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Pathological Video Game Use Among Youths: A Two-Year Longitudinal Study

Department of Psychology, College of Liberal Arts and Sciences, Iowa State University, Ames, Iowa 50011-3180, USA.
PEDIATRICS (Impact Factor: 5.47). 02/2011; 127(2):e319-29. DOI: 10.1542/peds.2010-1353
Source: PubMed
ABSTRACT
We aimed to measure the prevalence and length of the problem of pathological video gaming or Internet use, to identify risk and protective factors, to determine whether pathological gaming is a primary or secondary problem, and to identify outcomes for individuals who become or stop being pathological gamers.
A 2-year, longitudinal, panel study was performed with a general elementary and secondary school population in Singapore, including 3034 children in grades 3 (N = 743), 4 (N = 711), 7 (N = 916), and 8 (N = 664). Several hypothesized risk and protective factors for developing or overcoming pathological gaming were measured, including weekly amount of game play, impulsivity, social competence, depression, social phobia, anxiety, and school performance.
The prevalence of pathological gaming was similar to that in other countries (∼9%). Greater amounts of gaming, lower social competence, and greater impulsivity seemed to act as risk factors for becoming pathological gamers, whereas depression, anxiety, social phobias, and lower school performance seemed to act as outcomes of pathological gaming.
This study adds important information to the discussion about whether video game "addiction" is similar to other addictive behaviors, demonstrating that it can last for years and is not solely a symptom of comorbid disorders.

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Available from: Douglas A Gentile
Pathological Video Game Use Among Youths: A Two-Year Longitudinal Study
Douglas A. Gentile, PhD, Hyekyung Choo, PhD, Albert Liau, PhD, Timothy Sim, PhD, Dongdong Li, MA,
Daniel Fung, MD, and Angeline Khoo, PhD
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Page 1
Pathological Video Game Use Among Youths: A
Two-Year Longitudinal Study
WHAT’S KNOWN ON THIS SUBJECT: Several correlational studies
documented that participants who would be classified as
“pathological” video gamers demonstrate a pattern of
correlations with other variables that are comorbid (eg,
depression) or occur with (eg, poorer grades and increased
hostility) other addictions.
WHAT THIS STUDY ADDS: Following a large sample across 2
years, this study provides needed data on risk factors for
becoming a pathological gamer, how long pathological gaming
lasts, outcomes, and whether it is a primary problem or is a
symptom of comorbid problems.
abstract
OBJECTIVES: We aimed to measure the prevalence and length of the
problem of pathological video gaming or Internet use, to identify risk
and protective factors, to determine whether pathological gaming is a
primary or secondary problem, and to identify outcomes for individu-
als who become or stop being pathological gamers.
METHODS: A 2-year, longitudinal, panel study was performed with a
general elementary and secondary school population in Singapore,
including 3034 children in grades 3 (N 743), 4 (N 711), 7 (N 916),
and8(N 664). Several hypothesized risk and protective factors for
developing or overcoming pathological gaming were measured, in-
cluding weekly amount of game play, impulsivity, social competence,
depression, social phobia, anxiety, and school performance.
RESULTS: The prevalence of pathological gaming was similar to that in
other countries (9%). Greater amounts of gaming, lower social com-
petence, and greater impulsivity seemed to act as risk factors for
becoming pathological gamers, whereas depression, anxiety, social
phobias, and lower school performance seemed to act as outcomes of
pathological gaming.
CONCLUSION: This study adds important information to the discussion
about whether video game “addiction” is similar to other addictive
behaviors, demonstrating that it can last for years and is not solely a
symptom of comorbid disorders. Pediatrics 2011;127:e319–e329
AUTHORS: Douglas A. Gentile, PhD,
a
Hyekyung Choo, PhD,
b
Albert Liau, PhD,
c
Timothy Sim, PhD,
d
Dongdong Li, MA,
c
Daniel Fung, MD,
e
and Angeline Khoo, PhD
c
a
Department of Psychology, College of Liberal Arts and Sciences,
Iowa State University, Ames, Iowa;
b
Department of Social Work,
Faculty of Arts and Social Sciences, National University of
Singapore, Singapore;
c
Department of Psychological Studies,
National Institute of Education, Nanyang Technological
University, Singapore;
d
Department of Applied Social Sciences,
Faculty of Health and Social Sciences, Hong Kong Polytechnic
University, Hong Kong; and
e
Department of Child and Adolescent
Psychiatry, Institute of Mental Health, Singapore
KEY WORDS
pathological video game use, video game addiction, depression,
longitudinal, impulse control
ABBREVIATION
LAN—local area network
www.pediatrics.org/cgi/doi/10.1542/peds.2010-1353
doi:10.1542/peds.2010-1353
Accepted for publication Oct 29, 2010
Address correspondence to Douglas A. Gentile, PhD, Iowa State
University, College of Liberal Arts and Sciences, Department of
Psychology, W112 Lagomarcino Hall, Ames, IA 50011-3180. E-mail:
dgentile@iastate.edu
PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275).
Copyright © 2011 by the American Academy of Pediatrics
FINANCIAL DISCLOSURE: The authors have indicated they have
no financial relationships relevant to this article to disclose.
