The Impact of Screen-Use on Cognitive Function:
A Pilot Study
Jóhanna Ásta Þórarinsdóttir
2015
BSc in Psychology
Author: Jóhanna Ásta Þórarinsdóttir
ID number: 020392-3479
Department of Psychology
School of Business THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
2
Foreword and Acknowledgements
Submitted in partial fulfillment of the requirements of the BSc psychology degree, Reykjavík
University, this thesis is presented in the style of an article for submission to a peer-reviewed
journal.
THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
3
Abstract
Screen-use and its impact on cognitive function has not been studied thoroughly. The main
intention of the present study was to see if screen-use has an impact on cognitive function.
Furthermore, the aim was to look at possible moderating effects of factors such as depression,
sleep and psychosomatic symptoms that have been shown to decrease cognitive function in
earlier studies. A total of 45 undergraduate students at the Reykjavik University, Iceland were
recruited. The participants answered a questionnaire that included items about depression,
sleep, psychosomatic symptoms and screen-use. The subjects were then asked to complete a
computerized Sustained Attention to Response Task (SART) and a Stroop Color-Word task.
The results revealed a significant main effect of type of stroop task and a significant threeway interaction between type of stroop task, screen-use, and depression. Furthermore, a
significant two-way interaction between SART time (beginning, middle, end) and screen-use
was revealed. No other main effects or interactions were significant. Results indicate an
effect of screen use on cognitive function. The moderators in general did not impact the
relationship between screen use and cognitive function except for depression and stroop task.
Keywords: cognitive function, screen-use, stroop, depression, psychosomatic
symptoms, sleep-length, sleep-quality
Útdráttur
Áhrif skjááhorfs á hugræna virkni hafa ekki verið skoðuð mikið af fyrri rannsóknum. Aðal
áhersla rannsóknarinnar var að skoða hvort skjááhorf hafi áhrif á hugræna virkni. Einnig var
markmiðið að skoða hugsanleg samvirkniáhrif þátta eins og þunglyndi, svefn og sállíkamleg
einkenni sem sýnt hafa fram á tengsl við hugræna virkni í fyrri rannsóknum. Fjörutíu og
fimm nemar í Háskólanum í Reykjavík tóku þátt. Þátttakendur svöruðu spurningalista sem
innihélt spurningar um þunglyndi, svefnmynstur, sállíkamleg einkenni og skjááhorf.
Þátttakendur gengust svo undir athyglispróf Sustained Attention to Response Task (SART) og
stroop próf Stroop Color-Word task. Niðurstöðurnar sýndu marktæk meginhrif fyrir tegund
stroop verkefnis ásamt marktækri þríbreytu samvirkni á millli tegundar stroop verkefnis,
skjááhorfs og þunglyndiseinkenna. Jafnframt kom fram marktæk tvíbreytu samvirkni milli
SART tíma (byrjun, miðja, endir) og skjááhorfs. Engin önnur meginhrif eða samvirknihrif
voru marktæk. Niðurstöðurnar benda til þess að áhrif séu af skjánotkun á hugræna virkni.
Miðlunaráhif höfðu almennt ekki áhrif á sambandið á milli skjánotkunar og hugrænnar virkni
nema á milli þunglyndis og stroop prófsins.
Lykilhugtök: hugræn virkni, skjááhorf, stroop, þunglyndi, sállíkamleg einkenni,
svefnlengd, svefn gæði
THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
4
The Impact of Screen Use on Cognitive Function: A Pilot Study
Very few studies have explored the relationship between screen-use and cognitive
function precisely. Screen-use implies all kinds of screen-based activity through media and
gaming. It is normally assessed by asking about television, computers, smart phones and
tablets use.
Smart phones are devices that have developed incredibly fast in recent years and are
considered essential in many people’s lives. Over 90 percent of the population in Europe has
Internet access on their phone (Kotler & Keller, 2012). In Iceland, computer usage has
increased considerably over the last ten years from 85 percent in 2004 to 98.2 percent in
2014, where computer usage and Internet usage were the same (Statistics Iceland, 2004,
2014). No country in Europe has more computer usage than Iceland. Some studies have
examined the effects of electronic screen exposure on cognitive function, and many studies
have looked at the impact of video game playing on cognitive function, by recruiting video
game players (all of whom share the fact that they have high screen exposure) and non-video
game players (e.g., Karle, Watter, & Shedden, 2010). Bailey, West, and Anderson (2010)
found that routine action video game players had lower proactive cognitive control
(activating and sustaining goal related information before the stimulus appears) when using
Stroop Color-Word task (i.e., stroop task). However, no difference was found in performance
on the stroop task.
