The Relationship between Perceived Health and
Physical Activity Indoors, Outdoors in Built
Environments, and Outdoors in Nature
Tytti P. Pasanen*
University of Tampere, Finland
Liisa Tyrväinen
Finnish Forest Research Institute Metla, Finland
Kalevi M. Korpela
University of Tampere, Finland
Background: A body of evidence shows that both physical activity and exposure
to nature are connected to improved general and mental health. Experimental
studies have consistently found short term positive effects of physical activity in
nature compared with built environments. This study explores whether these
benefits are also evident in everyday life, perceived over repeated contact with
nature. The topic is important from the perspectives of city planning, individual
well-being, and public health. Methods: National survey data (n = 2,070) from
Finland was analysed using structural regression analyses. Perceived general
health, emotional well-being, and sleep quality were regressed on the weekly
frequency of physical activity indoors, outdoors in built environments, and in
nature. Socioeconomic factors and other plausible confounders were controlled
for. Results: Emotional well-being showed the most consistent positive connec-
tion to physical activity in nature, whereas general health was positively asso-
ciated with physical activity in both built and natural outdoor settings. Better
sleep quality was weakly connected to frequent physical activity in nature, but
the connection was outweighed by other factors. Conclusion: The results indi-
cate that nature provides an added value to the known benefits of physical
activity. Repeated exercise in nature is, in particular, connected to better emo-
tional well-being.
Keywords: Finland, mental health, natural environment, physical activity,
sleep, well-being
* Address for correspondence: Tytti Pasanen, School of Social Sciences and Humanities,
Kalevantie 5, 33014 University of Tampere, Tampere, Finland. Email: tytti.pasanen@uta.fi
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APPLIED PSYCHOLOGY: HEALTH AND WELL-BEING, 2014, 6 (3), 324–346
doi:10.1111/aphw.12031
© 2014 The Authors. Applied Psychology: Health and Well-Being published by John Wiley &
Sons Ltd on behalf of The International Association of Applied Psychology.
This is an open access article under the terms of the Creative Commons Attribution-
NonCommercial-NoDerivs License, which permits use and distribution in any medium,
provided the original work is properly cited, the use is non-commercial and no modifications or
adaptations are made.
INTRODUCTION
Being exposed to nature, whether it be viewing pictures of natural landscapes
or physical activity (PA) in the wild, has been found to have numerous health
benefits. Previous epidemiological studies have found positive correlations
between neighbourhood greenery and long term health indicators such
as morbidity and longevity (Maas et al., 2009; Takano, Nakamura, &
Watanabe, 2002). Experimental studies have focused on the short term posi-
tive effects of being exposed to green environments, such as changes in mood
and physiological stress measures (see for example, Hartig, Evans, Jamner,
Davis, & Gärling, 2003; Lee, Park, Tsunetsugu, Kagawa, & Miyazaki, 2009;
Pretty, Peacock, Sellens, & Griffin, 2005). These benefits have been generally
explained by involuntary attention recovery (Attention Restoration Theory,
ART; Kaplan, 1995) and stress reduction on psychological and physiological
levels (Ulrich, 1983; Ulrich et al., 1991; see Beute & de Kort, 2014, for a
recent review).
Epidemiological studies have explored the correlation between green space
indicators and improved well-being. The relationship has been established for
a variety of measures of neighbourhood greenery, such as proximity, per-
ceived quality, and the extent of the greenery (de Jong, Albin, Skärbäck,
Grahn, & Björk, 2012; Stigsdotter et al., 2010; van Dillen, de Vries,
Groenewegen, & Spreeuwenberg, 2012; Van Herzele & de Vries, 2012; Ward
Thompson & Aspinall, 2011). Plausible mechanisms behind this relation
include increased levels of PA (Mytton, Townsend, Rutter, & Foster, 2012),
reduced stress, and social cohesion (de Vries, van Dillen, Groenewegen, &
Spreeuwenberg, 2013). Interestingly, a recent epidemiological study found
that green exercise, but not the total amount of PA, mediated the connection
between green space proximity and well-being (de Vries et al., 2013). By
encouraging the above-mentioned healthy behaviour particularly in deprived
populations, exposure to green space has been found to balance socioeco-
nomic health inequalities (Mitchell & Popham, 2008; Ward Thompson &
Aspinall, 2011).
The strong positive connection between PA and general and mental health
has been widely accepted in previous research (Fox, 1999; Penedo & Dahn,
2005; Stephens, 1988). Whether exercising in nature, as opposed to other
environments, provides added value to these beneficial effects has been the
primary interest in many recent experimental studies. A meta-analysis by
Bowler, Buyung-Ali, Knight, and Pullin (2010) outlined that exercising in a
natural environment, rather than in built environments indoors or outdoors,
reduces negative emotions and improves attention, while physical measures
(blood pressure and cortisol concentration) had not shown systematic differ-
ences in the studies they reviewed. More specifically, the improvements in
mood and self-esteem are experienced within five minutes of exposure
PERCEIVED HEALTH AND ACTIVITY IN NATURE 325
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Sons Ltd on behalf of The International Association of Applied Psychology
(Barton & Pretty, 2010). A systematic review by Thompson Coon et al.
(2011) summarised that the benefits following exercise outdoors, compared
with similar activities indoors, include increased energy and feelings of revi-
talisation, as well as decreases in tension, confusion, anger, and depression.
The following sections provide empirical evidence showing the potential
effects of exposure to nature on stress and attention restoration, and the
consequent implications on well-being and health. We conclude the introduc-
tion by presenting the outline and hypothesis of the present study.
Exposure to Nature and Mental Well-Being
Maas et al. (2009) found an inverse relationship between living less than one
kilometre away from green space, and depression and anxiety disorders.
There is also evidence that the proportion of greenery in the neighbourhood
is connected to increased happiness (Van Herzele & de Vries, 2012), reduced
mental distress (White, Alcock, Wheeler, & Depledge, 2013), and reduced
stress on subjective and objective levels (Ward Thompson et al., 2012). Simi-
larly, the quantity of green areas, as well as the quantity and quality of
streetscape greenery, has been related to better mental health (van Dillen
et al., 2012). Being active outdoors has been associated with enhanced mental
well-being: the elderly showed fewer depressive symptoms the more time they
spent outdoors and being physically active (Kerr et al., 2012), while a study in
Scotland estimated that each additional weekly use of natural environment
lowers the risk of poor mental health by 6 per cent (Mitchell, 2013). Similarly,
a meta-analysis by Barton and Pretty (2010) found that those with mental
illnesses benefitted systematically from exposure to nature more than others.
On the other hand, in a population-based survey study, a positive measure of
mental well-being exhibited a stronger positive connection to the use of
non-natural environments than to nature (Mitchell, 2013).
Exposure to Nature and General Health
Good health has been associated with proximity to the nearest green space
(Stigsdotter et al., 2010) and the proportion of greenery in the surrounding
environment (Maas, Verheij, Groenewegen, de Vries, & Spreeuwenberg,
2006). However, the latter association has recently been questioned (de Jong
et al., 2012; Van Herzele & de Vries, 2012). Interestingly, although de Jong
et al. (2012) did not find a significant relationship between objective measures
of neighbourhood greenery (via GIS) and perceived health, health corre-
lated positively with perceived greenery. The latter result was also weakly
supported by a study by Ward Thompson and Aspinall (2011). Besides
quantity, better observed quality of the green areas and streets of the neigh-
bourhood have also shown a relation to higher perceived general health (Van
326 PASANEN ET AL.
© 2014 The Authors. Applied Psychology: Health and Well-Being published by John Wiley &
Sons Ltd on behalf of The International Association of Applied Psychology
Dillen et al., 2012). On an epidemiological level, living close to green elements
has been connected to longevity (Takano et al., 2002) and decreased mortal-
ity rates (Mitchell & Popham, 2008).
