A longitudinal study of the association between Compulsive Internet use
Linda D. Muusses
, Catrin Finkenauer
, Peter Kerkhof
, Cherrie Joy Billedo
Department of Social and Organizational Psychology, VU University Amsterdam, Van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands
Department of Clinical Child and Family Studies, VU University Amsterdam, Van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands
Department of Communication Science, VU University Amsterdam, Buitenveldertselaan 3, 1082 VA Amsterdam, The Netherlands
Available online 11 April 2014
Compulsive Internet use
Objective: Compulsive Internet Use (CIU) has been linked to lower wellbeing, especially among adoles-
cents. Yet, questions regarding the directionality of this association remain unanswered: CIU may inﬂu-
ence wellbeing and vice versa. Theoretically, both directions are plausible, yet so far no studies have
examined the directionality of these effects among adults. This article aims to shed light on the direction-
ality of the relation between CIU and both positive and negative wellbeing, using a prospective, longitu-
dinal sample of adults (n= 398).
Methods: Over the course of four years, participants completed ﬁve assessments of their CIU and both
positive and negative indicators of wellbeing. Participants were married couples who were recruited in
the municipalities where they were married.
Results: CIU predicted increases in depression, loneliness and stress over time, and a decrease in happi-
ness. No effect of CIU on the change in self-esteem was found. Further, happiness predicted a decrease in
CIU over time.
Conclusions: The results suggest CIU lowers wellbeing. This is important given that lowered wellbeing
may affect health. Happiness is suggested to be a buffer for developing CIU.
Ó2014 Elsevier Ltd. All rights reserved.
An increasing number of people ﬁnds it hard to regulate their
Internet use. As a result, they develop symptoms of compulsive
Internet Use (CIU): Internet use with addictive characteristics,
including withdrawal reactions when Internet use is impossible
(e.g., unpleasant emotions), lack of control over Internet use (e.g.,
use of the Internet despite the intention or desire to stop or to de-
crease the use), and cognitive and behavioral preoccupation with
the Internet (Van den Eijnden, Meerkerk, Vermulst, Spijkerman, &
Engels, 2008). Many studies have shown that CIU is associated with
lower psychological wellbeing (Chou, Condron, & Belland, 2005;
Widyanto & Grifﬁths, 2006). However, there is no consensus on
the directionality of this association (e.g., Armstrong, Phillips, &
Saling, 2000; Ha et al., 2007; Sum, Mathews, Hughes, & Campbell,
2008). While some researchers suggest that CIU causes lower well-
being (e.g., Moody, 2001), others argue that low wellbeing causes
an increase in CIU (e.g., LaRose, Lin, & Eastin, 2003). Although both
sides make theoretically compelling cases, it remains unclear
which direction has the strongest effects over time.
Almost all studies on CIU and wellbeing use samples of adoles-
cents or college students. We know surprisingly little about the
link between CIU and wellbeing among adults (Byun et al., 2009;
Chou et al., 2005; Kuss & Grifﬁths, 2011; Tokunaga & Rains,
2010; Widyanto & Grifﬁths, 2006). This is surprising, given that,
for example, in the Netherlands, a country which has the 8th high-
est Internet penetration rate in the world, (InternetWorldStats.,
2011), adults are the largest group of Internet users (Centraal
Bureau voor de Statistiek [Central Statistical Ofﬁce]., 2013). Fur-
thermore, the 78.15% of the Dutch population is over 18 years of
age, and only 7.11% of the Dutch population is a student of voca-
tional or academic education (Centraal Bureau voor de Statistiek
[Central Statistical Ofﬁce]., 2014). The present study aims to ex-
plore the long-term directionality of the association between CIU
and different indicators of wellbeing, using ﬁve consecutive sur-
veys that were conducted over a four year period among adults.
Psychological wellbeing, sometimes referred to as psychological
health or subjective wellbeing, is the evaluation of one’s quality of
0747-5632/Ó2014 Elsevier Ltd. All rights reserved.
This research was supported by a Grant to the third author from the
Netherlands Organization for Scientiﬁc Research (No. 452-05-322) awarded to
Corresponding author. Tel.: +31 (0)205980132.
E-mail addresses: firstname.lastname@example.org (L.D. Muusses), c.ﬁnkenauer@vu.nl
(C. Finkenauer), email@example.com (P. Kerkhof), firstname.lastname@example.org (C.J. Billedo).
Tel.: +31 (0)205988857.
Tel.: +31 (0)205986815.
Computers in Human Behavior 36 (2014) 21–28
Contents lists available at ScienceDirect
Computers in Human Behavior
journal homepage: www.elsevier.com/locate/comphumbeh
life or life satisfaction. It is positively related to physical health
(Mechanic & Hansell, 1987) and better social functioning (Diener,
1984; Diener, Suh, Lucas, & Smith, 1999; Okun, Stock, Haring, &
Witter, 1984). Given the independent contribution of positive
and negative aspects of wellbeing for health and human function-
ing, psychological wellbeing is considered as comprising both
dimensions. Typically, happiness, depression, stress, loneliness,
and self-esteem are indicators of psychological wellbeing (Augner
& Hacker, 2011; Crocker, Luhtanen, Blaine, & Broadnax, 1994;
Kang, 2007). The present study recognizes the multiple dimensions
of wellbeing, and includes these diverse indicators to examine
their relation with CIU.
1.1. Wellbeing and Compulsive Internet use
CIU is often described as being incapable to control one’s Inter-
net use (Chou & Hsiao, 2000; Johansson & Götestam, 2004). Related
terms in the literature are Internet addiction (e.g., Young, 1998),
problematic (e.g., Caplan, 2002; Morahan-Martin & Schumacher,
2000) or pathological Internet use (e.g., Davis, 2001), and Internet
dependence (e.g., Wang, 2001). Several studies have shown that
CIU is negatively associated with different indicators of wellbeing:
Compulsive Internet users are more depressed, stressed and lonely,
less happy and have lower self-esteem (for recent reviews and
meta-analyses see Byun et al., 2009; Chou et al., 2005; Tokunaga
& Rains, 2010; Widyanto & Grifﬁths, 2006).
Longitudinal studies yield mixed results. In a study of people’s
ﬁrst two years with a home Internet connection, greater use of
the Internet increased depression and loneliness (Kraut et al.,
1998). However, in the third year, these effects had dissipated
(Kraut et al., 2002). This begs the question what kind of results
can be expected now that people have been using the Internet
for almost twenty years, and how wellbeing relates to compulsive,
rather than frequent, use of the Internet.
