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Cognitive benefits of walking in natural versusbuilt environments

Andrew W. Bailey, Garrett Allen, Josh Herndon & Christian Demastus

To cite this article: Andrew W. Bailey, Garrett Allen, Josh Herndon & Christian Demastus (2018)Cognitive benefits of walking in natural versus built environments, World Leisure Journal, 60:4,293-305, DOI: 10.1080/16078055.2018.1445025

To link to this article: https://doi.org/10.1080/16078055.2018.1445025

Published online: 02 Mar 2018.

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Cognitive benefits of walking in natural versus builtenvironmentsAndrew W. Bailey, Garrett Allen, Josh Herndon and Christian Demastus

Health and Human Performance, University of Tennessee, Chattanooga, TN, USA

ABSTRACTLeisure time physical activity can be a robust preventer of physicaland mental illness. The health benefits of exercise have been well-documented, including decreased risk for obesity, diabetes, andheart disease, and increased mood, life-satisfaction and cognitiveperformance (An, Xiang, Yang, & Yan, 2016; Blair & Morris, 2009).Time spent outdoors is also emerging as a predictor of positivemental health, though the outcomes are often conflated withheightened levels of physical activity. This study sought toconfirm the cognitive benefits of exercise in outdoorenvironments using established cognitive tests while measuringbrainwave activity throughout the process using portableelectroencephalograph (EEG) headsets. Participants completedcognitive performance tests before and after walking indoors,then again on a different session while walking outdoors.Repeated-measures analyses of variance revealed significantcognitive improvement after both walking sessions, with elevatedmental restoration observed for the outdoor walking session, asmeasured by a Stroop Test. EEG measures revealed a significantlyhigher level of meditative state during the outdoor walkingsession, as compared to indoors. In addition, the gains in relaxedand meditative mental states were retained longer after walkingoutdoors. Results are discussed within the context of growingsupport for leisure time exposure to natural environments andoutdoor prescriptions. Implications for planning, health policy, andpublic education are proffered in light of these findings.

ARTICLE HISTORYReceived 9 June 2017Accepted 24 September 2017

KEYWORDSEEG; cognitive psychology;outdoor exercise; natureprescriptions

Introduction

It has long been known that physical activity can lead to increased cognitive function (Mid-dleton, Barnes, Lui, &Yaffe, 2010).However, the environmentwherein that physical activityis performedmaybe just as important as the act itself (ThompsonCoon et al., 2011).Naturalenvironments, as opposed to built environments, have a host of positive effects on physicaland mental health, including: increased physical activity and caloric expenditure, fewerdepressive symptoms, reductions in myopathy, reduced stress levels and increased feelingsof accomplishment (Brussoni et al., 2015; French, Ashby, Morgan, & Rose, 2013; Grahn &Stigsdotter, 2003). By contrast, an increasingly urbanized world, with associated distrac-tions and mental distress, may contribute to physical and mental disorders (Prince et al.,2007). Increasing screen time and decreasing outdoor time during leisure exacerbate this

© 2018 World Leisure Organization

CONTACT Andrew W. Bailey Andrew-Bailey@utc.edu

WORLD LEISURE JOURNAL2018, VOL. 60, NO. 4, 293–305https://doi.org/10.1080/16078055.2018.1445025

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issue (Bassett, John, Conger, Fitzhugh,&Coe, 2015). A better understanding of how variousenvironments directly impact mental well-being could provide support for policies andinfrastructure that increase opportunities for routine outdoor experiences.

While previous research has documented the effects of natural environments on mentalaptitude, technological advancements now provide the opportunity to understand theprocess whereby those outcomes are produced. Recently, portable EEG scanners havebeen utilized to demonstrate that green spaces reduce stress response and levels of frustra-tion (Aspinall, Mavros, Coyne, & Roe, 2015). Given that stress causes decreased cognitiveperformance, it may follow that walking outdoors would be better for cognitive functionthan walking indoors. Numerous studies support this assertion, (Berman, Jonides, &Kaplan, 2008; Bratman, Daily, Levy, & Gross, 2015). However, there is a dearth of researchcombining mobile EEG technology with common cognitive tests to compare the processand effects of walking indoors versus walking outdoors on mental states. This study buildsupon previous research to illustrate the influence outdoor environments have on the brainwhich could aid cognitive performance and mental well-being.

