Viewing an alpine environment positively affects emotional analytics in
patients with somatoform, depressive and anxiety disorders as well as
in healthy controls
Katharina Hüfner ( firstname.lastname@example.org )
Medizinische Universitat Innsbruck https://orcid.org/0000-0002-5453-8792
Medizinische Universitat Innsbruck
Medizinische Universitat Innsbruck
Medizinische Universitat Innsbruck
Medizinische Universitat Innsbruck
Medizinische Universitat Innsbruck
Keywords: alpine environment, resilience, self-perceived stress, self-assessment manikin, emotional analytics, psychosomatic disorders
License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
Background: Patients with somatoform, depressive or anxiety disorders often don´t respond well to medical treatment and experience many side
effects. It is thus of clinical relevance to identify alternative, scientically based, treatments. Our approach is based on the recent evidence that
urbanicity has been shown to be associated with an increased risk for mental disorders. Conversely green and blue environments show a dose-
dependent benecial impact on mental health.
Methods: Here we evaluate the effect of viewing stimuli of individuals in an alpine environment on emotional analytics in 183 patients with
psychiatric disorders (mostly somatoform, depressive and anxiety disorders) and 315 healthy controls (HC). Emotional analytics (valence:
unhappy vs happy, arousal: calm vs excited, dominance: controlled vs in control) were assessed using the Self-Assessment Manikin. Further
parameters related to mental health and physical activity were recorded.
Results: Emotional analytics of patients indicated that they feel less happy, less in control and had higher levels of arousal than HC when
viewing neutral stimuli. The comparison alpine>neutral stimuli showed a signicant a positive effect of alpine stimuli on emotional analytics in
both groups. Patients and HC both felt attracted to the scenes displayed in the alpine stimuli. Emotional analytics correlated positively with
resilience and inversely with perceived stress.
Conclusions: Preventive and therapeutic programs for patients with somatoform, depressive and anxiety disorders should consider taking the
benets of natural outdoor such as alpine environments, into account. Organizational barriers which are preventing the implementation of such
programs in clinical practice need to be identied and addressed.
The natural environment is known to improve physical and mental health: A meta-analysis reported an 8% reduction in all-cause mortality for
residents with the highest nature outdoor exposure compared with the lowest exposure group (Gascon et al. 2016). Discovering blue (de Bell et
al. 2017) and green (van den Berg et al. 2016) spaces is associated with psychological benets. Stress is an important mediator of the effect of
natural outdoor environments and mental well-being (Triguero-Mas et al. 2017). Green spaces have been shown to reduce cortisol levels as a
marker of stress (Twohig-Bennett and Jones 2018). Stress as important marker of mental health is signicantly reduced by the exposure to
nature even by only the visual stimulation with nature without physical exposure in a dose-response relationship (Hazer et al. 2018). Visual or
auditory nature stimuli can facilitate recovery from psychological stressful events (Brown et al. 2013; Alvarsson et al. 2010) and from physical
disease (Ulrich 1984). In mental health, chronic stress is among the strongest risk factors for depression but is also an important pathogenetic
factor in anxiety disorders, post-traumatic stress disorders or somatoform disorders (Slavich and Irwin 2014, Bangasser and Valentin 2014).
Another factor through which exposure to natural outdoor environments exerts its positive effect on mental health might be through the
strengthening of resilience (Ritchie et al. 2014; Panno et al. 2017). Resilience can be dened as one’s ability to cope with and recover from
adverse life events. Resilience is improved by physical activity performed in a natural outdoor environment but is not associated with physical
activity performed indoors (Ower et al. 2018). When the natural environment is used to perform physical activity the positive effects of physical
activity and natural environments can be combined: there is evidence that exercising outdoors results in greater improvements of mental well-
being than exercising indoors with greater feelings of delight, energy and revitalization, as well as decreases in frustration, tiredness and anger
(Thompson Coon et al. 2011).
The positive effects of the alpine natural environment have rarely been examined. One of the few available studies suggests that watching
grand mountain scenes triggers a greater mood improvement than mundane nature. Furthermore participants were feeling signicantly more
connected to others, more caring, and more spiritual after watching awe-inspiring nature condition (Joye and Bolderdijk 2015). Hikers of alpine
wilderness trails reported substantial stress reduction and mental rejuvenation following a day or overnight hike (Cole and Hall 2010).
Furthermore, in a crossover trial focusing on differences between indoor and alpine activity, mountain hiking showed signicantly greater
positive effects on affective valence and activation compared to indoor physical activity (Niedermeier et al. 2017a). It is unknown whether the
mechanisms linking different natural environments (green space, blues space, alpine) to mental health are due to similar or differential effects
(Gascon et al. 2015, Liu et al. 2020).
Although studies report an improvement on various psychological measures, as a result of exposure to alpine environments they do not refer to
a possible therapeutic effect in mental health. There are only few studies investigating therapeutic alpine interventions as treatment for patients
in mental health care. In a mountain hiking program for suicidal patients, participants reported signicant reduction in depression, hopelessness
and suicidal ideation (Sturm et al. 2012). In another study adults and youth with mental illness experienced signicant improvements in self-
esteem, mastery and resilience following activities like mountain biking and raft building (Bowen et al. 2016).
