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Health Benefits Derived from Forest: A Review

International Journal of

Environmental Research

and Public Health

Review

Health Beneﬁts Derived from Forest: A Review

Gianluca Grilli 1,2,3 and Sandro Sacchelli 1,*

1 Department of Agriculture, Food, Environment and Forestry, University of Florence, I-50144 Florence, Italy;

Gianluca.Grilli@esri.ie

2 Economic and Social Research Institute, D02 K138 Dublin, Ireland

3 Trinity College Dublin, D02 PN40 Dublin, Ireland

* Correspondence: sandro.sacchelli@uniﬁ.it

Received: 17 July 2020; Accepted: 21 August 2020; Published: 23 August 2020

Abstract: In this paper the scientiﬁc literature on the association between forests, stress relief

and relaxation is reviewed with the purpose to understand common patterns of research, the main

techniques used for analysis, ﬁndings relevant to forest-therapy-oriented management, and knowledge

gaps. The database of studies was collected with a keyword search on the Web, which returned a

set of 32 studies that were included in the analysis. The main ﬁndings and patterns were identiﬁed

with a text mining analysis of the abstract to search for keyword patterns across studies. The analysis

indicates that most studies compared rest and relaxation performances across urban and forest

environments and used a combination of self-reported measure of stress or rest collected with validate

scales, e.g., the Proﬁle of Mood of States (POMS) and the Restoration Outcome Scale (ROS), and a

minority-only set of these two groups of indicators. Results of this review indicate that primary studies

identiﬁed a positive association between forest exposure and mental well-being, in particular when

compared to urban environments, thus suggesting that forest are eﬀective in lowering stress levels.

This study found that, to date, the characteristics of forests and characteristics of the visit are little

investigated in the literature. For this reason, more research with a focus on forest variables such as

tree species composition, tree density and other variables aﬀecting forest landscape should be further

investigated to inform forest management. Similarly, the characteristics of the visits (e.g., length of

visit and frequency) should be further explored to provide robust forest therapy guidelines.

Keywords: forest therapy; forest recreation; relaxation; stress relief; quantitative analysis;

psycho-physiological indicators

1. Introduction

Recreation is a very important ecosystem service provided by natural areas. In particular, forested

areas provide countless recreational opportunities such as hiking, picnicking, mushroom and berry

picking, biking and horse riding. For this reason, there is an increasing trend in forest management to

leave some forested areas as dedicated areas for recreation. The propensity of a forest for recreation

is highly speciﬁc [1]. Previous research highlighted the importance of forest structure for recreation;

in particular, it has been observed that tree species composition, forest cover and forest structure

are important variables [2,3]. With respect to forest types, mixed and coniferous forests are often

preferred destinations for recreational purposes compared to broadleaf forests [4,5], while canopy cover

has been found to inﬂuence recreation based on transport and access to forests, with lower canopy

cover preferred by motorized recreationalists and higher density cover by non-motorized visitors [6].

The other examined preference studies linked forest structure in terms of shrubs, dead wood, and type

of higher trees to diﬀerent preferences in terms of recreational activities [7].

One of the recent trends in the recreational aspects of forest management is to explore the potential

of woodlands for stress relief and rest. This is especially important for forested areas in proximity

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www.mdpi.com/journal/ijerph

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of cities. Urban areas are increasingly seen as a source of stress, because industrialized society has

created a frenetic routine for individuals. Urbanization is increasing, particularly in some developing

nations, and, as a result, it is estimated that 67% of the world population will be living in urban areas

by 2050 [8]. Easy access to oases where one can rest and that are close to urban areas is important

for resting the mind and mental well-being. The health beneﬁts of being in contact with nature are

demonstrated by several studies, many of which were collected in the meta-analysis published by

Gascon et al. [9]. More recently, a study in England found that spending 120 min per week in nature is

suﬃcient to maintain good health and well-being levels [10]. The beneﬁts of green areas for well-being

are not necessarily linked to active visitation, as the simple exposure to natural environments is

equally beneﬁcial. In fact, several hospitals are now equipped with the so-called “healing gardens”,

where patients can spend some time to recover from mental diseases [11–13]. Due to the close link of

stress to urban life, most studies analyse the impact of within-city green spaces on mental well-being.

