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Can Different Forest Structures Lead to Different Levels of Therapeutic Effects? A Systematic Review and Meta-Analysis

healthcare

Systematic Review

Can Different Forest Structures Lead to Different Levels of

Therapeutic Effects? A Systematic Review and Meta-Analysis

Eunsoo Kim 1 , Sujin Park 1 , Soojin Kim 1, Yeji Choi 1 , Junghee Cho 1, Sung-il Cho 2,3 , Hae-ryoung Chun 2

and Geonwoo Kim 1,*

1 Future Forest Strategy Department, Forest Human Service Division, National Institute of Forest Science,

Seoul 02455, Korea; euncarp2@gmail.com (E.K.); snowshoe@korea.kr (S.P.); kimsoojinsj@korea.kr (S.K.);

usmile.choi@gmail.com (Y.C.); jjh6758@korea.kr (J.C.)

2 Department of Public Health Sciences, Graduate School of Public Health, Seoul National University,

Seoul 08826, Korea; persontime@hotmail.com (S.-i.C.); mamimihae@gmail.com (H.-r.C.)

3 Institute of Health and Environment, Seoul National University, Seoul 08826, Korea

* Correspondence: bkim5020@korea.kr; Tel.: +82-02-961-2862

Citation: Kim, E.; Park, S.; Kim, S.;

Choi, Y.; Cho, J.; Cho, S.-i.; Chun,

H.-r.; Kim, G. Can Different Forest

Structures Lead to Different Levels of

Therapeutic Effects? A Systematic

Review and Meta-Analysis.

Healthcare 2021, 9, 1427. https://

doi.org/10.3390/healthcare9111427

Academic Editor: Jesper Dahlgaard

Abstract: In recent decades, forests have expanded from natural resources for conservation and

production to health-promoting resources. With the growing body of evidence supporting the

therapeutic effects of forests, the number of investigations on the relationship between forest charac-

teristics and therapeutic effects have increased. However, quantitative synthesis of primary studies

has rarely been conducted due to a limited number of health studies including forest description

and high heterogeneity of forest variables. In this study, we conducted a systematic review and

meta-analysis to evaluate the relationship between the forest structure and the therapeutic effect.

We systematically searched the studies examining the therapeutic effects of forests with different

stand density and canopy density and synthesized the results. As a result of subgroup analysis,

we found that stand density modiﬁes the therapeutic effects. Emotional and cognitive restoration

showed greatest improvement in low-density forests with a stand density of less than 500/ha and

the therapeutic effects diminish as the stand density increases. The impact of canopy density was

not found due to a lack of studies reporting canopy density. Although some limitations remain, the

ﬁndings in this study have great signiﬁcance in providing the basis for establishing management

strategies of forests for therapeutic use.

Keywords: stand density; canopy density; affective restoration; cognitive restoration; physiological

relaxation; subgroup analysis

Received: 31 August 2021

Accepted: 19 October 2021

Published: 23 October 2021

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1. Introduction

Human use of nature to promote mental and physical health has a long history and

continues accumulating plenty of empirical evidence. In the 1980s, two representative

theoretical frameworks were proposed that explained how experiencing nature beneﬁts

human health. One is the attention restoration theory (ART) which suggests cognitive

recovery through experience in nature. According to ART, natural environments are rich

in components aiding recovery from mental fatigue [1,2]. Exposure to nature evokes

involuntary attention, rather than voluntary attention that causes directed attention fatigue,

result in the restoration of directed attention, giving more opportunities to restore cognitive

abilities [1–4]. The other one is the stress reduction theory (SRT) that emphasizes emotional

recovery and stress reduction through aesthetic experience in nature [5,6]. Viewing natural

scenery arouses positive emotions, limits negative thoughts, and alters the autonomic

nervous system activity towards being parasympathetic-dominant, leading to more relaxed

states [5–8]. In addition to these two approaches, several health-promoting pathways

in the natural environment have been proposed, such as promoting physical activity

and social contact, providing a quality environment for improving mental health [9,10].

Healthcare 2021, 9, 1427. https://doi.org/10.3390/healthcare9111427

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Plenty of studies have accumulated, leading to recognition of the therapeutic potential

of nature. Before 2007, however, most investigations did not distinguish types of the

natural environment because studies have focused primarily on demonstrating the health-

promoting effects of natural exposure and its mechanisms [11].

Since 2007, some East Asian and European countries have started to utilize urban

forests, peri-urban forests, and conservation areas to improve public health and well-

being [11–17]. Accordingly, research on forests, a speciﬁc natural environment, has been

increasing. Several clinical trials conducted walking, staying, and exercising sessions

in the forest and examined the therapeutic effect of forests. They demonstrate that for-

est have effects on reducing anxiety and depression [18–21], reducing stress [8,22–24],

promoting physiological relaxation [25–30], improving cognitive function [31–33], reliev-

ing inﬂammation [34–37], improving immune function [37–41], and preventing cardio-

vasculars [35,36,42,43]. Overall, these studies demonstrate that individuals gain health

beneﬁts through mental and physical interactions with forest elements. The forest en-

vironments consist of scenery, scent, sounds, phytoncides, anions, sunlight, microcli-

mate environments, and topography. These elements act as therapeutic factors by stim-

ulating the ﬁve senses [1,17,28,44–47], promoting psychological and physiological relax-

ation [25–30,48], supporting cognitive recovery [49], providing beneﬁcial chemicals to the

human body [50–57], and giving a recreational space [33,58].

Recently, studies have started to examine the relationship between therapeutic out-

comes and various forest variables. Investigations were conducted on a wide range of

forest variables such as spatial structure [59–61], openness-enclosure [59,62,63], vegetation

density [59,64,65], tree cover density [66], stand structure [67], species composition [44],

management [68,69], and physical environment [45,70–72]. Those investigations aimed

to assess and estimate the beneﬁts of the forest by its characteristics, determine adequate

levels of forest variables, and provide guidance to create and manage forest sites for ther-

apeutic use [73]. Furthermore, one systematic review examined the differences in health

effects according to the forest variables. However, few studies included forest variables

at the time of the review, and there was a limit to elucidating the relationship because

the forest variables investigated in each study were different. We recognized that more

articles have been reported since the publication of the review, and that the search needs

to be narrowed down to speciﬁc forest variables. Therefore, we conducted a systematic

review and a meta-analysis to update more recent evidence and identify the relationship

between the therapeutic effects and the structural variables of forests: stand density and

canopy density.

The structure of the forest affects the sense of openness, lightness, comfort, and

aesthetics that humans feel, so it can be expected that the structural characteristics will

change the therapeutic effect of the forest. Further, we focused on the density of trees, a

major component of the forest environment. Previous studies revealed that high-density

trees limit visual access and ease of movement, and may disturb immersion in nature

in that it threatens a sense of security [64,74,75]. Conversely, a forest setting with a low

vegetation density can give a sense of openness but it can create a boring and monotonous

landscape [66,76–79]. In this regard, we considered stand density—a number of trees per

hectare— and canopy density—the top density of trees—as a major factor in modifying

the therapeutic impacts of forest sites. Besides, we decided to include stand density and

canopy density for this review because these are measurable variables widely used in the

forestry sector and can be changed by short-term forest management.

A systematic review is a research method that summarizes the latest empirical evi-

dence relevant to a speciﬁc research question, and meta-analysis is a statistical method

that quantitatively synthesizes the data from searched studies. In general, meta-analysis is

performed to estimate the effect size more accurately by synthesizing homogeneous studies.

It can also be conducted to conﬁrm whether study-level variables change the outcomes

of each study [80]. The key challenge of this study was to investigate the moderating role

of stand density and canopy density. Therefore, we systemically summarized previous

Healthcare 2021, 9, 1427

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studies reporting both therapeutic effect and structural variables of the forest. Then we

performed a meta-analysis to investigate whether the structural variables of the forest

modify the therapeutic effect. We expected this study to contribute to developing strategies

to increase the therapeutic use of forests.

2. Materials and Methods

We followed the guidelines of the Cochrane Handbook for Systematic Reviews of

Interventions (Version 6.2) [80], and we identiﬁed and evaluated the available literature in

accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses

(PRISMA) checklist [81]. The study was registered in OSF database under the number

DOI:10.17605/OSF.IO/D7J5Q

2.1. PICO and Eligibility Criteria

In order to address a clearly formulated research question, we set our research ques-

tion by specifying the population, intervention, comparison, outcome (PICO). We also

established eligibility criteria following the PICOS framework for the study selection,

displayed in Table 1, as follows:

• P: All human participants (both healthy and unhealthy).

• I: Spending time in the forest or urban forest with the description of forest environment

in terms of stand density and canopy density—stand density, tree density, trees/ha,

basal area, canopy density, canopy openness, canopy closure, sky view factor etc.

• C: Studies with a control site or not.

• O: The studies that contained quantitative outcomes related to cognitive restoration,

psychological restoration, physiological relaxation, and stress reduction.

Table 1. Eligibility Criteria for Study Selection.

PICO

Participants

Intervention

Comparator

Outcome

Inclusion Criteria

Humans who are healthy or not

Activities

Spending time in the forest or urban

forest (e.g., forest walking, forest viewing,

forest bathing, forest therapy)

Undertaken area

Studies should describe forest

environment in terms of stand density or

canopy density (e.g., stand density, tree

density, trees/ha, basal area, canopy

density, canopy openness, canopy

closure, sky view factor, etc.)

Studies with a control site or not

Studies should report quantitative

outcomes related to cognitive restoration,

psychological restoration, physiological

relaxation, stress reduction.

Exclusion Criteria

Studies not including human

participants

Studies with the intervention

that does not match with

deﬁned activities

Studies not reporting stand

density nor canopy density

Studies not including

quantitative outcomes

2.2. Search Strategy

We took 24 pilot searches and ﬁnalized search keywords to gather relevant studies

and obtain reproducible search results. The search keywords contain keywords relevant to

forest-based activities, forest variables, and therapeutic effects (Table 2). We conducted a

systematic literature search of four databases PubMed, EBSCO, Web of Science, and Scopus.

We searched published articles in English from the inception year to the end of June 2020.

Healthcare 2021, 9, 1427

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Table 2. Search Keywords.

PICO

Intervention

Activities

Undertaken

Area

Outcome

Keywords

“forest therapy” OR “forest bathing” OR “shinrin-yoku” OR “shinrin yoku” OR

“forest walk” OR “forest walking” OR “forest recreation” OR “nature therapy”

OR “forest trip” OR “forest visit” OR (“trip” NEAR “forest”) OR (“visit” NEAR

“forest”) OR (“spending time” NEAR “forest”) OR (“walking” NEAR “forest”)

OR (“viewing” NEAR “forest”)

“stand density” OR “tree density” OR “strains/ha” OR “stands/ha” OR

“trees/ha” OR “basal area” OR “canopy density” OR “canopy openness” OR

“canopy closure” OR “sky view factor” OR “gap light analysis” OR (“managed”

NEAR “forest”) OR (“unmanaged” NEAR “forest”) OR (“thinned” NEAR

“forest”) OR (“unthinned” NEAR “forest”) OR “thinned condition” OR

“unthinned condition” OR “thinning intensity” OR (“light environment” NEAR

“tree vegetation”) OR (“light environment” NEAR “trees”) OR (“environmental

factor” NEAR “forest”) OR (“environmental factor” NEAR “tree vegetation”)

OR (“environmental factor” NEAR “trees”) OR (“microclimate” NEAR “forest”)

OR (“microclimate” NEAR “tree vegetation”) OR (“microclimate” NEAR

“trees”) OR (“physical environment” NEAR “forest”) OR (“physical

environment” NEAR “tree vegetation”) OR (“physical environment” NEAR

“trees”) OR (“forest” NEAR “structure”) OR “forest landscapes” OR “forest sites”

OR “forests”

“health” OR “well-being” OR “well-being” OR “psychological” OR

“physiological” OR “psycho-physiological” OR “restorativeness” OR

“restorative effect” OR “restorative experience” OR “psychological restoration”

OR “PRS” OR “perceived restorativeness schedule” OR “ROS” OR “restorative

outcome scale” OR “mood” OR “affective state” OR “POMS” OR “proﬁle and

mood state questionnaire” OR “PANAS” OR “positive and negative affect

schedule” OR “anxiety” OR “STAI” OR “Spielberger state-trait anxiety

inventory” OR “self-reporting anxiety scale” OR “depression” OR “BDI” OR

“Beck depression inventory” OR “self-reporting depression scale” OR “vitality”

OR “SVS” OR “subjective vitality scale” OR “psychological relaxation” OR

“physiological relaxation” OR “psycho-physiological relaxation” OR

“psychological response” OR “physiological response” OR “blood pressure” OR

“SBP” OR “systolic blood pressure” OR “DBP” OR “diastolic blood pressure”

OR “pulse rate” OR “heart rate” OR “HRV” OR “heart rate variability” OR

“SDNN” OR “RMSSD” OR “LF” OR “HF” OR “LF/HF” OR “skin conductance”

OR “SCR” OR “brain wave” OR “prefrontal activity” OR “SpO2” OR “EEG” OR

stress reduction” OR “stress recovery” OR “stress restoration” OR “cortisol” OR

“saliva”

2.3. Study Selection Process

From the database search, we found a total of 932 studies with 180 on PubMed, 68

on EBSCO, 113 on Web of Science, and 571 on Scopus. The results were exported to

EndNote Citation Manager software (Endnote X9.3.3, Clarivate Analytics, London, UK).

