Alexithymia components in excessive internet users: A multi-factorial analysis

Psychiatry Research 220 (2014) 348–355

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Alexithymia components in excessive internet users: A multi-factorial analysis Theodora A. Kandri a,n, Konstantinos S. Bonotis b, Georgios D. Floros c, Maria M. Zafiropoulou d a

University General Hospital of Larissa, Larissa, Greece Department of Psychiatry, University of Thessaly, Medical School, Larissa, Greece c Hellenic Association for the Study of Internet Addiction Disorder, Larissa, Greece d Laboratory of Developmental Psychology and Psychopathology, Department of Preschool Education, University of Thessaly, Volos, Greece b

art ic l e i nf o

a b s t r a c t

Article history: Received 4 November 2013 Received in revised form 13 July 2014 Accepted 27 July 2014 Available online 6 August 2014

The increasing use of computers and the internet – especially among young people – apart from its positive effects, sometimes leads to excessive and pathological use. The present study examined the relationship among the excessive use of the internet by university students, the alexithymia components and sociodemographic factors associated with internet users and their online activities. 515 university students from the University of Thessaly participated in the study. Participants anonymously completed: a) the Internet Addiction Test (IAT), b) the Toronto Alexithymia Test (TAS 20) and c) a questionnaire covering various aspects of internet use and demographic characteristics of internet users. Excessive use of the internet among Greek university students was studied within a multi-factorial context and was associated with the alexithymia and demographic factors in nonlinear correlations, forming thus a personalized emotional and demographic profile of the excessive internet users. & 2014 Elsevier Ireland Ltd. All rights reserved.

Keywords: Internet addiction Externally oriented thinking Non-linear canonical correlation analysis (OVERALS)

1. Introduction The increasing use of the internet – especially among young adults – apart from its positive effects, sometimes leads to excessive use with negative consequences for sociability and family life (Shen and Williams, 2010). Researchers have shown that excessive use of the internet (EIU) is related to academic and psychosocial impairment (Bartholow et al., 2005; Kubey et al., 2006), agitated and neglected nutritional needs (Tsai et al., 2009) and mental health problems, such as attention deficit hyperactivity disorder- ADHD (Yoo et al., 2004; Cho et al., 2008; Yen et al., 2009), insomnia (Choi et al., 2009; Cheung and Wong, 2011), depression (Ha et al., 2007; Ko et al., 2008), social anxiety (Chak and Leung, 2004; Caplan, 2007), psychosis (Bonotis et al., 2013) and other psychiatric conditions (Morrison and Gore, 2010; Ko et al., 2012). University students are considered as a high risk group for EIU (Kandell, 1998; Nalwa and Anand, 2003; Niemz et al., 2005). The definition and classification of EIU have been debated. Alternatively, named as an addictive (Young, 1998), pathological (Morahan-Martin and Schumacher, 2000), problematic (Caplan,

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Corresponding author. Tel./fax: þ 30 2413501055. E-mail address: [email protected] (T.A. Kandri).

http://dx.doi.org/10.1016/j.psychres.2014.07.066 0165-1781/& 2014 Elsevier Ireland Ltd. All rights reserved.

2002) or compulsive behavior (Greenfield, 1999), EIU has been described by four main components: (a) Internet overuse and loss of sense of time, (b) withdrawal symptoms; tension or depression when use is limited, (c) tolerance; e.g., need for more time online and (d) negative effects on social functioning (Chang and Law, 2008; Weinstein and Lejoyeux, 2010). In the present research, we used the term EIU, in line with Young's directions (Young, 1998), but in the reports of previous research we maintained the terms used by the authors. The concept of alexithymia was first coined by Sifneos (1973) to describe the lack of emotional skills originally found in psychosomatic patients. Alexithymia is characterized by reduced capacity to identify, analyze and verbalize feelings, impoverished imagination, and an externally oriented, concrete way of thinking. Furthermore, individuals with alexithymia have difficulty in discerning and assessing the emotions of others, which is thought to lead to ineffective emotional responding without empathy (Feldmanhall et al., 2013). After almost four decades of studies on alexithymia, the concept still remains rather unclear. Over the past few years, the main area of the debate has been revolved around whether alexithymia could be considered as a constant personality trait (Salminen et al., 2006; De Timary et al., 2008) or a defense mechanism as a consequence of a psychological distress, such as depression or anxiety (De Groot et al., 1995; Honkalampi et al.,