Bowler et al. [14] collected all studies speciﬁcally designed to understand the eﬀect of urban green

spaces on rest in a systematic meta-analysis and found that all primary sources identiﬁed some

degree of eﬀectiveness of green spaces on human health. While urban green spaces are recognized

environments where one can rest, the potential of close-to-city forests for therapy is less explored,

and little eﬀort has been dedicated to gather the existing scientiﬁc evidence to identify lessons learned

and knowledge gaps.

With this in mind, this contribution oﬀers a review of the literature exploring the association

between forest exposure and mental well-being, in particular with respect to relaxation or rest from

stress. The main objective of the paper is to provide a snapshot of the existing knowledge, techniques and

approaches implemented to study forest therapy. The analysis considers stress indicators, forest stands,

and forest characteristics associated with relaxation, as well as interesting avenues for future research.

Results are useful to indicate development paths for forest managers interested in forest therapy and

to inform researchers and analysis on evidence that is still missing on this relevant topic.

2. Materials and Methods

2.1. Database Collection

The data collection was carried out with a keyword search in Scopus, i.e., one of the largest

scientiﬁc databases available. Scopus was chosen because it is user-friendly and large enough to

include most papers indexed in other famous servers, for example Web of Science. A preliminary

qualitative study of four papers relevant to the topic [15–18] was undertaken to familiarize with

the speciﬁc nomenclature and select the most appropriate keywords. The keyword search was

then conducted using one word among “forest”, “woodlands” or “greenwoods” in combination

with one of the following: “therapy”, “stress”, “relaxation”, “restoration”, “bathing” (the Scopus

search was conducted on 7th August 2020 with the following string: (TITLE-ABS-KEY (forest) OR

TITLE-ABS-KEY (woodlands) OR TITLE-ABS-KEY (greenwoods) AND TITLE-ABS-KEY (therapy)

AND TITLE-ABS-KEY (stress) OR TITLE-ABS-KEY (restoration) OR TITLE-ABS-KEY (relaxation) OR

TITLE-ABS-KEY (bathing)). The criteria for the inclusion of a contribution were the following: (1) the

paper must be focused on beneﬁts obtained from exposure to forests; (2) the paper must explore

the eﬀects of relaxation or rest of forests; (3) the paper must follow a treatment–control experiment

approach; (4) the paper must use self-rated or physiological indicators of stress/rest. These criteria

were used to obtain a homogeneous set of manuscripts, which were comparable in terms of topic and

methodological approach. Manuscripts that did not satisfy all of these selection criteria were excluded

from the analysis. For example, many contributions concentrate on urban green spaces because they are

important for rest from city life. A large list of these papers has been collected previously [14]. Since one

of the main objectives of this review is to provide indications for therapy-oriented forest management,

urban green space papers were excluded due to the limited usefulness for forest management purposes.

Finally, both quantitative (based on text mining approach) and qualitative reviews (focused on the

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Int. J. Environ. Res. Public Health 2020, 17, x

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applied methods, the eﬀects of relaxation and rest of forests as well as inﬂuence of forest characteristics

quaolintasttirveessrerevcieowvesry(f)owcuesreedpoernfotrhme eadpp(Fliiegdurme e1t)h. ods, the effects of relaxation and rest of forests as

well as influence of forest characteristics on stress recovery) were performed (Figure 1).

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showulhdicbheshcoonusldidbeerecdonassidaerperdelaismainparerlyimteisntatroy tbeestintotebgeriantteedgriantefdutiunrfeuteuvraeluevaatilounasti.oTnhsi.sTqhuisaqnutiatanttiivtaetive

anaalynsailsysisisaisteaxttemxtinmininginexgeercxiesrecibsaesbedasoedn othnethtietlet,italeb,satrbascttraacntdankdeykweoyrwdoordf aolfl aelxlaemxianmedinepdappearps ers

