Open Journal of Depression
2014. Vol.3, No.1, 13-17
Published Online February 2014 in SciRes (http://www.scirp.org/journal/ojd) http://dx.doi.org/10.4236/ojd.2014.31005
Adolescents’ Compulsive Internet Use and Depression: A
Longitudinal Study
Einar B. Thorsteinsson, Lucy Davey
University of New England, Armidale, Australia
Received November 23rd, 2013; re vised January 6 th, 2014; accepted January 15th, 2014
Copyright © 2014 Einar B. Thorsteinsson, Lucy Davey. This is an open ac cess article distrib uted und er the Cre-
ative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any me-
dium, provided the origin al work is properly cited. In accordance of the Creative Co mmons Attribution License
all Copyrights © 201 4 are reserved for SCIRP and the owner o f the in tellectual prop erty Ein ar B. Th orst eins so n,
Lucy Davey. All Copyright © 2014 are guarded by law and by SCIRP as a guardian.
Background: The present longitudinal study examined predictors of compulsive internet use and depres-
sion. Method: Adolescents, 21 males and 20 females, completed online questionnaires with a 12-month
interval. Resu lts: Social internet use (i.e., using instant messaging and social networks) was associated
with decreased levels of depression. High support satisfaction, use of social networking, and instant mes-
saging contributed to lower changes in compulsive Internet use. Conclusion: The effects of social internet
use in combination with different psychosocial factors seem to have more positive effects than negative
ones on change in depression and the development of compulsive internet use.
Keywords: Adolescents; Compulsive Internet Use; Coping; Social Support; Self-Esteem; Depression
Introduction
Before social media took off on the Internet in 2003/2004
(e.g., Facebook), it had been suggested that internet use was
related to depression (e.g., Young & Rodgers, 1998). Young
and Rodgers reported mild to moderate depression levels being
associated with internet use among males and females. Gross
(2004) surveyed 12- and 15-year-old adolescents with a upper
middle-class background and enrolled in a public school in the
US. Gross found that most of the time adolescents spend on the
internet was spent on private communication and that most of
their private communication was through instant messaging.
Gross reported small correlations between time online and de-
pression and very little difference for boys and girls regarding
internet use. A longitudinal study conducted in 2003 and 2004
in the Netherlands on 12- to 15 -year-old a dolescents found that
instant messaging at Time 1 was moderately related to depres-
sion (r = 0.18) and compulsive internet use (r = 0.24) a t Time 2,
six months later (van den Eijnden, Meerkerk, Vermulst, Spij-
kerman, & Engels, 2008). It is probably not surprising that the
association between internet use and depression was not strong
given that much of the internet use (e.g., instant messaging and
social networking) was focused on strengthening social support
networks through communications with friends and family (e.g.,
Subrahmanyama, Reich, Waechter, & Espinoza, 2008). A study
by Shaw and Grant (2002) of university undergraduates in the
US found that internet chat sessions attenuated depression (d =
0.46) and loneliness (d = 0.41) and that these chat sessions in-
creased self-esteem (d = 0.11) and perceived social support (ds
from 0.45 to 1.06). This study was conducted as a “reply” to a
study by Kraut, Patterson, Lundmark, Kiesler, Mukopadhyay,
and Scherlis (1998) that suggested that Time 2 internet use in-
creased Time 3 depression (r = 0.15) and loneliness (r = 0.15)
and reduced communication (r = −0.08) and social support (r =
−0.04).
Compulsive Internet Use
Studies conducted after 2008 in Europe and the US, after Fa-
cebook reached 100 million users, have reported mixed results
for the relationship between problematic/addictive Internet use
and depression among adolescents. Some studies have reported
an association between Internet use and depression (e.g., Chris-
takis, Mor eno, Jelenc hick, Myai ng, & Zhou, 2011; Durkee, Kaess,
Floderus, Carli, & Wasserman, 2011; Liu, Desai, Krishnan-Sa-
rin, Cavallo, & Potenza, 2011). However, other studies have re-
ported that aspects of Internet use such as Facebook use (i.e.,
social Internet use) is not linked to depression (Jelenchick,
Eickhoff, & Moreno, 2013) despite warnings about so called
“Facebook depression” (e.g., O’Keeffe & Clarke-Pearson, 2011).