ARTICLES
PEDIATRICS Volume 127, Number 2, February 2011 e319
Page 2
Several researchers have begun test-
ing scientifically the concept of patho-
logical video game use, commonly
called video game “addiction.”
1–17
The
American Medical Association recog-
nized that it is worthy of study, and the
American Psychiatric Association con-
sidered it for inclusion in the Diagnos-
tic and Statistical Manual of Mental
Disorders, Fifth Edition, but decided
there was not yet sufficient research.
18
Most researchers have assumed that
it would be similar to pathological
gambling. The parallel seems justifi-
able, because both are assumed to be
behavioral addictions that begin as en-
tertainment that can stimulate emo-
tional responses and dopamine re-
lease.
14,19
People gamble or play video
games for many reasons, including re-
laxation, competence, autonomy, and
escape from daily concerns.
20,21
Play-
ing can produce “flow” states, in which
the player is focused, has a sense of
control, may lose a sense of time and
place, and finds playing intrinsically
rewarding.
22
Playing is not pathologi-
cal initially but becomes pathological
for some individuals when the activity
becomes dysfunctional, harming the
individual’s social, occupational, fam-
ily, school, and psychological function-
ing. There is by no means a consensus
on this issue, however. There still is
heated debate about how best to de-
fine addictions, including behavioral
addictions.
23–25
The purpose of this ar-
ticle is not to answer that debate but to
provide new data that may be useful.
A majority of studies have based a
screening tool for pathological video
game or Internet use on the Diagnostic
and Statistical Manual of Mental Disor-
ders, Fourth Edition, criteria for patho-
logical gambling.
5,7,8,14,26
Although the
existing studies involve different popu-
lations and measures, they are begin-
ning to yield fairly consistent evidence
for construct validity, that is, cases
that would be classified as “pathologi-
cal” demonstrate a pattern of correla-
tions with other variables that cooccur
(eg, poorer grades and increased hos-
tility) or are comorbid (eg, depression
and attention-deficit/hyperactivity dis-
order) with other addictions.
5,16,27–29
A
national weighted sample of 1178 US
youths found that 8.5% of gamers were
classified as pathological gamers.
5
Other samples in other countries
yielded similar proportions, including
10.3% in China,
30
8.0% in Australia,
31
11.9% in Germany,
32
and 7.5% in Tai-
wan.
33
This burgeoning consistency
notwithstanding, it is unclear whether
this dichotomous approach is the best
for screening, because most clinicians
tend to focus also on the severity of
symptoms. As an introductory ap-
proach to a new phenomenon, how-
ever, it is a reasonable starting place.
It is critical not to pathologize behav-
iors needlessly; therefore, the weight
of evidence would need to be strong as
well as consistent. The evidence has
not yet met these criteria.
Specifically, there are many issues
that we need to understand before it
would be reasonable to consider
pathological video game use as a diag-
nosis in any revision to the Diagnostic
and Statistical Manual of Mental Disor-
ders. These issues include (but are not
limited to) the following. (1) We need to
be able to define risk and protective
factors for becoming a pathological
gamer and its etiology. (2) We need to
understand the pattern of comorbidity
with pathological gaming. Is patholog-
ical gaming a primary or a secondary
problem? For example, if it is comorbid
with depression, does depression in-
crease the risk for a child to become a
pathological gamer, or does patholog-
ical gaming increase the risk for de-
pression? (It is worth noting that this is
something of a false dichotomy, given
that comorbid mental health issues of-
ten reinforce each other and simply
treating the “first” issue does not solve
the total problem.) (3) We need to un-
derstand the course of the problem.
How long does it last? Is it simply a
transitory (although perhaps acute)
problem, or does it last for years? (4)
We need to understand the typical out-
comes of the problem. (5) We need to
know how easily it can be overcome. If
help is needed, what type of help would
be most effective?
Almost all of the studies that have been
conducted to date have relied on mea-
surements at a single time point or on
case studies. Unfortunately, most of
the questions noted above cannot be
answered with these research designs
but require longitudinal studies. Fur-
thermore, because extreme behaviors
and conditions by definition affect only
small proportions of the population,
large samples are needed.
The present study monitored 3000
youths for 2 years, to begin to answer
several of the unanswered questions.
Specifically, we report on the preva-
lence and length of the problem, em-
pirically identified risk and protective
factors, whether the problem seems to
be primary or secondary, and out-
comes of both beginning and overcom-
ing the problem.
METHODS
Participants
A total of 3034 children and adoles-
cents in grades 3 (N 743), 4 (N
711), 7 (N 916), and 8 (N 664) in 6
primary schools and 6 secondary
schools participated; 5 schools were
boys’ schools. These students were
surveyed annually between 2007 and
2009. Parental consent was gathered
by the schools. The overall participa-
tion rate was 99%. Surveys were con-
ducted in classrooms by teachers
who had been trained by the re-
search team. Detailed procedures
and demographic features are de-
scribed elsewhere.
