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The consequences of compulsion: A 4-year longitudinal study of compulsive internet use and emotion regulation difficulties

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Little is known about how compulsive Internet use (CIU) relates developmentally to different aspects of emotion regulation. Do young people engage in CIU because they have difficulty regulating emotions (the "consequence" model), does CIU lead to emotion regulation problems (the "antecedent" model), or are there reciprocal influences? We examined the longitudinal relations between CIU and 6 facets of difficulties in emotion regulation. Adolescents (N = 2,809) across 17 Australian schools completed measures yearly from Grades 8 (MAge = 13.7) to 11. Structural equations modeling revealed that CIU preceded the development of some aspects of emotion dysregulation, such as difficulties setting goals and being clear about emotions, but not others (the antecedent model). We found no evidence that emotion regulation difficulties preceded the development of increases in CIU (the consequence model). Our findings indicate that teaching adolescents general emotion regulation skills may not be as effective in reducing CIU as more direct approaches of limiting Internet use. We discuss the implications of our findings for interventions designed to reduce CIU and highlight issues for future research. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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The Consequences of Compulsion: A Four-Year Longitudinal
Study of Compulsive Internet Use and Emotion Regulation
Difficulties
James N. Donalda
Joseph Ciarrochib
Baljinder K. Sahdrab
a University of Sydney Business School, Abercrombie Building (H70), Corner Abercrombie
Street and Codrington Street, NSW, 2006, Australia
b Institute for Positive Psychology and Education, Australian Catholic University,
25A Barker Rd, Strathfield, NSW, 2135, Australia
Corresponding author:
James N Donald
Abercrombie Building (H70), Corner Abercrombie Street and Codrington Street
The University of Sydney NSW 2006, Australia
Email: james.donald@sydney.edu.au
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Abstract
Little is known about how compulsive internet use (CIU) relates developmentally to different
aspects of emotion regulation. Do young people engage in CIU because they have difficulty
regulating emotions (the ‘consequencemodel), does CIU lead to emotion regulation problems
(the antecedentmodel), or are their reciprocal influences? We examined the longitudinal
relations between CIU and six facets of difficulties in emotion regulation (DER). Adolescents
(N = 2,809) across 17 Australian schools completed measures yearly from Grades 8 (MAge
=13.7) to 11. Structural equations modelling revealed that CIU preceded the development of
some aspects of emotion dysregulation, such as difficulties setting goals and being clear about
emotions, but not others (the ‘antecedent’ model). We found no evidence that emotion
regulation difficulties preceded the development of increases in CIU (the ‘consequence’
model). Our findings indicate that teaching adolescents general emotion regulation skills may
not be as effective in reducing CIU as more direct approaches of limiting the use of the internet.
We discuss the implications of our findings for interventions designed to reduce CIU and
highlight issues for future research.
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Global internet use is estimated to have grown almost ten-fold between 2000 and 2018,
and as of the end of 2018, 51% of the world’s population, or 3.9 billion people, used the internet
(International Telecommunication Union, 2018). This exponential growth is being led by
young people. A recent survey found that nearly all U.S. teenagers report having access to a
smartphone, and about half report being online almost constantly (Anderson & Jiang, 2018).
As adolescents spend ever-more time online, policy makers and researchers are grappling with
the implications of these changes for the well-being and social functioning of young people.
While there is evidence that online activity is, on average, associated with negative
mental health among adolescents (e.g., Huang, 2017; Twenge, Joiner, Rogers, & Martin, 2018),
other researchers have presented a more nuanced picture, for example, finding curvilinear
effects wherein moderate levels of online activity enhance well-being (e.g., via greater social
interaction), while more extensive use leads to harmful effects (e.g., Przybylski & Weinstein,
2017). While varying ‘doses’ of online activity have differing effects on adolescent well-being,
there is evidence that extensive online activity is associated with difficulty regulating one’s use
of the internet, and in-turn, the development of compulsive internet use (van den Eijnden,
Meerkerk, Vermulst, Spijkerman, & Engels, 2008; Van Der Aa et al., 2009). Further, there is
emerging evidence that the compulsive use of the internet has a range of unhealthy
consequences—though the breadth and magnitude of these consequences are not yet fully
understood. The present investigation focuses on the links between compulsive internet use
and adolescent emotion regulation – the first study we are aware of to do so.
Compulsive internet use (CIU; also referred to as “problematic internet use” and
“internet dependence”) is an inability to regulate one’s use of the internet, with associated
feelings of guilt about one’s lack of control, and reduced enjoyment of and engagement in other
activities (Caplan, 2003; Spada, 2014). While there is debate as to whether CIU represents a
behavioural addiction or an impulse control disorder (e.g., Ko, Yen, Yen, Chen, & Chen, 2012),
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conceptually, CIU appears to share similarities with other addictive disorders, such as the
experience of withdrawal, intolerance, and negative social consequences (Pies, 2009). Given
the mounting evidence regarding the negative consequences of CIU, examining it as a
phenomenon in its own right, rather than as a proxy for general addictive behaviour, is valuable
(Ciarrochi et al., 2016; Muusses, Finkenauer, Kerkhof, & Joy, 2014; van den Eijnden et al.,
2008).
There is mounting evidence that the compulsive use of the internet across various online
uses is associated with negative life outcomes, including worse mental ill-health (Carli et al.,
2013; Ciarrochi et al., 2016), poor self-concept (Donald, Ciarrochi, Parker, & Sahdra, 2019),
loneliness (Kim, La Rose, & Peng, 2009), stress (Muusses et al., 2014) and decrements in
general well-being (Muusses et al., 2014). Further, there is evidence that compulsive internet
use is a product of young people having a preference for online interaction, as a way of
maintaining self-esteem (Caplan, 2003; Gámez-Guadix, Calvete, Orue, & Las Hayas, 2015).
This suggests that a general compulsive internet use across the range of internet uses is worthy
of investigation (e.g., Ciarrochi et al., 2016; Muusses et al., 2014; van den Eijnden et al., 2008).
Several recent reviews have called for more longitudinal research to better understand
relations between generalized CIU and adolescent well-being and functioning (Carli et al.,
2013; Durkee et al., 2012; Ko et al., 2012; Spada, 2014). Longitudinal research on CIU has to-
date focused on the links between CIU and life outcomes such as mental health (Ciarrochi et
al., 2016), self-concept (Donald et al., 2018) and well-being (for a review see Huang, 2010).
No research to our knowledge has examined the extent to which CIU attenuates, amplifies or
has no effect on different dimensions of emotion regulation skills, and vice-versa. In the context
of youth development, focusing on the issue of emotion regulation is of critical importance,
given the central role effective emotion regulation plays in adolescent functioning (Garnefski,
Kraaij, & Etten, 2005; Silk, Steinberg, & Morris, 2003), and the negative consequences in later
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life of emotion regulation difficulties in adolescence (e.g., Bradley, 2000; Cole, Michel, & Teti,
L., 1994; Gross, 1998).
Difficulties with emotion regulation
Emotion regulation has been described as the process of modifying one’s emotions,
responses to emotions, or the situations that generate emotions, so-as to adapt appropriately to
environmental demands (Gratz & Roemer, 2004; Gross, 1998; Gross & Munoz, 1995). In
studying emotion regulation processes, some scholars have distinguished between efforts to
regulate the form of emotions (e.g., controlling emotional expression, and reducing emotional
arousal; see Cortez & Bugental, 1994; Garner & Spears, 2000; Kopp, 1989; Zeman & Garber,
1996), and efforts to regulate the function of emotional experience (e.g., Cole, Michel, & Teti,
1994; Hayes, Strosahl, & Wilson, 1999). These latter approaches form the focus of the present
paper and suggest that difficulties in experiencing, identifying and accepting the full range of
emotions, as well as acting in situationally-flexible ways in the presence of strong emotions,
may be just as maladaptive as deficiencies in the ability to attenuate and modulate strong
emotions (Cole et al., 1994; Gross & Munoz, 1995; Paivio & Greenberg, 1998).
Several aspects of emotion regulation are arguably reflective of an emphasis on the
functionality of individuals’ responses to emotions. Gratz and Roemer (2004), in their work on
difficulties with emotion regulation, identify six emotion regulation difficulties that have
received considerable research attention, and also have links to important life outcomes (Aldao,
Nolen-Hoeksema, & Schweizer, 2010; Gratz & Roemer, 2004; Neumann, van Lier, Gratz, &
Koot, 2010).