The limited and mixed findings regarding the association
between social Internet use and negative mental health out-
comes (e.g., depression) suggest that compulsive Internet use
needs to be measured independently. A study by van den Eijn-
den et al. (2008) only found weak or no associations between
compulsive Internet use and depression, and instant messaging
and depression in adolescents. However, Meerkerk, van den Ei-
jnden, Vermulst, and Garretsen (2007) reported compulsive In-
ternet use in adults to be associated with negative mental health
outcomes such as increased depressive symptoms (r = 0.30)
and loneliness (r = 0.24) and reduced self-esteem (r = −.31) and
life satisfaction (r = −.26).
Social Support, Self-Es teem and Coping
Psychosocial factors that are potential risk factors in the de-
OPEN ACCESS 13

E. B. THORSTEINSSON, L. DAVEY
velopment of depression such as social support, self-esteem,
and coping might also affect the development of compulsive
Internet use in adolescents and therefore need to be examined
carefully. Social support is a potentially important moderator of
the effects of stressful situations on health in adults (Thorsteins-
son & James, 1999; Thorsteinsson, James, Douglas, & Omodei,
2011; Thorsteinsson, Ryan, & Sveinbjornsdottir, 2013) and
adolescents (Burke & Weir, 1978; Levitt, Guacci Franco, & Le-
vitt, 1993). Moreover, poor self-esteem is well established as
one of the symptoms that specifically relates to depression in
adolescents (Lewinsohn, Gotlib, & Seeley, 1997) and can pro-
spectively predict the likelihood of future depression in adoles-
cents (Orth, Robins, & Roberts, 2008).
Maladaptive coping such as rumination, acting out, and anger
has been reported as a risk factor for depression in adolescents
(Jose et al., 1998; Kosterman, Hawkins, Mason, Herrenkohl,
Lengua, & McCauley, 2010; Skitch & Abela, 2008; Thorstein-
sson, Ryan, et al., 2013). Maladaptive coping has also been
suggested as a factor that sustains depression (Galaif, Sussman,
Chou, & Wills, 2003; Murberg & Bru, 2005). Therefore, mala-
daptive coping may potentially augment and sustain depression
in adolescents. Furthermore, increased rumination has been
found to be associated with increased distress and reduced sa-
tisfaction with life (Thorsteinsson, Sveinbjornsdottir, Dintsi, &
Rooke, 2013), both potential pathways to increased depression.
The present study aims to increase our theoretical under-
standing regarding the predictors of compulsive Internet use
and depression in adolescents. It will also examine the strength
of the relationship between different psychological factors and
social Internet use; both positive and negative relationships.
Hypotheses
The purpose of the present study was to examine the pros-
pective effects of coping, social support, self-esteem, instant
messaging and use of social networking sites on compulsive
Internet use and symptoms of depression. Several hypotheses
were tested: (1) there would be an association between instant
messaging and social networking at Time 1 with depression at
Time 2 and change in depression from Time 1 to Time 2; (2)
high compulsive Internet use at Time 1 would be associated
with high depression levels at Time 2 and change in depression
from Time 1 to Time 2; and (3) self-esteem, coping, social sup-
port, social networking and instant messaging at Time 1 will
predict change in compulsive Internet use and depression from
Time 1 to Time 2.
Method
Parti cipant s
Participants were recruited from an independent school from
a small coastal town in northern New South Wales, Australia.
