visits undertaken
voluntarily, presumably because they had features important to those individuals47 and these eects may not be
found if individuals were to regularly spend 120 mins a week in a natural environment of less personal relevance
(e.g. those who self-identied as ‘White European’). Our estimates also explicitly excluded time spent in one’s own
garden which can be an important form of meaningful nature contact for many people48. All of these issues will
need greater consideration in future research.
To conclude, although this research suggests that spending ≥120 mins a week in nature may be an important
“threshold” for health and well-being across a broad range of the adult population in England, we believe that
more prospective cohort, longitudinal, and experimental studies are required before any clear conclusions can
be drawn. In addition to improving the duration-exposure estimates used here, more research is also needed to
understand the impact of dierent activities undertaken, as well as the eect of environmental quality and per-
sonal meaning. Nevertheless, we see our ndings as an important starting point for discussions around providing
simple, evidence-based recommendations about the amount of time spent in natural settings that could result in
meaningful promotion of health and well-being.
Methods
Participants & procedure. Participants were drawn from Waves 6 and 7 (2014–2015/2015–2016) of the
Monitor of Engagement with the Natural Environment (MENE) survey (the only Waves where our key out-
comes were consistently measured). e survey, which is part of the UK government’s National Statistics, is repeat
cross-sectional (dierent people take part in each wave), and is conducted across the whole of England and
throughout the year (approx. 4,000 people per week) to reduce potential geographical and seasonal biases49. As
part of the UK’s ocial statistics, sampling protocols are extensive, to ensure as representative a sample of the
adult English population as possible. Full details can be found in the annual MENE Technical Reports49 with
key features including: (a) “a computerised sampling system which integrates the Post Oce Address le with
the 2001 Census small area data at output area level. is enables replicated waves of multi-stage stratied sam-
ples”; (b) “the areas within each Standard Region are stratied into population density bands and within band, in
descending order by percentage of the population in socio-economic Grade I and II”; (c) “[in order to] maximise
the statistical accuracy of the sampling, sequential waves of eldwork are allocated systematically across the sam-
pling frame to ensure maximum geographical dispersion”; (d) “to ensure a balanced sample of adults within the
eective contacted addresses, a quota is set by sex (male, female housewife, female non-housewife); within the
female housewife quota, presence of children and working status and within the male quota, working status”; and
(e) “the survey data is weighted to ensure that the sample is representative of the UK population in terms of the
standard demographic characteristics” (ref.49, p.5). Data is collected using in-home face-to-face interviews with
responses recorded using Computer Assisted Personal Interviewing (CAPI) soware.
Although the total sample for these years was n = 91,190, the health and well-being questions were only asked
in every fourth sampling week (i.e. monthly, rather than weekly) resulting in a reduced sample of n = 20,264.
In order to account for any residual biases in sampling at this monthly level, special ‘month’ survey weights are
included in the data set. ese were applied in the current analysis to ensure that results remained generalisable to
the entire adult population of England. All data were anonymised by Natural England and are publically accessi-
ble at: http://publications.naturalengland.org.uk/publication/2248731?category=47018. Ethical approval was not
required for this secondary analysis of publically available National Statistics.
Outcomes: Self-reported health & subjective well-being. Self-reported health (henceforth: health)
was assessed using the single-item: ‘How is your health in general?’ (sometimes referred to as ‘SF1’). Response
options were: ‘Very bad’, ‘Bad’, ‘Fair’, ‘Good’ and ‘Very good’. Responses are robustly associated with use of med-
ical services50 and mortality51; and crucially, for current purposes, neighbourhood greenspace13. Following ear-
lier work we dichotomised responses into ‘Good’ (‘Good/very good’, weighted = 76.5%) and ‘Not good’ (‘Fair/
bad/very bad’, 23.5%)52. Subjective well-being (henceforth: well-being) was assessed using the ‘Life Satisfaction’
measure, one of the UK’s national well-being measures53: ‘Overall how satised are you with life nowadays?’
with responses ranging from 0 ‘Not at all’ to 10 ‘Completely’. Again, following earlier studies we dichotomised
responses into ‘High’ (8–10, 60.2%) and Low (0–7, 39.8%) well-being54. Histograms of the (non-normal) distribu-
tions for both outcome variables are presented in Appendix A. Of note although the dichotomisation points were
based on prior research, they are consistent with the current data; the 50th percentile for health was in the ‘good’
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response and for wellbeing in ‘8’. Sensitivity analyses conducted on ordinal (both health and wellbeing) and linear
(wellbeing only) variations of these variables are presented in Appendix E.