Impulsiveness. Individuals can struggle with reactivity to strong emotions (Neumann et
al., 2010). Difficulty with impulse control is a key element of emotion dysregulation and has
been linked to a host of negative outcomes, including worse mental health (Neumann et al.,
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2010), interpersonal difficulties (Gratz & Roemer, 2004), and unhealthy substance use (Fox,
Axelrod, Paliwal, Sleeper, & Sinha, 2007).
Difficulties pursuing goals. In the presence of difficult emotions, individuals may have
trouble remaining focused on and persisting toward valued goals (Hayes et al., 1999).
Difficulty pursuing goals is often linked to experiential avoidance or the avoidance of
distressing thoughts and feelings (Gratz & Roemer, 2004; Hayes et al., 1999). In these
situations, the presence of unwanted emotions overwhelms a person’s efforts to engage in
intentional behaviour toward goals (Aldao et al., 2010; Gratz & Roemer, 2004; Neumann et
al., 2010).
Difficulties identifying emotion regulation strategies. Another form of emotion
regulation is the capacity to flexibly identify ways of managing emotions, and central to this is
feeling self-efficacy in responding to emotions (Gratz & Roemer, 2004). Individuals with such
self-efficacy feel they have personal agency and the strategies to respond to their emotional
experiences (Parker, Ciarrochi, et al., 2015). Conversely, people who believe they lack efficacy
in managing their emotions are more likely to report greater experiential avoidance, and among
women, report more self-harm, while among men, report greater partner violence (Gratz &
Roemer, 2004). Further, among adolescents, an inability to identify strategies for managing
emotions is associated with anxiety and depression, and engagement in aggressive behaviour
(Neumann et al., 2010; Sarıtas-Atalar, Gencoz, & Ozen, 2015).
Non-acceptance of emotions. Non-acceptance of emotions has been shown to precede
strong negative emotions, as well as maladaptive behaviours aimed at suppressing or avoiding
emotional experience (Hayes et al., 1999). An accumulating body of evidence indicates that
the non-acceptance of emotions is a general process that underpins a host of maladaptive
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behaviours, including addictive behaviours, antisocial behaviour and reduced well-being
(Hayes et al., 1999; Kashdan, Barrios, Forsyth, & Steger, 2006).
Difficulties with emotional clarity. Individuals who are unable to identify their emotions
are more likely to misinterpret and not accept their emotions, and are more likely to engage in
unhealthy emotion regulation strategies (Parolin et al., 2018). Difficulties identifying one’s
emotions, also known as alexithymia, has been linked with a range of psycho-social difficulties,
such as substance abuse (Kauhanen, Wilson, Salonen, Kaplan, & Julkunen, 1993), and social
and interpersonal problems (Spitzer, Siebel-Jurges, Barnow, Grabe, & Freyberger, 2005), and
among young people, worse social support (Heaven, Ciarrochi, & Hurrell, 2010), obesity
(Baldaro et al., 2003), and greater dissociative tendencies (Sayer, Kose, Grabe, & Topbas,
2005).
Difficulties with awareness of emotions. Relatedly, being able to observe one’s
emotions is arguably a precursor to responding effectively to them (Gratz & Roemer, 2004).
Conversely, a lack of emotional awareness has been linked to unhealthy behaviours including
problematic gambling (Williams, Grisham, Erskine, & Cassedy, 2012) and substance use
(Parolin et al., 2018).
How might generalized CIU relate to these various emotion regulation difficulties over
time? We explore three possible models: CIU as an antecedent (the antecedentmodel), a
consequence (the ‘consequence’ model), or both a cause and a consequence of emotion
regulation difficulties (the ‘reciprocal influencemodel). We now discuss each of these models
and develop associated hypotheses.
Antecedent model
CIU can be thought of as difficulty regulating internet behaviour in the presence of
internet-related impulses (e.g., “I’ve got to improve my score”) and feelings (e.g., fear of
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missing out). Engaging in CIU is, in a sense, like practicing poor behavioural regulation.
That is, because CIU involves diminished self-regulatory capacities pertaining to internet use,
and has been shown to be linked to reduced self-regulation more broadly (Billieux & Van
Der Linden, 2012; Van Der Aa et al., 2009), we hypothesize that CIU leads to difficulties
with behavioural control in the presence of strong emotions.
More specifically, we anticipate that CIU will precede difficulties with both the
regulation of impulsive behaviour (i.e., impulse control), as well as the regulation of
persisting with behaviour toward valued goals, in the presence of negative emotions (i.e.,
goal-pursuit). In support of this, CIU has been correlated with difficulties in impulse control
among adolescents (Cao, Su, Liu, & Gao, 2007), emerging adults (Mottram & Fleming,
2009) and online gamers (Kim, Namkoong, Ku, & Joo, 2008). Further, CIU has been
associated with difficulties in seeing the longer-term consequences of present decisions and
making values-based decisions (Sun, Chen, & Ma, 2009), which speaks to the capacity to
pursue valued goals in the presence of emotions (Gratz & Roemer, 2004).
We also anticipate that CIU may precede reductions in the ability to identify and label
feelings (emotional clarity). There is considerable evidence that other forms of addiction
precede alexithymia, or difficulties identifying emotions (Parolin et al., 2018). Based on this
body of alexithymia and addiction research, it may be that the self-regulatory difficulties
implicated in CIU lead to broader difficulties processing information and making decisions,
which in-turn leads to problems identifying emotions (Parolin et al., 2018). This leads to our
first hypothesis:
Hypothesis 1: CIU will predict less emotional clarity, impulse control and goal-
directed behaviour in the presence of emotions.
Consequence model
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Perhaps young people turn to online activity as a form of emotion regulation, because
their other emotion regulation skills are failing them. If so, then young people who are
struggling with emotion regulation in life will find it increasingly difficult to regulate their
internet related behaviour. Evidence from research on other forms of addiction is consistent
with this prediction. For example, Berking et al. (2011) found that difficulty in emotion-
regulation predicted alcohol dependence both during and following a treatment program.
Further, Williams, Grisham, Erskine, & Cassedy (2012) found that emotional regulation
difficulties predicted pathological gambling behaviours, while Fox, Axelrod, Paliwal, Sleeper,
& Sinha (2007) found a similar effect for cocaine addiction, in relation to impulse control.
Also, there is meta-analytic evidence that general social and emotional learning interventions
for young people result in reductions in drug use and fewer conduct problems (Taylor, Oberle,
Durlak, & Weissberg, 2017), suggesting that socio-emotional regulation difficulties will result
in maladaptive behaviours, including CIU.
Our leading candidates for the consequence model were emotional clarity, difficulties
with impulse control, and difficulties with goal directed behaviour. Poor emotional clarity has
been clearly implicated in other forms of addiction (Lindsay & Ciarrochi, 2009; Parolin et al.,
2018). Problems with emotional clarity has also been shown to proceed social and emotional
problems (Ciarrochi, Heaven, & Supavadeeprasit, 2008; Rowsell, Ciarrochi, Deane, & Heaven,
2014). In addition, young people with difficulties with impulse control and goal directed
behaviour may struggle to put long term goals (e.g., school work) ahead of online activity,
consistent with evidence from other forms of addictive behaviour (Fox et al., 2007; Williams
et al., 2012). This leads to our second hypothesis:
Hypothesis 2: Difficulties with emotional clarity, impulse control and goal-directed
behaviour will predict the development of CIU.
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We did not have strong predictions for the other emotion regulation strategies, so we
set out to explore the extent to which different aspects of emotion regulation difficulties (i.e.,
identifying strategies, accepting emotions, and emotional clarity and awareness) predict the
development of CIU.
Reciprocal influence model
As a final possibility, there may be a reciprocal cycle linking CIU and various emotion
regulation difficulties. Specifically, it may be that CIU promotes a narrowing of social skills
and meaningful daily activity (Babic et al., 2017; Lin & Tsai, 2002), which leads to an erosion
of emotion regulation skills. In-turn, this may lead to further CIU as a way of reducing or
avoiding the negative thoughts and feelings associated with such experiences.
The present study
In the present study, we examined the longitudinal relations between CIU and emotion
regulation difficulties among an adolescent sample over four years (Grades 8 to 11). Given the
limited research in this field, some of our hypotheses were necessarily exploratory.
Methods
Participants and procedure
The present investigation was a part of the Australian Character Study (ACS), a multi-
year research project exploring Australian adolescent behaviours, relationships, beliefs,
aspirations and self-evaluations. Study participants were drawn from 17 Catholic high schools
in the states of New South Wales and Queensland. Catholic schools make up 20.52% of
secondary schools in Australia (Australian Bureau of Statistics, 2012). The schools included in
this study were mainly in cities - Wollongong (New South Wales) and Cairns (Queensland).