T.A. Kandri et al. / Psychiatry Research 220 (2014) 348–355

2000). Recent studies provide strong evidence in favor of alexithymia as a multidimensional personality construct (Parker et al., 2008; Mattila et al., 2010). Based on the hypothesis that individuals with alexithymia try to regulate their emotions through impulsive behaviors, alexithymia was associated with addictive or obsessive compulsive spectrum disorders such as pathological gambling (Parker et al., 2005), substance abuse disorders (Pinars et al., 1996) and eating disorders (Spence and Courbasson, 2012; Zeeck et al., 2011). Pathological use of the internet has also been described, into the same context, as an obsessive-compulsive spectrum disorder (Goldsmith et al., 1998). Interestingly, evidences from different studies indicate a possible common neurobiological background between alexithymia and ΕIU. Lin et al. (2012) found that internet addiction is associated with reductions of fractional anisotropy (FA) in white matter in brain regions involved with the processing of emotions, attention, decision making and cognitive functions. Among other regions, abnormal white matter integrity has been observed in the anterior cingulate cortex (ACC) of internet addicts. ACC connects to the frontal lobes and the limbic system, playing an important role in cognitive control, emotional processing and craving (Goldstein and Volkow, 2002). On the other hand, few studies have associated alexithymia with deficiency in the activation of the anterior cingulate cortex (ACC) during emotional processing (Lane et al., 1997; Kano et al., 2003; Karlsson et al., 2008; McRae et al., 2008). To our knowledge, there are only three studies assessing the relationship between alexithymia and EIU. De Berardis et al. (2009) found that undergraduate students with alexithymia reported higher potential risk for internet addiction. According to the results, the difficulty in identifying feelings was associated with a higher risk for developing IA. The difficulty in identifying feelings along with the difficulty in describing feelings were also positively correlated with the severity of IA in a more recent research (Dalbudak et al., 2013). Yates et al. (2012) assessed a developmental process model of problematic internet use, in which the expected association between child maltreatment and problematic internet use would be explained by alexithymia. The results supported a model, wherein child maltreatment might cause cognitive–affective vulnerabilities that, in turn, make individuals prone to problematic internet use. In these three studies different tools were applied to measure the internet use so their findings cannot easily been compared. Furthermore, according to researchers, individuals with alexithymia presented lower life satisfaction (Mattila et al., 2007), lower performance in remembering emotional words (Luminet et al., 2006), difficulties in identifying facial expressions (Grynberg et al., 2012) and problems in their social life (Vanheule et al., 2007). It has also been suggested that difficulties in social interaction may be addressed in an online environment because of the absence of physical presence, the anonymity and the enhanced control over the time and pace of interactions (McKenna and Bargh, 2000). Hence, we hypothesized that individuals with alexithymia may be prone to developing EIU in their effort to fulfill their unmet social needs. Therefore, EIU is a new multidimensional clinical phenomenon, associated with emotional and social factors, and therefore, its study must be conducted within a multi-factorial context. On the other hand, the common statistical methods do not allow for the analysis of a large number of different variable sets. In the present study an advanced statistical method was applied (non-linear canonical correlation analysis), that allowed for the analysis of the relationship among EIU, the alexithymia components and factors that in previous research have been linked with EIU, namely with gender (Chou and Hsiao, 2000; Liang, 2003), the online activities and the field of study (School) of university students (Jalalinejad et al., 2012). We hypothesized that there is

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a correlation between the emotional needs of university students and the use of the internet. We assumed that students that had difficulty in regulating their feelings would find the online environment attractive, probably due to the absence of physical presence and the anonymity, and therefore, could be more vulnerable to EIU. Given the lack of previous research, there were no specific predictions about the interactions between the examined variables. The results of the present study were approached in an exploratory way, trying to investigate all the possible correlations among the examined variables.