Literature review

Leisure time physical activity

An increase in sedentary lifestyles has led to myriad health concerns. Workplace physicalactivity has seen precipitous declines over the last 50 years, with 80% of modern occu-pations labelled as sedentary (Church et al., 2011). Given an inherent lack of choice inworkplace activities, health professionals commonly focus on leisure pursuits as vehiclesto healthy lifestyles. Routine leisure behaviours can influence physical and mentalhealth as well as overall life satisfaction (Bailey, Kang, & Schmidt, 2016; Iso-Ahola,1980). Unfortunately, residents of developed countries consistently pursue isolated, seden-tary activities (e.g. television, social media) above healthier, prosocial and active opportu-nities (e.g. sports, visits to parks) for leisure and recreation (Bureau of Labor Statistics,2017). Leisure time physical inactivity has been tied to a host of health concerns, including:obesity, diabetes, high blood pressure, and cardiovascular disease (An, Xiang, Yang, &Yan, 2016). Physical activity has many benefits for human health both mentally and phys-ically. Myriad studies conclude that regular doses of physical activity influence overallhealth. Individuals who exercised 150 min/week at moderate intensity reduce the risk ofnumerous chronic diseases, preserve health and function into old age, and extend longev-ity (Blair & Morris, 2009).

Not only does physical activity benefit physical health but it can enhance mental health,as well. Kim et al. (2012, p. 458) states that, “… physical activity can reduce depressivesymptoms in individuals diagnosed with major depression… Regular physical activityappears to be protective against anxiety disorders.” Vankim and Nelson (2013) foundthat college students who reported regular, vigorous physical activity were less likely tohave poor mental health and perceived stress than students who did not perform vigorousactivity. Running and walking have been shown to induce a relaxed state of mind, evi-denced by increased alpha brain waves post workout (Schneider et al., 2009; Woo, Kim,Kim, Petruzzello, & Hatfield, 2009). Given the established benefits of physical activityfor physical and mental health, this study incorporated the recommended daily dose of

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light/moderate exercise (i.e. 30 min a day) into a repeated-measures design. In this way,physical activity was held constant across two differing environmental conditions.

Natural and built environments

While active leisure is beneficial in many ways, outcomes may also be enhanced by theenvironment. Numerous studies provide evidence to support the effectiveness of activitiesdone outdoors above and beyond those performed in a built environment. Outdoor exer-cise has been shown to: induce greater feelings of revitalization and positive engagement,increase energy, and decrease depression, anxiety, anger, and frustration (Mitchell, 2013;Pasanen, Tyrväinen, & Korpela, 2014; Thompson Coon et al., 2011). Even when control-ling for routine physical activity, time spent outdoors can enhance psychological well-being and happiness (Bailey et al., 2016). The influence of natural environments hasbeen expounded in the Attention Restoration Theory (ART). ART maintains thaturban landscapes and built environments contain distractions that exhaust the brain’sability to focus on a task (Kaplan, 1995). Urban and built environments present a plethoraof competing stimuli that render it difficult for the brain to filter out distractions. Thisexhaustion of top-down, voluntary attentional control renders the completion ofarduous tasks requiring concentration more difficult after being in an urban landscapeor indoors around other people (Berto, 2005; Kaplan, 1995). By contrast, natural environ-ments contain fewer extraneous stimuli, allowing for a deeper state of relaxation. ARTasserts that natural environments give one a sense of “being away” from the world,thereby reducing voluntary attention necessitated by urban areas, and restoring degradedattention (Berto, 2005). This influence helps one to perform better on tasks of attentionafter being in a natural environment.

Attention Restoration Theory has been verified through multiple studies. Taylor andKuo (2009) found that children with ADHD performed better on a task of concentrationafter walking in a park compared to walking in a downtown environment. A similar studyconfirmed a positive influence of park space on tasks of attention for adults withoutADHD (Berman et al., 2008). The influence of nature can even provide benefits indirectly.Tennessen and Cimprich (1995), for instance, found that college students having a view ofnature from their dormitories performed better on tests that required excessive concen-tration. These findings informed our current study, the purpose of which is to illustratethe influence of outdoor versus indoor walking on cognitive performance tests. A second-ary purpose was to explore the changes in mental state under testing and walking con-ditions in natural and built environments. This was accomplished with the use ofmobile electroencephalography (EEG).

