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How to Waste a Break: Using Portable Electronic Devices Substantially Counteracts Attention Enhancement Effects of Green Spaces

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Overuse of portable electronic devices depletes one’s attention capacity, a critical cognitive resource. Although contact with nature promotes attentional functioning, we do not know the extent to which exposure to nature and the use of electronic devices interact to promote or inhibit attentional functioning. In this study, 81 participants performed cognitive tasks and then were randomly assigned to one of four rest treatments: green settings with or without a laptop computer and barren settings with or without a laptop computer. Attention was measured three times. Analysis showed a significant effect for both setting and use of a laptop as well as a significant interaction between setting and laptop use. A further analysis controlling for time spent focused on the laptop screen produced similar results. The findings show that using an electronic device in green settings substantially counteracts the attention enhancement benefits of green spaces.
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https://doi.org/10.1177/0013916518788603
Environment and Behavior
2019, Vol. 51(9-10) 1133 –1160
© The Author(s) 2018
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DOI: 10.1177/0013916518788603
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Article
How to Waste a Break:
Using Portable
Electronic Devices
Substantially Counteracts
Attention Enhancement
Effects of Green Spaces
Bin Jiang1, Rose Schmillen2,
and William C. Sullivan2
Abstract
Overuse of portable electronic devices depletes one’s attention capacity,
a critical cognitive resource. Although contact with nature promotes
attentional functioning, we do not know the extent to which exposure
to nature and the use of electronic devices interact to promote or inhibit
attentional functioning. In this study, 81 participants performed cognitive
tasks and then were randomly assigned to one of four rest treatments: green
settings with or without a laptop computer and barren settings with or
without a laptop computer. Attention was measured three times. Analysis
showed a significant effect for both setting and use of a laptop as well as
a significant interaction between setting and laptop use. A further analysis
controlling for time spent focused on the laptop screen produced similar
results. The findings show that using an electronic device in green settings
substantially counteracts the attention enhancement benefits of green
spaces.
1The University of Hong Kong, Hong Kong SAR, China
2University of Illinois at Urbana–Champaign, USA
Corresponding Author:
Bin Jiang, Virtual Reality Lab of Urban Environments and Human Health, HKUrbanLab,
Division of Landscape Architecture, Department of Architecture, The University of Hong
Kong, 614 Knowles Building, Pokfulam Road, Hong Kong SAR, China.
Email: jiangbin@hku.hk
788603EABXXX10.1177/0013916518788603Environment and BehaviorJiang et al.
research-article2018
1134 Environment and Behavior 51(9-10)
Keywords
electronic device, green space, barren space, short rest period, attentional
functioning, urban environment
Introduction
Portable electronic devices make it easy to stay connected to people we care
about from almost anywhere and provide easy access to information at a
global scale. It is not surprising then, that it has become normal to see people
staring into their screens no matter where they are: on a train, on a bus, in an
office, on the street, or in a park.
Being constantly connected to the world through our electronic devices
demands one of our most precious resources: our attention. This constant
demand on our attention can leave us cognitively depleted and in need of
restoration. One effective source of enhancing attention functioning is to be
in, or have a view of, nature—spaces that contain vegetation (Kaplan, 1995;
Li & Sullivan, 2016; Roe & Aspinall, 2011; Tennessen & Cimprich, 1995).
We do not know, however, the extent to which engaging with electronic
devices such as phones, tablets, or laptops affects attentional functioning
while we are in nature-rich places.
We explore this issue through a randomized experimental design that
compares attentional functioning with and without the use of a laptop in
green and barren landscapes. We ask, “To what extent do green settings
enhance attentional functioning while people are using laptop computers?”
Rapidly Increased Electronic Device Use
Excessive use of electronic devices has become a worldwide problem and its
negative health consequences have been recognized by the World Health
Organization (WHO, 2015). With the availability of the smartphone, iPad,
Netbook, and laptop, we can be connected anywhere, from the beach to the
bedroom, and still pursue work or leisure (Perlow, 2012). At the global scale,
the estimated number of Internet users in 2018 is 4.02 billion and estimated
time spent online in 2018 is approximately one billion years (Kemp, 2018).
The excessive use of electronic devices is even more remarkable if we add
usage of electronic devices unconnected to the Internet. In 2012, the average
American consumed 13.6 hr of media per day that was not work related,
sometimes simultaneously, such as the case of a person browsing the Internet
while watching a television program (Short, 2013). By the end of 2016, 78%
of U.S. adults owned a desktop or laptop, 95% of adults owned a cell phone,
Jiang et al. 1135
77% of adults owned a smart phone, 51% of adults owned a tablet computer,
and 22% owned an e-reader (Mobile Fact Sheet, 2017). With smart phones,
high-speed cellular connections, and public hotspots, people can be con-
nected to the Internet, and therefore to one another, almost everywhere they
go. It is clear that hundreds of millions of people in contemporary society are
deeply engaged with portable electronic devices across the world (Weiss,
Baer, Allan, Saran, & Schibuk, 2011).
There are health risks associated with frequent use of electronic devices
(WHO, 2015). Addiction to electronic devices is a growing issue around the
world, and excessive use of electronic devices has been correlated with atten-
tion-deficit/hyperactivity disorder (ADHD) (Lee, Han, Kim, & Renshaw,
2013; Weiss et al., 2011). This addiction is more prevalent in people younger
than 30 years of age, and even more common in persons younger than the age
of 19 (Sahin, 2011), suggesting that this problem is likely to get worse in the
coming decades.
Heavy reliance on electronic devices can negatively affect health and
well-being in a variety of ways. Electronic device distractions reduce peo-
ple’s ability to pay attention, lower productivity, lead to procrastination, and
jeopardize academic and work performance (Ai, 2012). Heavy use of elec-
tronic devices is correlated with mental health issues: loneliness, low self-
esteem, and depressive moods (van der Aa et al., 2009), as well as alcohol,
tobacco, and substance abuse (Lee et al., 2013). In addition, reliance on digi-
tal screens is changing the way we obtain and process information: A study in
Science indicates the Internet is depleting our memory capability (Sparrow,
Liu, & Wegner, 2011). Heavy reliance on electronic devices is also a source
of anxiety and stress (Cheever, Rosen, Carrier, & Chavez, 2014). Studies sug-
gest that overuse of electronic devices can be triggered by mentally demand-
ing activities and that mental fatigue and stress can be caused by overuse of
electronic devices, leading to a vicious cycle (Ai, 2012).
One of the greatest costs of being constantly connected to the world
through digital technology is the demand it puts on our attention. Our atten-
tion is a limited resource: it fatigues with use and requires a period of rest
before it is fully restored (Kaplan, 1995; Sullivan, 2015). As anyone who
uses a smart phone, tablet, or computer knows, using electronic screens can
be mentally distracting and fatiguing, leaving people feeling their attention
depleted and their ability to focus reduced (Attia, Baig, Marzouk, & Khan,
2017; Swing, 2012).
Attachment to electronic devices may affect the ways humans interact
with urban green environments. It is possible that when people are in an
urban green space they may be immersed in a totally different world: the digi-
tal world delivered through electronic screens. This possibility has important
1136 Environment and Behavior 51(9-10)
consequences because of the impact green environments have on our ability
to pay attention.
Nature and Attention
Recently, there has been considerable interest in examining the relationships
between exposure to urban nature and human well-being (Frumkin et al.,
2017; Jiang, Zhang, & Sullivan, 2015; Sullivan, Frumkin, Jackson, & Chang,
2014). One of the most influential works in this area of scholarship is Kaplan
and Kaplan’s attention restoration theory (Kaplan, 1995; Kaplan & Kaplan,
1989). Many studies have demonstrated the attention enhancement effects of
spending time in nature (Beil & Hanes, 2013; Berto, 2005; Korpela, Ylen,
Tyrvainen, & Silvennoinen, 2010; Pretty, Peacock, Sellens, & Griffin, 2005;
Taylor, Kuo, & Sullivan, 2001). Exposure to a wide variety of natural ele-
ments and settings, even urban green settings, helps people recover from
mental fatigue (Li & Sullivan, 2016; Wang, Rodiek, Wu, Chen, & Li, 2016).
The costs of low attentional functioning are considerable (Sullivan &
Kaplan, 2015). A person who cannot focus his or her attention is likely to
miss important details and have trouble remembering details. Compared with
someone who is not mentally fatigued, a person with low attention function-
ing is more likely to be irritable, have trouble with self-management, struggle
to resist temptations, and miss subtle social cues. When a person is mentally
fatigued, he or she is less effective in pursuing goals and interacting with oth-
ers (Kaplan, 1995). A person with depleted attention is more likely to say or
do things he/she might later regret, which can affect relationships, work per-
formance, and even personal goals such as losing weight or saving money. In
short, we are not at our best when our attention is depleted (Kaplan & Berman,
2010; Kaplan & Kaplan, 2003; Kuo & Sullivan, 2001; Poon, Teng, Wong, &
Chen, 2016; Sullivan & Chang, 2011).