However, the study included a number of schools in more rural locations, thereby ensuring the
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socioeconomic and geographic diversity of study participants. The Australian Government’s
Index of Community Socio-Educational Advantage (ICSEA) provides an indication of the
level of a school’s educational advantage relative to other schools
(https://www.myschool.edu.au/more-information/information-for-parents/making-a-fair-
comparison/). The schools in the present study had an ICSEA ranking (1,025; SD = 43) almost
identical to the Australian average of 1,000. Of the 17 schools in the current study, one school
(ICSEA = 783) had an ICSEA score less than one standard deviation (i.e., ICSEA = 957) below
the ICSEA average of 1,000. In contrast, five schools in our sample had ICSEA scores
(1061,1062,1071,1077, and 1083) that were more than one standard deviation (ISCEA = 1043)
above the ICSEA average. This suggests the schools in this sample were moderately skewed
toward a higher socio-educational status, with the majority (11 schools) falling in the
‘moderate’ range (i.e., between one standard deviation above and below the mean ICSEA
score).
Study participants completed a battery of survey instruments three-quarters of the way
through the school year in each of the four years of the study (Grades 8 to 11). In Grade 8, the
average participant age was 13.7 years (SD = .45). A total of 2,809 students participated in the
study (1395 or 49.7% male, 1399 or 49.8% female, 15 unknown). Statistical power calculations
for an effect size of 0.05 (based on effect sizes observed in similar studies of CIU), and to
obtain power of 95%, indicated a sample size requirement of N = 262. This indicates that the
present study was very well powered to detect actual effects. The University of Wollongong
provided ethics approval for the study and all study participants completed consent forms prior
to participating. We note that data from the ACS project have been published on the links
between compulsive internet use and mental health (Ciarrochi et al., 2016) and self-esteem and
hope (Donald et al., 2019).
Measures
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Compulsive internet use. CIU was measured with the compulsive internet use scale
(CIUS; Meerkerk, Van Den Eijnden, Vermulst, & Garretsen, 2009). The CIUS was designed
to capture the central features of addictive behaviour (per the DSM-IV and elsewhere),
including elements such as withdrawal symptoms, loss of control, preoccupation, conflict with
other activities, and lying to hide addictive behaviour (Meerkerk et al., 2009). Due to space
constraints, and following similar approaches elsewhere (e.g., Ciarrochi et al., 2016; Donald et
al., 2018), we used a 10-item version of this scale (dropping items 11 to 14; see Meerkerk et
al., 2009). The 10-item version has demonstrated acceptable psychometric properties,
including factorial stability across time, and good convergent validity (Ciarrochi et al., 2016;
Donald et al., 2018). Cronbach’s alphas for this measure in among the present sample were
acceptable (Grade 8, α = .88; Grade 9, α = .89; Grade 10, α = .89; Grade 11, α = .89). Scale
responses range from 0 (never) to 4 (very often). Sample items include “Do you find it difficult
to stop using the Internet when you are online?” and “Do you feel restless, frustrated, or irritated
when you cannot use the Internet?”
Difficulties in emotion regulation. Drawing on the approach taken by Gratz & Roemer
(2004), we operationalized the six broad forms of emotion regulation reviewed above using the
Difficulties in Emotion Regulation Scale (DERS; Gratz & Roemer, 2004). This 36-item scale
is comprised of six subscales: 1) difficulties with awareness of emotions (‘awareness’; 6 items,
e.g., “I am attentive to my feelings”); 2) difficulty clearly understanding one’s emotions
(‘clarity’; 6 items, e.g., “I have no idea how I am feeling”); 3) difficulty accepting one’s
emotions (‘acceptance’; 6 items, e.g., “When I’m upset, I feel guilty for feeling that way”); 4)
difficulties with impulsivity (‘impulse’; 6 items, e.g., “When I’m upset, I lose control over my
behaviors”), 5) an inability to engage in goal-directed behaviour (‘goals’; 5 items, e.g., “When
I’m upset, I have difficulty getting work done”); and 6) low levels of self-efficacy in identifying
emotion regulation strategies (‘strategies’; 8 items, e.g., “When I’m upset, I believe that there
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is nothing I can do to make myself feel better”). Items are scored on a 5-point scale ranging
from 1 (almost never) to 5 (almost always).
The DERS has displayed acceptable psychometric properties, including high internal
consistency, good test–retest reliability, and adequate construct and predictive validity (Gratz
& Roemer, 2004; Neuman et al., 2010). In factor analyses, the six subscales have been found
to have acceptable psychometric properties and be modestly correlated with one another among
adults (e.g., Gratz & Roemer, 2004; Neumann et al., 2010) and adolescents (Sarıtas-Atalar et
al., 2015). However, we note that in some studies, the ‘awareness’ DERS subscale has shown
divergent correlations with the other DERS subscales and has repeatedly shown a divergent
pattern of relations with constructs theoretically related to emotion regulation (Bardeen et al.,
2016; Bardeen, Fergus, & Orcutt, 2012; Benfer et al., 2019; Lee, Witte, Bardeen, Davis, &
Weathers, 2016). In the present study, we therefore modelled each DERS component separately
so as to isolate effects of specific DERS components, following similar approaches elsewhere
(e.g., Bardeen et al., 2012; Gratz & Roemer, 2004; Neumann et al., 2010).
Cronbach’s alphas for each of the DERS subscales, across the four years of the study,
had the following ranges: for impulse, α = .88 to α = .91; for goals, α = .83 to α = .87; for
strategies, α = .86 to α = .90; for acceptance, α = .85 to α = .89; clarity, α = .65 to α = .77; and
for awareness, α = .81 to α = .85. Cronbach’s alphas for the total DERS were α = .91 in Grade
8; α = .92 in Grade 9; α = .93 in Grade 10; and α = .94 in Grade 11.
Statistical Analyses
Random intercept cross-lagged panel models
To examine the longitudinal relations among compulsive internet use and emotion
regulation, we used random intercept autoregressive cross-lagged panel (RI-ACP) models
(Hamaker, Kuiper, & Grasman, 2015). The RI-ACP modelling approach has several
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advantages. First, by using latent factor scores (i.e., an SEM approach), measurement error is
explicitly modelled, enhancing model fit to the data (Parker, Marsh, Morin, Seaton, & Zanden,
2015). Second, the RI-ACP approach explicitly models both the trait and the state components
of variables, meaning that within-person (state) changes over time can be estimated without
the trait component of variables confounding within-person effects, which is not done in
traditional ACP SEMs (see Hamaker et al., 2015, for a discussion). Third, the autoregressive
and cross-lagged components of these models provide a conservative test of temporal
precedence, by explicitly modelling within-variable change over time (i.e., the autoregressive
component of the model). Figure 1 shows the lay-out of the RI-ACP model.
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Figure 1. Random-intercept autoregressive cross-lagged panel (RI-ACP) model. Note. Kappa and
omega represent the trait component of CIU and DER respectively; p and q represent the within-
person component of CIU and DER respectively. Alpha and delta represent the within-person change
in CIU and DER over time, respectively. Beta represents the cross-lagged effect of DER on CIU,
while gamma represents the cross-lagged effect of CIU on DER.
Together, RI-ACP modelling enabled us to identify the likely temporal ordering of
changes in both CIU and DER over time, and the extent to which these changes were uni- or
bi-directional (Parker, Marsh, et al., 2015), controlling for trait-components of these variables.
In the present study, we tested whether: (a) CIU predicts difficulties in emotion regulation (i.e.,
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an antecedent model); (b) DER precedes the development of CIU (i.e., a consequence model);
or (c) the development of CIU and DER are mutually-reinforcing (i.e., a reciprocal influence
model).
Modelling approach
In the present study, we examined the links between CIU and each of the six facets of
emotion regulation difficulties. For each CIU–emotion regulation relationship, we tested a
series of five progressively more constrained models. In all five models, we explicitly modelled
the trait (κ and ω) and the state (p and q) components of CIU and DER components (see Figure
1). First, we ran a configural confirmatory factor analytic (CFA) model, in which all time-
varying model parameters were allowed to vary across time, and the regression paths of interest
(i.e., the regression paths between p and q) were not modelled. If this unconstrained model
displays acceptable fit to the data, models with constraints can be tested (Bollen, 1989).
Second, we estimated a measurement CFA model in which we constrained factor-
loadings to be equal across the four waves of the study, for both CIU and DER components.