Exposure to Nature and Sleep Quality
An epidemiological study in Australia found that people who live close to less
green areas are more likely to sleep fewer hours per night (Astell-Burt, Feng,
& Kolt, 2013). Apart from this study, the topic has received little research
attention, although it has high relevance to public health. In Finland, for
example, frequent insomnia-related symptoms are prevalent in 10 per cent of
men and 14 per cent of women (Lallukka et al., 2012). Nocturnal awakening
has been related to decreased quality of life (Väätäinen et al., 2013). Being in
nature is likely to involve exposure to natural light and physical exercise, both
of which have been found to have independent associations with sleep quality
(Driver & Taylor, 2000). Similarly, a study on the elderly indicated that
nocturnal sleep quality is associated with being active during the day as well
as exposure to naturalistic light (Hood, Bruck, & Kennedy, 2004). The evi-
dence on the health effects of light exposure on sleep is currently more
extensive than the evidence regarding exposure to nature and sleep (Beute &
de Kort, 2014). As exposure to nature and to daylight often coincides, their
associated health benefits are likely to overlap (Beute & de Kort, 2014).
The Present Study
In line with the above studies, we hypothesise that the well-being benefits of
PA differ between activities that occur in nature, indoors, and in built
outdoor environments. We assume that attention and stress restoration,
shown in previous experimental and epidemiological studies, produces long-
term health and well-being benefits in an everyday context over repeated
contacts with nature. Two of our outcomes, emotional well-being and per-
ceived general health, have already been widely studied, whereas the third,
sleep quality, has not been previously explored in this context.
MATERIALS AND METHODS
Data Collection
Our data were collected in two rounds (with a random sample of 4,000 people
per round) in the winter and spring of 2009. Thus, the natural environments
on which the responses are based include green, aquatic (“blue”), and snow-
covered (“white”) nature areas. The survey was part of the nationwide
Outdoor Recreation Demand Inventory (LVVI2), conducted by the Finnish
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Sons Ltd on behalf of The International Association of Applied Psychology
Forest Research Institute, and it consisted of six survey rounds altogether
with partly varying themes. In the first two survey rounds, analysed in this
study, 3,060 Finnish respondents (response rate 38%), aged 15–74 years,
completed and returned the questionnaire. When they were initially con-
tacted, a letter was sent to each respondent with a link to an online question-
naire, personal username and password, along with a brochure about the
study in general. The second reminder (of three), in addition, included a
paper copy of the questionnaire.
The respondents represented the original sample fairly well although
women were overrepresented relative to men by 3.4 percentage points, and
the younger age groups were underrepresented (15- to 24-year-olds by 4.9
percentage points and 25- to 44-year-olds by 3.1 percentage points) relative to
the older age groups (45- to 64-year-olds overrepresented by 5.1 percentage
points and 65- to 75-year-olds by 2.9 percentage points; original figures from
Virtanen, Nyberg, Salonen, Neuvonen, & Sievänen, 2011). To inspect the
validity of the LVVI2 survey series, a phone survey for a random sample of
the non-respondents in the final survey round (n = 301) was conducted
(Virtanen et al., 2011).
Participation in the survey was voluntary and based on informed consent;
it did not expose the respondents to any harm, and the respondents were
given all the necessary information regarding the study. Therefore, according
to the Finnish Advisory Board on Research Integrity, an ethical review of the
study was not required (http://www.tenk.fi/en/).
Measures
Outcomes. Emotional well-being was measured by five statements that
comprise the Emotional Well-Being subscale in the RAND 36-item health
survey 1.0 (Hays, Sherbourne, & Mazel, 1993; validated in Finland by Aalto,
Aro, & Teperi, 1999; see Appendix 1, in the Supplementary Data, for the
complete items of the scale). The items were measured on a 6-point Likert
scale ranging from “All of the time” (1) to “Not at all” (6). In the analyses, if
necessary, the outcomes were inverted so that higher values indicated better
well-being. Perceived general health was investigated with a single question
phrased, “In general, would you say your health is”: with the options “good”
(1), “fairly good” (2), “average” (3), “fairly poor” (4) and “poor” (5). Assess-
ing perceived general health by a single item is a widely acknowledged prac-
tice in health research (Idler & Benyamini, 1997; Miilunpalo, Vuori, Oja,
Pasanen, & Urponen, 1997). Likewise, sleep quality was investigated with a
single question asking how often in the past four weeks the respondents had
experienced sleep problems or poor sleep quality, with the same options as
the items of the emotional well-being scale. A comprehensive and widely used
sleep assessment scale, the Pittsburgh Sleep Quality Index (PSQI; Buysse,
328 PASANEN ET AL.
© 2014 The Authors. Applied Psychology: Health and Well-Being published by John Wiley &
Sons Ltd on behalf of The International Association of Applied Psychology
Reynolds, Monk, Berman, & Kupfer, 1989), also assesses sleep quality in the
past four weeks by a single item, and the item has shown the highest corre-
lation with the overall score of the same index (Carpenter & Andrykowski,
1998).
Physical Activity. The PA measures were quantified by merging two
variables, one estimating the weekly frequency of PA and the other recording
the locations where the PA takes place. Regarding the locations of PA, the
respondents were instructed to estimate the proportions of their spare time
PA that take place in seven types of environment (with examples in brackets).
These environments were grouped into three categories for the purposes of
this study. Indoor sports settings (such as indoor sports hall, gym, or swim-
ming pool) and indoors at home (such as heavy housework or home gym-
nastics) were classified in the group indoors. Outdoors in a built setting (such
as streets, cycle lanes, or sports fields) formed its own category. Outdoors
around home (such as gardening, clearing the snow, or ball games) was
excluded from the analysis because it could refer to both natural and built
settings. The final category, nature, included the natural environment near
home (such as nearby forests or urban parks), the natural environment
around one’s second home, and the natural environment elsewhere. Regard-
ing the weekly frequency of PA, the respondents were instructed to only
consider active bouts that lasted at least 20 minutes (a common measure in
previous Finnish population studies; for example, Peltonen et al., 2008),
during which they breathed more heavily and broke into a sweat (excluding
walking, cycling, running, etc. as part of their normal daily activities).
Finally, the measures in the analyses were obtained by multiplying the
weekly frequency of PA by the proportions taking place in each type of
setting. For example, if the respondent claimed to exercise twice a week, 50
per cent of the time indoors and 50 per cent in nature, the new variables PA
indoors and PA in nature would both be equal to 2 × 0.5 = 1.
An estimate of how active the respondent had been in the past four weeks,
indicating the intensity of PA, was derived from a variable general activity.
The instrument, adapted from the widely used scale “Self-reported Physical
Activity”, developed by Saltin and Grimby (1968), has been found to have
stronger connections to mental health than an objective measure of physical
fitness (Lindwall, Ljung, Hadžibajramovic´, & Jonsdottir, 2012). The measure
consists of four response categories that depict the respondent’s activity and
exertion level (see Appendix 1, Supplementary Data).
