Abstract
Excessive Internet use has demonstrated comorbidity with other psychological symptoms and psychiatric disorders, as well as impairments in the management of daily life, relationships and emotional stability. Recent findings in the literature have consistently supported the relationship between impulsivity and Internet addiction. The present study hypothesized that, in addition to impulsivity, a further predictor of Internet addiction might be relational co-dependency, which is also associated in the literature with addiction phenomena, but mainly substance addiction. This paper investigates the role and predictive weight of impulsivity and codependency on Internet addiction on a sample of young adult university students (n = 481) by using a hierarchical regression analysis. The participants were administered the UADI-2, the BIS-11 and the SFCDS. In terms of percentage distribution, 38 % of the participants were in the dependency range, while 37.7 % demonstrated Internet abuse behavior. The results confirmed the role of impulsiveness (β = 0.312) and added to the literature by showing the significant role of relational codependency (β = 0.275), gender (β = 0.174) and age (β = 0.196). Thus, male participants were more dependent, more impulsive and more co-dependent, with increasing age in the given range (18–30). The present study shed light to the presence of this issue among young adults and that, as a preventive and restraining measure, there is a need not only for targeted awareness-raising programmes but also for interventions to promote greater emotional control and a more balanced management of personal relationships.
Introduction
The Internet is one of the most widespread and accessible media for young people: chatting, role-playing, etc., are increasingly the routinary activities for them and the growing use of this media has led to the emergence of psychological problems linked to its possible maladaptive use in young people. The phenomenon of Internet abuse has been called by different names such as computer addiction, compulsive Internet use, Internet mania, problematic or pathological Internet use, and finally Internet Addiction (IA) (1–5). Young (6), Young and Rogers (1) bring Internet Addiction Disorder to the center of the scientific debate, shifting the diagnostic reference from substance-related problems to those found in pathological gambling problems (GAP) and in fact placing Internet addiction within impulse control disorders. Individuals with Internet addiction may lose control over their Internet use, resulting in impairments in the management of daily life, relationships and emotional stability (1, 2, 4, 7).
A critical level is identified when the excessive Internet use impedes the management of the young individual's developmental activities and negative consequences come to light in an overt way (for example, decline in school performance, excessive limitation of outside activities, permanent conflicts with parents and friends, etc.) (8–11). When it happens, except the use of Internet, several other activities and interests are neglected, despite they are consciously perceived as significant, while individual continue to massively use the Internet despite the possible harmful consequences, a phenomenon known as “harmful consumption” (12, 13).
Compared with the past, currently Internet abuse is classified not as an impulse control disorder ma as a (potential) addiction, i.e., the fact the tendency is to define addiction to specific online activities (as seen in section III of DSM-5 and ICD-11), rather than Internet addiction in general.
Currently, the main forms of addiction associated with the excessive use of Internet are: Cyber-relational addiction, characterized by an excessive tendency to establish friendship or love relationships with people met online, mainly via chat rooms, forums or social networks (14). In this condition, online relationships quickly become over-involving and individuals tend to neglect their relationships in presence with friends and family. Information overload, characterized by an obsessive search for information on the web: individuals spend increasing amounts of time searching for and organizing data on the web (15). Cybersexual addiction, which is characterized by compulsive use of pornography and virtual sex sites. Individuals usually download and use online pornography, engage in adult-only chats and may have compulsive masturbation (16). Offline gaming, characterized by a tendency to over-involve in virtual games that do not involve multi-player interaction and are not played over a network (17). Online gaming, in which excessive involvement and compulsive behaviors related to various online activities such as gambling, compulsive shopping, role-playing games are evident (18, 19).
Excessive Internet use has been found to be in co-morbidity with other psychological symptoms and psychiatric disorders (4). Internet addiction has been found to be associated with attention deficit hyperactivity disorder (20, 21), low self-esteem (22), shyness (23), depressive symptoms (1, 23–26), hostility (27, 28), interpersonal sensitivity (27, 29), disturbances in relationships (30, 31), obsessive-compulsive symptoms (OCS) (20, 24, 25), and impulsivity (32, 33).
Harmful Internet use, like substance abuse, triggers individuals' preoccupation with details, nervousness, irritability, aggression and impulsivity (4, 34). Previous studies have also shown that obsessive-compulsive symptoms are associated with the severity of Internet addiction (20, 24, 25). Cao et al. (32) reported that adolescents with Internet addiction show increased impulsivity and have various comorbid psychiatric disorders, which may be associated with Internet addiction. For those with behavioral inhibition issues, the Internet can serve as an area where individuals can receive short-term rewards through gaming, surfing or social networking, and be reinforced by immediate gratification (7, 35). A further study suggested that impulsivity can be considered as an endophenotype of addictive behavior (36). Impulsive individuals have problems in managing their behavior, showing recurrent failures to resist impulses to engage in a specified behavior and a feeling of lack of control while engaging in the behavior. A large body of the literature in this area concerns impulsiveness impacting the addictive tendencies (37, 38). Consistent with this, recent findings in the literature have consistently supported the relationship between impulsivity and Internet Addiction (33, 39–44).
Another construct that has been associated with addiction phenomena (predominantly substance addiction) is that of codependency. Codependency is often referred to as “relationship addiction”. It's an emotional and behavioral condition that interferes with an individual's ability to develop a healthy, mutually satisfying relationship. But over the years it's been expanded to include individuals who maintain one-sided, emotionally destructive, or abusive relationships (45–47). Researchers have identified several factors that are often linked with codependency: lack of trust in self or others; fear of being alone or abandoned; a need to control other people; chronic anger; frequent lying; poor communication skills; trouble making decisions; problems with intimacy; difficulty establishing boundaries; trouble adjusting to change; an extreme need for approval and recognition (48–50). The role of codependency among the variables associated with gambling disorder has been reported by Barrera-Algarín and Vázquez-Fernández (51). In contrast, an interesting contribution by Lu (52) recently illustrated the link between virtual community codependency and virtual community addiction: the virtual community codependency will need individuals to have a desire to derive compensation from the virtual community that cannot be achieved in the real world. If people in this community have similar needs, priorities, and goals, increasing the use of Facebook will lead to an increase in virtual community addiction. The author argues that codependency is a pattern of dysfunction in interpersonal relationships. According to the social compensation theory, if people feel insecurity and negative social identity in real life interpersonal networks, they may spend more time using virtual communities as compensation. Lu's study (52) tested and reported a direct impact of virtual community codependency on virtual community addiction. Furthermore, the increased use of Facebook when there is a sense of the ‘spirit of belonging together' can lead to increased tendency to virtual community addiction. In more general terms, Shishkov et al. (53) have first suggested a direct association between internet addiction and codependency, while, with reference to the set of patterns of thinking and behavioral characteristics of the codependent personality, Artemtseva and Malkina (54) pointed out that the codependents make cognitive errors about the consequences of their behavior in order to constantly protect themselves from uncertainty.
While the role of impulsivity has been widely analyzed in the literature of Internet Addiction, there is still a lack of studies that consider codependency as another possible factor associated to excessive Internet use. The present work had therefore the following objectives: evaluate the importance of Internet abuse and dependence in a sample of young adults, by also considering the gender of the participants; investigate the possible role of Impulsivity and codependency in explaining Internet Addiction. Other studies have confirmed for this age group the relationship between impulsivity and problems associated with various forms of addiction (55–59), and this can be even more true considering the important personal limitations in terms of mobility and relationships related to COVID-19 pandemic, which have not only solicited an increase in addictive practices (60, 61) but also a deterioration in perceived safety in relationships with others, amplifying the compensatory search for codependent relationship patterns that Internet use can offer (62–65). On the basis of the literature presented hitherto, we hypothesized that relational codependency might be in young adults, in addition to impulsivity, a further significant predictor of Internet addiction.