Exposure: Recreational nature contact in last 7 days. Recreational nature contact, or time spent in
natural environments in the last week, was derived by multiplying the number of reported recreational visits
per week by the length of a randomly selected visit in the last week. Participants were introduced to the survey
as follows: “I am going to ask you about occasions in the last week when you spent your time out of doors. By out
of doors we mean open spaces in and around towns and cities, including parks, canals and nature areas; the coast
and beaches; and the countryside including farmland, woodland, hills and rivers. is could be anything from a few
minutes to all day. It may include time spent close to your home or workplace, further aeld or while on holiday in
England. However this does not include: routine shopping trips or; time spent in your own garden.” en they were
asked “how many times, if at all, did you make this type of visit yesterday/on <DAY > ” for each of the previous
seven days. Ninety-eight percent of respondents reported ≤7 visits last week. e remaining 2% were capped at 7
visits to avoid dramatically skewing weekly duration estimates.
Aer basic details of each visit (up to 3 per day) were recorded, a single visit was selected at random by the
CAPI soware, for the interviewer to ask further questions about, including: “How long did this visit last alto-
gether?” (Hours & Minutes). Due to random selection, even if the selected visit was not necessarily representative
for any given individual, the randomisation procedure should reduce potential bias at the population level at
which our analyses were conducted. Weekly duration estimates were thus derived by multiplying the duration
for this randomly selected visit by the number of stated visits in the last seven days (capped at 7). Following the
approach of earlier exposure-response studies in the eld (e.g. Shanahan et al., 2016), duration was categorised
into 7 categories: 0 mins (n = 11,668); 1–59 mins (n = 355); 60–119 mins (n = 1,113); 120–179 mins (n = 1,290);
180–239 mins (n = 1,014); 240–299 mins (n = 882); ≥300 mins (n = 3,484). An alternative banding at 30 minutes
was problematic because of very low Ns for some bands (e.g. 1–29 mins, n = 85), reecting the fact that weekly
duration estimates clustered around the hour marks, e.g. 78% of the unweighted observations within the 120–
179 mins band were precisely 120 mins (See Appendix A, Figure C for duration histogram). e highest band was
capped at ≥300 mins due to the large positive skew of the data.
Control variables. Health and well-being are associated with socio-demographic and environmental charac-
teristics at both neighbourhood (e.g. area deprivation) and individual (e.g. relationship status) levels55. As many
of these variables may also be related to nature exposure they were controlled for in the adjusted analyses.
Area level control variables. Area level covariate data was assigned on the spatial level of the Census 2001
Lower-layer Super Output Areas (LSOAs) in which individuals lived. ere were 32,482 LSOAs in England, each
containing approximately 1,500 people within a mean physical area of 4km2.
Neighbourhood greenspace. In order to understand how much greenspace is in an individual’s neigh-
bourhood, we derived an area density metric using the Generalised Land Use Database (GLUD)56. e GLUD
provides, for each LSOA in England, the area covered by greenspace and domestic gardens. ese were summed
and divided by the total LSOA area to provide the greenspace density metric. is metric was allocated to each
individual in the sample, based on LSOA of residence. Following previous literature, individuals were assigned to
one of ve quintiles of greenspace based on this denition (ranging from least green to most green)33. Rather than
derive quintiles of greenspace from the current sample (i.e. divide the current sample into ve equal parts based
on the percentage of greenspace in their LSOA), we assigned individuals instead to one of ve pre-determined
greenspace quintiles based on the distribution of greenspace across all 32,482 LSOAs in England. Although this
meant that we did not get exactly equal 20% shares of our current sample across greenspace quintiles (although
due to the sampling protocol we were still very close to this, see Appendix B) this approach allowed inferences to
be made across the entire country, rather than simply to the current sample. In exploratory sensitivity analyses
we dened greenspace as the GLUD category ‘greenspace’ only, with the GLUD category ‘gardens’ excluded. is
produced very similar results, so we focused on the more inclusive denition including both aspects. In further
exploratory sensitivity analyses, we assigned individuals to ve greenspace categories dened by equal ranges of
greenspace coverage (e.g. 0–20%, 21–40%, 41–60% etc.) rather than quintiles based on percentages of the pop-
ulation. is also produced very similar results, so again we decided to go with the most common approach. In
subsequent analyses the least green quintile acted as the reference category.
Area deprivation. Each LSOA in England is assessed in terms of several parameters of deprivation, includ-
ing unemployment and crime, levels of educational, income, health metrics, barriers to housing and services, and
the living environment. A total Index of Multiple Deprivation (IMD) score is derived from these subdomains57.
Following previous studies52, we assigned individuals into deprivation quintiles based on the LSOA in which
they lived. As with greenspace, the cut points for area deprivation quintiles were also based on all LSOAs in
England, rather than those in the current sample, to allow inference to the population as a whole (most deprived
quintile = ref).
Air pollution. An indicative measure of air pollution was operationalised as LSOA background PM10 assigned
to tertiles of all LSOAs in England (lowest particulate concentration = ref). PM10 concentrations, based on
Pollution Climate Mapping (PCM) model simulations58, were averaged over the period 2002–2012, and aggre-
gated from 1 km square resolution to LSOAs.