Covariates. The first demographic covariate was gender. Within the
Finnish population, gender has not shown a relationship to perceived health
(Kallio, 2006) or emotional well-being (Aalto et al., 1999). Conversely, sleep
problems are more common in females (Lallukka et al., 2012; Ohayon &
PERCEIVED HEALTH AND ACTIVITY IN NATURE 329
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Sons Ltd on behalf of The International Association of Applied Psychology
Partinen, 2002), with the exception of sleep apnoea, which is more prevalent
in men (Kronholm et al., 2009). The second demographic covariate, age, has
been found to correlate negatively with perceived health (in Finland, Aalto
et al., 1999) and sleep quality (Driver & Taylor, 2000) but not with mental
health (Aalto et al., 1999). There is additional evidence that different age
groups respond differently to exposure to nature (Barton & Pretty, 2010;
Maas et al., 2009). Low levels of the third covariate, monthly household
income, have been associated with lower emotional well-being (Lahelma,
Laaksonen, Martikainen, Rahkonen, & Sarlio-Lähteenkorva, 2006) and
sleeping fewer hours per night (Lallukka et al., 2012) in Finland. In general,
subjective socioeconomic status and perceived health correlate positively
(Kallio, 2006). Epidemiological studies have found fewer income-related
health inequalities in the greener neighbourhoods (Maas et al., 2009; Mitchell
& Popham, 2008). The fourth demographic covariate, household size, was
added to control for the bias it could cause in income.
In addition, we tested for alternative explanations for the well-being meas-
ures, as suggested by Spector and Brannick (2011). Recent unusual events
might affect well-being; this was considered by asking the respondents
whether they had been going through an exceptional situation in life in the
previous four weeks, and if the phase had been easier or more difficult than
usual. Permanent or long term illnesses or disabilities may affect physical and
mental functioning, so we enquired whether the respondents had long term
disabilities that impede their physical activities outdoors. To consider any
other less permanent disadvantages in life the respondents may experience,
such as lack of time or facilities, we asked whether they had been able to
exercise outdoors as much as they had wished (constraints) in the previous 12
months. Finally, season (winter or spring) affects natural light exposure
which may cause fluctuations in sleep quality (Hood et al., 2004), impact
emotional well-being (Graw, Recker, Sand, Kräuchi, & Wirz-Justice, 1999),
and alter exercise routines (Tucker & Gilliland, 2007).
Statistical Analysis
There were in total 28 (0.9%) respondents that had a long term illness or
disability that prevented them from going outdoors, and they were excluded
from the analyses. In addition, multivariate outliers, evaluated by
Mahalanobis distances, were detected and excluded from the data iteratively
following the guidelines of Tabachnick and Fidell (2007). Only the most
distinguishable extreme cases were excluded each time, and after each exclu-
sion, the Mahalanobis distances were re-calculated and re-inspected. The
exclusion of outliers was complete when the largest Mahalanobis value was
close to the selected critical value (based on the χ2 distribution with df =
“number of variables” − 1) and the normal probability plot ceased to have a
330 PASANEN ET AL.
© 2014 The Authors. Applied Psychology: Health and Well-Being published by John Wiley &
Sons Ltd on behalf of The International Association of Applied Psychology
distinguishable tail at the larger end. After the exclusion, the correlations
were re-examined and compared to the pre-filtered data.
For the main analyses, all variables were incorporated into structural
regression models using Mplus version 7. The main reason for choosing a
structural model was its ability to control for the covariance between inde-
pendent variables as well as the health measures. Emotional well-being was
specified as a latent factor with five ordinal indicators, and general health and
sleep quality were treated as latent single indicator factors. We used the
recommended approach in the model building where the models of interest
are tested immediately without a preceding measurement model for the latent
constructs (Hayduk & Glaser, 2000). As all observed outcome variables were
ordinal, a normal transformation was performed and a diagonally weighted
least squares (WLSMV) estimator was used (Muthén & Muthén, 1998–2012).
Independent variables were added in the constructed models in four main
steps that were specified following the suggestions of Spector and Brannick
(2011). The authors recommend first examining the relationships that are
theoretically expected, after which other feasible alternatives should be
tested. The first step consisted of PA indoors, in built outdoor environments,
and in nature regressing on emotional well-being, general health, and sleep
quality. In the second step, a measure of the intensity of activity was added. In
the third step, we added the covariates. If a covariate was not significant in
most analyses, either the insignificant paths or the covariate itself was deleted
from the following models. The fourth and the final step included alternative
explanations for the health outcomes.
The SEM models were evaluated with the χ2 test and the related fit indices
available in the WLSMV estimation. The Bentler comparative fit index (CFI)
measures how far the specified model is from the baseline model with no
connections (Raykov & Marcoulides, 2011), and the general guidelines are
0.95 for close fit, whereas less than 0.90 is considered unacceptable (Hu &
Bentler, 1999; Yu, 2002). As for the root mean-square error of approximation
(RMSEA), the maximum acceptable value varies between 0.05 and 0.08
(Browne & Cudeck, 1992; Hu & Bentler, 1999). In addition, we compared the
observed outcome values with the probabilities that their model-estimated
value is correct.
RESULTS
Univariate and Bivariate Distributions
All outcomes had a skewed distribution, with the average responses
approaching higher rather than lower values of well-being (see Table 1 for all
observed distributions). The emotional well-being scale, for which compara-
tive population figures were readily available, was distributed with the same
PERCEIVED HEALTH AND ACTIVITY IN NATURE 331
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Sons Ltd on behalf of The International Association of Applied Psychology
median (80) as the national norms, although in our sample, mean value was
slightly greater (78 versus 74) and standard deviation smaller (15 versus 20)
than the available population values (Aalto et al., 1999).
Regarding bivariate distributions, all independent variables had a signifi-
cant correlation coefficient (p < .05) with at least one of the three well-being
measures (Table 2; see Appendix 2a for correlations within the independent
variables and Appendix 2b, in the Supplementary Data, for correlations
within the outcomes). Some of the significant correlations were small in
magnitude which is common in many research fields, including applied
TABLE 1
Distributions of the Observed Variables (n = 2,070)
Variable Scale or range Mean (SD) or %
Emotional well-being (Cronbach’s
α = 0.846)
0–100 (summary score) 77.6 (15.4)
Perceived general health Good (1)–Poor (5) 2.0 (0.93)
Poor sleep quality or sleep problems All of the time (1)–Not at
all (6)
4.8 (1.16)
Gender (%) Male 44.4
Female 55.6
Age 15–74 years 45.2 (14.8)
Household size 1–8 people 2.5 (1.2)
Monthly household incomea (%) €1,000 or less 5.9
€1,001–3,000 32.6
€3,001–5,000 31.7
€5,001–7,000 19.2
€7,001–9,000 6.1
More than €9,000 4.4
Long term disability (%) None 82.9
Yes, but can still exercise
outdoors
17.1
Exceptional situation in life in the
past 4 weeks (%)
More stressful than usual 27.3
Not exceptional 64.6
Easier than usual 8.1
Constraints on outdoor exercise in the
past 12 months (%)
None 55.6
Some 44.4
General activity in the past four
weeks (%)
Inactive 14.2
Moderate activity 53.0
Vigorous activity 30.9
Competitive sports 1.9
Weekly frequency of PA by location
(max 5 times)
Indoors 0.7 (0.8)
Outdoors (built) 0.5 (0.7)
In nature 0.9 (0.9)
Season Winter 51.9
Spring 48.1
a shortened scale, in the analysis we used 11 categories.