The primary aim of the present study was to investigate whether stimuli depicting alpine environments would elicit differential or similar
emotional analytics in patients with somatoform, depressive and anxiety disorders and healthy controls in order to judge the potential
usefulness for a therapeutic intervention program. This aim was approached by the following study setting:
1. We assessed emotional analytics upon viewing neutral and alpine stimuli in patients with somatoform, depressive and anxiety disorders
and healthy controls. The alpine stimuli depicted individuals while engaged in physical activity in an alpine environment
2. We investigated whether there was a correlation of emotional analytics with resilience or perceived stress in patients and healthy controls.
3. We measured the amount of self-performed physical activity in an alpine environment as a marker of previous exposure to the depicted
stimuli in a natural environment.
2.1 Study design
This is a cross-sectional observational study including a quasi-experimental part (gure 1). The whole study was performed online. The rst part
of the study contained questionnaires, while the second part recorded emotional reaction to visual stimuli. It was not possible to skip one
question or a questionnaire. The current data is part of a larger study examining the effect of physical activity in an alpine environment on
mental health, part of which has been published (Ower et al. 2018). Innsbruck is one of few urban spaces located directly within the Alps and
thus allows for easy access to the alpine environment. The ethics commission of the Medical University of Innsbruck reviewed and approved the
study protocol. After being informed in detail about the study aims and procedures, participants provided informed consent prior to study
participation. Study recruitment was conducted over a four-month period in 2016.
Participants and recruiting are described in Ower et al. 2018, participant numbers vary slightly compared to the previous publication due to
missing data in individual participants. In brief, a total of 1029 individuals participated in an open online-only survey. They were recruited via
email (mailing lists), social media and classied websites or whilst treated at the Department of Psychiatry, Psychotherapy and Psychosomatics
(Division of Psychiatry II/Psychosomatic Medicine) at Innsbruck Medical University at the inpatient or outpatient clinic. We included mainly
patients with the diagnosis of somatoform, depressive and anxiety disorders. For the present analysis participants who terminated the
questionnaire early i.e. prior to the Self-Assessment Manikin (SAM) ratings (missing data n = 436, Figure 2) were excluded from the study. This
high drop-out rate was mainly due to the fact that SAM ratings of emotional analytics were performed as the nal phase of the questionnaire
and it was not possible to skip questions. Comparison of participants terminating early with those included in the data analysis showed that the
former were signicantly older (mean age ± standard deviation, 33.5 ± 12.1 years vs 29.7 ± 10.1 years, p<0.001, Mann-Whitney U-Test) and that
a larger proportion of them was female (68.4 % vs 61.2%, p=0.017, Chi-square test). Despite statistical signicance, the differences in age (effect
size d= 0.34) and sex distribution (odds ratio = 1.37) were comparatively small. Furthermore participants that reported implausible values (n =8),
screened positively for alcohol abuse only (n =54) or for an eating disorder only (Anorexia nervosa and Bulimia nervosa; n=33) were excluded
from the present analysis (Figure 2). In Anorexia nervosa or Bulimia nervosa it is known that high levels of PA are used as tool for losing weight
and therefore are an expression of disease. Therefore, these patients were excluded (Bezzina et al. 2019). There were 4% to 13% missing values
for individual SAM ratings. The 498 participants included in the present analysis consisted of two groups. Patients screened positively for
mental health disorder on the Patient Health Questionnaire (PHQ, n =183). Participants without positive PHQ screening (n =315) formed the
control group (=HC).
Stimuli were alternating 5 neutral pictures (re-staged to ocial International Affective Picture System (IAPS) pictures (slide no. 6150, 7009, 5661,
5500, 7150)) and 5 alpine stimuli (Figure 3). Neutral pictures displayed gural subjects of daily life (e.g. mug, wall, umbrella). Alpine stimuli
displayed alpine environments with individuals performing some sort of physical activity therein (e.g. hiking, biking, skiing). The pictures were
presented to all participants in the same order. Two picture stimuli had to be excluded due considerations related to the displayed content
(canyon wall in the neutral stimuli and paraglider in the mountains in the alpine stimuli) and their mean ratings for at least one of the analyzed
dimensions ranging two standard deviations outside the mean of the other stimuli in the group. Pictures were displayed for 5 seconds before the
page with the emotional analytic ratings appeared. Each stimulus could only be observed once (Figure 1).
Socio-demographic parameters included information on age, sex, education and marital status. Mental health was assessed using the German
version of Patient Health Questionnaire (Gräfe et al. 2004). Additionally, open text elds were provided for entering psychiatric diagnoses.
Resilience was measured using the Brief Resilience Score (BRS) (Smith et al. 2008), self-perceived stress using the Perceived Stress Scale (PSS)
(Cohen et al. 1983) and Physical activity (PA) using the Global Physical Activity Questionnaire (GPAQ-2) (Bull et al. 2009). PA is calculated using
metabolic equivalents of task (METs) as a unit for energy expense. As determined of the World Health Organization we classied PA in
moderate and vigorous intensity. We adapted the standard questionnaire to measure PA performed in the alpine environment.