There is clear evidence supporting the predictions of attention restoration
theory for people in age categories across most of the human life span.
Compared with their counterparts who spend time in low-nature urban set-
tings, individuals who spend time in nature-rich urban settings are better able
to improve their ability to pay attention (Berman, Jonides, & Kaplan, 2008).
Children with ADHD performed better on attention tests after taking a walk
in a park compared with taking a walk in a neighborhood or urban setting
(Kuo & Taylor, 2004). In a study of 101 high schools in Michigan, schools
with greener views from cafeteria and classroom windows had significantly
higher graduation rates, standardized tests scores, percentage of students
planning to attend a 4-year college, and fewer occurrences of criminal behav-
ior (Matsuoka, 2010). A recent study reported positive association between
Jiang et al. 1137
tree density on a school campus and students’ academic performance (Kweon,
Ellis, Lee, & Jacobs, 2017). In another study, high school students were ran-
domly assigned to classrooms with and without views to green spaces. The
students in classrooms with green views performed significantly better on
tests of attention than their peers without green views (Li & Sullivan, 2016).
Exposure to vegetation and other forms of nature has also been shown to
promote attentional functioning in adults, when photographs of green set-
tings (Berto, 2005) or window views to green settings (Chang & Chen, 2005)
were the stimuli.
To what extent does engaging with electronic devices affect attentional
functioning for individuals in green spaces? Put another way, does engaging
in a leisure activity with an electronic device in a green setting allow a person
to reap the attention-enhancement benefits of such a space? To address this
question, we conducted an experiment using a randomized controlled design
in which individuals either used or did not use a laptop computer in a rela-
tively barren or green space.
Method
Experimental Settings and Participants
We selected barren and green sites based on two criteria. First, each site had
a nearby building with private indoor rooms of comparable size and layout.
Second, the indoor rooms had to be less than a 2-min walk to both an outdoor
barren setting and an outdoor green setting. We selected four barren settings
in which participants saw no trees or other vegetation, only human-made ele-
ments such as parking lots, walls, or the sides of buildings (Figure 1). We also
selected four green settings in which participants saw considerable vegeta-
tion, especially trees (Figure 2). All outdoor settings had wireless Internet
access.
We used the software G*Power 3.0.10. to decide the appropriate sample
size, which is a method suggested by the UCLA Institute for Digital Research
and Education (n.d.). We set effect size at .5, alpha error probability value at
.05, power value at .95, and found that the total sample size should be at least
40. To ensure a satisfactory power value, we adjusted the total sample size to
80, and in doing so produced a power value of .99. Eighty-one individuals
participated in this study (50 females, 31 males). After we removed four out-
liers and one participant because it rained during the outdoor portion of the
experiment, the power remained at .99.
Participants were recruited with flyers placed in various locations both on
and off of the university campus. Individuals were not eligible to participate
1138 Environment and Behavior 51(9-10)
in the study if they had consumed caffeine within the past 2 hr, alcohol within
the past 12 hr, food or drink within the past hour, or tobacco products within
the past hour. If prospective participants had been diagnosed with a mental
illness, they did not participate in the study.
We excluded data from five individuals because it either rained during the
outdoor portion of the experiment or because the participants’ attention scores
were more than 2 SDs from the mean. Participants’ ages ranged from 17 to 35
years (M = 20.76, SD = 3.02). Participants’ sociodemographic information is
shown in Table 1.
Experimental Conditions
Participants were randomly assigned to one of four conditions: (a) a barren
setting in which they used a laptop, (b) a barren setting with no laptop, (c) a
green setting in which they used a laptop, or (d) a green setting with no laptop
(Table 2).
Figure 1. The four barren settings to which participants were randomly assigned
during the rest portion of the experiment.
Jiang et al. 1139
Procedure
After giving consent to engage in the experiment, participants reviewed the
inclusion criteria to confirm that they had not consumed any substance that
would affect their normal cognitive performance. To account for confound-
ing factors, all participants completed a general background questionnaire to
report their age, gender, race, native language, major of study, and self-
reported chronic mental fatigue.
The experiment began in an indoor room in which only the facilitating
research assistant and the participant were present. The experimental proce-
dure involved only one participant at a time. Before the experiments, the
indoor rooms were arranged in the same configuration and with a consistent
room temperature. Participants sat at a table opposite the research assistant.
Research assistants were trained to follow an identical procedure (Figure 3).
In total, three research assistants administered the experiment to participants.
No one research assistant was assigned to any one condition, such that there
Figure 2. The four green settings to which participants were randomly assigned
during the rest portion of the experiment.
1140 Environment and Behavior 51(9-10)
was an even distribution of different research assistants to the four different
conditions studied.
After collecting background information from participants, we assessed
their attentional functioning with baseline attention tests described in the
Table 1. Demographic Characteristics of Participants.
Demographic variables Number (percentage)
Gender
Male 27 (35.5)
Female 49 (64.5)
Race
African American 3 (3.9)
Asian or Pacific Islanders 45 (59.2)
Caucasian 21 (27.6)
Hispanic 5 (6.6)
Other 1 (1.3)
Native language
English 36 (47.4)
Other 40 (52.6)
Table 2. Participants Were Randomly Assigned to One of Four Groups With
Nearly Equal Representation.
Laptop use No laptop use
Barren setting n = 19 n = 18
Green setting n = 20 n = 19
Figure 3. The procedure used in this experiment included five main activities.
Jiang et al. 1141
measures section below. Next, to induce a same level of cognitive load among
participants, we required participants to engage in 10 min of cognitive activi-
ties. These activities included a 5-min proofreading task (Kaplan, 1995;
Laumann, Garling, & Stormark, 2003; Li & Sullivan, 2016) and a 5-min
subtraction task (Jiang, Chang, & Sullivan, 2014; Li & Sullivan, 2016).
For the proofreading task, participants were given sheets of paper covered
in lines of seemingly random sequences of letters. Looking at one line at a
time, they were instructed to memorize the first three letters in the line and
then identify how many times that exact sequence of letters was repeated in
the remainder of that line. They wrote the answer at the end of the line. The
three-letter sequence was different for every line. Participants were told to
complete as much of the task as they could within 5 min.
For the subtraction task, participants were given a four-digit number (e.g.,
1,038) and asked to continuously subtract 13 from that number. If partici-
pants made an error, the research assistant instructed them to begin again.
Following these activities, participants completed the attention tests for a sec-
ond time.
The research assistant administering the experiment then took the partici-
pant to the assigned outdoor setting. All outdoor settings used were less than
a 1-min walk from the indoor spaces. Participants were instructed to sit on a
fixed chair or bench in the shade to maintain a comfortable temperature and
to reduce screen glare for participants in the laptop group. Participants
assigned to use their laptop were instructed to use their laptop in a manner
similar to when they were taking a break. The break lasted 15 min.
The investigator left the assigned outdoor spot but remained approxi-
mately 50 to 75 ft behind the participant during the rest period. The distance
and out-of-sight direction were chosen to avoid creating psychological stress
on participants and to ensure the investigator could observe and take accurate
notes on participants’ behaviors and the environmental conditions during the
break period.
We reinforced the notion of taking a break by asking participants not to
use their laptops for anything related to work. Individuals in the laptop group
were given the following list of suggested activities to reinforce the instruc-
tion that they were to use their laptops for leisure activities: social media
sites, news sites, YouTube or other video-sharing sites, blogs, online games,
online shopping websites, emails (unrelated to work).
Measures of Attention Functioning
Attentional functioning was assessed with the Digit Span Forward and Digit
Span Backward tests of attentional functioning (Berman et al., 2008; Li &
1142 Environment and Behavior 51(9-10)
Sullivan, 2016; Wang et al., 2016). Both tests required participants to repeat
increasing lengths of digit sequences in forward or reverse order until they
reached two consecutive failures at the same length. We recorded the number
of correct trials before the two consecutive errors as a measure of attentional
functioning. These tests have been used to measure attentional capacity,
short-term memory, and working memory. Each of these measures was
employed three times throughout the experiment: (a) at baseline, (b) after the
cognitive activities, and (c) after the rest period.
Measures of Potential Confounding Factors
We measured three categories of potential confounding factors and controlled
them in statistical analysis (Table 3). First, immediately before the experi-
ment, participants were asked to answer five questions about their normal
attention capabilities (Taylor et al., 2001). These include overall academic
performance of the past 12 months, how well they started necessary tasks
during the past week, how well they concentrated during the past week, how
well they remained patient during the past week, and the frequency of having
problems of memory, attention, or taking action during the past week.
