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Background and aims: Problematic smartphone use (PSU) has been described as a growing public health issue. In the current study, we aimed to provide a unique and comprehensive test of the pathway model of PSU. This model posits three distinct developmental pathways leading to PSU: (1) the excessive reassurance pathway, (2) the impulsive pathway and (3) the extraversion pathway. Methods: Undergraduate students (n = 795, 69.8% female, mean age = 23.80 years, sd = 3.02) completed online self-report measures of PSU (addictive use, antisocial use and dangerous use) and the psychological features (personality traits and psychopathological symptoms) underlying the three pathways. Results: Bayesian analyses revealed that addictive use is mainly driven by the excessive reassurance pathway and the impulsive pathway, for which candidate etiopathological factors include heightened negative urgency, a hyperactive behavioural inhibition system and symptoms of social anxiety. Dangerous and antisocial use are mainly driven by the impulsive pathway and the extraversion pathway, for which candidate etiopathological factors include specific impulsivity components (lack of premeditation and sensation seeking) and primary psychopathy (inclination to lie, lack of remorse, callousness and manipulativeness). Discussion and conclusions: The present study constitutes the first comprehensive test of the pathway model of PSU. We provide robust and original results regarding the psychological dimensions associated with each of the postulated pathways of PSU, which should be taken into account when considering regulation of smartphone use or tailoring prevention protocols to reduce problematic usage patterns.
A test of the pathway model of problematic
smartphone use
and JO
Department of Developmental and Social Psychology, University of Padova, Padova, Italy
Department of General Psychology, University of Padova, Padova, Italy
Department of Psychology, University of Milano-Bicocca, Milan, Italy
Institute of Psychology, University of Lausanne, Lausanne, Switzerland
Received: September 18, 2020 Revised manuscript received: December 05, 2020 Accepted: December 06, 2020
Background and aims: Problematic smartphone use (PSU) has been described as a growing public
health issue. In the current study, we aimed to provide a unique and comprehensive test of the pathway
model of PSU. This model posits three distinct developmental pathways leading to PSU: (1) the
excessive reassurance pathway, (2) the impulsive pathway and (3) the extraversion pathway. Methods:
Undergraduate students (n5795, 69.8% female, mean age 523.80 years, sd 53.02) completed online
self-report measures of PSU (addictive use, antisocial use and dangerous use) and the psychological
features (personality traits and psychopathological symptoms) underlying the three pathways. Results:
Bayesian analyses revealed that addictive use is mainly driven by the excessive reassurance pathway and
the impulsive pathway, for which candidate etiopathological factors include heightened negative ur-
gency, a hyperactive behavioural inhibition system and symptoms of social anxiety. Dangerous and
antisocial use are mainly driven by the impulsive pathway and the extraversion pathway, for which
candidate etiopathological factors include specific impulsivity components (lack of premeditation and
sensation seeking) and primary psychopathy (inclination to lie, lack of remorse, callousness and
manipulativeness). Discussion and conclusions: The present study constitutes the first comprehensive
test of the pathway model of PSU. We provide robust and original results regarding the psychological
dimensions associated with each of the postulated pathways of PSU, which should be taken into account
when considering regulation of smartphone use or tailoring prevention protocols to reduce problematic
usage patterns.
problematic smartphone use, pathway model, undergraduate students, personality traits, psychopathological
symptoms, Bayesian analytical approach
In the last two decades, a growing number of studies have found that excessive smartphone
use is related to a wide range of negative consequences, such as addiction-like symptoms (e.g.
loss of control, apparent withdrawal and tolerance, and interference with daily life),
emotional symptoms (e.g. depression and anxiety), road accidents and interference with sleep
(Billieux, 2012; De-Sola Guti
errez, Rodr
ıguez de Fonseca, & Rubio, 2016; Elhai, Dvorak,
Levine, & Hall, 2017). From a developmental perspective, the ongoing digital technology
revolution naturally increases and expands the ordinary adolescent proclivities to explore,
seek and learn from socially and affectively salient experiences (Giovanelli, Ozer, & Dahl,
2020). Previous research suggests that children, adolescents and young adults (often
described as digital natives) might be a particularly vulnerable population, as they frequently
Journal of Behavioral
© 2020 The Author(s)
pCorresponding author.
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display heavy mobile phone usage patterns (Cheever, Rosen,
Carrier, & Chavez, 2014; Giovanelli, Ozer, & Dahl, 2020;
Lepp, Barkley, & Karpinski, 2014; Lepp, Li, Barkley, &
Salehi-Esfahani, 2015; Smetaniuk, 2014; Walsh et al., 2020).
In view of these issues, it is of paramount importance to
focus research on the public health implications and risk
associated with excessive problematic mobile phone use.
Problematic smartphone use (PSU) has been defined as an
uncontrolled pattern of smartphone use linked with
impairment in academic, occupational and/or social func-
tioning (Billieux, Maurage, Lopez-Fernandez, Kuss, & Grif-
ths, 2015). Although PSU presents overlapping symptoms
with addictive behaviours (e.g., loss of control, tolerance-like
and withdrawal-like phenomena) (see De-Sola Guti
et al., 2016 for a review), its recognition as a potential
addictive disorder is debated (Billieux et al., 2017; Montag,
Wegmann, Sariyska, Demetrovics, & Brand, 2020; Panova &
Carbonell, 2018).
Despite the blossoming of PSU research, uncertainty still
abounds regarding the psychological factors that may be
implicated in PSU, as most research in this field has been
conducted in the absence of theoretical models. Although
PSU has been generally viewed either as a genuine addictive
disorder (a behavioural addiction) (Choliz, 2010; Montag
et al., 2020)orascompensatorybehaviour displayed by
individuals who present emotional symptoms (e.g. anxiety,
depression, stress) and maladaptive emotion regulation
strategies (Elhai, Yang, & Montag, 2019a; Elhai, Levine, &
Hall, 2019b), few theoretical models of PSU have been
formulated. One notable exception is the framework pro-
posed by Billieux and colleagues (Billieux, 2012; Billieux
et al., 2015) to account for the multiple forms and etiologies
of PSU. This model proposed three distinct developmental
pathways to PSU, each being associated with distinct psy-
chosocial and psychopathological variables: (1) the excessive
reassurance pathway, (2) the impulsive pathway and (3) the
extraversion pathway. The excessive reassurance pathway
comprises individuals whose PSU is mainly driven by the
necessity to maintain relationships and obtain reassurance
from others (Ha, Chin, Park, Ryu, & Yu, 2008; Lu, Katoh,
Chen, Nagata, & Kitamura, 2014). This pathway is postu-
lated to typically result in perceived dependence on the
mobile phone and/or addiction-like symptoms (e.g., toler-
ance). Such a pathway was formulated on the basis of
numerous studies that linked PSU to childhood maltreat-
ment and a poor self-model of adult attachment, low self-
esteem, and heightened levels of general and social anxiety,
depression or neuroticism (Emirtekin, 2019; Igarashi,
Motoyoshi, Takai, & Yoshida, 2008; Lu et al., 2014; Lepp
et al., 2015). This excessive reassurance pathway is consis-
tent with the hypothesis proposed by Elhai and colleagues
(2019a, b) that PSU is mainly driven by negative rein-
forcement (e.g. a way for anxious or depressed people to
cope with negative emotions and distract themselves from
these emotions) and can be viewed as a maladaptive coping
strategy. The impulsive pathway corresponds to PSU pat-
terns promoted by poor impulse control, ultimately resulting
in uncontrolled urges and dysregulated use. The existence of
this pathway is supported by research that linked PSU with
specic impulsivity traits, such as lack of planning/pre-
meditation and urgency (the tendency to act rashly in
intense emotional contexts) (Billieux, Van der Linden, &
Rochat, 2008; Fjeldsoe, Marshall, & Miller, 2009; Khang,
Kim, & Kim, 2013). It was also suggested that this pathway
characterises individuals with aggressive and antisocial
personality traits. Notably, this impulsive pathway might
lead to various forms of PSU, including addictive use,
antisocial use (e.g. using the mobile phone in banned places
or in an aggressive way) or dangerous use (e.g. using the
mobile phone while driving and in other hazardous situa-
tions) (Billieux, 2012; Billieux et al., 2008). Finally, the ex-
traversion pathway is dened as the consequence of reward
and excitement-driven smartphone usage patterns poten-
tially linked to a wide range of risky behaviours (e.g.
phoning while driving, sexting). The existence of this pattern
is supported by studies that showed that PSU is linked with
high extraversion and increased levels of sensation seeking
(Augner & Hacker, 2012; Ehrenberg, Juckes, White, &
Walsh, 2008), need for stimulation and excitement, and a
high sensitivity to rewards (Igarashi et al., 2008). A funda-
mental aspect of this model is that it assumes not only that
various pathways can lead to different manifestations of PSU
(addictive, dangerous and antisocial use), but also that
various pathways can lead to similar manifestations or
symptomsthrough the interplay of different etiological
mechanisms. In particular, within this model, addictive use
of the mobile phone can be viewed as the result of either
emotional and attachment-related variables (excessive reas-
surance pathway) or impulsivity and self-control-related
variables (impulsive pathway), which might resolve the
recurrent debate that opposes the addictive behaviour
(Choliz, 2010; Montag et al., 2020) versus maladaptive
coping hypotheses of PSU (Elhai et al., 2019a,b).