To measure emotional response we used the Self-Assessment Manikin (SAM) 9-point Likert-scale. This scale measures emotional analytics in
the three dimensions valence, arousal and dominance (J Lang et al. 2008). The valence scale ranges from a frowning, unhappy (adjectives used
in the SAM manual: unhappy, annoyed, unsatised, melancholic, despaired, bored; lower values) to a smiling, happy gure (happy, pleased,
satised, contented, hopeful). The arousal scale displays the lowest value with a calm, eyes-closed gure (relaxed, calm, sluggish, dull, sleepy,
unaroused), whilst the highest value is represented by an excited gure (stimulated, excited, frenzied, jittery, wide-awake, aroused). The lowest
values in the dominance scale are symbolized by a controlled small gure (controlled, inuenced, cared-for, awed, submissive, guided.) whilst
highest values are represented by a dominant and oversized gure (controlling, inuential, in control, important, dominant, autonomous). After
presenting a picture for ve seconds participants were asked to rate their emotional reaction in the three dimensions. For alpine stimuli, we
added a fourth dimension asking about ones attraction to the situation, labelled motivational direction. The 9 point Likert-scale ranged from “I
don’t want to be in this situation” to “I want to be in the situation”.
2.4 Statistical methods
Metric variables were analyzed for normal distribution prior to applying further statistical tests by assessing their skewness and their kurtosis,
considering skewness values > 0.5 or < -0.5 (Lehman 1991) and kurtosis values > 1 or <-1 (https://brownmath.com/stat/shape.htm) as
deviations from a normal distribution requiring non-parametric testing. To compare emotional reactions between overall neutral and alpine
pictures we created a mean score for each category. In each category one picture was excluded due to statistical outliers (paraglide in alpine
pictures; red wall in neutral pictures). Mean scores were calculated for each emotional dimension per person if at least three scores were
completed. Group comparisons (patients vs. HC) were performed using t-test, Mann-Whitney U-test and Chi-square test, depending on the
variable type and distribution. As the two groups differed signicantly in their age; education, marital status, and work situation, we also
performed analyses of covariance with adjustment for these potential confounders. As the emotional analytic ratings displayed missing values
(4% to 13%), we performed an additional analysis where missing ratings were replaced by imputed values. The SPSS Missing Value Analysis
procedure with Little’s test for missingness completely at random (MCAR) and imputation by expectation-maximization (EM) was used for this
purpose (IBM SPSS manual). The relationship between resilience, self-perceived stress, PA and emotional analytics was investigated on a
descriptive level by means of correlation analysis. Spearman rank correlation coecients were used as most the variables involved showed
deviations from a normal distribution.
3.1 Sociodemographic characteristics and clinical features
The sociodemographic characteristics of patients and HC are displayed in Table 1. Patients diagnoses according to PHQ were in decreasing
frequency: somatoform disorder (n=101, 55.2 %), major depressive syndrome (n=67, 36.6%), other anxiety syndrome (n=45, 24.6%), panic
syndrome (n=36, 19.7%), other depressive syndrome (n=34, 18.6%), alcohol abuse (n=31, 16.9%), binge eating disorder (n=23, 12.6%), bulimia
nervosa (n=10, 5.5%) and others (n=2, 1.1%). More than half of the patients (n=100, 51.9%) were diagnosed with more than one mental health
disorder, the most prevalent combination was somatoform disorder and major depressive syndrome (n=42, 23.0%).
3.2 Comparison of resilience, self-perceived stress and emotional analytics in patients and HC
The mean score of the Brief Resilience Scale (BRS) was signicantly lower in patients than in HC (Mann-Whitney U Test-Test, p<0.001; Table 2).
Furthermore the total score of the PSS was signicantly higher in patients than in HC (Mann Whitney U Test, p<0.001; Table 2).
Comparing the mean emotional analytics score in neutral and alpine stimuli, patients reported signicantly lower values for valence (both
ps<0.001) indicating that they felt less happy than HC, and dominance (neutral: p=0.021, alpine: p<0.001; Table 2) indicating that they felt less
in control than HC. Arousal when viewing neutral stimuli was signicantly higher (p<0.001) for patients indicating that they felt more aroused or
jittery than the HC at baseline. In alpine pictures the difference in arousal was not signicant between patients and HC (p=0.223; Table 2). In the
fourth dimension asking about attraction to the displayed alpine situation, the mean score was signicantly lower in patients as in HC (p< 0.001
table 2) although both groups showed a high attraction to the alpine stimuli. All statistically signicant differences in Table 2 remained
signicant when adjusting for age, education, marital status, and work situation by analysis of covariance. Missing value analysis for emotional
analytics revealed that SAM ratings were not missing completely at random (Little’s test, c² = 3607.5, d.f.= 3314, p < 0.001). Replacement of
missing emotional analytics ratings by the EM imputation method led to comparable results as the analysis without replacement. Mean ratings
changed by less than 0.1 in both groups. Moreover, all signicant group differences were retained.