The impact of electronic media on attention has been broadly studied, but limited in
most parts to television (Healy, 2004; Levine & Waite, 2000; Swing, Gentile, Anderson, &
Walsh, 2010). Zimmerman, and Christakis (2007) researched television viewing among
young children and found it to be related to problems with attention regulation later in life,
and that the association was specific to non-educational television viewing. Dworak, Schierl,
Burns, and Struder (2007) reported an increased risk of extreme screen-use causing negative
THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
5
impact on slow wave sleep and verbal cognitive performance, thus increasing risk for
attention-related disorders such as attention deficit hyperactivity disorder (ADHD).
The Sustained Attention to Response task (SART) has been used to investigate
ADHD in children (Johnson, Kelly, et al., 2007; Johnson, Robertson, et al., 2007). The task
activates the same right fronto-parietal attention network that seems dysfunctional in ADHD
(Fassbender et al., 2004; Manly et al., 2003; Silk, 2005). SART was developed to measure
lapses of sustained attention in short durations. By necessitating participants to respond
continuously, a powerful automatic response set is created beforehand, which must be
actively inhibited when a digit that is a no go trial (the digit ‘3’) appears. This task is
considered to require incredible amount of controlled attention (Robertson, Manly, Andrade,
Baddeley, & Yiend, 1997). The task has been questioned regarding whether it measures
attention instead of primary measures, like participant’s impulsivity and response strategy
(Stevenson, Russell, & Helton, 2011). Whyte, Grieb-Neff, Gantz, and Polansky (2006)
reported concerns about the validity and reliability of the SART task after failing to replicate
the original study by Robertson et al. (1997). At the same time, other researchers have
claimed the task to be robust, by using it with good results (e.g., Dockree et al., 2004; Farrin,
Hull, Unwin, Wykes, & David, 2003; Fassbender et al., 2004; Greene, Bellgrove, Gill, &
Robertson, 2009; Johnson, Kelly, et al., 2007; Johnson, Robertson, et al., 2007; Linden,
Keijsers, Eling, & Schaijk, 2005; Smilek, Carriere, & Cheyne, 2010). Farrin et al. (2003)
reported that mild to moderate depression was associated with measurable deficits in
sustained attention when using the SART task. Studies have shown that attention complains
are predictors of depression (e.g., Carriere, Cheyne, & Smilek, 2008; Smallwood, Fitzgerald,
Miles, & Phillips, 2009).
Screen-use has been linked to depression, but few studies have examined the
relationship without using cross sectional methods rather than longitudinal research or other
THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
6
controlled experiments (Primack, Swanier, Georgiopoulos, Land, & Fine, 2009). It is
important to know causality, because, the researcher cannot know if the depression was
caused by screen-use or vise versa. Since we are looking at a condition that has already
happened, it is also difficult to know the individuals actions prior to their depression
diagnosis. In summary, screen-use could cause depression, but it is also possible that
depressed individuals increase their screen-use (Campbell, Cumming, & Hughes, 2006). The
possibility of an unknown third factor that causes both depression and increased screen-use
cannot be excluded.
Primack et al. (2009) used a longitudinal design. Their analysis started in 1994 and
lasted until 2002. Their data was gathered in 1995 and those who had valid data from the
beginning were asked to participate again seven years later. They wanted to see the
development of depression, so they excluded everyone that claimed depression at baseline.
The final study had 4142 participants who did not feel depressed at baseline. A shortened
version of The Centers for Epidemiologic Studies – Scale (CES-D) was used to screen for
depression in participants and included nine validated questions to measure depression. They
asked about television viewing and videocassettes, computer gaming and radio using, and for
duration of this media use. Those who reported more depression at follow-up had watched
significantly more television at baseline. The authors wondered why there was a stronger
impact from TV watching than other medias and concluded that while watching television,
the motion pictures look more realistic than those that appear while playing computer games
and listening to the radio. The authors also concluded that development of depression is
linked to several factors such as poor sleep, self-comparison, anxiety provoking content and
stereotyping. Especially, television and overall media exposure in early childhood was linked
to the development of depressive symptoms in early adulthood. It would be informative to
see if this association still exists by replicating the study and looking at general screen-use,
THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
7
not merely TV viewing. Other studies have shown that screen-use has negative impact on
mental well-being (de Wit, van Straten, Lamers, Cuijpers, & Penninx, 2011; Hamer,
Stamatakis, & Mishra, 2010; Yang, Helgason, Sigfusdottir, & Kristjansson, 2013).
Several studies have concluded that screen-use negatively affects sleep, mental health,
and psychosomatic symptoms (e.g., Iannotti, Kogan, Janssen, & Boyce, 2009; Taehtinen,
Sigfusdottir, Helgason, & Kristjansson, 2014; Van den Bulck, 2007; Ybarra, Alexander, &
Mitchell, 2005). Furthermore, sleep deprivation, mental health problems and psychosomatic
symptoms have shown to reduce cognitive function (e.g., Hall, Kuzminskyte, Pedersen,
Ørnbøl, & Fink, 2011; Hinkelmann et al., 2009; Kim, Kim, Park, Choi, & Lee, 2011; Miyata
et al., 2013; Murrough, Iacoviello, Neumeister, Charney, & Iosifescu, 2011; Williamson &
Feyer, 2000).