Methods and materials
Participants
Participants were recruited by forwarding an email to students enrolled at a university in central-southern Italy. This email defined the goals as well as the function of the study. Subjects were invited to enter a specific link found in the same notice, after which they filled in and posted the answers telematically and digitally. Participants were assured anonymity and also the use of information in aggregate type for research purposes. They also provided their written informed consent to participate in this study. The protocol was approved by the local university Institutional Review Board and tools administration took place in April and May 2020. A total of 1,500 emails were sent out. As far as the drop-out ratio is concerned, 86 participants dropped out after beginning to fill it in, therefore 481, including 219 (45.5 %) males and 262 females (54.5 %) with an average age of 21.79 and SD = 4.16 and age range 18–30, completed questionnaires were finally collected.
Tools
- Uso-Abuso e Dipendenza da Internet [Internet use-abuse and addiction] (UADI-2), (66), assesses the psychopathological risk of Internet abuse and the psychological use that users make of the network (example items: “I happen to have flashbacks or disconnected thoughts during or after a long Internet connection”; “Sometimes I like to lie on the net”; “On the Internet I happen to look for erotic material or talk about sex”). The instrument measures the psychological and psychopathological aspects related to the use and abuse of the Internet and has been designed to be administered both off-line (by filling in the U.A.D.I. in paper form) and on-line (by filling it in via Internet). The instrument consists of 24 items that the person must answer on a 5-point scale ranging from 1 (Absolutely false for me) to 5 (Absolutely true for me). The UADI-2 allows scoring with reference to four dimensions: Dissociation (describes some dissociative symptoms as bizarre sensory experiences, de-personalization, de-realization, along with the tendency to alienation and estrangement-escape from reality), Impact on Real Life (contains items describing the real-life consequences i.e., any changes in habits, social relationships, mood as a result of continued Internet use), Addiction Symptoms (contains items that address some behaviors and symptoms of addiction, particularly with reference to gradually increasing linkage period, abstinence, compulsiveness, and hyperinvolvement), Identity and Sexuality (contains items describing manipulation of true personal identity online and the tendency to search for sexually oriented content). The scoring has three score ranges: up to 62, normal Internet use; 63–74, Internet abuse; over 74, Internet addiction. Cronbach's alpha for this study was 0.867.
- Barratt Impulsiveness Scale-11 [BIS-11; (67, 68)] is a 30-item self-report questionnaire designed to assess general impulsivity taking into account the multifactorial nature of the construct. The structure of the instrument allows the assessment of six first-order factors (attention, motor, self-control, cognitive complexity, perseverance, cognitive instability) and three second-order factors: attentional impulsivity, motor impulsivity (motor and perseverance), unplanned impulsivity (self-control and cognitive complexity). Example items: “I do things without thinking”; “I act on the spur of the moment”; “I often have extraneous thoughts when thinking”. The person is asked to respond regarding how often he or she generally (not referring to a specific time interval) acts and thinks similarly to the items on the scale. The total score is obtained by summing up the first and second order factors. The items are distributed on a four-point scale (Rarely/Never = 1, Occasionally = 2, Often = 3, Almost Always/Ever = 4). In the present study, the Italian version by Fossati et al. (68) was used. Cronbach's alpha for this study was 0.835.
- Spann-Fisher Codependency Scale [SFCDS; (69)]. Codependency is referred as a dysfunctional pattern of relating to others with an extreme focus outside of oneself, lack of expression of feelings, and personal meaning derived from relationships with others. The tool is an unidimensional 16-item 6-point scale, ranging in score from 16 to 96 with higher scores reflecting codependency (example items: “It is hard for me to make decisions”, “I don't usually let others see the “real” me”, or “When someone upsets me I will hold it in for a long time, but once in a while I explode”). The mean Spann-Fischer co-dependency score is approximated with a midpoint of 52.6, a “high” score of 67.2 and a “low” score of 37.3 suggested by Fischer, Spann, and Crawford (69). The codependent person puts a lot of effort into satisfying the needs of others, constantly trying to be helpful and organizing others' lives, losing sight of and disregarding their own needs. For the purposes of this study, we obtained an Italian version of the questionnaire through back-translation procedures. We performed an exploratory factor analysis (Maximum Likelihood, promax rotation) on The Italian Spann-Fischer Codependency Scale items. Our results revealed a one-dimensional structure. A test for internal consistency and item-total correlations confirmed that excluding one poor functioning item, best preserved the reliability of the questionnaire, and we therefore decided to exclude it from the final Italian version. After this adjustment, the scale consisted of 15 items and showed good internal consistency (Cronbach's α = 0.820).
Statistical analysis
Descriptive analyses (percentages, means, standard deviation, skewness and kurtosis, confidence intervals); t-test for comparison of scores with respect to gender; Pearson's bivariate correlations; testing of univariate and multivariate regression assumptions; and hierarchical regression were conducted.
Results
Descriptively, 38.0% (n = 183) of the sample were in the range of Internet addiction (with a mean score on the UADI-2 > 74). The 27.7% (n = 133) of the sample were found to be in the Internet abuse range (with a mean score between 63 and 74). The remaining 34.3% (n = 175) were in the normal range of Internet use. Significant differences emerged, however, in relation to gender. Amongst males, 45.2% (n = 99) were addicted to the Internet, while 30.1% (n = 66) had Internet abuse behavior. Among females, 32.1% (n = 84) were addicted, while 25.6% (n = 67) abused the Internet. These differences were more specifically highlighted in Table 1 where the t-test comparisons between the two groups and the respective breakdowns in the range of full dependency, abuse and normal Internet use are shown.
Table 1
| Males | Females | ||||
|---|---|---|---|---|---|
| Dependence | M (SD) | M (SD) | CI 95% | p | d |
| 86.703 (8.37) | 82.71 (4.95) | [1.26; 5.37] | < 0.005 | 0.48 | |
| Abuse | M (SD) | M (SD) | CI 95% | p | d |
| 67.79 (3.42) | 68.78 (3.46) | [−2.17;0.192] | >0.05 | 0.28 | |
| Normal use | M (SD) | M (SD) | CI 95% | p | d |
| 55.48 (6.14) | 53.42 (5.93) | [0.092; 4.02] | <0.05 | 0.34 |
Differences in the level of Internet addiction with respect to gender of participants.
In Table 2 below it can be seen that the level of male dependence was higher both in terms of the overall score and in relation to the subscales of Dissociation, Identity and Sexuality and Impact on Real Life, while the manifestation of Addiction Symptoms did not significantly differ between genders (p > 0.05).
Table 2
| Males | Females | ||||
|---|---|---|---|---|---|
| General addiction | M (SD) | M (SD) | CI 95% | p | d |
| 73.00 (14.33) | 66.74 (13.57) | [3.75; 8.76] | <0.001 | 0.45 | |
| Dissociation | M (SD) | M (SD) | CI 95% | p | d |
| 21.21 (4.38) | 19.16 (4.35) | [1.27; 2.84] | <0.001 | 0.46 | |
| Real life impact | M (SD) | M (SD) | CI 95% | p | d |
| 14.18 (2.40) | 13.61 (2.46) | [.129; 1.00] | <0.05 | 0.24 | |
| Addiction symptoms | M (SD) | M (SD) | CI 95% | p | d |
| 23.05 (5.77) | 22.54 (5.59) | [−0.515; 1.52] | >0.05 | 0.09 | |
| Identity and sexuality | M (SD) | M (SD) | CI 95% | p | d |
| 14.56 (5.10) | 11.43 (5.11) | [2.21; 4.05] | <0.001 | 0.61 |
General and specific dimensions of Internet addiction with respect to gender of participants.