332 PASANEN ET AL.
© 2014 The Authors. Applied Psychology: Health and Well-Being published by John Wiley &
Sons Ltd on behalf of The International Association of Applied Psychology
psychology (Møller & Jennions, 2002; Tabachnick & Fidell, 2007). The well-
being effects of contact with nature are generally smaller than the well-being
effects of socioeconomic or behavioural characteristics (Hartig, Mitchell, de
Vries, & Frumkin, 2014).
In all, 2,122 subjects (70% of the 3,032 that were able to go outdoors) had a
valid response to the 19 questions included in the analysis. The final sample
consisted of 2,070 cases where 52 respondents (2.5% of valid cases) had been
excluded due to multivariate non-normality, evaluated by Mahalanobis dis-
tances (with p < .001, the critical value is χ2(18) = 43.8). Although these cases
were excluded, the univariate distributions and the bivariate correlations of the
original and the cleaned data did not differ substantially. Altogether, the
sample in this study consists of 26 per cent of the 8,000 individuals to whom
the survey was sent.
Structural Regression Models
In the first step of the analysis (see Appendix 3a, Supplementary Data, for an
overview), PA in nature exhibited a positive connection to emotional well-
being (B = 0.21, standard error (SE) = 0.03, p < .001; Table 3), perceived
general health (B = 0.14, SE = 0.03, p < .01), and sleep quality (B = 0.09, SE
= 0.02, p < .01). PA in built outdoor settings was positively associated
with emotional well-being (B = 0.10, SE = 0.04, p < .01) and general health
TABLE 2
Correlation Coefficients (Spearman if One or Both of the Variable Pair are
Ordinal, Pearson in Italics) between the Observed Independent and Outcome
Variables (n = 2,070)
Emotional well-being
(RAND-36 score)
Perceived
general health
Sleep
quality
PA indoors 0.05* 0.25*** 0.04
PA outdoors (built) 0.07** 0.20*** 0.02
PA in nature 0.17*** 0.15*** 0.08***
General activity 0.17*** 0.40*** 0.11***
Gendera −0.02 0.08*** 0.00
Age 0.20*** −0.20*** 0.05*
Household size −0.03 0.09*** 0.03
Income 0.12*** 0.11*** 0.07**
Exceptional situation in life 0.36*** 0.11*** 0.19***
Disability −0.07** −0.36*** −0.10***
Constraints −0.23*** −0.23*** −0.14***
Season −0.02 0.01 0.06*
* p < .05; ** p < .01; *** p < .001.
a 0 = male, 1 = female.
PERCEIVED HEALTH AND ACTIVITY IN NATURE 333
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334 PASANEN ET AL.
© 2014 The Authors. Applied Psychology: Health and Well-Being published by John Wiley &
Sons Ltd on behalf of The International Association of Applied Psychology
(B = 0.22, SE = 0.04, p < .01). The only outcome that PA indoors was
significantly (and positively) related to was general health (B = 0.32, SE =
0.03, p < .01).
In the second estimated model (Appendix 3b, Supplementary Data), the
added variable general activity was significantly and strongly related to all
outcomes, and it reduced the coefficient estimates of the PA variables com-
pared with the previous model. Associations that remained significant were
those between PA in nature and emotional well-being (B = 0.16, SE = 0.03, p <
.01) and sleep quality (B = 0.06, SE = 0.03, p = .02), PA outdoors (built) and
general health (B = 0.14,SE = 0.04, p < .01), and PA indoors and general health
(B = 0.10, SE = 0.03, p < .01). Unexpectedly, a new significant negative
relationship appeared between PA indoors and emotional well-being (B =
−0.09, SE = 0.04, p = .02).
The third estimated model (Appendix 3c, Supplementary Data), in which
demographic covariates were added, required adjustment. Household size
showed no significant connections to any outcomes and, therefore, it was
removed from the subsequent analyses. In line with bivariate correlations
(Table 2), gender was only related to perceived general health, and removing
its connections with the other two outcomes improved the model fit. Higher
income level was significantly and positively associated with each of the three
outcomes. Perceived health decreased and emotional well-being and sleep
quality increased with age. By adding these covariates, all connections
between PA indoors and the outcomes appeared non-significant, whereas PA
in nature continued to show a positive association with emotional well-being
(B = 0.13, SE = 0.03, p < .01), general health (B = 0.09, SE = 0.03, p < .01), and
sleep quality (B = 0.05, SE = 0.03, p = .04). The frequency of PA in built
outdoor settings was, additionally, positively connected to general health
(B = 0.15, SE = 0.04, p < .01).
Apart from season, all new regressor variables added in model 4 (Figure 1)
were strongly and significantly related to the health outcomes. Those who had
responded to the survey in spring reported slightly better sleep quality than the
ones who had responded in winter. Having a longer-term disability or other
constraint on exercising outdoors was negatively related to emotional well-
being, general health, and sleep quality. Going through an exceptionally
positive phase in life had a positive relationship with these outcomes. There
was some overlap with the independent variables introduced and the PA
measures, as the coefficients of the PA measures reduced and became less
significant. PA in built outdoor settings and in nature were only weakly related
to general health (Boutdoors(built) = 0.09,SE= 0.04,p= .02; Bin nature = 0.06,SE= 0.03,
p = .04), and the weak positive connection that PA in nature previously shown
to sleep quality was no longer apparent (B = 0.02,SE= 0.03, p= .38). Emotional
well-being and PA in nature continued to be significantly related, although the
estimated coefficient was somewhat reduced (B = 0.08, SE = 0.03, p < .01).
PERCEIVED HEALTH AND ACTIVITY IN NATURE 335
© 2014 The Authors. Applied Psychology: Health and Well-Being published by John Wiley &
Sons Ltd on behalf of The International Association of Applied Psychology
Another difference from the previous model was that age and sleep quality
were no longer significantly connected. The final model explained 26 per cent
of the variation in emotional well-being, 38 per cent of the variation in
perceived general health, and 8 per cent of the variation in sleep quality.
The model fit improved and the variances explained increased in each step
(Table 3). Besides the χ2 test, which rejected each model, the fit indices showed
acceptable fit in all models (except the first model where the RMSEA was 0.09).
In the final model, almost all the higher scores of the emotional well-being
indicators were correctly estimated, whereas the low scores were not predicted
so well. The values of general health were well estimated throughout the scale.
Regarding sleep quality, the high values were estimated better than low values,
although many values throughout the scale were incorrectly estimated.
DISCUSSION AND CONCLUSIONS
Evidence on the Relationship between PA in Different
Environments and Perceived Health
Overall, repeated PA in nature, in comparison with built locations, seems to
provide added benefits for subjective health. PA in nature and emotional
FIGURE 1. Diagram of model 4. χ2 = 518, df = 59, p < .0001, CFI = 0.96, RMSEA
= 0.06. Only significant (p < .05) connections are shown (arrows). The dashed
arrows represent negative connections. The regression coefficients between
independent and dependent variables are provided in Table 3.