Although the pathway model developed by Billieux and
colleagues (2015) is frequently used to theoretically anchor
PSU research and/or interpret related research ndings, this
model has received little direct empirical support to date. In
a recent study conducted in a representative sample of Swiss
men, Dey et al. (2019) tested how psychological factors
underlying the three postulated pathways predicted addic-
tive use of smartphones. These authors found that psycho-
logical factors underlying the excessive reassurance pathway
(depression, social anxiety) and the impulsive pathway
(attention decit hyperactivity disorder, aggression-hostil-
ity), but not the extraversion pathway, predicted addictive
smartphone use. In another study, Pivetta et al. (2019)
provided partial support for the pathway model of PSU
through a path analysis in which psychological factors
included in the pathway model were used to predict different
types of PSU. The results showed that trait neuroticism
specically predicted addictive smartphone use, whereas
impulsivity traits (assessed by the Barratt Impulsivity Scale;
Patton, Stanford, & Barratt, 1995) predicted all types of PSU,
namely addictive, dangerous and antisocial use. Contrary to
what was formulated in the model, extraversion was not
found to predict PSU. The major limitation of these
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preliminary attempts to validate the pathway model of PSU
is the absence of some key model variables, including
emotion-related impulsivity traits (which have been sys-
tematically linked to PSU and which are not measured by
the Barratt Impulsivity Scale) or psychopathy-related per-
sonality traits.
To address this gap in the literature, we aimed in the
present study to provide the first comprehensive test of the
pathway model of PSU in young adults (e.g. undergraduate
students) by considering the specific personality traits that
were not included in the preliminary validation attempts of
the model (Dey et al., 2019; Pivetta, Harkin, Billieux, Kanjo,
& Kuss, 2019). Notably, Billieux et al. (2015) stated that the
three PSU pathways should not be considered as mutually
exclusive, and it is possible that interactions among different
factors may occur in some cases of PSU (e.g. a smartphone
user characterised simultaneously by poor self-esteem and
high impulsivity). In order to directly take into account this
model premise, in the present study we used a Bayesian
approach that allowed us to (statistically) consider the three
pathways as being not mutually exclusive.
Capitalising on a Bayesian approach, we were able to test
all possible combinations among personality traits and
psychopathological features characterising the assessed
developmental pathways leading to PSU (i.e. addictive,
dangerous and antisocial use). In other words, starting from
the prior assumptionthat all the personality traits and
psychopathological features have the same probability of
being included in the three pathways that are postulated in
the model, we tested whether their posterior probabilities in
describing addictive, antisocial and dangerous patterns of
smartphone use matched the relationships hypothesised by
the pathway model of PSU. In line with the initial formu-
lations of the pathway model of PSU (Billieux et al., 2015),
we predicted that (1) psychological factors associated with
the three pathways (e.g. impulsivity traits, emotional
symptoms) would be associated with addictive use of the
smartphone and (2) psychological factors underlying the
impulsive pathway (e.g. impulsivity trait, psychopathic trait,
aggressiveness) and the extraversion pathway (e.g. sensation
seeking, extraversion, sensitivity to reward) would be asso-
ciated with dangerous and antisocial smartphone use.
In this study, we used data from a cross-sectional online
survey available from June 13 to October 15, 2019. Partici-
pants were recruited through online advertisements shared
in local online messaging boards and social network groups.
Interested participants were directed to an online informed
consent statement. Inclusion criteria included (1) being a
college student (2) being young adults aged 1835 years and
(3) being fluent in Italian. Participants had the opportunity
to receive compensation (25 Euros) by taking part in a lot-
tery of 10 prizes. Anonymity of the participants was guar-
anteed (no personal data or Internet Protocol addresses were
collected) unless participants wanted to take part in the
lottery by providing their e-mail address and personal details
(name and surname) at the end of the survey (these data
were dissociated from the data set before the analyses to
ensure anonymity). The entire online survey (173 items)
took approximately 30 min to complete. In total, 1,397
participants started the survey, of whom 187 were excluded
because they did not meet the inclusion criteria. Another
413 potential participants were removed because either (1)
they did not respond correctly on a set of four items that
aimed to identify careless answering (e.g. click now on
number 3) or (2) they did not complete all of the items in
the survey. Participants who reported non-binary gender
self-identication were also excluded because of the small
number in our sample (n52). The nal sample comprised
795 participants whose demographic characteristics are
presented in Table 2. This study was part of a larger research
project on smartphone use in Italian college students; other
data not related to this study will be presented elsewhere.
After completing a series of questions on demographics (i.e.
gender, age, employment, relationship status and place of
residence), participants were invited to fill in a series of
validated questionnaires.
Questionnaires included in the online survey were
selected to prioritise instruments that have been validated
and for which published versions exist in Italian. PSU was
assessed by using the short version of the Problematic Mo-
bile Phone Use Questionnaire (Lopez-Fernandez et al.,
2018), which measures different types of PSU, namely
addictive use (e.g. difculty controlling smartphone use),
antisocial use (e.g. using the mobile phone in banned places)
and dangerous use (e.g. using the mobile phone while
Other questionnaires were selected to assess the psy-
chological factors (personality traits and psychopathological
symptoms) underlying the three pathways (i.e. reassurance
seeking, impulsive, extraversion) postulated by Billieux et al.
(2015). Impulsivity traits (i.e. lack of premeditation, lack of
perseverance, negative urgency, positive urgency and
sensation seeking) were assessed by using the Italian version
of the Short UPPS-P Impulsive Behaviour Scale (D'Orta
et al., 2015). Aggressive traits were measured with the Italian
validation of the Aggression Questionnaire (Fossati, Maffei,
Acquarini, & Di Ceglie, 2003). Extraversion and neuroticism
were measured by selecting specic subscales of the Big Five
Questionnaire Short Version (Italian version: Caprara,
Barbaranelli, Borgogna, & Perugini, 1993). Psychopathic
traits were measured with the Italian version of the Levenson
Self-Report Psychopathy Scale (Somma, Fossati, Patrick,
Maffei, & Borroni, 2014). The Behavioural Activation Sys-
tem (i.e., reward responsiveness, reward drive and fun
seeking) and the Behavioural Inhibition System were
assessed by using the Italian version of the BIS/BAS scales
(Leone, Pierro, & Mannetti, 2002). A series of additional
questionnaires were used to measure psychopathological
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symptoms. Social anxiety was measured with the Italian
version of the Social Interaction Anxiety Scale (Sica et al.,
2007). Emotional symptoms (stress, anxiety and depression)
were measured with the short-form version of the Depres-
sion Anxiety Stress Scales (Italian version: Bottesi et al.,
2015). Finally, self-esteem was measured by using the Italian
version of the Rosenberg Self-Esteem Scale (Prezza, Trom-
baccia, & Armento, 1997). Table 1 comprehensively de-
scribes all the questionnaires and variables included in the
present study.
Analyses were performed with R software (R Core Team,
2018). The existence of three developmental pathways
leading to PSU was tested by assessing the possible contri-
butions of personality measures and psychopathological
symptoms (i.e. statistical predictors) to addictive, antisocial
and dangerous mobile phone use (i.e. dependent variables)
through a Bayesian approach, which is considered a
powerful procedure for testing hypotheses in psychological
research (Wagenmakers et al., 2018; West, 2016). More
specically, Bayesian inference permits quantication and
assessment of evidence in favour of both the null hypothesis
and of alternative hypotheses (Wagenmakers et al., 2018),
and the Bayesian parameter estimation is not affected by the
sampling plan (Sch
onbrodt, Wagenmakers, Zehetleitner, &
Perugini, 2017). Here, Bayesian adaptive sampling for vari-
able selection and model averaging (R package: BAS; the R
script is available at
Bayesian-model-averaging;Clyde, Ghosh, & Littman, 2011)
was used to assess what combination of statistical predictors
provided an adequate description of the distributions that
generated the observed addictive, antisocial and dangerous
patterns of mobile phone use. Specically, 19,792 models (all
possible combinations between predictors when taking
gender into account) were estimated by a Markov Chain
Monte Carlo sampling method by using the ZellnerSiow
Cauchy prior on the coefcients (i.e. all Bayes factors are
compared with the null model) and a uniform prior distri-
bution over the models (i.e. by assigning equal probabilities
to all models). The null hypothesis was rejected when the
95% Bayesian credibility intervals (BCIs) did not include the
null value (Kruschke, 2014). As an extension of Bayesian
inference, this approach considers parameter uncertainty
through prior distribution and model uncertainty and ob-
tains posterior distributions for the model parameters and
the model by using Bayestheorem, allowing for model se-
lection and combined estimation (Clyde, 2011; Fragoso,
Bertoli, & Louzada, 2018).
Multicollinearity was monitored by examining the vari-
ance inflation factor (VIF). In this study, the VIF indicated
that multicollinearity was not a concern (see Supplement,
section 1).
Residual plots were used to evaluate the normality and
homogeneity of variance. The scatterplot of the standardised
residuals showed that the data met the assumptions of
homogeneity of variance and linearity (see Supplement,
section 2).
Ethical clearance was obtained from the ethical committee of
the University of Padova (protocol number: 3,104). This
study did not involve human and/or animal experimenta-
Descriptive statistics and correlations between variables are
reported in Table 2. In the present study, skewness and
kurtosis values were all less than 0.80 and 0.56, respectively,
indicating that the distributions of variables closely mirrored
a normal distribution.