In the, to our knowledge only longitudinal study of CIU and
wellbeing, incoming freshmen were recruited for a three-wave pa-
nel study in the summer before their ﬁrst year of college (Tokuna-
ga, 2012). Results showed that psychosocial problems such as
loneliness and depression predicted later CIU, which in turn pre-
dicted later functional impairment (i.e., vocational impairment,
impairment in friendships and in family relationships). All three
waves were administered within half a year, from the summer be-
fore freshmen started college to the end of the ﬁrst semester. At
this time these young people probably underwent important life
changes (e.g., moving away from their parents, living in a new
environment), which may have affected the results and which lim-
its the generalizability of these ﬁndings. Finally, a 2-wave study
among adolescents aged 12–15 found that Internet use for com-
munication purposes predicted an increase in depression six
months later (van den Eijnden et al., 2008). Surprisingly, it also
yielded an opposite effect: loneliness predicted a decrease of com-
puter-mediated communication over time. Taken together, these
results suggest that both directions of inﬂuence are plausible. Over
time, CIU might affect wellbeing, but wellbeing might also affect
CIU. However, there is no consensus in the literature.
1.2. Directionality of effects
The literature provides theoretical reasons for both directions of
inﬂuence. Wellbeing might affect CIU because people with low
self-esteem may develop a preference for online over ofﬂine social
interactions, which they experience as a safer way of expressing
themselves (Caplan, 2003, 2006). The preference for online interac-
tions, in turn, may increase their dependence on the Internet,
leading to CIU. Further, the consumption of different media can alter
prevailing mood states, and people’s selection of speciﬁc kinds of
media content often serves to regulate their mood (Zillmann,
1988). Depressed individuals may therefore create media habits to
alleviate depressed moods, leading to CIU (LaRose et al., 2003). Sim-
ilarly, people may develop CIU because they use the Internet to dis-
sociate and protect themselves from memories of loss, neglect, and
abuse experienced in childhood (Schimmenti, Guglielmucci, Barba-
sio, & Granieri, 2012; Schimmenti, Passanisi, Gervasi, Manzella, &
Famà, 2013). However, the opposite effect has also been described.
CIU might affect wellbeing because time spent online with weak
ties, such as acquaintances, might substitute time spent on strong
ties, such as family members (Nie & Erbring, 2000; Vitalari, Venk-
atesh, & Gronhaug, 1985). CIU may also negatively affect other life
outcomes, including school or work performance. Such outcomes
can isolate individuals from healthy social activities and increase
their feelings of loneliness (Kim, LaRose, & Peng, 2009; Tokunaga,
2012). Finally, certain types of online content and online interac-
tions may affect wellbeing. To illustrate, excessive use of social med-
ia might decrease one’s self-esteem or lead to depression, because it
provides content that can be used for social comparison (Pantic et al.,
2012). Thus, according to the literature, both directional paths seem
plausible. It may even be the case that they mutually reinforce each
other, in that lower wellbeing may lead to more CIU, which in turn
may decrease wellbeing even further. The present study sought to
examine the directionality of the link between CIU and wellbeing.
1.3. The present study
Our study aims to shed light on the long-term directionality of
the link between CIU and psychological wellbeing. Based on the
existing literature, we expected to replicate the negative associa-
tion between CIU and wellbeing, and extend these ﬁndings by
exploring the long-term effects of CIU and wellbeing. Given that
positive and negative indicators are partly independent of one an-
other (Huppert & Whittington, 2003), and psychological wellbeing
is considered a multi-dimensional construct, we examine both
negative (i.e., depression, stress, and loneliness) and positive (i.e.,
happiness and self-esteem) indicators of wellbeing.
Because the literature describes effects in both directions, we
pose the research question: What are the long-term effects of
CIU on wellbeing, and of wellbeing on CIU? To examine these asso-
ciations as well as the directionality of effects, we use data from a
5-year prospective study among married adult couples. These cou-
ples were recruited through the municipalities in which they were
married, and are representative of Dutch married couples. Married
people make up 41.48% of all people in the Netherlands (Centraal
Bureau voor de Statistiek, 2013). Therefore it is an important and
representative group to study. Furthermore, CIU has adverse social
and relational effects; not only does it contributes to greater loss of
self-control, but it also undermines trust (Muusses, Finkenauer,
Kerkhof, & Righetti, 2013). These examples show that married peo-
ple are at risk for CIU related issues, which begs the question of
how CIU and wellbeing are related in married people.
The longitudinal design of our study allows us to examine the
long-term directional effects of CIU and wellbeing. Because the
data is dyadic, we use analyses that correct for this non-indepen-
dence of the data. Furthermore, we provide across-partner correla-
tions for the variables of interest. Although our study is
correlational, the results will contribute to our understanding of
the importance of both directions.
The data used for this study are derived from the VU University
Panel on Marriage and Well-Being, a 5-wave, longitudinal study
22 L.D. Muusses et al. / Computers in Human Behavior 36 (2014) 21–28
among newlywed couples in the Netherlands. In the ﬁve waves
199, 195, 190, 157, and 140 newlywed couples participated,
respectively. At the ﬁrst wave, the mean age of husbands (coded
as 0) was 32.07 years (SD = 4.86) and the mean age of wives (coded
as 1) was 29.20 years (SD = 4.28). Couples had been romantically
involved on average for 5.71 years (SD = 3.03) and had been living
together for an average of 3.81 years (SD = 2.31). The ﬁrst wave of
this study took place about one month after marriage (for more
information, including ethical board, consent and assent proce-
dures, see Finkenauer, Kerkhof, Righetti, & Branje, 2009; Pollmann
& Finkenauer, 2009). Nearly all the couples (98.5% of the husbands
and 96.4% of the wives) were Dutch.
Participants were recruited via the municipalities in which they
got married. The municipalities were average sized Dutch cities.
Selection criteria were that (1) for all participants this was their
ﬁrst marriage, (2) at the ﬁrst data collection, couples had no chil-
dren from this marriage or from previous relationship partners,
(3) both partners were between 25 and 40 years old, and (4) cou-
ples were heterosexual. Nineteen percent of the couples who were
sent a letter of invitation to participate in the study agreed to par-
ticipate. This response rate is similar to other studies recruiting
participants from public records in the United States (e.g., Kurdek,
The study was introduced to participants as a study on the
inﬂuence of personal dispositions, behavior in the relationship,
and partner perception on marital wellbeing in the ﬁrst years of
marriage. Wave one took place in 2005, 1–2 months after they
got married. The following waves took place at one-year intervals.