Table 3 below presents the descriptive statistics of all the variables used in the study.
Table 3
| Skewness | (SE) | Kurtosis | (SE) | Mean | (SD) | |
|---|---|---|---|---|---|---|
| Age | 0.757 | 0.111 | −0.932 | 0.222 | 21.79 | 4.16 |
| General Internet addiction | 0.123 | 0.111 | −0.560 | 0.222 | 69.59 | 14.26 |
| Dissociation | −0.020 | 0.111 | −0.411 | 0.222 | 20.09 | 4.48 |
| Real life impact | −0.026 | 0.111 | 0.495 | 0.222 | 13.87 | 2.45 |
| Addiction symptoms | −0.272 | 0.111 | −0.362 | 0.222 | 22.77 | 5.67 |
| Identity and sexuality | 0.083 | 0.111 | −0.891 | 0.222 | 12.85 | 5.33 |
| Codependency | −0.275 | 0.111 | 0.495 | 0.222 | 51.36 | 10.88 |
| Total impulsivity | −0.410 | 0.111 | −0.242 | 0.222 | 68.03 | 10.47 |
| Motor impulsivity | −0.150 | 0.111 | −0.726 | 0.222 | 22.87 | 5.18 |
| Attentional impulsivity | −0.183 | 0.111 | −0.343 | 0.222 | 18.04 | 3.23 |
| Non planning | −0.122 | 0.111 | 0.630 | 0.222 | 27.12 | 4.34 |
Descriptive statistics of the variables.
SE, Standard Error; SD, Standard Deviation.
Table 4 below shows the bivariate correlations between the measures used in the study. It can be seen that there were significant associations with both the Codependency scale (0.347**) and the Impulsivity scale (0.349**). More specifically for the latter measure, Internet Addiction reported correlations with the subscale of the Attentional Impulsiveness (0.379**) and Motor Impulsiveness (0.365**), while the association with the subscale of non-planning was not significant.
Table 4
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
|---|---|---|---|---|---|---|---|---|---|---|
| General Internet addiction (UADI-2) | 1 | |||||||||
| Dissociation (UADI-2) | 0.861** | 1 | ||||||||
| Real life impact (UADI-2) | 0.387** | 0.288** | 1 | |||||||
| Addiction symptoms (UADI-2) | 0.832** | 0.615** | 0.038 | 1 | ||||||
| Identity and sexuality (UADI-2) | 0.887** | 0.676** | 0.293** | 0.628** | 1 | |||||
| Codependency (SFCDS) | 0.347** | 0.230** | 0.017 | 0.394** | 0.306** | 1 | ||||
| Total impulsivity (BIS-11) | 0.349** | 0.320** | 0.248** | 0.157** | 0.382** | 0.138** | 1 | |||
| Motor impulsivity (BIS-11) | 0.365** | 0.335** | 0.233** | 0.190** | 0.384** | 0.168** | 0.878** | 1 | ||
| Attentional impulsivity (BIS-11) | 0.379** | 0.312** | 0.198** | 0.218** | 0.428** | 0.182** | 0.805** | 0.647** | 1 | |
| Non planning (BIS-11) | 0.124** | 0.140** | 0.174** | −0.010 | 0.144** | −0.003 | 0.765** | 0.443** | 0.425** | 1 |
| Age | 0.173** | 0.092* | 0.089* | 0.136** | 0.225** | 0.232** | −0.110** | −0.077 | −0.022 | −0.169** |
Bivariate correlations.
N = 481.
**Correlation is significant at the 0.01 level (2-tailed). *Correlation is significant at the 0.05 level (2-tailed).
For Age Spearman's correlation has been used. Pearson's for the other variables.
In order to identify predictors of Internet addiction, a hierarchical regression was performed on the variables of Codependency and Impulsivity. The preliminary verifications of the regression assumptions excluded the presence of multivariate outliers. Mardia's multivariate kurtosis index (62.33) was in fact below the critical value [p (p + 2) = 99]; therefore, the relationship between the variables can be considered substantially linear. Low co-linearity was indicated by the low variance inflation factor (VIF) values <2 and high tolerance values > 0.60. For verification of the assumptions on the residuals, the average between the standardized and raw residuals was equal to 0; the Durbin–Watson test had a value of 1.96 and was therefore indicative of the absence of autocorrelation.
A hierarchical multiple regression was run to determine if the addition of Codependency, Impulsivity, Age, and Gender improved the prediction of the Internet Addiction. The full model resulted statistically significant, R2 = 0.289, F(4,480) = 48.119, p < 0.001; adjusted R2 = 0.283.The regression model included Codependency and Impulsivity at step 1, Age at step 2, Gender at step 3. The results of the hierarchical multiple linear regressions are presented in Table 5. In the regression model, with Internet Addiction as outcome variable, Codependency and Impulsivity jointly explained a 22% portion of the outcome variability. Adding Age at the second step provided a significant improvement in the explained variance, which reached 26%. By adding Gender at the third step, the explained variance further significantly increased to 29%. Standardized beta values were significant. with a positive sign for Codependency, Impulsivity, Age, and a negative sign for Gender. The order reflects the relative importance assigned to each predictor. Since this study intended to give special emphasis as a predictor to codependency, agreeing with what has been argued in this regard in the recent literature cited above, this variable appears to have taken precedence in the entry over that of impulsivity, which is dominant in the less recent literature. As a third consideration, age was included, with respect to which some studies reported an inverse association with the level of addiction (70–72), while others reiterated the linear direction with increasing levels of Internet addiction (73–75). It was interesting to understand what the predictive relationship between age and problematic internet use might be in the sample of young adults considered. Finally, the gender variable was included, which according to other studies is predictive of different male and female susceptibility to problematic and pathological internet use. Thus, it was deemed that the four variables, considered in this order of entry into the predictive model, could provide a significant explanatory portion of the phenomenon under study.
Table 5
| Outcome variable | |||
|---|---|---|---|
| Internet addiction | |||
| Independent variable | Adjust R2 | ΔR2 | β |
| Step 1 | 0.220*** | 0.223*** | |
| Codependency | 0.310*** | ||
| Impulsivity | 0.318*** | ||
| Step 2 | 0.255*** | 0.037*** | |
| Codependency | 0.265*** | ||
| Impulsivity | 0.342*** | ||
| Age | 0.197*** | ||
| Step 3 | 0.283*** | 0.029*** | |
| Codependency | 0.275*** | ||
| Impulsivity | 0.312*** | ||
| Age | 0.196*** | ||
| Gender | −0.174*** | ||
Results of hierarchical linear regression analyses.
N = 481; β = standardized beta value.