Second, immediately after the experiment, participants were asked to
answer two questions about their habit of using electronic devices. The ques-
tions measured (a) Hours of using electronic screen per week measured by
the 10-cm Visual Analog Scale, which is defined by 11 anchors as 10 equal
segments (Jiang, Li, Larsen, & Sullivan, 2016) and (b) Reliance on electronic
screen for recreation or entertainment measured by the 5-point rating scale
(not at all, a little, somewhat, a lot, or very much; Jiang, Larsen, Deal, &
Sullivan, 2015). Third, participants reported the extent to which they used the
laptop during the rest period and what portion of the rest period the laptop
played sound measured by the 6-point rating scale (100%, 99%-75%, 74%-
50%, 49%-25%, 24%-1%, or 0%). Finally, they reported how many types of
laptop activities they participated in during the rest period (social media sites,
news sites, YouTube or other video-sharing sites, blogs, online games, retail
sites, emails unrelated to work, or other).
The experiment was conducted with one individual at a time between 8:30
a.m. and 5:30 p.m. when temperatures were between 70 and 86°F. The
weather during the experiment was cloudy, partially cloudy and sunny, or
sunny. The experiments were rescheduled when the temperature or weather
did not meet these criteria. Data were collected during summer months in a
university campus in the Midwestern United States to ensure that the green
landscapes were lush and to ensure that the outdoor temperature was com-
fortable. Another reason for choosing summer time was because the campus
1143
Table 3. Three Categories of Potential Confounding Factors Were Surveyed Before or After the Experiment.
Categories Factors n M SD Range Minimum Maximum Instrument
Normal
attention
capabilities
Overall academic performance of
past 12 months
76 3.16 0.59 0-4 2 4 5-point rating scale
How well to start necessary
tasks during the past week
76 3.04 0.60 0-4 2 4 5-point rating scale
How well to concentrate during
the past week
76 3.20 0.57 0-4 2 4 5-point rating scale
How well to remain patient
during the past week
76 3.25 0.70 0-4 2 4 5-point rating scale
Frequency of having problems of
memory, attention, or action
during the past week
76 1.11 0.84 0-4 0 4 5-point rating scale
Habit of using
electronic
device
Hours of using electronic screen
per week
76 21.49 10.11 0-40 3 40 Visual Analog Scale with
11 anchors (0-40)
Reliance on electronic screen for
recreation or entertainment
76 2.92 0.83 0-4 1 4 5-point rating scale
Environmental/
behavioral
factors of the
experiment
Weather 76 1.38 0.54 0-2 0 2 3-point rating scale
Temperature 76 76.47 7.48 0-120 65 86 Degree Fahrenheit
How much of the rest period
used the laptop
39 2.49 0.64 0-5 2 4 6-point rating scale
How much of the rest period the
laptop played the sound
39 1.41 1.48 0-5 0 4 6-point rating scale
How many types of laptop
activities participated in during
the rest period
39 2.31 0.92 0-7 1 4 Seven fixed options and
one open-ended option
Note. There were no missing data. The range is for the available range provided by the questionnaire or device.
1144 Environment and Behavior 51(9-10)
during the summer had few distractions caused by human activities com-
pared with the spring or fall semesters.
Results
Results are presented in three sections. First, we provide descriptive statistics
for three attention tests for each treatment condition. We compare the base-
line and postactivity attention levels among the four conditions to ensure that
there are no pretreatment group differences. Second, we explore the effect of
the treatment conditions on participants’ attention levels after the break and
explore the possibility of an interaction between the conditions. Finally, we
address behavioral factors that could have affected the results.
Pretreatment Attention Scores
To compare attention scores across groups, we created a summary attention
score from the Digit Span Forward and Digit Span Backward tests and exam-
ined the mean differences among the groups before the cognitive tasks, after
the cognitive tasks, and after the rest period. Table 4 includes the summary of
main descriptive statistics of three tests for four conditions. Did the groups dif-
fer before being exposed to the treatment conditions? No, an ANOVA shows
there were no significant differences among the attention scores at baseline,
F(3, 72) = 1.86, p = .144. A repeated-measures ANOVA showed no significant
differences in the participants’ attention levels among the four conditions after
participants engaged in the cognitive tasks, F(3, 72) = 0.35, p = .793. The
Table 4. Descriptive Statistics of Three Attention Tests for Four Conditions.
Conditions n
Baseline test Second test Third test
Range M SD Range M SD Range M SD
Green without
laptop
19 5.0-9.0 6.50 1.20 4.5-9.5 6.32 1.11 5.0-10.0 7.24 1.37
Green with
laptop
20 4.5-7.0 5.83 0.82 4.5-8.0 6.02 1.01 4.5-7.5 6.15 0.86
Barren without
laptop
18 4.5-7.5 5.86 0.82 5.0-7.5 6.08 1.01 5.0-8.0 6.31 0.86
Barren with
laptop
19 4.5-8.0 5.97 1.06 4.5-8.0 6.05 1.04 5.5-8.0 6.24 0.82
Note. The range is from measured minimum scores to measured maximum scores.
Jiang et al. 1145
difference between scores of the baseline and second cognitive tasks are also
not significant, t(75) = −1.04, p = .303. None of the groups had advantages or
disadvantages before engaging in the cognitive tasks or before the break.
Effect of Four Treatment Conditions
Did the treatment conditions affect participants’ attention? We expected par-
ticipants’ scores on attention tests would be higher after a 15-min break than
immediately after the cognitive activities. And indeed, attention scores
increased an average of 0.4 trials higher, a significant difference, t(76) = 4.23,
p < .001. A multivariate ANOVA confirms there is a significant main effect
for the four conditions, F(3, 72) = 5.69, p = .002, partial eta squared
(ηp² hereafter) = .19. Further analyses found a significant main effect for
setting (green vs. barren setting), F(1, 72) = 4.14, p = .046, ηp² = .05, and a
significant main effect for laptop use (with or without laptop), F(1, 72) = 7.03,
p = .010, ηp² = .09. Moreover, as can be seen in Figure 4, there is a significant
Figure 4. Changes in attentional functioning after the rest treatment for four
conditions.
1146 Environment and Behavior 51(9-10)
interaction between setting and laptop use, F(1, 72) = 5.81, p = .019, ηp² = .08.
The model passes the Levene’s Test, F(3, 72) = 0.35, p = .793, indicating
there is equal variance across four treatment conditions.
Do attention scores increase equally across all treatments? To answer this
question, we conducted paired-samples t tests for each condition and, as can
be seen in Table 5, the Green without laptop is the only condition that yielded
a significant change in attention scores, MD = −0.92, SD = 0.80, t(18) = 4.99,
p < .001.
We asked a further question, “Do the findings in Table 3 hold after consid-
ering four categories of potential confounding factors?” That is, when we add
variables related to demographics, self-assessment of attention functioning,
use of electronic devices, and weather conditions during the experiment, do
the findings above change? To address this question, we ran a multivariate
ANOVA in which these potentially confounding variables and the treatment
conditions were included.
The results were indeed the same. We found a significant main effect for
setting (green vs. barren setting), F(1, 59) = 5.82, p = .019, ηp² = .09, and a
significant main effect for laptop use (with or without laptop), F(1, 59) = 7.97,
p = .006, ηp² = .12. Moreover, as can be seen in Figure 4, there is a significant
interaction between setting and laptop use, F(1, 59) = 5.11, p = .027, ηp² = .08.
Other potential confounding factors yielded nonsignificant results (Table 6).
The only condition that produced an increase of attentional functioning
was a green setting in which the participants did not use a laptop. The model
passes the Levene’s Test, F(3, 72) = 0.96, p = .417, indicating there is equal
variance across four treatment conditions. The pairwise comparisons show
that Green without laptop condition yielded significantly better attention
functioning than other three conditions. There were no differences among the
other three groups (Table 7 and Figure 4).
The significant main effect of physical setting demonstrates that the
green setting yielded better attention functioning than the barren setting. The
Table 5. Results of Paired-Samples t Test for Four Conditions (Comparison of
Attention Functioning Before and After the Rest).
n MD SD SE t df Significance
Green without laptop 19 −0.92 0.80 0.18 4.99 18 .001
Green with laptop 20 −0.13 0.63 0.14 0.89 19 .383
Barren without laptop 18 −0.22 0.65 0.15 1.46 17 .163
Barren with laptop 19 −0.18 0.65 0.15 1.24 18 .233
Jiang et al. 1147
Table 6. Results of Multivariate ANOVA Analysis for Four Conditions.