In summary, studies indicate that increased screen-use has a negative effect on
cognitive performance, as well as other factors such as mental health and sleep, which also
are known to affect cognitive function negatively. Few studies, however, have examined
whether the impact of screen use on cognitive function is moderated by factors such as
mental health (i.e., psychosomatic symptoms and depression), and sleep. The aim of the
current study is to examine the impact of screen use on cognitive function and whether the
impact varies depending on mental-health or sleep. The present study is a pilot study, for a
potentially larger research on the impact of screen use on cognitive function. In this pilot
study, two cognitive tasks measuring attention will be used, SART and Stroop Color-Word
task, and mean reaction times (RT) measured. Depression will be measured by using the
Symptom Check List (Derogatis, Lipman, & Covi, 1973), psychosomatic symptoms, sleep
and screen use will be assessed as well, through a questionnaire. This study presents practical
knowledge for the scientific community, by providing up to date information about screenuse and its impact on cognitive function.
THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
8
The following hypotheses are tested in this study: Hypothesis 1: High screen-use
individuals will show slower RT when conducting SART task compared to low screen-use
individuals. Hypothesis 2: High screen-use individuals will show higher depression
symptoms, psychosomatic symptoms and lower sleep quality and length compared to low
screen-use individuals on the SART task. Hypothesis 3: High screen-use individuals will
show slower RT when conducting Stroop Color-Word task compared to low screen-use
individuals. Hypothesis 4: The performance of high screen-use individuals on cognitive tasks
will vary with factors such as sleep, mental health and psychosomatic symptoms.
Method
Participants
A total of 45 participants took part in the study (nine men and 36 women, Mage =
25.18) and their age ranged from 20 to 53 years (SD = 6.05). An email was sent to students,
requesting participation and the study was furthermore introduced in classes that took place
in Reykjavik University. Participants were all undergraduate students at School of Business,
most of them were psychology students from the department’s research participant pool who
received course credits for their participation.
Instruments and Stimuli
High screen-use/low screen-use. Screen-use was assessed with a questionnaire by
asking participants what kind of electronic device they had access to/and or owned at home
“television”, “laptop”, “desktop”, “smart-phone”, “tablet”, “kindle”, and then there was a
space where the participant could add some other device that required screen-use. The
participants were next asked to specify which device they used the most, of the ones selected
above. The participants were subsequently asked how much time they spent on this specific
device each day, ranging from “almost no time”, to “six hours or more” per day.
THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
9
Cognitive function. Attention was measured by asking participants to complete the
Sustained Attention to Response Task (SART) (Robertson et al., 1997). The SART was
administered while participants were in front of a computer screen in a quiet room. The
SART task presented digits ranging from 0-9 that appeared randomly in the center of a
computer-screen. Digits were presented in black with the size of 30 millimeters on white
background. The task was conducted on MacBook pro retina 13-inch laptop. Participants
were trained to push either the ‘space’ or the ‘enter’ buttons as quickly as possible, except
when the digit ‘3’ appeared (which occurred randomly, on average 10% of the time). The
program recorded RT in milliseconds (ms) and whether or not a response was given. The
time between numbers was 1125 ms, if the participant’s reaction was slower than that time
the response was marked as missed. The task lasted for nine minutes and 375 ms where 500
digits were presented, each digit was displayed for 250 ms. The mean RT of all trials that
were correctly answered were used in data analysis.
Participants also completed a Stroop Color-Word task (Stroop, 1935) where they were
asked to identify the color of the font of particular color names that they saw instead of the
meaning of the word. Because the meaning is processed faster than the color, the results show
a slower RT when color and word do not match. Time was calculated both for baseline stroop
(with matching font color and color name, blue in blue font), and for an experimental
condition where font color and color name did not match (e.g., blue in yellow font).
Depression. Depression was measured by asking participants questions from the
Symptom Check List (SCL-90) (Derogatis et al., 1973; Derogatis & Unger, 2010).
Participants were asked how often during the last 30 days they had experienced discomfort
related to the following statements: “I was sad or had little interest in doing things”, “I felt
lonely”, “I had a hard time falling asleep or keep myself asleep”, “I had little appetite”, “I felt
sad or blue”, “I was slow or had little energy”, “I cried easily or wanted to cry”, “The future
THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
10
seemed hopeless”, “I was not excited in doing things”, and “I thought of committing suicide”.
Participants answered using a four point Likert scale ranging from 1 = “almost never”, 2 =
“seldom”, 3 = “sometimes” to 4 = “often”.