34
e320 GENTILE et al
Page 3
Of the participants who provided con-
sent, 2998 completed a survey at time 1
(2179 boys and 819 girls; 72.6% Chi-
nese, 14.2% Malay, 8.8% Indian, and
4.3% other races). Sixty students did
not provide identifying information
and were lost to follow-up monitoring
(which left 2974 participants); 2605
and 2532 questionnaires were col-
lected in years 2 and 3, respectively,
which yielded attrition rates of 12.3%
by time 2 and 14.7% by time 3. Compar-
isons of dropouts with remaining par-
ticipants at time 3 showed no differ-
ences in their time 1 and time 2 levels
of pathological gaming symptoms. At-
trition was mainly attributable to ad-
ministrative reasons, rather than stu-
dents’ refusal to participate. At time 1,
4 classes at each educational level
were selected to participate. The stu-
dents were reassigned to different
classes each year, which made track-
ing of students difficult. Most students,
however, stayed within the same
schools across all 3 years.
Measures
The appendix describes the measures
used. Pathological gaming was defined
similarly to other American Psychiat-
ric Association disorders, such that at
least one-half of the items (5 of the 10
items) needed to be endorsed for a
case to be considered pathologi-
cal.
5,7,8,14,26
We were interested in exam-
ining how pathological gaming was re-
lated to several potential predictor or
outcome variables, including amount
of gaming, social competence, impul-
sivity, social phobia, depression, anxi-
ety, parent-child relationship quality,
and school performance. The ques-
tionnaires were administered in coun-
terbalanced order.
Data Analyses
Latent growth mixture modeling was
used to determine groups of students
who were similar with respect to their
growth trajectories in pathological
gaming. Comparisons were made be-
tween the latent classes with respect
to risk factors and outcome variables.
RESULTS
Most (83%) of our subjects reported
playing video games at least occasion-
ally, with an additional 10% reporting
that they used to play. The average
amount of time playing was 20.5 hours
per week (SD: 25.8 hours per week) at
time 1, 22.5 hours per week (SD: 24.2
hours per week) at time 2, and 20.9
hours per week (SD: 22.7 hours per
week) at time 3. Boys played more at
each wave (Table 1).
The average numbers of pathological
gaming symptoms reported (mean
SD) were small, that is, 2.28 1.78
at time 1, 2.05 1.86 at time 2, and
1.78 1.80 at time 3. The number of
symptoms reported was correlated
moderately with the amount of playing
time at each wave (r 0.33 at time 1,
r 0.35 at time 2, and r 0.37 at time
3). Boys were more likely to play video
games and to endorse more symp-
toms at each time (Table 1). Overall,
the proportions of students in our
sample who would meet the
5-symptom requirement to be consid-
ered pathological gamers were 9.9%
at time 1, 8.8% at time 2, and 7.6% at
time 3. Boys were more likely than girls
to meet this requirement at all 3 times
(time 1: boys, 12.0%; girls, 4.6%; time 2:
boys, 11.2%; girls, 2.6%; time 3: boys,
9.2%; girls, 3.3%; all
2
P .001).
Longitudinal latent class analysis was
conducted by using the growth mix-
ture model analysis in Mplus 5.21
(Muthén & Muthén, Los Angeles, CA).
This analyzes each child’s changes
across time and groups together chil-
dren who change similarly. Six classes
were found that fit with theoretical
predictions. Multiple criteria were
used to choose the 6-class model as
the best fit. First, classification quality
is assessed on the basis of entropy,
which is a standardized summary
measure, with higher values indicating
more-accurate classification. Entropy
continued improving with additional
classes. Second, the Bayesian Informa-
tion Criterion statistic becomes
smaller with improved fit, and 6
classes were better than 5. Third, the
bootstrap Lo-Mendell-Rubin test com-
pares the fit with k classes and the fit
with k 1 classes, providing a P value
to determine whether there is a statis-
tically significant improvement in fit
with more classes. The Bootstrap like-
lihood ratio test yielded nonsignificant
results for both 5 and 6 classes.
Fourth, the classes need to fit theoret-
ical predictions, and each of the 6
classes was theoretically sensible.
One latent class of children fit the def-
inition of being pathological video
game players (by endorsing at least
one-half [ie, 5] of the symptoms) at
time 1 but dropped below that thresh-
old by time 3 (hereafter called the
TABLE 1 Differences Between Boys and Girls in Weekly Amounts of Game Play and Numbers of
Pathological Gaming Symptoms Reported
Overall Boys Girls tdfP
Weekly amount of game play,
mean SD, h
Time 1 20.53 25.78 21.54 26.46 18.28 23.89 3.08 3010 .002
Time 2 22.52 24.15 23.47 24.66 19.88 22.50 3.19 2358 .001
Time 3 20.95 22.72 21.44 23.07 19.51 21.63 1.75 2230 .081
No. of pathological gaming
symptoms, mean SD
Time 1 2.28 1.78 2.49 1.83 1.76 1.55 9.63 2695 .001
Time 2 2.05 1.86 2.29 1.93 1.45 1.48 10.14 2455 .001
Time 3 1.78 1.80 1.97 1.86 1.25 1.51 8.75 2342 .001
ARTICLES
PEDIATRICS Volume 127, Number 2, February 2011 e321
Page 4
stops group). One latent class began
the study below the 5-symptom line but
increased to be over it by time 3 (the
starts group). One latent class began
the study above the 5-symptom line
and held fairly constant across 2 years
(the stays group). Finally, 3 latent
classes stayed below the 5-symptom
line (1 remaining stable across time, 1
increasing in symptoms, and 1 de-
creasing in symptoms), and these 3
classes were combined (the never
pathological group). Figure 1 shows
the trajectories of these 4 groups.