After removing 159 duplicates, the titles and abstracts of the 773 publications were reviewed.

After removing 504 explicitly irrelevant cases, two investigators independently reviewed

the full text for 269 studies based on eligibility criteria (E.K. and Y.C.). Disagreements

during the screening process were resolved by two other investigators (G.K. and S.P.). We

included ten studies from our database search and added two studies that were manually

identiﬁed. Thus, in total, 12 studies were included in the review (Figure 1).

2.4. Data Extraction

Two investigators (E.K. and S.K.) independently used the same data extraction form

and cross-checked them. Data were extracted from individual studies to identify studies,

determine the homogeneity between studies, and estimate the effect size of individual

studies. The extracted data included study information, samples, forest characteristics,

environmental characteristics, intervention, and outcome measurements as follows.

Healthcare 2021, 9, 1427

Healthcare 2021, 9, x FOR PEER REVIEW

5 of 28

5 of 31

2.3.•StudSdytauStedel,yetciitmniofenoroPmfrmoactieeosasns:uyreemareonft publication, author, country, city, study design, conducted

• FroSmamthpeledsa:tasabmaspelseesairzceh, ,gwenedfeoru,nadnda atogteal of 932 studies with 180 on PubMed, 68 on

EBS•CO, F1o1r3eostncWhaerbacotfeSricsiteincsc:el,oanngdit5u7d1eo, nlaStictoupdues, .aTlthiteurdees,uslttas nwdedreenexsiptoyr(tterdeetso/Ehnad),Ncaonteopy

CitationdMenasniatyge(%r s),odftiwamareete(rEantdbnroetaestXh9e.3ig.3h,tC(clamri)v, ahteeigAhnta(mlyt)i,cdso, mLoinnadnotnt,rUeeKs)p. eAcifetesr re-

mov•ing E1n59vidrounpmliceantteasl, cthheartaitclteesriasntidcsa:btsetmrapcetsraotfutrhee(◦7C73), preulbatliicvaetihounms wideitrye (r%ev),ierwadeida.nAt hf-eat

ter r•emo(v◦iCn)g, w50i4ndexsppleiceidtly(mir/resl)e, vilalunmt ciansaensc, etw(lox)i,nnvoeissteiglaetvoerls(idnBd)ependently reviewed the

full•text Ifnotre2rv69ensttiuodni:eascbtiavsietdieso,nacetliivgiitbyildituyrcartiitoenri,aa(nEd.Kfr.eaqnudenYc.Cy .). Disagreements during

the•screeOnuintcgompreo:cemsseawsuerremreesnotlvienddibceys,twporeo-mtheearsiunrveemsteingtat(oMrs ±(GS.KD.),anpdosSt-.Pm.)e.aWsuereimn-ent

cluded t(eMn s±tuSdDie)s, cfhroamngeouinr mdaetasbuarseemseanrtc(hMa±ndSaDd)d, iendtetrw-toriastlucdoirerseltahtaiotnwere manually

identified. Thus, in total, 12 studies were included in the review (Figure 1).

Articles identified through

database searching

N = 932

PubMed (n = 180)

EBSCO (n = 68)

Web of Science (n = 113)

Scopus (n = 571)

Title and abstract level screening

(n = 773)

Duplicates removed

(n = 159)

Articles excluded

(n = 504)

Full-text articles assessed for

eligibility

(n = 269)

Full-text publications excluded, with reasons

Irrelevant intervention (n = 13)

without quatitative forest description (n = 132)

without quantitative health outcomes (n = 7)

Duplicated data (n = 2)

Reviews (n = 105)

Total articles included in the review

(n = 12, 28 cases)

Additional articles identified manually (n = 2)

Quantitative synthesis

(n = 9, 11 cases)

FiguFriegu1r.eFl1o.wFldowiagdriaamgrailmlusiltlruastitnragtitnhge tsheelescetiloecntipornocpersosc.ess.

Healthcare 2021, 9, 1427

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In the case of stand density reported as basal area (m2/ha) and growing stock (m3/ha),

we converted it into the number of trees per hectare. In the case of canopy density reported

as canopy openness, we reversed and uniﬁed it.

2.5. Methodological Quality

To collect all relevant studies reporting forest structural variables, we did not impose

strict restrictions on study design and included non-randomized studies in the review.

ROBINS-I was used to evaluate the study’s methodological quality, according to the

recommendations of the Cochrane Handbook [80]. ROBINS-I is a useful methodological as-

sessment tool in a systematic literature review that includes non-randomization studies [82].

ROBINS-I evaluates seven areas of bias at the stage of pre-intervention, at intervention, and

post-intervention. Two investigators (E.K. and G.K.) independently assessed the risk of

bias by answering the signaling questions for each bias area. The degree of bias is derived

as low risk of bias, moderate risk of bias, serious risk of bias, critical risk of bias, and no

bias information. If the evaluation result is a low risk of bias, it can be regarded as a study

similar to a well-performed randomized controlled trial. In the case of a moderate risk of

bias, it provides fair evidence but is difﬁcult to compare with a randomized controlled trial.

In the case of a serious risk of bias, there is a signiﬁcant defect in at least one area, but it is

difﬁcult to regard the overall quality as low. Critical risk of bias means that there are many

problems to provide useful evidence and cannot be used to synthesize research results.

2.6. Statistical Analysis

We conducted the statistical analysis using R 4.04 and R Studio with the “metafor”

and other R packages. We extracted effect estimates from individual studies, estimates

overall effect, conducted subgroup analysis and sensitivity analysis.

2.6.1. Extracting Estimates of Effect

Since the control group for each study was different, pre-and post-measurement

of individual forest environments were extracted and used for analysis. We calculated

standardized mean differences (SMD) to estimate the effect size. The mean, standard

deviation, number of samples, and inter-trial correlation of pre-and post-measured values

were used for SMD calculation. If the study did not report the inter-trial correlation,

we estimated inter-trial correlation according to the recommendations of the Cochrane

Handbook [80]. When several forest environments were investigated in one study, we

extracted the effect size separately for each forest environment. As Cohens et al. [83]

suggested, we interpreted the result of SMD 0.20–0.49 as “small” effect size, 0.50–0.79 as

“medium” effect size, and ≥0.80 as “large” effect size.

2.6.2. Estimating Overall Effect Size

There are two statistical models for meta-analysis. One is a ﬁxed-effect model, which

assumes that studies share a common effect size and that differences in results result from

sampling error. The other is a random-effects model, which assumes that the true effect

size varies by study and is distributed around the overall mean. As we hypothesized that

the therapeutic effect of forest varies by forest structure, we used a random-effects model

to estimate effect size.

2.6.3. Sub-Group Analysis

We conducted a subgroup analysis to investigate the relationship between the struc-

tural variable of forest sites and their therapeutic effect. We divided structural variables

into three or four subgroups based on their homogeneity and calculated the proper sample

size using G*Power 3.1. software to ensure a satisfactory power value. To assess inter-study

heterogeneity in subgroup analysis, we used Cochrane’s Q test (p < 0.10 for statistical sig-

niﬁcance) and I2 (I2 > 50% used as a threshold for signiﬁcant heterogeneity). We observed

whether the two structural variables affected the healing effect by the signiﬁcance test

Healthcare 2021, 9, 1427

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of subgroup differences and the comparison of heterogeneity according to the subgroup

analysis. We also report the 95% conﬁdence interval of SMD for each subgroup.

2.6.4. Sensitivity Analysis

Finally, we took a sensitivity analysis to check whether individual studies affect the

effect estimation. The leave-one-out method was used for sensitivity analysis, and we

assessed the degree of change through Rstudent, difﬁts, Cook’s D, covratio, T2, Qresid,

hat value, and DFBETAS values (the results are available in the Supplementary Materials

Figure S1).

3. Results

3.1. Study Characteristics

A total of 12 studies were included in this review and characteristics of those are shown

in Table 3. Of the 12 included studies, one was a randomized controlled trial, seven were

randomized crossover studies, and four were non-randomized studies. All studies were

conducted in forest settings, and eight studies conducted interventions in multiple forest

sites. Studies were conducted in seven countries, including Japan (four studies; nine sites),

China (three studies; nine sites), Korea (one study, one site), Taiwan (one study, one site),

Finland (one study; four sites), Poland (one study, one site), Spain (one study, one site).

Thus, a total of 26 different forest sites were examined. The main intervention methods

were staying (seven studies), walking (four studies), or both (one study). The duration of

interventions in most studies was within 120 min (11 studies), and one study conducted a

180 min-length intervention.

A total of 685 participants were included in 12 studies, and the number of samples

ranged from 8 to 346. The average age of participants in the included study ranged from

20.8 to 60.0 years. Four studies included only young adults, one study included only

middle-aged adults, six included both young and middle-aged, and one included both

middle-aged and elderly.

The therapeutic effects were reported as emotional restoration, cognitive restoration,

and physiological relaxation. Emotional restoration included improvement in mood states

such as anxiety (eight studies; eighteen sites), depression (seven studies; sixteen sites),

anger (seven studies; sixteen sites), confusion (six studies; ﬁfteen sites), fatigue (six studies;

ﬁfteen sites), vigor (seven studies; nineteen sites), negative and positive affect (four studies;

nine sites). Cognitive restoration included restorative experience (five studies; thirteen sites).

Physiological relaxation included lowering blood pressure (four studies; seven sites), heart

rate and pulse rate (four studies; seven sites), relaxation effects measured through heart rate

variability and stress hormone (one study; two sites). Additionally, one study measured

the blood concentration of monoterpenes as an indicator of therapeutic effect.

Prior to the meta-analysis, we reviewed participant characteristics, species composi-

tions, microclimate, noise, and the timing of measurement to rule out possible confounding

factors. As a result, nine studies of twelve included studies were used for meta-analysis.

Since thermal discomfort can disrupt the therapeutic experience, we excluded three studies

that are difﬁcult to be considered homogeneous in terms of environmental characteristics

(temperature and humidity). There were some differences in how each forest was managed.

However, it was not thought that there would be a signiﬁcant difference in the perception of

the forest environment among the participants, mainly non-experts, as most of the studies

investigated conifer-dominant mixed forest.

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Table 3. Characteristics of included studies.

First

Author

and Year

2019

[84]

Bach

2021

[85]

Bielinis

2019

[27]

Elsadek

2019

[86]

Sample

Size

(M/F)

(7/6)

(6/4)

(12/9)

346

(200/164)

Participant

Characteristics

Stand

Density

(Trees/ha)

DBH

(cm)

Canopy

Den-

sity

Forest

Characteristics

Healthy university students

Age: 21.8 ± 1.9

Healthy university students with

no abnormalities in the respiratory

or immune system

Age: 36.4 ± 6.5

3 different forest sites

(no control assigned)

1667/ha

1867/ha

1993/ha

13.26 0.56

10.35 0.75

14.39 0.78

One forest site and

urban site (control)

10500/ha

6.43

0.95

Location:

43◦51 N, 125◦18 E

Betula platyphylla

Acer triﬂorum

Quercus mongolica

Location:

41◦73 N, 2◦44 E

Altitude:

860–972 m

Dominant species:

Quercus ilex

Healthy university students and

non-student volunteers with no

mental or physical diseases or

metabolic syndromes

Age: 23.9 ± 2.7

One forest group

(no control)

1200/ha

NA NA

Location:

53◦90 N, 20◦35 E

Dominant species:

Picea abies and Pinus sylvestris

3 different forest sites and

urban site(control)

Location:

31◦27 N, 121◦46 E

0.13

Urban road

(control)

Healthy university students with

no mental or physical diseases

Age: 23.0 ± 4.6

0.49

0.89

Prunus serrulata

Plantanus hispanica

0.94

Metasequoia glyptroboides

Environmental

Characteristics

Outcome

Measurement

Activity

Type

Time of

Measure

Study

Design

Temp: 18 ◦C

(10–25 ◦C)

Relative humidity: 45%

Wind speed: 3.33 m/s

Illuminance: 4617 lx

Illuminance: 1124 lx

Illuminance: 1012 lx

SBP; DBP; HR

SBP(/); DBP(/); HR(/)

SBP(/); DBP(/); HR(+)

SBP(+/); DBP(+/); HR(+/)

Staying

(30 min)

Sep, 2017

(8:30–

12:00)

Randomized

Cross-over

Temp: 23.87 ± 0.18 ◦C

Relative humidity:

53.7%

Blood concentration of Alpha-pinene(/);

Beta-pinene(/); Alpha-phellandrene(/);

Limonene(/); All monoterpene(/)

Walking

(120 min)

July, 2018

(10:00–

12:00)

Randomized

Controlled

Trial

Temp: 25 ◦C

Relative humidity: 46%

Wind speed: 6.11 m/s

Noise:

38.08 ± 5.19 dB

Illuminance:

37755.24 lx

Temp: 31.8 ◦C

Relative humidity:

42.8%

Wind speed:

0.66 m/s

Temp: 31.2 ◦C

Relative humidity:

47.0%

Wind speed:

0.23 m/s

Temp: 29.0 ◦C

Relative humidity:

50.9%

Wind speed: 0.15m/s

Temp: 28.1 ◦C

Relative humidity:

52.9%

Wind speed:

0.11 m/s

SBP(+); DBP(/); PR(+); MAP(+);

POMS(Confusion(+) Fatigue(+/)

Anger(+) Anxiety(+) Depression(+)

Vigor(/)); PANAS(PA(/) NA(+));

ROS(+); SVS(+)

POMS; STAI; ROS; SVS; PET

POMS(Anxiety(−) Depression(−)

Anger(−) Fatigue(−) Confusion(−)

Vigor(−)); STAI(−); ROS(−); SVS(−)

POMS(Anxiety(+) Depression(+)

Anger(+) Fatigue(+) Confusion(+)

Vigor(+)); STAI(+); ROS(+); SVS(+)

POMS(Anxiety(+) Depression(+)

Anger(+) Fatigue(+) Confusion(+)

Vigor(+)); STAI(+); ROS(+); SVS(+)

POMS(Anxiety(+) Depression(+)

Anger(+) Fatigue(+) Confusion(+)

Vigor(+)); STAI(+); ROS(+); SVS(+)

Staying

(60 min)

May, 2018

(15:45–

16:15)

Uncontrolled

Before and

After

Walking

(15 min)

May, 2018

(10:00–

15:30)

Randomized

Crossover

Healthcare 2021, 9, 1427

9 of 28

First

Author

and Year

Lee

2020

[87]

Oishi

2003

[88]

Saito

2019

[89]

Table 3. Cont.