To measure the effect of the alpine stimuli normalized to the neutral baseline, we evaluated the difference of each emotional dimension
between alpine and neutral pictures. The comparison alpine > neutral stimuli was signicantly greater than 0 for both patients and HC indicating
a positive effect of alpine stimuli on emotional analytics. For valence and dominance this comparison of alpine > neutral stimuli did not differ
signicantly between patients and HC (Table 2). For arousal the difference was signicantly smaller in patients than in HC due to higher
baseline arousal values in patients (p<0.001; Table 2).
3.3 Correlation between resilience, self-perceived stress, physical activity in an alpine environment and emotional analytics
For the correlation analysis between resilience, self-perceived stress and emotional response, we combined the patient and HC group to one total
sample. Resilience correlated positively in both neutral and alpine stimuli with the emotional analytics for valence, dominance and attraction (all
ps<0.001, Table 3) indicating that greater resilience was associated with higher emotional ratings. Self-perceived stress correlated negatively
with valence, dominance and attraction in both neutral and alpine stimuli (all ps<0.05; Table 3) demonstrating that higher stress levels were
associated with lower emotional ratings (Table 3).
Arousal while viewing neutral pictures correlated in an inverse way: negatively with resilience and positively with perceived stress. Subanalyses
demonstrated that this was mostly due to patients´ values (not shown). This demonstrates that individuals with low resilience and high levels of
stress feel more aroused or jittery at baseline compared to resilient individuals who feel calmer when viewing neutral stimuli. Physical activity in
an alpine environment correlated positively with all four emotional analytics in alpine stimuli (all p<0.001), whilst there was no signicant
correlation with neutral stimuli (Table 3).
In the present study we evaluated the effect of viewing alpine stimuli on emotional analytics in patients with somatoform, depressive and
anxiety disorders and healthy controls. The major ndings were: 1) the emotional analytics valence and dominance were signicantly lower in
patients compared to HC for both alpine and neutral stimuli. Baseline arousal when viewing neutral stimuli was signicantly higher in patients,
2) the emotional analytic scores were signicantly higher for alpine compared to neutral pictures for patients as well as for HC, 3) Emotional
analytics of alpine pictures correlated positively with resilience and physical activity in an alpine environment and inversely with perceived
4.1 Resilience and psychosomatic stress in patients with psychosomatic disorders
In patients with somatoform, depressive and anxiety disorders we observed lower levels of resilience and higher levels of perceived stress
compared to HC. These ndings are in line with previous studies showing that patients with mental disorders often lack strategies of a resilient
mindset, which can improved during recovery (Lee et al. 2017). Likewise perceived stress has been shown to be elevated in states of emotional-
ill being (Kadzikowska-Wrzosek 2012). Impaired resilience and higher perceived stress, are part of the current vulnerability-stress-model of
psychosomatic disorders (Fava et al. 2017).
4.2 Emotional analytics in response to neutral and alpine stimuli in patients with somatoform, depressive and anxiety disorders
We found lower levels of valence and dominance in patients than in HC over all (neutral and alpine) stimuli. The lower levels of valence (i.e.
more unhappy) reect the fact that our largest subgroup in our patient group was „depressive disorders” (55,2%). This conrms previous studies
showing that patients suffering from depression tend to show lower levels of valence as they describe a feeling of numbness und joylessness in
their lives (Dai et al. 2016). A dysfunction in emotional processing might be the underlying pathophysiological concept (Kemmis et al. 2017).
Viewing alpine stimuli lead to a comparable increase in valence (feeling happier) and dominance (feeling more in control) in patients and
controls. Baseline arousal was higher in the patients than HC a nding previously described in individuals with depressive symptoms (Gilbert et
al. 2019). This led to a signicantly smaller increase in arousal between neutral and alpine stimuli for patients than controls.
4.3 Association of resilience, perceived stress and emotional analytics
The association of resilience and perceived stress with emotional analytics was found not only in patients with somatoform, depressive and
anxiety disorders but also in healthy controls. This underlines the theory that there is a continuum of health and disease also for somatoform,
depressive and anxiety disorders, and that mechanisms of overtly ill patients are also present in individuals with sub-syndromal forms of
psychosomatic disorders pointing towards general mechanisms of mental health (Keyes 2007). The inverse correlation of arousal while viewing
neutral pictures (negatively with resilience and positively with perceived stress) were mostly due to patients´ values: They are more jittery or
aroused at baseline which ts well with their predominant diagnoses of somatoform, depressive and anxiety disorders (Jung et al. 2016).
4.4 The effect of alpine stimuli on emotional analytics
The effect the alpine environment on mental health has rarely been researched to date, most studies where performed on other natural
environments. In the present study we found that both patients and HC reacted to alpine stimuli in form of a signicant increase in valence,
arousal and dominance compared to neutral stimuli. This nding of a positive impact on emotional analytics is in line with previous studies
evaluating psychological and physical reactions to visual natural stimuli. Comparing reactions to urban with those to natural scenery a
signicant increased positive affect in emotional response could be found in nature condition only using virtual reality stimuli (Valtchanov et al.