At Time 1 there were a total of 96 participants, 52 m ale s and 4 4
females, ranging from 12 to 18 years of age (M = 13.88, SD =
1.42, mode = 15). At Time 2 there were 77 participants, 33
males and 44 females, aged from 13 to 16 years (M = 14.61, SD
= 1.06, mode = 15). Forty-one participants (21 males and 20
females) completed both Time 1 and Time 2 (dropout of 57%).
Their ages ranged from 12 to 15 years (M = 13.32, SD = 1.15,
mode = 12). Table 1 summarizes demographics for the key
variables at Time 1.
Comparison of participants that completed both Time 1 and
Table 1.
Means and standard deviations of key variables at time 1.
Range
Measure n M SD Possible Actual
Self-est eem 41 2.24 0.76 1 - 5 0.60 - 4.00
Social support s atisfacti on 39 5.23 1.01 1 - 6 1.83 - 6.00
Social support number 40 5.68 2.24 0 - 9 0.83 - 9.00
Distraction 35 1.28 0.57 0 - 3 0 - 2.50
Acting out 35 0.54 0.72 0 - 3 0 - 2. 83
Rumination 35 1.02 0.65 0 - 3 0 - 2.67
Seeking soc i al support 35 1.12 0.56 0 - 3 0.14 - 2.57
Self-care 35 1.07 0.58 0 - 3 0.14 - 2.86
Social networking 32 2.25 1.53 1 - 5 1.00 - 5.00
Instant messaging 32 2.58 1.38 1 - 5 1.00 - 5.00
Compulsive internet Use 33 2.15 0.89 1 - 5 1.00 - 4.14
Depressi on 38 0.46 0.42 0 - 3 0 - 2.14
Time 2 (n = 41) and participants that only filled in Time 1 (n =
96) revealed that the former were one year younger (M = 13.32,
SD = 1.15) than then latter (M = 14.23, SD = 1.47). The Time 1
and Time 2 participants also had higher self-esteem, a larger
social support network (number), and a lower depression score.
The two groups had similar scores for distraction, acting out,
rumination, seeking social support, self-care, social support sa-
tisfaction, instant messaging, social network, and compulsive
Internet use.
Measures
The questionnaire package included demographic items re-
garding age and gender. Time 1 measures covered coping, so-
cial support, and self-esteem. Time 1 and Time 2 measures in-
cluded compulsive Internet use, use of instant messaging and
social networking, and depressive symptoms. The time interval
between Time 1 and Time 2 was 12 months.
Compulsive Internet use. Compulsive Internet use was
measured using the Compulsive Internet Use Scale (CIUS;
Meerkerk, van den Eijnden, Vermulst, & Garretsen, 2009). The
CIUS consists of 14 statements that were adjusted in wording
with Australian adolescents in mind rather than adults. Partici-
pants were asked to answer statements such as “Do you con-
tinue to use the Internet even when you intend to stop?” and
“Do you think you should use the Internet less often?” on a 5-
point scale from 0 (Never) to 4 (Very often). For data base pur-
poses the scale was coded from 1 to 5, respectively. The CIUS
has been found to be a reliable and valid instrument. Internal
consistency has been reported and found to range from 0.82 to
0.85 (van den Eijnden et al., 2008). Internal consistency for the
CIUS in the present study was 0.82 at Time 1 and 0.94 at Time 2.
Depression. Symptoms of depression were measured using
the Depression sub-scale of the Depression, Anxiety, and Stress
Scale (DASS-21; Lovibond & Lovibond, 1995). The DASS-21
has good internal consistency for depression and adequate va-
lidity in a variety of populations (Antony, Bieling, Cox, Enns,
& Swinson, 1998). The scale has questions such as “I felt that
life was meaningless” and is answered on a 4-point Likert scale
14

E. B. THORSTEINSSON, L. DAVEY
ranging from 0 (Did not apply to me) to 3 (Applied to me very
much, or most of the time). Internal consistency for depression
was 0.78 at Time 1 and 0.93 at Time 2.