At the data collections, both members of the couple separately
ﬁlled out an extensive questionnaire at home in the presence of
a trained interviewer, who visited them at home. The interviewer’s
presence ensured that partners independently completed the
questionnaires without consulting each other. The questionnaire
took about 90 min to complete. At each data collection, after they
completed the questionnaire, couples received 15 Euros and a
small gift (e.g., pen-set, gift voucher). To increase commitment,
we sent birthday cards to each participant. Also, participants were
able to get updates about the progress of the study via the study
All measures were assessed in all ﬁve waves except loneliness,
which was only measured in the last two waves. Therefore, all
analyses concerning loneliness include only waves 4 and 5. All
other analyses include all ﬁve waves. For details on the study see
Finkenauer, Wijngaards-de Meij, Reis, and Rusbult (2010), Kerkhof,
Finkenauer, and Muusses (2011), Muusses et al. (2013). Only scales
relevant to the present hypotheses and research question are
2.3.1. Measures of Internet use
Compulsive Internet Use (CIU) was assessed using a shortened
version of the Compulsive Internet Use Scale (CIUS; Meerkerk,
van den Eijnden, Vermulst, & Garretsen, 2009). For a description
of the shortened scale and data supporting the reliability and valid-
ity of the scale, see Muusses et al. (2013). The items are: ‘‘How
often.... (1) do you ﬁnd it difﬁcult to stop using the Internet when
you are online? (2) do you continue to use the Internet despite
your intention to stop? (3) do you prefer to use the Internet instead
of spending time with others (e.g., partner, children, parents,
friends)? (4) are you short of sleep because of the Internet? (5)
do you feel restless, frustrated, or irritated when you cannot use
the Internet?’’ (
for the ﬁve waves respectively .74, .81, .79, .82
and .84) (for descriptive information, see Tables 1 and 2 and Fig. 1).
To assess Frequency of Internet use, participants reported how
many days per week and how many hours per day during these
days they used the Internet for private purposes (as opposed to
using the Internet for work). The product of the two questions re-
sulted in a score for the frequency of Internet use, ranging from 0 to
686 (h). Theoretically, the measure should have a maximum value
of 168 (7 days in a week ⁄24 h in a day). Consequently, the three
participants who scored higher than the theoretical maximum
probably misunderstood the question. Therefore, for the analyses
that included frequency of Internet use, values higher than the the-
oretical maximum were replaced with missing values. Recoding
the outlier values changed only 1 of the possible 28 results. The
remaining 27 results did not change in signiﬁcance or direction.
2.3.2. Measures of personal wellbeing
Happiness was assessed using Lyubomirsky and Lepper’s (1999)
Subjective Happiness Scale. The questionnaire contained four
items. The items are: (1) ‘‘In general, I consider myself:’’ (1 = not
a very happy person to 7 = a very happy person);(2) ‘‘Compared to
most of my peers, I consider myself:’’ (1 = less happy to 7 = more
Means and SD’s of the variables over time.
Wave Internet frequency CIU Happiness Depression Stress Loneliness Self-esteem
Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD)
1 7.98 (11.51) 1.57 (.54) 5.72 (.86) 1.35 (.30) 2.06 (.49) 4.05 (.44)
2 18.3 (75.68) 1.62 (.61) 5.67 (.91) 1.37 (.32) 2.11 (.49) 4.08 (.46)
3 12.35 (53.25) 1.63 (.54) 5.69 (.86) 1.36 (.31) 2.12 (.48) 4.12 (.47)
4 15.15 (59.57) 1.61 (.57) 5.66 (.85) 1.36 (.29) 2.05 (.49) 1.21 (.30) 4.10 (.43)
5 17.51 (64.33) 1.60 (.57) 5.60 (.91) 1.36 (.30) 2.08 (.48) 1.21 (.31) 4.11 (.50)
Note: CIU = Compulsive Internet Use; Happiness = Subjective Happiness Scale; Depression = Center for Epidemiologic Studies Depression Scale; Stress = Perceived Stress
Scale; Loneliness = Loneliness Scale; Self-Esteem = Self-Esteem Scale.
Within and across partner correlations, means and SD’s for the assessed variables.
Variable 1 2 3 4 5 6
CIU .04 .02 .01 .11
.03 .05 .04 .04
.03 .03 .01 .01
M 1.60 5.67 1.36 2.08 1.21 4.09
SD .57 .87 .30 .49 .30 .46
Note: CIU = Compulsive Internet Use; Happiness = Subjective Happiness Scale;
Depression = Center for Epidemiologic Studies Depression Scale; Stress = Perceived
Stress Scale; Loneliness = Loneliness Scale; Self-Esteem = Self-Esteem Scale.
Note: The correlations on and above the diagonal are across partner correlations
between model variables. The correlations below the diagonal line are within-
L.D. Muusses et al. / Computers in Human Behavior 36 (2014) 21–28 23
happy);(3) ‘‘Some people are generally very happy. To enjoy life
regardless of what is going on, getting the most out of everything.
To what extent does this characterization describe you?’’ (1 = not at
all to 7 = a great deal);(4) ‘‘Some people are generally not very hap-
py. Although they are not depressed, they never seem as happy as
they might be. To what extent does this characterization describe
you?’’ (1 = not at all to 7 = a great deal).The scale showed good reli-
ability in our study (Cronbach’s
for men = .78, .84, .78; .75, and
.82; and for women = . 76, .81, .79, .77, and .84 for all ﬁve waves
respectively) and has good validity (Lyubomirsky & Lepper, 1999).
Depression was assessed using Radloff’s (1977) Center for Epide-
miologic Studies Depression Scale (CES-D Scale). The scale con-
tained 20 items, rated on a 4-point Likert scale; for example,
‘‘During the past week, I was bothered by things that usually don’t
bother me.’’ [1 = Rarely or non of the time (less than 1 day);2=Some
or a little of the time (1–2 days);3=Occasionally or a moderate
amount of time (3–4 days);4=Most or all of the time (5–7 days)].
The scale showed good reliability (Cronbach’s
for men = .71,
.87, .83, .78, and .85; and for women = .85, .85, .87 .87, and .85
for all ﬁve waves respectively).