2010). The restorative effect of the natural environment, even if only present within visual stimuli, might be explained by a reduction in stress
levels induced by exposure to views of nature (Valtchanov et al. 2010). Patients and HC showed higher emotional analytics for valence and
dominance, but we also detected an increase in arousal in response to the alpine stimuli. This is in contrast with several studies pointing
towards relaxation and tranquility felt while viewing natural environment (Davis 2004). One possible explanation of our diverging nding is that
most of the alpine pictures shown in this study displayed physically active persons (e.g. downhill skiing). Comparable data were published by
IAPS showing high arousal ratings in the SAM scale when viewing stimuli of physically active persons in alpine surroundings (J Lang et al.
2008). People living in perceived safe, lively and beautiful neighborhoods were more likely to engage in PA, and people living in perceived boring
and depressing neighborhoods were less likely to engage in PA (Wang et al
2019). Multilevel modeling results showed that after controlling for
depressive symptoms at baseline, symptoms decreased in neighborhoods where physical environment and social environment were better
(Wang et al. 2018).
4.5 The effect of physical activity in an alpine environment on mental health
Physical activity by itself and especially when performed in an outdoor/green/alpine environment is known to improve mental health. Few pilot
studies could conrm the positive effect of the alpine environment when performing physical activity (Sturm et al. 2012; Niedermeier et al.
2017a; Ower et al. 2018). This is in line with our nding that self-performed physical activity (METs) correlates with higher valence and
dominance felt by participants after viewing alpine but not neutral stimuli. Conversely, some studies did not detect any differences in affective
response when comparing alpine to indoor physical exercise (Niedermeier et al. 2017b). Furthermore, no effect of anthropogenic elements in the
alpine environment on acute stress-related physiological responses was found (Niedermeier et al. 2019). Though importantly the latter studies
as well as the present one showed a positive correlation of outdoor physical activity on parameters of mental well-being.
The main limitation of the study is that in a survey study no causal relationship between the emotional analytics and mental health can be
obtained. Furthermore, the exposure in our study was applied in form of visual stimuli instead of actually spending time in an alpine
environment. The present study does not allow the differentiation which components of viewing alpine environment lead to the observed
positive effects on the emotional analytics. This was a cross sectional study which cannot give any evidence about the long term effects on
emotional analytics. Due to the spread of the study invitation via social media, yers, classied websites and mailing list, we cannot report the
Conclusion And Consequences For Clinical Practice
4.7 Conclusion and consequences for clinical practice Therapeutic programs for patients with somatoform, depressive and anxiety disorders
should contain physical activity and according to our results, also consider taking the effect of nature into account. The results from the current
study indicate that patients with somatoform, depressive and anxiety disorders have a positive attitude towards physical activity in an alpine
environment and that emotional analytics such as valence and dominance increase in patients and HC in a comparable manner. Practical
strategies to implement such programs should be discussed. Obvious practical barriers to the implementation of such programs are primarily of
a nancial origin, since in our medical system money for medications and inpatient hospital stays is readily available while therapeutic
programs including physical activity in an alpine environment are not nanced by public healthcare. To further elucidate the effect of physical
activity in an alpine environment on mental health longitudinal intervention studies are needed. The current study indicates that such studies
could be promising.
Ethics approval and consent to participate
The study was appoved by the ethics committee of Innsbruck Medical University (AN2014-0243). After being informed in detail about the
study aims and procedures, participants provided informed consent prior to study participation.
Consent for publication
Availability of data and materials
Data are available from the rst author upon request.
The authors report no conict of interest.
This research did not receive any specic grant from funding agencies in the public, commercial, or not-for-prot sectors.
Study design: K.H., C.O., C.M., G.K., B.S-U.
Data Collection: K.H, C.O., C. M.
Data analysis: K.H., C.O., G.K., T.V.,
Data interpretation: all authors
Writing and review of manuscript: all authors
We thank Dr. Thomas Post, Dr. Ulrike Weber-Lau, Dr. Barbara Mangweth-Matzek, for help with patient recruitment and Dr. Christian
Widschwendter for helpful discussion. This study is part of the doctoral thesis of Cornelia Ower.
1. Alvarsson, J. J., Wiens, S., & Nilsson, M. E. (2010). Stress Recovery during Exposure to Nature Sound and Environmental Noise.
2. Bangasser, D. A., & Valentino, R. J. (2014). Sex differences in stress-related psychiatric disorders: neurobiological perspectives. Frontiers in
neuroendocrinology, 35(3), 303–319. https://doi.org/10.1016/j.yfrne.2014.03.008
3. Bezzina, L., Touyz, S., Young, S. et al. (2019) Accuracy of self-reported physical activity in patients with anorexia nervosa: links with clinical
features. J Eat Disord 7, 28. https://doi.org/10.1186/s40337-019-0258-y
4. Bowen, D. J., Neill, J. T., & Crisp, S. J. R. (2016). Wilderness adventure therapy effects on the mental health of youth participants.
Program Plann, 58
, 49-59, doi:10.1016/j.evalprogplan.2016.05.005.
5. Brown, D. K., Barton, J. L., & Gladwell, V. F. (2013). Viewing nature scenes positively affects recovery of autonomic function following acute-
Environ Sci Technol, 47
(11), 5562-5569, doi:10.1021/es305019p.
6. Bull, F. C., Maslin, T. S., & Armstrong, T. (2009). Global physical activity questionnaire (GPAQ): nine country reliability and validity study.