336 PASANEN ET AL.
© 2014 The Authors. Applied Psychology: Health and Well-Being published by John Wiley &
Sons Ltd on behalf of The International Association of Applied Psychology
well-being showed a significant connection in every phase of the analysis,
whereas PA in other locations did not. The connection was evident even after
controlling for general activity level and unusual life events. This result is in
line with previous studies where green exercise has been related to better
mental well-being in the long term (in the past four weeks; de Vries et al.,
2013) and short term (immediately after exposure; Barton & Pretty, 2010;
Bowler et al., 2010; Thompson Coon et al., 2011). Another result supporting
previous evidence (de Vries et al., 2013) was the positive connection between
general health and PA outdoors, although this was not specific to natural
environments. The result is, furthermore, in agreement with the epidemio-
logical studies that have shown the connection between green space proximity
and general health (de Jong et al., 2012; Maas et al., 2006; Stigsdotter et al.,
2010; Ward Thompson & Aspinall, 2011). Finally, engaging more frequently
in PA in nature and having better sleep quality were weakly connected in all
structural analyses except the final model. These conflicting results indicate
that PA in nature and sleep quality are related, but the associated mechanism
might be dependent on other factors.
Sleep quality was the least adequately explained outcome in our models,
with the highest R2 being only 8 per cent. Even though we found factors that
were connected to sleep quality, at this point the results are suggestive. We
suspect that either the selected regressors or the single-item measurement was
insufficient to capture all relevant aspects of sleep quality. Most sleep quality
indicators in sleep research comprise several items on different aspects of
sleep quality (see Lomeli et al., 2008, for a review). In applied sleep research,
however, single items have been found useful as they minimise response
burden and allow individuals to determine themselves which aspects of sleep
quality are relevant (Cappelleri et al., 2009). A single item can be indicative of
more comprehensive sleep measures (Carpenter & Andrykowski, 1998) but,
based on this study, we cannot infer whether the low variance explained was
due to the model itself or the selected measure.
The situational factors showed both unexpected and expected dependencies.
Contrary to the general preconceptions, season (winter or spring) barely
correlated with the well-being outcomes or with the frequency measures of PA.
Indeed, a reliability analysis of the LVVI2 survey series (of which the data in
this study were a part) has shown that, in Finland, there are no differences
between seasons in the most popular forms of PA such as walking for pleasure
or fitness, Nordic walking, and jogging (Korpela, Borodulin, Neuvonen,
Paronen, & Tyrväinen, 2014). Then again, as could be expected, unusual life
events and constraints and disabilities that partly (but not totally) constricted
the respondents’ outdoor visits were negatively correlated with each PA
measure, as well as each outcome. By adding them in the model, most
relationships between PA and health measures were suppressed as their coef-
ficients decreased and significance levels increased. It seems that having a long
PERCEIVED HEALTH AND ACTIVITY IN NATURE 337
© 2014 The Authors. Applied Psychology: Health and Well-Being published by John Wiley &
Sons Ltd on behalf of The International Association of Applied Psychology
term disability or other hindrance to exercise outdoors, or going through a
difficult phase in life, does not necessarily eliminate the benefits of exercising,
albeit they do reduce the effects of these relationships. Nonetheless, the
principal finding of this study was that the relationships between frequent PA
in nature and better emotional well-being and perceived health remained
significant even though these wider situational factors were controlled for.
The demographic covariates, with the exception of gender, behaved mainly
as expected based on previous research. Higher income was positively related
to all three outcomes, and perceived health decreased with age. Unexpectedly,
age was positively associated with mental well-being, and its relationship to
sleep quality was also weakly positive (unlike the suggestion of Driver &
Taylor, 2000). We had expected women to have more sleep problems but no
such connection was found. Instead, women rated their health higher than
men.
A weakness in all of our analyses was that the models failed to explain low
values of the two main outcomes. Those whose emotional well-being was low,
as well as those who had been experiencing poor sleep quality most of the
time, were poorly estimated in all our analyses. The models thus explained
well-being rather than “ill-being”. In this sense, the results differ from the
study by Mitchell (2013) where exercising in nature was more closely related
to the risk of poor mental health than to the positive measure of well-being.
Limitations of the study include the use of cross-sectional data that do not
permit the assessment of causalities. In addition, self-report measures have
been widely acknowledged to suffer from social desirability and memory bias
effects (Cooper, 1998). The theme of the survey was outdoor recreation, and
we acknowledge the risk of self-selection. People who enjoy recreational
activities outdoors may be more inclined to respond to a survey on this theme,
even though the theme covered activities in all types of outdoor environments,
including both natural and built environments. However, the validity study
(see the section on data collection) of the related survey concluded that the
respondents and non-respondents did not differ substantially from each other
with regard to their participation in outdoor recreation (Virtanen et al., 2011).
As the response rates and demographics between the sample in this study and
the sample for which the validity analysis was conducted were similar
(Virtanen et al., 2011), we have a strong basis to believe that this validity
analysis also applies to the data we analysed. Therefore, we consider the
potential effect of selection bias on the validity of our results to be only minor.
It is, in addition, possible that some of our findings are culturally affected.
Finns and other Nordic peoples are more active in outdoor recreation com-
pared with citizens in other parts of Europe (Bell, Tyrväinen, Sievänen,
Pröbstl, & Simpson, 2007), and may therefore perceive natural environments
more positively than those who are less familiar with nature. Then again, with
Finnish people living on average within 700 metres distance from a forest
338 PASANEN ET AL.
© 2014 The Authors. Applied Psychology: Health and Well-Being published by John Wiley &
Sons Ltd on behalf of The International Association of Applied Psychology
(Sievänen & Neuvonen, 2011), the results are not likely to have been affected
by inequalities in access to natural facilities.
Finally, there are some alternative explanations for the results that were
not considered in the analyses. First, no information on the nearby greenery
of the respondents’ living environments was available. Nearby greenery may,
for instance, encourage engagement in physical activities (Mytton et al.,
2012) and impact sleep quality through light pollution and noise. Second, we
do not know whether the average duration of exercise indoors, outdoors in
built settings, and in nature varies. If visits to the natural environments tend
to last longer, the increased amount of physical activity and exposure to
natural light could, at least partly, explain the strong relationship between
exercise in nature and well-being. Third, the broad categorisation into
indoor, built outdoor, and natural environments ignored the qualitative dif-
ferences within the environments in the same category. Different types of
natural environments may induce different restorative effects (Korpela, Ylén,
Tyrväinen, & Silvennoinen, 2010; Tyrväinen et al., 2014) and therefore, the
detected associations may not apply to all natural environments.
Validity of the Methods and Results
Most of our measures are standard in research practice in health sciences and
gave us no reason to suspect their validity or reliability. Measures that
necessitate some discussion are the estimation of the amount and intensity
of PA. Compared with controlled experiments, a self-report questionnaire
inevitably provides less accurate measures of PA. Nonetheless, estimating
one’s activities in weekly intervals was considered sufficiently easy for the
respondents to estimate. Another problem with the measure was that it
assumed that PA in different types of settings is something one does in a
generally consistent way from week to week, which might not be the case and,
consequently, may bias the estimate. Our measure of the general intensity of
PA, however, has previously been shown to be a stronger connection to mental
health than an objective measure of aerobic fitness (Lindwall et al., 2012).
Nevertheless, we consider our estimates of frequency and intensity of PA
capable of at least clearly distinguishing the most active from those who
exercise only occasionally.