EEG

EEG is a technique widely used to analyze brain wave patterns in living organisms. EEGdevices measure the currents created by electrochemical gradients that flow through nervecells when excited by a stimulus (Teplan, 2002). This is accomplished by the conductivesurfaces of the EEG headgear making direct contact with surface of the subject’s scalp.The brain waves recorded by the EEG can be characterized by their frequencies as a par-ticular emotion or state of consciousness and be used to interpret the subject’s level of

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stress or cognitive intensity (Teplan, 2002). EEG primarily measures the activity of the cer-ebral cortex due to its size and position within the brain in relation to the scalp (Teplan,2002). This is beneficial for our study since the cerebral cortex is responsible for cognition,logical thinking, and emotional expression. Previous research has shown that naturalscenery reduces stress and restores mental attention more than urban scenery (Berto,2005; Roe, Aspinall, Mavros, & Coyne, 2013). A recent study that employed the use ofmobile EMOTIV EEGs found that subjects walking in green spaces outdoors showlower levels of stress and higher levels of meditation and happiness compared to subjectswalking in urban areas outdoors (Aspinall et al., 2015). For our purposes, EMOTIV EEGheadsets were worn by participants as they exercised outdoors or indoors and while takingcognitive tests. The associated brainwaves were interpreted to infer the subjects’ level offocus, approach motivation, stress, relaxation, and meditative state.

Methods

The participants of this study included ten students (50% female, Mean age = 20) from apublic university in the southeastern United States. Participants were recruited fromhealth science classes that encourage research participation. No incentives were offeredand there were no repercussions for declining or withdrawing early. These participantswere screened to avoid complications from physical or neurocognitive disabilities.

The students selected dates of participation from a proposed schedule spanning threeweeks in the mid-spring, each attending on two separate days. The participants were ran-domly assigned to the indoor or outdoor portion of the experiment on their first day, andthe complementary portion of the experiment on their second day. For all portions of theexperiment the participants were fitted with a mobile EMOTIV EEG headset to monitortheir brain wave activity before partaking in any testing or walking.

To establish a baseline brainwave measure of each participant, they were first asked torelax for ten seconds with their eyes open and then repeat that with their eyes closed. Theparticipants were then asked to complete two cognitive exams in an indoor setting at theAquatic Recreation Center (ARC). This was the testing location for both the outdoor andindoor portions of the experiment. The first exam was the Stroop Effect Test, where par-ticipants were tasked with verbally identifying the printed colour of a word that is nor-mally associated with another colour. The second exam, the Backward Digit Span Task(BDST), required the participants to verbally repeat a set of digits in the reverse orderthat they were given.

After cognitive testing, the participants assigned to the outdoor portion of the exper-iment walked to a nature trail on campus, and walked the entirety of this trail. The par-ticipants then walked back to the ARC with the entire route, from the ARC to theGreenway trail and back, taking thirty minutes. After this thirty-minute walk, theStroop Effect test and the BDST were administered in the same fashion to the participants.This concluded the outdoor portion of the experiment. The indoor portion of the exper-iment (completed on a different day) was identical, except the participants were insteadasked to walk for thirty minutes along the indoor track inside the ARC. During eachwalk, a researcher guided the participants by walking in front of them while simul-taneously video-recording the path taken. This was done to account for any spuriousactivities that may have influenced brainwave data (student interactions, etc.).

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Measures

EegThe EMOTIV headset is a brand of mobile EEG that has been utilized in various otherstudies (Aspinall et al., 2013; Bailey, Johann, & Kang, 2017). Emotiv headsets have beenverified as comparable to high-end research EEG systems (Badcock et al., 2015). TheInsight version uses five sensors to collect brainwaves from five frequencies at a rate of128 Hz. In general, lower frequencies (Delta = .5–3 Hz, Theta = 4–7 Hz) are associatedwith less intense brain functions such as sleep, meditation, and daydreaming. Alphawaves (8–15 Hz) indicate a relaxed brain that is ready for action. Higher frequencywaves (Beta = 16–31 Hz, Gamma = 32–100 Hz) are associated with heavier mental loads(i.e. concentration, stress, etc.).