Coping. The Measure of Adolescent Coping Strategies (Sve-
inbjornsdottir & Thorsteinsson, in press) was used to assess
what coping methods participants employ in stressful situations.
The MACS was selected based on its psychometric properties
and due to the limitations of other adolescent coping question-
naires (Sveinbjornsdottir & Thorsteinsson, 2008). Participants
were asked to think of a stressful situation that has happened to
them and to then select which coping strategies they employed
to cope with this situation, for example, “I talked with someone”
(social support) and “I cried” (rumination). The 34-item ques-
tionnaire consists of five factors: distraction/stoicism, acting
out, rumination, social support, and sel f-care. It is rated on a 4-
point scale ranging from 0 (I did not use) to 3 (I used almost all
the time). The MACS has good internal consistency, ranging
from 0.70 to 0. 81, and good test re-test reliability, ranging from
0.59 to 0.74 (Sveinbjornsdottir & Thorsteinsson, in press). The
MACS is based on several large data sets (e.g., 3034 and 534
Australian adolescents and 6908 Icelandic adolescents). Inter-
nal consistency for the MACS in the present study at Time 1
was 0.78 for distraction, 0.84 for acting out, 0.70 for rumination,
and 0.82 for seeking social support, and 0.65 for self-care.
Social support. The Social Support Questionnaire—Short
Form (SSQ-6; Sarason, Sarason, Shearin, & Pierce, 1987) was
used to measure support satisfaction and size of support net-
work. The SSQ-6 consists of six items from the original 27-
item version with reliability greater than 0.90 for both versions
(Sarason et al., 1987). Two scores were obtained from each
item. First, participants are asked to identify persons that might
provide support in the situation described (e.g., “Who can you
count on to console you when you are very upset?”). Partici-
pants could select up to nine supporters for each question. Par-
ticipants then rated their level of satisfaction from 1 (Very dis-
satisfied) to 6 (Very satisfied) with the support perceived. The
SSQ-6 has high internal reliability and good convergent validi-
ty (Sarason et al., 1987). Internal consistency in the present
study at Time 1 was 0.95 for social support number and 0.95
for social support satisfaction.
Self-esteem. The Rosenbe rg Gl oba l Self-Esteem Scale (RSES;
Rosenberg, 1965) adapted by Bachman (1978) was employed
to measure self-esteem. The RSES consists of ten items rated
on a 5-point scale. The questions ask participants to describe
what sort of a person they are by selecting how often from 1
(Never true) to 5 (Almost always true) each of the statements
are true for them. Six of the items are positively worded (e.g.,
“I feel that I have a number of good qualities”), and four items
are negatively worded (e.g., “I feel I can’t do anything right”).
The RSES has high internal consistency at 0.93, good test-retest
reliability (two-week interval) at 0.85, and validity ranges from
0.56 to 0.83 (Chiu, 1988; Hagborg, 1993). Internal consistency
in the present study was 0.94 at Time 1.
Social Internet use profile. Based on the research conducted
by van den Eijnden et al. (2008), participants were asked ques-
tions about how often they engaged in instant messaging and
social networking. Answers were given on a 5-point scale rang-
ing from 1 (Less than once a week) to 5 (D aily). Participants
were then asked to rate how important these functions are for
keeping in contact with friends from 1 (Not important at all) to
5 (Very important). These scores were then aggregated to create
a mean score for instant messaging and social networking.
Procedure
Ethics approval was sought from the University of New Eng-
land Human Research Ethics Committee. The questionnaire
was uploaded to a secure Internet-based survey provider (sur-
veymonkey.com) where a link to the survey was established.
Participants were provided with an information statement and
consent form and consent was obtained from the participant’s
parent or guardian prior to proceeding. Teachers then directed
students to the survey link and the questionnaire was completed
anonymously online under teacher supervision. Time 1 and
Time 2 data were linked by matching unique identifiers across
the data sets. The identifiers were created by participants at the
start of Time 1 and Time 2 and were based on answers to ques-
tions that once combined, created the identifier.
