Can Nature Walks With Psychological Tasks Improve Mood, Self-Reported Restoration, and Sustained Attention? Results From Two Experimental Field Studies

Tytti Pasanen; Katherine Johnson; Kate Lee; Kalevi Korpela

doi:10.3389/fpsyg.2018.02057

. 2018 Oct 30;9:2057. doi: 10.3389/fpsyg.2018.02057

Can Nature Walks With Psychological Tasks Improve Mood, Self-Reported Restoration, and Sustained Attention? Results From Two Experimental Field Studies

Tytti Pasanen ^1,^*, Katherine Johnson ², Kate Lee ³, Kalevi Korpela ¹

PMCID: PMC6218585 PMID: 30425671

Abstract

The evidence for restorative effects of contact with nature is vast. Drawing from two well-known theories in Environmental Psychology, Stress reduction theory and Attention restoration theory, restoration can be seen as a sequential, interactive process that begins with physiological relaxation and results in affective and attention restoration and broader life reflection. This interaction between a person and their environment may be facilitated by actively engaging with the environment but this has been understudied. We examined engagement with the environment by asking participants to complete psychological, restoration theory-driven tasks designed to enhance physiological, affective and attention restoration, while walking on nature trails. We conducted two experimental field studies (conceptual replications) in Finland in a coniferous forest (Study 1; n = 128) and an urban park (Study 2; n = 121). The participants walked at their own pace for 4-6 km with or without psychological tasks. Those in the task conditions completed either theory-based restoration-enhancement tasks or alternative tasks that we expected to be less restorative (Study 1: the same tasks in the reverse order; Study 2: awareness-enhancement tasks). The participants completed self-reports on valence, activation, and restoration, and the Sustained Attention to Response Task, before, and after, the walk. We compared the change between measurements using regression models grouped by study conditions, with age, recent stress, difficulties with wayfinding, start time, and navigation method (Study 2 only) as covariates. Valence and self-reported restoration improved after the walk, but there was no additional benefit from the psychological tasks. In both studies, sustained attention consistently improved following different versions of the restoration-enhancement tasks and, to some extent, after a walk without the tasks. Participants who were more stressed experienced greater improvements in valence and self-reported restoration (Study 1) and sustained attention (Study 2). The results support both Stress reduction theory and Attention restoration theory, and imply that some forms of active engagement with the environment can aid sustained attention but not affective restoration. Future research efforts are needed to replicate these findings and to assess any potential long-term or multiplicative effects of engagement-based tasks, or other strategies that could enhance positive engagement with the environment.

Keywords: natural environments, restorative environments, green exercise, sustained attention, engagement, psychological well-being

Introduction

Contact with natural environments has consistently been shown to improve psychological and cognitive outcomes (Hartig et al., 2014). A vast amount of past research has focused on contrasting the effects of urban and natural environments (summarized in a systematic review by Bowler et al., 2010) or on the specific qualities of environments that promote affective or attention restoration (for example, Stigsdotter and Grahn, 2011; Gatersleben and Andrews, 2013). The cognitive processes and the quality of interaction with nature leading to a restorative experience have, however, been underexplored (Markevych et al., 2017) although they are key components in the dominant theories explaining the benefits of contact with nature, Attention restoration theory (Kaplan and Kaplan, 1989) and Stress reduction theory (Ulrich, 1983). In particular, we do not know if the benefits of a nature experience are a result of gaining distance from everyday concerns or if they are rather a result of positive engagement with natural elements (Hartig et al., 2014). Preliminary evidence suggests that focusing on the surrounding environment during nature visits is connected to greater recalled restoration, although it is not the only means of experiencing it (Pasanen et al., 2018). Thus, it may be that active engagement and interaction with the surrounding environment is not a precondition for restorative experiences but it may facilitate them.

Attention restoration theory states that the benefits of interaction with nature are largely due to cognitive benefits and “soft,” effortless fascination (Kaplan and Kaplan, 1989). The theory identifies four qualities that contribute to a restorative experience. Fascination implies that there is something in the surroundings that captures one’s attention in a non-depleting, replenishing way (Kaplan and Kaplan, 1989). Extent assumes that the environment should have coherent scope such that one feels like being in a whole other world (Kaplan and Kaplan, 1989). Being away means being mentally detached from everyday worries and concerns (Kaplan and Kaplan, 1989). Finally, the environment should match one’s current needs to support restoration, thus, compatibility is important (Kaplan and Kaplan, 1989). In applied research in environmental psychology, these four qualities have often been interpreted as external, physical qualities, even though Attention restoration theory describes them as components of person-environment interaction (Kaplan, 2001). From this interaction perspective, the role of an individual in need of restoration is an active one, as opposed to being a passive recipient of some pre-determined restorative cues. This idea of active engagement in environmental experiences has been implied in Attention restoration theory, although applied research has not emphasized it (Kaplan, 2001).

Supporting the notion of attention restoration, the cognitive benefits of contact with nature have been demonstrated, from exposure times ranging from 40 s to 55 min (Berto, 2005; Berman et al., 2008; Lee et al., 2015; Pilotti et al., 2015). Recent evidence has suggested that some of the associated cognitive benefits can be enhanced by targeting active engagement with the environment. In a study by Lin et al. (2014), participants were shown five pictures of urban streetscape with trees for a total of 100 s, and their directed attention was measured by the digit span backward task before and after viewing the images. The participants who were instructed to pay special attention to the greenery (trees and plants) in the images improved their directed attention more than another group who were instructed to observe the environment in general (Lin et al., 2014). Thus, focusing specifically on natural features seems to enhance attention restoration.