Participant’s sleep. Sleep was measured in two ways, by asking participants about
length of sleep (how many hours) and sleep quality. The sleep length was rated by asking
“How many hours do you sleep during weekends”, and “How many hours do you sleep
during weekdays” ranging from “Less than four hours”, to “Longer than ten hours”, and
“Over the last two weeks how often do you think you got plenty of sleep” ranging from
“Always”, to “Never”.
The items assessing sleep quality were; “If you think about your sleep quality during
the last two weeks how long did it take you to fall asleep”, and could be responded to on a
five point scale from “it only took me a few minutes to fall asleep” to “It took me more than
three hours or I did not fall asleep”. “How often during the last two weeks have you on
average woken up in the middle of the night” ranging from “Never”, to “Three times or
more”. There was also an option in this item where participants could mark “I have no idea”
and that was rated as a missing value. The last item in the sleep quality category was “How
often during the last two weeks have you woken up unnecessarily early”. Ranging from
“Never” to “Every morning/night”. The questions used to measure sleep length and sleep
quality are based on various sleep scales (e.g., Insomnia severity index, Athens insomnia
scale and Pittsburgh sleep index) (Bastien, 2001; Buysse, Reynolds, Monk, Berman, &
Kupfer, 1989; Soldatos, Dikeos, & Paparrigopoulos, 2000, 2003).
Psychosomatic symptoms. Participant’s psychosomatic symptoms were assessed by
summing up two items on the questionnaire, “How often during the last 30 days have you
experienced headaches”, and “How often during the last 30 days have you experienced
stomachaches” and the scale was from “almost never”, to “often”.
THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
11
Procedure
Before the study was conducted, The Data Protection Authority was informed and the
study was approved at the BSc Psychology Course Committee at Reykjavik University. An
email was sent to students with very specific information about the study and those students
that were interested to participate could reply to the email and the researcher sent a link to a
Google sheet, where the participant could sign up for an available meeting session. The study
was carried out in two different rooms at Reykjavik University with one participant at a time.
Each session took about 45 to 50 minutes. The researcher welcomed the participant, and after
introducing herself, he or she was invited to have a seat. The participant was then shown the
same information that they received in the email (see appendix A, p. 27) and the researcher
made sure that they fully understood what was expected of them. Following that, the
informed consent form was signed (see appendix A, p. 28).
The anonymity of the study was repeated verbally and if something was unclear about
the questionnaire (see appendix B, p.30) the researcher had his own copy to look at and
specific details were explained individually to each participant if requested. Finally, the
participant was asked not to speak to others about the study.
After the questionnaire had been filled out the SART task was conducted (see
appendix D, p. 38). Before the first session, participants got to practice by testing the task
with ten cues (with random numbers appearing, and inhibiting response when ‘3’ appeared).
After the practice session was over the subjects were asked if they understood what was
expected of them. If they replied “no”, the instructions were repeated verbally. Lastly, the
Stroop Color-Word task was completed (see appendix C, p. 36). Participants were informed
about the study’s hypotheses after their participation and the researcher thanked them for
their participation.
Research Design and Data Analysis
THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
12
Mean RT for the SART task was calculated for the first part, the middle part and the
last part of the task (this was done to see if performance changed over time). The data was
then analyzed in four 3 x 2 x 2 mixed ANOVAs with SART time (beginning, middle, end),
screen use (high, low) and deficits (sleep length/sleep quality/psychosomatic
symptoms/depression [high, low]) as the independent variables. For the RT in the stroop task,
the data was analyzed in four 2 x 2 x 2 with stroop (matching, non-matching task), screen use
(high, low) and deficit (sleep length/sleep quality/psychosomatic symptoms/depression [high,
low]) as the independent variables. The screen-use question (how many hours per day a
device is used) was divided into two parts with median split to divide participants into high
and low screen-use groups (high = 25, low = 19). One participant forgot to answer the
question and was therefore treated as missing. The questions on depression (high = 22, low =
22), psychosomatic symptoms (high = 29, low = 15), and sleep (sleep quality [high = 20, low
= 24] sleep length [high = 25, low = 19]) were summed together and participants’ scores
divided into high and low using median split. Before the sleep length items were summed up
and divided, one question was rotated to be consistent with the others “How often during the
last two weeks have you got enough sleep”.
The hypotheses were one tailed which specifies a directional relationship between
variables, therefore the p values were divided by two. Mauchly’s test indicated that the
assumption of sphericity had been violated in all circumstances, except between screen-use
and sleep length, and therefore the data was corrected using Huyn-Feldt correction, which
resulted in corrected degrees of freedom. Chronbach’s Alpha reliability coefficient was
calculated for the ten questions that measured depression, which had good reliability, α = .84,
Alpha for the psychosomatic questions was α = .65, for sleep length α = .58 and sleep quality
α = .61. Effect sizes (partial eta square) were measured in the analysis (.02 = small, .13 =
THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
13
medium, .16 = large). The statistical analysis was performed with IBM SPSS statistics
version 22.