The 4 latent classes allowed us to esti-
mate what proportions of youths
stopped or started being pathological
gamers over 2 years. Of 219 youths
who were pathological gamers at time
1, 16.4% (36 youths) had significantly
decreased symptoms within 2 years.
Of 2741 youths who were not patholog-
ical gamers at time 1, 1.3% (35 youths)
became pathological gamers within 2
years. This answers one critical ques-
tion, namely, whether pathological
gaming is a condition that is transient
or stable. Most (84%) of the youths
who were pathological gamers at time
1 were still pathological gamers 2
years later.
These longitudinal data also allowed
us to begin to answer questions about
whether some variables act as risk
factors for becoming a pathological
gamer and what the outcomes are for
starting or stopping pathological gam-
ing. A series of planned comparison
analyses of covariance compared
these 2 groups, controlling for race
and gender (Table 2). Table 2 presents
significant differences that seem to in-
dicate risk factors for becoming a
pathological gamer by virtue of being
different particularly at time 1 and sig-
nificant differences that seem to indi-
cate outcomes of becoming a patho-
logical gamer by virtue of being
different particularly at times 2 and 3
and not at time 1.
A second series of planned compari-
sons were conducted for 2 groups of
youths who began the study as patho-
logical gamers, that is, those who
stayed and those who stopped being
pathological gamers. Table 3 presents
differences that were seen between
these 2 groups at time 1, which may
give some hint regarding why some
youths were able to stop, and differ-
ences that were seen between these
2 groups only later, which suggest
outcomes of stopping pathological
gaming.
The planned comparisons provide lon-
gitudinal data on risk factors, out-
comes, and comorbidity and suggest
some intriguing hypotheses. The pat-
terns of results in Tables 2 and 3 dem-
onstrate that many of the relation-
ships between pathological gaming
and other variables are not as simple
as first imagined. For example, al-
though impulsivity is a risk factor for
becoming a pathological gamer, im-
pulsivity worsens after a youth be-
comes a pathological gamer. Further-
more, depression, anxiety, and social
phobias worsen after a youth becomes
a pathological gamer and improves if
an individual stops being a pathologi-
cal gamer. These findings suggest that
pathological gaming is not simply a
symptom of other problems but con-
tributes to those problems. To test this
hypothesis, a longitudinal growth
model, in which several risk factors at
time 1 (weekly amount of video game
play, social competence, and impulsiv-
ity) were hypothesized to predict
changes in pathological gaming symp-
toms, which in turn were hypothesized
to predict time 3 outcomes (depres-
sion, anxiety, social phobia, and school
performance), with controlling for
gender, was tested. Figure 2 presents
the results of this hypothesized model.
The model shows that amount of gam-
ing and impulsivity significantly pre-
dict the number of pathological gam-
ing symptoms at time 1 (pathological
gaming intercept), such that more
time gaming and higher impulsivity
predict greater tendency to be a patho-
logical gamer. Time 1 social compe-
tence and impulsivity also predict who
changes to have more pathological
gaming symptoms (pathological gam-
ing change), such that children with
lower social competence and greater
impulsivity exhibit increases in their
pathological gaming symptoms. Chil-
dren who begin with more pathologi-
cal gaming symptoms at time 1 demon-
strate higher levels of depression,
anxiety, and social phobia and lower
grades at time 3. If they change to ex-
hibit more pathological gaming symp-
toms, then this further increases their
FIGURE 1
Mean trajectories of pathological gaming symptoms for 4 distinct groups identified through latent
class analysis.
e322 GENTILE et al
Page 5
TABLE 2 Significant Differences Between Latent Classes of Gamers Who Became Pathological Gamers and Those Who Never Were Pathological Gamers
Variable Mean SE F df P Partial
2
Starts Group Never Group
Potential risk factors
Impulsivity score
Time 1 2.49 0.09 2.25 0.01 7.63 1,2427 .006 .003
Time 2 2.58 0.09 2.24 0.01 15.61 1,2234 .001 .007
Time 3 2.69 0.09 2.22 0.01 29.41 1,2167 .001 .013
Social competence score
Time 1 2.58 0.12 2.96 0.01 10.54 1,2418 .001 .004
Time 2 2.76 0.10 2.97 0.01 4.09 1,2244 .043 .002