A similar effect of active engagement on improved cognition has been shown over longer periods in intervention studies (Duvall, 2011, 2013; Lymeus et al., 2018). Lymeus et al. (2018) found improved performance in an attention task followed by 5 weeks of restoration skills training in garden settings, compared with conventional mindfulness training in a classroom with no outdoor views. In Duvall’s studies (Duvall, 2011, 2013), participants were divided into two 2-week walking interventions: a standard condition with planned walking schedules, and an engagement condition where the participants were additionally given several options for engaging with the environment during the planned walks (so called awareness plans). The participants in the engagement group experienced better attentional functioning and less frustration at the end of the study, whereas there was no similar change in the reference group (Duvall, 2011). These results suggest that engagement may be useful for short-term attentional functioning and day-to-day replenishment of cognitive resources.

In the Stress reduction theory (Ulrich, 1983), interaction with the environment is described to start with physiological and initial affective responses, and continue with more elaborated affective, cognitive, and behavioral changes (Ulrich et al., 1991; Hartig et al., 2003). Stress plays a key role in this theory: affective and physiological restoration presumes that the participant is in an initially stressed, highly aroused state that a natural environment helps to restore (Ulrich, 1983). Accordingly, exposure to natural environments have been suggested to function as a buffer that reduces the negative effects of stress on well-being (Wells and Evans, 2003; Mitchell and Popham, 2008; Hartig et al., 2014). Regarding different aspects of stress markers, the evidence is stronger for positive affective changes followed by exposure to natural versus built environments compared with physiological stress indicators (Barton and Pretty, 2010; Bowler et al., 2010; McMahan and Estes, 2015). Thus, it is likely that the physiological effects of exposure to a restorative environment not only appear but also diminish quickly (Hartig et al., 2003).

Potential stress-reducing effects of contact with nature may guide stressed individuals to seek natural environments repeatedly (Russell and Snodgrass, 1987; Gulwadi, 2006). This idea of using and choosing environments for coping is incorporated in the concept of favorite places (Korpela, 2003). Favorite places combine the ideas of self- and emotion-regulation, place attachment, place identity, and restoration theories (Korpela, 2012). Most identified favorite places are in natural settings or nearby water, and visits to them provide the more self-reported restoration compared with other types of favorite places (Korpela et al., 2010). However, it is currently not known how common it is to use an environment as a means of stress and emotional regulation (Hartig et al., 2014). Some evidence suggests that adults prefer to go to “classic” natural environment when feeling either happy or sad more than to other types of environments such as urban areas, “unsafe” nature, living rooms, and shopping malls (Johnsen and Rydstedt, 2013).

Even though the restorative experiences described in Stress reduction theory and Attention restoration theory are conceptually different, they have been seen as complementary processes that interact with each other (Kaplan, 1995; Markevych et al., 2017). Stress reduction theory assumes that restoration is a response to visual properties in the environment and their preference evaluation, which quickly results in physiological and affective relaxation (Ulrich, 1983). In Attention restoration theory, the first phase of restoration involves ‘clearing the head,’ that is, removing excessive cognitive residue, followed by recovery of directed attention, facing challenges in one’s mind, and finally, more general life reflection (Kaplan and Kaplan, 1989; Korpela and Hartig, 1996). Integrating these perspectives, Hartig et al. (1991) proposed that a restorative experience begins with physiological and attentional recovery, which are followed by affective changes.

Drawing together Attention restoration theory, Stress reduction theory, and favorite place studies, restoration can be seen as a multi-phasic experience in which individuals can have an active role by interacting with an environment that supports their (restoration) needs. Restorative experiences, in turn, can be important for more general well-being (Hartig et al., 2014). In this paper, we explore whether affective and attention restoration could be enhanced by psychological instructions that aim to deepen the different phases of a restorative experience by conducting two experimental field studies.

The Present Studies

To study the restorative effects of instructed interaction with the environments, we conducted two field experiments where participants walked along a nature trail, either with or without psychological tasks (descriptives in Table 1). Both studies had two versions of the tasks, one that was hypothesized to be more restorative than the other. The tasks that we hypothesized to be the most restorative were based on restoration theories (Attention restoration theory, Stress reduction theory, and favorite place studies) and their contents followed the different phases of restoration described in the introduction: physiological and affective relaxation, mood-enhancement, building an affective relationship with a place, and general life reflection (Korpela et al., 2017). We labeled these ‘restoration-enhancement tasks.’ The comparison tasks were either the same tasks in the reverse order, that is, mismatched with the hypothesized phases of restoration (Study 1), or ‘awareness-enhancement tasks’ inspired by Duvall’s studies (Duvall, 2011, 2013; Study 2). The participants completed self-evaluated questionnaires on restoration and mood (valence, activation) and a behavioral task on sustained attention before and after the walk.

Table 1.

Descriptive information of the study settings and the participants.