For this study, low and high pass filters removed all Delta frequencies (< 3 Hz) andGamma frequencies above 43 Hz. This filter was employed by the hardware before con-verting the raw data into distinct wavelengths via Fast Fourier Transformation (FFT).Filters reduce unwanted artifacts in the data, produced by muscle movements andambient electrical signals in the atmosphere. All data were first screened for removal ofobvious artifacts, then transformed into emotional states using established formulasfrom previous research. Focus (i.e. concentration) was measured as the presence of highfrequency waves across the frontal lobe (Coelli et al., 2015). Approach motivation(AM), or an individual’s interest and enjoyment, was measured via frontal asymmetry(Coelli et al., 2015). Higher relative activity on the left frontal cortex is associated witha more enjoyable or interesting experience, while right frontal activity indicates a desireto withdraw from the situation. Anxiety was measured as high frequency waves in the pos-terior cortex (Oathes et al., 2008). Relaxation was measured as relative global alpha poweracross all sensors (Harmon-Jones, Gable, & Peterson, 2010). Finally, meditative state wasmeasured as the presence of more powerful lower frequency waves in the frontal cortex

Figure 1. Smoothed data points for five mental states for a single participant session. 1)Baseline,2)Stroop, 3)BDST, 4)Walking, 5)Stroop, 6)BDST.

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(Theta) and stronger alpha waves in the posterior (Lagopoulos et al., 2009). This measureof meditative state is associated with nondirective rather than concentrative styles of med-itation. Whereas concentrative meditation (e.g. Transcendental) would filter outextraneous thoughts through intense focus on a single element (e.g. sound or mantra),non-directive meditation (e.g. Mindfulness) allows thoughts to flow freely through themind, without focusing on anything in particular. It should be noted that not all neuro-science literature agrees on EEG indicators, with various measures and labels beingreported in previous research.

Given constant flux in brainwave activity, analytical approaches to this type of researchvary. Figure 1 presents the overall test data for a single participant, demonstrating theamount of variation across and within each condition during the entire session.

A common approach is to EEG analysis is to select consecutive blocks of time (i.e.epochs) and measure variation between time points for each mental state (Wilson et al.,2015). The appropriate length of each epoch depends on the research questions underinvestigation. For this study, it was deemed appropriate to create a single epoch foreach condition during the research session. Thus, a participant received a score for eachmental state for the baseline (10 s of relaxed inactivity), stroop test, backward digit spantask, walking, post-stroop test, and post-backward digit span task conditions. Thisallowed for an overall comparison of mental states over the duration of each conditionfor the indoor and outdoor walking sessions. While ignoring a large amount of minor vari-ation, this provided a clearer picture of the overall influence of natural and built environ-ments. Since brain activity varies largely across individuals, and can vary withinindividuals due to contextual factors (i.e. lack of sleep, time of day, etc.), all test andwalking conditions were baseline-adjusted by subtracting the baseline average fromeach following condition (Hu et al., 2014). Thus, a negative Mean score for relaxationduring the Stroop test, for example, would indicate that, on average, participants wereless relaxed during the test than at baseline. The Mean for each condition was then ana-lyzed via repeated-measures Analysis of Variance (RM ANOVA) with the natural versusbuilt environment as a between-subjects variable.

The Stroop TestThe Stroop Test is a cognitive test designed to measure level of concentration. The testtakes advantage of the interference caused by word meaning and the actual colour theword is printed in, which slows down the time of response. This phenomenon is com-monly known as the Stroop Effect (Hanslmayr et al., 2008). The test begins with thepure Stroop Test where the participants read aloud the name of thirty colours that areprinted in their respective colour (e.g. the word “green” appears in green coloured text).The time it takes for the participants to read the colours out loud is recorded. The partici-pants then complete the interference Stroop test by reading aloud printed words of thirtycolours, which are printed in a different colour than the word. The time it takes to com-plete this task is also recorded (in seconds). The Stroop score is calculated using the time ofthe pure Stroop test subtracted from the time it takes to read the interference test(MacLeod, 1991). Using this method a Stroop score was produced for every participantat four time points: (1) Pretest indoor, (2) Post test indoor, (3) Pretest outdoor, (4)Post test outdoor. The stroop test was analyzed with a RM AOVA with time as therepeated measure and environment as a within subjects variable.