Phys Act Health, 6
7. Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A Global Measure of Perceived Stress.
J Health Soc Behav, 24
8. Cole, D. N., & Hall, T. E. (2010). Experiencing the Restorative Components of Wilderness Environments: Does Congestion Interfere and Does
Length of Exposure Matter?
(6), 806-823, doi:10.1177/0013916509347248.
9. Dai, Q., Wei, J., Shu, X., & Feng, Z. (2016). Negativity bias for sad faces in depression: An event-related potential study.
(12), 3552-3560, doi:10.1016/j.clinph.2016.10.003.
10. Davis, J. (2004). Psychological Benets of Nature Experiences: An Outline of Research and Theory- with Special Reference to transpersonal
Psychology. In N. U. a. S. o. L. Borders (Ed.).
11. de Bell, S., Graham, H., Jarvis, S., & White, P. (2017). The importance of nature in mediating social and psychological benets associated
with visits to freshwater blue space.
Landscape and Urban Planning, 167
, 118-127, doi:https://doi.org/10.1016/j.landurbplan.2017.06.003.
12. Fava, G. A., Cosci, F., & Sonino, N. (2017). Current Psychosomatic Practice.
Psychother Psychosom, 86
(1), 13-30, doi:10.1159/000448856.
13. Gascon, M., Triguero-Mas, M., Martínez, D., Dadvand, P., Rojas-Rueda, D., Plasència, A., et al. (2016). Residential green spaces and mortality:
A systematic review.
Environment International, 86
, 60-67, doi:https://doi.org/10.1016/j.envint.2015.10.013.
14. Gascon, M., Triguero-Mas, M., Martínez, D., Dadvand, P., Forns, J., Plasència, A., & Nieuwenhuijsen, M. J. (2015). Mental health benets of
long-term exposure to residential green and blue spaces: a systematic review.
International journal of environmental research and public
(4), 4354–4379. https://doi.org/10.3390/ijerph120404354
15. Gilbert, K. E., Tonge, N. A., & Thompson, R. J. (2019). Associations between depression, anxious arousal and manifestations of
J Behav Ther Exp Psychiatry, 62
, 88-96, doi:10.1016/j.jbtep.2018.09.006.
16. Gräfe, K., Zipfel, S., Herzog, W., & Löwe, B. (2004). Screening psychischer Störungen mit dem “Gesundheitsfragebogen für Patienten (PHQ-
(4), 171-181, doi:10.1026/0012-1922.214.171.124.
17. Hazer, M., Formica, M. K., Dieterlen, S., & Morley, C. P. (2018). The relationship between self-reported exposure to greenspace and human
stress in Baltimore, MD.
Landscape and Urban Planning, 169
, 47-56, doi:https://doi.org/10.1016/j.landurbplan.2017.08.006.
18. https://brownmath.com/stat/shape.htm (retrieved 2570572020)
19. IBM SPSS manual,
20. Joye, Y., & Bolderdijk, J. W. (2015). An exploratory study into the effects of extraordinary nature on emotions, mood, and prosociality.
Frontiers in Psychology, 5
, 1577-1577, doi:10.3389/fpsyg.2014.01577.
21. Jung, S., Proske, M., Kahl, K. G., Krüger, T. H., & Wollmer, M. A. (2016). The New Hamburg-Hannover Agitation Scale in Clinical Samples:
Manifestation and Differences of Agitation in Depression, Anxiety, and Borderline Personality Disorder.
22. Kadzikowska-Wrzosek, R. (2012). Perceived stress , emotional ill-being and psychosomatic symptoms in high school students : the
moderating effect of self-regulation competences.
Archives of Psychiatry and Psychotherapy
(3 ), 25–33.
23. Kemmis, L. K., Wanigaratne, S., & Ehntholt, K. A. (2017). Emotional Processing in Individuals with Substance Use Disorder and
Posttraumatic Stress Disorder.
Int J Ment Health Addict, 15
(4), 900-918, doi:10.1007/s11469-016-9727-6.
24. Keyes, C. L. M. (2007). Promoting and protecting mental health as ourishing: A complementary strategy for improving national mental
American Psychologist, 62
(2), 95-108, doi:10.1037/0003-066X.62.2.95.
25. Lang, P. J., M Bradley, M., & Cuthbert, B. (2008).
International Affective Picture System (IAPS): Affective Ratings of Pictures and Instruction
Manual (Rep. No. A-8)
26. Lee, D., Cha, B., Park, C.-S., Kim, B.-J., Lee, C.-S., Lee, S.-J., et al. (2017). Effects of resilience on quality of life in patients with bipolar disorder.
J Affect Disord, 207
, 434-441, doi:https://doi.org/10.1016/j.jad.2016.08.075.
27. Lehman, R.S. (1991). Statistics and research design in the behavioral sciences. Wadsworth/Thomson Learning Belmont CA, ISBN: 0-534-
28. Liu, Y; Wang, R; Lu, Y; Li, Z; Chen, H; Cao, M; Zhang, Y; (2020). Natural outdoor environment, neighbourhood social cohesion and mental
health: Using multilevel structural equation modelling, streetscape and remote-sensing metrics. Urban Forestry & Urban Greening , 48 ,
Article 126576. 10.1016/j.ufug.2019.126576.