The associations between potential predictors and
addictive, antisocial and dangerous patterns of smartphone
use were modelled through a Bayesian approach. Consid-
ering the addictive pattern of smartphone use as the
dependent variable, social anxiety, behavioural inhibition,
negative urgency, primary psychopathy and BAS-RR
showed marginal posterior inclusion probabilities (PIPs) of
>0.5. However, across the 19,792 models, the combination of
social anxiety, behavioural inhibition, negative urgency and
primary psychopathy was the more plausible in describing
the addictive pattern of use; i.e. it received the highest pos-
terior probability (Bayes factor (BF) 51; R
Therefore, we focused on these predictors in subsequent
analyses. The 95% BCIs showed that higher levels of an
addictive pattern of mobile use were associated with higher
negative urgency (b50.22, 95% BCI 5[0.14; 0.30]), higher
behavioural inhibition (b50.11, 95% BCI 5[0.06; 0.16]),
higher primary psychopathy (b50.06, 95% BCI 5[0.03;
0.10]) and higher social anxiety (b50.02, 95% BCI 5[0.01;
Considering the antisocial pattern of smartphone use as
the dependent variable, lack of premeditation, sensation
seeking, aggressive traits and primary psychopathy showed
marginal PIPs of >0.5, and their combination was the more
plausible in describing the antisocial pattern of use (BF 51;
50.14). The 95% BCIs showed that higher levels of an
antisocial pattern of smartphone use were associated with
higher lack of premeditation (b50.17, 95% BCI 5[0.09;
0.24]), higher sensation seeking (b50.08, 95% BCI 5[0.02;
0.13]), higher levels of aggressive traits (b50.02, 95% BCI
5[0.01; 0.03]) and higher primary psychopathy (b50.07,
95% BCI 5[0.04; 0.10]).
Finally, considering the dangerous pattern of smart-
phone use as the dependent variable, lack of premeditation,
sensation seeking and primary psychopathy showed mar-
ginal PIPs of >0.5, and their combination was the more
plausible in describing the antisocial pattern of use (BF 51;
50.16). The 95% BCIs showed that higher levels of an
antisocial pattern of mobile use were associated with higher
lack of premeditation (b50.30, 95% BCI 5[0.22; 0.39]),
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Table 1. Characteristics and reliability for the scales
Authors (Italian
version) Definition Items Likert response format
Reliability values
(Cronbachs alpha)
Henry & Crawford,
2005 (Bottesi et al.,
Emotional distress in
three subcategories of
depression (e.g. loss of
and depressed mood),
anxiety (e.g. fear and
anticipation of negative
events) and stress (e.g.
persistent state of over
arousal and low
frustration tolerance)
21 4-point ranging from
0 (never or almost
never) to 3 (almost
always or always)
Total score: 0.93 Reassurance
SIAS Mattick & Clarke, 1998
(Sica et al., 2007)
Anxiety over social
interactions (e.g. I have
difculty talking with
other people)
19 5-point ranging from
0 (Not at all) to 4
Total score: 0.92 Reassurance
Carver & White 1994
(Leone, Pierro &
Mannetti, 2002)
Behavioural inhibition
system (BIS): sensitivity
to aversive stimuli (e.g.
worrying about making
7 5-point ranging from 1
(not describe me at all)
to 5 (describes me
completely) for both
the BIS and BAS scales
BIS: 0.83 Reassurance
Behavioural activation
system scales (BASs)
Reward Responsiveness
(RRs): sensitivity to
13 BAS-RR: 0.78 Extraversion
BAS Drive: motivation
to achieve desired goals;
BAS-Drive: 0.70
BAS Fun Seeking:
willingness to approach
new appetitive stimuli
BAS-Fun: 0.78
Caprara, Barbaranelli,
Borgogna & Perugini,
Emotional Stability/
Neuroticism: Capability
of controlling ones affect
and emotional reactions
8 5-point ranging from 1
(completely true) to 5
(completely false)
Emotional Stability:
Introversion: activity,
enthusiasm, and self-
condence (e.g. I like to
Extraversion: 0.75 Extraversion
RSES Rosenberg, 1965
(Prezza, Trombaccia &
Armento, 1997)
Self-Esteem: a persons
overall evaluation of his
or her worthiness as a
human being
10 4-point ranging from 1
(strongly disagree) to 4
(strongly agree)
0.90 Reassurance
Billieux et al., 2012
(D'Orta et al., 2015)
Negative urgency:
tendency to experience
strong reactions under
conditions of intense
negative affect
20 4-point ranging from 1
(strongly agree) to 4
(strongly disagree)
Negative urgency:
Positive urgency:
tendency to experience
strong reactions under
conditions of intense
positive affect
Positive urgency: 0.79
Lack of premeditation:
tendency to fail to think
and reect on the
consequences of an act
Lack of
Premeditation: 0.82
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Table 1. Continued
Authors (Italian
version) Definition Items Likert response format
Reliability values
(Cronbachs alpha)
before engaging in that
Lack of perseverance:
difculties remaining
focused on a task that
may be long, boring, or
Lack of Perseverance:
Sensation-seeking: the
tendency to enjoy and
pursue exciting activities
and an openness to
trying new experiences
that may or may not be
Sensation Seeking:
AQ Buss & Perry, 1992
(Fossati, Maffei,
Acquarini & Di Ceglie,
Trait aggressiveness
which measures four
components of
aggression: physical
aggression; verbal
aggression; anger; and
29 5-point ranging from 1
(uncharacteristic of
me) to 5 (very
characteristic of me)
0.89 Impulsive
LSRP Levenson, Kiehl &
Fitzpatrick, 1995
(Somma, Fossati,
Patrick, Maffei &
Borroni, 2014)
Primary psychopathy:
inclination to lie, lack of
remorse, callousness, and
manipulativeness (e.g. I
enjoy manipulating other
peoples feelings)
26 4-point ranging from 1
(strongly disagree) to 4
(strongly agree)
Primary psychopathy:
Secondary psychopathy:
frustration tolerance,
quick-temperedness, and
lack of long-term goals
(e.g. Ind myself in the
same kinds of trouble,
time after time)
psychopathy: 0.70
Lopez-Fernandez et al.,
Addictive use: perceived
dependence on the
smartphone (e.g. It is
hard for me to turn my
mobile phone off);
15 4-point ranging from 1
(strongly agree) to 4
(strongly disagree)
Addictive use: 0.81
Antisocial use: the
tendency to use mobile
phones in contexts where
they are banned (e.g. I
don't use my mobile
phone in a library,
cinema or hospital)
Antisocial use: 0.60
Dangerous use:
unequivocally risky
behaviours (e.g. I use my
mobile phone while
Dangerous use:0.84
List of tools: Depression, Anxiety and Stress Scales 21 (DASS 21); Social Interaction Anxiety Scale (SIAS); Carver and White
Questionnaire (Behavioural Inhibition System: BIS; Behavioural Activation System: BAS); Italian Version of the Big Five Questionnaire
Short Version (BFQ SV); Rosenberg Self-Esteem Scale (RSES); Short UPPS-P Impulsive Behaviour Scale (S-UPPS-P); Aggression
Questionnaire (AQ); Levenson Self-Report Psychopathy Scale (LSRP); Problematic Mobile Phone Use Questionnaire Short Version
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Table 2. (a) Descriptive statistics
N of cases/Mean (SD) Range
Sex Male: 240; Female: 555
Age 23.8 (3.02) 1835
Employment a:484; b:132; c:43; d:66; e:70
Relationship a:264; b:39; c:468; d:6; e:2; f:16
Living place a:26; b:9; c:411; d:223; e:61; f:65
Addictive mobile phone use 12.88 (3.03) 520
Antisocial mobile phone use 9.78 (2.35) 516
Dangerous mobile phone use 7.94 (2.95) 519
Negative urgency 9.16 (2.60) 416
Positive urgency 9.24 (2.41) 416
Lack of premeditation 7.56 (2.21) 416
Sensation seeking 9.39 (2.83) 416
Self-esteem 28.50 (5.65) 1140
Aggression 70.92 (15.95) 36128
Neuroticism 11.94 (3.26) 420
Extraversion/introversion 8.88 (2.73) 418
Social anxiety 28.48 (13.10) 171
Stress, depression and anxiety 20.32 (11.06) 062
Primary psychopathy 30.13 (6.15) 1952
Secondary psychopathy 20.97 (4.57) 1038
BAS-reward responsiveness 19.55 (3.09) 1125
BAS-drive 12.33 (2.74) 520
BAS-fun seeking 11.63 (3.07) 420
Behavioural inhibition 25.17 (4.69) 1335
a5Nothing; b 5xed-term, part-time; c 5permanent, part-time; d 5xed-term, full-time; e 5permanent, full-time.
a5Single; b 5casually date; c 5in a committed relationship; d 5married; e 5divorced; f 5widow/widower; g 5I prefer not to answer.
a5student residence; b 5college; c 5at your parentshouse; d 5house for rent with other students; e 5alone in a house for rent; f 5other. ppp P< .001; pp P< .01; pP< .05.
Journal of Behavioral Addictions 7
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higher sensation seeking (b50.11, 95% BCI 5[0.04; 0.18])
and higher primary psychopathy (b50.11, 95% BCI 5
[0.07; 0.14]). See Table 3 for a summary of these results.
The aim of the current study was to provide a first
comprehensive test of the pathway model of PSU (Billieux
et al., 2015) by capitalising on a Bayesian analytical
approach. This analysis revealed robust and original results
regarding the psychological dimensions associated with each
of the postulated pathways of PSU. Consistent with the
hypotheses formulated in the pathway model of PSU (Bil-
lieux et al., 2015), it appeared that some psychological di-
mensions played a unique role in explaining PSU (e.g. social
anxiety is only associated with addictive smartphone use),
whereas other psychological dimensions contribute to more
than one type of PSU (e.g. sensation seeking predicts both
antisocial and dangerous patterns of PSU). The results for
each type of PSU are elaborated below in relation to each of
the distinct pathways theorised in the model.