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softswofatrwe aTr-eLTa-bL(awbw(www.tlwa.bt.liatb),.iat)t,oaotloboalsbeadseodn othnethleexilceoxmicoemtriectraipcparpoparcoha[c1h9][.1B9]a.sBedasoend tohnetbhiegbdigatda ata

framfreawmoerwko, trekx,ttemxtinminignidnegrdiveersivpeasttpeartntserwnsithwiinththinetchoerpcourspsuuscshuacshianscilnusctleursitnegri,ncgo,nccoenpcteepxttreaxcttriaocntion

andasnedntsiemnetinmt eanntalaynsaisly. Isniso. uInr wouorrkw, woreku, wseeduthseedmtuhletimdiumlteindsiimonenalsisocnalailnsgcaaplipnrgoaapchpr(oMaDchS)(M[2D0]S. )In[20].

MDISn, MthrDoSu,gthhrthoeugaphptlhiceaatipopnliocfaStiaomnmoof nS’asmalmgoornit’hs malg[2o0r]i,thremlat[i2o0n],shreiplastaiomnoshngiplsemammoansginlethmemcoarspiunsthe

arecroerppruesseanrteedrepinreasegnratepdhiicnaal wgraayp(hdicisatlawncaeya(nddistpaonscietioannd). pLoemsitmioans).aLreemdemnoasteadrebdyecniroctleeds bofysciinrgcllees of

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obstehreveodbsdeirsvseimd idlaisrsitiymmilaartirtiyx mamatornixgatmheonlegmtmhealseamnmd athseanesdtitmheateesdtimonaet.ed one.

3. Results and Discussion

The search provided an initial list of 162 documents, but many papers did not satisfy all the

coTnhseidseeraerdchcripterroivai.dAedftearnreianditiinagl ltihset aobfs1tr6a2ctdso, tchuemliesnt tosf, dbouctummaennytspwaapsefrusrdthiderncoletasraetdis, fayndaltlhtehﬁe nal

consseidt einrecdludcreidte3ri6a.pAapfteerrs,rweahdiicnhgwtheereahbosmtraocgtes,ntehoeuslilsyt oorfgdanoiczuemdeanntds awdaesqfuuarttehfeorrctlheaerpeudr, paonsde tohfethis

finarlesveitewin.cMludoestds3e6lf-praeppeorrst,ewd hscicahlews uerseedhoinmporgiemnaeroyusstlyudoiregsaanriezekdnoanwdnaidnetqhueastceiefonrtitﬁhcelpituerrpatousreeowf ith

thisarcerovnieywm. sMaonsdt asbelbfr-reevpiaotritoends,swcahleicshuasreedrienpoprrtiemdairnyTsatubdleie1s. aTrheekfnuollwlinstionftshteudscyieisnstihfiocwlinteirnatTuarbele 2.

witOh uatcroofntyhmesse adnodcuambbernetvs,ia2t4iownes,rewchaircrhiedaroeurtecpoonrtseiddeirninTgambleea1su. Trehserefuglalrldisint gofsesltfu-rdayteids ssthroewssnorinrest,

Tab2l7e 2fo. cOuustedofotnhepsheydsoioculomgiecnatlsm, 2e4awsuerrees,caarnrdie1d7ouustecdonbositdhearpinpgromaecahseus.reMs roesgt acrodnitnrgibsuetlifo-nrastewdesretrreescsent,

or rwesitt,h2o7nfolycuosneedpoanppehr ypsuioblloisghiceadl mbeefaosruer2e0s0, 2a;ntdh1is7iunsdeidcabtoesththaaptpsrcoiaecnhtieﬁsc. Matotesnt ctioonntroibnuftoiorensstwtheerreapy

receanntd, wstirtehssonrelyduocnteiopnaipsear vpeurbylinsehwedtobpefico.reIn20te0r2m; tshoisf ienxdpiecraitmesetnhtaatl ssectietinntgifsi,cmatotestntsitoundioens ifnovreosltved

theroanp-syitaenddatsatrceoslslercetidounc, twiohnicihs ma evaenrsythnaetwthteopreiccr.uIintetdersmams polfe ewxapserbirmouenghtatltosetthtienfgosr,esmt otostaslltouwdifeosrest

invoimlvmederosino-nsi.teAdsamtaalcleorllsehctairoeno, fwehxipcehrimmeeanntss wtheartetchoenrdeucrcuteitdedinsaamlabplseewttiansgbbryoumgehatntsootfhveifrotureasltretoality

allo(wVRfo) rdeesvt iicmesmtehrastiosinm. Aulastmedalnleartushraalreenovfierxopnemriemntesn. ts were conducted in a lab setting by means of

virtual reality (VR) devices that simulated natural environments.