Stress was assessed using a short form of the Cohen, Kamarck,
and Mermelstein (1983) Perceived Stress Scale (PSS). The question-
naire contained 11 items. An example item is ‘‘In the last month,
how often have you been upset because of something that hap-
pened unexpectedly.’’ (1 = never to 5 = very often). The scale
showed good reliability (Cronbach’s
for men = .77, .85, .81; .87,
and .85; and for women = .84, .86, .87, .86, and .88 for all ﬁve waves
Self-esteem was assessed using the Rosenberg’s (1965) Self-Es-
teem scale. The scale contained 10 items, rated on a 5-point Likert
scale; for example, ‘‘I feel that I’m a person of worth, at least on an
equal plane with others.’’ (1 = does not apply to 7 = does apply). The
scale showed good reliability (Cronbach’s
for men = .78, .86, .85,
.85, and .82; and for women = .79, .84, .87 .87, and .86 for all ﬁve
Loneliness was assessed only in wave 4 and 5, using the De Jong
Gierveld and van Tiburg (1999) Loneliness Scale, measuring over-
all, emotional, and social loneliness. The scale contained 11 items.
An example item is ‘‘In the last month, how often have you been
upset because of something that happened unexpectedly.’’
(1 = yes,2=more or less,3=no). The scale showed good reliability
for men = .77 and .81; and for women = .87 and .88
for both waves respectively).
Finally, to be able to control for Commitment, we assessed com-
mitment using the Rusbult, Martz, and Agnew (1998) Scale (8
for men = .86, .90, .93, .93, and .94; and for wo-
men = .91, .91, .93 .93, and .93 for all ﬁve waves respectively).
2.4. Strategy of analysis
Data provided by a given participant on multiple research occa-
sions are non-independent, as is data that results from two part-
ners in a given relationship. Accordingly, we analyzed our data
using hierarchical linear modeling (Raudenbush & Bryk, 2002). This
technique accounts for the nonindependence of observations by
simultaneously examining variance associated with each level of
nesting, thereby providing unbiased hypothesis tests. Following
recommended procedures for couples data, we represented inter-
cept terms as random effects and represented slope terms as ﬁxed
effects (Kenny, Mannetti, Pierro, Livi, & Kashy, 2002).
To test for longitudinal effects, we performed multilevel residu-
alized lagged regression analyses. In these analyses, we regressed
each criterion variable onto the earlier predictor and the earlier
measure of the criterion. These analyses allowed us to assess the
extent to which the predictor variable accounts for the change in
the criterion over time. We performed lagged analyses, in which
we simultaneously predict Time 2 criteria from Time 1 predictors,
Time 3 criteria from Time 2 predictors, Time 4 criteria from Time 3
predictors and Time 5 criteria from Time 4 predictors.
3.1. Descriptive analyses
As suggested by the literature (Chou et al., 2005), gender was
signiﬁcantly associated with CIU indicating that men were more
likely to use the Internet compulsively than women (see Table 3).
We also found some associations between gender and the wellbe-
ing variables. Consistent with the literature (Kessler, McGonagle,
Swartz, Blazer, & Nelson, 1993; Kling, Hyde, Showers, & Buswell,
1999; Lundberg, 2002), women reported more depression and
stress and less self-esteem, but we found no associations with gen-
der for happiness and loneliness (see Table 3).
The means and standard deviations as well as the individual
and cross-partner correlations of the main variables are presented
in Table 2. Apart from the main variables’ signiﬁcant within per-
sons correlations, it is interesting to note that CIU in one partner
is positively associated with the other partner’s stress. Put differ-
ently, the more one partner uses the Internet compulsively, the
more stressed is the other partner (and vice versa). Furthermore,
1st wave 2nd
Fig. 1. Standardized mean values of the main variables over time. Note: CIU = Compulsive Internet Use; Happiness = Subjective Happiness Scale; Depression = Center for
Epidemiologic Studies Depression Scale; Stress = Perceived Stress Scale; Loneliness = Loneliness Scale; Self-Esteem = Self-Esteem Scale.
The results for the longitudinal dyadic data using hierarchical linear models and
lagged analyses are displayed in beta’s. These beta’s can be considered measures of
effect. Effect sizes for these results were not provided, because to our knowledge, no
suitable measure of effect size is commonly used with these types of analyses.
24 L.D. Muusses et al. / Computers in Human Behavior 36 (2014) 21–28
happiness, stress and loneliness are correlated within couples. That
is, when one partner is happy, stressed, or lonely, the other partner
is more likely to be happy, stressed, or lonely too.
Apart from suggesting gender effects for CIU and the wellbeing
effect separately, the literature does not suggest that the relations
between CIU and wellbeing differ for men and women, and hus-
band-wife dyads should thus be treated as indistinguishable. To
test if the data supports this assumption, we performed prelimin-
ary analyses to explore possible moderation by participant gender,
by including main effects and interaction effects for gender to the
analyses testing all possible main effects of the variables. Impor-
tantly, none of the 15 possible associations of the main variables
of interest changed in direction or level of signiﬁcance when add-
ing gender as a main effect or interaction term. Therefore, we omit-
ted participant gender from the analyses and treated dyad
members as indistinguishable.
To ensure that the relation between CIU and the indicators of
wellbeing was not attributable to commitment, we also controlled
for commitment in our main analyses. None of the associations
changed in signiﬁcance or direction (positive or negative), indicat-
ing that commitment did not change the relation between CIU and
3.2. Predicting key model variables cross-sectionally
Using hierarchical linear modeling, the critical relations were
tested for all ﬁve time points (Time 1, 2, 3, 4 and 5) simultaneously,
except for loneliness, which was only tested in wave 4 and 5. CIU
was signiﬁcantly negatively associated with happiness and self-
esteem, and signiﬁcantly positively with depression, stress, and
loneliness (see Table 3). Furthermore, to test the validity of our
model, we tested whether it held above and beyond Internet
frequency, by including main effects and interaction effects for
Internet frequency to the analyses. None of the effects of CIU
changed in signiﬁcance or direction, and Internet frequency was
not signiﬁcantly related to any of the wellbeing indicators.
3.3. Longitudinal analyses
As described in our strategy of analysis, to test the effects longi-
tudinally and explore the change over time in the criteria, we per-
formed multilevel residualized lagged analyses. In these analyses
the earlier independent variable was regressed on the dependent
variable one year later, controlling for the dependent variable
one year earlier. All effects were tested while controlling for the
earlier measure of the criterion variable (e.g., earlier CIU predicts
later happiness, while controlling for earlier happiness). Each effect
therefore predicts the change in the dependent variable.
We tested both the effects of CIU on the wellbeing variables, as
well as the opposite direction: the effects of the wellbeing
variables on CIU. Firstly, CIU was found to signiﬁcantly predict
the changes in happiness, depression, stress and loneliness
=.05, p = .04; b
= .06, p= .03; b
p= .04; b
= .13, p= .01) but not for self-esteem (see Table 4).