Time 3 2.76 0.10 3.06 0.01 8.20 1,2154 .004 .004
Emotional regulation score
Time 1 2.53 0.11 2.77 0.01 5.39 1,2421 .020 .002
Time 2 2.41 0.09 2.81 0.01 17.45 1,2249 .000 .008
Time 3 2.55 0.09 2.87 0.01 11.57 1,2156 .001 .005
Empathy score
Time 1 2.20 0.07 2.34 0.01 3.90 1,2507 .048 .002
Time 2 2.16 0.07 2.34 0.01 7.38 1,2265 .007 .003
Time 3 2.15 0.07 2.34 0.01 7.57 1,2172 .006 .003
Weekly amount of video game play, h
Time 1 31.12 4.19 19.28 0.48 7.90 1,2730 .005 .003
Time 2 37.10 4.21 20.98 0.51 14.47 1,2166 .001 .007
Time 3 40.66 3.76 19.19 0.47 32.06 1,2050 .001 .015
LAN center frequency score
Time 1 1.00 0.07 0.84 0.01 5.23 1,2559 .020 .002
Time 2 1.09 0.06 0.78 0.01 27.10 1,2287 .001 .012
Time 3 1.02 0.05 0.73 0.01 31.51 1,2195 .001 .014
Problematic gaming symptoms score
Time 1 3.44 0.34 1.44 0.04 33.51 1,2481 .001 .013
Time 2 3.61 0.26 1.01 0.03 99.22 1,2242 .001 .042
Time 3 5.72 0.23 0.83 0.03 452.97 1,2155 .001 .174
Identification with game characters score
Time 1 2.25 0.17 1.96 0.02 2.85 1,2131 .091 .001
Time 2 2.15 0.15 1.77 0.02 6.862 1,1980 .009 .003
Time 3 2.30 0.13 1.67 0.02 22.07 1,1802 .001 .012
Potential outcomes
School performance score
Time 2 2.76 0.22 3.24 0.03 4.76 1,2309 .030 .002
Time 3 2.69 0.22 3.10 0.03 3.46 1,2242 .060 .002
Goal-setting score at time 2 2.73 0.10 2.93 0.01 3.90 1,2250 .050 .002
Parent-child relationship score at time 3 3.00 0.11 3.46 0.01 18.88 1,2143 .001 .009
Violent game exposure score
Time 2 7.40 0.76 3.94 0.09 20.54 1,2002 .001 .010
Time 3 7.34 0.68 3.38 0.09 33.85 1,1816 .001 .018
Normative beliefs about aggression score
Time 2 2.21 0.12 1.82 0.01 10.46 1,2282 .001 .005
Time 3 2.44 0.11 1.79 0.01 35.08 1,2152 .001 .016
Aggressive fantasies score
Time 2 2.45 0.12 1.85 0.01 26.29 1,2277 .001 .011
Time 3 2.48 0.11 1.80 0.01 35.78 1,2168 .001 .016
Hostile attribution bias score at time 3 0.47 0.04 0.29 0.01 18.06 1,2231 .001 .008
Physically aggressive behavior score
Time 2 2.17 0.11 1.59 0.01 29.71 1,2283 .001 .013
Time 3 2.35 0.10 1.49 0.01 75.08 1,2175 .001 .033
Relationally aggressive behavior score
Time 2 2.28 0.10 1.73 0.01 27.21 1,2282 .001 .012
Time 3 2.41 0.10 1.64 0.01 56.64 1,2174 .001 .025
Physical victimization score
Time 2 2.08 0.13 1.63 0.02 11.52 1,2281 .001 .005
Time 3 2.10 0.12 1.49 0.02 24.09 1,2171 .001 .011
Relational victimization score
Time 2 2.19 0.12 1.78 0.02 11.32 1,2281 .001 .005
Time 3 2.29 0.12 1.70 0.02 22.77 1,2171 .001 .010
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PEDIATRICS Volume 127, Number 2, February 2011 e323
Page 6
time 3 depression, anxiety, and social
phobia levels and poor school perfor-
mance. Overall, this model fits the data
extremely well (
2
70.4, P .001;
comparative fit index 0.99, Tucker-
Lewis index 0.96, root mean square
error of approximation 0.04, stan-
dardized root mean square residual
0.02). This model accounted for a sig-
nificant amount of variance for each of
the outcome variables (depression,
R
2
0.49; anxiety, R
2
0.29; social
phobia, R
2
0.20; grades, R
2
0.14;
all P .001).
DISCUSSION
Although many studies have described
correlates of pathological video game
play, the present study provides
needed data on risk factors for becom-
ing a pathological gamer, how long
pathological gaming lasts, what the
outcomes are, and whether it is a pri-
mary problem or is simply a symptom
of other comorbid problems. With a Di-
agnostic and Statistical Manual of
Mental Disorders-style approach to
definition, in which people exhibiting at
least one-half of the symptoms are
considered to be pathological gamers,
between 7.6% and 9.9% of our sample
would be classified as pathological
gamers at any point in time. This range
is similar to those for samples from
other countries.
5,30–33
Knowing the
TABLE 2 Continued
Variable Mean SE F df P Partial
2
Starts Group Never Group
Online game play score
Time 2 2.70 0.20 2.32 0.02 3.80 1,1989 .051 .002
Time 3 2.96 0.20 2.37 0.03 9.16 1,1804 .003 .005
ADHD symptoms score
Time 2 2.28 0.09 1.74 0.01 34.40 1,2261 .001 .015
Time 3 2.65 0.09 1.69 0.01 117.74 1,2207 .001 .051
Anxiety score
Time 2 0.84 0.07 0.65 0.01 8.04 1,2241 .005 .004
Time 3 0.95 0.07 0.63 0.01 21.21 1,2203 .001 .010
Social phobia score
Time 2 2.60 0.14 2.27 0.02 5.14 1,2216 .023 .002
Time 3 2.46 0.13 2.19 0.02 4.09 1,2164 .043 .002
Depression score
Time 2 3.17 0.14 2.24 0.02 43.56 1,2295 .001 .019
Time 3 2.85 0.14 2.17 0.02 25.26 1,2188 .001 .011
ADHD indicates attention-deficit/hyperactivity disorder.