29. Niedermeier, M., Einwanger, J., Hartl, A., & Kopp, M. (2017a). Affective responses in mountain hiking-A randomized crossover trial focusing
on differences between indoor and outdoor activity.
PLoS One, 12
(5), e0177719, doi:10.1371/journal.pone.0177719.
30. Niedermeier, M., Grafetstatter, C., Hartl, A., & Kopp, M. (2017b). A Randomized Crossover Trial on Acute Stress-Related Physiological
Responses to Mountain Hiking.
Int J Environ Res Public Health, 14
31. Niedermeier, M., Grafetstatter, C., Kopp, M., Huber, D., Mayr, M., Pichler, C., et al. (2019). The Role of Anthropogenic Elements in the
Environment for Affective States and Cortisol Concentration in Mountain Hiking-A Crossover Trial.
Int J Environ Res Public Health, 16
32. Ower, C., Kemmler, G., Vill, T., Martini, C., Schmitt, A., Sperner-Unterweger, B., et al. (2018). The effect of physical activity in an alpine
environment on quality of life is mediated by resilience in patients with psychosomatic disorders and healthy controls.
Eur Arch Psychiatry
33. Panno, A., Carrus, G., Lafortezza, R., Mariani, L., & Sanesi, G. (2017). Nature-based solutions to promote human resilience and wellbeing in
cities during increasingly hot summers.
Environ Res, 159
, 249-256, doi:https://doi.org/10.1016/j.envres.2017.08.016.
34. Ritchie, S. D., Wabano, M. J., Russell, K., Enosse, L., & Young, N. L. (2014). Promoting resilience and wellbeing through an outdoor
intervention designed for Aboriginal adolescents.
Rural Remote Health, 14
35. Slavich, G. M., & Irwin, M. R. (2014). From stress to inammation and major depressive disorder: a social signal transduction theory of
Psychol Bull, 140
(3), 774-815, doi:10.1037/a0035302.
36. Smith, B. W., Dalen, J., Wiggins, K., Tooley, E., Christopher, P., & Bernard, J. (2008). The brief resilience scale: assessing the ability to bounce
Int J Behav Med, 15
(3), 194-200, doi:10.1080/10705500802222972.
37. Sturm, J., Ploderl, M., Fartacek, C., Kralovec, K., Neunhauserer, D., Niederseer, D., et al. (2012). Physical exercise through mountain hiking in
high-risk suicide patients. A randomized crossover trial.
Acta Psychiatr Scand, 126
(6), 467-475, doi:10.1111/j.1600-0447.2012.01860.x.
38. 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.
Sci Technol, 45
(5), 1761-1772, doi:10.1021/es102947t.
39. Triguero-Mas, M., Donaire-Gonzalez, D., Seto, E., Valentín, A., Martínez, D., Smith, G., et al. (2017). Natural outdoor environments and mental
health: Stress as a possible mechanism.
Environ Res, 159
, 629-638, doi:https://doi.org/10.1016/j.envres.2017.08.048.
40. Twohig-Bennett, C., & Jones, A. (2018). The health benets of the great outdoors: A systematic review and meta-analysis of greenspace
exposure and health outcomes.
Environ Res, 166
, 628-637, doi:https://doi.org/10.1016/j.envres.2018.06.030.
41. Ulrich, R. S. (1984). View through a window may inuence recovery from surgery.
42. Valtchanov, D., Barton, K. R., & Ellard, C. (2010). Restorative effects of virtual nature settings.
Cyberpsychol Behav Soc Netw, 13
43. van den Berg, M., van Poppel, M., van Kamp, I., Andrusaityte, S., Balseviciene, B., Cirach, M., et al. (2016). Visiting green space is associated
with mental health and vitality: A cross-sectional study in four european cities.
Health Place, 38
44. Wang, Y., Chen, Y. C., Shen, H. W., & Morrow-Howell, N. (2018). Neighborhood and Depressive Symptoms: A Comparison of Rural and Urban
Chinese Older Adults.
(1), 68–78. https://doi.org/10.1093/geront/gnx063
45. Wang, R., Liu, Y., Lu, Y.
(2019). The linkage between the perception of neighbourhood and physical activity in Guangzhou, China: using
street view imagery with deep learning techniques.