In the opposite direction, happiness predicted the change in CIU
over time (b=.06, p< .01), but none of the other wellbeing indi-
cators predicted the change in CIU over time (see Table 4).
This study investigated the long-term directionality of the rela-
tionship between CIU and psychological wellbeing using an adult
sample. Previous research found negative relations between CIU
and wellbeing (for recent reviews and meta-analyses see Byun
et al., 2009; Chou et al., 2005; Tokunaga & Rains, 2010; Widyanto
& Grifﬁths, 2006). This study replicated these ﬁndings on a range of
wellbeing indicators, both positive and negative: Compulsive
Internet use was negatively related to happiness and self-esteem,
and positively related to depression, stress, and loneliness.
Crucially extending these ﬁndings, the current study examined
the directionality of the long-term associations. Longitudinally, the
data showed stronger support for the suggestion that CIU affects
wellbeing over time, than that wellbeing affects CIU: CIU predicted
changes in depression, stress, and loneliness positively, and the
change in happiness negatively. No effect of CIU on changes in
self-esteem was found. These results suggest CIU decreases peo-
ple’s wellbeing. Happiness however, did predict the change in
CIU negatively over time, suggesting happiness could protect peo-
ple from developing CIU.
4.1. Implications and future directions
4.1.1. CIU lowers psychological wellbeing
Our ﬁndings provide support for the suggestion that CIU con-
tributes to a decline in psychological wellbeing. In particular, we
found that CIU predicted greater depression, stress, and loneliness
over time. Previous research suggests several reasons as to why
this may happen. One is that the more time people spend online,
the less time they spend on real life interactions such as communi-
cation with ofﬂine signiﬁcant others (Kraut et al., 1998). This may
give rise to feelings of isolation and disconnection, which are often
associated with depression and loneliness (Kim et al., 2009;
Moody, 2001; Tokunaga, 2012). Another reason why CIU may be
associated with low wellbeing is that the Internet exposes a person
to different forms of aversive experiences. Prolonged and excessive
use of social media has been shown to lead to negative feelings
Hierarchical linear modeling b’s over T= 1, 2, 3, 4 and 5.
CIU Gender Frequency of Internet Use
Note: CIU = Compulsive Internet Use; Happiness = Subjective Happiness Scale;
Depression = Center for Epidemiologic Studies Depression Scale; Stress = Perceived
Stress Scale; Loneliness = Loneliness Scale; Self-Esteem = Self-Esteem Scale.
Residualized lagged analyses b’s for the predicted change in CIU by wellbeing factors,
and predicted change in wellbeing factors by CIU.
Criterion at earlier
Effect on change in
Happiness ?CIU .70
Depression ?CIU .71
Stress ?CIU .71
Loneliness ?CIU .78
Self-esteem ?CIU .71
CIU ?Happiness .65
CIU ?Depression .27
CIU ?Stress .49
CIU ?Loneliness .66
CIU ?Self-esteem .71
Note: CIU = Compulsive Internet Use; Happiness = Subjective Happiness Scale;
Depression = Center for Epidemiologic Studies Depression Scale; Stress = Perceived
Stress Scale; Loneliness = Loneliness Scale; Self-Esteem = Self-Esteem Scale.
L.D. Muusses et al. / Computers in Human Behavior 36 (2014) 21–28 25
about the self and even depression, because it evokes social com-
parison (Pantic et al., 2012). In support of this explanation, Krasno-
va, Wenninger, Widjaja, and Buxmann (2013) found that the
relation of life satisfaction and Facebook use was mediated by
envy, particularly among passive users. Furthermore, the intensity
of passive following was likely to reduce a user’s life satisfaction in
the long run because it triggered this type of upward social
Our results show that CIU has the strongest effect on loneliness
compared to the other indicators of wellbeing. This provides sup-
port for previous studies that show that extensive use of the Inter-
net, even for communication purposes, leads to loneliness (Kraut
et al., 1998; Moody, 2001). An explanation may be that people high
on CIU displace time spent on strong ties ofﬂine with time spent on
weak ties online (Moody, 2001). Note, though, that we measured
loneliness only in the last two waves, so we know less about the
stability and robustness of this effect than for the other wellbeing
Our results did not provide support for Tokunaga’s (2012) ﬁnd-
ings that loneliness and depression predicted CIU over time. Toku-
naga’s sample included incoming college freshmen. Measures were
administered from before they started college until the end of the
ﬁrst semester of their ﬁrst year. The present study was conducted
among newlywed adults from about two months after marriage
until the ﬁfth year of marriage. Apart from different time lags,
we can also speculate that the difference in results could be ex-
plained by differences between the samples and their life stages.
Going to college and getting married are two signiﬁcant events
that may have very different implications for the lives of individu-
als (e.g., going to college means being away from family and
friends vs. getting married means having a constant companion).
Also, emotional stability tends to increase across the adult life span
(Brose, Scheibe, & Schmiedek, 2012), which could account for
greater vulnerability of college students to loneliness and
The data did not provide support for the long-term directional-
ity of the link between CIU and self-esteem found among adoles-
cents. Research shows that self-esteem stability tends to increase
gradually throughout adulthood (Robins & Trzesniewski, 2005;
Trzesniewski, Donnellan, & Robins, 2003). It is possible that self-
esteem among adults is relatively stable, making it less susceptible
to the long-term effects of CIU.
Furthermore, it is important to highlight that the negative im-
pact of CIU in our study was not limited to the individual. Our ﬁnd-
ings showed that the more one partner uses the Internet
compulsively, the more stressed is the other partner. Additionally,
within couples, happiness, stress, and loneliness were correlated.
These results indicate that one’s personal low wellbeing is associ-
ated with the low wellbeing of the partner. Intuitively, such a sit-
uation may intensify low wellbeing, contributing to a downward
spiral of negative emotions (Peterson & Seligman, 1984). This sug-
gests that when CIU takes time away from social activities with off-
line signiﬁcant others, it may lead not only to the decline of one’s
own well-being (Moody, 2001), but may also affect the wellbeing
of other people in the social network.
4.1.2. The bidirectional relationship of CIU and happiness
Given that CIU is associated with increases in depression, stress,
and loneliness over time, intuitively one would expect CIU to have
the opposite effect on happiness. The results support this, and
showed that CIU had a long-term negative effect on happiness.