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Backward digit span testThe BDST represented a second validated measure of concentration (Hale, Hoeppner, &Fiorello, 2002). In each trial of this task, the researcher reads a sequence of numbers aloud(one digit per second), and the participant is asked to repeat the numbers in reverse order(Bratman et al., 2015). For the first trial, participants are given two numbers to remember.If the participant correctly answers the sequence correctly (recalling all digits in the correctorder), the researcher increases the length of the digit span by one number, and the partici-pant gets one trial at the new length. This continues until the participant fails to recall bothtrials at a particular length, or he/she has completed the maximum length of 8 digits(Bratman et al., 2015). For each set correctly recalled, a participant receives one point (regard-less of length), this is known as the “all-or-nothing unit scoring” used by Berman et al. (2008).The total number of points received by the participant will be the participant’s score on thistest. Our study utilized a version of backward digit span that ranged from two to eight digitsin length (Bratman et al., 2015). The BDSTwas analyzed with a RMANOVAwith time as therepeated measure and environment as a within subjects variable.

Results

Cognitive tests

All variables were normally distributed, excepting one score for the pure stroop test. Eachindividual completed the stroop test four times over the course of the study. One individ-ual’s stroop pure test was identified as an outlier when compared with his other three purestroop measures. This single response was replaced with the Mean of that individual’sother three stroop pure responses. Table 1 includes Mean scores and standard deviationsfor all participants for each test. The score for the outdoors and indoors Stroop tests aremeasured in seconds, with a negative score indicating that the participant and the scoresfor the BDST are measured as the total number of sets completed.

Participants did better on the post-Stroop test after walking indoors and after walkingoutdoors, indicated by a main effect for exercise in both environments (F1,18 = 15.625, p= .008, h2p = 0.331). Additionally, the participants completed the Stroop Test 1.50 s fasterafter walking outside versus 0.25 s faster after walking inside. RM ANOVA revealed a sig-nificant time × environment interaction (F1,18 = 8.464, p = .042, h2p = 0.211), confirmingthe additional influence of the natural environment beyond that of light exercise. TheBDST demonstrated much less of an influence, with the indoor session showing noimprovement and the outdoor session, only one-third of a set gain from the walking con-dition. There was no significant main effect for the BDST for time (F1,18 = 1.767, p = .200)nor for the time x environment interaction (F1,18 = 0.000, p = 1.000).

Table 1. Descriptive statistics for stroop and BDST scores.Indoors Outdoors

Mean Std. Dev. Mean Std. Dev

Stroop Pretest 6.44 3.518 8.24 3.403Stroop Post-test 6.11 1.661 6.07 2.536BDST Pretest 4.00 0.471 3.60 0.516BDST Post-test 4.30 1.059 3.90 1.370

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Mental states

Differences in EEG-based cognitive states were assessed using a RM MANOVA, with theindoor/outdoor condition as a fixed variable. This provided insight into the change of fivemental states over the duration of each session, as well as a comparison of time pointsacross sessions. Since the only significance in objective measures was found for theStroop test, repeated-measures analyses were conducted on the pre-Stroop test, whilewalking, and the post-Stroop test. All measures were baseline-adjusted, so negativeMeans indicate a lower score during that time point than at baseline. Results for these ana-lyses can be seen in Table 2.

Significant differences were evident in all cognitive states across three time points, withthe exception of Approach Motivation (AM). This indicates that focus and stress wereelevated during the Stroop tests, while meditative and relaxed states were elevatedduring the walk for both conditions. The outdoor walking condition also induced ahigher meditative state than the indoor walking condition. Post hoc analyses helped toprovide a deeper understanding of the nature of influence for each condition. A poly-nomial contrast applied to all cognitive states over three time points revealed a significantquadratic slope (“U” shape or inverse “U” shape) for all mental states (p < .001) except forAM. However, when accounting for the time x environment interaction effect, the med-itative (p = .033) and relaxed (p = .58) mindsets demonstrated a linear trend, thus remain-ing elevated and losing significance for the quadratic slope altogether. This implies thatsomething about the outdoor condition enabled the relaxed and meditative cognitivestates to endure even through the stress-inducing post-Stroop test.