***p ≤ 0.001.
Discussion
The present study was aimed to evaluate the importance of Internet abuse and dependence in a sample of young adults and it aimed to clarify the possible role of impulsivity, codependency, gender and age in explaining Internet addiction. Among the instruments in the Italian context to measure Internet addiction, the UADI, although not recent, has been preferred over others such as the Generalized Problematic Internet Use Scale-2 [GPIUS-2, (76); Italian valid. (77)] or the classic Internet Addiction Test [IAT, (1); Italian valid (78)], because, in addition to having in other studies confirmed good psychometric properties (79–83), it allowed us to assess two dimensions not present in the other instruments mentioned above, and which we considered significant for their possible association with the impulsivity and codependency variables, namely dissociation experiences and identity manipulations on the web. First of all, the results showed a substantial percentage of young people in the addiction phase (one third of the total sample). Moreover, another third of the sample demonstrated Internet abuse behavior. This clearly indicates that there was an issue of control over the use of the Internet among the young adults involved. Nevertheless, we recognize that there might be an overestimation, especially referred to the classification of “abuse” of the Internet. This can be due to the fact that the instrument was originally carried out in 2005 when the average use of the Internet and social networks was still limited. Over the years, we have seen a significant increase in the use of the Internet, especially among young people, due to a natural expansion of connectivity possibilities and as a normal evolution of a behavior of consultation and search for information. Moreover, the use of messaging for interactions with friends and acquaintances has also highly increased. Another aspect that should definitely be considered is that the UADI does not differentiate between different forms of addiction (smartphone, social media, cybersex, game addiction), while it measures a general prevalence of addiction. In light of current developments, we believe there is a need to provide adequate distinctions between different types of addiction and to differentiate areas affected by possible problems. Considering that the administrations took place after the period of greatest impact of the COVID-19 pandemic in Italy (84) which, as we know, imposed a prolonged isolation and reduction in direct contacts, it is probable that these percentages are affected by the impact of social isolation (85, 86) and that this has contributed to a compensatory search on the Internet. The results are, however, similar to the findings of the study by Salarvand et al. (87), also conducted with university students. Consulting the existing literature related to the period of COVID-19 lockdown (the same period in which we conducted our survey), has shown that the rates of general addiction increased as compared to the pre-COVID period. For example, the study of Burkauskas et al. (88) has shown that Internet Gaming Disorder (IGD) has increased 1.6 times (compared to the pre-COVID period) while the prevalence of the Problematic Internet USE (PIU) has increased 1.5 times. The same increase (1.6 times) during the COVID-19 pandemic of PIU has been also remarked by (89) in both adults and young people. This increase is particularly critical among young people as pointed out by several studies. For example, Zhao et al. (90) estimated the PIU prevalence rate in a sample of university students to be 28.4%, while a Swiss study by Mohler-Kuo et al. (91) estimated the PIU prevalence rate to be 21.3% for young adults.
Of particular interest, however, is the recent meta-analysis by Meng et al. (92), which includes 504 studies from 64 countries conducted before November 2021 and from which the importance of the varying incidence of specific modes of Internet addiction can be clearly understood. The study reports prevalence estimates of 26.99% (95% CI, 22.73–31.73) for smartphone addiction, 17.42% (95% CI, 12.42–23.89) for social media addiction, 14.22% (95% CI, 12.90–15.65) for Internet addiction, 8.23% (95% CI, 5.75–11.66) for cybersex addiction, and 6.04% (95% CI, 4.80–7.57) for game addiction.
Underlying the differences in prevalence estimates among the studies should certainly be noted the incidence of the instrument used. In our case, the results reported using the UADI-2 suffer from a lack of classificatory articulation and a normative update that may be reflected in some overestimation of problematic incidence.
However, in the enforced form of preventive isolation, a vicious circle is created that pushes people to seek comfort, entertainment, distraction and relief on the Internet, putting aside the real discomforts, which in this way are not resolved and addressed (93). In other words, the Internet acts as a deterrent and an escape route for people who experience difficulties in socializing in real life. Due to character traits such as shyness or situations of social isolation, the use of new technologies and social networks seem to become a privileged source of intense and satisfying emotions and sensations, albeit originating from entirely virtual dimensions, so that the Internet can represent a means of escaping from everyday reality and taking refuge in an illusory and gratifying world, in which the virtual element makes it possible to overcome the difficulties and inhibitions that can characterize real interactions, thus triggering pathological mechanisms that severely affect the social relationships, the financial situation and the mental health of the people involved (92).
Internet addictions are more frequent in people with a basic emotional fragility. They are triggered in people who are already experiencing psychological difficulties such as depression, obsessive-compulsive disorders and anxiety disorders (94). The immoderate and improper use of mobile phones and the Internet not only can cause huge gaps between people, but can also lead them to withdraw into themselves, to develop relational insecurities or a fear of rejection, to feel inadequate and in need of support, even if this is external and for its own sake. It should not be forgotten that among these forms of addiction, there is also the so-called ludopathy, i.e., addiction to games and gambling, to which mobile devices also contribute on a large scale (95, 96).
Our results underline the male prevalence of Internet addiction, in line with other studies carried out during the same period (97, 98). Regarding gender differences, the literature indicates that men are generally attracted to sex sites and online games. Women are more likely to spend time flirting in chat rooms. Men prefer visual stimuli and focused on sexual experiences, while women are more focused on relationships and interactions (99–102). These features are congruent with the findings regarding gender comparisons of the UADI-2 addiction scale components. The significantly higher score on the dissociation scale for males is associated with increased gaming [see also (103–105)], whereas the score on the identity and sexuality scale is more likely to relate to behavior related to searching the Internet for sexually oriented content or masking one's identity in chat rooms or role-playing games [see also (106, 107)]. While no gender differences were found with regard to the manifestation of specific addiction-related symptoms, the negative impact on real life (work, study, social relationships, general wellbeing) was greater for males.
The analysis of the bivariate correlations clearly confirmed both the association with impulsiveness and that with codependency. The subsequent hierarchical regression also confirmed the hypothesis of the present study. In terms of the weights of the regression coefficients, impulsivity remains the main predictor (β = 0.312), as indicated by most of the above literature, but it is flanked by co-dependency, which shows a regressive weight just below the former (β = 0.275).
To the best of our knowledge, the only study that explicitly relates codependency to Internet addiction is that of Shishkov et al. (53). Their contribution shows that higher levels of Internet addiction were associated with an increase in codependency. Although the authors do not carry out a regression analysis, but limit themselves to correlation associations, they comment on the results, pointing out that the prerequisites for Internet addiction as well as for codependency are in the family.
In contrast to the study of Shishkov et al., in which both Internet addiction and codependency were greater in younger individuals, our results show the opposite trend: within the 18–30 age group, it is the older participants who are more dependent, both on the Internet and in terms of relationships. This result is particularly relevant as it raises interesting questions about the potential extension of addiction problems into the fully adult age group.
Some confirmation with respect to the age trend involved in such issues comes from studies that have recently focused on the Internet addiction of workers and professionals (108–111). Other studies also point out the association between Internet addiction (in both adults and young adults) with depression (43, 112–114), hyperactivity and attention deficit (115–119).
The prevalence of Internet addiction in the adults leads us to consider the growing incidence of attention disorders such as ADHD in this age group. Although ADHD is a disorder that begins in childhood, if it is not recognized and properly treated, it can develop into adult ADHD. Although hyperactivity often tends to diminish over time, emotional restlessness and instability in interpersonal relations sometimes persist, together with difficulty in organizing oneself and managing several tasks in parallel (120–123); attention difficulties persist, manifesting themselves as difficulties in tasks such as keeping appointments and meeting deadlines. These consequences negatively affect different aspects of the adult's life, often leading to financial and work difficulties, interpersonal and relationship problems (124, 125). The significant association and predictive estimation, which emerged in our study, of motor and attentional impulsiveness with Internet addiction, suggests that at the basis of this addiction there may also be problems of attention and impulse management that can be traced back to adult ADHD.
As regards codependency, this predictor usually includes personal relationship problems, also within the family context. We found only one study that explicitly considered family functioning, attentional impulsivity and Internet addiction in a sample of young adults in a single explanatory model (43). In this model, attentional impulsivity is proposed as a mediator of the relationship between family functioning and Internet addiction. Although our study does not test this mediation, it has shed light to the role of these predictors in explaining Internet addiction.