2. Methods 2.1. Participants In the study 589 students of the University of Thessaly participated. Of these, 74 were excluded due to reduced validity of their questionnaires (failure in one or more of the four validity items or incomplete filling). The final sample consisted of 515 students. 2.2. Procedures A cross-sectional study design was used to evaluate the study objectives. The data was collected from a convenience sample. The participants voluntarily and anonymously completed the self-report questionnaires which are described below. The collection of data was performed during academic traditions and other student activities. There were no course credits or other recompense for the participation. 2.3. Materials Excessive internet use was assessed using the Young's Internet Addiction Test – IAT (Young, 1998). The IAT is a self-report scale consisting of 20 items, which are rated on a 5-point Likert scale, ranging from 1, rare, to 5, always, with total scores ranging from 20 to 100. It covers the degree to which internet use affect daily routine, social life, productivity, sleeping pattern, and feeling. Three types of internet-user groups can be identified in accordance with the original scheme of Young: average users (20–39), moderate – at risk users (40–69) and excessive users (70–100). It was translated into Greek (forward and back-translation) by two independent translators. The IAT evidenced good internal consistency in this sample (Cronbach α ¼0.897), which is consisted with other studies (Yang, 2001; Widyanto and McMurran, 2004; Yang et al., 2005). Exploratory factor analysis with a varimax rotation accounted for 54.457% of the variance revealed four homogeneous factors: emotional self regulation that explained 19.912% of the variance, impact on social life that explained 13.607% of the variance, loss of sense of time that explained 12.255% of the variance and academic impairment that explained 9.382% of the variance. Alexithymia was assessed with the Greek version of the TAS-20 (Anagnostopoulou and Kioseoglou, 2000; Bagby et al., 1994). The TAS-20 is a selfreport scale consisting of 20 items, which are rated on a 5-point Likert scale, ranging from 1, strongly disagree, to 5, strongly agree, with total scores ranging from 20 to 100. The first factor in the three-factor model for the TAS-20 consists of seven items assessing the ability to identify feelings (DIF), the second factor consists of five items assessing the ability to describe feelings (DDF) and the third factor consists of eight items assessing externally oriented thinking (EOT). The TAS20 uses cut-off scoring: a score of equal to or less than 51 indicates no alexithymia, 52–60 points indicate possible alexithymia, and a score of equal to or greater than 61 indicates alexithymia (Bagby et al., 1994). In the present study Cronbach's alpha for the TAS-20 was .781. An improvised self-report questionnaire was applied asking for information about age, field of study and internet activities. Four validity items were also included ( i.e. “mark the c answer”). 2.4. Statistical analysis At first, descriptive statistics were calculated and internet users were categorized according to levels of internet use and alexithymia. Between-groups comparisons were evaluated with the non-parametric chi-square test (χ2). For analyzing gender differences of measured continuous variables Mann–Whitney Z test was employed because of parametric data and Cohen's d effect size was calculated in order to estimate the magnitude of the differences. A Krusklal–Wallis test was performed for examining the relationship among the IAT groups and the alexithymia total score and its factors. Mann and Whitney testing with a Bonferroni correction evaluated the IAT groups differences. Two non-linear canonical correlation analysis (OVERALS) were performed in order to explore the possible relationships between the IAT groups and the investigated

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variables (identifying and expressing feelings, externally oriented thinking, gender, field of study and internet activities). The OVERALS procedure is available as an SPSS module, allowing for the analysis of nonlinear relationships among a large number of different variable sets with variables scaled as either nominal, ordinal, or numerical. The OVERALS approach analyzes relationships among K sets of variables and searches for what is common among sets of variables measured on the same objects (Van der Burg, 1988). The first analysis examined the relationship between basic demographics, on line activities and IAT groups. The second analysis investigated the relationship between gender, selected online activities, alexithymia and IAT groups.

variables, in the second set, internet activities as single nominal variables and in the third set, scores from the IAT grouped as an ordinal variable (‘IAT Groups’). The second analysis focused on the relationship between Gender in set 1, certain selected online activities in set 2 (their selection based on calculations of their incidence in our sample), scores from the TAS questionnaire grouped in an ordinal variable (‘Alexithymia groups’) in set 3 and ‘IAT Groups’ in set 4. The results of the analyses are presented in Tables 4 and 5 and include loss values, component loadings, eigenvalues and fit. Eigen values indicate the level of relationship shown by each dimension. Depending on the number of dimensions used in the analysis, the sum of the eigenvalues denotes the percentage of the calculated variation. Loss represents the proportion of variation in object scores for each dimension and set. Small loss values indicate large multiple correlations between weighted sums of our optimally scaled variables. The component loadings give the correlations between object scores and optimal scaled variables. They represent the coordinates of the variable points on the graphs, with the distance from the origin to each variable point approximating the importance of that variable. The plot of centroids shows how well variables separate groups of objects with centroids being in the center of gravity of the objects. In order to understand the relationships between variables, matching clusters of categories in centroid plots need to be identified and for that reason we have included the respective diagrams, permitting us to better understand individual relationships between our variables. The distance from the origin to each variable point approximates the importance of that variable. The most effective variables, in relationships among variable sets, are the ones that are positioned far away from the origin. Furthermore, variables close to each other have more similarities than variables that are far apart (Greenacre, 2007). Fig. 1 presents the relationship among the IAT groups, the internet activities, the Gender and the field of study. At the lower right quadrant and far from the origin, variables EIU and Technological Studies were positioned together and equally spaced from Male and Female gender. This means that EIU was strongly related to