Sample

Size

(M/F)

(5/11)

(23/21)

(17/-)

Participant

Characteristics

Housewives, freelancers and ofﬁce

workers

Age: 41.6 ± 1.8

Stand

Density

(Trees/ha)

DBH

(cm)

Canopy

Den-

sity

Forest

Characteristics

One forest site and

one outdoor site(control)

156/ha

27.4 NA

Location:

35◦83 N, 128◦76 E

Dominant species:

Pinus rigida and Quercus aliena

5 different forest sites and

one site outside the

forest(control)

Location:

39◦75 N, 141◦03 E

250/ha

Pinus densiﬂora

Healthy residents including

students and workers

Age: 24.4 ± 9.9

750/ha

34.9

1700/ha

10.6

2275/ha

10.4

2875/ha

9.5

2 different forest sites:

managed forest and

unmanaged forest(control)

healthy male volunteer had no

history of cardiovascular disease or

mental illness

Age: 40.2 ± 6.2

1024/ha

19.78 0.62

1208/ha

15.75 0.91

Cyptomeria japonica

Quercus serrata

Pinus densiﬂora

Machilus thunbergii

Pinus densiﬂora

Quercus serrata

Location:

35◦41 N, 138◦86 E

Mixed forest

(Broadleaf trees: 82%

Coniferous trees: 18%)

Mixed forest

(Broadleaf trees: 50%

Coniferous trees: 50%)

Environmental

Characteristics

Outcome

Measurement

Activity

Type

Time of

Measure

Study

Design

DEP(+/); ANX(+/); AGG(/);

ST-IN-S(+); ST-V-RG-P(+)

Staying

(120 min)

May–

Aug,

2017

Controlled

Before and

After

Temp:

19.67 ± 3.31 ◦C

Relative humidity:

68.9%

Temp:

20.29 ± 3.66 ◦C

Relative humidity:

69.3%

Temp:

19.91 ± 3.40 ◦C

Relative humidity:

75.8%

Temp:

20.17 ± 3.19 ◦C

Relative humidity:

73.3%

Temp:

18.63 ± 3.16 ◦C

Relative humidity:

76.4%

Wind speed: m/s

Temp: 24.3 ± 2.8 ◦C

Relative humidity:

73.3%

Illuminance:

255.0 ± 97.6 lx

Temp: 24.2 ± 2.8 ◦C

Relative humidity:

70.0%

Illuminance:

119.2 ± 48.5 lx

POMS

POMS(Anxiety(+) Depression(+)

Anger(+) Fatigue(+) Confusion(+)

Vigor(+))

POMS(Anxiety(+) Depression(+)

Anger(+) Fatigue(+) Confusion(+)

Vigor(+))

POMS(Anxiety(+) Depression(+)

Anger(+) Fatigue(+) Confusion(+)

Vigor(+))

POMS(Anxiety(+) Depression(+)

Anger(+) Fatigue(+) Confusion(+)

Vigor(+))

POMS(Anxiety(+) Depression(+)

Anger(+) Fatigue(+) Confusion(+)

Vigor(+))

SBP; DBP; MAP; HR; HF; LF/HF; saliva

cortisol

SBP(+); DBP(+); MAP(+); HR(/);

HF(+/); LF/HF(+); saliva cortisol(+)

SBP(+); DBP(+); MAP(+); HR; HF(−/);

LF/HF(−); saliva cortisol(+/)

Staying

(10 min)

Staying

(15 min)

Jul–Sep,

2001

(8:45–

13:10)

Non-

Randomized

Cross-over

July, 2014 Randomized

Crossover

Healthcare 2021, 9, 1427

10 of 28

First

Author

and Year

Simkin

2020

[90]

Takayama

2017a

[91]

Table 3. Cont.

Sample

Size

(M/F)

(41/59)

(18/-)

Participant

Characteristics

City workers aged from 26–65

Age: 43.4 ± 10.7

Stand

Density

(Trees/ha)

DBH

(cm)

Canopy

Den-

sity

Forest

Characteristics

4 different forest sites

(no control assigned)

374/ha

424/ha

520/ha

1746/ha

Picea abies-dominated forests

in Finland

Location:

60◦45 N, 25◦19 E

(stand age > 120)

Location:

60◦22 N, 24◦92 E

(stand age: 95)

Location:

60◦33 N, 25◦18 E

(stand age: 100)

Location:

60◦39 N, 25◦18 E

(stand age: 40)

2 different forest sites:

managed forest and

unmanaged forest(control)

Location:

35◦41 N, 138◦86 E

Healthy male volunteer without

history of cardiovascular disease or

mental illness

Age: 40.2 ± 6.4

1056/ha

23.12 0.62

Larix kaempferi 66%,

Cornus controversa 10%

Pinus densiﬂora 7%

1212/ha

18.54 0.91

Larix kaempferi 66%

Cornus controversa 7%

Pinus densiﬂora 7%

Environmental

Characteristics

Outcome

Measurement

Activity

Type

Time of

Measure

Study

Design

Temp: 12.8 ± 4.4 ◦C

Relative humidity:

39–96%

Temp: 15.8 ± 4.2 ◦C

Relative humidity:

29–100%

Temp: 15.9 ± 5.8 ◦C

Relative humidity:

39–91%

Temp: 15.3 ± 4.8 ◦C

Relative humidity:

27–98%

ROS; SVS; PANAS

ROS(+); SVS(+); PANAS(+)

Staying

(15 min)

and

Walking

(45 min)

Aug–Oct,

2016

April–

June,

2017

(15:00–

17:00)

Randomized

Crossover

Temp: 24.3 ± 7.9 ◦C

Relative humidity:

73 ± 39%

Wind velocity:

0.13 m/s

Radiant Heat:

25.5 ± 10.5 ◦C

Illuminance:

255.0 ± 97.6 lx

Noise: 41.5 ± 3.8 dB

Temp: 24.2 ± 7.6 ◦C

Relative humidity:

70 ± 34%

Wind velocity:

0.23 m/s

Radiant Heat:

25.3 ± 9.8 ◦C

Illuminance:

119.2 ± 48.5 lx

Noise: 39.0 ± 3.9 dB

POMS; PANAS; ROS; PRS

POMS(Anxiety(+) Depression(/)

Anger(/) Fatigue(+/) Confusion(+)

Vigor(+)); PANAS(PA(/)NA(+)); ROS(+)

POMS(Anxiety(+) Depression(/)

Anger(/) Fatigue(/) Confusion(+)

Vigor(+)); PANAS(PA(/)NA(+/))

ROS(/)

Staying

(15 min)

July, 2014 Randomized

Crossover

Healthcare 2021, 9, 1427

11 of 28

Table 3. Cont.

First

Author

and Year

Takayama

2017

[92]

Sample

Size

(M/F)

(11/4)

Participant

Characteristics

Stand

Density

(Trees/ha)

DBH

(cm)

Canopy

Den-

sity

Forest

Characteristics

2 different forest sites:

thinned forest and

unthinned forest(control)

Location: 35◦41 N, 138◦86 E

University students and mid-aged

faculties without had no history of

cardiovascular disease or mental

illness

Age: 35.9 ± 8.2

1056/ha

23.12 0.52

1200/ha

22.76 0.59

Larix kaempferi 66%,

Cornus controversa 10%

Pinus densiﬂora 7%

Larix kaempferi 66%,

Cornus controversa 10%

Pinus densiﬂora 7%

Environmental

Characteristics

Outcome

Measurement

Temp: 18.6 ± 0.92 ◦C

Relative humidity:

92 ± 4.6%

Wind velocity:

0.16 m/s

Radiant Heat:

18.8 ± 1.1 ◦C

Illuminance:

336 ± 172 lx

Temp: 17.1 ± 2.52 ◦C

Relative humidity:

38 ± 20%

Wind velocity:

0.30 m/s

Radiant Heat:

21.8 ± 2.8 ◦C

Illuminance:

668 ± 237 lx

POMS; PANAS; ROS; PRS

POMS(Anxiety(+) Depression(+)

Anger(+) Fatigue(+/) Confusion(+)

Vigor(/)); PANAS(PA(/)NA(/))

ROS(+/)

POMS(Anxiety(+) Depression(+)

Anger(/) Fatigue(+) Confusion(+)

Vigor(/)); PANAS(PA(/)NA(/)) ROS(+)

Activity

Type

Time of

Measure

Study

Design

Staying

(15 min)

Oct,

Randomized

May, 2013 Crossover

Middle-aged and elderly subjects

One forest site

aged 45 to 86 years No disease

123

(n = 69), hypertension (n = 25),

(no control)

Location:

24◦09 N, 121◦18 E

[93]

(43/85)

diabetes (n = 9), heart disease

Dominant species:

(n = 8)

1200/ha

NA NA

Cryptomeria japonica

Age: 60.0 ± 7.4

Temp: 22.6 ± 1.4 ◦C

Relative humidity:

87.4%

Wind speed:

0.1 ± 0.2 m/s

PR(+); SBP(+); DBP(+); HF(/);

LF/HF(/); POMS(Anxiety(+)

Depression(+) Anger(+) Fatigue(+)

Confusion(+) Vigor(+)); STAI(+)

Walking

(180 min)

July, 2016

(8:30–

12:00)

Uncontrolled

Before and

After

Zhou

2019

[94]

(8/35)

University students free from

diagnosed cardiovascular, allergic,

physical disease, or mental diseases

Age: 20.8 ± 0.9

2 different forest sites

(no control assigned)

0.20–

0.75

0.50–

0.75

Location:

26◦11 N–26◦55 N

106◦07 E–107◦17 E

Cinnamomum camphora

Quercus fabri

Platanus acerifolia

Cinnamomum camphora

Celtis sinensis

Catalpa bungei

Celtis sinensis

Temp: 22.6 ± 1.4 ◦C

Relative humidity:

87.4%

Wind speed:

0.1 ± 0.2 m/s

Anti-anxiety scores

Anti-anxiety scores (ﬁnancial states(+);

exam pressure(+); campus life(/); love

affair relationship(+))

Anti-anxiety scores(ﬁnancial states(+);

exam pressure(+); campus life(+); love

affair relationship(/))

walking

(60 min)

Dec, 2016

Randomized

Cross-over

AGG 7—questions on aggression, ANX 11—questions on anxiety, DBP—diastolic blood pressure, DEP 11—questions on depression, HBP—high-frequency, HR—heart rate, LF/HF—ratio of low-frequency and high

frequency, MAP—mean arterial pressure, PANAS—positive and negative affect schedule, POMS—proﬁle of mood states, PR—pulse rate, PRS—perceived restorativeness scale, ROS—restorative outcomes scale,

SBP—systolic blood pressure, STAI—state trait anxiety inventory, ST-V-IN-S—ﬁve questions on stress vulnerability-interpersonal sensitivity, ST-V-RG-P—ﬁve questions on stress vulnerability-self-regulation

problems, SVS—subjective vitality scale, Temp—temperature. +: signiﬁcant effect on positive health outcome; +/: including both signiﬁcant and nonsigniﬁcant effect on positive health outcome; /: nonsigniﬁcant

effect; -/: including both signiﬁcant and nonsigniﬁcant effect on negative health outcome -: signiﬁcant effect on negative outcome.

Healthcare 2021, 9, 1427

12 of 28

3.2. Methodological Quality

Methodological quality was assessed using ROBINS-I. Overall, the risk of bias was

moderate in most studies (Table 4). At the pre-intervention stage, low confounding bias was

found in most studies, with the exception of two studies that did not provide information

on potential confounding factors. Selection bias was mainly low, however moderate

selection bias was found in four studies due to lack of randomization and control groups.

At the intervention stage, all studies found low classiﬁcation bias due to clearly deﬁned

forest sites, activities. At the post-intervention stage, most of the studies found low bias

by deviations and missing data, with the exception of one study that did not provide

sufﬁcient information. Moderate bias in measurement found in most studies reﬂects the

lack of blinding in studies using self-reported measures. Most studies were concerned

that there was a moderate bias in their reporting due to the lack of evidence to justify the

analytical methods, such as pre-registered protocol. One study was assessed as having

serious reporting bias due to incompletely reported results.