	Study 1	Study 2
Length (km)	6	4
Environment	Coniferous/mixed forest in the countryside	Urban park near the city center
Where were the tasks read from?	Signposts along the trail	Mobile application
Alternative tasks	Same tasks in the reverse order	Awareness-enhancement tasks (Duvall, 2011)
Design	2 × 2 × 2 (pre-post, tasks/no tasks, route direction)	2 × 3 (pre-post, tasks/no tasks/alternative tasks)
Participants (valid)	150 (127)	122 (119)
Mean age [range]	50 [18–81]	40 [18–63]
Women (%)	80	87

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We hypothesized that walking the nature trails would provide initial benefits: (1a) enhance restoration and valence and reduce activation, and (1b) reduce errors and shorten and stabilize response times in the sustained attention task (Ulrich, 1983; Kaplan and Kaplan, 1989; McMahan and Estes, 2015). We further hypothesized that the above benefits (1a-b) would differ between the study conditions: (2a) the benefits would be greatest after conducting the restoration-enhancement tasks that follow the theory-driven phases of restoration, (2b) the benefits would be smallest after walking without the tasks (due to less interaction with the environment), and (2c) the benefits for those who conduct the comparison tasks would lie between those two. The studies are conceptual replications of each other, with similar procedures (depicted in Figure 1). Study 1 assesses whether any potential restorative effects of conducting the restoration-enhancement tasks depend on the order of the tasks. Is the theory-driven order ideal in terms of experienced restoration after a nature walk? In Study 2, we focus on exploring if the restoration-enhancement tasks have a similar effect as other types of psychological tasks that guide interaction with the environment but do not address restoration in particular. How relevant is the content of the tasks for restorative outcomes? In the next sections, we present the two studies in more detail. At the end of this paper, we return to a more general discussion on the common themes of the studies.

Study 1 – Coniferous Forest

We began investigating the topic of instructed engagement with the environment during nature visits on a nature trail that had been developed for another project in 2010 (Korpela et al., 2017). For the present study, the trail was equipped with signposts containing the theory-based restoration-enhancement tasks aimed to strengthen affective and attention restoration. We were specifically interested in (1) whether these psychological tasks would aid restoration in general, compared with a walk without tasks, and (2) if the effects of these tasks were stronger when conducted in a theoretically and empirically determined order that mirrored the phases of a restorative experience (physiological, affective, cognitive), compared with the reverse order. Conducting the tasks in the reverse order provided a strong theoretical test, and it was relevant from practical perspective, as the circular route containing the signposts could just as easily be walked in the opposite direction in real life. As the signposts were built into the ground, we assigned four separate groups of participants to walk the route in both directions, with and without the restoration-enhancement tasks.

Materials and Methods

The study site

The 6-km-long circular trail was located in Ikaalinen, a small municipality in Pirkanmaa, Finland. The before and after measurements were taken at meeting rooms at Ikaalinen Spa, a commercial wellness center that provides both recreational and rehabilitation services. The scenery along the route varied, although it was predominantly a typical Finnish natural environment with lakes, some residential houses, a large sandpit, and forests that were both unpleasant (recently clear-cut forest) and pleasant (a scenic viewpoint by a lake). By the Corine Land Cover 25 ha (2012) classification, approximately 3.2 km of the trail was situated within a ‘coniferous forest,’ 1.2 km (beginning and end around the spa) of the trail were classified as ‘industrial or commercial units’ (with a lake on the side), 1.1 km as ‘mixed forest’ (with the scenic viewpoint), and 0.5 km as ‘fields.’

On average (measured by median and mode), it took 103 min to walk the route, with a range of 65–155 min. The route contained several crossings where the participants were guided by yellow ribbons and printed instructions, containing both pictures and written guidance. Originally, the route with the signposts was marked with arrows that guided visitors to walk in the clockwise direction.

Participants

Altogether 150 volunteers participated in 35 sessions (Table 1). Contrary to our initial plan, we could not recruit visitors at the spa and consequently, the majority of participants signed up after reading about the study in a regional newspaper and via the project’s Facebook page. Other recruitment means included a local newspaper, e-mail invitations to local companies, and advertisements at supermarkets in nearby areas. The study was called ‘Forest walk study,’ and the participants were given information about the procedure and the type of measures (e.g., an attention task) but no specific information about the experimental conditions. We conducted one pilot study with volunteer psychology students (n = 6) who received no compensation for participation, and a second pilot (n = 6), after which the procedure was significantly clarified. Of the remaining 144 participants, a further 15 were excluded due to the following criteria: not walking the instructed route (n = 7), problems with the procedure during one study session (n = 6), impaired senses (n = 1), and personal withdrawal (n = 1). For five participants, the attentional task was either not valid or missing. Ten sessions were canceled due to bad weather. The final sample consisted of 129 participants.

For the majority of the sample (92%), the route was new. Many participants showed a special interest in natural environments (we explored this indirectly in the social stressor task, described in Section “Procedure”). In the whole sample, the participants reported visiting nature 3.9 times per week on average, which is more than the national mean of 2-3 times per week (Sievänen and Neuvonen, 2011).

Procedure

The procedure is illustrated in Figure 1. The participants came in groups of 2-6 people, mainly from the surrounding municipalities in the region. They were seated in a meeting room in front of a desk with a laptop, a pen, and an envelope that contained the written tasks. First the researchers (most commonly two project workers) introduced themselves, the study, and the procedure, after which the participants signed the informed consent. Further information about the experiment was then detailed. The participants were asked not to talk aloud during the measurements and to refrain from using mobile phones during the study.

We conducted the experiments during the holiday season (May-September 2016) when stress levels may be lower than usual (de Bloom et al., 2010). To induce a mildly stressed state that could potentiate restorative effects (Ulrich, 1983), we started with a social stressor task, after which the participants completed the self-reported questionnaires and the behavioral measurements. When they were finished, the participants left the room in their own pace and they were given verbal and written instructions for the walk one by one outside the study room. The participants were instructed to walk by themselves. Before and after the walk, the participants could help themselves to some fruit, fresh juice, and water. After the walk, the respondents returned to the study room to complete the tasks in the same order as before the walk. At the end of the session, we showed each participant descriptive statistics of their attention task results, asked for feedback on the study, and gave everyone a cinema voucher. The procedure took approximately 2.5-3 h per person.