First, our results suggest that addictive smartphone use is
mainly driven by the excessive reassurance pathway and the
impulsive pathway, for which candidate etiopathological
factors include heightened negative urgency (i.e. emotion-
related impulsive behaviours), the hyperactive behavioural
inhibition system (i.e. a greater sensitivity to punishment)
and symptoms of social anxiety. This pattern of results is
consistent with the view that addictive smartphone use is
mostly fuelled by negative reinforcement and reflects a
compensatory mechanism to regulate aversive emotions
(Chen et al., 2017; Elhai et al., 2017) or to alleviate social
anxiety symptoms by preferring online social interactions
over face-to-face interactions (Enez Darcin et al., 2016).
Furthermore, our results are consistent with previous
research that linked addictive mobile phone use with nega-
tive urgency traits (see Billieux, 2012 for a review) and the
hyperactive behavioural inhibition system (Kim et al., 2016).
In line with previous research (Elhai et al., 2019a,b), the
present study revealed that an addictive pattern of smart-
phone use is more likely to occur in the context of intense
distressing emotions. This view is consistent with previous
evidence suggesting that intense negative emotional states
impair decision making (Bechara, 2004; Dreisbach, 2006)by
causing less discretionary use of information (Forgas &
Bower, 1987) or greater distractibility (Dreisbach &
Goschke, 2004) or, alternatively, by interfering in ones
ability to control the urge to use a smartphone (Contractor,
Weiss, Tull, & Elhai, 2017). Thus, individuals with higher
negative urgency may be motivated by immediately rein-
forcing experiences that help to reduce or distract from
negative affect in the short term (Inzlicht & Schmeichel,
2012). The current research also revealed a positive associ-
ation between social anxiety and addictive smartphone use,
conrming previous evidence (Darcin, Noyan, Nurmedov,
Yilmaz, & Dilbaz, 2015; Elhai, Tiamiyu, & Weeks, 2018).
Table 2. (b) Pearsons correlations
(b) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19.
1. Addictive mobile
phone use
2. Antisocial mobile
phone use
0.30ppp 1
3. Dangerous mobile
phone use
0.18ppp 0.56ppp 1
4. Negative urgency 0.26ppp 0.15ppp 0.17ppp 1
5. Positive urgency 0.18ppp 0.20ppp 0.21ppp 0.54ppp 1
6. Lack of premeditation 0.08p0.25ppp 0.31ppp 0.41ppp 0.43ppp 1
7. Sensation seeking 0.03 0.19ppp 0.21ppp 0.11pp 0.28ppp 0.22ppp 1
8. Self-esteem 0.16ppp
0.07 0.03 0.23ppp
0.07 0.03 1
9. Aggression 0.19ppp 0.23ppp 0.22ppp 0.49ppp 0.34ppp 0.27ppp 0.17ppp
0.34ppp 1
10. Neuroticism 0.24ppp 0.04 0.01 0.32ppp 0.20ppp 0.05 0.12ppp
0.51ppp 0.35ppp 1
11. Extraversion/
0.15ppp 0.11pp 0.10pp 0.13ppp 0.04 0.17ppp
0.43ppp 0.08p0.21ppp 1
12. Social anxiety 0.22ppp 0.04 0.01 0.18ppp 0.08p
0.04 0.10pp
0.55ppp 0.23ppp 0.37ppp 0.52ppp 1
13. Stress, depression and
0.19ppp 0.07p0.11pp 0.27ppp 0.16ppp 0.08p0.10pp
0.57ppp 0.46ppp 0.54ppp 0.24ppp 0.39ppp 1
14. Primary psychopathy 0.13ppp 0.29ppp 0.31ppp 0.22ppp 0.29ppp 0.25ppp 0.28ppp
0.01 0.16ppp 0.07 0.17ppp 1
15. Secondary
0.23ppp 0.25ppp 0.24ppp 0.47ppp 0.37ppp 0.40ppp 0.20ppp
0.47ppp 0.58ppp 0.38ppp 0.38ppp 0.36ppp 0.49ppp 0.39ppp 1
16. BAS-reward
0.11pp 0.01 0.10pp 0.10pp 0.18ppp
0.15ppp 0.09p0.12ppp 0.09p0.05 0.30ppp
0.06 0.05 0.02 0.12ppp 1
17. BAS-drive 0.04 0.08p0.12ppp 0.14ppp 0.21ppp 0.06 0.26ppp 0.12pp 0.21ppp
0.03 0.30ppp
0.15ppp 0.07p0.30ppp 0.00 0.38ppp 1
18. BAS-fun seeking 0.05 0.21ppp 0.18ppp 0.21ppp 0.43ppp 0.31ppp 0.60ppp
0.01 0.26ppp
0.03 0.10pp
0.06 0.11pp 0.33ppp 0.30ppp 0.24ppp 0.35ppp 1
19. Behavioural inhibition 0.23ppp
0.06 0.14ppp 0.19ppp 0.09p
0.45ppp 0.15ppp 0.57ppp 0.13ppp 0.45ppp 0.32ppp
0.22ppp 0.13ppp 0.25ppp
0.16ppp 1
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According to previous studies (Darcin et al., 2015; Enez
Darcin et al., 2016), individuals with social anxiety symp-
toms can be more at risk of displaying addictive smartphone
use because social anxiety causes avoidance of face-to-face
relationships. For these individuals, virtual socialisation (and
communication) via smartphone use can alleviate their fear
or concern of showing signs of physiological arousal, and
may be guided either by the need to feel free or by the need
to behave without the perception of social pressure.
Our results are important in relation to the current de-
bates about the conceptualisation of addictive smartphone
use and suggest that this problematic behaviour should
preferentially be understood as a maladaptive form of coping
rather than a genuine addictive disorder (Harris, Regan,
Schueler, & Fields, 2020; Panova & Carbonell, 2018). It is
worth noting that we cannot exclude the possibility that
specic activities performed via the smartphone (such as
video gaming or online gambling) constitute discrete
addictive behaviours, yet the present study focused on
general smartphone use and cannot answer this question.
We also found that primary psychopathy characterised by
callousness, shallow affect, manipulation and supercial
charm (Levenson, Kiehl, & Fitzpatrick, 1995; Somma et al.,
2014)is an important predictor of addictive smartphone
use. A tenable explanation for this nding is that individuals
who are prone to primary psychopathy might perceive
themselves as being dependent on the smartphone, as this
tool has much functionality that allow one to efciently
manipulate or deceive others.
The present study also revealed that dangerous and
antisocial smartphone use are mainly driven by the impul-
sive pathway and the extraversion pathway, for which
candidate etiopathological factors include specific impul-
sivity components (lack of premeditation and sensation
seeking) and primary psychopathy. More specifically, lack of
premeditation was reported to have the strongest association
with both antisocial and dangerous smartphone use. This
specific impulsivity component has been linked to short-
term-based choices and risky decision-making (Zermatten,
Van der Linden, d'Acremont, Jermann, & Bechara, 2005),
and it is thus likely that short-term-based choices and a
myopia towards long-term consequences favour both anti-
social smartphone use (e.g. nes or public disapproval
resulting from using the smartphone in a prohibited area)
and dangerous use (e.g. lethal risks associated with using the
smartphone while driving). In line with the results reported
by Billieux et al. (2008), our ndings conrm that lack of
premeditation is related to prohibited use of the mobile
phone and demonstrate (for the rst time) its additional
association with the dangerous pattern of use. Primary
psychopathy also showed positive associations with both
dangerous and antisocial smartphone use, while aggressive
traits were uniquely linked to an antisocial pattern of
smartphone use. These results suggest that individuals with
primary psychopathy traits who are aggressive prone
possibly use their smartphone as a medium to behave in an
antisocial manner (e.g. cyberbullying via social networking
sites, Orue & Calvete, 2019). Lastly, sensation seeking was
identied as explaining both the antisocial and the
dangerous use of a smartphone. This nding is consistent
with previous research showing that heightened sensation
seeking is linked to mobile phone-based aggressive behav-
iours (Kokkinos, Antoniadou, & Markos, 2014), phoning
while driving (Billieux et al., 2008) and sexting (e.g. ex-
change of sexual pictures via the smartphone) (Dir & Cy-
ders, 2015), implying that various risky and/or antisocial
smartphone-mediated behaviours might be displayed in
order to fuel a need for pleasure, excitement or stimulation.
We found dangerous and antisocial smartphone use to be
specically related to sensation seeking, as previously found
Table 3. Hypotheses based on the pathway model of Billieux et al. (2015) and results
Reassurance Impulsive Extraversion
Psychological factors
Social anxiety
Psychological distress#
Behavioural inhibition
Negative urgency ●○ ○
Positive urgency ○○ ○
Lack of premeditation ○● ●
Aggression ○● ○
Primary psychopathy ●● ●
Secondary psychopathy ○○ ○
Sensation seeking ○● ●
Extraversion ○○ ○
Reward responsiveness ○○ ○
Drive ○○ ○
Fun seeking ○○ ○
Type of problematic use Addictive Addictive Antisocial Dangerous Addictive Antisocial Dangerous
5hypothesised association; 5hypothesised and credible association; #Stress, Depression, and Anxiety.