TABLE 3 Significant Differences Between Latent Classes of Gamers Who Stopped Being Pathological Gamers and Those Who Stayed Pathological Gamers
Variable Mean SE F df P Partial
2
Stops Group Stays Group
Potential protective factors
School performance score at time 1 2.52 0..23 3.10 0.10 5.39 1,186 .021 .028
Goal-setting score
Time 1 2.99 0.11 2.70 0.05 5.97 1,179 .016 .032
Time 2 2.75 0.10 2.76 0.05 1 1,176 NS .000
Time 3 3.02 0.10 2.77 0.04 5.40 1,167 .021 .031
Problematic gaming symptoms score
Time 1 6.05 0.42 4.95 0.19 5.52 1,198 .020 .027
Time 3 0.66 0.32 3.02 0.14 46.10 1,168 .001 .215
Potential outcomes
Empathy score at time 3 3.13 0.09 2.89 0.04 5.78 1,167 .017 .033
Impulsivity score at time 3 2.35 0.07 2.55 0.03 6.19 1,168 .014 .036
Violent game exposure score at time 3 4.43 1.07 6.72 0.46 3.79 1,165 .053 .022
Aggressive fantasies score at time 3 1.99 0.13 2.33 0.06 5.30 1,172 .022 .030
Physically aggressive behavior score at time 3 1.56 0.12 1.86 0.05 5.12 1,173 .025 .029
Relationally aggressive behavior score at time 3 1.77 0.12 2.02 0.05 3.73 1,173 .055 .021
Anxiety score at time 3 0.58 0.08 0.89 0.04 12.98 1,175 .001 .069
Social phobia score
Time 2 2.33 0.16 2.73 0.07 5.42 1,181 .021 .029
Time 3 2.21 0.13 2.70 0.06 11.16 1,168 .001 .062
Depression score at time 3 2.28 0.15 2.71 0.07 6.88 1,175 .009 .038
NS indicates not significant.
e324 GENTILE et al
Page 7
prevalence, however, only tells us what
proportion of children are experienc-
ing dysfunction at a single point in
time. This is not necessarily important
if pathological gaming is a transient
problem. The data here demonstrate,
however, that most pathological gam-
ers (84%) are still pathological gam-
ers 2 years later. Furthermore, in the
same 2-year window, only 1% of chil-
dren became pathological gamers.
Therefore, pathological gaming is not
simply a “phase” that most children go
through.
In examination of what early predic-
tors discriminate between those who
become pathological gamers and
those who do not (Table 2), several
personal characteristics and gaming
habits seem to act as risk factors.
Youths who are more impulsive, have
lower social competence and empathy,
and have poorer emotional regulation
skills are more likely to become patho-
logical gamers. This pattern fits with
theoretical predictions, given that, if
pathological gaming were to be classi-
fied as a disorder, it probably would be
considered an impulse control disor-
der. Although the amount of gaming is
not sufficient to define pathological
gaming,
5,35,36
several variables related
to the amount of gaming clearly differ-
entiate individuals who are at risk
of becoming pathological gamers.
Youths who became pathological gam-
ers started with an average of 31
hours of play per week, compared with
19 hours per week for those who never
became pathological gamers. Going to
local area network (LAN) computer
centers (which are popular in Singa-
pore) more frequently was a risk fac-
tor. These issues differentiated be-
coming a pathological gamer but also
tended to become more discrepant
over time. Furthermore, greater time 1
identification with game characters
predicted becoming a pathological
gamer but online gaming did not (al-
though online gaming levels were sig-
nificantly greater at time 2 and time 3).
Once players became pathological
gamers, they began to have poorer
grades and poorer relationships with
their parents and to be exposed to
more violent games (Table 2). This is of
concern, given that several studies
have demonstrated both short-term
and long-term effects of violent games
on aggression.
37–54
In this study, chil-
dren who began consuming more vio-
lent games also began to have more
normative beliefs about aggression,
hostile attribution biases, and aggres-
sive fantasies and to engage in more
physically and relationally aggressive
behaviors (they also became more
likely to be victims of aggression). This
pattern is mirrored for those who stop
being pathological gamers; they end
up with lower levels of violent game
exposure (marginally significant), ag-
gressive fantasies, and aggressive be-
haviors (Table 3).
Of particular note, however, are the re-
sults regarding other mental health
variables. Many clinicians think that
pathological gaming is not a serious
concern because it has been assumed
that gaming either is a symptom of
other issues or is a secondary issue.
That is, many clinicians assume that
children may be depressed or anxious
and therefore retreat into games as a
coping strategy. Our data demonstrate
that this assumption is overly simplis-
tic. Although children do use games as
a coping mechanism, it is not simply a
symptom of other problems. Youths
who became pathological gamers
ended up with increased levels of de-
pression, anxiety, and social phobia
(Table 2). Conversely, those who
FIGURE 2
Longitudinal growth curve model, testing risk factors and outcomes of pathological gaming. VG
indicates video gaming; T1, time 1; T3, time 3; ns, not significant; CFI, comparative fit index; TLI,
Tucker-Lewis index; RMSEA, root mean square error of approximation; SRMR, standardized root mean
square residual. indicates P .10; a, P .05; b, P .01; C, P .001.