Support for this model indicates that the constructs being measured (i.e., CIU and DER
components) tap the same phenomenon at each time point, and is a central assumption of RI-
ACP models and indeed all ACP models (Ciarrochi et al., 2016).
Following tests of CFA models, we ran a series of three structural equation models
(SEMs), which included regression coefficients between latent variables (i.e., CIU and DERS
components). The first SEM was a ‘fully-forward’ model wherein regression coefficients for
all paths (i.e., autoregressive and cross-lagged) were estimated, including lags across multiple
time-points (i.e., Time 1 to Time 2, Time 1 to Time 3, and Time 1 to Time 4, etc). In the second
SEM, lags greater than one timeinterval were removed from the model, and single-lag
regression coefficients were estimated (as represented by α, β, ϒ, and δ in Figure 1). In a final
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SEM, regression coefficients across single-year lags (i.e., both autoregressive and cross-lagged
paths) were constrained to be equal, known as a ‘developmental equilibrium’ model, thereby
testing whether effects were consistent across time (see Figure 1).
The data for this study had a nested structure with the 2,809 students nested within 17
schools. As our hypotheses related to individual differences, we controlled for differences in
effects due to school membership. To do this, we used a ‘no pooling’ approach, in which each
of the 17 schools was included in all models as a set of dummy variables (Gelman & Hill,
2009). This approach is more conservative than a classic multi-level modelling approach
(‘partial pooling’), as it does not force random effects to be normally distributed, thereby
allowing for greater heterogeneity in school-level effects (Gelman & Hill, 2009).
Missing data
Participant attrition was a potential problem in this study, given its longitudinal design
and use of high-school students. Of the 2,809 participants, 966 had data from all four waves
(50.2% female), 837 had data from three waves (49.2% female), 532 had data from two waves
(49.5% female), and 470 (52.0% female) had data from only one wave of the study. Where
participant attrition is not due to random factors, this can result in biased parameter estimates
if ad-hoc methods for handling missing data, such as pair- or list-wise deletion, are used
(Baraldi & Enders, 2010). In the present study, we used full information maximum likelihood
estimation (FIML) methods for handling missing data in all models. An advantage of the FIML
approach is that it uses all the available information for parameter estimationboth complete
and incomplete cases—and generates parameter values with the greatest likelihood of
reproducing the sample data (Baraldi & Enders, 2010). There were differences in scores on
CIU and DER facets between participants who completed all waves of data and those who did
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not, though these differences were small. Further detail on these analyses is in Supplemental
Material.
Fit statistics
Where parameter estimates are consistent with the theory proposed, the solution is well
defined, and model fit indices are acceptable, models are considered to fit the data well
(McDonald & Marsh, 1990). In the present study, we used three fit indices, in addition to the
chi-squared statistic: the Tucker–Lewis index (TLI), the comparative fix index (CFI), and the
root mean square error of approximation (RMSEA). These three fit indices have the advantage
of not being sensitive to sample size in the same way the chi-squared statistic is (Cheung &
Rensvold, 2002). For TLI and CFI, generally accepted minimum fit thresholds are .90, while
for RMSEA the figure is .08 (Chen, 2007; Cheung & Rensvold, 2002). In comparing the
deterioration in fit of successively more restrictive models (i.e., the two CFAs and three SEMs),
we used the criteria provided by Cheung and Rensvold (2002), who suggest that invariance
exists between nested models if changes in CFI is <.01 (we used the same criteria for the TLI).
For RMSEA, we used criteria provided by Chen (2007), who suggests invariance between
nested models exists if changes in RMSEA is ≤ .015.
Results
Preliminary analyses
Latent means and standard deviations for CIU and DER are shown in Table 1. As Table
1 shows, means were relatively consistent across the four waves of the study for DER and
increased moderately for CIU.
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Table 1
Latent means and standard deviations for study variables across the four study years
Grade 8
Grade 9
Grade 10
Grade 11
SD
Mean
SD
Mean
SD
Mean
SD
CIU
1.01
2.18
0.99
2.26
1.00
2.34
1.00
DER
0.93
3.05
0.87
3.03
0.90
2.99
0.93
Impulse
0.94
2.95
0.95
3.00
1.00
2.97
0.98
Goals
0.88
3.54
0.88
3.62
0.82
3.71
0.90
Strategies
0.88
2.38
0.98
2.52
1.06
2.54
1.01
Acceptance
0.78
2.88
0.83
2.99
0.91
3.05
0.88
Clarity
0.89
2.69
0.94
2.72
0.97
2.71
0.96
Awareness
0.63
2.55
0.63
2.47
0.67
2.46
0.73
Note. CIU = Compulsive internet use. DER = Difficulties in emotion regulation.
Bivariate correlations between CIU and DER components were moderate and positive,
with except for the ‘awareness’ component, which displayed a very small positive correlation
with CIU and mixed (both positive and negative) correlations with other DER components.
Notably, correlations were of a similar magnitude for both males and females. These
correlations are illustrated in Supplemental Material (Table 3 for Grade 8 and Table 4 for Grade
11, to reflect the span of the study). Information on test-retest correlations among study
variables is in Table 2 of Supplemental Material, noting that these were in the medium range
(.29 to .64), and were generally strongest at proximal time-points and weakest at distal time-
points, across all study variables.
To examine the extent of CIU among our sample, we inspected CIU count data across
the four years of the study (Figure 1 of Supplemental Material shows bar plots of these data).
Although the CIUS is a continuous measure of CIU (Meerkerk et al., 2009), ratings of 3
RUNNING HEAD: EMOTION REGULATION AND COMPULSIVE INTERNET USE
20
(“often”) and 4 (“very often”) on the CIUS may be taken to indicate relatively high levels of
CIU. The percentage of study participants in this category were: 5.01% in Grade 8; 6.26% in
Grade 9; 6.78% in Grade 10; and 6.30% in Grade 11.
Main analyses
We next examined our main research question: the longitudinal relations between
CIU and the development of difficulties in emotion regulation. As described above, for each
CIU-DERS-component relationship, we tested a series of five separate models (two CFA
models and three SEMs) wherein each successive model added constraints to the previous
one. Where the deterioration in fit was within accepted thresholds, the more restrictive model
was preferred.
Model fit
For CIU and DERS variables, there were relatively large covariances between the error
terms of items as indicated by high modification indices. Where items with high modification
indices are substantively very similar and linking them in a model makes theoretical sense, and
where the sample is relatively large, allowing such items to covary in latent models is
acceptable and can substantially improve model fit (Weston & Gore, 2006). Given these
conditions were met in the present study, items that were a) substantively similar and b) had
high inter-item modification indices were allowed to covary in all models. There were five
such items from the CIU scale, and eight items from the 36-item DERS (see Supplemental
Material for more information on these items and the basis for linking them in our models).
Following adjustment for high modification indices, model fit was acceptable for all
models. Notably, for all the CIU-DER models included in this study, the stability of the factor-
structure across time was supported (i.e., the time-invariant CFA). Second, for all relationships
tested, the structural model in which only single-lags are estimated had acceptable fit to the
RUNNING HEAD: EMOTION REGULATION AND COMPULSIVE INTERNET USE
21
data, meaning it was appropriate to test only single-lagged effects across time. Finally, for all
CIU-DER relationships we tested, the fit of the developmental equilibrium SEM (i.e., the
model in which cross-year lags were specified to be equivalent) did not deteriorate beyond the
thresholds outlined above, making this the preferred model, and providing evidence of stability
in year-on-year effects across time (see Supplemental Material for further information).
Path coefficients for developmental equilibrium models
Figure 2 displays the cross-lagged relations between CIU predicting changes in all six
DER components (i.e., the ‘CIU-as-antecedent’ hypothesis), and DERS facets predicting
changes in CIU (i.e., the ‘CIU-as-consequence’ hypothesis), from each of the models we ran.
Both 90% and 95% confidence intervals (CIs) are shown around each estimate. Assuming a
normally distributed population and known population variance, a 95% CI indicates an 83%
likelihood that the effect size estimate of a replication study would lie within the interval, while
a 90% CI indicates a 76% likelihood that the effect size estimate of a replication study would
lie within the CI (Cumming & Maillardet, 2006; Cumming, 2013).
RUNNING HEAD: EMOTION REGULATION AND COMPULSIVE INTERNET USE
22
Figure 2. Forest plot of standardized cross-lagged estimates from developmental equilibrium
models with CIU predicting the six DER strategies (‘antecedent’) and vice-versa
(‘consequence’).