Source df F Significance ηp²
Gender 1 0.51 .479 .01
Age 1 0.01 .915 .01
Race 1 3.40 .070 .06
Language 1 1.28 .262 .02
Academic performance 1 1.58 .213 .03
Capability of starting tasks 1 0.13 .722 .01
Capability of concentration 1 1.29 .261 .02
Capability of remaining patience 1 0.01 .905 .01
Capability of memory 1 0.14 .711 .01
Hours per week of using e-device 1 0.64 .428 .01
Reliance of e-device for
recreation/entertainment
1 0.49 .486 .01
Temperature 1 0.29 .595 .01
Weather 1 0.90 .347 .02
Laptop condition 1 7.97 .006 .12
Setting condition 1 5.82 .019 .09
Laptop × Setting 1 5.11 .027 .08
Error 59
Total 76
Corrected total 75
Note. R-squared = .34 (adjusted R-squared = .15).
Table 7. Results of Pairwise Comparisons Show Difference Among Four
Conditions on Attention Enhancement.
Green without
laptop
Green with
laptop
Barren without
laptop
Barren with
laptop
Green without
laptop
Green with laptop 0.80*** (0.22)
Barren without
laptop
0.70** (0.23) −0.10 (0.22)
Barren with laptop 0.88** (0.24) −0.06 (0.22) 0.04 (0.23)
Note. The results are presented as “mean difference (standard error).” The difference is
presented as a column value minus a row value.
The significance levels of mean difference are marked as p ≥ .05. **p < .01. ***p < .001.
1148 Environment and Behavior 51(9-10)
significant effect of laptop use demonstrates that the without laptop condition
yielded enhanced attentional performance than the with laptop condition. The
interaction and pairwise comparisons make clear that for a person’s attention
to be enhanced, it was not enough to be in a green space; people must be in a
green space without engaging with their electronic device.
A Further Comparison Examining Six Treatment Conditions
Although we have found that laptop conditions yielded significantly different
results on tests of attention, it is possible that participants’ different amounts
of time gazing at the screen contributed to these findings. To examine this
possibility, we further divide the two laptop conditions based on the amount
of time participants reported looking at their computer screen during the
experiment. Data from the posttreatment survey showed that 17 (43.6%) of
the 39 participants looked at their screen at least 50% of time while 22
(56.4%) participants looked at their screen less than 50% of time (Table 8).
Thus, we divide participants’ treatments into six conditions. The six condi-
tions include (a) Green without laptop, (b) Barren without laptop, (c) Green
with laptop (<50% screen time), (d) Barren with laptop (<50% screen time),
(e) Green with laptop (≥50% screen time), and (f) Barren with laptop (≥50%
screen time). Table 9 presents a summary of the main descriptive statistics of
three tests for six conditions.
After this recategorization, we conducted paired-samples t test for each
condition to see whether it yielded significant attention enhancement effects
after the assigned rest treatment. Again, the results show the Green without
laptop is the only condition that yielded a significant enhancement in atten-
tional performance (Table 10).
Next, we ran a multivariate ANOVA analysis to examine the difference
among six conditions. This analysis resulted in similar findings: After con-
trolling for sociodemographic and chronic psychological factors, there is a
main effect for laptop conditions, F(2, 57) = 4.07, p = .022, ηp² = .13.
However, the effect of setting conditions and the interactive effect between
Table 8. Participants Were Randomly Assigned to One of Six Groups With
Nearly Equal Representation.
Laptop use
(≥50% screen time)
Laptop use
(<50% screen time)
No laptop
use
Green setting n = 8 n = 12 n = 19
Barren setting n = 9 n = 10 n = 18
Jiang et al. 1149
laptop and setting conditions are nonsignificant. In addition, all potential
confounding factors yielded nonsignificant effects (Table 11). The analysis
passes Levene’s Test, F(5, 70) = 1.32, p = .267. Furthermore, results of pair-
wise comparisons show the “Green without laptop” condition yielded signifi-
cantly better attention performance than any of the other five conditions
(Table 12 and Figure 5). There is no significant difference between any other
pair of the five other conditions. These results further confirm physically
being in a green space without engaging with an electronic device is the only
condition that produced significant enhancement of attentional functioning.
Table 9. Descriptive Statistics of Three Attention Tests for Six Conditions.
Conditions n
Baseline test Second test Third test
Range M SD Range M SD Range M SD
Green without laptop 19 5.0-9.0 6.50 1.20 4.5-9.5 6.32 1.11 5.0-10.0 7.23 1.37
Barren without
laptop
18 4.5-7.5 5.86 0.89 5.0-7.5 6.08 0.77 5.0-8.0 6.31 0.84
Green with laptop
(<50% screen time)
12 4.5-7.0 6.04 0.72 4.5-8.0 6.13 1.09 5.0-7.5 6.17 0.86
Barren with laptop
(<50% screen time)
10 4.5-7.0 5.90 0.88 5.0-7.0 5.85 0.67 5.5-7.0 6.05 0.55
Green with laptop
(≥50% screen time)
8 4.5-6.5 5.50 0.89 4.5-7.0 5.88 0.92 4.5-7.0 6.13 0.92
Barren with laptop
(≥50% screen time)
9 4.5-8.0 6.06 1.29 4.5-8.0 6.28 1.35 5.5-8.0 6.44 1.04
Note. The range is from measured minimum scores to measured maximum scores.
Table 10. Results of Paired-Samples t test for Six Conditions (Comparison of
Attention Functioning After and Before the Rest).
n MD SD SE t df Significance
Green without laptop 19 −0.92 0.80 0.18 −4.99 18 .001
Barren without laptop 18 −0.22 0.65 0.15 −1.46 17 .163
Green with laptop
(<50% screen time)
12 −0.04 0.75 0.22 −0.19 11 .851
Barren with laptop
(<50% screen time)
10 −0.20 0.67 0.21 −0.68 9 .373
Green with laptop
(≥50% screen time)
8 −0.25 0.38 0.13 −1.87 7 .104
Barren with laptop
(≥50% screen time)
9 −0.16 0.66 0.22 −0.76 8 .471
1150 Environment and Behavior 51(9-10)
Table 11. Results of Multivariate ANOVA Analysis for Six Conditions.
Source df F Significance ηp²
Gender 1 0.43 .512 .01
Age 1 0.02 .887 .01
Race 1 3.34 .073 .06
Language 1 1.51 .224 .03
Academic performance 1 1.27 .265 .02
Capability of starting tasks 1 0.17 .681 .01
Capability of concentration 1 1.03 .314 .02
Capability of remaining patience 1 0.01 .921 .01
Capability of memory 1 0.06 .810 .01
Hours per week of using e-device 1 0.67 .416 .01
Reliance of e-device for
recreation/entertainment
1 0.51 .479 .01
Temperature 1 0.18 .669 .01
Weather 1 0.92 .341 .02
Laptop condition 2 4.07 .022 .13
Setting condition 1 2.56 .115 .04
Laptop × Setting 2 2.40 .100 .08
Error 57
Total 76
Corrected total 75
Note. R-squared = .34 (adjusted R-squared = .14).
Table 12. Results of Pairwise Comparison Analysis Shows Difference among Six
Conditions on Attention Enhancement.
1 2 3 4 5 6
1
2 0.70** (0.23)
3 0.88*** (0.26) 0.18 (0.26)
4 0.72** (0.27) 0.02 (0.27) −0.16 (0.30)
5 0.67* (0.29) −0.03 (0.29) −0.21 (0.32) −0.05 (0.33)
6 0.75** (0.28) 0.05 (0.28) −0.13 (0.31) 0.03 (0.32) 0.08 (0.34)
Note. The numbers indicate six conditions: (a) green without laptop, (b) barren without
laptop, (c) green with laptop (<50% screen time), (d) barren with laptop (<50% screen time),
(e) green with laptop (≥50% screen time), and (f) barren with laptop (≥50% screen time). The
difference is presented as a column value minus a row value. The results are presented as
“mean difference (standard error).”
The significance levels of mean difference are marked as p ≥ .05. *p < .05. **p < .01. ***p < .001.
Jiang et al. 1151
Discussion
In this study, 81 healthy adults were randomly assigned to outdoor spaces that
had views to green or barren settings and, within those spaces, to either use
their laptop computers for leisure activities or to sit and relax without using
their laptops. At the end of the experiment, participants in the green view
condition who did not use their laptops scored significantly higher on tests of
attentional functioning than their peers in the other three conditions. There
were no significant differences in attentional functioning after the break
among participants in the Green with Laptop condition(s) or the two barren
conditions. These findings establish a causal relationship: When individuals
spend time in green outdoor environments without engaging their laptop
computers, their attentional functioning improves. The same is not true for
individuals who use their laptops in green settings or for those assigned to the
barren environment. For these individuals, a 15-min break was the equivalent
of no break at all.