Discussion

The purpose of this study was to determine differences in cognitive performance tests afterexercise in natural and built environments and assess differences in mental state that maycontribute to that influence. The results indicate that participants did significantly betteron the Stroop Test after walking and that the outdoor environment had an additional

Table 2. Descriptive statistics and significant differences for EEG-based mental states across sessions.

Indoor Session Outdoor SessionANOVA (3 time points: Pre, walking,

post)

Mental State Mean Std. Deviation Mean Std. Deviation F Sig partial eta squared

Med Pre Stroop 0.12 0.84 −0.13 0.41 91.506 .000 .836Med Walking 1.54 1.02 2.16 0.68Med Post Stroop −0.06 1.00 0.17 0.61Relax Pre Stroop −0.02 0.83 −0.04 0.55 154.109 .000 .895Relax Walking 2.06 1.10 2.44 0.61Relax Post Stroop −0.17 1.01 0.21 0.48Focus Pre Stroop 0.03 0.22 0.01 0.32 17.274 .000 .490Focus Walking −0.20 0.30 −0.31 0.34Focus Post Stroop 0.00 0.30 0.02 0.29AM Pre Stroop −0.17 1.05 0.20 0.73 .187 .774 .010AM Walking 0.15 0.50 0.08 0.54AM Post Stroop 0.02 0.70 0.23 0.56Anxiety Pre Stroop −0.06 0.78 −0.11 0.80 27.530 .000 .605Anxiety Walking −1.19 0.60 −0.88 0.99Anxiety Post Stroop −0.08 0.38 −0.12 0.53

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positive influence. The outdoor walking session also induced a more meditative state thanthe indoor session. While instructive, the results from this study should be interpretedwithin the context of its limitations. This study could have benefitted from a largersample size, though EEG studies are commonly small and criteria for power and effectsizes were met. While the sample size is appropriate for the analyses used, it precludesextrapolation without confirmatory research. The testing location posed another limit-ation. To reduce spurious influences on performance tests, all tests were conducted inthe same location and the walking condition proceeded directly from the testing location(i.e. participants did not relax during transport to the green space). Future research couldincorporate a longer time frame and/or a testing location that is more centralized tovarying levels of green space for comparison of multiple environments. This study did,however, produce notable results given its scope.

The two cognitive tests produced disparate results in this study. The BDST did not yieldsignificant results for the main effect of walking, nor for the indoor or outdoor conditions.This finding is consistent with the results of Bratman et al. (2015) but at odds with that ofTaylor and Kuo (2009). The latter study utilized only the BDST with no other cognitivemeasures, while Bratman et al. (2015) administered the BDST as the third in a batteryof tests before and after a nature walk. It is possible that the effect of the walk was depletedby the other tests, thus reducing the measurable influence. To test that assertion with thedata in our current study, a post hoc repeated-measures ANOVA was conducted onmental states between Stroop and BDST conditions both before and after walking. No sig-nificant differences were found for any of the five mental states measured. Future researchwill need to be done to determine the longevity of the impacts from walking indoors andoutdoors, as well as the specific influence of natural environments on areas of the brainunder load during each cognitive test.

The outdoor walking condition did significantly influence improvement in Stroop testscores. According the “selective attention” theory of the Stroop effect (Lavie, 2005), thesetest scores are related to the allocation of direct attention to the task at hand. Individualswho are less distracted and have more mental energy, overcome the interference of theword/colour discrepancy faster and with better accuracy. This supports the premise ofAttention Restoration Theory (Kaplan, 1995), indicating that directed attention is betterrestored through natural environments rather than built or urban environments. Effectsizes for exercise and for the exercise x environment interaction were equal to orgreater than those produced by standard ADHD medications (Swanson et al., 2004;Taylor & Kuo, 2009). Given previous similar findings, Taylor and Kuo (2009) called forthe immediate implementation of nature prescriptions as part of the mental healthtoolkit. Standard protocols already exist for the prescription of exercise (Chen, Frederic-son, Matheson, & Phillips, 2013) and consistent findings for the additional influence ofnatural environments are compelling. Nature prescription programmes are emergingacross the country in a piecemeal fashion (c.f. http://www.outdoorsrx.org/). Given thelack of grossly negative side effects, nature prescriptions could be a viable alternative topharmaceutical solutions.