Based on the RMSEA and the CFI, our models 2–4 fit the data well. The fact
that the CFIs somewhat worsened and the RMSEAs improved as new inde-
pendent variables were added is a common phenomenon (Kenny & McCoach,
2003). The χ2 values, on the contrary, were far from the ideal. We deduced
three reasons for the failure of this test. First, the χ2 test is known to be too
sensitive when sample sizes are large (N > 300; Kline, 2011). The RMSEA, a
similar measure that is less sensitive to sample size, supported most of the
tested models. Second, the models better explained positive rather than
PERCEIVED HEALTH AND ACTIVITY IN NATURE 339
© 2014 The Authors. Applied Psychology: Health and Well-Being published by John Wiley &
Sons Ltd on behalf of The International Association of Applied Psychology
negative emotional well-being and sleep quality, resulting in some large resid-
uals that evidently influenced the χ2 value. Third, the largest residuals between
the observed and estimated correlations appeared between the items of the
RAND-36. Had we constrained some of their residual variances, the fit could
have been artificially improved, but we found it theoretically unjustifiable.
Conclusions
This study provides a new type of survey evidence supporting the theories on
health-enhancing effects of contact with nature by suggesting that repeated
exercise in nature is related to improved long term well-being more explicitly
than repeated exercise in built environments indoors or outdoors. Good
emotional well-being, in particular, seems to be evidently associated with
more frequent, active visits to natural environments. Moreover, perceived
general health appears to be connected to PA both in nature and in built
outdoor settings. The evidence on sleep quality, on the other hand, was
weaker in this study. More research is needed to better understand the
connection between exposure to nature and sleep. Whether this relationship
is, for example, mediated by improved psychological well-being or neigh-
bourhood qualities is worthy of further examination.
ACKNOWLEDGEMENTS
Many thanks go to Tuija Sievänen, Marjo Neuvonen, Miisa Pietilä, and Ann
Ojala of the Finnish Forest Research Institute (Metla) for their contributions
to the project. This work was supported by the Academy of Finland (project
number 2501255431) and the Finnish Ministry of Education and Culture,
and the Ministry of the Environment (grant number YTA022).
CONFLICT OF INTEREST
None.
REFERENCES
Aalto, A., Aro, A.R., & Teperi, J. (1999). RAND-36 terveyteen liittyvän elämänlaadun
mittarina: mittarin luotettavuus ja suomalaiset väestöarvot [RAND-36 as a measure
of health-related quality of life: Reliability, construct validity and reference values
in the Finnish general population]. Helsinki: Stakes.
Astell-Burt, T., Feng, X., & Kolt, G.S. (2013). Does access to neighbourhood
green space promote a healthy duration of sleep? Novel findings from a cross-
sectional study of 259 319 Australians. BMJ Open, 3(8). doi:10.1136/bmjopen-
2013-003094
340 PASANEN ET AL.
© 2014 The Authors. Applied Psychology: Health and Well-Being published by John Wiley &
Sons Ltd on behalf of The International Association of Applied Psychology
Barton, J., & Pretty, J. (2010). What is the best dose of nature and green exercise for
improving mental health? A multi-study analysis. Environmental Science & Tech-
nology, 44(10), 3947–3955. doi:10.1021/es903183r
Bell, S., Tyrväinen, L., Sievänen, T., Pröbstl, U., & Simpson, M. (2007). Outdoor
recreation and nature tourism: A European perspective. Living Reviews in Land-
scape Research, 1(2), 1–46.
Beute, F., & de Kort, Y.A.W. (2014). Salutogenic effects of the environment: Review
of health protective effects of nature and daylight. Applied Psychology: Health and
Well-Being, 6(1), 67–95. doi:10.1111/aphw.12019
Bowler, D.E., Buyung-Ali, L., Knight, T.M., & Pullin, A.S. (2010). A systematic
review of evidence for the added benefits to health of exposure to natural environ-
ments. BMC Public Health, 10, 456–465.
Browne, M.W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Socio-
logical Methods & Research, 21(2), 230–258.
Buysse, D.J., Reynolds III, C.F., Monk, T.H., Berman, S.R., & Kupfer, D.J. (1989).
The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice
and research. Psychiatry Research, 28(2), 193–213. doi:10.1016/0165-
1781(89)90047-4
Cappelleri, J., Bushmakin, A., McDermott, A., Sadosky, A., Petrie, C., & Martin, S.
(2009). Psychometric properties of a single-item scale to assess sleep quality
among individuals with fibromyalgia. Health and Quality of Life Outcomes, 7(1),
54.
Carpenter, J.S., & Andrykowski, M.A. (1998). Psychometric evaluation of the Pitts-
burgh Sleep Quality Index. Journal of Psychosomatic Research, 45(1), 5–13.
doi:10.1016/S0022-3999(97)00298-5
Cooper, C. (1998). Individual differences. London: Arnold.
De Jong, K., Albin, M., Skärbäck, E., Grahn, P., & Björk, J. (2012). Perceived green
qualities were associated with neighborhood satisfaction, physical activity, and
general health: Results from a cross-sectional study in suburban and rural Scania,
southern Sweden. Health & Place, 18(6), 1374–1380. doi:10.1016/j.healthplace
.2012.07.001
De Vries, S., van Dillen, S.M.E., Groenewegen, P.P., & Spreeuwenberg, P. (2013).
Streetscape greenery and health: Stress, social cohesion and physical activity as
mediators. Social Science & Medicine, 94(0), 26–33. doi:10.1016/j.socscimed.
2013.06.030
Driver, H.S., & Taylor, S.R. (2000). Exercise and sleep. Sleep Medicine Reviews, 4(4),
387–402. doi:10.1053/smrv.2000.0110
Fox, K.R. (1999). The influence of physical activity on mental well-being. Public
Health Nutrition, 2(3a), 411–418.
Graw, P., Recker, S., Sand, L., Kräuchi, K., & Wirz-Justice, A. (1999). Winter and
summer outdoor light exposure in women with and without seasonal affective
disorder. Journal of Affective Disorders, 56(2–3), 163–169. doi:10.1016/S0165-
0327(99)00037-3
Hartig, T., Evans, G.W., Jamner, L.D., Davis, D.S., & Gärling, T. (2003). Tracking
restoration in natural and urban field settings. Journal of Environmental Psychol-
ogy, 23(2), 109–123. doi:10.1016/S0272-4944(02)00109-3
PERCEIVED HEALTH AND ACTIVITY IN NATURE 341
© 2014 The Authors. Applied Psychology: Health and Well-Being published by John Wiley &
Sons Ltd on behalf of The International Association of Applied Psychology
Hartig, T., Mitchell, R., de Vries, S., & Frumkin, H. (2014). Nature and health.
Annual Review of Public Health, 35(1), 207–228. doi:10.1146/annurev-publhealth-
032013-182443
Hayduk, L.A., & Glaser, D.N. (2000). Jiving the four-step, waltzing around factor
analysis, and other serious fun. Structural Equation Modeling: A Multidisciplinary
Journal, 7(1), 1–35. doi:10.1207/S15328007SEM0701_01
Hays, R.D., Sherbourne, C.D., & Mazel, R.M. (1993). The RAND 36-item health
survey 1.0. Health Economics, 2(3), 217–227. doi:10.1002/hec.4730020305
Hood, B., Bruck, D., & Kennedy, G. (2004). Determinants of sleep quality in the
healthy aged: The role of physical, psychological, circadian and naturalistic light
variables. Age and Ageing, 33(2), 159–165.