Journal of Behavioral Addictions 9
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by Billieux et al. (2008) and Dey et al. (2019), but not
necessarily to the more overarching construct of extraver-
sion (in accordance with Pivetta et al., 2019; Dey et al.,
2019). In this regard, Zuckerman and Glicksohn (2016)
suggested that the link between sensation seeking and psy-
choticism is probably more pronounced than that between
sensation seeking and extraversion, mainly because both
sensation seeking and psychoticism (but not extraversion)
are characterised by recklessness and proneness to taking
risks (e.g. Jonason, Lyons, Bethell, & Ross, 2013; Zeigler-Hill
& Vonk, 2015). Thus, we believe that the extraversion
pathway should be further investigated and potentially
renamed the sensation seeking pathway. Although the
antisocial and the dangerous patterns of smartphone use
largely overlapped in terms of psychological antecedents, our
results showed a key difference between them, namely trait
aggression. Aggression is dened as any behaviour directed
towards another person aimed at causing harm (Anderson &
Bushman, 2002; Bushman & Huesmann, 2010), and trait
aggression is the individual disposition to behave aggres-
sively. Trait aggression was specically and solely associated
with the antisocial pattern (and not with the dangerous
pattern). This difference substantiates the typical behaviours
theorised by Billieux et al. (2015) for the two types of PSU.
Indeed, our results suggest that the antisocial pattern con-
sists in behaviours not intentionally (and solely) aimed at
causing harm to others (e.g. cyberbullying), which can be
traced back to trait aggression. Conversely, the dangerous
pattern consists of behaviours that, though potentially
dangerous for both the smartphone user and other in-
dividuals (e.g. phoning while driving might cause a car ac-
cident involving the user and other drivers), are not
primarily driven by the intention to harm.
Our study has several limitations. Data were collected via
self-report measures and were cross-sectional, which pre-
cludes any causality statement. Recent studies have used a
longitudinal design (Herrero, Torres, Vivas, & Urue~
na, 2019;
Lapierre, Zhao, & Custer, 2019; Lee et al., 2020) or a qual-
itative approach (Yang, Asbury, & Grifths, 2019) for the
study of smartphone use/dependency or PSU, and further
studies that use these alternative methodologies should be
conducted to complement our approach. The present study
focused on a specic demographic group (i.e., college stu-
dents with regular access to the Internet, predominantly
females), thus limiting the generalisability of our ndings.
Our results revealed, among the established risk factors for
each developmental pathway of PSU, the robust presence of
personality traits instead of psychopathological symptoms.
Although this result might suggest that stable personality
dispositions are better predictors of PSU than potentially
transient psychopathological symptoms, we cannot exclude
the possibility that it can be explained by the nature of our
sample (community sample instead of clinical sample).
Future research should focus on more representative,
gender-balanced samples of adults and also on clinical (or
subclinical) samples. Furthermore, the present study did not
consider some psychological factors (e.g. attentional bias and
cue reactivity) included in other theoretical frameworks such
as the I-PACE model of technology-mediated addictive
disorders (Brand et al., 2016, 2019). Finally, the present study
did not assess the objective use of smartphones (e.g. daily time
spent on social media, tracking data on the number of mi-
nutes of screen time and the number of phone screen unlocks;
Ellis et al., 2019)orspecic smartphone functionalities (e.g.
Lowe-Calverley & Pontes, 2020), and these aspects should be
considered and measured in future studies.
Overall, and despite these limitations, our study is the
first comprehensive test of the frequently cited pathway
model of PSU (Billieux et al., 2015), which was initially
formulated to guide research efforts in a research eld in
which most studies are conducted with an atheoretical and/
or symptom-focused approach. The present study, in addi-
tion to providing empirical support to the model, further
showed that PSU is a multi-determined and multi-faceted
construct, which has to be taken into account when regu-
lating smartphone use or tailoring preventive actions to
reduce problematic usage patterns.
Funding sources: This work was partially supported by an
intramural grant of the University of Padova (Year: 2018
prot. BIRD183124).
Authorscontribution: NC, TM, GB, AV, MD and JB are
responsible for the study concept and design. TM performed
analysis. NC and LP supervised the statistical analysis and
contribute to the interpretation of data. NC wrote the first
draft of the manuscript and all authors critically reviewed
and approved the final version of the manuscript.
Conflict of interest: The authors declare no conflict of in-
The online version of this article offers supplementary ma-
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... (depression, anxiety, stress, boredom proneness, rumination, suicidal ideation). Out of 25 studies, 24 reported that negative affectivity was a potential risk factor for PSU (Alavi et al., 2020;Canale et al., 2021;Choi et al., 2015;Cui et al., 2021;Elhai et al., 2018a;Elhai et al., 2020c;Enez Darcin et al., 2016;Forster et al., 2021;He et al., 2020;Hou et al., 2021;Khoury et al., 2019;Kim & Koh, 2018;Kim et al., 2019;Kuang-Tsan & Fu-Yuan, 2017;Kuru & Celenk, 2021;Liu et al., 2020;Liu et al., 2021;Long et al., 2016;Matar Boumosleh & Jaalouk, 2017;Xiao et al., 2021;Yang et al., 2020aYang et al., , 2020bYou et al., 2019;Yuan et al., 2021;Zhang et al., 2020a). Two showed a very small effect size (8.3%), ...
... Specifically, the studies found that depression (Alavi et al., 2020;Choi et al., 2015;Cui et al., 2021;Forster et al., 2021;Matar Boumosleh & Jaalouk, 2017;Yang et al., 2020aYang et al., , 2020bYuan et al., 2021;Zhang et al., 2020a), anxiety (Alavi et al., 2020;Choi et al., 2015;Hou et al., 2021;Khoury et al., 2019;Kim & Koh, 2018;Kuru & Celenk, 2021;Matar Boumosleh & Jaalouk, 2017), social anxiety (Canale et al., 2021;Enez Darcin et al., 2016;Xiao et al., 2021;You et al., 2019), depression/anxiety and suicidal ideation , stress (Forster et al., 2021;He et al., 2020;Kim et al., 2019;Kuang-Tsan & Fu-Yuan, 2017;Liu et al., 2020;Long et al., 2016), rumination (Elhai et al., 2020c;Liu et al., 2021) and boredom proneness (Elhai et al., 2018a;Yang et al., 2020a, b;Zhang et al., 2021) were risk factors. ...
... Impulsivity. Out of 4 studies, all reported impulsivity as a potential risk factor for PSU (Canale et al., 2021;Khoury et al., 2019;Roberts & Pirog III, 2013;Roberts et al., 2015). One showed a small effect size (25%), two a small one (50%) and one a mixed effect size (25%). ...
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University students are a high-risk population with problematic online behaviours that include generalized problematic Internet/smartphone use and specific problematic Internet uses (for example, social media or gaming). The study of their predictive factors is needed in order to develop preventative strategies. This systematic review aims to understand the current state of play by examining the terminology, assessment instruments, prevalence, and predictive factors associated with problematic smartphone use and specific problematic Internet uses in university students. A literature review was conducted according to the PRISMA guidelines using four major databases. A total of 117 studies were included, divided into four groups according to the domain of problem behaviour: problematic smartphone use ( n = 67), problematic social media use ( n = 39), Internet gaming disorder ( n = 9), and problematic online pornography use ( n = 2). Variability was found in terminology, assessment tools, and prevalence rates in the four groups. Ten predictors of problematic smartphone use, five predictors of problematic social media use, and one predictor of problematic online gaming were identified. Negative affectivity is found to be a common predictor for all three groups, while social media use, psychological well-being, and Fear of Missing Out are common to problematic smartphone and social media use. Our findings reaffirm the need to reach consistent diagnostic criteria in cyber addictions and allow us to make progress in the investigation of their predictive factors, thus allowing formulation of preventive strategies.
... Furthermore, individuals with low self-esteem may develop a preference for smartphone-mediated communication, ultimately leading to PSU, as this may constitute a useful alternative to maintaining interpersonal relationships while minimizing the discomfort that they typically experience in face-to-face interactions [23]. The personality trait of impulsivity, which entails the tendency to act rashly or without adequate forethought, with difficulty in delaying reward, and reduced inhibition capacity [104], was consistently associated with PSU symptoms in previous studies [16,40,46]. As explained by Mitchell and Hussain [64], individuals with high impulsivity present with proneness to fail to control urges to use their smartphones, for instance, by checking their notifications, which can increase unregulated smartphone use and its associated negative consequences. ...
... This study was part of a larger project on PSU which addressed two different research questions and resulted in two different studies. A first study empirically tested the Pathways Model of problematic smartphone use and was published elsewhere [16]. The second study is the current one. ...
... Most participants were classified into two groups of users (representing 75% of the sample) having a low-average impact of smartphones on their daily lives, which further supports the need to avoid overpathologizing smartphone use [10,66]. At the theoretical level, the present study emphasizes that the impact of smartphones on everyday life is highly heterogeneous and it depends on a wide range of psychological factors in accordance with the pathways model of problematic mobile phone use [9,16], which considers multiple forms and etiologies of PSU (e.g., excessive reassurance and impulsive pathways). At the clinical level, the heterogeneity found in the present study calls for the development of personalized (custom-made) interventions that target specific psychological mechanisms (e.g., [17,79]). ...