Int.InJ.t.EJn. Evinrvoinr.onR.eRs.esP. uPbulbiclicHHeaelatlhth22002200,,1177,,6x125

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Table 1. Acronym of self-rated indicators used in primary studies.

Nomenclature

NomePncAlaNtuArSe

Positive And Negative Affect Schedule

PANPAOSMS

PPorsoiftilveeOAfnMd oNoedgaSttiavteeAs ﬀect Schedule

POMSSVS

SubjectivPeroVﬁilteaOlitfyMSocoadleStates

SVSPSS

RPSASND 36

RASNDDMSD3M6

TMTDMD

STASTI AI

RORSOS

PerceivSeudbjSectrtievses VScitaalleity Scale

EmotionalPwerecleli-vbeedinSgtrsecsaslSecale

SemantiScEedmmiaoffnteitoriecnnadtliiﬀwaelermleln-ebttiehainlomgd secthaloed

Total mToootdaldmisotoudrbdainstcuerbance

State-TrSatiattAe-nTrxaieittyAnInxvieetnytIonrvyentory

RestoraRtieosntoOrauttioconmOeutSccoamlee Scale

MBMI BI

MaslachMBauslrancohuBtuIrnnvoeunttIonrvyentory

FigFuigruer2es2hoswhoswthsetgheeoggeraopghraicpahlicdailstdriibsutrtiibountioofnthoef stthuedisetsu.dJiaeps.anJaipsatnheismtohset rmeposret sreenptreedsecnotuendtry

wKoiwsctrhtoeuhua1di,nl0ieFtersiSsyntouwluwadetniihretdehsKc.a1oonC0rndehdsaiPtun,uoFcadtliaeniaednlnsadda.nrCedteahhaIceitnhnaUdlahySPa,aAoSnvlwdeaanetrtheddhreereeneaeUp,csShSrtweAuhsdiaetaviznreeeteser.tldrhaOernbpetdyrheeaesssnrtieuxdncdotaTeiuneadnsdi.wtbOrﬁyaivetnhsse,iewxasrltclhauownedurdiienethfstfir,ovairreeesesmssswpttauethlhdcleeetirerirevsan,efpuolryyrme,esssbwptteuehtrcdhitoleiieefvrsaoeSplnwoyyeu,etrhe

conodr utwctoedstuardeieIsta. ly, Sweden, Switzerland and Taiwan, all with a smaller number of one or two studies.

FFigiguurere22. .GGeeooggrraapphhiiccaall ddiissttrriibbuuttiioonnooffththeestsutuddieise.s.

Int. J. Environ. Res. Public Health 2020, 17, 6125

Authors

Ulrich et al.

Hartig et al.

Hansmann et al.

Park et al.

Li et al.

Annerstedt et al.

Beil and Hanes

Tsunetsugu et al.

Jiang et al.

Korpela et al.

Tirvainen et al.

Jung et al.

Ochiai et al.

Chiang et al.

Ohe et al.

Park et al.

Song et al.

Bielinis

Chen et al.

Bielinis et al.

Lee et al.

Song et al.

Wang et al.

Chia-Pin and Hsieh

Huang et al.

Kim et al.

Kuper

Sacchelli et al.

Janeczko et al.

Zeng et al.

Jiang

Bielinis et al.

Wang and Zao

Year

1991

2003

2007

2008

2009

2012

2013

2014

2015

2017

2018

2019

2020

Source

[17]

[21]

[22]

[23]

[24]

[25]

[26]

[18]

[27]

[28]

[29]

[30]

[31]

[32]

[33]

[34]

[35]

[36]

[37]

[38]

[39]

[40]

[41]

[42]

[43]

[44]

[45]

[46]

[47]

[48]

[49]

[50]

[51]

[52]

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Table 2. Studies included in the review.