Hu, L., & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure
analysis: Conventional criteria versus new alternatives. Structural Equation Mod-
eling: A Multidisciplinary Journal, 6(1), 1–55.
Idler, E.L., & Benyamini, Y. (1997). Self-rated health and mortality: A review of
twenty-seven community studies. Journal of Health and Social Behavior, 38(1),
21–37.
Kallio, J. (2006). Koettu terveys ja terveydellinen tasa-arvo kuudessa
hyvinvointivaltiossa [Self-perceived health and health differences in six welfare
states]. Sosiaalilääketieteellinen Aikakauslehti—Journal of Social Medicine, 43(3),
218–230.
Kaplan, S. (1995). The restorative benefits of nature: Toward an integrative frame-
work. Journal of Environmental Psychology, 15(3), 169–182. doi:10.1016/0272-
4944(95)90001-2
Kenny, D.A., & McCoach, D.B. (2003). Effect of the number of variables on measures
of fit in structural equation modeling. Structural Equation Modeling, 10(3), 333–
351.
Kerr, J., Marshall, S., Godbole, S., Neukam, S., Crist, K., Wasilenko, K. et al. (2012).
The relationship between outdoor activity and health in older adults using GPS.
International Journal of Environmental Research and Public Health, 9(12), 4615–
4625.
Kline, R.B. (2011). Principles and practice of structural equation modeling (3rd edn.).
New York: Guilford Press.
Korpela, K., Borodulin, K., Neuvonen, M., Paronen, O., & Tyrväinen, L. (2014).
Analyzing the mediators between nature-based outdoor recreation and emotional
well-being. Journal of Environmental Psychology, 37, 1–7. doi:10.1016/
j.jenvp.2013.11.003
Korpela, K.M., Ylén, M., Tyrväinen, L., & Silvennoinen, H. (2010). Favorite green,
waterside and urban environments, restorative experiences and perceived health in
Finland. Health Promotion International, 25(2), 200–209. doi:10.1093/heapro/
daq007
Kronholm, E., Sallinen, M., Suutama, T., Sulkava, R., Era, P., & Partonen, T. (2009).
Self-reported sleep duration and cognitive functioning in the general population.
Journal of Sleep Research, 18(4), 436–446. doi:10.1111/j.1365-2869.2009.00765.x
Lahelma, E., Laaksonen, M., Martikainen, P., Rahkonen, O., & Sarlio-
Lähteenkorva, S. (2006). Multiple measures of socioeconomic circumstances
342 PASANEN ET AL.
© 2014 The Authors. Applied Psychology: Health and Well-Being published by John Wiley &
Sons Ltd on behalf of The International Association of Applied Psychology
and common mental disorders. Social Science & Medicine, 63(5), 1383–1399.
doi:10.1016/j.socscimed.2006.03.027
Lallukka, T., Sares-Jaske, L., Kronholm, E., Saaksjarvi, K., Lundqvist, A., Partonen,
T. et al. (2012). Sociodemographic and socioeconomic differences in sleep duration
and insomnia-related symptoms in Finnish adults. BMC Public Health, 12(1),
565–586.
Lee, J., Park, B., Tsunetsugu, Y., Kagawa, T., & Miyazaki, Y. (2009). Restorative
effects of viewing real forest landscapes, based on a comparison with urban land-
scapes. Scandinavian Journal of Forest Research, 24(3), 227–234. doi:10.1080/
02827580902903341
Lindwall, M., Ljung, T., Hadžibajramovic´, E., & Jonsdottir, I.H. (2012). Self-
reported physical activity and aerobic fitness are differently related to mental
health. Mental Health and Physical Activity, 5(1), 28–34.
Lomeli, H.A., Perez-Olmos, I., Talero-Gutierrez, C., Moreno, C.B., Gonzalez-Reyes,
R., Palacios, L. et al. (2008). Sleep evaluation scales and questionnaires: A review.
Actas Espanolas De Psiquiatria, 36(1), 50–59.
Maas, J., Verheij, R.A., de Vries, S., Spreeuwenberg, P., Schellevis, F.G., &
Groenewegen, P.P. (2009). Morbidity is related to a green living environment.
Journal of Epidemiology and Community Health, 63(12), 967–973. doi:10.1136/
jech.2008.079038
Maas, J., Verheij, R.A., Groenewegen, P.P., de Vries, S., & Spreeuwenberg, P.
(2006). Green space, urbanity, and health: How strong is the relation? Journal
of Epidemiology and Community Health, 60(7), 587–592. doi:10.1136/jech
.2005.043125
Miilunpalo, S., Vuori, I., Oja, P., Pasanen, M., & Urponen, H. (1997). Self-rated
health status as a health measure: The predictive value of self-reported health
status on the use of physician services and on mortality in the working-age popu-
lation. Journal of Clinical Epidemiology, 50(5), 517–528. doi:10.1016/S0895-
4356(97)00045-0
Mitchell, R. (2013). Is physical activity in natural environments better for mental
health than physical activity in other environments? Social Science &Medicine, 91,
130–134. doi:10.1016/j.socscimed.2012.04.012
Mitchell, R., & Popham, F. (2008). Effect of exposure to natural environment on
health inequalities: An observational population study. The Lancet, 372(9650),
1655–1660. doi:10.1016/S0140-6736(08)61689-X
Møller, A., & Jennions, M.D. (2002). How much variance can be explained by
ecologists and evolutionary biologists? Oecologia, 132(4), 492–500.
Muthén, L.K., & Muthén, B.O. (1998–2012). Mplus user’s guide (7th edn.). Los
Angeles, CA: Muthén & Muthén.
Mytton, O.T., Townsend, N., Rutter, H., & Foster, C. (2012). Green space and
physical activity: An observational study using Health Survey for England data.
Health & Place, 18(5), 1034–1041. doi:10.1016/j.healthplace.2012.06.003
Ohayon, M.M., & Partinen, M. (2002). Insomnia and global sleep dissatisfaction in
Finland. Journal of Sleep Research, 11(4), 339–346.
Peltonen, M., Harald, K., Männistö, S., Saarikoski, L., Lund, L., Sundvall, J. et al.
(2008). Kansallinen FINRISKI 2007-terveystutkimus: Tutkimuksen toteutus ja
PERCEIVED HEALTH AND ACTIVITY IN NATURE 343
© 2014 The Authors. Applied Psychology: Health and Well-Being published by John Wiley &
Sons Ltd on behalf of The International Association of Applied Psychology
tulokset. taulukkoliite [The National FINRISKI 2007 Health Study: Procedure and
results. Table appendix]. Kansanterveyslaitos.
Penedo, F.J., & Dahn, J.R. (2005). Exercise and well-being: A review of mental and
physical health benefits associated with physical activity. Current Opinion in Psy-
chiatry, 18(2), 189–193.
Pretty, J., Peacock, J., Sellens, M., & Griffin, M. (2005). The mental and physical
health outcomes of green exercise. International Journal of Environmental Health
Research, 15(5), 319–337.
Raykov, T., & Marcoulides, G.A. (2011). Introduction to psychometric theory. New
York: Routledge.