Study

First

Author

Year

Bach

Bielinis

2019

2021

2019

Elsadek 2019

Lee

Oishi

Saito

Simkin

Takayama

Zhou

2020

2003

2019

2020

2017a

2017b

2017

2019

Table 4. Methodological Quality Assessment of Included Studies Using ROBINS-I.

Pre-Intervention

Bias Due to

Confound-

ing

Low

Bias in

Selection of

Participants

into the Study

Low

Moderate

Low

Moderate

Serious

Low

Moderate

Low

Moderate

Low

At Intervention

Bias in

Classiﬁcation of

Interventions

Low

Bias Due to

Deviations

from Intended

Interventions

Low

Information

Low

Post-Intervention

Bias Due to

Missing Data

Low

Information

Low

Bias in Mea-

surement of

Outcomes

Low

Moderate

Low

Moderate

Bias in

Selection of

the Reported

Result

Moderate

Low

Moderate

Serious

Moderate

Overall Risk

of Bias

Moderate

Low

Moderate

information

Serious

Moderate

Serious

Moderate

3.3. Stand Density as an Effect Modiﬁer of Therapeutic Effect

We classified stand density into four groups: low for <500/ha, medium for 500–1000/ha,

high for 1000–1500/ha, and very high for ≥1500/ha. In the case of emotional restoration,

improving anxiety, depression, anger, frustration, and fatigue, enhancing vitality, alleviat-

ing negative emotions, and promoting positive emotions were reported. The restorative

outcome scale (ROS), which measures restorative experience, was reported regarding

cognitive restoration. For physiological relaxation, lowering effects on blood pressure,

pulse rate, and heart rate were reported. The results for each outcome indices are described

in subsections.

3.3.1. Emotional Restoration

Anxiety

Twelve cases with different stand densities were used for meta-analysis. Anxiety

state signiﬁcantly relieved in the forest environment (SMD −0.52; 95% CI −0.76 to −0.28;

p < 0.0001; I2 = 76%; 106 participants). As a result of subgroup analysis, there was a signiﬁ-

cant difference in the effect size according to the stand density (Psubgroup < 0.0001; Figure 2).

“Big” effect in low-density environments (SMD −0.97; 95% CI −1.33 to −0.61; p < 0.0001;

I2 = 0%), and “medium” effect in high-density environments (SMD −0.77; 95% CI −0.96 to

−0.59; p < 0.0001; I2 = 9%) “small” effect in extremely high-density environments (SMD

−0.20; 95% CI −0.39 to −0.01; p = 0.0389; I2 = 0%) appeared. One case in an environment

with moderate stand density was excluded from the subgroup analysis.

Healthcare 2021, 9, 1427

< 0.0001; I2 = 76%; 106 participants). As a result of subgroup analysis, there was a signifi

cant difference in the effect size according to the stand density (Psubgroup < 0.0001; Figure 2)

“Big” effect in low-density environments (SMD −0.97; 95% CI −1.33 to −0.61; p < 0.0001; I

= 0%), and “medium” effect in high-density environments (SMD −0.77; 95)% CI −0.96 to

−0.59; p < 0.0001; I2 = 9%) “small” effect in extremely high-density environ13mofe2n8ts (SMD

−0.20; 95% CI −0.39 to −0.01; p = 0.0389; I2 = 0%) appeared. One case in an environmen

with moderate stand density was excluded from the subgroup analysis.

(Fmm(F<<iieg55ega0u0aun0r0nr/ed/ehhdi2fa2a.if)fe.),fT,rTehehhrhinrgeicregnheeehcse(fe1((ofSs01orM00e(r0S0seD–tM0s1)p–t5Da1lp0on50l)tdo0/sa0th9ons/a5fhdo)%t,afh9e)cte5,xoh%tecnrexheﬁtcmcadrhoneeeangmnlynfecieeghdilneiiyengniahthnencir(exga1vi5nhiaen0lxts(y0ti1e/e(5Cirthnv0yIas0as≤)iu/l.nsh)bT.as(ghTCu≤reh)obI.desugT)ipdr.ahoamTaetuhnaopdaenaladraydetnsaairiaatseamltprybhoeoyesrinrbtsseedotdbpatatnyoaotdrsmstttsdehatpdeaenrnnedbasdssoieadttnysrtedtt:onasimlznsotehidwtdepyar:redlosiewznetds

othveeroalvleerfafelclte. fTfehcetp. TlohtteedplsoqtutaerdessqduenaoretesSdMenDo, taenSdMthDe ,wahnisdktehrsedwenhoistekethrseidr 9e5n%otCe Itsh. eir 95% CIs.

Depression

Twelve cases with different stand densities were used for meta-analysis. Depres-

sion state signiﬁcantly relieved in the forest environment (SMD −0.29; 95% CI −0.47

to −0.10; p = 0.028; I2 = 78%; 106 participants). As a result of subgroup analysis, there

was a marginally signiﬁcant difference in the effect size according to the stand density

(Psubgroup = 0.0778; Figure 3). “Medium” effect in low-density (SMD −0.73; 95% CI −1.25

to −0.20; p = 0.0067; I2 = 54%) appeared, however, no signiﬁcant effect was shown in

high-density environments (SMD −0.31; 95% CI −0.64 to 0.01; p = 0.0594; I2 = 81%)

and extremely high-density environments (SMD −0.12; 95% CI −0.27 to 0.02; p = 0.1034;

I2 = 0%). One case in an environment with moderate stand density was excluded from the

subgroup analysis.

Anger

Twelve cases with different stand densities were used for meta-analysis. Anger

state signiﬁcantly relieved in the forest environment (SMD −0.35; 95% CI −0.58 to −0.12;

p = 0.0026; I2 = 73%; 106 participants). As a result of subgroup analysis, there was a

signiﬁcant difference in the effect size according to the stand density (Psubgroup = 0.0032;

Figure 4). A “medium” effect in low-density (SMD −0.78; 95% CI −1.12 to −0.43; p < 0.0001;

I2 = 0%), and a “small” effect in high-density environments (SMD −0.43; 95% CI −0.77

to −0.10; p = 0.01; I2 = 68%) appeared. While there was no signiﬁcance effect shown

in extremely high-density environments (SMD −0.07; 95% CI −0.27 to 0.12; p = 0.4469;

I2 = 0%). One case in an environment with moderate stand density was excluded from the

subgroup analysis.

Healthcare 2021, 9, 1427

state significantly relieved in the forest environment (SMD −0.29; 95% CI −0.47 to −0.10; p

= 0.028; I2 = 78%; 106 participants). As a result of subgroup analysis, there was a marginally

significant difference in the effect size according to the stand density (Psubgroup = 0.0778;

Figure 3). “Medium” effect in low-density (SMD −0.73; 95% CI −1.25 to −0.20; p = 0.0067; I2

= 54%) appeared, however, no significant effect was shown in high-density environments

(SMD −0.31; 95% CI −0.64 to 0.01; p = 0.0594; I2 = 81%) and extremely high-density14enofv2i8-

ronments (SMD −0.12; 95% CI −0.27 to 0.02; p = 0.1034; I2 = 0%). One case in an environment

with moderate stand density was excluded from the subgroup analysis.

Healthcare 2021, 9, x

FFiigguurree 33.. TThhrereeefofroersetstplpoltostos fotfhtehcehcahnagnegine dinepdreepsrseiosnsioinnsiunbsgurobugproaunpalaynsaislybsyisstbayndstadnednsditeyn: sloitwy:

(lo<5w00(/<h5a0)0, /hhigah),(1h0ig0h0–(11500000/–h1a5),0a0n/dhae)x,tarenmdeelxythreigmhe(ly15h0i0g/hha(≤1)5. 0T0h/ehdaa≤ta).aTrehreedpoatrateadreasrestpaonrdteadrda-s

issbzetoaentdnttosdmmtahredepanirozevedsedeirfnfametlsrleeteanhfnfceeeocdstvi.(feSTfreMhareleDlnpe)clfaeofesntctde(tS.d9MT5sh%qDeu)capoarlneonsftditdde9dee5nns%coqetueciaonSrntMeeﬁsrDdvdae,enlansnco(edtCeitInhSst)Mee. rTwDvhha,eilassdnk(ideaCrmtsIhsode).newdnThoahtitseektthdeheireasbimrdo9eott5nno%odm1te7CapttIohrstfe.eh-i3er1

95% CIs.

Anger

Twelve cases with different stand densities were used for meta-analysis. Anger state

significantly relieved in the forest environment (SMD −0.35; 95% CI −0.58 to −0.12; p =

0.0026; I2 = 73%; 106 participants). As a result of subgroup analysis, there was a significant

difference in the effect size according to the stand density (Psubgroup = 0.0032; Figure 4). A

“medium” effect in low-density (SMD −0.78; 95% CI −1.12 to −0.43; p < 0.0001; I2 = 0%), and

a “small” effect in high-density environments (SMD −0.43; 95% CI −0.77 to −0.10; p = 0.01;

I2 = 68%) appeared. While there was no significance effect shown in extremely high-den-

sity environments (SMD −0.07; 95% CI −0.27 to 0.12; p = 0.4469; I2 = 0%). One case in an

environment with moderate stand density was excluded from the subgroup analysis.

(F(F<<iig55g0u0u00rre//ehh44aa..)),T,Thhhhirgiregheehe(f1(of10or00er00es0s–tt–1p15plo05l0o0ts/0ths/oafho)tf,aha)te,nhacdenhcedahxnteagrxneetgmrieenemilanyenlhgayinegrhghiiegnr(h1si5un(01b0s5g/u0hrb0oag/u≤rh)po.aTua≤nhp)ae.aldynTsaahitlseaybsadyirasestbatrayeanprsdoetradrtneeeddpnosadirtsetyens:dtslaiontawysd:aslrtoadwn- -

dizaerddimzeedanmdeiafnfedreinffceersen(ScMesD(S)ManDd) 9a5n%d c9o5n%ficdoennﬁcedeinntceervinatlesr(vCaIlss).(CTIhse).dTiahme odniadmaotnthdeabtothtteombopttroem-

psreenstesntthsethoeveorvaellraelflfeecffte. cTth. Te hpelopttloedttesdqusqarueasredsednoenteoSteMSDM,Dan, adntdhethwehwishkisekrserdsedneonteottehtehirei9r59%5%CICs.Is.

Confusion

Eleven cases with different stand densities were used for meta-analysis. Confusion

state Ewleavsesnigcnaisﬁecsawntiltyhrdeliifefevreedntinsttahnedfodreensstiteinesviwroenrme uensetd(SfMorDm−et0a.-4a1n; a9l5y%sisC. IC−on0f.u60sioton

−st0a.t2e3w; pa<s 0si.g00n1if;iIc2an=t7ly1%re;l2ie2v6epdarintictihpeanfotsr)e.sAt senavriersounlmt oefnstu(bSgMroDup−0a.n41a;ly9s5is%, tCheIr−e0w.6a0stao

−0.23; p < 0.001; I2 = 71%; 226 participants). As a result of subgroup analysis, there was a

significant difference in the effect size according to the stand density (Psubgroup = 0.0020;

Figure 5). “Medium” effect in high-density (SMD −0.56; 95% CI −0.78 to −0.35; p < 0.0001;

I2 = 47%), and “small” effect in extremely high-density environments (SMD −0.19; 95% CI

−0.35 to −0.03; p = 0.0181; I2 = 0%) appeared. One case in the low- and moderate-density

Healthcare 2021, 9, 1427

sents the overall effect. The plotted squares denote SMD, and the whiskers denote their 95% CIs.

Confusion

Eleven cases with different stand densities were used for meta-analy1s5iosf.28Confusio

state was significantly relieved in the forest environment (SMD −0.41; 95% CI −0.60

−0.23; p < 0.001; I2 = 71%; 226 participants). As a result of subgroup analysis, there was

ssiiggnniiﬁficcaannt tddiffieffreernecnecine tihnetehffeecetffseizcet asciczoerdaicncgortoditnhge

FFiigguurree55).)“. M“Medeidumiu”mef”feecftfiencthiignhh-diegnhs-idtye(nSsMitDy −(S0M.5D6;

9s−tt5o0a%n.5tdhC6e;Id9−es5nt0%as.7int8yCdt(oIPd−−seu0n0b.g7.s3ri8o5tu;ytppo(=<P−000su...0b03g005r2o0;u01pp;;

0.002

0.000

II2 == 4477%%)),,aanndd“s“msmalla”lle”ffeecffteincteixntreexmterleymheiglyh-hdiegnhsi-tdyeennsviitryonemnvenirtson(SmMeDn−ts0(.S19M; 9D5%−0C.1I 9; 95%

−−00..3355 too −−0.0033;;pp==0.00.108118;1I2; I=2 0=%0)%ap)paepapreeda.rOedne. Ocansee icnatshee ilnowth- eanldowm-odaenrdatem-doednesritayte-densi

eennvviirroonnmmenetnwt wasaesxeclxucdlueddferdomfrothme stuhbegsrouubpgraonualpysaisn.alysis.