Self-Rated Measures

non-validated measures

POMS

PSS

POMS, ROS

RAND 36

ROS

MBI

SDM, POMS, TMD

POMS

PANAS

SDM

PANAS, POMS, ROS, SVS

POMS, STAI

ROS, POMS

non-validated measures

POMS, STAI

POMS

POMS, TMD

POMS

ROS

POMS, PANAS, ROS, SVS

SDM

ﬂuid procrastination

Physiological Measures

blood pressure, muscle tension

blood pressure

cortisol, electroencephalography (EEG)

cortisol, heart beat

blood pressure, heart beat, urine dopamine, and cardiovascular and metabolic parameters

cortisol, cardiovascular data

cortisol

blood pressure, heart beat, nervous activity

cortisol, skin conductance

cortisol

cortisol, heartbeat, natural killer cell

blood pressure

EEG

blood pressure, heart beat

heart rate, prefrontal cortex activity

heartbeat, blood pressure

salivary amylase, blood pressure, heart beat

blood pressure, heartbeat

nervous activity, heartbeat

skin conductance

heartbeat, blood pressure

EEG

heart rate, blood pressure

peripheral oxygen saturation, heartbeat, blood pressure

EEG, skin conductance, heartbeat, blood pressure

EEG

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3.1. Quantitative Review (Text Analysis)

The stress index of MDS output (0.092) indicates a positive correlation between the input matrix

and Sammon’s map [53].

The map displayed in Figure 3 shows that research interest has, to date, concentrated on some

topics. In the ﬁrst quadrant, the inﬂuence of forest variables on stress relief is shown. Lemmas such as

“tree” or “density” are related to “restoration” eﬀects or “preference” of people. The “attention” level

of interviewees as to the “exposure” to forests, in particular through the so-called “dose–response”

eﬀect are investigated with particular care. Forest seasonality was very inﬂuential on stress recovery

(due to lemmas “season”, “foliage”, “evergreen”, “winter” or speciﬁcation of month of the year).

Immersion of people in forests—on-site or using new “virtual” reality technologies—is indicated on

the map. Some studies report the importance of the “sound” of forests for stress recovery (analyzed

in literature, particularly in “park” and urban green areas). The second quadrant focuses on a

cluster of lemmas related to physiological indicators of “well-being”. Some scientiﬁc papers use

“cardiovascular” parameters, “salivary cortisol”, “prefrontal” performances (i.e., activity in highly

stress-related area of brain) or “parasympathetic” nervous system activation to deﬁne the status of

people in the case of forest exposition. Forest variables are included in the speciﬁc sense of “view”

and “landscape” evaluation. Activities (“walk”) and performances (“concentration”) are investigated

in general terms (“subject”) or for speciﬁc age and status (“student”). An interesting topic appears

in this quadrant, that is, the “tourism” tendency of forest bathing (“Shinrin-yoku”) is emphasized,

particularly for “Japan”. Undertaking analyses at diﬀerent times of the day seems to be relevant

for stress recovery (“morning” and “afternoon” lemmas). The third quadrant introduces the basic

concept linked to “forest”, “therapy”, “health” and “anxiety” as well as the evaluation of the most

investigated “physiological” parameters (“heart rate” and “nervous” trend). However, a focus on

psychological analysis is here outlined by the terms “score” (typical of questionnaires), “mood” and

“POMS” scale. The last quadrant also designates a cluster composed by general lemmas describing

both psychological (“questionnaire”, “scale”) and physiological (“systolic”) terms, interviewed group

(“young”, “adult”) and area (“Taiwan”). The preliminary text mining is based on a limited number

of papers in respect to the existing application of similar methods in the literature [54]; however,

it provides a good comparison with qualitative literature reviews described in this paper, facilitating

replicability of the analysis for diﬀerent countries or temporal trends. In future works, additional

quantitative evaluation could be introduced.