Saltin, B., & Grimby, G. (1968). Physiological analysis of middle-aged and old former
athletes: Comparison with still active athletes of the same ages. Circulation, 38(6),
1104–1115. doi:10.1161/01.CIR.38.6.1104
Sievänen, T., & Neuvonen, M. (2011). Luonnon virkistyskäytön kysyntä
2010 ja kysynnän muutos. [Outdoor recreation demand 2010 and
changes in the demand]. In T. Sievänen & M. Neuvonen (Eds.), Luonnon
virkistyskäyttö 2010 [Outdoor recreation 2010]. Working Papers of the
Finnish Forest Research Institute No. 212 (pp. 37–73). Vantaa:
Metsäntutkimuslaitos.
Spector, P.E., & Brannick, M.T. (2011). Methodological urban legends: The misuse of
statistical control variables. Organizational Research Methods, 14(2), 287–305.
doi:10.1177/1094428110369842
Stephens, T. (1988). Physical activity and mental health in the United States and
Canada: Evidence from four population surveys. Preventive Medicine, 17(1),
35–47. doi:10.1016/0091-7435(88)90070-9
Stigsdotter, U.K., Ekholm, O., Schipperijn, J., Toftager, M., Kamper-Jørgensen, F.,
& Randrup, T.B. (2010). Health promoting outdoor environments—associations
between green space, and health, health-related quality of life and stress based on
a Danish national representative survey. Scandinavian Journal of Public Health,
38(4), 411–417. doi:10.1177/1403494810367468
Tabachnick, B.G., & Fidell, L.S. (2007). Using multivariate statistics (5th edn.).
Boston, MA: Pearson Education.
Takano, T., Nakamura, K., & Watanabe, M. (2002). Urban residential environments
and senior citizens’ longevity in megacity areas: The importance of walkable green
spaces. Journal of Epidemiology and Community Health, 56(12), 913–918.
doi:10.1136/jech.56.12.913
Thompson Coon, J., Boddy, K., Stein, K., Whear, R., Barton, J., & Depledge, M.H.
(2011). Does participating in physical activity in outdoor natural environments
have a greater effect on physical and mental wellbeing than physical activity
indoors? A systematic review. Environmental Science & Technology, 45(5), 1761–
1772. doi:10.1021/es102947t
Tucker, P., & Gilliland, J. (2007). The effect of season and weather on physical acti-
vity: A systematic review. Public Health, 121(12), 909–922. doi:10.1016/j.puhe
.2007.04.009
Tyrväinen, L., Ojala, A., Korpela, K., Lanki, T., Tsunetsugu, Y., & Kagawa, T. (2014).
The influence of urban green environments on stress relief measures: A
344 PASANEN ET AL.
© 2014 The Authors. Applied Psychology: Health and Well-Being published by John Wiley &
Sons Ltd on behalf of The International Association of Applied Psychology
field experiment. Journal of Environmental Psychology, 38, 1–9. doi:10.1016/
j.jenvp.2013.12.005
Ulrich, R.S. (1983). Aesthetic and affective response to natural environment. Human
Behavior & Environment: Advances in Theory & Research, 6, 85–125.
Ulrich, R.S., Simons, R.F., Losito, B.D., Fiorito, E., Miles, M.A., & Zelson, M.
(1991). Stress recovery during exposure to natural and urban environments.
Journal of Environmental Psychology, 11(3), 201–230. doi:10.1016/S0272-
4944(05)80184-7
Väätäinen, S., Tuomilehto, H., Saramies, J., Tuomilehto, J., Uusitalo, H., Hussi, E.
et al. (2013). The health-related quality-of-life impact of nocturnal awakenings in
the middle-aged and older Finnish population. Quality of Life Research, 22(10),
2737–2748. doi:10.1007/s11136-013-0404-y
Van Dillen, S.M.E., de Vries, S., Groenewegen, P.P., & Spreeuwenberg, P. (2012).
Greenspace in urban neighbourhoods and residents’ health: Adding quality to
quantity. Journal of Epidemiology and Community Health, 66(6), e8. doi:10.1136/
jech.2009.104695
Van Herzele, A., & de Vries, S. (2012). Linking green space to health: A comparative
study of two urban neighbourhoods in Ghent, Belgium. Population and Environ-
ment, 34(2), 171–193. doi:10.1007/s11111-011-0153-1
Virtanen, V., Nyberg, R., Salonen, R., Neuvonen, M., & Sievänen, T. (2011). LVVI
2—seurantatutkimuksen aineisto ja menetelmät [The data and methods of the
LVVI2 follow-up survey]. In T. Sievänen, & M. Neuvonen (Eds.), Luonnon
virkistyskäyttö 2010 [Outdoor recreation 2010] (pp. 19–36). Vantaa:
Metsäntutkimuslaitos.
Ward Thompson, C., & Aspinall, P.A. (2011). Natural environments and their impact
on activity, health, and quality of life. Applied Psychology: Health and Well-Being,
3(3), 230–260. doi:10.1111/j.1758-0854.2011.01053.x
Ward Thompson, C., Roe, J., Aspinall, P., Mitchell, R., Clow, A., & Miller, D. (2012).
More green space is linked to less stress in deprived communities: Evidence from
salivary cortisol patterns. Landscape and Urban Planning, 105(3), 221–229.
doi:10.1016/j.landurbplan.2011.12.015
White, M.P., Alcock, I., Wheeler, B.W., & Depledge, M.H. (2013). Would you be
happier living in a greener urban area? A fixed-effects analysis of panel data.
Psychological Science, 24(6), 920–928. doi:10.1177/0956797612464659
Yu, C. (2002). Evaluating cutoff criteria of model fit indices for latent variable models
with binary and continuous outcomes. Doctoral dissertation, University of Cali-
fornia, Los Angeles.
SUPPORTING INFORMATION
Additional Supporting Information may be found in the online version of
this article at the publisher’s web-site:
Appendix 1. Scale for emotional well-being (RAND-36 subscale; Hays
et al., 1992), Scale for general activity (adapted from Saltin & Grimby,
1968).
PERCEIVED HEALTH AND ACTIVITY IN NATURE 345
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Sons Ltd on behalf of The International Association of Applied Psychology
Appendix 2a. Correlations between the observed independent variables
(Spearman if one or both of the variable pair are ordinal, Pearson in italics),
n = 2070.
Appendix 2b. Spearman correlations between the outcome variables (emo-
tional well-being as the summary score of the five RAND-36 items), n = 2070.
Appendix 3a. Diagram of model 1. χ2 = 405, df = 25, p < 0.0001, CFI = 0.97,
RMSEA = 0.09. Only significant (p < 0.05) connections are shown (arrows).
The regression coefficients between independent and dependent variables are
provided in Table 3.
Appendix 3b. Diagram of model 2. χ2 = 388, df = 29, p < 0.0001, CFI = 0.97,
RMSEA = 0.08. Only significant (p < 0.05) connections are shown (arrows).
The dashed arrow represents a negative connection. The regression coeffi-
cients between independent and dependent variables are provided in Table 3.
Appendix 3c. Diagram of model 3. χ2 = 463, df = 43, p < 0.0001, CFI = 0.97,
RMSEA = 0.07. Only significant (p < 0.05) connections are shown (arrows).
The dashed arrow represents a negative connection. The regression coeffi-
cients between independent and dependent variables are provided in Table 3.
346 PASANEN ET AL.
© 2014 The Authors. Applied Psychology: Health and Well-Being published by John Wiley &
Sons Ltd on behalf of The International Association of Applied Psychology