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Background The relationships between problematic smartphone use and psychological factors have been extensively investigated. However, previous studies generally used variable-centered approaches, which hinder an examination of the heterogeneity of smartphone impact on everyday lives. Objective In the present study, we capitalized on latent profile analysis to identify various classes of smartphone owners based on the impact associated with smartphone use in their daily lives (e.g., unregulated usage, preference for smartphone-mediated social relationships) and to compare these classes in terms of established psychological risk factors for problematic smartphone use. Method We surveyed 934 young adults with validated psychometric questionnaires to assess the impact of smartphones, psychopathological symptoms, self-esteem and impulsivity traits. Results Smartphone users fall into four latent profiles: users with low smartphone impact, users with average smartphone impact, problematic smartphone users, and users favoring online interactions. Individuals distributed in the problematic smartphone user profile were characterized by heightened psychopathological symptoms (stress, anxiety, depression, obsessive-compulsive tendencies) and impulsivity traits. Moreover, users who preferred online interactions exhibited the highest symptoms of social anxiety and the lowest levels of self-esteem. Conclusions These findings further demonstrate the multidimensionality and heterogeneity of the impact of smartphone use, calling for tailored prevention and intervention strategies.
... Las personas con alta urgencia positiva han demostrado ser más vulnerables a la interferencia cognitiva por la presencia de un dispositivo de teléfono inteligente (Canale et al., 2019). El tercer y último rasgo de impulsividad que caracteriza al grupo SA es la falta de premeditación, también ligada a trastornos adictivos y conductas de riesgo (López-Torres, León-Quismondo y Ibáñez, 2021; Minhas et al., 2021) y al PSU (Canale et al., 2021). Los perjuicios que el desarrollo de la SA provoca en los ámbitos social, laboral o académico pueden surgir por no valorar las consecuencias del uso excesivo. ...
... However, there would be a need to differentiate such a construct from other individual differences in psychology, particularly related to mental health and distress (Davidson et al., 2022). Further research could also model how specific pathways of problematic smartphone usage are related to objective behavior, as has been proposed in theoretical models such as the Pathways Model (Canale et al., 2021). These possibilities notwithstanding, this study highlights how there are severe limitations with the use of addiction models in understanding technological harm, and there is a need to encourage a greater diversity of psychological perspectives when it comes to understanding these phenomena. ...
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There has been a growing literature that has utilized logged behavior from smartphones to study the impacts of technology use on individuals. One of these proposed impacts has been that people become addicted to their smartphones. Measurements of smartphone addiction do not appear to strongly correlate with actual behavior logged from smartphones. Instead, smartphone addiction may be better explained by distress rather than disordered behavior, but this has not been adequately tested. This study examined the relative contributions of self-reported and actual smartphone behavior alongside key mental health and individual differences in a pre-registered, two-wave study with a two-week re-test. 511 smartphone users (391 at Time 2) completed measures of smartphone usage, attitudes towards smartphone usage, smartphone addiction, other behavioral addictions, mental health, and individual differences. The results suggest smartphone addiction is principally driven by perceived rather than actual usage, especially where these are discordant. Self-reported smartphone usage, other behavioral addictions, and the impulsivity facet of negative urgency are more predictive of smartphone addiction than logged behavior. These results suggest that volume of smartphone usage is insufficient in and of itself to explain problematic smartphone behavior and questions the criterion validity of smartphone addiction measurements.
... Excessive phone use at bedtime can significantly contribute to poor sleep quality [28,31,32] and insufficient sleep duration [33]. Based on the abovepresented evidence, MPA is a growing and serious problem, especially in emerging adults, that can negatively influence both the mental and physical health of an individual [34,35]. ...
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Emerging adulthood (EA) is a critical stage of life to develop and sustain a healthy lifestyle, but is also a time of vulnerability to poor physical and mental health outcomes. In this study, we conducted a path analysis (N = 1326) to examine associations among four dimensions of EA, levels of regular physical activity (PA), self-control, MPA tendency and irrational procrastination. We observed that: (1) higher levels of PA predicted both mobile phone addiction (MPA) tendency (β = −0.08, 95% CI: −0.11 to −0.06, p < 0.001) and irrational procrastination (β = −0.01, 95% CI: −0.17 to −0.008, p < 0.01) indirectly via self-control; (2) Instability (β = 0.13, 95% CI: 0.08 to 0.18, p < 0.01) and Responsibility (β = −0.06, 95% CI: −0.10 to −0.08, p = 0.03) exerted direct effects on irrational procrastination and Instability also indirectly predicted irrational procrastination via MPA tendency (β = 0.03, 95% CI: 0.02 to 0.05, p < 0.01). These findings suggested that perceived features of EA are linked to behavioral problems and supported the idea that regular PA plays a crucial role to protect mental health.
... As Billieux et al. (2015) proposed, research has found that psychological factors from the excessive reassurance (e.g., social anxiety), impulsive (e.g., urgency) and extraversion (e.g., sensation seeking) pathways differentially drive addictive, antisocial, and risky patterns of PSU (Canale et al., 2021;Pivetta et al., 2019). Therefore, it follows that certain motives may differentially influence these distinct patterns of PSU. ...
Full-text available
Motives for smartphone use may be key factors underlying problematic smartphone use (PSU). However, no study has reviewed the literature investigating the association of motives with PSU. As such, we conducted a systematic review to: (a) determine which smartphone use motives were associated with PSU; and (b) examine the potential indirect and moderating effects of motives in the relationship of psychosocial factors with PSU. We identified 44 studies suitable for inclusion in our systematic review. There was extensive heterogeneity in smartphone use motives measures across the studies, including 55 different labels applied to individual motives dimensions. Categorisation of these motives based on their definitions and item content identified seven motives that were broadly assessed across the included studies. Motives which reflected smartphone use for mood regulation, enhancement, self-identity/conformity, passing time, socialising, and safety were generally positively associated with PSU. There were indirect effects of depression, anxiety, and transdiagnostic factors linked to both psychopathologies on PSU via motives, particularly those reflecting mood regulation. Stress and anxiety variously interacted with pass-time, social, and a composite of enhancement and mood regulation motives to predict PSU. However, the heterogeneity in the measurement of smartphone use motives made it difficult to determine which motives were most robustly associated with PSU. This highlights the need for a valid and comprehensive smartphone use motives measure.
... There is a growing awareness that smartphone addiction is gradually becoming a significant public health issue (Busch & McCarthy, 2021;Canale et al., 2021;Zhou et al., 2022). However, the lack of consensus in the conceptualization of the phenomenon has led to controversy over using the term "smartphone addiction." ...
Problematic smartphone use (PSU) is a recent concern resulting from the dramatic increase in worldwide smartphone use. Although prior studies have indicated the between-person relationship between happiness motives and PSU, less is known about the within-person connection. This study investigated the week-to-week associations between PSU and two types of happiness motives (i.e., hedonic motives and eudaimonic motives) using a weekly diary design. A sample of 270 young adults (Mage = 19.06 years, SD = 0.88 years) completed the online questionnaires once a week for ten consecutive weeks. The results indicated that hedonic motives were positively linked with PSU while eudaimonic motives were negatively associated with it in the same week. More importantly, the multilevel cross-lagged path analysis showed that hedonic motives from the previous week positively predicted PSU in the following week. There was also a reverse relationship between them, whereas there was no predictive relationship between eudaimonic motives and PSU. These results provide convincing evidence that the two types of happiness motives play different roles in the development of PSU, and for the reciprocal relationships between hedonic motives and PSU.
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Zusammenfassung: Ziel: In der Fachwelt besteht noch große Uneinigkeit im Hinblick auf eine mögliche Klassifikation exzessiver Smartphone-Nutzung (ESN) als Störung aufgrund einer Verhaltenssucht. In diesem Positionspapier werden relevante inhaltliche und methodische Aspekte bisheriger Forschungsarbeiten zum Thema ESN dargestellt. Daraus werden Empfehlungen abgeleitet, welche Vorgehensweisen bei zukünftigen Forschungsarbeiten verstärkt Berücksichtigung finden sollten. Schlussfolgerungen: Unsere Empfehlungen umfassen folgende Punkte: 1. Entwicklung und konsistente Nutzung von Smartphone-basierten Messinstrumenten, die die Erhebung von Echtzeitnutzungsdaten und das Vorlegen von zeitgesteuerten Fragebögen erlauben, 2. Validierung dieser Instrumente an großen, repräsentativen Stichproben in Deutschland, 3. Untersuchungen mittels Echtzeit-Messinstrumenten zur Beantwortung der Frage, inwiefern ESN Suchtcharakter annehmen kann, sprich inwieweit zentrale Suchtkriterien erfüllt werden, 4. Klärung der zentralen Frage, inwiefern ESN spezifisch (d. h. die Nutzung von spezifischen Smartphone-Funktionen wie z. B. soziale Netzwerke) oder generalisiert (d. h. im Sinne eines Verhaltensmusters der allgemeinen Smartphone-Überbeanspruchung) erfolgt. Langfristig sollten weitere Studien zur Neurobiologie, sowie zur Langzeitstabilität von ESN durchgeführt werden, bevor die Klassifikation der ESN als Störung aufgrund einer Verhaltenssucht empfohlen werden kann.
Although many benefits emerge from the growing capabilities of smartphones, there are also concerns related to the long-term hyper-connected experience. Based on a systematic mapping method, this study investigates the primary factors of problematic smartphone use (PSU). Initially, this mapping considered ten academic databases, which allowed the analysis of 436 studies, and the creation of a taxonomy that categorises technology addiction topics such as the Internet, Smartphones, Video games, and Electronic devices. After the initial search and filtering, the study selected and deeply analysed 115 articles concerning the PSU influences on mental health, proposing a taxonomy to classify mental disorders and common symptoms related to PSU. The outcomes suggest that those who fear missing out on important events, females, depressed, anxious, and bored people are prone to PSU, reinforcing the importance of understanding the factors that lead a person to use smartphones in a problematic way and alternatives to help people cope with PSU. Scales such as the Smartphone Addiction Scale-Short Version (SAS-SV) and strategies such as cognitive-behavioural therapy (CBT) and limiting smartphone access are being used to handle PSU. Finally, this study presents implications and recommendations for future research in this area.