X-Axis

Figure 3. Sammon’s map showing relationships among lemmas (diﬀerent colours denote the

four quadrants).

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3.2. Performance Measurement

The eﬀectiveness of forests for relaxation and rest compared to urban areas or control groups has

been tested with two families of indicators: (1) self-rated measures and (2) physiological outcomes.

Self-rated measures are subjective answers of participants to a set of questions that capture the

perceived mood after stimuli administration. There are several ready-to-use questionnaires for this

purpose. The most common scale in the sample was the Proﬁle Of Mood States (POMS), whose purpose

is to evaluate individual moods associated with certain forest exposure [37–39]. The Positive And

Negative Aﬀect Schedule (PANAS) is instead one of the oldest but not so popular scales available [55],

and was used by Bielinis et al. [37,51] and Park et al. [36] for the evaluation of the therapeutic eﬀects

of forests. The evaluation of rest is conducted with a speciﬁc scale called the Restoration Outcome

Scale (ROS), which is composed of six items [56]. In the forest therapy literature, the use of ROS is

relatively popular [27,30,37,39,47]. Less popular measures are the Subjective Vitality Scale (SVS) [37],

the Perceived Stress Scale (PSS) [37], the emotional well-being scale (RAND 36) [29], the semantic

diﬀerential method (SDM) [32], total mood disturbance (TMD) [32], the Maslach Burnout Inventory

(MBI) [31] and the restored ability to work (RAW) [57]. Interestingly, one contribution investigated,

among other indicators, the “ﬂuid procrastination”, which indicates a pessimistic attitude to complete

a job. The psychological stress measure (PSM-9) is a popular approach to measure stress [58],

which uses a set of nine indicators; however, none of the studies considered in this review implemented

investigations based on PSM-9. This result is a potential indication that most research in forest therapy

considered rest potential and mood induced by forests with speciﬁc scales such as POMS and ROS,

rather than measures of stress levels. Lastly, some papers captured self-rated measures with scales that

were not previously validated in the literature [22,40].

With respect to physiological measures, saliva samples for cortisol and amylase represent the

most common indicator [18,24,26,28,30,31,38]. Two main other measures are blood pressure and

heartbeat [17,21,25,34,37]. Often cortisol, blood pressure and heartbeat are measured simultaneously.

Interestingly a minority of other contributions utilized indicators of natural killer cells [31].

Another important indicator of stress is the analysis of cerebral activity, collected with

electroencephalogram devices [33,47,52].

3.3. Forests’ Eﬀects of Relaxation and Rest

An initial interesting result is that all studies report a positive impact of exposure to forest

environments on measures related to stress and rest, regardless of the indicator used. Several Japanese

studies, where forest therapy is often referred to as “Shinrin-yoku” (taking in the atmosphere of the

forest), indicate that spending time in forests helps in reducing cortisol levels and blood pressure,

as well as contributing to a more stable heartbeat [16,23,24]. Several Chinese studies conﬁrmed a

positive association between stress relief and forest recreation [27,32,59]. Moving to Europe, in Nordic

countries, numerous studies demonstrated that in experiments that compare stress levels between

urban areas and forests, participants always show lower stress levels when exposed to forested

areas [29,30]. Similar ﬁndings were obtained in Italy using a combination of virtual reality, EEG and

ROS scales [47]. Another interesting application of virtual reality was oﬀered by Annerstedt et al. [26],

who compared three groups exposed to an urban setting, a forest and a forest including sounds of

nature, respectively. Results indicated that forest relaxation is highest when coupled with sounds of

nature. Beil and Hanes conducted a study in the USA where relaxation eﬀects were also conﬁrmed [18].

While no studies reported a failure of forest for relaxation, some studies reported a bad performance

of some of the indicators, in particular those related physiological outcome. Two studies in particular

reported a negligible or absent eﬀect of forest exposure on lowering saliva cortisol levels [18,30].

Authors attributed the result to the short duration of the stimuli, arguing that cortisol response to

external stimuli is slow, and that it requires long exposure of respondents to the treatments.