Figure 5. Two forest plots of the change in confusion in subgroup analysis by stand density: high

(F1i0g0u0–re1550.0T/hwao), efoxtrreesmt eplylohtsigohf(1th50e0c/hhaan≤g).eTihnecdoantafuasreiornepionrtseudbagsrsotuanpdaarndaizlyesdismbeyansdtaifnfedrednecenssity: high

((S1M00D0–) 1an50d09/5h%a)c, oenxﬁtrdeemnceelyinhteigrvhal(s15(C0I0s/)h. aT≤h)e. Tdihaemdoantdaaatrteherebpoottrotemdparsessetnatnsdtharedoivzeerdalml eeffaenctd. iffer-

Tenhecepslo(tStMedDsq)uaanrdes9d5e%nocteonSMfidDe,nacneditnhteewrvhaislske(rCsIds)e.nTohteethdeiiarm95o%ndCIast. the bottom presents the overall

effect. The plotted squares denote SMD, and the whiskers denote their 95% CIs.

Fatigue

Eleven cases with different stand densities were used for meta-analysis. Fatigue

state signiﬁcantly relieved in the forest environment (SMD −0.29; 95% CI −0.45 to −0.12;

p = 0.0006; I2 = 87%; 226 participants). As a result of subgroup analysis, there was a

signiﬁcant difference in the effect size according to the stand density (Psubgroup < 0.0001;

Figure 6). A “small” effect in high-density (SMD −0.42; 95% CI −0.61 to −0.22; p < 0.0001;

I2 = 75%), and a very “small” effect in extremely high-density environments (SMD −0.09;

95% CI −0.19 to −0.01; p = 0.0890; I2 = 0%) appeared. One case in the low- and moderate-

density environment was excluded from the subgroup analysis.

Vigor

Fifteen cases with different stand densities were used for meta-analysis. Vigor sig-

niﬁcantly improved in the forest environment (SMD 0.23; 95% CI 0.07 to 0.39; p = 0.0041;

I2 = 84%; 300 participants). As a result of subgroup analysis, there was a signiﬁcant dif-

ference in the effect size according to the stand density (Psubgroup = 0.0053; Figure 7). A

“small” effect in low-density (SMD 0.47; 95% CI 0.28 to 0.66; p = 0.0002; I2 = 55%) appeared,

however, there were no signiﬁcant effects shown in high-density environments (SMD 0.13;

95% CI −0.22 to 0.48; p = 0.4787; I2 = 90%) and extremely high-density environments (SMD

0.19; 95% CI −0.02 to 0.39; p = 0.0821; I2 = 70%). One case in an environment with moderate

stand density was excluded from the subgroup analysis.

Healthcare 2021, 9, 1427

significantly relieved in the forest environment (SMD −0.29; 95% CI −0.45 to −0.12; p =

0.0006; I2 = 87%; 226 participants). As a result of subgroup analysis, there was a significant

difference in the effect size according to the stand density (Psubgroup < 0.0001; Figure 6). A

“small” effect in high-density (SMD −0.42; 95% CI −0.61 to −0.22; p < 0.0001; I2 = 75%), and

a very “small” effect in extremely high-density environments (SMD −0.09; 95% CI −106.1o9f 2t8o

−0.01; p = 0.0890; I2 = 0%) appeared. One case in the low- and moderate-density environ-

ment was excluded from the subgroup analysis.

Healthcare 2021, 9, x

FFiigguurree 66.. TTwwoo ffoorreessttpplloottssooffththeechchanagnegeininfaftaigtiugeuienisnusbugbroguropuapnaalnyasliyssbisy bstyanstdanddendsietnys:ihtyig: hhigh

(d(e11in0f0fc00ee00rs–e–1(n1S5c5M0e00s0D//(hh)SaaaM))n,,Ddaan)9nd5ad%needxxctor9tren5em%fmideecleylonynhcﬁehigdiighnehtn(e1cr(5e1v05ai00nl/s0hte/(aCrh≤vI)asa.≤)lT.s)hT.(ehCTedIshad)te.iaadTmaahroeteanrddeaipraaeotmrtrthoeeenpddobarosattettsdottamhnaesdpbasroretdatstneiozndmetadsrptdmhrieezeseao1edn9vnedtmorsifafetfl3ahel1nre-

oevffeercatl.lTehffeecptl.oTttheedpslqouttaerdessqdueanroetsedSeMnDot,eaSnMdDth,eanwdhtishkeewrshdisekneortsedtheneiorte95t%heiCrI9s5. % CIs.

Vigor

Fifteen cases with different stand densities were used for meta-analysis. Vigor signif-

icantly improved in the forest environment (SMD 0.23; 95% CI 0.07 to 0.39; p = 0.0041; I2 =

84%; 300 participants). As a result of subgroup analysis, there was a significant difference

in the effect size according to the stand density (Psubgroup = 0.0053; Figure 7). A “small”

effect in low-density (SMD 0.47; 95% CI 0.28 to 0.66; p = 0.0002; I2 = 55%) appeared, how-

ever, there were no significant effects shown in high-density environments (SMD 0.13;

95% CI −0.22 to 0.48; p = 0.4787; I2 = 90%) and extremely high-density environments (SMD

0.19; 95% CI −0.02 to 0.39; p = 0.0821; I2 = 70%). One case in an environment with moderate

stand density was excluded from the subgroup analysis.

FFigiguurree77. .ThTrhereefeorfeosrtesptloptlsootsf tohfetchheacnhgaenigneviingovriignosruibngsruobugpraonuaplyasnisalbyysisstabnydsdtaenndsitdye: nloswity: low

(<(<550000/h/ah)a, )h, ihgihgh(1(010000–01–510500/0h/ah),ae),xetrxetrmemelyelhyihgihgh(1(510500/0h/ah≤)a.≤T)h.eTdhaetdaaatraearreeproerpteodrteads satsanstdaanrddairzdeidzed

mmeeaannddififffeerreenncceess((SSMMDD)) aanndd 9955%% ccoonnfﬁiddeennccee iinntteerrvvaallss((CCIIss))..TThheeddiiaammoonnddaatttthheebbootttotommppreresesenntststhe

thoeveorvaellraelflfeecftf.ecTth. eThpelopttleodttesdqusaqrueasrdeesndoetneoStMe SDM, aDn,danthdetwhehwiskheisrksedresndoetneottheetirhe9i5r%95C%IsC. Is.

NNeeggaattiviveeAAffffeecctt

s0dspsFi.ig0iiifgg<gf0nennu0i0rNfii1r.eNiﬁﬁe0;cnicc0aIni8cn2aa0n)ee=nne.1tclA;tti3cyalnday4Is“2as%etimefahls=fl;sleeewel1ewrvd3e2evii4ift0ianuifht%atehcpmetdce;aeddt”irdi1fisntiffe2iinfeficz0etnfriehretepehpcentaantheatcnetirecsnffttsfotosiftaelc)rroaocn.idenrpwtAdseidantss-sdindgtedzaeteneeestnnrvnno)eass.isviscirtittuihoctiAyrieolenoets(srmsnSsodwatMmwfieanesnnreeDrguetrndeesb−(ttuuSdogu(0slMreSstte.one7hMdoDud3sefip;fDtfos−9ysoatr50ua−rn%(.mbn5Pma06gdeslC.uy;er5tbdIotas96gari−-ue5;os-aun%ap,90npnst5.ah8=iaaC%tl8enlyy0Iyrtas.Ceso(−0ilPisy02Iws−.s.s8.uN7−ai02Nbs0s.;0eg,5eraFgt.7ot7goaihu;s0gatppie−tiugtriv0=<ovnre.ee4ei00−fwa28i..af;00c0)ffaa20.pfe.sen480Acc<221ttat;;;

“Im2 =ed0i%um), a”nedffaec“tsminallol”we-fdfeecntsiintyh(iSgMh-Dde−n0s.i7ty3;(9S5M%DC−I 0−.03.98;89t5o%−0C.5I 7−; 0p.6<00t.o00−001.;1I72 ;=p0=%0),.0a0n0d4;

a “small” effect in high-density (SMD −0.39; 95% CI −0.60 to −0.17; p = 0.0004; I2 = 0%)

appeared, one case in an environment with extremely high stand density was excluded

from the subgroup analysis, and no case with moderate stand density.

Healthcare 2021, 9, 1427

Negative Affect

Nine cases with different stand densities were used for meta-analysis. Negative affect

significantly alleviated in the forest environment (SMD −0.56; 95% CI −0.70 to −0.42; p <

0.0001; I2 = 34%; 120 participants). As a result of subgroup analysis, there was a significant

difference in the effect size according to the stand density (Psubgroup = 0.0282; Figure187)o.fA28

“medium” effect in low-density (SMD −0.73; 95% CI −0.88 to −0.57; p < 0.0001; I2 = 0%), and

a “small” effect in high-density (SMD −0.39; 95% CI −0.60 to −0.17; p = 0.0004; I2 = 0%)

aIp2p=ea0r%ed) ,aopnpeeacraesde,ionnaenceanseviirnonamn eenntvwiroitnhmeexntrtewmiethlyehxitgrhemstealnydhidgehnssittaynwd adsenexsictlyudweads

freoxmclutdheedsufrbogmrotuhpe asunbalgyrsoius,paanndanlyosicsa,saenwdintho mcaosdeewraitthe smtaondderdaetenssittayn.d density.

Healthcare 2021, 9, x

20 of 31

FFigiguurree88..TTwwoo ffoorreesstt pplloottss ooff tthhee cchhaannggee iinnnneeggaattiviveeaafffefeccttininsusubgbgroruouppanaanlaylsyissibsybystasntadnddednesnitsyi:tylo:w

l(o(9S<w5M5%0(0<Dc/5o)h0naa0ﬁn)/d,hdhea9ni)g,5chhe%i(ig1nch0toe0(nr01vf–0ia1d0l5es00n–(0cC1/eI5his0na).0)t.eT/hTrhvahea)e.ldsdTiaa(hCtmeaIodsan)ar.etdaTraehatpreteohdrrieteaepbmdoooartnttsoedsdmtaaatnpsdtrsheatesraednbniodzttasetrdotdhmmiezepeodarvneemsrdeaenialfltfnseerfdtefhienfecfcetoe.rsveTen(hSrcaeMelsplDlo) tatnedd

esfqfeucatr.eTshdeepnlootteteSdMsDqu, arneds tdheenwotheiSskMerDs ,daendottehtehweihr i9s5k%ersCdIse.note their 95% CIs.

PPoossiittiivvee AAffffeecctt

NNiinnee ccaasseess wwiitthh ddiiffffeerreenntt ssttaanndd ddeennssiittiieess wweerree uusseedd ffoorr mmeettaa--aannaallyyssiiss.. PPoossiittiivvee aaffffeecctt

IssI2i2igg==nn9ii2fﬁ9i%2ccaa%;nn1;tt2ll1yy02ipi0mmaprpptarriocrotivvpiceeaiddpniatinnsn)tt.thhsA)ee.sffooaArrreseesssattueerlnntevvsouiifrrlosotunnobmmfgeesrnonututb((pgSSrMMaonuDDapl0y0.a.s33ni44sa;,;l9t9yh55s%e%irse,CCwtIIh00ae..s0r0e5a5

ttoo 00..6633;; pp ==00..00220022;;

wsigans iaficsaingnt diﬁifcfaenr-t

ednifcfeerienntcheeineftfhecetesfifzeectasciczoeradcincogrdtointghetosttahnedstdaenndsidtyen(sPistuybg(rPouspu<bg0ro.0up00<1;0F.0i0g0u1r;eF9ig).uAre“9b)i.gA”

“ebffiegc”t einffelocwt i-ndleonwsi-tdye(nSsMityD(0S.M86D; 905.%86;C9I50%.6C0 Ito0.16.012to; p1.<120;.0p0<010;.I020=018;0I%2 )=a8p0p%e)araepdp,ehaorewd-,

ehvoewr,evneor,snigonsiifgicnainﬁtcaenffteecftfewctaws sahsoswhonwinn ihnighhig-hd-ednesnitsyityenevnivriornomnmenentsts(S(SMMDD00.0.000;; 9955%% CCII

−−00.2.211toto0.02.22;2p; =p 0=.908.2948;2I42;=I255=%5)5. %On).eOcansee cinasaeninenavniroennvmireonntmweitnht ewxtirthemexeltyrehmigehlysthaingdh

dsteannsditydewnasistyexwclausdeexdclfurdomedtfhreomsutbhgerosuubpgarnoaulpysainsa, alynsdisn, aoncdasneos cwaistehsmwoitdhemraotedesrtaantedsdtaennd-

sditeynswiteyreweexrceluexdceldu.ded.

FFiigguurree 99.. TTwwoo ffoorreesstt pplloottss ooff tthhee cchhaannggeeiinnppoossiittiivveeaaffffeeccttininssuubbggrroouuppaannaalylysissisbbyystsatnanddddenensistiyt:yl:ow

l(o<w50(0</5h0a0)/,hhai)g, hhi(g1h00(01–010500–01/5h00a/)h. aT)h. eTdhaetdaaatraearreeproerpteodrtaesdsatasnsdtaanrddiazreddizmedeamnedainffedriefnfecreesn(cSeMs (DS)MaDnd)

a9n5d%9c5o%nﬁcdonenficdeeinncteerinvtaelrsv(aClIss()C. TIsh).eTdhieamdioanmdoantdthaet tbhoettboomttopmrespernetssenthtse tohveeoravlelreaflfleecftf.eTcth.eTphleotted

psqloutaterdessdqeunaoretes SdMenDo,teanSdMtDhe, awnhdisthkerws hdiesnkoetres tdheeniro9te5%theCirIs9.5% CIs.