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ODREDNICE OVISNOSTI O INTERNETU ZAGREBAČKIH SREDNJOŠKOLACA [DETERMINANTS OF INTERNET ADDICTION AMONG HIGH SCHOOL STUDENTS IN THE CITY OF ZAGREB] Introduction In the last twenty years, technology has become an indispensable part of people’s lives around the world, and at the same time the availability and accessibility of the Internet has been increasing. Children are surrounded by modern technologies from birth, and almost all young people are highly dependent on the Internet during their education. As a result, it is not surprising that there are currently nearly five billion active Internet users in the world and a portion of the population has been developing problems related to excessive Internet use or Internet addiction. Young people, i.e., high school and college students, are continuously cited as a particularly vulnerable group for the development of Internet addiction because they belong to a generation that has grown up with an extraordinary accessibility to modern technologies and for whom the Internet is inseparable from almost all aspects of life. Although prevalence data vary and there is no consensus, findings suggest a slightly higher prevalence of Internet addiction among high school students in Asian countries, where it ranges from 14% to 20%, while in European countries it ranges from 1% to 5%. As for individual risk factors for developing Internet addiction, the most significant are younger age of first Internet use, shyness, loneliness, certain personality traits, low self-esteem and self-efficacy, and low self-control. Special emphasis is also placed on the presence of mental health problems such as depression, anxiety, and stress, as well as impulsivity, hyperactivity, and problems with maintaining attention. In addition to individual factors, environmental factors, especially family and peer factors, are also important. Family factors include frequent conflicts between parents and between parents and adolescents, inadequate parental control of Internet use, substance abuse and positive attitude of close family members towards it, parents’ mental health issues, and lower level of family functioning. The most important risk factors related to peer relationships are social loneliness, peer pressure, and peer rejection. The main goals of this study are to gain insight into the characteristics and habits of social networking and video game use among adolescents, to examine the prevalence of Internet addiction, and to identify possible differences in the characteristics of use and Internet addiction in relation to key personal and sociodemographic characteristics. In addition, the contribution of selected personal characteristics to Internet addiction among adolescents who prefer social networking sites and adolescents who prefer online video games will be examined. Methodology This research was conducted on a probabilistic sample of a total of 825 students from the first to the final grade of different high schools (three- and four-year vocational schools and grammar schools) from the City of Zagreb. This type of sampling allows us to generalize the results of the sample to the population of high school students in the City of Zagreb. The sample consists of a total of 49.0% boys, 50.7% girls and 0.3% students who did not provide information about their gender, and the participants’ ages ranged from 14 to 20 years (Mage=16.65 years; SDage=1.208). In order to provide a comprehensive response to the research goals and problems, an extensive battery of measurement instruments was used: (1) Questionnaire on participants' baseline characteristics; (2) Questionnaire on frequency of use of social networks and online video games; (3) Internet Addiction Test (Young, 1998); (4) International Personality Item Pool-20 (Donnellan et al., 2006); (5) Self-Description Questionnaire II (Marsh, 1992); (6) General Self-Efficacy Scale (Schwarzer and Jerusalem, 1995); (7) Hyperactivity - impulsivity - attention Scale (Vulić-Prtorić, 2006); (8) Questionnaire of emotional skills and competence (Takšić, 1998); (9) Depression, Anxiety and Stress Scale-21 (Lovibond and Lovibond, 1995); (10) Internet Motive Questionnaire for Adolescents (Bischof-Kastner, Kuntsche, and Wolstein, 2014); (11) Social Comparison Scale (Gibbons and Buunk, 1999); (12) Social and Emotional Loneliness Scale (diTommaso and Spinner, 1993). Prior to the start of this study, the approval of the Ethics Committee of the Faculty of Education and Rehabilitation Sciences of the University of Zagreb was obtained, as well as the approval of the Ministry of Science and Education of the Republic of Croatia with a positive opinion of the Education and Teacher Training Agency. Consent was obtained from the principals of the selected schools, and from the parents of the minors. After obtaining the above consents, the research in the schools began. It was conducted during the second semester of the 2020/2021 school year, using the "pencil and paper" method of self-reporting. The students gave their verbal consent to participate in the research after having been informed about the basic aim of the research, anonymity, and voluntary nature of participation, as well as the possibility to stop filling in the questionnaire at any time. Results The results show that the participants of this research use social networks the most, namely Instagram and YouTube. They are followed by Snapchat, which is used slightly less frequently than Instagram and YouTube, and in third place is TikTok. All four types of online video games, Facebook and Pinterest share the "fourth" place, meaning they are used less frequently compared to Instagram, YouTube, Snapchat and TikTok. Twitter is used the least. Almost all social networks (Instagram, Snapchat, TikTok and Pinterest), are used more often by girls, while only YouTube is used more by men, which points to significant gender differences in social media usage. Gender differences were not found in the use of Facebook and Twitter, which are those social networks that are used the least (almost not at all) by both girls and boys compared to other networks. As for the differences in the frequency of use of online video games, they are significant in favour of young men, i.e., they play them to a greater extent than girls. Most participants (79%) use the Internet in a way that helps them fulfil their obligations and occasionally provides entertainment without interfering with their daily routine. About 20.1% of adolescents have a moderate level of addiction, while 0.8% of them meet the criteria for a high level of addiction. In other words, one fifth of Zagreb high school students have certain problems related to Internet use, and their daily psychosocial functioning is impaired in almost all areas, such as in interpersonal relationships, academic success, reducing Internet usage, and the like. The results indicate differences in students’ gender and age and the type of secondary schools. It has been found that the prevalence of addiction is higher in girls than in boys. Furthermore, first grade students report the presence of certain symptoms of Internet addiction to a greater extent than older students. As far as the type of secondary schools is concerned, the results have shown that grammar school students express problems related to Internet addiction most often. Hierarchical regression analysis was performed and significant predictors of Internet addiction among adolescents who predominantly used social networks were found, such as the female gender; lower levels of conscientiousness and self-efficacy; more problems with attention; emotional loneliness; a greater tendency to compare oneself with others; motivation to use the Internet with the aim of social conformity (conformism), mood elevation, and as a coping strategy for stressful life situations; and more intensive use of social networks. Significant predictors of Internet addiction among adolescents who predominantly played online video games were higher levels of attention problems, motivation to use the Internet as a coping strategy for stressful life situations, and more intensive use of online video games. Conclusion In conclusion, this study has certainly contributed to the expansion of the body of knowledge in scientific and professional contexts. The characteristics and habits of Internet use among Zagreb high school students were investigated, especially in relation to the frequency and intensity of use of certain online activities. The prevalence of Internet addiction among Zagreb high school students was determined, which allowed comparison of the results with foreign and domestic studies. The correlates of Internet addiction among adolescents depending on the dominant online activity were investigated and the role of certain factors in explaining Internet addiction, which are included but not clearly enough described in the model I- PACE, was described in more detail. The research findings point to the need for changes in the way Internet addiction is measured, with a particular focus on measuring addiction to specific online activities. The findings provide a starting point for the development and implementation of effective prevention and treatment interventions for adolescents.
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As technologies continue to evolve at exponential rates, online platforms are becoming an increasingly salient social context for adolescents. Adolescents are often early adopters, savvy users, and innovators of technology use. This not only creates new vulnerabilities but also presents new opportunities for positive impact—particularly, the use of technology to promote healthy learning and adaptation during developmental windows of opportunity. For example, early adolescence appears to represent a developmental inflection point in health trajectories and in technology use in ways that may be strategically targeted for prevention and intervention. The field of adolescent health can capitalize on technology use during developmental windows of opportunity to promote well-being and behavior change in the following ways: (1) through a deeper understanding of the specific ways that developmental changes create new opportunities for motivation and engagement with technologies; (2) by leveraging these insights for more effective use of technology in intervention and prevention efforts; and (3) by combining developmental science-informed targeting with broader-reach technologic approaches to health behavior change at the population level (e.g., leveraging and changing social norms). Collaboration across disciplines—including developmental science, medicine, psychology, public health, and computer science—can create compelling innovations to use digital technology to promote health in adolescents.
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The clinical course of problematic smartphone use (PSU) remains largely unknown due to a lack of longitudinal studies. We recruited 193 subjects with smartphone addiction problems for the present study. After providing informed consent, the subjects completed surveys and underwent comprehensive interviews regarding smartphone usage. A total of 56 subjects among the 193 initially recruited subjects were followed up for six months. We compared baseline characteristics between persistent addicted users and recovered users at the end of the 6-month follow-up. Persistent problematic smartphone users displayed higher baseline smartphone addiction severity and were more prone to develop mental health problems at the follow-up. However, baseline depressive or anxiety status did not significantly influence the course of PSU. PSU behaved more like an addictive disorder rather than a secondary psychiatric disorder. Harm avoidance, impulsivity, higher Internet use, and less conversation time with mothers were identified as poor prognostic factors in PSU. Lower quality of life, low perceived happiness, and goal instability also contributed to persistent PSU, while recovery increased these scores as well as measures of self-esteem. These findings suggest that the Matthew effect is found in the recovery of PSU with better premorbid psychosocial adjustment leading to a more successful recovery. Greater clinical resources are required for interventions in vulnerable populations to modify the course of this increasingly prevalent problematic behavior worldwide.