ARTICLES
PEDIATRICS Volume 127, Number 2, February 2011 e325
Page 8
stopped being pathological gamers
ended up with lower levels of depres-
sion, anxiety, and social phobia than
did those who remained pathological
gamers. To our knowledge, these are
the first data to demonstrate that gam-
ing predicts other mental health disor-
ders longitudinally, rather than simply
being correlated with them. Figure 2
demonstrates that these empirically
defined risk factors and outcomes
fit together as hypothesized; early
amounts of gaming, social compe-
tence (a protective factor that is nega-
tively related), and impulsivity predict
initial pathological gaming symptoms
and increases in pathological gaming,
which in turn predict increased levels
of depression, anxiety, and social pho-
bia and poorer grades.
Although these data provide evidence
that pathological gaming can influence
other mental health issues, we expect
that many of the relationships be-
tween variables are in fact reciprocal,
given that many mental health disor-
ders tend to be comorbid and mutually
reinforcing.
55
That is, although chil-
dren who are depressed may retreat
into gaming, the gaming increases the
depression, and vice versa. Longer lon-
gitudinal studies are needed to test
this.
Although this study identified several
risk factors for becoming a pathologi-
cal gamer, we did not find a systematic
pattern of protective factors that
helped some pathological gamers
overcome their dysfunction (Table 3).
It may be that other variables will need
to be measured, or larger samples
may be required to yield sufficient sta-
tistical power. Furthermore, these re-
sults will need to be replicated in other
samples. It is unclear whether cultural
differences limit the generalizeability
of the results. The prevalence of patho-
logical gaming is similar to that in
other countries. The effects of proso-
cial and violent gaming are similar to
those in other countries.
46,55
There
seems to be no specific reason to as-
sume that the relationships between
variables would be different in other
countries, but additional studies are
needed. For example, LAN computer
centers are more prevalent in Asia
than in the West, and they were related
to pathological gaming. It is likely that
this relationship signifies increased
access and not some risks unique to
Singapore, but this remains to be
tested. This study also would have
been improved by gathering informa-
tion from additional sources, such as
teachers and parents.
This study begins to provide data to
answer questions about the risk fac-
tors, cause, course, and outcomes of
pathological gaming. Pathological
gaming seems not to be simply sec-
ondary to other disorders but to pre-
dict poorer functioning longitudinally,
and it can last for several years. Sev-
eral important questions remain to be
answered, including information on
protective factors, how children can
be helped, and what types of help
might be most effective.
ACKNOWLEDGMENTS
This research was supported by a
grant from the Ministry of Education
and the Media Development Authority
of Singapore.
We thank the participating schools,
teachers, and students for their assis-
tance with this study.
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APPENDIX Measures Used in Study
Name of Measure Variables Measured Items Sample Items Response Options and Scores Reliability
Wave 1 Wave 2 Wave 3
General Media Habits
Questionnaire
38,45
Weekly amount of video
game play
6 On a usual school day, how many hours do you play
computer/video games in the morning?
Hours NA NA NA
Frequency of visiting
LAN center
2 How often do you visit game arcades or gaming
centers on weekdays/weekends?
Never (1) to almost always (4) NA NA NA
Violent game exposure 4 (for each of 3
games)
How often do you shoot or kill other players in this
game?
Never (1) to almost always (4) NA NA NA
Identification with game
characters
2 (for each of 3
games)
How much is your in-game character like you in
real life?
Never (1) to almost always (4) NA NA NA
Online game play 1 (for each of 3
games)
Is the game played online with other people? Never (1) to almost always (4) NA NA NA
Barratt Impulsiveness Scale
56
Impulsivity 14 I talk even when I know I shouldn’t. Strongly disagree (1) to strongly agree (4) 0.69 0.77 0.78
Personal Strengths Inventory
II
57
Social competence 4 I get along well with other people. Strongly disagree (1) to strongly agree (4) 0.73 0.78 0.80
Emotional regulation 6 I know how to deal with stress. Strongly disagree (1) to strongly agree (4) 0.62 0.71 0.73
Goal-setting 5 I set goals and plan how to reach those goals. Strongly disagree (1) to strongly agree (4) 0.71 0.76 0.76
Children’s Empathic Attitudes
Questionnaire
58
Empathy 15 I understand how other students feel. No, maybe, or yes 0.86 0.87 0.86
Normative beliefs about
aggression
59
Normative beliefs about
aggression
20 In general, it is OK to hit other people. It’s perfectly OK (1) to it’s really wrong (4) 0.94 0.95 0.95
Hostile attribution bias
60
Hostile attribution bias 12 6 scenarios 0.79 0.81 0.83
Aggressive fantasies
61,62
Aggressive fantasies 6 Do you ever daydream about people getting killed? Never (1) to almost always (4) 0.78 0.82 0.84
Self-report of aggression
63
Physically aggressive
behavior
6 When someone has angered or provoked me in
some way, I have reacted by hitting that person.
Not true at all (1) to very true (4) NA 0.86 0.87
Relationally aggressive
behavior
6 I have spread rumors about a person just to be
unkind.
Not true at all (1) to very true (4) NA 0.78 0.80
Physical victimization 3 I often get hit or kicked by others. Not true at all (1) to very true (4) NA 0.84 0.85
Relational victimization 3 I get ignored by others when they are angry with
me.