Practical implications of the study
Once some of the possible significant predictors have been identified, it seems appropriate to identify the containment interventions to be put in place. In this regard, the review by Xu et al. (126) on psychological interventions on Internet addiction suggests the formation of targeted and personalized intervention programmes. For impulsivity, which has been proposed as a potential indicator and treatment target of Internet addiction (127, 128), The Reality Therapy approach is suggested to assist individuals in controlling their behavior and making alternative Internet-related choices (129). Reality therapy is based on choice theory, which holds that people are in charge of their lives and what they do, feel, and think (126, 130). It focuses on goal-directed choices and self-control, which are very important aspects for young people (131, 132) directly by assisting individuals in reflecting on their behaviors, evaluating their options, and planning to choose more effective options (130, 133). Reality therapy may help people with addictions and impulsivity issues improve their self-control and reduce problem behaviors. Despite the fact that there have been very few studies of Internet addiction intervention using reality therapy alone, this method has been linked to improved self-esteem. Similar effects have been observed in studies of reality therapy for substance abuse (134, 135). Although more research is needed, preliminary findings suggest that reality therapy may play a role in the treatment of Internet addiction (130). Because good family functioning was linked to a lower risk of experiencing Internet addiction, family factors may be important targets for Internet addiction interventions (136). Family therapy is not a specific process, but rather a set of interventions aimed at improving family functions and relationships rather than directly addressing addictive behaviors. The therapies are designed to improve communication and relationships while shifting psychological needs fulfillment away from the internet and toward interactions and building relationships with family members (137, 138). Shek et al. (139, 140) used a combination of motivational interviewing and family-based therapy. Participants reported less Internet addiction and improved family functioning.
Since our study reveals the predictive role of codependency, and this is certainly associated with problems of poor relationship functioning, it can be assumed that both family therapy and other interventions or compound approaches may help. Mindfulness-oriented recovery enhancement (MORE), for example, combines mindfulness training with cognitive restructuring (the process of learning to identify and modify maladaptive thoughts through methods such as logical disputation) (141). Some studies have looked into combining two different psychosocial treatments. According to Yao et al. (142), combining reality therapy and mindfulness meditation had a significant effect on Internet gaming disorder.
Given that an inverse relationship between internet addiction and information literacy has emerged in several studies (143–145), further preventive and restraining interventions could include ad hoc media and information literacy enhancement programs, which have been found to be effective in addressing other youth issues such as various addictions (146–148), doping consumption in sports (149, 150), eating disorders (151–153), ciberbullismo (154, 155), youth aggressiveness and deviant behaviours (156, 157).
With regard to the above-mentioned interventions, it should be noted that since most of them are conducted with small groups of adolescents, it remains open to question the extent of their effectiveness with a different target group such as young adults and adults. For example, both adult co-dependency and adult hyperactivity problems would require further experimentation, taking into account the different contexts and the actual limitations/opportunities of the current living conditions. Further research and implementation of targeted and customized programmes will certainly be necessary.
Limitations of the study
Our findings should be interpreted while acknowledging some limitations. First, the sample size for this study was small and the statistical power can be affected. This limitation was due to the difficulty of getting more students involved in the study during the COVID-19 emergency, but we believe that future studies could benefit from a larger sample size and selecting participants from other parts of the country. Second, the participants in our sample were all university students. This choice was made bearing in mind the results of recent meta-analyses conducted in different countries that have shown a high prevalence of Internet addiction in this population [e.g., (87, 158, 159)] and have raised the urgence to orientate policy strategies to this emerging issue for young adults. However future research will be needed to replicate these findings in other groups. Third, it should considered that the UADI-2 instrument does not differentiate between different forms of addiction (smartphone, social media, cybersex, game addiction) and the measure is indicative of a general prevalence, which in light of current developments, would instead need a specific distinction to adequately and differentially define the areas affected by possible problematicness. Furthermore, results reported may reflect some overestimation of problematic incidence due to this lack of classificatory articulation and normative update since the moment of validation of the instrument UADI-2 carried out in 2005. In addition, future studies could include more variables (such as socio-economic status, including clinical data as depression, anxiety, feeling of loneliness, interpersonal issues, maladaptive cognitions) and more covariates variables. Finally, it was a cross-sectional study, therefore, causalities could not be entirely clarified.
Conclusion
This study investigates the role and predictive weight of impulsivity and codependency on Internet addiction on a sample of young adult university students by using a hierarchical regression analysis. The results confirmed that both impulsivity and codependency play a role in problems related to Internet use, moreover they showed the relative importance of gender and age. The study demonstrated that maladaptive and addicted use of the Internet is a critical issue also among young adults, and it suggests that preventive and restraint measures are needed. These can include not only targeted awareness programs, but also interventions aimed at encouraging a greater emotional and attentional control and a more balanced management of personal relationships among young people.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Statements
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
The studies involving human participants were reviewed and approved by Institutional Review Board of the University of Cassino and Southern Lazio. The participants provided their written informed consent to participate in this study.
Author contributions
PD, SM, and SC designed the study and drafted the manuscript. PD, SM, SC, and ADR analyzed the data and discussed the results. EC, LG, and AC revised the manuscript. All authors contributed to the article and approved the submitted version.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
References
1.
YoungKSRogersRC. The relationship between depression and Internet addiction. Cyberpsychol Behavior. (1998) 1:25–8. 10.1089/cpb.1998.1.25
2.
DavisRA. A cognitive-behavioral model of pathological Internet use. Comput Human Behav. (2001) 17:187–95. 10.1016/S0747-5632(00)00041-8
3.
MeerkerkGJVan Den EijndenRJVermulstAAGarretsenHF. The compulsive internet use scale (CIUS): some psychometric properties. Cyberpsychology & behavior. (2009) 12:1–6. 10.1089/cpb.2008.0181
4.
KoCHYenJYYenCFChenCSChenCC. The association between Internet addiction and psychiatric disorder: a review of the literature. Eur Psychiat. (2012) 27:1–8. 10.1016/j.eurpsy.2010.04.011
5.
ShapiraNALessigMCGoldsmithTDSzaboSTLazoritzMGoldMSet al. (2003). Problematic internet use: proposed classification and diagnostic criteria. Depress. Anxiety. 17:207–16. 10.1002/da.10094
6.
YoungKS. Internet addiction: the emergence of a new clinical disorder. Cyberpsychol Behav Soc Netw. (1996) 3:237–44. 10.1089/cpb.1998.1.237
7.
AndersonELSteenEStavropoulosV. Internet use and problematic internet use: A systematic review of longitudinal research trends in adolescence and emergent adulthood. Int J Adolesc Youth. (2017) 22:430–54. 10.1080/02673843.2016.1227716
8.
PremsinghJGPrajinaPV. A study on the impact of internet addiction among adolescents. Int J Scientific Res. (2013) 2:499–501. 10.15373/22778179/AUG2013/164
9.
MissaouiSGBrahimTBourigaWAbdelazizAB. Prevalence and consequences of internet addiction in a cohort of tunisian adolescents: a pilot study. J Child Adolesc Behav. (2015) 3:257. 10.4172/2375-4494.1000257
10.
BaturayMHTokerS. Internet addiction among college students: some causes and effects. Educ Inf Technol. (2019) 24:2863–85. 10.1007/s10639-019-09894-3
11.
MohamedGBernoussR. A cross-sectional study on Internet addiction among Moroccan high school students, its prevalence and association with poor scholastic performance. Int J Adolesc Youth. (2020) 25:479–90. 10.1080/02673843.2019.1674165
12.