However, happiness differed from the other indicators of psycho-
logical wellbeing, because it also predicted a decrease in CIU over
We found that an increase in happiness predicted lower CIU
over time. This ﬁndings indicate that the happier people are, the
less prone they are to becoming Compulsive Internet users. Why
may this be the case? Fredrickson’s broaden-and-build theory of
positive emotions (2001) might provide a plausible explanation
for this association. According to this theory, positive emotions
broaden people’s momentary thought-action repertoires and build
people’s personal resources. These personal resources function as
reserves, improving people’s ability to cope and survive. The pres-
ent study suggests that happiness may broaden people’s repertoire
of behaviors and build personal resources including self-regulatory
resources, making them less susceptible to CIU.
Taking happiness as the starting point, our results suggest that
low levels of happiness can lead to an increase in CIU. Consistent
with previous ﬁndings, unhappy individuals may seek contexts
where they can experience stimulating experiences. The Internet
can provide such contexts (Caplan 2003, 2006). Zillmann (1988)
asserted that media use has the ability to alter one’s mood and
emotional states, and that individuals choose media content to
get the desired mood. By using the Internet, individuals are able
to experience immediate pleasure, thereby regulating their mood
and emotional states. On social network sites, for instance, users
receive encouragement from their online social network, which
may add to positive feelings (Kuss & Grifﬁths, 2011). The incentive
of consciously gratifying a need through media use motivates
media consumption behavior that may eventually become a condi-
tioned response to certain moods. These automatic media behav-
iors threaten self-regulation (Rosengren, Wenner, & Palmgreen,
1985). Lower self-control (akin to self-regulation) increases CIU
over time (Muusses et al., 2013). Therefore, once self-regulation
is impaired by media habits to alter one’s mood, one is more prone
According to Lyubomirsky (2008) one of the determinants of
sustainable happiness is engagement in intentional and effortful
activities. These activities are resistant to adaptation/habituation
effects and they can create a self-sustaining cycle of positive
change (Lyubomirsky, 2008). Positive activity increases positive
emotion, which in turn, enhances wellbeing (Lyubomirsky &
Layous, 2013). This provides further support to the proposition that
positive emotion builds psychological resilience and trigger up-
ward spirals of positive emotions (Fredrickson, 2001). Because
the present study showed that happiness could be a protective fac-
tor against CIU, it points to the importance of intentional engage-
ment in activities, preferably outside of the Internet, to attain
happiness. While the above described implications are tenable
assumptions, more research is needed to examine our suggestions
on the protective role of happiness on CIU (and on problematic
Internet use in general) and the impact of CIU on the downward
spiral of negative wellbeing.
4.2. Strengths and limitations
Previous research shows strong support for a negative relation
between CIU and wellbeing. The present study sought to circum-
vent shortcomings of existing research by examining the direction-
ality of the link between CIU and wellbeing among a considerable
sample of adults with ﬁve measurements over a period of four
To get a better understanding of the relationship of CIU and psy-
chological wellbeing, it was important to tease apart different com-
ponents of psychological wellbeing. Our study included both
positive indicators (i.e., happiness and self-esteem) and negative
indicators (i.e., depression, stress, and loneliness) of psychological
wellbeing. We found that CIU predicted positive and negative indi-
cators in the predicted direction. Importantly, we found that hap-
piness also predicted a decrease in CIU over time. These
processes had thus far not been studied simultaneously. Our ﬁnd-
ings emphasize the need to look at both sets of indicators because
26 L.D. Muusses et al. / Computers in Human Behavior 36 (2014) 21–28
they coexist but are partly independent from each other (Huppert
& Whittington, 2003).
This study contributes to the literature by focusing on adult
Internet users. While Kraut et al. (1998) also studied adults, there
are notable differences from the sample of this research. Most
importantly, they did not look at CIU but at frequency of Internet
use. Later research showed that almost all substantial negative ef-
fects are related to CIU rather than frequency of Internet use (e.g.
Kerkhof et al., 2011). Another difference is the experiences the
samples have had with the Internet. While Kraut et al’s (1998)
study included adults in their ﬁrst and second years of Internet
use in a time when Internet technology was relatively new, the
current sample has been exposed to the Internet for a much longer
period of time. A lot of applications, such as social network sites,
were not yet available during the earlier research. Much like Kraut
et al.’s (1998) study that examined people in the same household
(a large part of whom are in a relationship or married), this study
examined married couples. Although married people are one of the
largest demographics, future research might include adult or el-
derly single users. One could imagine the relation between CIU
and wellbeing might be different for this group of people. One
example is that perhaps loneliness is a bigger reason for single peo-
ple to use the Internet compulsively. Although the current research
provides knowledge about adult Internet users and their wellbeing
not yet present in the literature, more research is needed to inves-
tigate the impact of different internet applications, and crucially
whether the link between CIU and wellbeing changes with longer
use of the Internet.
This study explored the directionality between CIU and psycho-
logical wellbeing among adults. Psychological wellbeing was
examined using both positive indicators (happiness and self-es-
teem) and negative indicators (depression, stress and loneliness).
The results revealed that CIU lowers wellbeing over time. The high-
er the CIU, the more depressed, stressed, and lonely, and the less
happy participants became. Importantly, happiness prevented
CIU over time. Thus, while CIU seems to make adults more vulner-
able to decreases in wellbeing, positive wellbeing seems to protect
individuals from developing CIU in the long run.
The authors would like to acknowledge the input and participa-
tion in the data analyses of Asuman Buyukcan Tetik.
Armstrong, L., Phillips, J. G., & Saling, L. L. (2000). Potential determinants of heavier
Internet usage. International Journal of Human–Computer Studies, 53, 537–550.
Augner, C., & Hacker, G. W. (2011). Associations between problematic mobile phone
use and psychological parameters in young adults. International Journal of Public
Health (pp. 1–5), doi: 10.1007/s00038-011-0234-z.
Brose, A., Scheibe, S., & Schmiedek, F. (2012). Life contexts make a difference:
Emotional stability in younger and older adults. Psychology and Aging. Advance
online publication. doi: 10.1037/a0030047.
Byun, S., Rufﬁni, C., Mills, J. E., Douglas, A. C., Niang, M., Stepchenkova, S., et al.
(2009). Internet addiction: Metasynthesis of 1996–2006 quantitative research.
CyberPsychology & Behavior, 12, 203–207. http://dx.doi.org/10.1089/
Caplan, S. E. (2002). Problematic Internet use and psychosocial well-being:
Development of a theory-based cognitive–behavioral measurement
instrument. Computers in Human Behavior, 18, 553–575. http://dx.doi.org/
Caplan, S. E. (2003). Preference for online social interaction: A theory of problematic
Internet use and psychosocial well-being. Communication Research, 30,
Caplan, S. E. (2006). A social skill account of problematic Internet use. Journal of
Communication, 55, 721–736. http://dx.doi.org/10.1111/j.1460-2466.2005.