3. Results Of the 515 participants 3.5% (18) were categorized as excessive – pathological users and 55.3% (285) as moderate – at risk users (Table 2). Sixty nine (12.5%) were categorized as alexithymics and 54 (30.7%) as possibly alexithymics (Table 1). Table 2 presents the gender differences in age, IAT scores and TAS scores. Males reported higher scores on IAT total score and subscales Emotional regulation, Impact on studies, Online socialization, Loss of sense of time, Academic impairment and Externally-Oriented Thinking. Males also tended to be classified in a higher internet usage group, as it can easily be seen in Table 2. In order to examine the relationship among the IAT groups and the TAS-20 total score and its factors, we performed a Kruskal– Wallis test. Results indicated that all alexithymia factors tended to increase significantly with the intensification of internet use. Mann–Whitney testing with a Bonferroni correction between the IAT groups revealed that the most prominent shifts occurred in the DDF factor (Table 3). To explore possible relationships between the IAT groups and the variables of interest (identifying and expressing feelings, externally oriented thinking, gender, field of study and internet activities) non-linear canonical correlation analysis (OVERALS) was performed. Two OVERALS analyses were performed with a twodimensional solution being optimal. The first analysis examined the relationship between three sets of variables. In the first set, Gender and ‘Field of study’ (Technological Schools vs Schools of Human, Social and Health Studies) were entered as single nominal

Table 1 Categorization of Internet users according to levels of Internet use and alexithymia. Categories

Average internet user Moderate-at risk internet user Excessive internet user Non-alexithymic Possibly alexithymic Alexithymic

χ2

Gender Totals

Male

Female

212 (41.2%) 285 (55.3%) 18 (3.5%) 295 147 69

58 112 7 100 54 22

154 173 11 195 93 47

(32.8%) (63.3%) (4%) (56.8%) (30.7%) (12.5%)

χ2(2)¼ 7.85, p ¼ 0.02

(45.6%) (51.2%) (3.3%) (58.2%) (27.8%) (14%)

χ2(2)¼ 0.581, NS

Table 2 Between-gender comparisons of measured continuous variables with effect sizes for the magnitude of the differences. Variable

Age IAT total score IAT F1 – Emotional regulation IAT F2 – Online socialization IAT F3 – Loss of sense of time IAT F4 – Impact on studies TAS – Difficulty identifying Feelings TAS – Difficulty describing feelings TAS – Externally-Oriented Thinking

Male (mean/S.D.)

Female (mean/S.D.)

Comparison (Mann–Whitney)

N ¼177

N ¼ 338

Z

p

d

20.81 45.09 15.83 10.76 13.97 3.97 15.55 13.02 19.8

20.77 41.47 14.68 9.6 13.09 3.32 17.13 13.09 18.4

0.833 3.336 3.198 3.444 2.685 4.056 2.924 0.084 3.054

NS 0.001 0.001 0.001 0.007 o0.001 0.003 NS 0.002

0.019 0.305 0.219 0.326 0.252 0.374 0.290 0.016 0.310

(1.98) (11.27) (5) (3.65) (3.55) (1.83) (5.37) (3.95) (4.68)

(2.29) (11.96) (5.47) (3.48) (3.45) (1.54) (5.49) (3.95) (4.43)

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Technological Studies, equally involving male and female students. Moderate use was also equally spaced from male and female gender, while average use was closer to female. Studying in Technological Schools also had greater impact on the overall correlation than studying in Schools of Human, Social and Health Studies. When we controlled for the field of study, the Gender had a minor impact on the transition from the Average (that is more frequent in female students) to Moderate use and EIU. As it can be discerned from Fig. 1, students in Schools of Human, Social and Health Sciences appeared not to play online games, shop online and connect to social networking sites, forums and chat rooms, as the respective variables were positioned together at the upper left. It was also found that not playing online games or not shopping online did not mean that student was protected from potential EIU, as the “no” answer (to playing and shopping) was closer to the origin than the “yes” answer. On the other hand, not connecting to social networking sites, forums and chat rooms and not sending e-mails was related to average use among students in Schools of Human, Social and Health Sciences. Yet performing these activities was not necessarily related to the EIU, something logical since they are not typically associated with EIU.