TokunagaRS. A meta-analysis of the relationships between psychosocial problems and internet habits: synthesizing internet addiction, problematic internet use, and deficient self-regulation research. Commun Monogr. (2017) 84:423–46. 10.1080/03637751.2017.1332419
13.
TripathiA. Impact of internet addiction on mental health: an integrative therapy is needed. Integr Med Int. (2017) 4:215–22. 10.1159/000491997
14.
ShawMBlackDW. Internet addiction. CNS Drugs. (2008) 22:353–65. 10.2165/00023210-200822050-00001
15.
AlHeneidiHHSmithAP. Exploring the influence of information overload, internet addiction, and social network addiction, on students' well-being and academic outcomes. In: International Symposium on Human Mental Workload: Models and Applications. Cham: Springer. (2021) p. 116–135. 10.1007/978-3-030-91408-0_8
16.
AgastyaIGNSisteKNasrunMWSKusumadewiI. Cybersex addiction: an overview of the development and treatment of a newly emerging disorder. Med J Indonesia. (2020) 29:233â−41. 10.13181/mji.rev.203464
17.
BodiGMaintenantCPennequinV. The role of maladaptive cognitions in gaming disorder: differences between online and offline gaming types. Addict Behav. (2021) 112:106595. 10.1016/j.addbeh.2020.106595
18.
MüllerALaskowskiNMWegmannESteins-LoeberSBrandM. Problematic online buying-shopping: is it time to considering the concept of an online subtype of compulsive buying-shopping disorder or a specific internet-use disorder?. Curr Addict Rep. (2021) 8:494–9. 10.1007/s40429-021-00395-3
19.
Rosendo-RiosVTrottSShuklaP. Systematic literature review online gaming addiction among children and young adults: a framework and research agenda. Addict Behav. (2022) 107238. 10.1016/j.addbeh.2022.107238
20.
CarliVDurkeeTWassermanDHadlaczkyGDespalinsRKramarzEet al. The association between pathological internet use and comorbid psychopathology: a systematic review. Psychopathology. (2013) 46:1–13. 10.1159/000337971
21.
ChenYLChenSHGauSS. ADHD and autistic traits, family function, parenting style, and social adjustment for Internet addiction among children and adolescents in Taiwan: a longitudinal study. Res Dev Disabil. (2015) 39:20–31. 10.1016/j.ridd.2014.12.025
22.
KimHKDavisKE. Toward a comprehensive theory of problematic Internet use: Evaluating the role of self-esteem, anxiety, flow, and the self-rated importance of Internet activities. Computers in Human. (2009) 25:490–500. 10.1016/j.chb.2008.11.001
23.
TreuerTFábiánZFürediJ. Internet addiction associated with features of impulse control disorder: is it a real psychiatric disorder?. J Affect Dis. (2001) 66:283. 10.1016/S0165-0327(00)00261-5
24.
HaJHKimSYBaeSCBaeSKimHSimMet al. Depression and internet addiction in adolescents. Psychopathology. (2007) 40:424–30. 10.1159/000107426
25.
JangKSHwangSYChoiJY. Internet addiction and psychiatric symptoms among Korean adolescents. J School Health. (2008) 78:165–71. 10.1111/j.1746-1561.2007.00279.x
26.
DiotaiutiPGirelliLManconeSCorradoSValenteGCavicchioloE. impulsivity and depressive brooding in internet addiction: A study with a sample of italian adolescents during covid-19 lockdown. Front Psychiatry. (2022) 13:941313. 10.3389/fpsyt.2022.941313
27.
KoCHYenJYYenCFLinHCYangMJ. Factors predictive for incidence and remission of internet addiction in young adolescents: a prospective study. CyberPsychol Beh. (2007) 10:545–51. 10.1089/cpb.2007.9992
28.
YenJYKoCHYenCFWuHYYangMJ. The comorbid psychiatric symptoms of Internet addiction: attention deficit and hyperactivity disorder (ADHD), depression, social phobia, and hostility. J. Adolesc Health. (2007) 41:93–8. 10.1016/j.jadohealth.2007.02.002
29.
DiotaiutiPValenteGManconeSGramboneAChiricoA. (2021). Metric goodness and measurement invariance of the italian brief version of interpersonal reactivity index: A study with young adults. Front Psychol. (2021) 12:773363. 10.3389/fpsyg.2021.773363
30.
MilaniLOsualdellaDDi BlasioP. Interpersonal relationships, coping strategies and problematic internet use in adolescence: an italian study. Stud Health Technol Inform. (2009) 144:69–71. 10.3389/conf.neuro.14.2009.06.068
31.
MilaniLLa TorreGFioreMGrumiSGentileDAFerranteMet al. Internet gaming addiction in adolescence: risk factors and maladjustment correlates. Int J Ment Health Addict. (2018) 16:888–904. 10.1007/s11469-017-9750-2
32.
CaoFSuLLiuTGaoX. The relationship between impulsivity and Internet addiction in a sample of Chinese adolescents. Eur Psychiat. (2007) 22:466–71. 10.1016/j.eurpsy.2007.05.004
33.
MazhariS. The prevalence of problematic internet use and the related factors in medical students, Kerman, Iran. Addict Health Summer-Autumn. (2012) 4:87–94.
34.
KayişARSaticiSAYilmazMFSimşekDCeyhanEBakiogluF. Big five-personality trait and internet addiction: a meta-analytic review. Comput Hum Behav. (2016) 63:35–40. 10.1016/j.chb.2016.05.012
35.
PetruccelliFDiotaiutiPVerrastroVPetruccelliICarentiMLDe BerardisDet al. Obsessive-compulsive aspects and pathological gambling in an Italian sample. BioMed Res Int. (2014) 2014:167438. 10.1155/2014/167438
36.
Verdejo-GarcíaALawrenceAJClarkL. Impulsivity as a vulnerability marker for substance-use disorders: review of findings from high-risk research, problem gamblers and genetic association studies. Neurosci Biobehav Rev. (2008) 32:777–810. 10.1016/j.neubiorev.2007.11.003
37.
ZhangYLiuZZhaoY. Impulsivity, social support and depression are associated with latent profiles of internet addiction among male college freshmen. Front Psychiatry. (2021) 12:642914. 10.3389/fpsyt.2021.642914
38.
Rømer ThomsenKCallesenMBHesseMKvammeTLPedersenMMPedersenMUet al. Impulsivity traits and addiction-related behaviors in youth. J Behav Addict. (2018) 7:317–30. 10.1556/2006.7.2018.22
39.
LeeHWChoiJSShinYCLeeJYJungHYKwonJS. Impulsivity in internet addiction: a comparison with pathological gambling. Cyberpsychol Behav Soc Netw. (2012) 15:373–7. 10.1089/cyber.2012.0063
40.
ZhangYMeiSLiLChaiJLiJDuH. The relationship between impulsivity and internet addiction in chinese college students: a moderated mediation analysis of meaning in life and self-esteem. PLoS ONE. (2015) 10:e0131597. 10.1371/journal.pone.0131597
41.
YücensBÜzerA. The relationship between internet addiction, social anxiety, impulsivity, self-esteem, and depression in a sample of Turkish undergraduate medical students. Psychiatry Res. (2018) 267:313–8. 10.1016/j.psychres.2018.06.033
42.
LiQDaiWZhongYWangLDaiBLiuX. The mediating role of coping styles on impulsivity, behavioral inhibition/approach system, and internet addiction in adolescents from a gender perspective. Front Psychol. (2019) 10:2402. 10.3389/fpsyg.2019.02402
43.