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Individual level controls. Individual level controls comparable to earlier studies in this area6,7,12,13,15 included: sex
(male = ref); age (categorised as 16–64 = ref; 65+); occupational social grade (AB (highest, e.g. managerial), C1,
C2 and DE (lowest, e.g. unskilled labour, = ref) as a proxy for individual socio-economic status (SES); employ-
ment status (full-time, part-time, in education, retired, not working/unemployed = ref); relationship status (mar-
ried/cohabiting; single/separated/divorced/widowed = ref); ethnicity (White British; other = ref); number of
children in the household (≥1 vs. 0 = ref); and dog ownership (Yes; No = ref).
Two further control variables were particularly important. First, the survey asked: ‘Do you have any long stand-
ing illness, health problem or disability that limits your daily activities or the kind of work you can do?’ (‘Restricted
functioning’: Yes; No = ref). Including this variable, at least in part, controls for reverse causality. If similar asso-
ciations between nature exposure and health and well-being are found for both those with and without restricted
functioning, this would support the notion that the associations are not merely due to healthier, more mobile
people visiting nature more oen.
We also controlled for the number of days per week people reported engaging in physical activity >30 mins; in
the current analysis dichotomised as either meeting or not meeting guidelines of 150 mins per week (i.e. 5 days in
the week with physical activity >30 mins). Some people achieve this guideline though physical activity in natural
settings35, thus, any association between time spent in nature and health may simply be due to the physical activity
engaged in these settings. We believe this is not the case in the current context because the (rank order) correla-
tion between weekly nature contact and the number of days a week an individual engaged in >30 mins of physical
activity was just rs = 0.27. Nevertheless, by controlling for weekly activity levels, modelled relationships between
time in nature and health have less bias from this source, and, therefore, improved estimates of association with
nature exposure per se.
Temporal controls. Due to the multi-year pooled nature of the data, year/wave was also controlled for.
Preliminary analysis found no eect of the season in which the data were collected so this was excluded from
nal analyses.
Analysis strategy. Survey weighted binomial logistic regressions were used to predict the relative odds that
an individual would have ‘Good’ health or ‘High’ well-being as a function of weekly nature exposure in terms of
duration categories per week. Model t was provided by pseudo R2; here the more conservative Cox and Snell
estimate. e outcome binary variables were rst regressed against the exposure duration categories to test direct
relationships; adjusted models were then specied to include the individual and area level control variables.
Due to missing area level data for a small minority of participants (n = 456), our estimation samples for these
adjusted models were n = 19,808. Preliminary analysis found that the weighted descriptive proportions among
this reduced estimation sample diered only negligibly from those among all available observations in the wider
MENE sample, suggesting our complete case analysis approach did not distort the population representativeness
of the estimation sample. e full n = 20,264 sample was maintained for the unadjusted model to provide the
most accurate, weighted representation of the data, as reducing unadjusted models to n = 19,808 produced prac-
tically identical results. Although our main analyses used duration categories of weekly nature contact, an explor-
atory analysis used generalized additive models incorporating a penalized cubic regression spline of duration as a
continuous variable (adjusting for the same set of covariates). is enabled us to produce a ‘smoother’ plot of the
data. Analyses and plotting was done using R version 3.4.1, using packages mgcv and visreg59.
To explore the generalisability of any pattern across dierent socio-demographic groups, we also a priori
stratied the analyses on several area and individual covariates (as dened above) which have been found to
be important in previous studies: (a) Urbanicity; (b) Neighbourhood greenspace; (c) Area deprivation; (d) Sex;
(e) Age; (f) Restricted functioning; (g) Individual socio-economic status (SES); (f) Ethnicity; and (g) Physical
activity. In the case of the three multi-category predictors (area greenspace/deprivation, individual SES), binary
classications were derived for the stratied analyses to maintain robust sample sizes in each category. In the case
of LSOA greenspace and deprivation binary splits were made based on the median cut-point for all LSOAs in
England; SES was dichotomised by collapsing the social grade categories in the standard way, A/B/C1 vs. C2/D/E.
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Acknowledgements
is work was supported by the National Institute for Health Research Health Protection Research Unit (NIHR
HPRU) in Environmental Change and Health at the London School of Hygiene and Tropical Medicine in
partnership with Public Health England (PHE), and in collaboration with the University of Exeter, University
College London, and the Met Oce. e funders had no role in the study design, analysis, interpretation of data,
or decision to submit the article for publication. e views expressed are those of the author(s) and not necessarily
those of the NHS, the NIHR, the Department of Health, or Public Health England. We would like thank an earlier
reviewer and the editorial board team for suggestions on how to improve an earlier version of this manuscript.
Author Contributions
M.W. conceived of the study in discussion with T.H., M.D. and L.E.F.; M.W., I.A. and J.G. conducted the analyses;
B.W., S.W. and A.B. made additional analysis suggestions and provided text/references on specic sections. All
authors contributed to the text of the manuscript and reviewed the nal submission.
Additional Information
Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-019-44097-3.
Competing Interests: e authors declare no competing interests.
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