]
Figure 2 shows some support for Hypothesis 1. There were modest though consistent
effects for CIU predicting difficulties persisting in goals in the presence of distress and being
clear about one’s emotions (i.e., Impulse and Clarity, in top panel of Figure 2). We did not find
evidence for CIU preceding other aspects of emotional dysregulation. Also, we did not find
support for Hypothesis 2 (i.e., the lower panel in Figure 2). Problems with impulse control
preceded less CIU, as did difficulties identifying strategies to deal with emotions. We did not
find evidence that other aspects of emotional dysregulation preceded CIU. Standardised
regression coefficients, standard errors and confidence intervals for these effects are shown in
Table 2.
RUNNING HEAD: EMOTION REGULATION AND COMPULSIVE INTERNET USE
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Table 2
Standardized regression coefficients, standard errors and p-values from developmental equilibrium models, for all CIU-DERS subscale models
Time 1 Time 2 Coeff. SE p-
value Time 1 Time 2 Coeff. SE p-
value Time 1 Time 2 Coeff. SE p-
value
Impulse Impulse 0.20 0.04 <.001 Strategies Strategies 0.32 0.04 <.001 Goals Goals 0.19 0.04 <.001
CIU CIU 0.50 0.04 <.001 CIU CIU 0.52 0.04 <.001 CIU CIU 0.48 0.04 <.001
Impulse CIU -0.09 0.03 0.002 Strategies CIU -0.10 0.03 0.001 Goals CIU -0.02 0.03 0.482
CIU Impulse 0.04 0.03 0.293 CIU Strategies 0.04 0.04 0.297 CIU Goals 0.10 0.03 0.003
Time 1 Time 2 Coeff. SE
p-
value
Time 1 Time 2 Time 1 Time 2 Coeff. SE
p-
value
Acceptance Acceptance 0.24 0.04 <.001 Clarity Clarity 0.27 0.05 <.001 Awareness Awareness 0.34 0.04 <.001
CIU CIU 0.48 0.04 <.001 CIU CIU 0.49 0.04 <.001 CIU CIU 0.48 0.04 <.001
Acceptance CIU -0.02 0.03 0.465 Clarity CIU -0.05 0.03 0.162 Awareness CIU 0.03 0.04 0.413
CIU Acceptance 0.04 0.03 0.270 CIU Clarity 0.10 0.04 0.007 CIU Awareness 0.02 0.03 0.536
Note. Coeff. = Standardized regression coefficient; SE = standard errors.
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Discussion
Drawing on theories of emotion regulation, the present study tested whether CIU
predicts various emotion regulation difficulties (the ‘antecedentmodel; Hypothesis 1);
whether emotion regulation difficulties predict CIU (the ‘consequence’ model; Hypothesis 2);
or whether these effects are reciprocal (the ‘reciprocal influencemodel). We found some
support for the antecedent model and none for the consequence model.
CIU as an antecedent of emotion regulation difficulties
We found modest though consistent effects of CIU predicting difficulties with
regulating behaviour in the presence of emotions (i.e., difficulties pursuing goals in the
presence of distress; Hypothesis 1). This finding lends weight to the idea that CIU inhibits self-
regulatory capacities, in particular the ability to pursue valued goals and aspirations in the
presence of difficult emotions. This is consistent with previous research on the links between
CIU and self-regulation (Billieux & Van Der Linden, 2012; LaRose et al., 2003), but provides
multi-year, longitudinal evidence for these effects. Further, these findings suggest that CIU has
consequences for adolescent development, as self-regulatory difficulties in adolescence have
important longer-term consequences for mental-health and social functioning (Garnefski et al.,
2005).
Further, we found evidence for CIU predicting less clarity regarding emotions. This is
consistent with research on other forms of addiction and alexithymia, for example individuals
with substance addictions experience difficulties identifying and labelling their emotions
(Kauhanen, Wilson, Salonen, Kaplan, & Julkunen, 1993; Parolin et al., 2018). Our findings
suggest that CIU has similar effects, constraining individuals’ capacity to clearly identify and
label the emotions they are experiencing. Our findings contribute to broader research on
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25
alexithymia (e.g., Parolin et al., 2018), providing novel evidence that addiction (i.e., CIU)
precedes difficulties identifying and labelling emotions.
The effect sizes of CIU preceding self-regulation toward goals, and emotional clarity
across a year would generally be considered in the small range (i.e., .10), and the policy
significance of relatively small effects of CIU obtained across large samples have recently been
called into question (e.g., Orben & Przybylski, 2019). However, the effects we identified were
stable across all four years of our study. This means that if an individual is high in CIU (i.e., 2
standard deviations above average) in Grade 8, and continues to be at this level through to
Grade 10, we would expect that person to lose 0.6 of a standard deviation in emotional clarity
and distress tolerance for goals by Grade 11 (0.2 SD per year x 3 year lags). Thus, whilst a
single year of CIU may not be particularly detrimental, persistent CIU across adolescence may
have more substantial negative effects on these emotion regulation capacities.
Notably, we did not find evidence that CIU precedes other forms of emotional
dysregulation (i.e., emotional non-acceptance, lack of awareness, impulsiveness, or difficulties
identifying strategies for managing emotions). These findings suggest that CIU has moderate
effects on some aspects of emotional dysregulation and not others. Specifically, our findings
suggest that CIU influences more effortful and cognitively complex forms of emotion
regulation (i.e., difficulties pursuing life-goals and accurately understanding one’s emotions),
rather than regulations that are more spontaneous and closely tied to the affective experience
(i.e., impulse control and emotional avoidance).
Further, in contrast to findings from longitudinal studies that CIU precedes decrements
in general mental health (e.g., Ciarrochi et al., 2016) and well-being outcomes (e.g., (Muusses
et al., 2014), the present study provides insights into the specific types of emotion dysregulation
that are longitudinally predicted by (and that predict) CIU. This, in-turn, has implications for
RUNNING HEAD: EMOTION REGULATION AND COMPULSIVE INTERNET USE
26
the evaluation of psychological interventions aimed at reducing CIU, highlighting the kinds of
emotion regulation outcomes that are more (versus less) likely to be impacted by CIU
interventions. Our findings suggest that young people who engage in compulsive internet use
may need support with emotional labelling and persisting at goals in the presence of distress.
CIU as a consequence of emotion regulation difficulties
There is meta-analytic evidence suggesting that developing general emotion-regulation
skills among young people, for example through social and emotional learning interventions,
reduces maladaptive behaviours such as drug use and antisocial behaviours (Taylor et al.,
2017). There is also longitudinal evidence that emotion regulation skills predict the
development of social and emotional wellbeing (Ciarrochi et al., 2008; Rowsell et al., 2014).
Based on these past findings, we anticipated that general difficulties with emotion regulation
would lead to increases in a related maladaptive behaviour, namely CIU (Hypothesis 2). We
did not find evidence for this hypothesis for any of the six aspects of emotion dysregulation.
A key feature of the difficulties with emotion regulation scale (Gratz & Roener, 2004)
is that it is domain general. It does not refer specifically to compulsive internet use. Future
research is needed to examine whether domain specific forms of emotion dysregulation (e.g.,
regulating internet-related emotions) predict CIU over time. Alternatively, perhaps CIU is not
driven by poor emotion regulation skill. It may be that CIU is driven more by contextual factors,
such as access to the internet, access to devices, time spent alone, quality of family and peer
support, and the availability of non-internet pursuits (Kim et al., 2009; Yao, He, Ko, & Pang,
2014). Further research is needed to explore these possibilities.
We unexpectedly found that two aspects of dysregulation—impulse control and
difficulty identifying strategies for managing emotion—predicted less CIU over time. This is
clearly inconsistent with the consequence model we proposed in the present study. We
RUNNING HEAD: EMOTION REGULATION AND COMPULSIVE INTERNET USE
27
speculate here that perhaps difficulties with impulse control and emotion regulation strategies
make it less likely for a young person to be able to engage in online activity for extended
times, and therefore develop CIU. Past research has shown that both impulsivity and
difficulty with emotion regulation strategies have negative consequences in general
(Evenden, 1999; Gratz & Roemer, 2004), even if they may not lead to compulsive internet
use. Further research is needed to replicate this finding and explore the processes driving it.