MarzilliECernigliaLBallarottoGCiminoS. Internet addiction among young adult university students: the complex interplay between family functioning, impulsivity, depression, and anxiety. Int J Environ Res Public Health. (2020) 17:8231. 10.3390/ijerph17218231
44.
KhanbabaeiSAbdollahiMHShahgholianM. The predictive role of working memory and impulsivity in internet addiction, an investigation about the mediating role of time perception. Pers Individ Dif. (2022) 185:111280. 10.1016/j.paid.2021.111280
45.
KaraşarB. Codependency: An evaluation in terms of depression, need for social approval and self-love/self-efficacy. Kastamonu Egitim Dergisi. (2021) 29:117−26. 10.24106/kefdergi.738845
46.
OrbonMCBasariaDDewiFIRGumaraoMSMergalVCHengPH. Codependency among family members as predicted by family functioning and personality type. In: International Conference on Economics, Business, Social, and Humanities (ICEBSH 2021). Atlantis Press. (2021) p. 1388–1393. 10.2991/assehr.k.210805.218
47.
BerdichevskyAAPadunMAGagarinaMAArkhipovaMV. Emotional regulation in individuals, standing in codependent relationship. Clin Psychol. (2021) 10:185–204. 10.17759/cpse.2021100409
48.
AimaganbetovaOHSagnayevaTZBimagambetovaZTAdilovaETKasymL. Study of independence as social-psychological factor influencing on the personal features of the co-dependent person. Bull. KazNU. Psychol. Sociol. Ser. (2018) 2:146-153. https://bulletin-psysoc.kaznu.kz/index.php/1-psy/article/view/86810.26577/JPSS-2018-2-660
49.
RozhnovaTMKostyukSVMalyginVLEnikolopovSNNikolenkoVN. The phenomenon of codependency: psychological and medical genetic aspects. Neurol Neuropsychiat Psychosomat. (2020) 12:53–9. 10.14412/2074-2711-2020-5-53-59
50.
BaconIMcKayEReynoldsFMcIntyreA. The lived experience of codependency: An interpretative phenomenological analysis. Int J Ment Health Addict. (2020) 18:754–71. 10.1007/s11469-018-9983-8
51.
Barrera-AlgarínEVázquez-FernándezMJ. The rise of online sports betting, its fallout, and the onset of a new profile in gambling disorder: young people. J Addict Dis. (2021) 39:363–72. 10.1080/10550887.2021.1886567
52.
LuLCTsaiCT. The effect of virtual community codependency on virtual community addiction: exploring the mediation effects. In: 2018 Global Marketing Conference at Tokyo (pp. 1169-1173). Available online at: http://db.koreascholar.com/article.aspx?code=35168210.15444/GMC2018.09.08.02
53.
ShishkovVVKokurenkovaPASokolovARUlyanovaMYIlyichevABPozdnyakVVet al. Correlation of internet addiction with codependency and temperament. In: Medical Scientific Bulletin of Central Chernozemye (Naučno-medicinskij vestnik Central′nogo Cernozem′â). (2021) p. 45–51. Available online at: https://new.vestniksurgery.com/index.php/1990-472X/article/view/6786
54.
ArtemtsevaNGMalkinaMA. Cognitive mistakes of codependents as a way to protect against uncertainty. Vestnik Of Samara State Technical University Psychological And Pedagogical Sciences. (2022) 19:153–66. 10.17673/vsgtu-pps.2022.1.11
55.
SchreiberLRGrantJEOdlaugBL. Emotion regulation and impulsivity in young adults. J Psychiatr Res. (2012) 46:651–8. 10.1016/j.jpsychires.2012.02.005
56.
ZiadaKEBeckerDBakhietSFDuttonEEssaYAS. Impulsivity among young adults: Differences between and within Western and Arabian populations in the BIS-11. Curr Psychol. (2020) 39:464–73. 10.1007/s12144-018-0032-3
57.
Di CarloFPettorrusoMAlessiMCPicuttiECollevecchioRMigliaraGet al. Characterizing the building blocks of Problematic Use of the Internet (PUI): The role of obsessional impulses and impulsivity traits among Italian young adults. Compr Psychiatry. (2021) 106:152225. 10.1016/j.comppsych.2021.152225
58.
O'DonnellBFSkosnikPDHetrickWPFridbergDJ. Decision making and impulsivity in young adult cannabis users. Front Psychol. (2021) 12:2594. 10.3389/fpsyg.2021.679904
59.
SalvarliSIGriffithsMD. The association between internet gaming disorder and impulsivity: A systematic review of literature. Int J Ment Health Addict. (2022) 20:92–118. 10.1007/s11469-019-00126-w
60.
ServidioRBartoloMGPalermitiALCostabileA. Fear of COVID-19, depression, anxiety, and their association with Internet addiction disorder in a sample of Italian students. J Affect Dis Rep. (2021) 4:100097. 10.1016/j.jadr.2021.100097
61.
SinclairDLVanderplasschenWSavahlSFlorenceMBestDSussmanS. Substitute addictions in the context of the COVID-19 pandemic. J Behav Addict. (2021) 9:1098–102. 10.1556/2006.2020.00091
62.
AbbottAAskelsonNSchererAMAfifiRA. Critical reflections on COVID-19 communication efforts targeting adolescents and young adults. J Adolescent Health. (2020) 67:159–60. 10.1016/j.jadohealth.2020.05.013
63.
AliverniniFManganelliSGirelliLCozzolinoMLucidiFCavicchioloE. Physical distancing behavior: the role of emotions, personality, motivations, and moral decision-making. J Pediatr Psychol. (2021) 46:15–26. 10.1093/jpepsy/jsaa122
64.
CavicchioloEManganelliSGirelliLCozzolinoMLucidiFAliverniniF. Adolescents at a distance: the importance of socio-cognitive factors in preventive behavior during the COVID-19 pandemic. Eur J Health Psychol. (2021) 28:161–70. 10.1027/2512-8442/a000083
65.
DiotaiutiPValenteGManconeSFaleseLBellizziFAnastasiDet al. Perception of risk, self-efficacy and social trust during the diffusion of Covid-19 in Italy. Int. J. Environ. Res. Public Health. (2021) 18:3421–7. 10.3390/ijerph18073427
66.
BaioccoRMancaMDel MiglioCCerrutiRCouyomdjiamA. Uso e abuso di Internet in adolescenza: quale relazione con i disturbi psicosomatici? Internet use and abuse in adolescence: what relationship with psychosomatic disorders?Psicotech. (2005) 2:47–60. 10.1400/69127
67.
PattonJHStanfordMSBarrattES. Factor structure of the Barratt impulsiveness scale. J Clin Psychol. (1995) 51:768–74. 10.1002/1097-4679(199511)51:6<768::AID-JCLP2270510607>3.0.CO;2-1
68.
FossatiADi CeglieAAcquariniEBarrattES. Psychometric properties of an Italian version of the Barratt Impulsiveness Scale-11 (BIS-11) in nonclinical subjects. J Clin Psychol. (2001) 57:815–28. 10.1002/jclp.1051
69.
FischerJLSpannLCrawfordDW. Measuring codependency. Alcohol Treat Q. (1991) 8:87–100. 10.1300/J020V08N01_06
70.
RosenthalSRChaYClarkMA. The internet addiction test in a young adult US population. Cyberpsychol Behav Soc Netw. (2018) 21:661–6. 10.1089/cyber.2018.0143
71.
WolniewiczCATiamiyuMFWeeksJWElhaiJD. Problematic smartphone use and relations with negative affect, fear of missing out, and fear of negative and positive evaluation. Psychiatry Res. (2018) 262:618–23. 10.1016/j.psychres.2017.09.058
72.