EEG monitoring of the process provides insight into how the natural environmentmay influence the brain to enable better post-test performance. Meditative state (i.e.frontal theta) differed significantly across environments, remaining higher eventhrough the post-Stroop test for outdoor walkers. The meditative state measured in

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http://www.outdoorsrx.org/

this study is consistent with that reported during nondirective meditation (Lagopouloset al., 2009). While concentrative forms of meditation enhance high frequency bands(e.g. gamma) through focused attention on a sound or mantra, nondirective meditation(i.e. open monitoring) facilitates a free mental attitude, where any thought or sensationis allowed to pass through the consciousness without any attempt to control the content(Lagopoulos et al., 2009; Lutz, Slagter, Dunne, & Davidson, 2008). Other open-monitor-ing methods of meditation (e.g. mindfulness) have been shown to improve sustainedattention, executive function, and visual-spatial performance for experienced meditatorsand those with only initial training (Moore & Malinowski, 2009; Teper & Inzlicht, 2013;Zeidan, Johnson, Diamond, David, & Goolkasian, 2010). Theoretically, this occursthrough the improvement of resource allocation processes, thus preparing the brainfor better performance when under cognitive load (Berman et al., 2008; Malinowski,2013). Natural environments may employ similar mechanics as nondirective meditationin regards to the stimulation of lower-frequency brainwaves. Future research will need todirectly compare meditative practice with time spent in natural environments in order toconfirm this assertion.

Paradoxically, the presence of theta waves has been associated with ADHD in EEGresearch, with a high theta/beta ratio being a leading indicator for diagnosis (Arns,Conners, & Kraemer, 2013). That an increase of theta waves in the frontal lobe wouldenhance performance on a task of concentration is unexpected. However, the lineartrend of relaxation (i.e. alpha) and meditative states through the post-Stroop test couldindicate that participants are transitioning from lower states of activity to a relaxedstate that is ready for action. The ability to self-regulate mental states based on the taskat hand would likely enhance performance and mental health over many contexts(Pigott, 2017). Future research can help shed light on the relationship between timespent outdoors, neuroplasticity and long-term mental health. Research has shown thattime spent outdoors has a positive influence on psychological, social, and subjectivewell-being (Bailey et al., 2016; Mutz & Müller, 2016). It is feasible that frequent visits tonatural environments can produce long-term state and/or trait-like changes to one’sdisposition.

The results from this study have implications for parents, educators, urban plannersand practitioners interested in facilitating health outcomes. The benefits of activity inoutdoor environments can be reaped through programmed outdoor excursions or inci-dental daily encounters. Visitors of local parks typically report fewer visits to thedoctor for chronic illness (Godbey, 2009) and outdoor-related programmes mayinduce a host of mental and physical benefits (Hattie, Marsh, Neill, & Richards,1997). Positive outcomes of active outdoor leisure pursuits have been reported fordecades, but we may now gain knowledge of the neural mechanisms by which theseoutcomes occur. There is no universal remedy for improving mental attention, butactive leisure and time spent in the natural environment are viable options. Mountingevidence compels us to be intentional about logging off, unplugging, and interactingwith the natural environment to protect our mental and physical health andenhance overall performance and well-being. Planners and programmers shouldclearly communicate the need for outdoor leisure and facilitate access to safe, opengreen spaces for all.

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Disclosure statement

No potential conflict of interest was reported by the authors.

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AbstractIntroductionLiterature reviewLeisure time physical activityNatural and built environmentsEEG

MethodsMeasuresEegThe Stroop TestBackward digit span test

ResultsCognitive testsMental states

DiscussionDisclosure statementReferences