Centraal Bureau voor de Statistiek [Central Statistical Ofﬁce]. (2013). Retrieved
February 18th, 2013, from <http://statline.cbs.nl/StatWeb/publication/
Centraal Bureau voor de Statistiek [Central Statistical Ofﬁce]. (2014). Retrieved
March 5th, 2014, from <http://statline.cbs.nl/StatWeb/publication/?VW=
Chou, C., Condron, L., & Belland, J. C. (2005). A review of the research on Internet
addiction. Educational Psychology Review, 17, 363–388. http://dx.doi.org/
Chou, C., & Hsiao, M. C. (2000). Internet addiction, usage, gratiﬁcation, and pleasure
experience: The Taiwan college students’ case. Computers & Education, 35,
Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived
stress. Journal of Health and Social Behavior, 385–396. doi: 2136404.
Crocker, J., Luhtanen, R., Blaine, B., & Broadnax, S. (1994). Collective self-esteem and
psychological well-being among White, Black, and Asian college students.
Personality and Social Psychology Bulletin, 20, 503–513. http://dx.doi.org/
Davis, R. A. (2001). A cognitive-behavioral model of pathological Internet use.
Computers in Human Behavior, 17, 187–195. http://dx.doi.org/10.1016/S0747-
De Jong Gierveld, J., & van Tiburg, T. (1999). Manual of the Loneliness Scale. Department
of Social Research Methodology, Vrije Universiteit Amsterdam, Amsterdam
(updated version 18.01.02). Unpublished manuscript retrieved March 4th, 2014,
Diener, E. (1984). Subjective well-being. Psychological Bulletin, 95, 542–575.
Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective well-being: Three
decades of progress. Psychological Bulletin, 125, 276.
Finkenauer, C., Kerkhof, P., Righetti, F., & Branje, S. (2009). Living together apart:
Perceived concealment as a signal of exclusion in marital relationships.
Personality and Social Psychology Bulletin, 35, 1410–1422. http://dx.doi.org/
Finkenauer, C., Wijngaards-de Meij, L., Reis, H. T., & Rusbult, C. E. (2010). The
importance of seeing what is not there: A quasi-signal detection analysis of
positive and negative behavior in newlywed couples. Personal Relationships, 17,
615–633. doi: 10.1111/j.1475-6811.2010.01300.x.
Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: The
broaden-and-build theory of positive emotions. American Psychologist, 56,
Ha, J. H., Kim, S. Y., Bae, S. C., Bae, S., Kim, H., Sim, M., et al. (2007). Depression and
Internet addiction in adolescents. Psychopathology, 40, 424–430. http://
Huppert, F. A., & Whittington, J. E. (2003). Evidence for the independence of positive
and negative well-being: Implications for quality of life assessment. British
Journal of Health Psychology, 8, 107–122. http://dx.doi.org/10.1348/
InternetWorldStats. (2011). Retrieved March 8th, 2013, from <http://
Johansson, A., & Götestam, K. G. (2004). Internet addiction: Characteristics of a
questionnaire and prevalence in Norwegian youth (12–18 years). Scandinavian
Journal of Psychology, 45, 223–229. http://dx.doi.org/10.1111/j.1467-9450.2004.
Kang, S. (2007). Disembodiment in online social interaction: Impact of online chat
on social support and psychosocial well-being. CyberPsychology & Behavior, 10,
Kenny, D. A., Mannetti, L., Pierro, A., Livi, S., & Kashy, D. A. (2002). The statistical
analysis of data from small groups. Journal of Personality and Social Psychology,
83, 126–137. http://dx.doi.org/10.1037//0022-35184.108.40.206.
Kerkhof, P., Finkenauer, C., & Muusses, L. D. (2011). Relational consequences of
Compulsive Internet use: A longitudinal study among newlyweds. Human
Communication Research, 37, 147–173. http://dx.doi.org/10.1111/j.1468-
Kessler, R. C., McGonagle, K. A., Swartz, M., Blazer, D. G., & Nelson, C. B. (1993). Sex
and depression in the National Comorbidity Survey I: Lifetime prevalence,
chronicity and recurrence. Journal of Affective Disorders, 29, 85–96. http://
Kim, J., LaRose, R., & Peng, W. (2009). Loneliness as the cause and the effect of
problematic Internet use: The relationship between Internet use and
psychological well-being. CyberPsychology & Behavior, 12, 451–455. http://
Kling, K. C., Hyde, J. S., Showers, C. J., & Buswell, B. N. (1999). Gender differences in
self-esteem: A meta-analysis. Psychological Bulletin, 125, 470. doi: 0033-2909/
Krasnova, H., Wenninger, H., Widjaja, T., & Buxmann, P. (2013, February-March).
Envy on Facebook: A hidden threat to users’ life satisfaction? Presentation
delivered at 11th international conference on wirtschaftsinformatik, Leipzig,
Germany. Retrieved from <http://warhol.wiwi.hu-berlin.de/~hkrasnova/
Ongoing_Research_ﬁles/WI 2013 Final Submission Krasnova.pdf>.
Kraut, R., Kiesler, S., Boneva, B., Cummings, J., Helgeson, V., & Crawford, A. (2002).
Internet paradox revisited. Journal of Social Issues, 58, 49–74. http://dx.doi.org/
L.D. Muusses et al. / Computers in Human Behavior 36 (2014) 21–28 27
Kraut, R., Patterson, M., Lundmark, V., Kiesler, S., Mukophadhyay, T., & Scherlis, W.
(1998). Internet paradox: A social technology that reduces social involvement
and psychological well-being? American Psychologist, 53, 1017. doi: 0003-066X/
Kurdek, L. A. (1993). Predicting marital dissolution: A 5-year prospective
longitudinal study of newlywed couples. Journal of Personality and Social
Psychology, 64, 221–242. http://dx.doi.org/10.1037/0022-35220.127.116.11.
Kuss, D. J., & Grifﬁths, M. D. (2011). Online social networking and addiction – A
review of the psychological literature. International Journal of Environmental
Research and Public Health, 8, 3528–3552. http://dx.doi.org/10.3390/
LaRose, R., Lin, C. A., & Eastin, M. S. (2003). Unregulated Internet usage: Addiction,
habit, or deﬁcient self-regulation? Media Psychology, 5, 225–253. http://
Lundberg, U. (2002). Psychophysiology of work: Stress, gender, endocrine response,
and work-related upper extremity disorders. American Journal of Industrial
Medicine, 41, 383–392. http://dx.doi.org/10.1002/ajim.10038.