Not true at all (1) to very true (4) NA 0.64 0.69
Parent-family
connectedness
64
Parent-child
relationship
6 I feel close to my mother. Strongly disagree (1) to strongly agree (4) 0.90 NA 0.89
Pathological video game use
5
Pathological gaming 10 In the past year, has your schoolwork suffered
because you spent too much time playing
computer/video games?
Yes, no, or sometimes 0.71 0.77 0.79
Problematic gaming
1
Problematic gaming 10 Have you skipped meals, baths, or sleep so you
could play more computer/video games?
Yes, no, or sometimes 0.85 0.83 0.84
ADHD screen
65
ADHD 18 I feel restless. Never or rarely (1) to very often (4) NA 0.92 0.93
Asian adolescent depression
scale
66
Depression 22 I often feel like crying. Strongly disagree (1) to strongly agree (5) NA 0.95 0.96
Anxiety
67
Anxiety 20 I am scared to go to school. Not true or hardly ever true (0) to very
true or often true (2)
NA 0.90 0.92
Social phobia
68
Social phobia 17 I avoid speaking to anyone in authority. Not at all (1) to extremely (5) NA 0.93 0.92
School performance School performance 4 What marks did you get for your last exam for the
following subjects?
50 (1) to 90 (6) NA NA NA
ADHD indicates attention-deficit/hyperactivity disorder; NA, not applicable.
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PEDIATRICS Volume 127, Number 2, February 2011 e329
Page 12
  • Source
    • "First, the vast majority of studies conducted on IA and psychopathology thus far have been cross-sectional, whereas longitudinal research is scarcely available yet urgently needed to assess causal influences (Carli, et al., 2013). Second, most prior longitudinal studies relating depressive symptoms to internet or video game addiction have been conducted with children and adolescents (Brunborg, et al., 2014; Cho, Sung, Shin, Lim, & Shin, 2013; Gentile et al., 2011; Ko et al., 2014; Ko, Yen, Chen, Yeh, & Yen, 2009; van den Eijnden, Meerkerk, Vermulst, Spijkerman, & Engels, 2008), whereas limited longitudinal research has investigated depressive symptoms and internet-related addictive behaviors in emerging adults (Dong, Lu, Zhou, & Zhao, 2011). Finally, longitudinal studies have only observed the relationship between internet-related addictive behaviors and depressive symptoms in general without considering particular facets such as sadness or anhedonia (i.e., diminished pleasure in normally enjoyable activities). "
    [Show abstract] [Hide abstract] ABSTRACT: Internet addiction (including online gaming) has been associated with depression. However, most prior research relating internet addiction symptomatology to depressive symptoms has been cross-sectional, conducted with children and adolescents, and only examined depressive symptoms as a broad construct. The purpose of the current study was to examine potential longitudinal associations between anhedonia (i.e., difficulty experiencing pleasure, a key facet of depression) and internet-related addictive behaviors in 503 at-risk emerging adults (former attendees of alternative high schools). Participants completed surveys at baseline and approximately one year later (9–18 months later). Results indicated that trait anhedonia prospectively predicted greater levels of compulsive internet use and addiction to online activities as well as a greater likelihood of addiction to online/offline video games. These findings suggest that anhedonia may contribute to the development of internet-related addictive behaviors in the emerging adult population. Thus, interventions that target anhedonia in emerging adulthood (e.g., bupropion treatment or behavioral activation therapy) may help prevent or treat internet addiction.
    Full-text · Article · Sep 2016 · Computers in Human Behavior
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    • "These six criteria were also recognized as the core elements of behavioral addictions (Brown, 1993; Griffiths, 1999; Marks, 1990) and used for the development of most game addiction measures (King, Haagsma, Delfabbro, Gradisar, & Griffiths, 2013). Based on thorough consideration by a multidisciplinary expert group (see Petry et al., 2014), the APA decided to include three additional criteria when defining the criteria for the DSM-5 diagnosis of IGD, namely deception (e.g., Demetrovics et al., 2012; Gentile et al., 2011), displacement (e.g., Huang, Wang, Qian, Zhong, & Tao, 2007; Rehbein, Kleimann, & M€ ossle, 2010), and conflict (e.g., Lemmens et al., 2009; Young, 1996). Moreover, several authors in the field of IGD refer to relapse as persistence, to mood modification as escape, and to external consequences as problems (Lemmens et al., 2015; Petry et al., 2014). "
    [Show abstract] [Hide abstract] ABSTRACT: There is growing evidence that social media addiction is an evolving problem, particularly among adolescents. However, the absence of an instrument measuring social media addiction hinders further development of the research field. The present study, therefore, aimed to test the reliability and validity of a short and easy to administer Social Media Disorder (SMD) Scale that contains a clear diagnostic cut-off point to distinguish between disordered (i.e. addicted) and high-engaging non-disordered social media users.
    Full-text · Article · Aug 2016 · Computers in Human Behavior
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    • "television viewing, video gaming, and Internet connectivity) for entertainment may lead to addiction and pathological behaviours (e.g. Gentile et al. 2011). As discussed relative to the ambiguity of the relationship between leisure and media technology, media technology does not necessarily lead to positive leisure outcomes. "
    Full-text · Article · May 2016 · Annals of Leisure Research
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