Int J Health Geogr
18, 18. https://doi.org/10.1186/s12942-019-0182-z
Variable Groups Comparison
Test statistics D.f. p-value
Age in yearsa, 36.0±12.8 32.8±11.7 Z=2.42c 0.016
Female genderb117 (63.9) 187 (58.4) c=1.02d1 0.313
Educationb- - c=30.989d3 <0.001
University 41 (22.4) 111 (35.2) - -
Secondary school 62(33.9) 133(42.2) - -
Vocational training 53(29.0) 34 (10.8) - -
Compulsory school and other 27 (14.8) 37 (11.7) - -
Marital statusb- - c=13.699d2 0.001
Single 105 (57.4) 194 (61.6) - -
Married 56 (30.6) 110 (34.9) - -
Separated/divorced/widowed 22 (12.0) 11 (3.5) - -
Employmentb- - c=66.81d2 <0.001
Full-/part-timeemployment 75 (41.0) 177 (56.2)
In education/study/vocational training 49 (26.8) 122 (38.7)
Unemployed 59 (32.2) 16 (5.1)
babsolute number (percent)
ctest statistic for Mann Whitney U test
dtest statistics for Chi-Square test
Table 1: Sociodemographic characteristics of patients and healthy controls (adapted with participant numbers for the current
analysis from Ower et al. 2018)
Table 2: Resilience, self-perceived stress and emotional analytics (SAM ratings) in patients and controls
Variable Group Comparison
Mean ± SD
Mean ± SD
Test statistics Effect size, d p-valuea
Resilience (BRS mean score) 2.78 ±0.85 ¯ 3.76 ± 0.66 Z=-11.84 -1.33 <0.001
Stress (PSS score) 9.53 ± 3.61 4.73 ± 2.50 Z=-13.47 1.62 <0.001
Valence 5.09 ± 1.06 ¯ 5.65 ± 1.21 Z=-4.696 -0.48 <0.001
Arousal 4.13 ± 1.31 3.38 ± 1.23 Z= 5.848 0.60 <0.001
Dominance 4.78 ± 1.08 ¯ 5.13 ± 1.35 Z=-2.312 -0.15 0.021
Valence 6.99 ± 1.68 ¯ 7.85 ± 1.12 Z=-5.661 -0.64 <0.001
Arousal 5.01 ± 1.76 5.17 ± 1.94 Z=-1.218 -0.09 0.223
Dominance 5.85 ± 1.52 ¯ 6.42 ± 1.58 Z=-3.655 -0.37 <0.001
Attraction 6.62 ± 2.10 ¯ 7.52 ± 1.48 Z=-4.106 -0.52 <0.001
Valence 1.91 ± 1.80 *** 2.19 ± 1.42 *** Z=-1.466 -0.18 0.143
Arousal 0.87 ± 2.11 ¯** 1.79 ± 1.91 *** Z=-4.741 -0.46 <0.001
Dominance 1.09 ± 1.61 *** 1.29 ±1.67 *** Z=-1.465 -0.12 0.143
ap-values were calculated using Mann Whitney U Test
Significantly higher scores in patients than in healthy controls
¯ Significantly lower scores in patients than in healthy controls
** Difference “alpine – neutral” significantly greater than 0, Z=3.25, p<0.01
*** Difference “alpine – neutral” significantly greater than 0, always Z ≥ 4.5, p<0.001
Abbreviations: BRS: Brief Resilience Scale 13, PSS: Perceived Stress Scale, SD: standard
Correlation of emotional analytics (SAM) with resilience, self-perceived stress and PA in alpine environment
Total sample (n=498)
BRS PSS PA in alpine environment (MET)
Valence rs0.188** -0.249** 0.081
p 0.000 0.000 0.078
Arousal rs-0.183** 0.187** -0.091
p 0.000 0.000 0.051
Dominance rs0.227** -0.150** -0.021
p 0.000 0.002 0.656
Valence rs0.303** -0.276** 0.440**
p 0.000 0.000 0.000
Arousal rs0.073 -0.096* 0.225**
p 0.121 0.040 0.000
Dominance rs0.209** -0.172** 0.277**
p 0.000 0.000 0.000
Attraction rs0.222** -0.172** 0.413**
p 0.000 0.000 0.000
Valence rs0.125** -0.043 0.316**
p 0.007 0.358 0.000
Arousal rs0.175** -0.188** 0.266**
p 0.000 0.000 0.000
Dominance rs0.043 -0.025 0.278**
p 0.368 0.604 0.000
Abbreviations MET: metabolic equivalents, BRS: brief resilience scale, PSS: perceived stress scale
rs: Spearman rank correlation coefficient, p: p-value, *p<0.05, **p<0.01. ***p<0.001
Flow chart of the overall study design including details of the quasi experimental part with presentation of alpine stimuli and emotional analytic
ratings (boxed section shaded in grey). Specic questionnaires are indicated BRS = Brief resilience scale, PHQ = Patient health questionnaire,
PSS = Perceived stress scale, GPAQ = General Physical Activity Questionnaire. (…) indicates that there were questionnaires at the indicated point
in the study design not analyzed in the current study but in Ower et al. 2018.
Flowchart of patient and healthy control recruitment (adapted with participant numbers for the current analysis from Ower et al. 2018). Excluded
cases terminated early, reported implausible values or had a single diagnose of alcohol abuse or eating disorder. Abbreviations: BRS = Brief
resilience scale, GPAQ = General Physical Activity Questionnaire, PHQ = Patient health questionnaire, PSS = Perceived stress scale, SAM ratings
= Self-Assessment Manikin for emotional analytic ratings. (…) indicates that there were questionnaires at the indicated point in the study design
not analyzed in the current study but in Ower et al. 2018.
Examples of alpine stimuli depicting individuals performing physical activity in an alpine environment. Neutral stimuli are not depicted since this
is not considered good scientic practice for the IAPS picture collection (J Lang et al. 2008).