3.3.2. Cognitive Restoration

Restorative Experience

Nine cases with different stand densities were used for meta-analysis. Restorative

experience measured by ROS significantly increased in the forest environment (SMD 0.87;

Healthcare 2021, 9, 1427

Healthcare 2021, 9, x

18 of 28

3.4. Cognitive Restoration

Restorative Experience

Nine cases with different stand densities were used for meta-analysis. Restorative

experience measured by ROS signiﬁcantly increased in the forest environment (SMD 0.87;

95% CI 0.62 to 1.11; p < 0.0001; I2 = 60%; 120 participants). As a result of subgroup

analysis, there was a signiﬁcant difference in the effect size according to the stand density

(Psubgroup = 0.0385; Figure 10). A “big” effect in low-density (SMD 1.15; 95% CI 0.91 to 1.38;

p < 0.0001; I2 = 20%), and a “medium” effect in high-density (SMD 0.62; 95% CI 0.26 to

0.97; p = 0.0007; I2 = 43%) appeared. One case in an environment with extremely high

stand density was excluded from the subgroup analysis, and no cases with moder2a1teofst3a1nd

density were excluded.

FigFuirgeu1re0.1T0w. oTfworoesfot rpelsottspolof ttsheofchthanegcehiannRgeOSininRsOuSbginrousupbagnraoluypsisanbaylystsains dbydesntsaintyd: ldoewnsity: low

(<5(0<05/0h0a/),hhai)g, hi(g1h00(100–0105–0105/h00a/).hTah).eTdhaetadartae arereporerpteodrtaesdsatasnsdtaanrdaizrdedizmedemanedaniffdeirfefenrceensc(eSsM(SDM)Dan) dand

95%95c%oncfoidnﬁendceenicneteinrvtearlvsa(lCsI(sC).ITs)h. eTdhieadmiaomndonatdthatetbhoettboomttopmrepsernestsenthtsetohveeoravlelreaflfleecftf.eTcht.eTphleotptelodtted

squsaqrueasrdesendoetneoSteMSDM, Dan, dantdhethwehwishkiesrksedrsendoenteottheetihre9ir5%95%CIsC.Is.

3.33.3.5. .PPhhyyssiioologiiccaall RReelalaxxaatitoinon

Diastolic Blood Pressure

DiastoliSceBvelonocdasPersewssiuthredifferent stand densities were used for meta-analysis. Diastolic blood

preSsesvuerne sciagsnesiﬁwcainthtlydilfofwerernetdsitnantdhedfeonrseisttieesnwvierorenmuseendt (fSoMr mDe−ta0-.a1n8a; l9y5s%is. CDIia−s0to.2li9cto

blo−o0d.0p8r;epss=u0re.0s0i0g8n;iIf2ic=an0t%ly; 1lo7w9 peraerdticiinptahnetsf)o. rAestaernevsiurlotnomf seunbt g(SroMuDp a−n0a.1ly8;si9s5, %theCrIe−w0a.2s9no

to s−i0g.n08iﬁ; cpa=nt0.d0i0f0fe8r;eIn2 c=e0i%n ;th17e9efpfaercticsipzeanatcsc)o. rAdsinagretosutlhteosftsaunbdgdroeunpsitayn(aPlysusbisg,rotuhper=e0w.9a0s27;

noFsiiggunriefic1a1n).t Adif“fsemreanlcl”e einffethcteaepffpeecatrseizdebaoctchoirndihnigghto-dtehnessitayn(dSMdeDns−it0y.1(P8;su9b5gr%oupC=I0−.900.2370; to

Fig−u0r.e061;1p).=A0“.0s0m31a;llI”2 e=ff0e%ct) apnpdeeaxrterdembeoltyhhinigh-idghen-dsietnyseitnyv(irSoMnmDe−n0t.s18(S; M95D%−C0I.2−00;.3905%toCI

−0.−060;.4p3=to0.−0003.014; ;Ip2 ==00%.5)3;aIn2d=e0x%tr)e.mNeolycahsiegshw-diethnsloitwy -eannvdiromnomdenrattse(-SdMenDsit−y0e.2n0v;i9ro5n%mCeInts

−0.w43erteor−e0p.o0r4t;epd.= 0.53; I2 = 0%). No cases with low- and moderate-density environments

were reported.

Systolic Blood Pressure

Seven cases with different stand densities were used for meta-analysis. Systolic blood

pressure signiﬁcantly lowered in the forest environment (SMD −0.23; 95% CI −0.43 to

−0.03; p = 0.0249; I2 = 61%; 179 participants). As a result of subgroup analysis, there was

no signiﬁcant difference in the effect size according to the stand density (Psubgroup = 0.2179;

Figure 12). A “small” effect appeared in high-density environments (SMD −0.33; 95% CI

−0.58 to −0.08; p = 0.05; I2 = 63%), however, no signiﬁcant effect was shown in extremely

high-density environments (SMD −0.06; 95% CI −0.41 to 0.30; p = 0.7459; I2 = 56%). No

cases with low- and moderate-density environments were reported.

Figure 11. Two forest plots of the change in DBP in subgroup analysis by stand density: high

Healthcare 2021, 9, 1427

to −0.08; p = 0.0008; I2 = 0%; 179 participants). As a result of subgroup analysis, there wa

no significant difference in the effect size according to the stand density (Psubgroup = 0.9027

Figure 11). A “small” effect appeared both in high-density (SMD −0.18; 95% CI −0.30 t

−0.06; p = 0.0031; I2 = 0%) and extremely high-density environments (SMD19−o0f.2280; 95% C

−0.43 to −0.04; p = 0.53; I2 = 0%). No cases with low- and moderate-density environment

were reported.

Healthcare 2021, 9, x

22 of 31

significant difference in the effect size according to the stand density (Psubgroup = 0.2179;

FFFiiggiguuurreree11112.1).T.TwAwoo“fsofmorersaetlslpt”lpoeltofsfteoscfottfhatephecphecahanargenedgieniniDnhBDiPgBhiPn-disnuesbnugsbriotgyurpoeunapnvaiarlnyosanilsymsbieysnsbttsyan(sdStaMdnedDnds−iet0yn.:s3ih3tyi;g:9hh5i%ghCI

−(1(010.005008–01t–5o1050−0/00h./0ah)8a,;e),pxter=xetm0re.e0mly5e;hlIiyg2 hh=i(g165h30%0(1/)5h,0ah0≤o/h)w. aT≤eh)ve. eTdrha,teandoaraestairgeapnroiefritrecedapnaostrsteetfadfnedacastrsdwtiazanesddsamhredoaiwnzedndififmnereeeanxncterdseimffeerl-y

h(SeiMngchDe-s)da(eSnnMdsi9Dt5y)%eacnnodvni9ﬁr5od%ennmcceoeninnftitsder(evSnaMclseD(iCn−Itse0)r..v0Ta6hl;se9(d5Ci%aImsC).oITn−dh0ea.4td1tihatemobo0on.t3tdo0ma; tppt=hree0s.eb7no4tt5st9oth;meI2op=vree5rs6ae%lnl t)es.ffNtehcoet. coavseersall

wTehifetfhepclltoo.tTwtehd-easpqnuldoatrmteesdoddseqenuroataetreeS-sMddDeen,nasonittdyetSehMne vwDihr, ioasnknemdrstehdneetnswowtheiesthrkeeirrse9dp5%eonrCoteItsed. .their 95% CIs.

Systolic Blood Pressure

Seven cases with different stand densities were used for meta-analysis. Systolic bloo

pressure significantly lowered in the forest environment (SMD −0.23; 95% CI −0.43 t

−0.03; p = 0.0249; I2 = 61%; 179 participants). As a result of subgroup analysis, there was n

FFiigguurree 1122.. TTwwoo ffoorreesstt pplloottss ooff tthheecchhaannggee iinnSSBBPPiinnssuubbggrroouupp aannaallyyssiiss bbyy ssttaanndd ddeennssiittyy:: hhiigghh

((11000000––11550000//hhaa)),, eexxttrreemmeelylyhihgihgh(15(10500/0h/ah≤a)≤.)T. hTehdeadtaataarearreeproerpteodrtaesdstaasnsdtaarnddizaerddimzeeadnmdiefafenrednicfefesr-

e(SnMceDs )(SaMndD9)5a%ndco9n5ﬁ%decnocnefiindteenrvcealisn(tCerIsv)a. lTsh(Ce Idsi)a.mThoneddaiatmthoenbdotattotmhepbreostetnotms tphreeosvenertasltlheeffoecvte. rall

eTfhfeecpt.loTthteedpslqoutaterdessdqeunaorteesSdMeDno, atendSMthDe w, ahnidsktehrse dwehnoistkeetrhseidre9n5o%teCtIhs.eir 95% CIs.

Heart Rate or Pulse Rate

HearSteRvaetnecoarsePsuwlsiethRdaitfeferent stand densities were used for meta-analysis. Heart rate or

pulseSreavtensicgansieﬁscawnittlhy dloiwffeerendtinstathnedfodreensst ietnievsirwonermeeunste(dSMfoDr m−0e.t1a9-a; 9n5a%lysCisI .−H0e.3a3rttorate or

p−u0l.s0e5;rpat=e0s.i0g0n91if;icI2an=t8ly3%lo; w17e9repdartinicitphaentfso)r. eAsst aenrevsiuroltnomf seunbtg(rSoMupDa−n0a.l1y9si;s9, 5th%ereCwI −a0s.33 to

−n0o.0s5ig;npiﬁ=c0a.n0t0d9i1ff;eIr2e=nc8e3%in;t1h7e9efpfeacrttisciizpeaanctcso)r.dAinsgatoretshuelsttoafndsudbegnrsoituyp(Pasnuabglryosuips,=th0.e5r5e6w5; as no

hsF−hcF−i0aiiiiggg0gg.s3n.euhuh39sirr--9fddeewittcoeeo11ainn3t−3n−hs)s0).tii0.l.ttA0oy.dAy06wi6e“e;f;“n-fsnpepsmavvrm=n=ieiardr0onal0ol.nl.c”m0n0lem”00emo77fieedfn66enefe;;fnctreItIstta2h2csat(te==eSp(-SaM77pedpM99feeDf%p%aneDres)c−)e,ait,−dthr0yh0seo.iio1.endwz14wne4he;iv;nev9iai9gvre5ch5ohr%ec,%ni-rogndm,CroheCndIen-soIidn−sing−itestgs0n0inyg..iw3s3ﬁten8oi8ecntiayrftvtteionohicertra0e0onee.n.np1vfs1tf0motie0are;rc;oefntptpefnndwe=dt=mcs.ad0t0s(e..weS22nsnM5h5tas2so2sDi8w8t(s;yS;−hnIIM2o(20iP=n=w.D2sue773nbx5−5;gti%r0%9ron5.eu2))p%me..3x=NN;eCtl9roo0yIe5.c5m%a5se6Cel5ysI;

with low- and moderate-density environments were reported.

Healthcare 2021, 9, 1427

pulse rate significantly lowered in the forest environment (SMD −0.19; 95% CI −0.33 to

−0.05; p = 0.0091; I2 = 83%; 179 participants). As a result of subgroup analysis, there was no

significant difference in the effect size according to the stand density (Psubgroup = 0.5565;

Figure 13). A “small” effect appeared in high-density environments (SMD −0.23; 95% CI

−0.39 to −0.06; p = 0.0076; I2 = 79%), however, no significant effect was shown in extr2e0moef l2y8

high-density environments (SMD −0.14; 95% CI −0.38 to 0.10; p = 0.2528; I2 = 75%). No cases

with low- and moderate-density environments were reported.

Healthcare 2021, 9, x

Figure 13. TTwo forest plots of the change in heart rate and pulse rate in subgroup analysis by stand

sdteansditdye: nHsitgyh: H(1i0g0h0–(10500–/1h5a0)0, /ehxat)r,eemxetrleymheiglyhh(1ig5h00(/15h0a0≤/h).a≤T)h. eTdheatdaaatraearreeproerpteodrteads satsans2tda3anroddfair3zd1e-d

imzeedanmdeiaffnerdeinffceerse(nScMesD()SaMnDd)9a5n%dc9o5n%ﬁdceonncfeidienntecrevianlste(rCvIasl)s. (TChIes)d. iTahmeodnidamatotnhde batottthoemboptrteosmenptsreth- e

soevnetrsatlhl eefofevcetr. aTllheefpfelcott.teTdhesqpuloatrteesddseqnuoatreeSsMdeDn,oatnedSMthDe w, ahnidsktehres wdehnisokteertshdeiern9o5t%e tCheIsir. 95% CIs.

3.46.TCChaaenncooappnyyoDDpeeynnsdsiitetyynasaistyanwEaffsecctlaMssoidfiiefﬁiderinoftoThtherraepeeugtriocuEpffse:cltow for <50%, medium for 50–

70%, anTdhehicgahnofopry≥d70en%s.itWy ewcaosndcluacstseiﬁdesdubingtroouthpraeneaglyrosiusposn: blolowodfoprr<es5s0u%re, ,mheeadriturmatefo, r

an5d0–p7u0l%se, arnatde,hwighhicfohra≥re70in%d.iWcaetocrosnodfupctheydssioulboggriocaulpraenlaaxlyatsiiosnonefbfelocot.dDpureestsourthe,ehleimarittreadte,

nuamndbepruolsfestruadtei,esw, hwicehcaanrenointdciocnadtourcstosfupbhgryosuioploagniaclaylsriesloaxnaetmionoteioffneaclt.aDnduecotognthiteivleimreitse-d

tonrautmiobne. rThofe srteusudlitess,fowr eeaccahnenfofetcctoinndduiccattosruabrgeroduepscrainbaeldysinissounbseemctoiotinosn. al and cognitive

restoration. The results for each effect indicator are described in subsections.