Results
The aim of the study was to examine the impact of screen use on cognitive function
and to see whether the impact varies depending on mental-health psychosomatic symptoms,
and sleep. Data was analyzed in four 3 x 2 x 2 (SART x Screen-use x Deficit [high, low])
Mixed ANOVAs for the SART and four 2 x 2 x 2 (Stroop x Screen-use x Deficit [high, low])
Mixed ANOVAs for the stroop task. The alpha level of significance was set at .05 one-tailed.
SART task
Table 1 sums up the descriptive statistics for the SART task (beginning, middle and
end), and the between group variables, depression, psychosomatic symptoms, sleep length
and sleep quality.
Table 1
Mean Reaction Time and Std. Deviation for SART (beginning, middle and the end) and the
Different Dependent Variables
Measure
Depressive symptoms
Low depression
SART beginning
Low
High
screenscreenuse
use
M (SD)
SART middle
Low
High
screen-use screen-use
M (SD)
SART end
Low
High
screenscreenuse
use
M (SD)
.394
(.069)
.378
(.101)
.368
(.060)
.384
(.121)
.365
(.074)
.364
(.117)
.349
(.053)
.356
(.032)
.329
(.064)
.380
(.089)
.325
(.093)
.368
(.091)
.389
(.075)
.388
(.118)
.358
(.062)
.390
(.120)
.333
(.110)
.385
(.123)
High symptoms
.356
(.054)
.369
(.053)
.338
(.067)
.378
(.102)
.347
(.074)
.356
(.097)
Low sleep length
.388
(.080)
.372
(.102)
.356
(.072)
.379
(.133)
.346
(.071)
.356
(.132)
High sleep length
.356
(.050)
.365
(.051)
.339
(.061)
.385
(.079)
.339
(.097)
.374
(.075)
.367
(.049)
.371
(.101)
.350
(.065)
.381
(.106)
.346
(.107)
.374
(.104)
.371
(.081)
.365
(.045)
.339
(.066)
.383
(.110)
.336
(.052)
.356
(.108)
High depression
Psychosomatic symptoms
Low symptoms
Sleep length
Sleep quality
Low sleep quality
High sleep quality
Note. M = mean in seconds; SD = Standard Deviation; SART = Sustained Attention to Response task.
THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
14
Those with lower sleep quality and high screen-use all had slower mean RT than
those with low sleep quality and low screen use. Interestingly, those who had more sleep and
high screen exposure had slower RT in all conditions on the SART task than those who
reported high sleep length and low screen-use. The same trend appeared for those who
reported high screen-use and high psychosomatic symptoms.
The results of the four 3 x 2 x 2 Mixed ANOVAs for the SART data revealed a
significant two-way interaction between SART time and screen-use F(1.87; 78.54) = 2.55, p
= .044, ηp² = .016. No other main effects or interactions were significant. As can be seen in
figure 1, those who had high and low screen exposure were fairly equal in RT in the
beginning of the SART task. However, in the middle and in the end, those who reported high
screen exposure had significantly slower RT than those who reported low screen-use.
0,390
.382
Reaction Time
0,380
0,370
0,360
.368
.368
0,350
.366
Low screen-use
.345
.342
0,340
High screen-use
0,330
0,320
Beginning
Middle
End
SART
Figure 1. Interaction between low and high screen-use on reaction time.
Stroop task
Table 2 summarizes the descriptive statistics for the stroop tasks, for screen use,
depression, psychosomatic symptoms and sleep. The highest mean was on high depressive
symptoms and low screen-use on the matching stroop task. However, the lowest mean was
THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
15
measured for individuals with low depressive symptoms and low screen-use at baseline.
Interestingly, those who had high depressive symptoms and high screen-use had higher RT in
the matching stroop task. However, those who had high screen use and high depressive
symptoms on the nonmatching task had lower RT. A similar trend appeared for the
psychosomatic symptoms and low sleep quality.
Table 2
Mean Reaction Time and Std. Deviation for Stroop Matching and the Stroop Nonmatching
for the Various Dependent Variables
Measure
Depressive symptoms
Low depression
High depression
Psychosomatic symptoms
Low symptoms
High symptoms
Sleep length
Low sleep length
High sleep length
Sleep quality
Low sleep quality
High sleep quality
Stroop matching
Low
High
screenscreenuse
use
M (SD)
Stroop nonmatching
Low
High
screenscreenuse
use
M (SD)
14.42
(3.53)
17.10
(4.16)
24.78
(5.70)
28.98
(8.65)
16.74
(3.66)
17.76
(2.24)
31.63
(11.21)
25.41
(4.23)
15.68
(3.85)
17.50
(4.20)
27.28
(8.10)
25.61
(4.91)
15.81
(3.78)
17.34
(3.11)
29.66
(10.81)
28.25
(7.99)
17.28
(4.22)
17.30
(2.84)
29.95
(6.19)
28.65
(8.36)
14.87
(3.21)
17.47
(3.97)
28.10
(11.52)
26.25
(5.94)
15.31
(3.60)
17.16
(3.64)
29.07
(12.26)
26.82
(5.66)
16.38
(3.98)
17.63
(3.26)
28.38
(5.34)
28.03
(8.71)
Note. M = mean in seconds; SD = Standard Deviation.