In addition to the measures reported in this paper, the participants completed self-reported measures of empathic feelings and vitality and a behavioral task of frustration tolerance, but these are reported elsewhere due to space constraints and different theoretical reasoning. The study was carried out in accordance with the recommendations for “Responsible conduct of research and procedures for handling allegations of misconduct in Finland 2012” by the Finnish advisory board on research integrity (TENK). The protocol was approved by the Ethics Committee of the Tampere Region. All subjects gave written informed consent in accordance with the Declaration of Helsinki.

Study conditions

To control for any effects of weather, the participants were randomly allocated to different walking conditions each study day: 1/3 were assigned to a walk with the restoration-enhancement tasks completed in the designed, theory-based order (which we will call ‘clockwise order’ because they walked the route in the clockwise [C] direction), 1/3 were assigned to a walk with the restoration-enhancement tasks completed in the reverse order (hence, they walked the route in the reverse [R] direction), and the rest to a walk without tasks, of which one half (1/6 of the sample) walked the clockwise (C) and another half (1/6) the reverse (R) route. The participants in the ‘no task’ conditions walked the route in opposite directions to account for any potential environmental differences, and the initial idea was to combine these conditions for the analyses.

The psychological instructions

The instructions on the signposts were based on Stress reduction theory (Ulrich, 1983; Ulrich et al., 1991), Attention restoration theory (Kaplan and Kaplan, 1989), and favorite place studies (Korpela et al., 2008; Korpela and Ylén, 2009). Integrating these theories, a restorative experience has been suggested to start with physiological relaxation, followed by affective and mood-enhancing responses, and advance to building an affective relationship with the place and reflection on one’s current situation in life (Korpela et al., 2017). Thus, the first three signposts related to physical relaxation and observing the environment (for example, “[…] Keep looking around and let yourself be enchanted by your surroundings. Keep breathing peacefully.”), the next two to favorite place identification and reminiscence (“Find your favorite place in this area […] Choose a detail by which you may remember this place, perhaps for years.”), and the final two to clearing the mind and life reflection (“Look around for something representing you or your current situation in life […] Are you gaining new thoughts?”).

Pre- and post-walk measures

Self-reported restoration was measured with the 6-item Restoration Outcome Scale (ROS; Korpela et al., 2008; see also Hartig et al., 1998; Staats et al., 2003). The scale is a self-evaluation of attention restoration (one item: “I feel alert and able to concentrate”), relaxation and calmness (three items, for example, “I feel restored and relaxed”), and clearing one’s thoughts (two items, for example, “My thoughts are clear”). Participants rated their current state on a 7-point rating scale ranging from “Describes my experience…” 1 = not at all to 7 = completely. We calculated the mean summary score of the responses in both pre- and post-measurements (Cronbach’s α = 0.85 and 0.89, respectively).

Mood was measured with a two-dimensional affect grid (Russell et al., 1989) in which the participants are asked to evaluate their mood by marking a single cross in a 9 × 9 grid. The axes reflect core affects, valence (horizontal axis) and activation (vertical axis; Russell et al., 1989; Västfjäll and Gärling, 2007).

Sustained attention was measured using the Random version of the Sustained Attention to Response Task (SART), a test of sustained attention (Robertson et al., 1997). In the SART, participants respond to the digits 1–9, presented in a random order (each shown 25 times in five different font sizes) on a screen for 4.3 min. They were instructed to press the space bar whenever they saw any digit (Go) except the digit 3 (No-Go). The participants were asked to pay equal attention to speed and response accuracy. The stimulus was shown for 250 ms, followed by a mask (a white cross within a circle) for 900 ms. We used the source code provided by Stothart (2015) in the open-source software PsychoPy (Peirce, 2009), in which we translated the instructions into Finnish. The participants were seated approximately 40 cm from the screen of a Dell Latitude laptop, although they were free to move further or closer during the experiment. Both pre- and post-tests were preceded by a practice round with 18 digits where the participants received immediate feedback on the accuracy of the response (correct/incorrect).

The SART provides a number of sustained attention measures. Commission errors – the number of responses made to the No-Go digit ‘3’, reflect response accuracy, controlled attention (Manly et al., 2003), and response inhibition (Johnson et al., 2007). Omission errors - the number of non-responses to a Go digit - had a median of 1 and thus, there was little variation to examine and we excluded the measure from the analyses. The mean and standard deviation (SD) of response time (RT) were calculated after excluding responses to the digit ‘3’ and RTs < 100 ms. SDRT reflects the stability of the response style, with larger variability indicating more attentional lapses (Robertson et al., 1997; Manly et al., 2003; Smilek et al., 2010). The sequence of 225 RTs per participant was further analyzed using a Fast Fourier Transform (FFT) based on the method described in Johnson et al. (2007). Two dependent measures were derived from these FFT analyses – the slow (SFAUS) and fast (FFAUS) frequency areas under the spectra. For the SFAUS, the RT data were analyzed over the entire task. For the FFAUS, the RT data were analyzed in a first half versus second-half analysis. The SFAUS is a measure of all sources of variability in RT slower than 0.0772 Hz, which is derived from the Fixed version of the SART and represents one cycle of a presentation of the digits 1–9 (Johnson et al., 2007), and it measures gradual change in speed of responding over the course of the task. The FFAUS is a measure of all sources of variability faster than 0.0772 Hz, representing trial-to-trial variability in responding, and it measures moment-to-moment variability in responding.