Statistical Analysis
SPSS version 20 was used for general statistical analysis
such as multiple regressions, correlations, and exploratory fac-
tor analysis. AMOS version 20 was used for confirmatory fac-
tor analysis of the MACS examining indices such as the Com-
parative Fit Index (CFI) with values above 0.90 suggesting a
good fit, the Tucker-Lewis coefficient (TLI) with values close
to 1 indicating a good fit, the Goodness-of-Fit index (GFI) an
absolute fix index with values above 0.95 indicating a good fit,
the root mean square error of approximation (RMSEA), and
squared root mean residual (SRMR). RMSEA values below
0.08 and SRMR values below 0.10 are generally interpreted as
favorable. An SRMR value of 0 represents a perfect fit between
the model and the population covariance matrix. The confirma-
tory factor analysis of the MACS showed strong support for the
proposed factor structure, CFI = 0.94, TLI = 0.92, GFI = 0.95,
RMSEA = 0.054 [90% CI 0.045, 0.064], and SRMR = 0.063
(Sveinbjornsdottir & Thorsteinsson, in press).
Results
Examining the first hypothesis, Table 2 shows that there was
a strong negative association between instant messaging at
Time 1 and depression at Time 2 and change in depression.
High social networking at Time 1 was associated with lower
increases in levels of depression (change). The second hypothe-
sis was not supported. Compulsive Internet use at Time 1 only
had a small association with depression at Time 2 and change
in depression (see Table 2). The results for change in compul-
sive Internet use, in the third hypothesis, showed that 43% of
the variance in compulsive Internet use change from Time 1 to
Time 2 was explained by self-esteem, coping, social support,
social networking, and instant messaging at Time 1, adjusted R2
= 0.43, F(7, 19) = 3.81, p = 0.009 (see Table 3). Examining the
change in depression showed that 13% of the variance was
explained by self-esteem, adaptive coping, social support, so-
cial networking, and instant messaging at Time 1, adjusted R2 =
0.20, F(8, 19) = 1.83, p = 0.134 (see Table 4).
Discussion
The results show that the hypotheses were partly supported.
Instant messaging and social networking use was associated
with a reduction in depression over time. However, only high
levels of instant messaging a t Time 1 were associated with lowe r
depression levels at Time 2 and lower change in depression
OPEN ACCESS 15

E. B. THORSTEINSSON, L. DAVEY
Table 2.
Correlation matrix between key variables.
Depression (Time 2) Depression (C hange)
Measure (Time 1) r p n r p n
Instant messaging −0.40 0.015 30 −0.46 0.005 30
Social networking 0.13 0.252 30 −0.28 0.065 30
Compulsive internet use −0.02 0. 454 31 −0.13 0.248 31
Note: Change = Time 2 – Time 1. p values ar e one-tailed.
Table 3.
Regression models summary for change in compulsive internet use
from time 1 to time 2.
95% CI for B
Predict or (Time 1) B Lower Upper β r sr
Self-est eem 0.18 −0.40 0.75 0.13 0.34 0.10
Distractiona −0.16 −0.97 0.65 −0.09 0.24 −0.06
Self-carea 0.88 0.16 1.59 0.48 0.53 0.38
Social support
satisfact ion −0.30 −0.72 0.11 −0.27 −0.16 −0.23
Social support
number
0.03 −0.17 0.23 0.06 0.34 0.05
Social networking −0.16 −0.52 0.19 −0.22 −0.39 −0.14
Instant messaging −0.21 −0.57 0.15 −0.28 −0.57 −0.18
Note: Fit for model R2 = 0.58, adjusted R2 = 0.43, F(7, 19) = 3.81, p = 0.009. The
r given is for the zero-order correlation. aOnly coping factors that correlated >
0.20 with change i n compulsive Inte rnet use were entered into the regres sion.