Figure 5. Changes in attentional functioning after the rest treatment for six
conditions.
1152 Environment and Behavior 51(9-10)
Contributions
To the best of our knowledge, this is the first study to compare the combined
effect of exposure to green landscapes and the use of electronic devices on
attentional functioning. The findings demonstrate that the attention enhance-
ment effects of green settings are undermined by the use of an electronic
device. When individuals used their electronic device in a green setting, they
did not experience the benefits to their capacity to pay attention that green
spaces typically convey. That is, taking a break in a restorative setting while
using a laptop had the same impact on attentional functioning as taking no
break at all.
Why did taking a break in a green setting while using a laptop not result in
higher attentional scores than participants who took a break in a barren set-
ting? The green settings, after all, were quite green and these same settings
produced significant improvements in attention scores for those individuals
who did not use their laptop computers. The explanation is likely found in
Kaplan’s explanation of attention restoration theory (Kaplan, 1995), which
describes two ways of paying attention: top-down and bottom-up. Kaplan
divides bottom-up attention into two categories: soft fascination and hard
fascination. The proposed mechanism behind attention restoration is that
urban nature engages soft fascination, where leaves blowing in a breeze or a
running stream gently capture people’s attention while allowing their mind to
wander (Kaplan, 1995).
The participants in the green-no laptop condition likely experienced soft
fascination while they looked at the green landscape. Engaging their capacity
for soft fascination gave their top-down attention an opportunity to rest and
improve. For these participants, the 15-min break resulted in significantly
higher scores on the tests of attention. Participants in the green-laptop treat-
ment, however, continued to use their top-down attention as they engaged
their computers and thus did not rest or improve their attentional capacities.
Note that although participants used their laptops for leisure activities, these
activities still required top-down attention. It is interesting to note that even
individuals who spent less than 50% of the 15-min break looking at their
computer screen in the green setting did not gain significant attention
enhancement. Attention restoration theory predicts that without an opportu-
nity to rest one’s top-down attention, there will be little to no enhancement of
attentional functioning (Kaplan & Berman, 2010). The results here support
this prediction to the extent that they show an improvement in attentional
functioning only in the green without laptop condition.
Finally, we found the laptop condition (with or without a laptop) had a
stronger effect than the setting condition (green or barren) on attention
Jiang et al. 1153
functioning and that there was a significant interactive effect between them.
These findings suggest the impacts of electronic devices on our mental health
should not be neglected by environmental designers. Content of virtual real-
ity or augmented reality (virtual reality within the context of a physical set-
ting) would be a ubiquitous, even inseparable part of future urban residents’
daily contact with physical settings (Chicchi Giglioli et al., 2016). Moreover,
it is possible that paying too much attention to electronic devices would
reduce the positive impact that some kinds of physical settings (typically
green) have on mental health. The findings reveal a new phenomenon that
needs environmental planners and designers’ great attention (Chi, Kang, &
Wang, 2013).
Implications
The initial relationships between treatments and attention held after a number
of potentially confounding factors were considered. The evidence presented
here demonstrates that human behavior in a restorative setting can affect
attentional functioning. Planners, designers, and policy makers can intervene
to provide green spaces for different users, but if those users choose to use
their laptops or other electronic devices in those spaces, they likely will not
reap the attention enhancement benefit of taking a break in a restorative set-
ting. Two implications emerge from these findings.
First, this research is consistent with previous findings showing it is
important to transform barren urban settings into greener settings because
barren settings do not foster improvements in attention functioning—no
matter if people are using computing devices or not. Thus, providing easy
access to green spaces (e.g., urban parks, streets lined with trees, roof
gardens, rain gardens) is especially crucial for individuals learning and
working in settings that are devoid of vegetation (Suppakittpaisarn, Jiang,
Slavenas, & Sullivan, 2018). The attentional demands placed on individu-
als today result in the majority of people experiencing mental fatigue on
a daily basis (Sullivan, 2015). Mental fatigue makes individuals prone to
errors, impulsivity, and irritability (Kaplan, 1995). Thus, providing
greater access to green spaces will help individuals function at higher
levels than if they had only barren spaces around their homes, work
places, or schools.
Second, although the availability of green environments is important, it is
not enough. Given that using an electronic device in a restorative setting miti-
gates the benefits on attention of that setting, individuals would be wise to put
away their laptops and other electronic devices in restorative settings to
restore their capacity to pay attention.
1154 Environment and Behavior 51(9-10)
Future Directions
This is the first study to demonstrate that the use of portable electronic devices
mitigates the attention enhancing effects of taking a break in a green setting.
Certainly, these findings must be replicated in a variety of conditions before
we are convinced of their generalizability. Given that electronic devices are
nearly ubiquitous and that many people have easy access to smart phones and
tablets, this study should be replicated with participants using a variety of
mobile electronic devices because screen size (de Kort, Meijnders, Sponselee,
& Ijsselsteijn, 2006) and content used may be confounding factors. Based on
the existing literature, our findings suggest that the relationship between lap-
top use and attention enhancement may be similar for individuals in different
age categories. Just as we know that exposure to green settings improves
attentional functioning for adolescents (Li & Sullivan, 2016), adults (Berto,
2005; Roe & Aspinall, 2011), and seniors (Gamble, Howard, & Howard,
2014), we expect the findings of the current study will generalize to people
across the age spectrum too—from school children to older adults. In addition,
we suggest future researchers will examine this research question by using
other measures of attention functioning (Ohly et al., 2016).
Although the results of this study are best applied to individual behavior,
there are some implications for planners and designers. At first glance, the
findings here suggest that a good design of physical spaces can hardly sup-
port attention enhancement if people engage with their electronic devices.
But perhaps that notion underestimates the power of design. If we were to
challenge designers to create settings that encouraged engagement of bottom-
up attention in a more compelling fashion than did the spaces we put our
participants in, would such spaces pull users’ attention away from their elec-
tronic devices for long enough periods that their top-down attention would
improve? The findings here pose questions for future research about the
design of restorative settings (Sullivan et al., 2014).
What might spaces that draw more heavily on bottom-up attention be like?
We propose they would contain elements from nature that grab and hold bot-
tom-up attention more than a typical green space on a campus. These settings
might include moving water, wildlife, fire, or other natural elements that
move and change. The advent of augmented reality—a technology that super-
imposes a computer-generated image on a user’s view of the real world, thus
providing a composite view—might also be a way of creating such places
(Marques, Cladera, & Tenedório, 2017). But that, of course, would require
the use of electronic devices.
In this study, 10 min of cognitive tasks did not yield a significant decrease
in participants’ attentional performance. We see two possible explanations
Jiang et al. 1155
regarding why attention scores did not diminish after the 10-min tasks. On
the one hand, it is possible that participants were mentally fatigued when they
arrived for the experiment. This possibility seems reasonable because, during
the summer semester, participants were likely taking courses or were involved
in research that put considerable demands on their attention. In addition, per-
vious research using a 5-min Sustained Attention Response Task (SART) has
been shown to degrade attention (Berto, 2005). That 10-min of cognitive
tasks did not reduce attention scores suggests that participants were fatigued
when they began the experiment.
On the other hand, it is possible that the 10-min session was not long
enough to produce measurable declines in attentional functioning. Although
we cannot rule this out as a possibility, it seems less likely than that the par-
ticipants arrived in a fatigued state.
Although the task designed to fatigue participants’ attention did not result
in lower attention scores, the treatments did produce significant differences.
The only group to have a significant increase in attentional functioning at the
end of the treatment period was the Green-no laptop condition. These find-
ings beg the question, “To whom do these findings generalize? Are they
applicable to people who are mentally fatigued, or to people who are not
mentally fatigued?” At this moment, we cannot say; this is clearly an area that
deserves further study.
Future research might also examine the extent to which esthetic value or
other attributes of a place influence visitors’ preference and behavior (Gobster,
Nassauer, Daniel, & Fry, 2007; Nassauer, 2011). To what extent can settings
be designed to promote walking or sitting in a fashion that is engaging enough
that people are not drawn to use their electronic devices?
Conclusion
In this study, using an electronic device substantially counteracted the atten-
tion enhancing benefits of being in a green space. These findings suggest that
a common assumption people have about taking a break by using an elec-
tronic device may be counterproductive. Individuals in this study who were
randomly assigned to take a break while using an electronic device showed
no improvement in their attentional performance after a 15-min break. To
reap the benefits of being in a restorative green space, it appears that one
needs to take in the softly fascinating objects in the landscape. Perhaps the
most important lesson from this study is that to enhance your attention func-
tioning it is not enough to go to a green space; the evidence here suggests you
have to put aside your electronic devices in that space.