Strengths and Limitations
This is the first study we are aware of to examine the longitudinal association between
CIU and a range of emotion regulation difficulties. Emotion regulation skills have important
consequences for adolescent development and functioning (e.g., Bradley, 2000; Cole et al.,
1994; Gross, 1998), making them an important behaviour to explore in the context of studying
CIU among adolescents. The present study goes beyond previous longitudinal studies of CIU
and general wellbeing (Donald et al., 2018; Van Der Aa et al., 2009) and mental health
(Ciarrochi et al., 2016), to examine specific aspects of emotional dysregulation. A
methodological strength of this study was the use of state-of-the-art RI-ACP structural equation
modelling approaches (Hamaker et al., 2015), wherein we controlled for trait-stability in CIU
and DER over time, and tested whether the within-person longitudinal relations between CIU
and emotion regulation were consistent over time. This means we can draw relatively strong
conclusions regarding the stability and causal ordering of the effects we have observed
(Hamaker et al., 2015). However, this study has several limitations.
First, it is possible that additional, unmeasured variables explain the longitudinal
association between CIU and DER. A strength of the RI-ACP models we used was they control
for trait-levels of both CIU and DER components, and also control for within-person
autoregressive effects over time (Hamaker et al., 2015). However, we cannot rule out the
RUNNING HEAD: EMOTION REGULATION AND COMPULSIVE INTERNET USE
28
possibility that unmeasured third variablesfor example, demographic factors, environmental
factors such as internet access, time spent alone, and family and peer support, and genetic
factorscaused the effects observed in this study. Future longitudinal research on CIU could
include these additional variables, and, ideally, use experimental designs to test the relations
between CIU and DER.
Second, this study focused on the compulsive use of the internet and did not explore
the specific types of internet activities that may be predictive of emotion regulation difficulties
over time. There is evidence that gaming and social media use are the online activities most
strongly associated with CIU in adolescents (Ciarrochi et al., 2016; Muusses et al., 2014).
Perhaps, for example, online gaming has bigger effects on aspects of emotion regulation such
as pursuing goals and impulse control than browsing the internet for information-gathering.
Future research could explore these possibilities.
Third, this study focused on early-to-mid adolescence. There is evidence that CIU
begins in the pre-adolescent years (Lan & Lee, 2013; Vondráčková & Gabrhelík, 2016). It may
be that the longitudinal relations between CIU and emotion dysregulation have been
established by adolescence, explaining the relatively modest effect sizes we observed in the
present study, and that effects are larger among pre-adolescents. Future research needs to
examine the longitudinal effects of CIU and DER over a wider timespan, including among pre-
adolescents.
Fourth, our sample of schools had a socioeconomic ranking (1,025; SD = 43) almost
identical to the Australian average of 1,000. However, the sample was not fully representative,
comprising only Australian Catholic schools, and with the sample skewed toward somewhat
wealthier schools.
RUNNING HEAD: EMOTION REGULATION AND COMPULSIVE INTERNET USE
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Finally, there is a need to focus on ways to most effectively prevent CIU. Reviews of
CIU interventions have proposed a range of skills to be targeted in CIU interventions, including
educating adolescents’ immediate social networks on the development of healthy internet-use
practices; training young people in self-regulatory skills; teaching skills in coping with stress;
developing face-to-face interpersonal skills; and teaching skills in managing daily routine
(Vondráčková & Gabrhelík, 2016). Our findings indicate that interventions targeting CIU
specifically may be more effective than training general emotion regulation skills (i.e., we did
not find any evidence for the ‘consequencemodel). More research is needed to understand the
most effective CIU interventions, their mechanisms, and their impact on emotion regulation
abilities.
Conclusions
We examined the longitudinal effects of CIU on emotion regulation during
adolescence, a time of critical importance for the development of healthy emotion regulation
skills. Our findings suggest that CIU precedes some aspects of emotion dysregulation (i.e.,
goal-directed behaviour and less emotional clarity), but not others, underscoring the value of
examining various components of emotion dysregulation and their relation to CIU. Conversely,
we did not find evidence that emotion dysregulation precedes increases in CIU. This suggests
that interventions targeting general emotion regulation skills development are unlikely to
influence CIU, and that either more specific emotion regulation interventions are needed, or
else attention needs to be paid to environmental factors that might influence the development
of the compulsive use of the internet.
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... The subject of compulsive internet use (CIU), particularly as it regards online peer communication, has received increasing attention in recent years (Van Zalk & Lee, 2020). This is largely due to ongoing findings linking compulsive internet use to a range of negative outcomes, including those related to emotional regulation (Donald, Ciarrochi, & Sahdra, 2020), health-related behaviors (Khazaal et al., 2021), and worse academic achievement (Tóth-Király et al., 2021). ...
... In understanding the relationship of CIU to social interaction and emotional health, one of the more persistent controversies has been whether CIU represents an antecedent of observed mental health outcomes, or a consequence. Support has been found in longitudinal studies for CIU leading to lower self-esteem and hope (Donald, Ciarrochi, Parker, & Sahdra, 2019), evidence of the antecedent model, as well as emotional dysregulation (Donald et al., 2020), rather than cause of these. Similarly, further support of the antecedent model includes CIU predicting depressive symptoms (Kojima, Shinohara, Akiyama, Yokomichi, & Yamagata, 2021), but not itself predicted by such. ...
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Despite offering the potential for increased connection, intimacy, and positive outcomes, online social interaction also holds the potential to become problematic and create negative outcomes. The present study sought to address the question of the role of attachment style in affecting online behavior, as well as to better understand the way in which specific dimensions of online interaction may correspond to specific attachment styles. The study explored attachment style, particularly anxious and avoidant attachment, in predicting problematic internet use, online intimate self-disclosure, and positive and negative attitudes toward technology in a sample of young adults aged 18–25. A quantitative approach employing correlations, regressions, and ANOVAs was used. It was found that both anxious and avoidant attachments were positively related to and both predicted problematic internet use. Although neither anxious or avoidant attachment style predicted online intimate disclosure, preoccupied individuals were found to disclose significantly more online than fearful individuals. Both anxiety and avoidance positively related to negative attitudes toward technology, with avoidant attachment style significantly predicting it, while only anxious attachment predicted positive attitudes toward technology.
... The study faced the challenges related to both ER and PSU being evolving concepts with different measurements capturing different aspects of both conditions. In a very recent 4year longitudinal study looking at the causal relationship between compulsive internet use (CIU) and emotion regulation difficulties, it was found that CIU preceded the development of some aspects of ED, with no proof that emotion regulation difficulties preceded the development of increases in CIU [101]. Due to CIU carrying some degree of similarity with PSU, there is a likelihood that such results may replicate in the case of ER and PSU. ...
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Emotion Dysregulation (ED) and Problematic Smartphone Use (PSU) are two rising global issues requiring further understanding on how they are linked. This paper aims to summarize the evidence pertaining to this relationship. Five databases were systematically searched for published literature from inception until 29 March 2021 using appropriate search strategies. Each study was screened for eligibility based on the set criteria, assessed for its quality and its level of evidence was determined. The Comprehensive Meta-Analysis software program (CMA) was employed to run further analyses of the data. Twenty-one studies were included in the systematic review. Nine studies with extractable data for meta-analysis had high across-studies heterogeneity, hence subgroup analyses were performed that confirmed a significant moderate positive correlation between ED and PSU (pooled correlation coefficient, r = 0.416 (four studies, n = 1462) and r = 0.42 (three studies, n = 899), respectively) and a weak positive correlation between “expressive suppression” and PSU (pooled correlation coefficient, r = 0.14 (two studies, n = 608)). Meta-regression analysis showed a stronger correlation between ED and PSU (R2 = 1.0, p = 0.0006) in the younger age group. Further studies to establish and explore the mechanisms that contribute towards the positive link between ED and PSU are required to guide in the planning of targeted interventions in addressing both issues.
... Still, the relationship between the ED de cits and Internet addiction has been debated. For instance, Donald et al. (22) did not nd evidence that emotion regulation di culties preceded the development of compulsive Internet use and suggested that teaching general emotion regulation skills may not be as effective in reducing compulsive Internet use. ...
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Background: In recent decades, with the significant developments in technology, the Internet has become a main part of peoples' lives. The widespread use of the Internet has raised significant concerns about problematic Internet behaviors and their consequences. This study aimed to examine if Internet addiction significantly predicts obesity and whether Internet addiction and obesity are significantly predicted by emotion dysregulation. Mthods: 367 school-attending adolescents (M age = 13.35; SD = 0.82; 49% girls) in Tekab were recruited and completed the Difficulties in Emotion Regulation Scale (DERS) and Internet Addiction Test (IAT) measures, while their BMI scores were calculated to examine the participants' obesity levels. Results: The results indicated that Internet addiction significantly predicted obesity, while they both were significantly predicted by emotion dysregulations. Conclusion: Our findings could be informative for clinicians working with individuals suffering from Internet addiction and obesity.