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3.4. Inﬂuence of Forest Characteristics

Results indicated that, to date, most scientiﬁc attention has concentrated on stress levels associated

with the urban–forest dichotomy, and that all contributions conﬁrm good forest performance in relation

to stress reduction. However, forests are not all the same and vary based on tree species composition,

structure, canopy cover, dead wood and other variables that shape forest landscapes. Therefore,

the potential of forests to relieve stress may be unevenly distributed across diﬀerent forest stands,

with some forests more appropriate than others. In this respect, the literature is scarcer of contributions,

and only ﬁve studies collected in this review considered forest characteristics in their experimental

design. A pioneer study in this regard was made by Ulrich et al. [17], who compared blood pressure

and muscle tension of participants across six diﬀerent forests. However, the study did not consider

single forest characteristics and focused only on diﬀerent types of forest landscape. Forest density was

considered by Chiang et al. [33], whose results indicated that forest density correlates with stress levels.

In their study, high density forests caused higher attention levels of participants, but medium density

forests were reported to be favoured according to self-rated measures. Bielinis et al. investigated

seasonal variations of forest stands for stress recovery, comparing the results of forest therapy between

spring and winter [37]. Sacchelli et al. [47] implemented a virtual reality study where four diﬀerent

forest types were compared in winter, and they found that coniferous forests and Douglas ﬁr in

particular were more appropriate for stress relief purposes. In China, another virtual reality study by

Wang and Zhao [52] conﬁrmed that evergreen trees are more eﬀective to maximize the stress relief

potential of forests.

4. Knowledge Gaps

Despite a few number of papers currently focusing on the topic “forests for therapy”, from a

methodological point of view, additional quantitative evaluation could be introduced, especially in

the case of largely investigated techniques (e.g., scales applied in questionnaires, neuroscientiﬁc tools,

visitors preferences, etc.). Text mining gives an objective picture of the analyzed theme, facilitating the

summary and extraction of take-home messages. It allows replicability in diﬀerent times and for a

diversiﬁed study area. However, a speciﬁc guideline on how to use the potentially large amount of

available grey literature about forests and health should also be deﬁned.

A certain lack of knowledge is detected in regard to the social beneﬁts—derived from forest

activities—that inﬂuence mental and physical well-being of individuals, such as strengthening or

developing new social relationships [60]. Speciﬁc attention should be paid to the dose–response eﬀect,

concentrating in particular on diﬀerences between application of psychological and physiological

measurement of stress relief as well as on the inﬂuence of frequency and duration of activities.

A ﬁnal remark is related to the potential negative impacts of forests on people’s health. That is,

risks due to allergenic reactions, pests, insects, falling branches and trees or wildﬁres should be

considered—in particular in rural–urban interfaces—to allow the investigation of trade-oﬀs among

ecosystem services and disservices.

5. Conclusions

The issue of forest characteristics is relevant because the simple comparison of urban versus forest

environments provides very little information to forest management. The available scientiﬁc evidence

gives little information on the fundamental managerial question “How should the forest be managed

to increase forest therapy potential?” For this reason, more research on forest characteristics associated

with relaxation is necessary to better inform forest-therapy-oriented management. This aspect concerns

forest types and tree species composition as well as age structure, cover density, amount of deadwood

and, more generally, all the variables that have an impact on forest landscape.

Int. J. Environ. Res. Public Health 2020, 17, 6125

9 of 11

Author Contributions: Conceptualization, Section 2.2, Section 3.1, supervision and funding acquisition, S.S.;

Introduction, Section 2.1, Section 3, Section 3.2, Section 3.3, Section 3.4, G.G.; Section 4, S.S. and G.G. in equal parts.

All authors have read and agreed to the published version of the manuscript.

Funding: This study was conducted in the frame of the “Pianiﬁcazione strategica di impresa per la valorizzazione

sostenibile delle ﬁliere e dei servizi ecosistemici forestali”, Grant number: Research fund 2017 n.18080, project,

co-ﬁnanced by Fondazione Cassa di Risparmio di Firenze.

Conﬂicts of Interest: The authors declare no conﬂict of interest. The funder had no role in the design of the study;

in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish

the results.

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