1156 Environment and Behavior 51(9-10)
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research,
authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research,
authorship, and/or publication of this article: This work was supported by the USDA
Forst Service Northern Research Station and with laboratory seed funding from the
Faculty of Architecture at the University of Hong Kong.
References
Ai, M. (2012). How computer and Internet use influences mental health: A five-wave
latent growth model. Asian Journal of Communication, 23, 175-190. doi:10.108
0/01292986.2012.725179
Attia, N. A., Baig, L., Marzouk, Y. I., & Khan, A. (2017). The potential effect of
technology and distractions on undergraduate students’ concentration. Pakistan
Journal of Medical Sciences, 33, 860-865. doi:10.12669/pjms.334.12560
Beil, K., & Hanes, D. (2013). The influence of urban natural and built environ-
ments on physiological and psychological measures of stress-A pilot study.
International Journal of Environmental Research and Public Health, 10, 1250-
1267. doi:10.3390/ijerph10041250
Berman, M. G., Jonides, J., & Kaplan, S. (2008). The cognitive benefits of inter-
acting with nature. Psychological Science, 19, 1207-1212. doi:10.1111/j.1467-
9280.2008.02225.x
Berto, R. (2005). Exposure to restorative environments helps restore attentional
capacity. Journal of Environmental Psychology, 25, 249-259. doi:10.1016/j.
jenvp.2005.07.001
Chang, C. Y., & Chen, P. K. (2005). Human response to window views and indoor
plants in the workplace. HortScience, 40, 1354-1359.
Cheever, N. A., Rosen, L. D., Carrier, L. M., & Chavez, A. (2014). Out of sight is
not out of mind: The impact of restricting wireless mobile device use on anxiety
levels among low, moderate and high users. Computers in Human Behavior, 37,
290-297. doi:10.1016/j.chb.2014.05.002
Chi, H.-L., Kang, S.-C., & Wang, X. (2013). Research trends and opportunities of
augmented reality applications in architecture, engineering, and construction.
Automation in Construction, 33, 116-122. doi:10.1016/j.autcon.2012.12.017
Chicchi Giglioli, I. A., Chirico, A., Cipresso, P., Serino, S., Pedroli, E., Pallavicini,
F., & Riva, G. (2016). Feeling ghost food as real one: Psychometric assessment
of presence engagement exposing to food in augmented reality. In S. Serino,
A. Matic, D. Giakoumis, G. Lopez, & P. Cipresso (Eds.), Communications in
computer and information science (Vol. 604. pp. 99-109). Pervasive com-
puting paradigms for mental health. MindCare 2015. Springer. Cham. 6330
doi:10.1007/978-3-319-32270-4_10
Jiang et al. 1157
de Kort, Y. A. W., Meijnders, A. L., Sponselee, A. A. G., & Ijsselsteijn, W. A.
(2006). What’s wrong with virtual trees? Restoring from stress in a mediated
environment. Journal of Environmental Psychology, 26, 309-320. doi:10.1016/j.
jenvp.2006.09.001
Frumkin, H., Bratman, G. H., Breslow, S. J., Cochran, B., Kahn, P. H., Lawler, J. L.,
& Wood, S. A. (2017). Nature contact and human health: A research agenda.
Environmental Health Perspectives, 125(7), 075001. doi:10.1289/EHP1663
Gamble, K. R., Howard, J. H., Jr., & Howard, D. V. (2014). Not just scenery: Viewing
nature pictures improves executive attention in older adults. Experimental Aging
Research, 40, 513-530. doi:10.1080/0361073x.2014.956618
Gobster, P. H., Nassauer, J. I., Daniel, T. C., & Fry, G. (2007). The shared landscape:
What does aesthetics have to do with ecology? Landscape Ecology, 22, 959-972.
doi:10.1007/s10980-007-9110-x
Jiang, B., Chang, C.-Y., & Sullivan, W. C. (2014). A dose of nature: Tree cover, stress
reduction, and gender differences. Landscape and Urban Planning, 132, 26-36.
doi:10.1016/j.landurbplan.2014.08.005
Jiang, B., Larsen, L., Deal, B., & Sullivan, W. C. (2015). A dose–response curve
describing the relationship between tree cover density and landscape prefer-
ence. Landscape and Urban Planning, 139, 16-25. doi:10.1016/j.landurb-
plan.2015.02.018
Jiang, B., Li, D., Larsen, L., & Sullivan, W. C. (2016). A dose-response curve describing
the relationship between urban tree cover density and self-reported stress recov-
ery. Environment and Behavior, 48, 607-629. doi:10.1177/0013916514552321
Jiang, B., Zhang, T., & Sullivan, W. C. (2015). Healthy cities: Mechanisms and
research questions regarding the impacts of urban green landscapes on public
health and well-being. Landscape Architecture Frontiers, 3, 24-35.
Kaplan, R., & Kaplan, S. (1989). The experience of nature: A psychological perspec-
tive. New York, NY: Cambridge University Press.
Kaplan, S. (1995). The restorative benefits of nature—Toward an integrative frame-
work. Journal of Environmental Psychology, 15, 169-182.
Kaplan, S., & Berman, M. G. (2010). Directed attention as a common resource
for executive functioning and self-regulation. Perspectives on Psychological
Science, 5, 43-57. doi:10.1177/1745691609356784
Kaplan, S., & Kaplan, R. (2003). Health, supportive environments, and the reasonable
person model. American Journal of Public Health, 93, 1484-1489.
Kemp, S. (2018). Digital in 2018: World’s Internet users pass the 4 billion mark.
Available from https://wearesocial.com/
Korpela, K. M., Ylen, M., Tyrvainen, L., & Silvennoinen, H. (2010). Favorite green,
waterside and urban environments, restorative experiences and perceived health
in Finland. Health Promotion International, 25, 200-209. doi:10.1093/heapro/
daq007
Kuo, F. E., & Sullivan, W. C. (2001). Aggression and violence in the inner city—
Effects of environment via mental fatigue. Environment and Behavior, 33, 543-
571.
1158 Environment and Behavior 51(9-10)
Kuo, F. E., & Taylor, A. F. (2004). A potential natural treatment for attention-deficit/
hyperactivity disorder: Evidence from a national study. American Journal of
Public Health, 94, 1580-1586.
Kweon, B.-S., Ellis, C. D., Lee, J., & Jacobs, K. (2017). The link between school
environments and student academic performance. Urban Forestry & Urban
Greening, 23, 35-43. doi:10.1016/j.ufug.2017.02.002
Laumann, K., Garling, T., & Stormark, K. M. (2003). Selective attention and heart
rate responses to natural and urban environments. Journal of Environmental
Psychology, 23, 125-134. doi:10.1016/s0272-4944(02)00110-x
Lee, Y. S., Han, D. H., Kim, S. M., & Renshaw, P. F. (2013). Substance abuse pre-
cedes Internet addiction. Addictive Behaviors, 38, 2022-2025. doi:10.1016/j.add-
beh.2012.12.024
Li, D., & Sullivan, W. C. (2016). Impact of views to school landscapes on recovery
from stress and mental fatigue. Landscape and Urban Planning, 148, 149-158.
doi:10.1016/j.landurbplan.2015.12.015
Marques, L., Cladera, J. R., & Tenedório, J. A. (2017, October). Valorisation of urban
elements through 3D models generated from image matching point clouds and
augmented reality visualization based in mobile platforms. Paper presented at the
SPIE Remote Sensing.
Matsuoka, R. H. (2010). Student performance and high school landscapes: Examining
the links. Landscape and Urban Planning, 97, 273-282. doi:10.1016/j.landurb-
plan.2010.06.011
Mobile Fact Sheet. (2017). Retrieved from http://www.pewinternet.org/fact-sheet/
mobile/
Nassauer, J. I. (2011). Care and stewardship: From home to planet. Landscape and
Urban Planning, 100, 321-323. doi:10.1016/j.landurbplan.2011.02.022
Ohly, H., White, M. P., Wheeler, B. W., Bethel, A., Ukoumunne, O. C., Nikolaou,
V., & Garside, R. (2016). Attention restoration theory: A systematic review of
the attention restoration potential of exposure to natural environments. Journal
of Toxicology and Environmental Health, Part B, 19, 305-343. doi:10.1080/109
37404.2016.1196155
Perlow, L. A. (2012). Sleeping with your smartphone: How to break the 24/7 habit
and change the way you work. Boston, MA: Harvard Business Press.
