Adolescents’ Compulsive Internet Use and Depression: A Longitudinal Study

doi:10.4236/ojd.2014.31005

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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

Email: [email protected]

Received November 23rd, 2013; re vised January 6 th, 2014; accepted January 15th, 2014

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,

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

OPEN ACCESS

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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