Table 4.
Regression models summary for change in depression from time 1 to
time 2.
95% CI for B
Predictor (Time 1) B Lo wer Upper β r sr
Self-est eem −0.27 −0.90 0.36 −0.23 0.12 −0.16
Acting outa 0.10 −0.57 0.77 0.08 −0.26 0.06
Ruminationa −0.57 −1.39 0.26 −0.40 −0.30 −0.25
Seeking soc i al
support a 0.29 −0.68 1.26 0.18 0.17 0.11
Social support
satisfact ion
0.30 −0.13 0.72 0.34 0.37 0.25
Social support
number
0.00 −0.23 0.23 −0.01 0.13 −0.01
Social networking 0.06 −0.31 0.44 0.11 −0.33 0.06
Instant messaging −0.34 −0.74 0.07 −0.50 −0.48 −0.30
Note: Fit for model R2 = 0.43, adjusted R2 = 0.20, F(8, 19) = 1.83, p = 0.134. The
r given is for the zero-order correlation. aOnly coping factors that correlated >
0.20 with change i n depression were entered into the regression.
levels. The effects of compulsive Internet use at Time 1 on
depression at Time 2 and change in depression were weak or
negligible. Several factors seem to influence change in compul-
sive Internet use (beta > 0.20). High self -care contributed to aug-
mented compulsive Internet use levels while social support sa-
tisfaction, social networking, and instant messaging attenuated
change in compulsive Internet use. These findings suggest that
to protect against compulsive Internet use adolescents need to
have a solid support structure, possibly sustained by social net-
working and instant messaging, with low emphasis on coping
by using self-care.
Four factors influenced change in depression strongly (beta >
0.20). These factors were high self-esteem, rumination, and use
of instant messaging while depression was augmented through
high support satisfaction. These findings suggest that adoles-
cents that have a combination of high self-esteem, high instant
messaging use, high rumination, and low support satisfaction
are more likely to have a reduction or no change in depression
levels. The association between high support satisfaction and
depression may reflect the connection between seeking social
support as a coping mechanism and change in depression (beta
= 0.18). Thus adolescents may be seeking social support to deal
with changes in depression levels resulting in increased social
support that in turn explains increased social support satisfac-
tion levels.
Limitations
A small sample size increases the risk of moderate to large
effect sizes not being statistically significant. However, despite
a small sample size, many of the theorized effects were found
to be statistically significant. The sample is limited to one in-
dependent Anglican school only further reducing any generali-
zation from this study. However, this study does enable future
studies to examine how theorized models relate to the use of in-
stant messaging and social networking, compulsive Internet use,
and depression. Future studies should consider a shorter time
interval to reduce dropout. However, the time interval needs to
be long enough for observable chances to occur.
Conclusion
If we only look for the negative psychological effects of so-
cial internet use on psychological and physical health using a
plethora of different measures then we will find negative effects,
be they meaningful or not (Shaw & Gant, 2002). The present
study suggests that the direction of effects is not self-evident
when it comes to predictors of change in compulsive internet
use and depression. The results indicate that mood may not be
as important as previously thought in explaining adolescent be-
havior on the internet. Indeed, according to this study, the use
of social networking and instant messaging may have beneficial
effects for adolescents’ mental he alth.
It is not new for technology to be at the forefront of discus-
sion when it comes to adolescents. Where television, for in-
stance, was once predicted to erode social and emotional func-
tioning, the internet has more recently been in the spotlight.
Perhaps the predicted deleterious effect of technology on psy-
chological functioning is better explained by other factors in-
dependent of the technology. Excessive use of any technology
is likely explained by pre-existi ng psych opa th ology and may be
better considered a symptom of a wider problem. Importantly,
we may be overlooking the potential benefits of the internet for
adolescents if researchers only focus on pathology.
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