... They clearly outline the predictors of poor impulse control, and a lack of social skills as one of those predictors. Other studies suggest PIU being responsible for emotion dysregulation rather than vice versa [111]. They also specify that PIU only affects the more complex forms of emotion dysregulation such as pursuing life goals rather than impulse control. ...
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Studies in recent years and especially since the beginning of the COVID-19 pandemic have shown a significant increase in the problematic use of computer games and social media. Adolescents having difficulties in regulating their unpleasant emotions are especially prone to Problematic Internet Use (PIU), which is why emotion dysregulation has been considered a risk factor for PIU. The aim of the present study was to assess problematic internet use (PIU) in adolescents after the third wave (nearly 1.5 years after the onset in Europe) of the COVID-19 pandemic. In the German region of Siegen-Wittgenstein, all students 12 years and older from secondary-level schools, vocational schools and universities were offered a prioritized vaccination in August 2021 with an approved vaccine against COVID-19. In this context, the participants filled out the Short Compulsive Internet Use Scale (SCIUS) and two additional items to capture a possible change in digital media usage time and regulation of negative affect due to the COVID-19 pandemic. A multiple regression analysis was performed to identify predictors of PIU. The original sample consisted of 1477 participants, and after excluding invalid cases the final sample size amounted to 1268 adolescents aged 12–17 (x = 14.37 years, SD = 1.64). The average prevalence of PIU was 43.69%. Gender, age, digital media usage time and the intensity of negative emotions during the COVID-19 pandemic were all found to be significant predictors of PIU: female gender, increasing age, longer digital media usage time and higher intensity of negative emotions during the COVID-19 pandemic were associated with higher SCIUS total scores. This study found a very high prevalence of PIU among 12- to 17-year-olds for the period after the third wave of the COVID-19 pandemic, which has increased significantly compared to pre-pandemic prevalence rates. PIU is emerging as a serious problem among young people in the pandemic. Besides gender and age, pandemic-associated time of digital media use and emotion regulation have an impact on PIU, which provides starting points for preventive interventions.
... If individuals have difficulties in emotional expression (Cisler et al., 2010), they may prefer MP conversation to avoid in-person interactions Sapacz et al., 2016). Previous researchers have also shown that a reduction in the ability to identify and label emotions is associated with problematic internet use (Donald et al., 2020) and problematic MP use . As social support and interpersonal contact have beneficial effects on ER, and the MP is almost the best solution for social interaction or emotion expression (e.g. ...
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In the modern world, the mobile phone has become an indispensable part of modern life. On the one hand, the mobile phone allows maintaining interpersonal contacts and fulfilling work or school duties regardless of time and location. It enables individuals to plan their daily routines and their free times. On the other hand, a mobile phone is a tool that can cause several psychological and physical problems. Nomophobia, which is considered the phobia of the modern era, is only one of these problems. In the simplest terms, nomophobia is the fear of being without a mobile phone and the intense anxiety and distress experienced in the absence of a mobile phone. Although technological addictions such as smartphone addiction and internet addiction have been studied extensively in the psychology literature, it is striking that nomophobia is a neglected psychological problem. However, nomophobia is emerging as a common phenomenon among young adults, as most young adults use the mobile phone for about 5 hours a day. Some users define the mobile phone as a friend and the meaning of life. More importantly, prevalence studies have revealed that about half of young adults suffer from nomophobia. Since nomophobia causes many serious consequences such as physical pain, social problems and a decrease in academic achievement, nomophobia studies are important and beneficial especially for the younger generation. This book has been written to emphasize the importance of nomophobia and to provide detailed information about the diagnosis, treatment, prevalence, predictors and symptoms of nomophobia. In addition, this book aimed to conceptualize nomophobia theoretically. Also, based on the theoretical conceptualization, psychological structures that can cause nomophobia have been identified. The theoretical conceptualization has been tested and validated using scientific methods. This book, which contains a comprehensive literature review and scientific research, can shed light on researchers for future nomophobia studies. I also believe that this book will make valuable contributions to the clinical field by providing a better understanding of the factors that should be considered in prevention programs and treatment interventions developed for nomophobia. I hope that scholars, clinicians, and students from a variety of disciplines will find my efforts helpful.
... Study done by (Wartberg, Kriston, & Thomasius, 2020) predicted emotional distress as base cause of problematic internet use. Though same type of study done in large sample of Australian adolescents concluded that PIU in fact leads to emotional problems keeping distress side by side (Donald, Ciarrochi, & Sahdra, 2020). ...
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21st century has been era of digitalization which is totally assisted by internet networks. Internet has been developing its wings in every possible way in order to make human life simpler, luxurious and entertaining. Withholding many beauties, it has been favoriting all generation people. But young millennials who are born simultaneously with the emerge of internet technology are found to be more fascinated and attracted by this technology and thus are in the 1st rank in internet use. The COVID-19 pandemic has something powerful to change many things. Along with human health and psychology, it had also changed the internet use pattern of the people worldwide. In this review, impact of pandemic on internet use behavior mostly by college studying youths is analyzed. Time spent by surfing internet is hiked unexpectedly due to this pandemic as daily scheduled has of many citizens is altered. It is found that some of the students have taken this as an opportunity to explore and develop knowledge about new technologies. Online class has been normalized in many places and they are not deprived of education. But various disastrous impact is also seen. Third world countries students are having problem in study due to poor internet management. Students are facing problem of Internet Addiction using it all time. Mental stress, anxiety, aggressiveness, etc. can be seen when they are disconnected from internet for certain period of time. Here seems to drag attention of socialists and psychologist towards measures that needs to be adopted to minimize these dreadful impacts of internet use and emphasize more on benefits.
... More specifically, children prone to difficulties in dealing with negative emotions should be restricted or monitored more closely in their video game playing behavior. This falls in line with the proposition of Donald et al. (61) to reduce video gaming by restricting access to devices (despite them having considered ED as a result of GD instead of ED predicting GD, as we have found). At the same time, more adequate coping and action alternatives should be offered that both act as an adaptive strategy for dealing with negative emotions and can contribute to experiencing positive feelings. ...
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As the online world plays an increasing role in young peoples’ lives, research on compulsive internet use (CIU) is receiving growing attention. Given the social richness of the online world, there is a need to better understand how CIU influences adolescents’ social support and vice versa. Drawing on ecological systems theory, we examined the longitudinal links between adolescents’ CIU and perceived social support from three sources (parents, teachers, and friends) across 4 critical years of adolescence (Grades 8–11). Using random intercept cross-lagged modeling, we found that CIU consistently preceded reduced social support from teachers, whereas social support from parents preceded increases in CIU over time. We discuss the implications of our findings for parents and schools seeking to support young people experiencing CIU.
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Background and aims We aimed to systematically identify, evaluate and summarize the research on adolescent emotion dysregulation and problematic technology use. We critically appraise strengths and limitations and provide recommendations for future research. Methods We followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and conducted a systematic review of published original reports on adolescent emotion dysregulation and problematic technology use published until March 1, 2022. A thorough search preceded the selection of studies matching prespecified criteria. Strengths and limitations of selected studies, regarding design and reporting, were identified based on current best practices. Results 39 studies met inclusion criteria. All of these studies provided on the relationship between adolescent emotion dysregulation and problematic technology use severity based on self-report data. Discussion There was a positive correlation between adolescent emotion dysregulation and the severity of problematic technology use. Beyond this, other variables (such as anxiety, depression, self-esteem, etc.) were also closely related to emotion dysregulation and problematic technology use. Such studies are of importance to better understand cause-effect relations regarding both variables.
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Background: Emotional dysregulation (ED) is a transdiagnostic construct defined as the inability to regulate the intensity and quality of emotions (such as, fear, anger, sadness), in order to generate an appropriate emotional response, to handle excitability, mood instability, and emotional overreactivity, and to come down to an emotional baseline. Because ED has not been defined as a clinical entity, and because ED plays a major role in child and adolescent psychopathology, we decided to summarize current knowledge on this topic based on a narrative review of the current literature. Methods: This narrative review is based on a literature search of peer-reviewed journals. We searched the databases ERIC, PsycARTICLES, PsycINFO and PSYNDEX on June 2, 2020 for peer reviewed articles published between 2000 and 2020 in English language for the preschool, school, and adolescent age (2–17 years) using the following search terms: “emotional dysregulation” OR “affect dysregulation,” retrieving 943 articles. Results: The results of the literature search are presented in the following sections: the relationship between ED and psychiatric disorders (ADHD, Mood Disorders, Psychological Trauma, Posttraumatic Stress Disorder, Non-suicidal Self-Injury, Eating Disorders, Oppositional Defiant Disorder, Conduct Disorder, Disruptive Disruptive Mood Dysregulation Disorder, Personality Disorders, Substance Use Disorder, Developmental Disorders, Autism Spectrum Disorder, Psychosis and Schizophrenia, and Gaming Disorder), prevention, and treatment of ED. Conclusion: Basic conditions of ED are genetic disposition, the experience of trauma, especially sexual or physical abuse, emotional neglect in childhood or adolescence, and personal stress. ED is a complex construct and a comprehensive concept, aggravating a number of various mental disorders. Differential treatment is mandatory for individual and social functioning.