3.4.1. Physiological Relaxation

3.6.1. Physiological Relaxation

DDiaisatsotloicliBc lBoloododPrPersessusruere

FiFvievecacasesesswwiitthh differeennttccaannooppyydednesnistietisews ewreerueseudsefodr fmoretam-aentaa-laynsiasl.yDsiisa.stDoilaicstbolloiocd

blporoedsspurresssiugrneiﬁsciganitfliycalonwtlyerleodwinertehde ifnortehset (fSoMreDst −(S0M.1D9; −905.%19C; 9I 5−%0.C37I t−o0.−370.t0o2;−p0.=020;.0p3=28;

0.0I23=280;%I2; =300p%a;r3ti0cippaarnttisc)i.pAanstas)r.eAsusltaorfessuubltgorofuspubagnraolyuspisa, nthaelyresiws,atshaermeawrgaisnallmyasrigniniﬁacllaynt

sigdnififfeicreanctediinffethrenecfefeicnt sthizeeeafcfceoctrdsinzeg atocctohredcinangotpoythdeencasintyop(Pysdubegnrosuitpy=(P0s.u0b8gr9o0up; F=i0g.u0r8e901;4).

FiAgu“rsem1a4l)l.”Aef“fsemctaallp”peefaferecdt aipnpheiagrhe-ddiennshiitgyhe-dnvenirsoitnymeennvtisro(SnMmeDnt−s 0(.S3M4;D95−%0.3C4I; 9−50%.5C9 Ito

−0−.509.1to0;−p0.=100; .p00=606.;00I266=; I02%=)0w%h) iwlehniloe nsoigsniigﬁnciafinctaneftfefcftecwtawsasshsohwonwnininmmodoedreartaet-ed-denesni-ty

siteynevnirvoinromnemnetsn(tSsM(SDMD−0−.00.30;39; 59%5%CCI I−−00..2299 ttoo 0.22; p = 00..88000055;;II22==00%%).)N. Noocacsaesseswwithithlolwow

cacnaonpoypydednesnitsiietisews wereerererpeoprotretde.d.

FiFgiugruer1e41.4T.wTwo ofofroersetspt lpoltostosfotfhtehechcahnagnegeininDDBPBPininsusubgbrgoruouppananalaylysissisbbyycacannooppyyddeennsistiyty: :mmooddeer-rate

at(e5(05–07–07%0%),)e,xetxrtermemeleylyhihgihgh(7(07%0%≤≤)).. TThhee ddaattaa aarree rreeppoorrtteedd aass ssttaannddaarrddiizzeedd mmeeaann ddiiffffeerreenncceess (SMD)

(SaMnDd)9a5n%d c9o5n%ﬁcdoennfcideeinncteerivnatlesrv(CalIss)(.CITsh).eTdhieamdioamndonadt tahtethbeotbtootmtomprepsreensetsnttshethoevoevrearllalelfefeffcetc. t.The

ThpeloptltoetdtesdqusqarueasrdesendoetneoSteMSDM, Dan, dantdhethwehwishkiesrksedrsendoetneottheetihre9i5r%95C%IsC. Is.

Systolic Blood Pressure

Five cases with different canopy densities were used for meta-analysis. Systolic blood

pressure significantly lowered in the forest (SMD −0.11; 95% CI −0.35 to 0.13; p = 0.3744; I2

= 48%; 30 participants). As a result of subgroup analysis, there was a significant difference

in the effect size according to the canopy density (Psubgroup = 0.00271; Figure 15). A “small”

Healthcare 2021, 9, 1427

Healthcare 2021, 9, x

21 of 28

Systolic Blood Pressure

Five cases with different canopy densities were used for meta-analysis. Systolic blood

pressure signiﬁcantly lowered in the forest (SMD −0.11; 95% CI −0.35 to 0.13; p = 0.3744;

I2 = 48%; 30 participants). As a result of subgroup analysis, there was a signiﬁcant differ2-4 of 31

ence in the effect size according to the canopy density (Psubgroup = 0.00271; Figure 15). A

“small” effect appeared in moderate-density environments (SMD −0.31; 95% CI −0.35 to

0.13; p = 0.0096; I2 = 0%) while no signiﬁcance effect was shown in high-density environ-

ments (SMD 0.08; 95% CI −0.17 to 0.34; p = 0.5288; I2 = 6%). No cases with low24coafn3o1py

densities were reported.

Figure 15. Two forest plots of the change in SBP in subgroup analysis by canopy density: moder-

ate(50–70%), extremely high (70%≤). The data are reported as standardized mean differences

(TaFStiheMg(Fe(5u5iD0pgr0–eu–l)7o7r1a0et05n%t%.e1dT)5d),,.w9eseT5xoqxw%ttufrrooeeacrmmfreooeseenrstlelyfypsditdlhehopeinitgglnsoohhcottsee(f(77otiS00hnfM%%etthe≤≤cDreh)v).,ac.TahanTlhnagshenede(gdCitdneahIatisSeant)Ba.waSPTarBehrihPenireseirsknepdueposibraourgstmrbretdogdeourednoanpsaudoaspstnatseaatatnnatltyhndahseldaeyiirsarsdbribd9sioyz5itbeztc%ydoeadmncmCaomneIppsaoeyr.npaednysdeeiddnfnfeiseftnifsrtesyetrin:theymcne:ecosmoedvsoee(drSr-eaMrlalDtee)ffect.

(SManDd) a9n5%d 9c5o%nﬁcdonenficdeenincteeirnvtaelrsv(aClsIs()C. ITs)h. eThdeiadmiaomnodnadt atht ethbeobtotottmomprperseesnetnststhteheovoverearlallleefffefecct.t. The

HThepealporlttoteRtdteadstqesuqoaurraersPeduseldnseoenteoRtSaeMtSeMD,Da,nadntdhtehwe hwishkisekrserdsedneonteottehethirei9r59%5%CICs.Is.

HeHaretFaRrivtaeRteactoaersoePrsuPwlsueiltsRheadRteiaftfeerent canopy densities were used for meta-analysis. There was no

−sws−ii00gga..ns−22nnsios11i0iFf,af.sit2ititciocvohsg1Faaein0eing0nvt.ricto0.nﬁeeta03ccis03wcch;faea.ih;pan0scaspan3ats=weng;ncs=a0ihegpttw.0ha1seid.in=4ni1dgtii6gh4fnnih00eff6if.;eedﬁ1ehi0aIrni4rc2;efere6aft=haInen0n2errrc5t;ttaa=ee1ctrnId%rae52titainn1fr;o=tcfoa%e3earttp0ren5h;poey1oepo3nur%dpra0cleepysre;pftenpufuid3isceanill0ieincssrtpneitttepeahitscishsnaiieniitnertwpzisteteieh)fafetsc.ofhnerieaArwpeetcefcsaosutesc)fnrtrssao.oeeteierAsrszrdndu)teee.svsssefionauetniaArcrdglvoetcrmsinnofeortoomorvaesfdrntuisaetrrimmulnhen-otabsgteeenonugnt(mtaafcrlStotol-asM(yaeunSoutsnnhpMDfoibatsepsgl.aDy−(ucTynrS0saboah−Mi.nd0gsuley0o.9rerpD.sop;eTn0iu9ys9awhs−,5p;nied0att%9rahys.ea5e0lenn%nyC(9rwsaPose;IiCliatsy9suysI-b,5gt%rhouepCr=eI

0−w00..00a((.212PSs686Msau0;0b;D9s;giF5rFg−oi%nigug0piuf.uC1ir=cer8Iae0;−n1.9060t152).6d%3.6)i20A.fC;ftAeFIo“ri−seg−“mnu00scra..me3e0l2l41ia”n6;tlo)lept.”fh−fA=eee00c“fe.tf.0s0fem4wf1ce;ta2caplt6swl=”;saiIae0zp2fs.epf0=e1eac12apct16rcwpe%;odeIar2)dasiw=riannep1hgdhp1iite%lgioeanh)rnt-ewhhddoeeihgisnincilhsaeghi-ntnndiyogioepfheinsy-cndisagvdienntineyricnsoﬁeisnectiyeamntyfnevfecnei(nerPvctoitessrunfwobf(gmenSraocMmuetspnewDs=nthastoss(wSMn iDn

m−0o.s1hd8oe;wr9a5nt%ei-nCdIme−no0sd.i3te2yrateoten−-vd0i.e0rno4s;niptmy=ee0nn.0tvs1i2r(o6S;nMIm2 D=en101ts.%0(2)S;wM9h5Di%le0.nC0o2I;s−9ig05n.%0i8fiCctaIon−0ce0.1.e02f8;feptcot=0w0.1a.27s;4sp0h9o=;wI0n2.7=i4n009%; ). No

cmaosI2eds=erw0a%tiet)-h.dNelonowsictaycsaeensnovwpiriytohndlmeonwensicttsaine(SsoMpwyDedr0ee.n0rs2ei;tpi9eo5sr%tweCedrI.e−r0e.p08orttoed0..12; p = 0.7409; I2 = 0%). No

cases with low canopy densities were reported.

oFFompipigegdFyyauuiendgdrrneeuedesnr1nii1tefs6syf6i.e1ti:.tyTr6myTe:w.n:mwToomcwdoeofesdooofrde(roafSeroetraMsererttease(Dsp5ttt(le)05pop–a0(tll7sn5oo–007dttos%s0–f9%o7o)t5hf,0f)%ete,%thxehccet)xhroe,tceanrehcmenfxhaimgtdenareleegenylniemynghcieehnihegileiihghynnaehtr(haeht7(rrie0r7tvgaa%0raht%rale≤tst(≤ear7)()n.aCa0. dTtnT%Ieshdhp)≤ae.eup)nTd.ldusdhTaalesetthpeaardeauraaitaaldrertsemeeaeinrtroiearennspapuasdtoouebrrraebgttiteegnrrdtodrehosupaaeuupssopbbrsasogtattntaearntanodnoaldydumlyasaapssirrpsiddsasrbiitenbzzyasayeeneclddncydaatnmsasni-rsoedapbinyzyecdan-

mtheedaoinfvfeedrrieafnlflecerefesfne(cScteM. sTD(hS)eMapnlDodt)t9ea5dn%dsqc9uo5an%rﬁedscedonencnfeioditneetnSecMrevDainl,sate(nCrdvIstah)l.seT(wChheIissd)k.ieaTrmhs oednedndioaatmet ttohhneedirbao9tt5t%tohmeCIbpsor. etstoenmtsptrheesents

theovoevrearlal lelffeefcfte.cTt.hTe hpelopttleodttseqduasrqeus adreensodteeSnMotDe,SaMndDt,haenwdhtihskeewrshdiesnkoetrestdheeinro9t5e%thCeIisr. 95% CIs.

3.5. Other Possible Effect Modifier

3.5. OTthheeirnPclousdsiebdlestEufdfeicetsMweordeifcioernducted on forest sites in East Asia, Central Europe, and

NorthTehren iEnuclruopdeed(Fsitguudreies17w).eWreecoanlsdouccltaesdsifoiendfomreasntasgietmeseinnt EtyapstesAosifat,hCeefnotrreasltEinutroope, and

N“coormthmeerrnciaElufororepset,”(Faimguanreag1e7d).foWreset faolrsotimclbaesrspifrioedducmtiaonnaagnedmcoemntmteyrpcieasl uosfe,t“hfeorfeosrtest into

“recsoemrvme,”eracsiatrlicfotlryepstr,o”teactmedanfoargeestd, afnodre“sutrfboarntifmorbesetrs”pfroordeustcltoicoanteadnwdicthoimn mwaelrkciinagl udisse-, “forest

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22 of 28

3.7. Other Possible Effect Modiﬁer

The included studies were conducted on forest sites in East Asia, Central Europe,

and Northern Europe (Figure 17). We also classiﬁed management types of the forest into

“commercial forest,” a managed forest for timber production and commercial use, “forest

reserve,” a strictly protected forest, and “urban forests” forest located within walking

distance from the city. In addition to the structural characteristics, we found region and

management type as a possible effect-modiﬁer. We compared the therapeutic effects on

mental health, including emotional and cognitive restoration, by region and manage2m5 oefnt31

type. By region, the biggest effect was shown in Northern Europe (mean 0.912, median

0.905), followed by East Asia (mean 0.738, median 0.715), and Central Europe (mean 0.612,

median 0.650). By management type, the effect size showed the biggest in a forest reserve

inthteheorodrdereroof fNNoortrhtheerrnnEEuurrooppee((mmeeaann 00..991122;; mmeeddiiaann 00..990055)),, EEaassttAAssiiaa((mmeeaann00.7.73388; ;mmeeddiainan

00.7.71155),),ananddCCenetnrtarlaEl uEruorpoep(em(meaena0n.601.621; 2m; emdeiadnia0n.605.06)5.0B).yBmyamnaagneamgeemntetnytpteyapnedarnedgiroengiionn

EiansEt AasstiaA,sfioar,efsotrreessterrevseer(vmee(amne0a.7n308.;7m38e;dmiaend0ia.7n105.)7h1a5d) hthade bthigegbeisgtgeefsfet cetffseizcte,sifzoell,ofwolelodwbeyd

ubrybaunrfboarnesfto(rmeseta(nm0e.2a9n2;0m.29e2d;iamne0d.3ia2n0)0a.3n2d0c)oamndmecorcmiaml feorrceisatl (fmoreeasnt 0(m.15e1a;nm0e.1d5i1an; m0.e1d30ia)n.