Covariates

Stress in the past 4 weeks, which potentiates restoration effects (Ulrich, 1983), was measured by 10-item Perceived Stress Scale (Cohen et al., 1983), of which we calculated the summary score (Cronbach’s α = 0.84). Age was asked in full years. Older samples have been found to experience greater affective changes after nature exposure (McMahan and Estes, 2015) but we also hypothesized that older participants may find the lengthy route more exhausting, which could be reflected in lower restorative changes. For the majority of participants, the start time was at 10.30 am but it varied from 10 am to 4 pm to accommodate as many participants as possible. Time of day can, however, influence the level of alertness and task performance (Monk and Leng, 1982). We coded the start times as -1 = morning (10 - 10.30 am), 0 = midday (12 am - 1 pm), and 1 = afternoon (3 - 4 pm). As a post hoc measure, we recorded if the participants reported problems with wayfinding during the walk. Having to focus on navigation in a new environment requires mental effort which can reduce any potential restorative effect (both attentional and affective; Gatersleben and Andrews, 2013). We also recorded walk duration, weather, temperature, gender, and the number of hours slept the night before but these were not related to the outcomes in either of the two studies (Appendices A, B, D, E).

Data analysis

The a priori sample size was calculated as a 3 × 2 between-group repeated measures MANOVA with several correlating dependent variables, with a power of = 0.95 and alpha = 0.05. In this type of design, a medium effect size of 0.25 would be detected with a sample size of 165 participants (Gpower 3.1 software; Faul et al., 2007). However, as the final number of valid cases was lower than we aimed for, the following analyses have less statistical power than we expected to have.

Prior to the actual analysis, we checked that there were no differences between the groups at baseline in any of the outcomes with a one-way analysis of variance (ANOVA) in SPSS version 24 (provided in Appendix C). We also checked for differences in the outcomes between the two ‘no task’ groups that walked the route in different directions. Our initial plan had been to combine these two groups but as there were differences between them, we kept them separate in the analyses. However, we interpreted the results related to them with caution due to their smaller sample size.

We compared the change between pre- and post-measurements with multigroup regression analysis using Mplus version 7.4. The data was continuous but non-normally distributed so the MLR estimator was used (Muthén and Muthén, 1998/2012). The grouping was based on the direction of the route (clockwise/reverse), and completing the restoration-enhancement tasks was an explanatory variable (for simplicity, however, we present these estimates in the results as the difference between within-group intercepts, that is, the estimated within-group means). To retain more power in the analyses, we pre-selected those covariates that correlated significantly (p < 0.05) or showed a significant mean difference (in ANOVA) in at least one of the outcomes in either Study 1 or Study 2 (if applicable; these analyses are provided in Appendices A, B, D, E). Continuous covariates were centered and ordinal/dichotomous covariates were recoded so that their midpoint was at 0. In the initial models, the covariates were assumed to have a similar effect in both groups. If the standardized residuals for the covariates were large (>|1.96|), we relaxed this assumption and retained the modified model if the overall model fit improved.

In addition to the residuals, we checked how the models fit with the data and compared the models with the following criteria: a non-significant χ²-test, Satorra–Bentler corrected χ² difference-test (for model comparison), smaller values for information criteria (Akaike’s Information Criteria [AIC], Bayesian information criteria [BIC], and sample-adjusted BIC), Comparative Fit Index (CFI) and Tucker-Lewis Fit Index (TLI) ≥ 0.95, the Root Mean Square Error of Approximation (RMSEA) ≤ 0.05, and the Standardized Root Mean Square Residual (SRMR) ≤ 0.08 (Tucker and Lewis, 1973; Bentler, 1990; Browne and Cudeck, 1992; Hu and Bentler, 1999; Satorra and Bentler, 2010; Kline, 2016). To check for influential outliers we examined Cook’s distances in the first models for each block of outcomes, and if they exceeded 1.00 (Tabachnick and Fidell, 2014), the analyses were re-run without the most influential cases by excluding them one by one. If excluding an influential outlier improved the model fit, we retained the improved model.

To account for correlations between related outcomes but to retain more power in the analyses, we analyzed the outcomes in blocks of three: (1) self-reported measures (restoration, valence, and activation); (2) traditional SART measures (commission errors, RT, and SDRT); (3) refined SART variability measures (FFAUS in the 1st and 2nd halves of the tests, and SFAUS).

Sensitivity analyses

If applicable, we ran two types of sensitivity analyses for the final models: (1) for those models where we deleted influential outlier(s), we re-ran the final models with those outliers, (2) for the model with refined SART variability measures, we re-ran the models excluding participants whose mean RT was > 500 ms. RTs > 500 ms are generally considered slow in SART studies with adult participants and slower RTs can be connected to inflated FFAUS and SFAUS, which, in turn, may bias the model estimates. We ran these second sensitivity analyses to assess whether the results for FFAUS and SFAUS were influenced by respondents with slow mean RTs.

Results

Self-reported restoration and mood

Participants in all conditions reported greater restoration after the walk but there were no differences between the conditions (supporting hypothesis 1a but not 2a-c; Figure 2 and Table 2). The estimated change varied, on average, between 0.48 and 0.67 units on the original 1-7 scale. Similarly, in terms of estimated valence, hypothesis 1a but not 2a-c gained support, as the participants reported feeling, on average, 1.27-2.16 units more pleasant after the walk in all conditions. Activation, in turn, did not change in most groups which was against our hypotheses 1a and 2a-c. The exception were the participants in the ‘no task’ (C) condition who felt 1.52 units calmer after the walk.