Poon, K.-T., Teng, F., Wong, W.-Y., & Chen, Z. (2016). When nature heals: Nature
exposure moderates the relationship between ostracism and aggression. Journal
of Environmental Psychology, 48, 159-168. doi:10.1016/j.jenvp.2016.10.002
Pretty, J., Peacock, J., Sellens, M., & Griffin, M. (2005). The mental and physi-
cal health outcomes of green exercise. International Journal of Environmental
Health Research, 15, 319-337. doi:10.1080/09603120500155963
Roe, J., & Aspinall, P. (2011). The restorative benefits of walking in urban and rural
settings in adults with good and poor mental health. Health & Place, 17, 103-113.
doi:10.1016/j.healthplace.2010.09.003
Sahin, C. (2011). An analysis of Internet addiction levels of individuals according
to various variables. Turkish Online Journal of Educational Technology, 10(4),
60-66.
Jiang et al. 1159
Short, J. E. (2013). Paying attention: How concurrent media exposure, cross media
viewership and multitasking are forever changing traditional media behaviors.
San Diego, CA: Marshall School of Business.
Sparrow, B., Liu, J., & Wegner, D. M. (2011). Google effects on memory: Cognitive
consequences of having information at our fingertips. Science, 333, 776-778.
doi:10.1126/science.1207745
Sullivan, W. C. (2015). In search of a clear head. In R. K. A. Basu (Ed.), Fostering
reasonableness: Supportive environments for bringing out our best (pp. 54-69).
Ann Arbor: University of Michigan Press.
Sullivan, W. C., & Chang, C. Y. (2011). Mental health and the built environment. In
A. L. Dannenberg, H. Frumkin, & R. J. Jackson (Eds.), Making healthy places:
Designing and building for health, well-being, and sustainability (pp. 106-116).
Washington, DC: Island Press.
Sullivan, W. C., Frumkin, H., Jackson, R. J., & Chang, C.-Y. (2014). Gaia meets
Asclepius: Creating healthy places. Landscape and Urban Planning, 127, 182-
184. doi:10.1016/j.landurbplan.2014.03.005
Sullivan, W. C., & Kaplan, R. (2015). Nature! Small steps that can make a big dif-
ference. HERD: Health Environments Research & Design Journal, 9(2), 6-10.
doi:10.1177/1937586715623664
Suppakittpaisarn, P., Jiang, B., Slavenas, M., & Sullivan, W. C. (2018). Does density
of green infrastructure predict preference? Urban Forestry & Urban Greening.
Advance online publication. doi:10.1016/j.ufug.2018.02.007
Swing, E. L. (2012). Plugged in: The effects of electronic media use on attention
problems, cognitive control, visual attention, and aggression (Doctoral disserta-
tion). Iowa State University. Retrieved from https://lib.dr.iastate.edu/cgi/view-
content.cgi?article=3607&context=etd
Taylor, A. F., Kuo, F., & Sullivan, W. C. (2001). Coping with ADD: The surpris-
ing connection to green play settings. Environment and Behavior, 33, 54-77.
doi:10.1177/00139160121972864
Tennessen, C. M., & Cimprich, B. (1995). Views to nature—Effects on attention.
Journal of Environmental Psychology, 15, 77-85.
UCLA Institute for Digital Research and Education. (n.d.). Power analysis for paired
sample t test | G*power data analysis examples. UCLA: Statistical Consulting
Group. Retrieved from https://stats.idre.ucla.edu/other/gpower/power-analysis-
for-paired-sample-t-test/
van der Aa, N., Overbeek, G., Engels, R. C. M. E., Scholte, R. H. J., Meerkerk, G.-J.,
& Van den Eijnden, R. J. (2009). Daily and compulsive Internet use and well-
being in adolescence: A diathesis-stress model based on big five personality traits.
Journal of Youth and Adolescence, 38, 765-776. doi:10.1007/s10964-008-9298-3
Wang, X., Rodiek, S., Wu, C., Chen, Y., & Li, Y. (2016). Stress recovery and restor-
ative effects of viewing different urban park scenes in Shanghai, China. Urban
Forestry & Urban Greening, 15, 112-122. doi:10.1016/j.ufug.2015.12.003
Weiss, M. D., Baer, S., Allan, B. A., Saran, K., & Schibuk, H. (2011). The screens
culture: Impact on ADHD. Attention Deficit and Hyperactivity Disorders, 3, 327-
334. doi:10.1007/s12402-011-0065-z
1160 Environment and Behavior 51(9-10)
World Health Organization. (2015). Public health implications of excessive use of
the Internet, computers, smartphones and similar electronic device: Meeting
report. Report of the meeting conducted at Main Meeting Hall, Foundation for
Promotion of Cancer Research, National Cancer Research Centre, Tokyo, Japan,
27-29 August 2014.
Author Biographies
Bin Jiang is an assistant professor in the Department of Architecture and a lab direc-
tor at the University of Hong Kong. He is a cochair of research and methods track in
the Council of Educators in Landscape Architecture (CELA). He holds a PhD in land-
scape architecture from the University of Illinois at Urbana–Champaign. His research
work examines the impacts of the built environment on human health, environmental
justice, environmental safety and criminology, and virtual reality technology.
Rose Schmillen is a freelance designer and consultant in Trinidad and Tobago. She
holds a master of landscape architecture from the University of Illinois at Urbana–
Champaign. Her research focuses on environment, human health, and human
behavior.
William C. Sullivan is professor and head of landscape architecture at the University
of Illinois at Urbana–Champaign. He examines the health impacts of having regular
contact with urban green infrastructure.
... If you are feeling a headache coming on, some mental fatigue, perhaps anxiety and panic or feelings of being overwhelmed, you might be suffering from technostress, which includes these documented symptoms as well as direct or indirect negative effects on behaviour, thought, attitude and psychology (Naas Cook 2015). We know that technology has a negative impact on our ability to restore our cognitive functions (Jiang, Schmillen and Sullivan 2019). ...
... Trees, grass, flowers and even just fresh (crisp winter) air offer us a sense of being away from our routine, everyday settings and allow our brains to rest. The catch is that our devices actually counteract the benefits we reap from natural settings (Atchley, Strayer and Atchley 2012;Jiang, Schmillen and Sullivan 2019). ...
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Byra, M. (2020). Teaching SPECTRUM style – Part 3: Learning through critical thinking. Runner Journal: Alberta Health and Physical Education Council, 51(1), 27-33. ABSTRACT: This article is the third in a series of three articles on the Spectrum of Teaching Styles (Mosston & Ashworth 2008). The primary purpose of this three article series is to help teachers expand their toolbox of instructional strategies to meet the diverse needs of their students and the multiple learning outcomes associated with teaching physical education K-12. The focus of this third article is on the Spectrum’s production cluster of teaching styles, styles that require the learners to discover knowledge through inductive and deductive reasoning, inquiry, and problem solving, styles that evoke “cognitive dissonance” (Mosston & Ashworth 2008). Within this cluster of teaching styles, there appear to be two groupings, styles F-H (student discovery learning) and styles I-K (student initiated learning). Styles F (Guided Discovery), G (Convergent Discovery), and H (Divergent Discovery) are based upon the premise of learners discovering knowledge. These three styles emphasize students’ cognitive development, triggering specific thinking processes like comparing, contrasting, hypothesizing, discovering, and creating (Chatoupis, 2013; Cleland, 1994; McBride, 1992). Styles I (Learner-designed Individual Program), J (Learner-initiated), and K (Self-teaching) also emphasize learners’ activation to seek knowledge, but through self-initiated learning. It has been found that styles I-K impact students’ perceptions of autonomy more than the act of discovering knowledge (Papaioannou, Theodorakis, & Goudas, 2011). In the remainder of this article, I describe Styles F-K within the structure of these two sub-groupings (F-H and I-K), provide example scenarios for the styles, and discuss them in light of Alberta’s K–12 physical education learning outcomes (Alberta Learning, 2000).
... Following these stress tasks, stress levels were again measured with the VAS for the second time (T2). The TSST combined with a proofreading test has been demonstrated to induce stress in participants (Jiang et al. 2019;Li and Sullivan 2016). ...
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Exposure to natural landscapes can benefit human health. However, several knowledge gaps remain regarding the impacts of duration and cultural differences on the health benefits of nature. If these gaps are filled, designers and planners can better design landscapes for stress recovery. This study examined the effects of durations of virtual exposure to the built and natural environments across three countries using an experimental design. Two-hundred and seventy people from USA, Taiwan, and Thailand were induced with an acute stressor and then randomly assigned to watch 360° videos of urban or natural landscapes for 1, 5, or 15 min. Self-reported stress recovery data were collected before and after the exposure. The results suggested that gender and duration of exposure to virtual natural landscapes impacted stress recovery. Female participants recovered from stress more when exposed to the virtual natural landscapes than urban landscapes. Among those participants who were exposed to virtual natural landscapes, 5 min of exposure resulted in greater stress recovery than shorter or longer durations of exposure. Perceived familiarity did not influence the extent of stress recovery. These findings support previous research on how nature exposure is related to stress recovery and varies by dosage, leading to better understanding toward landscape design. Future studies should explore other measures of stress, different landscape designs, participants' immersion, and levels of control in simulated nature scenes.