O0n.13th0e). oOtnhetrhehaonthde,rinhatnhde,cianstehoefcNasoerothf eNrnorEthuerronpEe,ufroorpees,tfroerseesrtvrees(emrveean(m0.e9a1n3;0.m91e3d;imane-

0d.9i0a5n)0h.a9s05th) ehabsigtgheesbt iegfgfeecsttseifzfee,cftoslilzoew, efodllboywceodmbmyecrocmiaml foerrceisatl(fmoreeasnt 0(m.73e0a;nm0e.7d3i0a;nm0e.6d8i5a)n

a0n.d68u5r)baanndfourrebsatn(mfoeraenst0(.m68e8a;nm0e.6d8ia8n; m0.e6d1i0a)n. 0.610).

FFigiguurere1177. .BBooxxpplolottssccoommppaarriinnggtthheetthheerraappeeuuttiicc eeffffeeccttss bbyy rreeggiioonnss ((CCeennttrraall EEuurrooppee,, EEaassttAAssiiaa,,aanndd

NNoortrhthEEuurorpope)ea) nadndfofroersetsttytpyepse(sco(cmommemrceiracliafol rfeosrte,sfto,rfeosrtersetsreersveer,vuer,buarnbafonrefostr)e.sTt)h.eTohveeorvlaeprplaepdpdeodts

pdreostesnptrsetsaenndt asrtadnizdeadrdmizeeadnmdeifafenrdenifcfeesre(nScMesD()SMonDe)moonteiomnoatliorensatlorreastitoonraatinodn caongdnciotigvneitrievsetorreasttioorna-

etxitornacetxetdrafrcotemd ifnroclmudinedclustdueddiesst.udies.

44. .DDisisccuusssioionn

InInrerecceennttyyeeaarsrs, ,ththeeththeerarappeeuutitcicppootetenntitaial loofffoforerestsstshhaassbbeeennggraradduuaalllylyrerceocoggnnizizededinin

vvaariroiouussccoouunntrtireies,s,aannddththeennuummbbeerroof fininvveestsitgigaatitoionnssoof fththeeththeerarappeeuutitciceefffefectctaaccocordrdininggtoto

ththeefoforereststcchhaararaccteterirsitsitciscshhaassininccrereaasesedd. .InInththisisrereggaardrd, ,wweetrtireieddtotoidideenntitfiyfyththeererelalatitoionnshshipip

bbeetwtweeeenntthheerraappeeuuttiicc effects aannddffoorreessttsstrturucctuturere. .WWe heyhpyopthoethsiezseizdetdhatthsattanstdanddendseitnysaitnyd

acnadnocapnyodpeyndsietnyswityillwaicllt aacst masamjoar jeofrfecffte-mct-omdiofdieirﬁsearsffaecffteinctginfgorfeosrtess’ttsh’ ethraepraepuetiuctiecffeefcftesctosn

oenmeomtiootnioanl raelsrteosrtaotriaotnio, cno, gcnogitniviteivresrteosrtaotrioanti,oann,danpdhypshioylsoiogliocaglicraelarxealatixoanti.oTnh.iTs hstius dstyusdyys-

styesmteamtiactailclayllsyeasrecahrecdh,esdu, msummamrizaerdiz,eadn,dansydnstyhnesthizeesdizperdevpiroeuvsiosutusdsiteusdeixeas mexinaimngintihneg tthheer-

thapereaupteicuetifcfeecftfsecotfsvoafrvioaurisofuosrefostreesntveinrovnirmonemntesnwtsitwhidthifdfeirfefenrtesnttrustcrtuucrtaulrvaal rviarbilaebsl.es.

AAssaareresusultltooffmmeetata-a-annaalylyssisis, ,sstatannddddeennssitiytyssigignniﬁifcicaannttlylymmooddiﬁifeieddtthheeeefffeeccttoonneemmoo--

titoionnaal laannddccooggnnitiitviveerreesstotoraratitoionn. .AAnnxxieietyty, ,aannggeer,ra, nanddnnegegataitviveeaafffefcetctddecerceraesaesdedththeemmooststinin

ththeeloloww-d-deennsistiytyfoforersetstwwhhereerethtehestsatnadnddednesnistyitywwasalselsesstshtahnan50500/0h/haa. .InInccoonntrtarasts,t,ththeeeefffefcetct

ssizizeebbeeccaammeessmmaalllelerroorrininsisgignniﬁifcicaannttaassththeetrtereeeddenensistiytyinincrceraeasesded. .TThheererelileievvininggeefffefcetctoonn

ccoonnfufussioionnaannddfafatitgiguueeaalslsooddeeccrereaasseeddaassstsatannddddeennsistiytyinincrcereaaseseaannddshshoowwededththeesmsmalallelsetst

effect size in the extremely high tree density of 1500/ha or higher. The improvement of

depression and enhancement of vitality and positive effects were significant only in the

low-density forest with a stand density less than 500/ha. There was the largest increase in

restorative experience in the low-density forest with a stand density less than 500 trees

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23 of 28

effect size in the extremely high tree density of 1500/ha or higher. The improvement of

depression and enhancement of vitality and positive effects were signiﬁcant only in the

low-density forest with a stand density less than 500/ha. There was the largest increase in

restorative experience in the low-density forest with a stand density less than 500 trees per

hectare. Hence, mental recovery was greatest in low-density forests and decreased as the

stand density of trees increased. On the other hand, there was no signiﬁcant difference in

physiological relaxation by stand density. It might be due to physiological outcomes were

investigated only under high or extremely high stand density conditions.

By dividing the subgroups, we observed a decrease in heterogeneity within each

subgroup and found signiﬁcant differences between the subgroups. It suggests that stand

density modiﬁes the therapeutic effect of forests. However, in depression, anger, fatigue,

and vitality, we found non-negligible heterogeneity in forest environments with high stand

density between 1000/ha and 1500/ha. High heterogeneity in those subgroups suggests

a need for more tightly divided subgroups with a stand density greater than 1000 trees

per hectare. Furthermore, few studies investigated moderate-density forests with a stand

density between 500/ha and 1000/ha. Considering that previous studies estimated the

most signiﬁcant therapeutic effect in the middle level of stand density [59,60,65], it is

necessary to investigate the therapeutic effect of moderate-density forests with a stand

density between 500/ha and 1000/ha.

Depending on canopy density, the physiological relaxation indices showed inconsis-

tent and opposite trends. This is thought to be due to the limited number of investigations

reporting the canopy density. A review of literature on the therapeutic effects of forests

noticed the insufﬁcient number of studies that measured and reported canopy density of

forest sites. Furthermore, most literature reported tree cover, which indicates a level of

greenness at the landscape level, not at the spatial level that humans can perceive. For fu-

ture guidance on the therapeutic use of forests, studies should include detailed descriptions

of the forest sites where the participants perform their activities.

The structural variables used in this study—stand density and canopy density—are

widely reported variables in forest management, growth monitoring, and tree ecology

studies. Through a review of publications from 1989 to 2021 reporting the health effects of

forests, we found that structural characteristics of forest environments began to be described

earnestly after 2017. This may reﬂect the international trends of increasing collaboration

between forestry and the health sector [13,95,96]. Forests have long been treated as natural

resources for production or conservation. However, in recent decades, several countries in

East Asia, Europe, and North America have begun to recognize forests’ preventive medical

potential and managed forests to promote public health [11,95–97]. For example, Japan has

continuously certiﬁed forests rich in therapeutic effects through on-site veriﬁcation since

2006 [98]. Korea has been creating national healing forests since 2010 [95,99]. Denmark

established the Healing Forest Garden Nacadia [100] and Finland established the ﬁrst

‘Forest Wellness Trail’ in 2010. The German state of Mecklenburg-Pomerania also started

operating a legal designation system for forests with excellent recreational and therapeutic

properties in 2019 [99]. In addition, Finland, North America, and Canada have recently

supported health promotion activities based on natural environments [24,97].

This study has several limitations. First, the study design was not limited to ran-

domized controlled trials (RCTs). Forest therapy has been studied in earnest over the

past decade, and quantitative forest descriptions have only recently begun to be included

in intervention information in clinical trials. Most RCTs were excluded from the review

process as the information on the forest environment was insufﬁcient. For this reason, we

included not only RCTs but also randomized cross-over studies and NRCTs as the scope of

this study. Secondly, we extracted effect size using pre-and post- measurements of each

forest site rather than differences in forest sites and controls. The studies used in the meta-

analysis did not contain the control group examining pre-and post-outcomes outside the

forest environment, rather, they assigned the forest site with different condition as controls

or did not assign controls among forest settings. Most of the included studies adopted

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cross-over designs to investigate the difference in the therapeutic effects in different forest

settings. Only Bach et al. [85] and Elsadek et al. [86] assigned controls to urban sites, but

their results were excluded with some reasons. Bach et al. [85] investigated blood levels

of natural volatile organic compounds that have not been investigated in other studies.

Elsadek et al. [86] conducted studies under high temperature conditions (28.1–31.8 ◦C)

that are difﬁcult to synthesize with other studies conducted under temperature conditions

around 20 ◦C. We may regard one of the forest setting as a control, however, it will not be

meaningful to combine urban settings and the forest settings as equivalent control groups.

Thus, we estimated effect size by calculating a standardized mean difference of single

groups. Accordingly, there would be a risk of resulting false positives and limitations

in precision in deriving the effect size of forest therapy in this way. Future RCTs with

control sites such as indoor or urban environments are required to derive more precise

and reliable effect estimates. Nevertheless, the value of this study is verifying whether the

therapeutic effect of the forest differs by structural characteristics of the forest environment.

Thirdly, as we subgrouped the cases from included studies, sufﬁcient sample size was

required. However, the pooled populations were smaller than the appropriate sample

sizes to examine the moderating effect on the alleviation of anxiety, depression, anger, and

reinforcement of positive affect. With respect to canopy density, the collected samples were

too small to conﬁrm the moderating effects. Therefore, further studies on the therapeutic

effects of forests in various canopy conditions are required.

The key challenge of this study was to investigate moderating role of stand density

and canopy density. Through this study, we found that stand density modiﬁes the level

of therapeutic effects in the forest. Our ﬁndings had a great signiﬁcance in suggesting

the differences in therapeutic effects according to the environmental characteristics of

nature. However, within the scope of this study, we cannot elucidate the underlying mech-

anisms. Several previous studies exploring differences in preference or beauty according

to the characteristics of forest settings explained the differences using semantic difference

measures [21,45,50,57,70] or physical-psychological predictors [74–77]. Especially, psycho-

logical mediators such as a sense of safety, visual access, and ease of movement were used

to explain the difference in preference among forest settings with respect to structural

variables [74–76]. The actual reason or psychological mechanisms are still under discus-

sion [101]. Thus, there is abundant space for further progress in analyzing underlying

mechanism that explains why forest sites with less stand density has more positive effects.

In addition, we found an intriguing trend as a result of additional analysis. There was

a difference in the distribution of the therapeutic effect by region and forest management.

The therapeutic effect of the forest reserve was the highest in all regions, and the order of

the therapeutic effect of an urban forest and commercial forest differed by region. Future

investigation on the impact of management type, landscape characteristics by country, and

public use pattern are required.

5. Conclusions

In this study, a systematic literature review and meta-analysis were conducted to

evaluate the relationship between the structural characteristics of forests and the therapeutic

effect. We systematically searched, selected, and statistically synthesized the literature

examining the therapeutic effects of forests with different stand density and canopy density.

Subgroup analysis showed that stand density modiﬁes the therapeutic effects. Emotional

and cognitive restoration showed the largest effect size in low-density forests with a stand

density of less than 500/ha and the therapeutic effects diminished as the stand density

increases. The impact of canopy density has not been identiﬁed due to a lack of studies

reporting canopy density. In addition, there would be a risk of false positives as we did not

control the experimental design and control group. Although some limitations, we expect

that our ﬁndings that highlight the impact of forest structure on the therapeutic effects of

forests will contribute to advances in the therapeutic use of forests. We also expect that our

ﬁndings may be used as a basis for creating adequate forest sites for future forest therapy.

Healthcare 2021, 9, 1427

25 of 28

Supplementary Materials: The following are available online at https://www.mdpi.com/article/10

.3390/healthcare9111427/s1, Table S1: PRISMA 2020 checklist; Figure S1: result of sensitivity analysis.

Author Contributions: Conceptualization, E.K., G.K. and S.P.; methodology, E.K. and G.K.; software,

E.K., Y.C. and H.-r.C.; formal analysis, E.K., J.C. and Y.C.; investigation, E.K., Y.C. and G.K.; resources,

E.K., G.K. and S.P.; data curation, E.K. and G.K.; writing—original draft preparation, E.K., S.K. and

Y.C.; writing—review and editing, G.K., S.P. and S.-i.C.; visualization, E.K. and H.-r.C.; supervision,

G.K.; project administration, S.P. and S.-i.C. All authors have read and agreed to the published version

of the manuscript.

Funding: This research received no external funding.

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

Acknowledgments: We thank the anonymous reviewers for their constructive comments on the

manuscript.

Conﬂicts of Interest: The authors declare no conﬂict of interest.

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