Adjusted means in different conditions for the self-reported measures in Study 1 (n = 129). Solid line: statistically significant between-group difference. ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001.

Table 2.

The results for multigroup regression models for the self-reported measures in Study 1 (n = 129).

		Self-reported restoration				Valence				Activation

		b	SE	p	β (C/R)	b	SE	p	β (C/R)	b	SE	p	β (C/R)
Mean difference, estimated	(1) Restoration-enhancement tasks (C)	0.67***	0.15	0.00	0.84	1.64***	0.30	0.00	0.99	-0.56	0.42	0.19	-0.23
	(2) Restoration-enhancement tasks (R)	0.48***	0.12	0.00	0.66	1.39***	0.30	0.00	0.84	-0.56	0.41	0.17	-0.28
	(3) No tasks (C)	0.50***	0.16	0.00	0.63	2.16***	0.42	0.00	1.30	-1.52**	0.57	0.01	-0.62
	(4) No tasks (R)	0.50***	0.17	0.00	0.69	1.27***	0.42	0.00	0.76	0.14	0.57	0.77	0.07
	Task × route interaction (difference ‘1–3’ – ‘2–4’)	-0.19	0.26	0.47		0.64	0.57	0.26		-1.66*	0.83	0.05
Covariates	Stress	0.24*	0.11	0.03	0.17/0.19	0.38	0.30	0.21	0.13	-0.29	0.36	0.42	-0.07/-0.08
	Start time	0.03	0.11	0.75	0.03/0.04	0.23	0.23	0.33	0.09/0.11	-0.24	0.32	0.45	-0.07/-0.10
	Age	-0.01	0.00	0.05	-0.15/-0.18	-0.01	0.01	0.57	-0.05	0.00	0.01	1.00	0.00
	Wayfinding problems	-0.70**	0.23	0.00	-0.28/-0.31	-1.25*	0.56	0.02	-0.24	-0.08	0.56	0.88	-0.01
R² (C/R)		0.20/0.21				0.11/0.11				0.04/0.04

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χ² = 7.89 (df = 15, p = 0.93), RMSEA < 0.001, CFI = 1.00, TLI = 1.28, SMRM = 0.04. Grouping is based on walking direction: C, clockwise; R, reverse route; figures separated by “/” if they differ between the groups. ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001.

The change in restoration was greater for younger and more stressed participants (Table 2). Having a problem with wayfinding was connected to a more negative change in both self-reported restoration and a less positive mood (Table 2). Start time was not connected to changes in the self-reported measures.

The model explained self-reported restoration best (R²’s 0.20-0.21), followed by valence (0.11) and activation (0.04). The model fit well with the data and no influential outliers were excluded or large residuals freed (Table 2).

Sustained attention – traditional measures

The participants who either walked without tasks or conducted the restoration-enhancement tasks in the reverse order made 1.49 - 2.57 less commission errors after the walk (Figure 3 and Table 3), supporting hypothesis 1b in these groups. The trend was the same for the participants who conducted the restoration-enhancement tasks in the clockwise order, although the estimate (-1.22) was not statistically different from zero (Table 3). Similarly, SDRT reduced significantly in the condition with the reversed restoration-enhancement tasks, and the trend was to the same direction in both ‘no task’ conditions (showing partial support for hypothesis 1b but not 2a-c). With mean RT, there were no significant changes before and after the walk in any of the conditions (contrary to hypothesis 1b) but there was an unexpected interaction effect between route and tasks. Conducting the tasks was associated with increased mean RT compared with not conducting them in the clockwise route, whereas in the reverse route, conducting the tasks was associated with decreased mean RT compared with not conducting them (Figure 3). All these results were in contrast with our hypotheses 2a-c because they indicated the least benefits from conducting the restoration-enhancement tasks in the clockwise order.

Adjusted means in different conditions for the traditional SART measures in Study 1 (n = 125). Solid line: statistically significant between-group difference. ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001.

Table 3.

The results for multigroup regression models for the traditional SART measures in Study 1 (n = 125).

		Commission errors				Mean RT (ms)				SDRT (ms)

		b	SE	p	β (C/R)	b	SE	p	β (C/R)	b	SE	p	β (C/R)
Mean difference, estimated	(1) Restoration-enhancement tasks (C)	-1.22	0.66	0.07	-0.30	16.64	16.29	0.31	0.20	-5.28	5.46	0.33	-0.17
	(2) Restoration-enhancement tasks (R)	-1.49^∗	0.73	0.04	-0.37	-0.86	12.35	0.94	-0.01	-11.86^∗	5.00	0.02	-0.44
	(3) No tasks (C)	-2.57^∗∗	0.94	0.01	-0.63	-5.83	15.31	0.70	-0.07	-13.44	7.77	0.08	-0.43
	(4) No tasks (R)	-2.24^∗∗	0.77	0.00	-0.56	29.21	15.31	0.10	0.41	-11.44	7.77	0.08	-0.42
	Task × route interaction (difference ‘1–3’–‘2–4’)	-0.61	1.47	0.68		-52.55^∗	25.10	0.04		-8.59	10.88	0.43
Covariates	Stress	-0.92	0.64	0.16	-0.13	30.06/-5.20	19.56/12.44	0.12/0.68	0.20/-0.04	-5.20	4.79	0.28	-0.09/-0.11
	Start time	-0.40	0.47	0.39	-0.07/-0.08	9.17	10.28	0.37	0.07/0.10	-2.91	3.87	0.45	-0.06/-0.09
	Age	-0.02	0.02	0.44	-0.06/-0.07	0.23	0.44	0.61	0.04/0.05	-0.19	0.19	0.32	-0.08/-0.11
	Wayfinding problems	2.91^∗∗	0.98	0.00	0.23/0.24	-57.44^∗	22.73	0.01	-0.22/-0.26	-15.15	8.60	0.08	-0.16/-0.18
R² (C/R)		0.09/0.09				0.12/0.13				0.05/0.07