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The widespread use of digital technologies by young people has spurred speculation that their regular use negatively impacts psychological well-being. Current empirical evidence supporting this idea is largely based on secondary analyses of large-scale social datasets. Though these datasets provide a valuable resource for highly powered investigations, their many variables and observations are often explored with an analytical flexibility that marks small effects as statistically significant, thereby leading to potential false positives and conflicting results. Here we address these methodological challenges by applying specification curve analysis (SCA) across three large-scale social datasets (total n = 355,358) to rigorously examine correlational evidence for the effects of digital technology on adolescents. The association we find between digital technology use and adolescent well-being is negative but small, explaining at most 0.4% of the variation in well-being. Taking the broader context of the data into account suggests that these effects are too small to warrant policy change. © 2019, The Author(s), under exclusive licence to Springer Nature Limited.
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Objective Compulsive internet use (CIU) has been linked to decrements in mental health and well‐being. However relatively little is known about how CIU relates to evaluations of the self, and in particular, whether CIU is antecedent to or is a consequence of negative evaluations of one's social worth (self‐esteem) and general efficacy (hope). To examine this, we explored the longitudinal relations between CIU and the development of self‐esteem and hope among adolescents over a four‐year period. Method 2,809 adolescents completed measures yearly from Grade 8 (MAge =13.7) to Grade 11. Autoregressive‐cross‐lagged structural equation models were used to test whether CIU influenced or was influenced by self‐esteem and hope. Results We found consistent support for a CIU‐as‐antecedent model. CIU preceded reductions in trait hope, and small reductions in self‐esteem. In contrast, we did not find evidence for a CIU‐as‐consequence model: Low self‐esteem and hope did not predict increases in CIU over time. Conclusions Our findings suggest that CIU has negative consequences for young people's feelings of goal‐efficacy, and that interventions that address the compulsive use of the internet are likely to strengthen hope and self‐esteem among young people. This article is protected by copyright. All rights reserved.
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This meta-analysis reviewed 82 school-based, universal social and emotional learning (SEL) interventions involving 97,406 kindergarten to high school students (Mage = 11.09 years; mean percent low socioeconomic status = 41.1; mean percent students of color = 45.9). Thirty-eight interventions took place outside the United States. Follow-up outcomes (collected 6 months to 18 years postintervention) demonstrate SEL's enhancement of positive youth development. Participants fared significantly better than controls in social-emotional skills, attitudes, and indicators of well-being. Benefits were similar regardless of students’ race, socioeconomic background, or school location. Postintervention social-emotional skill development was the strongest predictor of well-being at follow-up. Infrequently assessed but notable outcomes (e.g., graduation and safe sexual behaviors) illustrate SEL's improvement of critical aspects of students’ developmental trajectories.
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Background and aims Out of a large number of studies on Internet addiction, only a few have been published on the prevention of Internet addiction. The aim of this study is provide a systematic review of scientific articles regarding the prevention of Internet addiction and to identify the relevant topics published in this area of interest. Methods The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were adopted. The EBSCO, ProQuest Central, and PubMed databases were searched for texts published in English and Spanish between January 1995 and April 2016. A total of 179 original texts were obtained. After de-duplication and topic-relevance review, 108 texts were systematically classified and subjected to descriptive analysis and subsequent content analysis. Results The results of the content analysis yielded the following thematic areas: (a) target groups, (b) the improvement of specific skills, (c) program characteristics, and (d) environmental interventions. Discussion and conclusion Literature on the prevention of Internet addiction is scarce. There is an urgent need to introduce and implement new interventions for different at-risk populations, conduct well-designed research, and publish data on the effectiveness of these interventions. Developing prevention interventions should primarily target children and adolescents at risk of Internet addiction but also parents, teachers, peers, and others who are part of the formative environment of children and adolescents at risk of Internet addiction. Newly designed interventions focused on Internet addiction should be rigorously evaluated and the results published.
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Objectives: The present study aimed to elucidate the factor structure of the Difficulties in Emotion Regulation Scale (DERS; Gratz & Roemer, )-a widely used measure of emotion dysregulation. Method: Participants were 3 undergraduate samples (N = 840, 78.33% female, mean age = 20.30). Results: We began by using confirmatory factor analysis (CFA) to examine 3 existing models, finding that none consistently demonstrated adequate fit across samples. Subsequently, we conducted an exploratory factor analysis, identifying a novel 5-factor model that consistently resulted in adequate fit across samples. We also ran several CFA models after removing the Awareness subscale items-which have performed inconsistently in prior research-finding that a reduced-measure variant of the model retained by Gratz and Roemer () resulted in adequate fit across samples. No higher-order models consistently resulted in adequate fit across samples. Conclusions: Our findings are consistent with previous work in suggesting use of a DERS total score may not be appropriate. Additionally, further work is needed to examine the novel 5-factor model and the effect of reverse-scored items on the DERS factor structure.
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Is compulsive Internet use (CIU) an antecedent to poor mental health, a consequence, or both? Study 1 used a longitudinal design to track the development of CIU and mental health in Grade 8 (N = 1030 males, 1038 females, Mage = 13.7), 9, 10, and 11. Study 2 extended Study 1 by examining the kinds of Internet behaviors most strongly associated with CIU within males and females. Structural equation modeling revealed that CIU predicted the development of poor mental health, whereas poor mental health did not predict CIU development. Latent growth analyses showed that both females and males increased in CIU and mental health problems across the high school years. Females had higher CIU and worse mental health than males, and tended to engage in more social forms of Internet use. We discuss future directions for CIU intervention research. (PsycINFO Database Record
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Given recent attention to emotion regulation as a potentially unifying function of diverse symptom presentations, there is a need for comprehensive measures that adequately assess difficulties in emotion regulation among adults. This paper (a) proposes an integrative conceptualization of emotion regulation as involving not just the modulation of emotional arousal, but also the awareness, understanding, and acceptance of emotions, and the ability to act in desired ways regardless of emotional state; and (b) begins to explore the factor structure and psychometric properties of a new measure, the Difficulties in Emotion Regulation Scale (DERS). Two samples of undergraduate students completed questionnaire packets. Preliminary findings suggest that the DERS has high internal consistency, good test–retest reliability, and adequate construct and predictive validity.
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In two nationally representative surveys of U.S. adolescents in grades 8 through 12 (N = 506,820) and national statistics on suicide deaths for those ages 13 to 18, adolescents’ depressive symptoms, suicide-related outcomes, and suicide rates increased between 2010 and 2015, especially among females. Adolescents who spent more time on new media (including social media and electronic devices such as smartphones) were more likely to report mental health issues, and adolescents who spent more time on nonscreen activities (in-person social interaction, sports/exercise, homework, print media, and attending religious services) were less likely. Since 2010, iGen adolescents have spent more time on new media screen activities and less time on nonscreen activities, which may account for the increases in depression and suicide. In contrast, cyclical economic factors such as unemployment and the Dow Jones Index were not linked to depressive symptoms or suicide rates when matched by year.
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Although the time adolescents spend with digital technologies has sparked widespread concerns that their use might be negatively associated with mental well-being, these potential deleterious influences have not been rigorously studied. Using a preregistered plan for analyzing data collected from a representative sample of English adolescents ( n = 120,115), we obtained evidence that the links between digital-screen time and mental well-being are described by quadratic functions. Further, our results showed that these links vary as a function of when digital technologies are used (i.e., weekday vs. weekend), suggesting that a full understanding of the impact of these recreational activities will require examining their functionality among other daily pursuits. Overall, the evidence indicated that moderate use of digital technology is not intrinsically harmful and may be advantageous in a connected world. The findings inform recommendations for limiting adolescents' technology use and provide a template for conducting rigorous investigations into the relations between digital technology and children's and adolescents' health.