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Purpose Adolescents' mental well-being has become a growing public health concern. Adolescents' daily lives and their engagement in risks have changed dramatically in the course of the 21st century, leading to a need to update traditional models of risk to include new exposures and behaviors. To date, studies have examined the relationship between (mainly traditional) risk behaviors and adolescent mental well-being or looked at risk factors that jeopardize mental well-being such as lack of social support but have not combined them together to highlight the most significant risks for adolescent mental well-being today. The present study included new and traditional risk behaviors and risk factors, robustly derived an empirically based model of clusters of risk, and examined the relative association of these clusters to adolescent mental well-being. Methods Data from the 2017–2018 Health Behaviours in School-aged Children study were used. The sample included 32,884 adolescents (51.7% girls) aged 15 years from 37 countries and regions. The principal component analysis was used to determine the existence of clusters of risk, using 21 items related to adolescent mental well-being that included both risk behaviors (e.g., substance use) and risk factors (e.g., peer support). Analysis was conducted in both a randomly split training and test set and in gender separate models. Mixed-effects logistic regressions examined the association between clusters of risk and mental well-being indices (low life satisfaction and psychosomatic complaints). Results Seven clusters of risk were identified: substance use and early sex, low social support, insufficient nutrition, bullying, sugary foods and drinks, physical health risk, and problematic social media use (SMU). Low social support and SMU were the strongest predictors of low life satisfaction (odds ratios = 2.167 and 1.330, respectively) and psychosomatic complaints (odds ratio = 1.687 and 1.386, respectively). Few gender differences in predictors were found. Exposure to bullying was somewhat more associated with psychosomatic complaints for girls, whereas physical health risk was associated with reduced relative odds of low life satisfaction among boys. Split-sample validation and out-of-sample prediction confirmed the robustness of the results. Conclusions The results highlight the importance of contemporary clusters of risk, such as low social support and SMU in the mental well-being of young people and the need to focus on these as targets for prevention. We propose that future studies should use composite risk measures that take into account both risk behaviors and risk factors to explain adolescents' mental well-being.
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The popularity of smartphones is undeniable in nearly all facets of society. Despite the many benefits attributed to the technology, concern has grown over the potential for excessive smartphone use to become problematic in nature. Due to the growing concerns surrounding the recognized and unrecognized implications of smartphone use, great efforts have been made through research to evaluate, label and identify problematic smartphone use mostly through the development and administration of scales assessing the behavior. This study examines 78 existing validated scales that have been developed over the past 13 years to measure, identify or characterize excessive or problematic smartphone use by evaluating their theoretical foundations and their psychometric properties. Our review determined that, despite an abundance of self-report scales examining the construct, many published scales lack sufficient internal consistency and test-retest reliability. Additionally, there is a lack of research supporting the theoretical foundation of many of the scales evaluated. Future research is needed to better characterize problematic smartphone use so that assessment tools can be more efficiently developed to evaluate the behavior in order to avoid the excessive publication of seemingly redundant assessment tools.
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Aim : There is empirical evidence to suggest that problematic smartphone use (PSU) is associated with mental health problems including anxiety in educational settings. This qualitative study explored attitudes towards – and self-reported impacts of – smartphone use among British young adult students, as well as perceived causes of PSU. Methods : Free-response written accounts were gathered from 265 British undergraduates at an English university. Open-ended questions were asked about their attitudes towards smartphone use, their reasons for using their smartphones, and what they perceived as the consequences of their smartphone use. Narratives were analyzed using Framework Analysis and a thematic framework was identified. Results : The three main consequences of PSU described by participants were (i) uncontrolled frequent checking of smartphones, (ii) using smartphones late at night, and irrelevant use of smartphones in class. The main reported explanations for PSU were fear of missing messages, boredom in class, poor self-regulation, and external reasons (e.g., boring lectures). Smartphone use was reported to have both positive and negative impacts on young adults’ life satisfaction, social relationships, physical health and study. Many participants reported that they need to develop better self-regulation to address their PSU. Conclusions : Findings suggest that smartphone use can have benefits as well as potentially causing harm among university students. PSU can – in some cases – be understood as reflecting mental well-being issues, poor self-regulation, and social problems.
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Aims: The present theoretical paper introduces the smartphone technology as a challenge for diagnostics in the study of Internet use disorders and reflects on the term "smartphone addiction." Methods: Such a reflection is carried out against the background of a literature review and the inclusion of Gaming Disorder in ICD-11. Results: We believe that it is necessary to divide research on Internet use disorder (IUD) into a mobile and non-mobile IUD branch. This is important because certain applications such as the messenger application WhatsApp have originally been developed for smartphones and enfold their power and attractiveness mainly on mobile devices. Discussion and conclusions: Going beyond the argumentation for distinguishing between mobile and non-mobile IUD, it is of high relevance for scientists to better describe and understand what persons are actually (over-)using. This is stressed by a number of examples, explicitly targeting not only the diverse contents used in the online world, but also the exact behavior on each platform. Among others, it matters if a person is more of an active producer of content or passive consumer of social media.
The purpose of the present study was to revise the Barratt Impulsiveness Scale Version 10 (BIS-10), identify the factor structure of the items among normals, and compare their scores on the revised form (BIS-11) with psychiatric inpatients and prison inmates. The scale was administered to 412 college undergraduates, 248 psychiatric inpatients, and 73 male prison inmates. Exploratory principal components analysis of the items identified six primary factors and three second-order factors. The three second-order factors were labeled Attentional Impulsiveness, Motor Impulsiveness, and Nonplanning Impulsiveness. Two of the three second-order factors identified in the BIS-11 were consistent with those proposed by Barratt (1985), but no cognitive impulsiveness component was identified per se. The results of the present study suggest that the total score of the BIS-11 is an internally consistent measure of impulsiveness and has potential clinical utility for measuring impulsiveness among selected patient and inmate populations.
Smartphone use is ubiquitous, however, scholarly debate regarding the addictive nature of smartphones abounds. In this context, it is integral to distinguish between the content that users experience and the medium that facilitates access to the former, as users may experience addictive-like responses to the specific activities they engage in through the context experienced rather than the device that facilitates access to these activities. The present study aimed to explore conceptualizations of smartphone addiction by (a) investigating user preferences for specific smartphone functionalities, (b) examining behavioral changes associated with limited access to preferred functionalities, and (c) exploring links between aspects of smartphone use and self-reported psychological well-being. A total of 471 participants completed an online survey, providing data on sociodemographics, actual and hypothetical smartphone usage, and psychological well-being (depression, anxiety, and stress symptoms). The results showed that communication functionalities were most frequently cited as being preferred among smartphone users. Notably, participants reported that they would check their smartphones significantly fewer times if their top-three functionalities were inaccessible. This suggests that smartphone users are likely to become addicted to the functionalities they access on their smartphones (content) and not the smartphones themselves (medium), rendering unviable the notion of smartphone addiction as a construct. Further analyses suggested negligible to small correlations between aspects of smartphone use and psychological well-being variables. The findings imply that rather than focusing on frequency of smartphone use, it is recommended that future research examines the type and quality of specific smartphone usages and their effects on user well-being.
Purpose: The aim of the study was to determine the short-term longitudinal pathways between smartphone use, smartphone dependency, depressive symptoms, and loneliness among late adolescents. Methods: A two-wave longitudinal survey was used using adolescents between the ages of 17 and 20 years. The interval between wave 1 and wave 2 was between 2.5 and 3 months. Using convenience sampling, the total number of participants who completed both waves of data collection was 346. Validated measures assessed smartphone dependency, smartphone use, depressive symptoms, and loneliness. The longitudinal model was tested using path modeling techniques. Results: Among the 346 participants (33.6% male, mean [standard deviation] age at wave 1, 19.11 [.75] years, 56.9% response rate), longitudinal path models revealed that wave 1 smartphone dependency predicted loneliness (β = .08, standard error [SE] = .05, p = .043) and depressive symptoms (β = .11, SE = .05, p = .010) at wave 2, loneliness at wave 1 predicted depressive symptoms at wave 2 (β = .21, SE = .05, p < .001), and smartphone use at wave 1 predicted smartphone dependency at wave 2 (β = .08, SE = .05, p = .011). Conclusions: Considering the rates of smartphone ownership/use among late adolescents (95%), the association between smartphone use and smartphone dependency, and the deleterious effects of loneliness and depression within this population, health practitioners should communicate with patients and parents about the links between smartphone engagement and psychological well-being.
Given the prominent role that smartphones have in everyday life, research in the field has proliferated. From a theoretical perspective, problematic smartphone use (PSPU) is described as a multi-faceted phenomenon entailing a variety of dysfunctional manifestations (e.g., addictive, antisocial and dangerous use). To date, however, there is still a lack of empirical evidence supporting the identification of PSPU as a potential behavioural addiction. Driven by theory, the aim of the present study was to provide an empirically validated model by testing the contribution of specific factors leading to PSPU. Relationships among individual characteristics (internalised psychopathology, impulsivity and personality traits) and PSPU uses (addictive, antisocial and dangerous) were investigated according to the updated version of the theoretical framework provided by the Pathway Model of problematic smartphone use (Billieux et al., 2015). An online survey was administered to a convenience sample (N = 511) of smartphone users in order to examine their daily engagement, problematic usage patterns and related psychological correlates. Path analysis revealed important information about different PSPU components and results are discussed in light of the available literature. Recommendations for future research are proposed to further investigate the problematic behaviour, including the study of additional variables, such as the fear of missing out (FoMO), nomophobia and excessive social media use.