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Reading online newspapers and magazines was related to Moderate use among male students regardless school of attendance. Teleconferencing among female students, regardless school of attendance, was not related to IAT groups while teleconference among male students was related to Moderate use. Online banking was related to the EIU especially among male students, regardless of school of attendance, while among female students it was not related to the IAT groups. Fig. 2 presents the relationship among the IAT groups, Alexithymia groups, Gender and selected internet activities. As it can be easily seen at the upper right quadrant and far from the origin, variables Alexithymia and EIU were positioned together meanings that they are strongly related. Fig. 2 also includes a fit line for a regression plot for the relationship between our variables that indicates a 20.5% of the explained variance. The fit line indicated that there was some underlying commonality between

Table 5 Two dimensional solution results with component loadings for the OVERALS analysis of the relationship between gender, selected online activities, alexithymia and IAT groups. Sets

Dimension

Table 3 Differences between IAT groups on levels of alexithymia variables. Variables

TAS total score Difficulty identifying feelings Difficulty describing feelings ExternallyOriented Thinking

Kruskal Wallis χ2

p

22.945 13.419

o 0.001 a to b, a to c, b to c 0.001 a to b, a to c

20.054

1 Loss

Significant group differences (Bonferroni adjusted p o 0.05)

a,b

1 2

Gender Participating in a discussiona,b Online gaminga,b Following newsa,b Academic usea,b Alexithymia groupsb,c ΙΑΤ groupsb,c Mean loss Eigenvalue Fit

o 0.001 a to b, a to c, b to c 3 4

7.346

0.025 None

a

a.‘Minimal use’ IAT group. b.‘Moderate use’ IAT group. c.‘Addictive use’ IAT group.

b c

2 Loading Loss

0.662  0.585 0.847 0.482 0.311 0.498

Total loss Loading 0.394 0.293

1.508 0.98

0.326  0.110 0.535  0.527  0.307  0.249 0.892 0.309 0.504 0.699 0.556 0.670 0.969 0.505 0.607 0.352 0.295 0.648

1.397 1.525 1.112

Optimal Scaling Level: Single Nominal. Projections of the Single Quantified Variables in the Object Space. Optimal Scaling Level: Ordinal.

Table 4 Two dimensional solution results with component loadings for the OVERALS analysis of the relationship among the gender, university school, online activities and IAT groups. Sets

Dimension 1

Gendera,b Schoolb E-mailinga,b Teleconferencinga,b Online discussionsa,b Seeking information on goods / servicesa,b Online gaminga,b Following newsa,b Online bankinga,b Academic usea,b Online buysa,b ΙΑΤ groupsb,c Mean loss Eigenvalue Fit

1 2

3

a b c

Optimal Scaling Level: Single Nominal. Projections of the Single Quantified Variables in the Object Space. Optimal Scaling Level: Ordinal.

2

Total loss

Loss

Loading

Loss

Loading

0.480

0.554  0.559 0.135  0.219  0.332  0.207  0.299  0.532  0.380 0.201  0.455  0.623

0.411

0.507 0.452  0.387  0.059 0.078 0.428 0.311  0.247  0.075 0.202 0.190 0.078

0.417

0.617 0.505 0.495 0.888

0.417

0.994 0.607 0.393

0.891 0.834

1.612 1.112

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Fig. 1. Centroids plot for the OVERALS analysis of the relationship between basic demographics, online activities and IAT groups.

Fig. 2. Centroids plot for the OVERALS analysis of the relationship between gender, selected online activities, alexithymia and IAT groups.