Lyubomirsky, S. (2008). The how of happiness: A scientiﬁc approach to getting the life
you want. New York: Penguin Press.
Lyubomirsky, S., & Layous, K. (2013). How do simple positive activities increase
well-being? Current Directions in Psychological Science, 22, 57–62. http://
Lyubomirsky, S., & Lepper, H. S. (1999). A measure of subjective happiness:
Preliminary reliability and construct validation. Social Indicators Research, 46,
Mechanic, D., & Hansell, S. (1987). Adolescent competence, psychological well-
being, and self-assessed physical health. Journal of Health and Social Behavior,
Meerkerk, G. J., van den Eijnden, R. J. J. M., Vermulst, A. A., & Garretsen, H. F. L.
(2009). The Compulsive Internet use scale (CIUS): Some psychometric
properties. CyberPsychology & Behavior, 12, 1–6. http://dx.doi.org/10.1089/
Moody, E. J. (2001). Internet use and its relationship to loneliness. CyberPsychology
& Behavior, 4, 393–401. http://dx.doi.org/10.1089/109493101300210303.
Morahan-Martin, J., & Schumacher, P. (2000). Incidence and correlates of
pathological Internet use among college students. Computers in Human
Behavior, 16, 13–29. http://dx.doi.org/10.1016/S0747-5632(99)00049-7.
Muusses, L. D., Finkenauer, C., Kerkhof, P., & Righetti, F. (2013). Partner effects of
Compulsive Internet use: A self-control account. Communication Research,
Published Online. doi: 10.1177/0093650212469545..
Nie, N. H., & Erbring, L. (2000). Internet and society.A preliminary report. Palo Alto,
CA: Stanford University, Stanford Institute for the Quantitative Study of Society.
Unpublished manuscript, Retrieved March 4th, 2014, from <http://www-
Okun, M. A., Stock, W. A., Haring, M. J., & Witter, R. A. (1984). Health and subjective
well-being: A meta-analysis. The International Journal of Aging and Human
Development, 19, 111–132. http://dx.doi.org/10.2190/QGJN-0N81-5957-HAQD.
Pantic, I., Damjanovic, A., Todorovic, J., Topalovic, D., Bojovic-Jovic, D., & Ristic, S.
(2012). Association between online social networking and depression in high
school students: Behavioral physiology viewpoint. Psychiatria Danubina, 24,
Peterson, C., & Seligman, M. E. P. (1984). Causal explanations as a risk factor for
depression: Theory and evidence. Psychological Review, 91, 347–374.
Pollmann, M. M. H., & Finkenauer, C. (2009). Empathic forecasting: How do we
predict other people’s feelings? Cognition & Emotion, 23, 978–1001. http://
Radloff, L. S. (1977). The CES-D scale: A self-report depression scale for research in
the general population. Applied Psychological Measurement, 1, 385–401. http://
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and
data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.
Robins, R. W., & Trzesniewski, K. H. (2005). Self-esteem development across the
lifespan. Current Directions in Psychological Science, 14, 158–162. http://
Rosenberg, M. (1965). Rosenberg self-esteem scale (RSE). Acceptance and
Commitment Therapy. Measures Package, 61. Unpublished manuscript,
Retrieved March 4th, 2014, from <https://www-static.uow.edu.au/content/
Rosengren, K. E., Wenner, L. A., & Palmgreen, P. (Eds.). (1985). Media gratiﬁcations
research: Current perspectives. Beverly Hills, CA: Sage Publications.
Rusbult, C. E., Martz, J. M., & Agnew, C. R. (1998). The investment model scale:
Measuring commitment level, satisfaction level, quality of alternatives, and
investment size. Personal Relationships, 5, 357–391. http://dx.doi.org/10.1111/
Schimmenti, A., Guglielmucci, F., Barbasio, C., & Granieri, A. (2012). Attachment
disorganization and dissociation in virtual worlds: a study on problematic internet
useamong playersof online roleplaying games.Clinical Neuropsychiatry,9, 195–202.
Retrieved from: <http://www.researchgate.net/publication/234129558_
Schimmenti, A., Passanisi, A., Gervasi, A. M., Manzella, S., & Famà, F. I. (2013).
Insecure attachment attitudes in the onset of problematic Internet use among
late adolescents. Child Psychiatry & Human Development (pp. 1–8), doi: 10.1007/
Sum, S., Mathews, R. M., Hughes, I., & Campbell, A. (2008). Internet use and
loneliness in older adults. CyberPsychology & Behavior, 11, 208–211. http://
Tokunaga, R. S. (2012). A unique problem or the manifestation of a preexisting
disorder? The mediating role of problematic Internet use in the relationships
between psychosocial problems and functional impairment. Communication
Research, Published Online. doi: 10.1177/0093650212450910.
Tokunaga, R. S., & Rains, S. A. (2010). An evaluation of two characterizations of the
relationships between problematic Internet use, time spent using the Internet,
and psychosocial problems. Human Communication Research, 36, 512–545.
Trzesniewski, K. H., Donnellan, M. B., & Robins, R. W. (2003). Stability of self-esteem
across the lifespan. Journal of Personality and Social Psychology, 84, 205–220.
Van den Eijnden, R. J. J. M., Meerkerk, G. J., Vermulst, A. A., Spijkerman, R., & Engels,
R. C. M. E. (2008). Online communication, Compulsive Internet use, and
psychosocial well-being among adolescents: A longitudinal study.
Developmental Psychology, 44, 655–665. http://dx.doi.org/10.1037/0012-
Vitalari, N. P., Venkatesh, A., & Gronhaug, K. (1985). Computing in the home: Shifts
in the time allocation patterns of households. Communications of the ACM, 28,
Wang, W. (2001). Internet dependency and psychosocial maturity among college
students. International Journal of Human–Computer Studies, 55, 919–938. http://
Widyanto, L., & Grifﬁths, M. (2006). ‘‘Internet addiction’’: A critical review.
International Journal of Mental Health and Addiction, 4, 31–51. http://
Young, K. S. (1998). Internet addiction: The emergence of a new clinical disorder.
CyberPsychology & Behavior, 1, 237–244. http://dx.doi.org/10.1089/
Zillmann, D. (1988). Mood management: Using entertainment to full advantage.
Communication, Social Cognition, and Affect, 147–171.
28 L.D. Muusses et al. / Computers in Human Behavior 36 (2014) 21–28