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χ² = 2.87 (df = 13, p = 1.00), RMSEA < 0.001, CFI = 1.00, TLI = 1.25, SMRM = 0.02. Grouping is based on walking direction: C, clockwise; R, reverse; figures separated by “/” if they differ between the groups. Parameters freed across groups: RT regressed on stress, covariance between commission errors and SDRT. ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001.

Age, stress in the past week, or start time were not significantly connected to changes in the outcomes but reporting problems with wayfinding was (Table 3). Those who reported problems with wayfinding made almost three more commission errors and had a significantly faster mean RT after the walk (Table 3).

The variances explained were nearly 0.09 for changes in commission errors, 0.12-0.13 for changes in mean RT, and 0.05-0.07 for changes in SDRT. The model with two freed parameters fit the data well (Table 3).

Sustained attention – refined variability measures

In the refined SART variability measures, there were several influential outliers and even after deleting the four most influential ones, the standard errors of the intercepts were large (Figure 4 and Table 4). The participants had similar amounts of FFAUS in the first half of the tasks (against hypotheses 1b and 2a-c), whereas in the second half only the group who conducted the restoration-enhancement tasks in the reverse order showed reduced FFAUS (partially supporting hypothesis 1b; Figure 4 and Table 4). Similarly, this group performed the SART with less SFAUS throughout the whole test after the walk, whereas the other groups showed no change. Our hypothesis 1b was, therefore, supported in only one group, and this group was not the one we hypothesized (2a) to show the greatest improvements.

Adjusted means in different conditions for the refined SART variability measures in Study 1 (n = 118). Solid line: statistically significant between-group difference. ^∗p < 0.05, ^∗∗ p < 0.01, ^∗∗∗p < 0.001.

Table 4.

The results for multigroup regression models for the refined SART variability measures in Study 1 (n = 118).

		FFAUS, 1st half				FFAUS, 2nd half				SFAUS

		b	SE	p	β (C/R)	b	SE	p	β (C/R)	B	SE	p	β (C/R)
Mean difference, estimated	(1) Restoration-enhancement tasks (C)	-7.21	9.98	0.47	-0.09	-1.19	8.96	0.89	-0.02	3.51	48.72	0.94	0.01
	(2) Restoration-enhancement tasks (R)	-0.15	7.52	0.98	0.00	-23.23^∗	10.57	0.03	-0.42	-125.97^∗	64.06	0.05	-0.34
	(3) No tasks (C)	-32.48	22.20	0.14	-0.42	24.92	15.86	0.12	0.41	-29.36	87.85	0.74	-0.11
	(4) No tasks (R)	-21.05	12.27	0.09	-0.43	-13.97	15.86	0.26	-0.25	-49.81	87.85	0.61	-0.14
	Task × route interaction (difference ‘1–3’ – ‘2–4’)	-4.36	26.98	0.87		16.86	24.55	0.49		-109.03	128.90	0.40
Covariates	Stress	-7.45	9.20	0.42	-0.06/-0.09	5.02	8.67	0.56	0.05	45.65	36.32	0.21	0.09/0.07
	Start time	-14.53/9.98	10.19/6.34	0.15/0.12	-0.13/0.17	18.74^∗/-3.92	8.13/6.93	0.02/0.57	0.20/-0.06	-16.36	45.82	0.72	-0.04
	Age	-0.06	0.31	0.84	-0.01/-0.02	0.09	0.37	0.81	0.02/0.03	-1.17	1.75	0.50	-0.06/-0.05
	Wayfinding problems	-4.15	19.90	0.84	-0.02/-0.03	-19.55	22.48	0.39	-0.10/-0.12	-272.82^∗	118.34	0.02	-0.29/-0.24
R² (C/R)		0.03/0.06				0.07/0.02				0.12/0.08

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χ² = 9.70 (df = 13, p = 0.72), RMSEA < 0.001, CFI = 1.00, TLI = 1.27, SMRM = 0.04. Grouping is based on walking direction: C, clockwise; R, reverse; figures separated by “/” if they differed between the groups. 4 outliers deleted; parameters freed across groups: FFAUS (both halves) regressed on start time. ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001.

Those who participated later in the day (and walked the clockwise route) performed the SART with more FFAUS in the 2nd half of the test, whereas problems with wayfinding were connected to reduced SFAUS after the walk (Table 4). Stress and age were not connected to the refined SART variability measures (Table 4).

The variances explained were low for the FFAUS in the 1st (0.03-0.06%) and the 2nd half (0.02-0.07), merely exceeding the minimum recommended R² for practically significant effect of 0.04 (Ferguson, 2009). For the SFAUS, the model explained 0.08-0.12 of the change between the measurements. Altogether four outliers were deleted and two parameters freed to obtain a good fit with the data (Table 4).