For the four 2 x 2 x 2 mixed ANOVAs conducted for the stroop data, the results
revealed a significant main effect of type of stroop F(1, 40) = 96.14, p < .001, ηp² = .706, and
a significant three-way interaction between type of stroop, screen-use and depression F(1, 40)
THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
16
= 3.83, p = .029, ηp² = .087. No other main effects or interactions were significant, as can be
seen on figure 2 and 3. High screen-users were slower in RT on both stroop tasks, for low
depression. However, those who showed high depression and were high screen-users showed
Mean reaction time for the Stroop task
considerably better performance on the nonmatching stroop task than low-screen users.
35
28.965
30
25
20
24.779
17.096
15
Low screen-use
14.423
High screen-use
10
5
0
Stroop matching
Stroop nonmatching
Stroop task
Figure 2. Performance on stroop task for low and high screen-use for low depressive
Mean reaction time for the Stroop task
symptoms.
35
31.692
30
25.415
25
20
15
17.764
Low screen-use
16.735
High screen-use
10
5
0
Stroop matching
Stroop nonmatching
Stroop task
Figure 3. Performance on stroop task for low and high screen-use for high depressive
symptoms.
THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
17
Discussion
The current study provides insight into screen-use and its impact on cognitive
function. A significant interaction was attained between screen-use and the SART task as
well as between screen-use, type of stroop and depressive symptoms. The main aim of the
study was to examine the impact of screen use on cognitive function and whether the impact
varied depending on mental-health and sleep. The hypotheses that were argued in the
introduction were mostly not supported, though hypothesis 1 was partly supported, as there
was a significant interaction between SART and the participant’s screen-use where those who
had higher screen-use had slower RT for the middle and the end part of the task compared to
low screen-use participants. Hypothesis 4 was also partly supported, in the sense that the
impact of screen-use on stroop task varied depending on depressive symptoms. The other
hypotheses were not supported.
In the present study, the results from four 3 x 2 x 2 Mixed ANOVAs for the SART
data revealed a significant two-way interaction between SART time and screen-use which
indicates that screen-use had a significant negative impact on cognitive function. Moreover,
for the four 2 x 2 x 2 mixed ANOVAs conducted for the stroop data, the results revealed a
significant main effect of type of stroop, as well as a significant three-way interaction
between type of stroop, screen-use and depression. Primack et al. (2009) used a longitudinal
design and found that children with increased media use in adolescence had greater odds of
developing depressive symptoms at follow-up seven years later. However, the association
that was found in the present study between the stroop task, screen use and depression was
quite interesting. Those that were low on depression and were high screen-users showed
slower RT on the stroop task (both matching and nonmatching), however those who showed
high depression and were high screen-users showed considerably better performance on the
nonmatching stroop task. The results from the present study from the SART task were in
THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
18
accordance to previous studies, that have claimed that screen-use has a negative effect on
cognitive function, (e.g., Bailey et al., 2010; Dworak et al., 2007; Healy, 2004; Karle et al.,
2010; Levine & Waite, 2000; Swing et al., 2010; Zimmerman & Christakis, 2007). For
example, Karle et al. (2010) researched task switching performance between video game
players and non-video game players and found that video game players demonstrated more
task switching benefits compared to non-video game players, however, the benefit
disappeared when the proactive interference between tasks was increased. The current
findings reveal that those who had high and low screen exposure were fairly equal in RT in
the beginning of the SART task. However, in the middle and in the end, those who reported
high screen exposure had significantly slower RT than those who reported low screen-use.
The results were inconsistent compared to other studies in finding a relationship
between screen-use, sleep and psychosomatic symptoms (Kim et al., 2011; Miyata et al.,
2013; Taehtinen, Sigfusdottir, Helgason, & Kristjansson, 2014; Van den Bulck, 2007).
Miyata et al. (2013) researched older adults and found that sleep length and quality might
play a significant role in older adult’s cognitive performance. The present results found no
difference, however that might have occurred due to inappropriate items on the questionnaire.
Also, there might be a difference between older adults' sensitivity to sleep deprivation/quality
and young adults’. Taehtinen et al. (2014) found a cross-sectional relationship between
screen-use and psychosomatic symptoms, by measuring psychosomatic symptoms with four
items on a questionnaire. The present study found no association however, why those studies
are not in accordance with each other could be because of the cross-sectional method
(Campbell et al., 2006), or the fact that the present study only used two items measuring
psychosomatic symptoms while Taehtinen et al. (2014) measured four items. Further research
on this association is needed.
THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
19
Whether the SART task is a good way to measure sustained attention, might have had
impact on the current study, in terms of how much criticism the task has received for its
reliability and validity (e.g., Stevenson et al., 2011; Whyte et al., 2006). In spite of that, those
articles that criticize the test often fail to report basic statistics like effect sizes and show
incomplete analysis which can have an enormous impact on results, especially with results
that have few participants like Whyte et al. (2006) had in their study. It is very important to
report the effect sizes; those statistics are far too frequently disregarded. It is obvious that
failing to replicate studies with many participants is most certainly because of lack of
statistical power, even though small studies have often very good potential value (Smilek et
al., 2010).
The present research had several limitations. For example, low statistical power, as
there were only 45 participants that took part. The content validity of the study was
threatened to a certain extent by summing up the items on the questionnaires where the
Chronbach’s Alpha inner reliability coefficient was quite low, except for depression, which
indicates that the items on the questionnaire were possibly not measuring the construct that
they were intended to measure well enough. The participants were almost all familiar with
the questionnaire, which may have biased their answers by trying to be socially desirable,
even though the experiment was anonymous. The generalization problem is always a
possibility, as the homogeneity of the participants is too high and therefore difficult to
generalize to the rest of the population.
In summary, participants’ attention should not differ in conducting the SART task but
it could be that undergraduate psychology students experience less or more depression,
psychosomatic symptoms or sleep problems than the general population. The study was also
carried out in two different rooms, which can affect the participants’ experience, in where
one room had no distractions and no windows and the other room had a window and perhaps
THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
20
distracting noise outside. The variable that measured psychosomatic symptoms only
contained two items that assessed headaches and stomachaches. There are considerable more
psychosomatic symptoms that are affecting individuals in their daily lives, therefore should
future studies measure psychosomatic symptoms with additional items. Similarly, were the
items that measured sleep length and sleep quality summed together by few items in the
questionnaire, which does not cover all the sleep difficulties, that people may experience in
their lives. Therefore, future studies should use more validated scales and have more than
handful of items summed together.
Despite its limitations, the present study has theoretical value for the scientific
community due to the fact that the relationship between screen-use and cognitive function has
not been studied by any extent; these results are therefore valuable and up to date information
for larger studies that will be conducted. The present findings suggest that screen-use has an
effect on cognitive function. The item measuring screen-use was the device that the
participant reported in using the most. Thus, the overall participants’ screen use may perhaps
be beyond what was measured in current research.
In conclusion, further research is needed on sustained attention and screen-use in
general to discriminate whether and how the relationship exists. Future studies should
evaluate screen-use with validated scales and possibly add another cognitive performance
task to the method to compare tasks. There might be stronger associations between present
variables; future research should add participants for satisfactory statistical power. As can be
seen on previous discussion, screen-use and its’ association with cognitive function is a
complex matter, by further research we can understand the screen-use behavior that has, and
will probably increase for years to come. This pilot study provides a fundamental knowledge
for future studies in this field.
THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
21
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Appendix A
Kynning á B.Sc rannsókn
Ég heiti Jóhanna Ásta Þórarinsdóttir ([email protected]) og er á þriðja ári í sálfræði við
Háskólann í Reykjavík. Ég er að vinna að B.Sc rannsókn minni sem snýr að áhrifum skjááhorfs á
hugræna virkni hjá háskólanemum og óska eftir þátttöku þinni.
Markmið rannsóknarinnar er að:
Kanna hvort að skjááhorf hefur áhrif á hugræna virkni ungs fólks. En einnig að athuga hvort að
aðrir andlegir þættir hafi áhrif á sambandið.
Þér er boðið að taka þátt í þessari rannsókn.
Þátttakendur verða nemendur úr Háskólanum í Reykjavík sem bjóða sig fram til þátttöku.
Þátttaka í rannsókninni felst í því að svara spurningalistum og leysa verkefni af tölvuskjá.
Áætlað er að það taki um 10 - 15 mínútur að svara spurningalistanum og tölvuverkefnið taki um
40 mínútur. Spurningalistinn verður lagður fyrir á undan og þátttakendur fá því gott næði til að
svara honum í einrúmi. Þátttaka fer fram í Háskólanum í Reykjavík.
Spurt verður um eftirfarandi þætti: almennar upplýsingar,svefnvenjur, skjááhorf, andlega og
líkamlega líðan.
Ávinningur og áhætta: Vísindalegur ávinningur rannsóknarinnar er fólginn í að afla vitneskju
um skjááhorf ungs fólks. Framlag þitt og annarra þátttakenda í rannsókninni mun stuðla að
aukinni þekkingu á skjááhorfi og tengslum þess við hugræna virkni. Þeir sem skráðir eru í
námskeiðin E-215-HSKY og E-313-SATI eiga þess kost að fá 60 mínútur af þeim 5 klst sem þarf
til að uppfylla 10% af einkunn, sitji þeir rannsóknina til enda.