... At the first glance, these findings are surprising given that many studies have found that open spaces are benefitical for health although their effects might be smaller than those of forest (please refer to 4.1.2). The most frequently reported benefits include reducing stress, fatigue, and negative emotions (Jiang, Schmillen, et al., 2019;Ulrich et al., 1991); promoting physical activities (Giles-Corti et al., 2005); promoting social cohesion (Jennings & Bamkole, 2019;Schmidt et al., 2019); and reducing the incidence of noninfectious chronic diseases, which mainly include cardiovascular diseases, stroke, cancers, and diabetes . Open spaces, similar as other green spaces, might enhance human's resistance to infection risk through enhancing mental health and then immunization functioning, and reduce solid aerosols (e. g., PM 2.5) in the air that might serve as a SARS-CoV-2 carrier as we discussed in 4.4.1. ...
Article
The coronavirus pandemic is an ongoing global crisis that has profoundly harmed public health. Although studies found exposure to green spaces can provide multiple health benefits, the relationship between exposure to green spaces and the SARS-CoV-2 infection rate is unclear. This is a critical knowledge gap for research and practice. In this study, we examined the relationship between total green space, seven types of green space, and a year of SARS-CoV-2 infection data across 3,108 counties in the contiguous United States, after controlling for spatial autocorrelation and multiple types of covariates. First, we examined the association between total green space and SARS-CoV-2 infection rate. Next, we examined the association between different types of green space and SARS-CoV-2 infection rate. Then, we examined forest–infection rate association across five time periods and five urbanicity levels. Lastly, we examined the association between infection rate and population-weighted exposure to forest at varying buffer distances (100m to 4km). We found that total green space was negative associated with the SARS-CoV-2 infection rate. Furthermore, two forest variables (forest outside park and forest inside park) had the strongest negative association with the infection rate, while open space variables had mixed associations with the infection rate. Forest outside park was more effective than forest inside park. The optimal buffer distances associated with lowest infection rate are within 1,200m for forest outside park and within 600m for forest inside park. Altogether, the findings suggest that green spaces, especially nearby forest, may significantly mitigate risk of SARS-CoV-2 infection.
... Additionally, there may have been reduced potential for improvement given that participants were pinged on cell phones and required to take multiple surveys throughout their walk. Along these lines, previous research has found that using portable electronic devices while in a natural environment diminished attention restoration (Jiang et al., 2019). Future work should attempt to replicate these results, which may help determine boundary conditions under which cognitive improvements are or are not seen after exposure to natural environments. ...
Article
Research has consistently shown differences in affect and cognition after exposure to different physical environments. The time course of these differences emerging or fading during exploration of environments is less explored, as most studies measure dependent variables only before and after environmental exposure. In this within-subject study, we used repeated surveys to measure differences in thought content and affect throughout a one-hour environmental exploration of a nature conservatory and a large indoor mall. At each survey, participants reported on aspects of their most recent thoughts (e.g., thinking of the present moment vs. the future; thinking positively vs. negatively) and state affect. Using Bayesian multi-level models, we found that while visiting the conservatory, participants were more likely to report thoughts about the past, more positive and exciting thoughts, and higher feelings of positive affect and creativity. In the mall, participants were more likely to report thoughts about the future and higher feelings of impulsivity. Many of these differences in environments were present throughout the one-hour walk, however some differences were only evident at intermediary time points, indicating the importance of collecting data during exploration, as opposed to only before and after environmental exposures. We also measured cognitive performance with a dual n-back task. Results on 2-back trials replicated results from prior work that interacting with nature leads to improvements in working-memory performance. This study furthers our understanding of how thoughts and feelings are influenced by the surrounding physical environment and has implications for the design and use of public spaces.
... Prolonged electronic device use may increase the risk of depression and feelings of loneliness [42]. Visiting green spaces can provide a break from electronic device use [83]. ...
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Parks with green spaces afford many opportunities for and encourage a variety of outdoor leisure activities, which improves park users' physical and mental health in Western countries. However, little is known about whether and how leisure constraint factors relate to the mental health of park users in non-Western countries. This study explored the relationship between leisure constraint factors, demographic variables, and park users’ mental health using multivariate multiple regression analysis. Data were collected from June 22 to July 19, 2021, from 506 respondents in seven major parks in Ningbo, China. Analysis yielded five leisure constraint factors (facilities and environment, accessibility, stress, intrapersonal, time) and three mental health factors (social dysfunction, general dysphoria, anxiety) among park users, and further found accessibility and stress among constraint factors to be significant predictors of social dysfunction among mental health factors; in addition, stress and facilities and environment were significant predictors of general dysphoria, and stress was a significant predictor of anxiety, while intrapersonal and time predicted none. Of the demographic variables, group composition, visit frequency, education, and income were significant predictors of social dysfunction; sex and marital status were significant predictors of general dysphoria; and group composition and visit frequency were significant anxiety predictors. Management implications The findings have implications for urban managers and policymakers in urban parks and recreation in a Chinese urban park context. Firstly, we found the accessibility constraint factor and visit frequency were positively related to park users' mental health. Hence, managers may consider building more parks and green spaces, providing more urban park information, and holding more activities to reduce accessibility constraints and encourage more frequent urban park uses. Second, the facilities and environment constraint factors, such as old leisure facilities and old-fashioned leisure activities, were associated with lower mental health perceptions. Managers may consider designing more modern, livable, and user-friendly facilities and offer programs and leisure activities to attract more park users. Third, managers may consider holding social gatherings in parks to improve the mental health of solo visitors. Finally, the stress constraint factor was the most significant factor hindering park users' mental health. Therefore, to enhance people's mental health, urban governance should include planning and managing urban environments and make every effort to offer free and accessible urban parks where park users may relieve their daily life pressure and stress.
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Walking in nature provides several health benefits including restoring attention capacity. However, researchers do not know the extent to which the duration of green exercise affects attention restoration. This lack of evidence prevents designers and planners from effectively providing this nature's co-benefit. This study tested the extent to which a view of nature and durations of nature while walking may impact attention restoration. 79 participants completed attentionally demanding tasks and walked on a treadmill for 15 minutes; they were randomly assigned 0, 1, 5, and 15-minute views of nature. Participants who saw nature during the walk had greater attentional score improvements. No difference was found among durations. This finding suggested that only 1 minute of nature in a 15-minute walk has a potential to restore attention. This implied that designers should focus on distributing views of urban nature into barren spaces before adding nature to the places which are already green. This study was one of the first that compared duration of contact with nature for differences in attention restoration. More studies should explore differences in attention restoration with greater ranges of duration to allow landscape designers and urban planners to effectively design cities to reap the full co-benefits of green exercise.
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Green Infrastructure (GI) refers to the natural spaces in a city that improve urban ecology and bring social, economic, and environmental benefits to residents and communities. Although we know a good deal about people's preference for urban forests, we know little about how people reaction to other types of GI and even less about how varying levels of vegetation density influence preference. Without this knowledge, planners and designers risk creating landscapes that people experience as insufficiently restorative.To understand people's preference for different types and vegetation density levels of GI, we conducted three GI preference surveys and utilized a new technology called Brown Dog's Green Index Extractor to calculate vegetation density. We found that, overall, tree density and understory vegetation density are positively associated with preference in a power-curve relationship. The nature of the relationship between bioretention density and preference remains unclear, even though it is significant and positive.The findings presented here expand our knowledge of landscape preference to the emerging field of GI. Designers and planners can use these results to create preferred landscapes that manage stormwater that also promote human well-being. Future studies might explore the relationship between GI density and preference further by investigating other aspects of GI such as planting designs and maintenance and the relationships between GI's vegetation density and various health and well-being indicators.
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Prior studies have consistently shown that ostracism promotes aggression. The present research investigated the role of nature in reducing aggressive responses following ostracism. Three studies provided converging support to the prediction that nature exposure can weaken the relationship between ostracism and aggression. Compared with ostracized participants who viewed nature pictures, ostracized participants who viewed urban pictures indicated a higher willingness to assign a longer and colder exposure of painful chilled water to another person (Study 1), reported elevated aggressive urges in hypothetical situations (Study 2), and showed a higher intention to assign a spicier and larger amount of hot sauce to a person who hated spicy food (Study 3). Taken together, these findings highlight the importance of nature in influencing aggressive responses following ostracism. Implications are discussed.