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alexithymia and EIU, particularly in the point where those entities crossed the barrier of pathology, namely between possibly alexithymics on the one hand and ‘at risk’ and excessive users of the internet on the other. As it was also observed at the right upper quadrant, variables Moderate use, playing online games, visiting social networking sites and not using the internet for academic purposes were positioned together. This means that Moderate users tended to play online games, connect to social networking sites and did not use the internet for academic purposes. On the other hand at the left upper quadrant, average users tended to connect to the internet for academic purposes and did not play online games.

4. Discussion The alexithymia levels were in line with previous studies (Salminen et al., 1999; Franz et al., 2008). The prevalence rate of EIU was in line with some previous research works in European population (Johansson and Götestam, 2004; Kaltiala-Heino et al., 2004; Pallanti et al., 2006) but in contrast with other research works in Greek population that reported higher rates (Frangos et al., 2010, Siomos et al., 2008). This variation can be attributed to the different methodology of these studies and in particular to the different assessment tool or the lower cut off scores that were used. The Greek study for example, conducted by Tsitsika et al. (2011) assessed the problematic use of the internet with the IAT test (Young, 1998) using scores significantly lower (40–100). Nevertheless, in the present study the lower prevalence rate of EIU does not much affect our results, as it was found that all the alexithymia factors tended to increase with the intensification of internet use. Consistent with prior research (Chou and Hsiao, 2000; Liang, 2003), male participants reported higher levels of EIU than their female counterparts but when we controlled for the field of study, the gender had a minor impact on the transition from the Average to the Moderate use and to the EIU. In the present study the EIU was studied within a multifactorial context including emotional and sociodemographic factors and the data obtained were analyzed and the correlations among them were illustrated graphically. Previous studies have shown that EIU was related to specific internet activities, as online gaming (Siomos et al., 2008), cybersex, online gambling and social networking (Tsitsika et al., 2011). The present study revealed a personalized profile of the internet users according to which students chose their online activities in line with their gender, field of study and the IAT group they belonged to. Α special relationship between the IAT groups and the internet activities was also found as non-performing an internet activity did not imply a lower level of internet use than performing it and vice versa. In other words, EIU is not simply an excessive preoccupation with specific internet activities, as excessive users choose various activities to stay online, according to demographic factors (gender, field of study) and factors related to internet usage (IAT group, online activities). These findings are of great importance for the planning of prevention programs for university students who engage themselves in excessive use or are in danger of doing so. Outlining the emotional profile of university students that were engaged in excessive use of the internet, we could suggest that they were characterized by an impaired ability mainly to express and secondarily to identify their feelings and also to think in an imaginative way. As it has been suggested, emotions regulate social interaction, since the emotional expressions affect observers’ behavior causing inferential processes and affective reactions in them (Van Kleef, 2009). On the other hand

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it has been found that poor social skills and high level of social anxiety influence internet addiction (Liu and Kuo, 2007). Consequently, we can assume that students with difficulty in expressing their emotions and hence in communicating and interacting, may find an alternative way of expressing and socializing in the internet, and therefore may be more prone to excessive use. In addition, students with alexithymia may prefer to socialize online, avoiding, this way, the “face to face” interaction, which makes them feel uncomfortable. Interacting through the internet may also give them the chance to better regulate their emotions since they have greater control over the communication process by choosing, for example, the profile and the time of their login on and off. They may also turn to the internet in an attempt to compensate for their poor relationships in the real world. In conclusion, more research is needed to investigate the way that university students interact in the cyberspace and the personality traits, in addition to the alexithymia, that may predispose them to excessive use of the internet. The present study had several limitations. The cross sectional design of the research along with the convenience sample did not provide a good basis for establishing causality and generalization of our findings. In addition, the above mentioned assumptions were based on the hypothesis, in line with recent research (Parker et al., 2008; Mattila et al., 2010), that alexithymia is a multidimensional personality construct. On the other hand, literature has linked alexithymia with depression and anxiety (De Groot et al., 1995; Honkalampi et al., 2000), factors that they were not measured in the present research. This is a limitation of the present study and must be taken into account for future research. In addition, given that alexithymia is characterized by a difficulty in identifying and expressing emotions, the reliability of the selfreport questionnaires which were used in the present research, may be questioned. In conclusion, the excessive use of the internet among Greek university students was studied within a multi-factorial context and was associated with the alexithymia and demographic factors in nonlinear correlations, forming thus a personalized emotional and demographic profile of the excessive internet users. The present findings indicate the need for a personalized psychotherapeutic approach of excessive internet users that focuses on the development of their emotional and social skills.

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