Sensitivity analyses

In the first sensitivity model for the refined SART variability measures including the 4 outliers deleted from the final model, the model fit was extremely bad in terms of all assessed criteria (for example, CFI = 0.438) and thus we found it meaningless to assess its results. In the second sensitivity model excluding those whose mean RT was > 500 ms, the intercept estimates of SFAUS and FFAUS in the 2nd half were no longer statistically significantly different from 0 for the group who conducted the restoration-enhancement tasks in the reverse order (however, the trend was the same). Therefore, the result that conducting the tasks in the reverse order, but not in clockwise order, improved sustained attention in terms of reduced variability was only partly supported in this analysis.

Discussion

Our first main result was that self-reported restoration and valence improved in all conditions but this was not connected to conducting the psychological tasks. Activation remained mostly similar. The second main result was that overall, sustained attention performance, as measured by the number of commission errors, improved after the walk, whereas the speed and stability of responding did not change substantially. Unexpectedly, the participants who completed the restoration-enhancement tasks in the reverse order improved their sustained attention performance (evaluated by reduced commission errors and RT variability) most consistently, whereas those who conducted the tasks in the clockwise order showed no changes in sustained attention. In both ‘no task’ conditions, sustained attention improved only in terms of commission errors. Thus, comparing the two conditions where the restoration-enhancement tasks were conducted in different orders, it appeared that the reverse order was more ideal for attention restoration than the hypothesized, theory-driven order. Based on this consistent finding, we modified the contents of the restoration-enhancement tasks for Study 2.

One limitation of this study was that wayfinding was difficult for some. Those who reported problems with wayfinding (n = 15) systematically reported lower levels of restoration and valence after the walk. They also responded more impulsively in their sustained attention task, meaning that they performed the SART with consistently faster RTs, combined with an increased number of commission errors and reduced variability (probably due to the fast speed of responding). The fact that the trail included several crossings (which, nevertheless, were marked with yellow ribbons) and required looking at a map to spot the signposts irritated some participants. Furthermore, taking an incorrect turn and having to return was a nuisance for some, although some found minor wandering around in a new environment inevitable. Most, nevertheless, thought that the trail was well marked and easy to follow.

Another limitation was that the route was different depending on the direction of the walk, which could have affected the results for several reasons. Firstly, when walking the clockwise route, the unpleasant parts of the trail (recent clearings) were toward the end of the walk, whereas in the reverse direction the end was intact coniferous forest. Recently clear-cut forests are generally regarded as unpleasant compared to intact forests or forests that have been cut less invasively (Silvennoinen et al., 2002). In addition to being visually unpleasant, some participants verbally reported feeling upset about the ecological consequences of these clearances. These kinds of reactions to the environment may have shown in their post-walk measurements. Secondly, as the signposts were numbered, the participants who completed the instructions in the reverse order could infer that they were doing them in an “incorrect” order so they were not completely blind to the study conditions. Thirdly, the trail was originally designed to be walked in the clockwise direction and thus, it was marked with arrows and was more intuitive to follow that way. Even though we marked the whole trail with yellow ribbons for this study, we chose not to use arrows pointing in the reverse route to avoid confusion, and it is probable that there was more wayfinding involved when walking the reverse route.

For all the above reasons, the finding that the tasks improved, to some degree, sustained attention performance when they were completed in the reverse order is particularly interesting. We speculate that this may partly have to do with the contents of the final tasks and their congruence with the environment. In the clockwise route, the final task related to general life reflection which may induce all kinds of emotional responses, not solely positive ones (for example, rumination). This type of negative emotional response, especially when combined with the unpleasant scene, may have been the reason for reduced sustained attention restoration; a similar pattern was not found when walking the same route without the tasks. In the reverse route, although more difficult to follow, the end of the trail was more visually pleasant and the final task focused on physical and psychological relaxation. These factors could have induced a more fascinated and calm state and thus, according to attention restoration theory (Kaplan and Kaplan, 1989), lead to better sustained attention when walking this route.

Based on this field experiment, there was no evidence that favored completing the restoration-enhancement tasks in the designed, theory-driven order, although there seemed to be no negative effects of doing these tasks either. It is important to also note that we inspected only short-term effects. For example, reflection may not be restorative in the short-term but it can have a longer-term impact on well-being. To assess any potential longer-term effects on general well-being is, however, outside the scope of this study. Relatedly, we studied single nature visits that may not reveal the full potential of these kind of tasks. For some, it may take more time to “learn” to do the tasks, or more repetition to experience any added benefits on affective or attention restoration (Lymeus et al., 2018).

We would like to note that our participants were more nature-oriented than the general population (evaluated by the number of weekly nature visits). Participation alone required 2.5–3 h, and for most it took much longer because they traveled to the study site from other municipalities in the region. The motive to participate seemed, for many, related to an interest in visiting a new natural environment and/or research on the topic of natural environments. The fact that we found few differences between the participants who completed or did not complete the psychological tasks could also be related to the sample being nature-oriented. Some of the participants in the ‘no task’ conditions said that they had been disappointed because they were instructed not to do the tasks, but that they compensated by focusing on other, pleasant features during the walk (such as spotting new plant species and picking berries and mushroom while walking). It is plausible to assume that some nature-oriented people already know how they like to explore a new (natural) environment and that they are more prone to find elements there that they find interesting and engaging.

Study 2 – Urban Park

In Study 1 we found that self-reported restoration and valence improved after a forest walk in all groups, regardless of the tasks, whereas for sustained attention, conducting the restoration-enhancement tasks in the reversed order seemed the most beneficial. The aims for Study 2 were to conceptually replicate Study 1, addressing its major limitations, and to investigate the effects of urban nature. The hypotheses were the same as in Study 1 (see The Present Studies).