Áhætta af þátttöku felst aðallega í því að spurningarnar gætu valdið hjá þér uppnámi, en minnt er
á að þátttakandi getur valið að sleppa einstaka spurningum.
Aðgengi að rannsóknargögnum: Allar upplýsingar sem þátttakendur veita í rannsókninni, verða
meðhöndlaðar samkvæmt ströngustu reglum um trúnað og nafnleynd og farið að íslenskum
lögum varðandi persónuvernd, vinnslu og eyðingu frumgagna. Í tölfræðilegum úrvinnsluskrám
munu hvergi koma fram persónugreinanleg gögn. Rannsóknargögn verða færð í gagnagrunn án
persónuauðkenna. Þér er hér með heitið að gögnin verða eingöngu notuð í vísindalegum tilgangi,
þau verða meðhöndluð af fyllsta trúnaði og samkvæmt lögum um persónuvernd og meðferð
persónuupplýsinga nr. 77, frá 23. maí 2000.
Varðveisla gagna: Að rannsókn lokinni verða gögnin látin í hendur ábyrgðarmanns rannsóknar
Dr. Kamillu Rúnar Jóhannsdóttur ([email protected]) sem mun varðveita þau í tvö ár og eyða þeim
síðan.
Þér er frjálst að hafna þátttöku eða hætta í rannsókninni hvenær sem er, án frekari útskýringa.
Nemendur fá þann tíma sem þeir inna af hendi upp í 10% einkunn.
THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
28
Upplýst samþykki fyrir rannsókn
Samþykki þátttakanda:
Ég hef fengið fullnægjandi upplýsingar um eðli og framkvæmd rannsóknarinnar. Ég hef verið
upplýst(ur) um að ég geti dregið samþykki mitt um að taka þátt í rannsókninni fyrirvaralaust
til baka, án nokkurra skýringa eða neikvæðra afleiðinga fyrir sjálfan mig. Rannsakendur hafa
leyfi til að vinna með þau gögn sem hafa safnast fram að því.
Mér er ljóst að rannsóknargögnin verða geymd án persónuauðkenna næstu tvö ár hjá
ábyrgðarmanni rannsóknar
Hér veiti ég upplýst samþykki mitt um þátttöku í umræddri rannsókn
___________________________
Þátttakandi
___________________________
Rannsakandi
THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
29
Written Debriefing Form The Impact of Screen Use on Cognitive Function: A Pilot Study The aim of this study was to see if screen-­‐use has an impact on cognitive function. Furthermore, the aim was to look at possible moderating effect of factors such as depression, sleep and psychosomatic symptoms that have been shown to decrease cognitive function in earlier studies. Your results will remain confidential and unrecognizable to the experimenter, and all results will only be published anonymously as a group data. If you want to be informed of the results of the study, once it is completed you are welcome to contact me, Jóhanna Ásta Þórarinsdóttir, 771-­‐5997. The reason that you were refrained from talking about the experiment while it was still running was because there were others in your class that were participating and therefore it was valid not to bias the initial results. In this study we presented you with two cognitive tests, The Sustained Attention to Response Task (SART) and the Stroop Color-­‐Word task. By conducting these tasks we were able to measure your Reaction time (RT) to see if screen use (which is an information you gave in the questionnaire) affects it. We also looked at factors such as psychosomatic symptoms, depression and sleep that have been shown to decrease cognitive functions. If you are interested in this area of research, please look at these articles at the library, or online: Bailey, K., West, R., & Anderson, C. A. (2010). A negative association between video game experience and proactive cognitive control. Psychophysiology, 47(1), 34–42. http://doi.org/10.1111/j.1469-­‐8986.2009.00925.x Primack, B. A., Swanier, B., Georgiopoulos, A. M., Land, S. R., & Fine, M. J. (2009). Association Between Media Use in Adolescence and Depression in Young Adulthood: A Longitudinal Study. Archives of General Psychiatry, 66(2), 181. http://doi.org/10.1001/archgenpsychiatry.2008.532 Karle, J. W., Watter, S., & Shedden, J. M. (2010). Task switching in video game players: Benefits of selective attention but not resistance to proactive interference. Acta Psychologica, 134(1), 70–78. http://doi.org/10.1016/j.actpsy.2009.12.007 If you wish to express concern, complaints or have question about the experiment please
contact Dr. Kamilla Rún Jóhannsdóttir, [email protected], Head of the Department of
Psychology, Reykajvík University.
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Appendix B
Spurningalisti
30
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32
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Appendix C
36
THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
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THE IMPACT OF SCREEN USE ON COGNITIVE FUNCTION
Appendix D
SART task
38