AndradeALMScatenaABedendoAEnumoSRFDellazzana-ZanonLLPrebianchiHBet al. Findings on the relationship between Internet addiction and psychological symptoms in Brazilian adults. Int J Psychol. (2020) 55:941–50. 10.1002/ijop.12670
73.
IoannidisKTrederMSChamberlainSRKiralyFReddenSASteinDJet al. Problematic internet use as an age-related multifaceted problem: Evidence from a two-site survey. Addict Behav. (2018) 81:157–66. 10.1016/j.addbeh.2018.02.017
74.
MenonSNarayananLKahwajiAT. Internet addiction: A research study of college students in India. J Econ Bus. (2018) 1:100–6. 10.31014/aior.1992.01.01.9
75.
LinMP. Prevalence of internet addiction during the COVID-19 outbreak and its risk factors among junior high school students in Taiwan. Int J Environ Res Public Health. (2020) 17:8547. 10.3390/ijerph17228547
76.
CaplanSE. Theory and measurement of generalized problematic Internet use: A two-step approach. Comput Human Behav. (2010) 26:1089–97. 10.1016/j.chb.2010.03.012
77.
FioravantiGPrimiCCasaleS. Psychometric evaluation of the generalized problematic internet use scale 2 in an Italian sample. Cyberpsychol Behav Soc Netw. (2013) 16:761–6. 10.1089/cyber.2012.0429
78.
FioravantiGCasaleS. Evaluation of the psychometric properties of the Italian Internet Addiction Test. Cyberpsychol Behav Soc Netw. (2015) 18:120–8. 10.1089/cyber.2014.0493
79.
D'EliaFCalleaA. UADI: uno studio sulla dipendenza da internet. Int J Educ Psychol. (2010) 4:107–15.
80.
GnisciAPeruginiMPedoneRDi ConzaA. Construct validation of the use, abuse and dependence on the Internet inventory. Comput Human Behav. (2011) 27:240–7. 10.1016/j.chb.2010.08.002
81.
Di LorenzoMLanciniMSuttoraCZanellaTE. La dipendenza da internet in adolescenza tra normalità e psicopatologia: uno studio italiano. Psichiatria e psicoterapia. (2013) 101–35.
82.
MasiGBerloffaSMuratoriPPacielloMRossiMMiloneA. Internet addiction disorder in referred adolescents: a clinical study on comorbidity. Addict Res Theory. (2021) 29:205–11. 10.1080/16066359.2020.1772242
83.
SechiCLoiGCabrasC. Addictive internet behaviors: The role of trait emotional intelligence, self-esteem, age, and gender. Scand J Psychol. (2021) 62:409–17. 10.1111/sjop.12698
84.
Bruno G., Panzeri, A., Granziol, U., Alivernini, F., Chirico, A., Galli, F., et al. (2020). The Italian COVID-19 psychological research consortium (it c19prc): general overview and replication of the UK study. J Clin Med. 10(1). 10.3390/jcm10010052
85.
AliverniniFManganelliS. The classmates social isolation questionnaire (CSIQ): an initial validation. Eur J Developmental Psychol. (2016) 13:264–74. 10.1080/17405629.2016.1152174
86.
CavicchioloELucidiFDiotaiutiPChiricoAGalliFManganelliSet al. Adolescents' characteristics and peer relationships in class: a population study. Int J Environ Res Public Health. (2022) 19:8907. 10.3390/ijerph19158907
87.
SalarvandSNAlbatinehADalvandSBaghban KarimiEGhanei GheshlaghR. Prevalence of internet addiction among iranian university students: a systematic review and meta-analysis. Cyberpsychol Behav Soc Netw. (2022) 25:213–22. 10.1089/cyber.2021.0120
88.
BurkauskasJGecaite-StoncieneJDemetrovicsZGriffithsMDKirályO. Prevalence of problematic internet use during the COVID-19 pandemic. Curr Opin Behav Sci. (2022) 101179. 10.1016/j.cobeha.2022.101179
89.
OkaTHamamuraTMiyakeYKobayashiNHonjoMKawatoMet al. Prevalence and risk factors of internet gaming disorder and problematic internet use before and during the COVID-19 pandemic: a large online survey of Japanese adults. J Psychiatr Res. (2021) 142:218–25. 10.1016/j.jpsychires.2021.07.054
90.
ZhaoYJiangZGuoSWuPLuQXuYet al. Association of symptoms of attention deficit and hyperactivity with problematic internet use among university students in Wuhan, China during the COVID-19 pandemic. J Affect Disord. (2021) 286:220–7. 10.1016/j.jad.2021.02.078
91.
Mohler-KuoMDzemailiSFosterSWerlenLWalitzaS. Stress and mental health among children/adolescents, their parents, and young adults during the first COVID-19 lockdown in Switzerland. Int J Environ Res Public Health. (2021) 18:4668. 10.3390/ijerph18094668
92.
MengSQChengJLLiYYYangXQZhengJWChangXWet al. Global prevalence of digital addiction in general population: a systematic review and meta-analysis. Clin Psychol Rev. (2022) 102128. 10.1016/j.cpr.2022.102128
93.
GiallonardoVSampognaGDel VecchioVLucianoMAlbertUCarmassiCet al. The impact of quarantine and physical distancing following COVID-19 on mental health: study protocol of a multicentric Italian population trial. Front Psychiatry. (2020) 11:533. 10.3389/fpsyt.2020.00533
94.
GuzickAGCandelariAWieseADSchneiderSCGoodmanWKStorchEA. Obsessive-compulsive disorder during the COVID-19 pandemic: a systematic review. Curr Psychiatry Rep. (2021) 23:71. 10.1007/s11920-021-01284-2
95.
MånssonVWallHBermanAHJayaram-LindströmNRosendahlI. A Longitudinal study of gambling behaviors during the COVID-19 pandemic in Sweden. Front Psychiatry. (2021) 12:708037. 10.3389/fpsyg.2021.708037
96.
SalernoLPallantiS. COVID-19 Related Distress in Gambling Disorder. Front Psychiatry. (2021) 12:620661. 10.3389/fpsyt.2021.620661
97.
KumariRLangerBGuptaRGuptaRKMirMTShafiBet al. Prevalence and determinants of Internet addiction among the students of professional colleges in the Jammu region. Fam Med Prim Care Rev. (2022) 11:325–9. 10.4103/jfmpc.jfmpc_991_21
98.
ParajuliBR. Increased internet addiction during COVID-19 pandemics. Life Res. (2022) 5:1. 10.53388/life2021-0829-635
99.
DufourMBrunelleNTremblayJLeclercDCousineauMMKhazaalYet al. Gender difference in internet use and internet problems among Quebec high school students. Can J Psychiatry. (2016) 61:663–8. 10.1177/0706743716640755
100.
SuWHanXYuHWuYPotenzaMN. Do men become addicted to internet gaming and women to social media? A meta-analysis examining gender-related differences in specific internet addiction. Comput Human Behav. (2020) 113:106480. 10.1016/j.chb.2020.106480
101.
HassanTAlamMMWahabAHawladerMD. Prevalence and associated factors of internet addiction among young adults in Bangladesh. J Egypt Public Health Assoc. (2020) 95:1–8. 10.1186/s42506-019-0032-7
102.
TianYZuoTSunQSunLCaoSQinN. The association between generalized and specific problematic internet use and its gender differences across different educational levels. Front Psychol. (2021) 12. 10.3389/fpsyg.2021.634581
103.
GuglielmucciFMontiMFranzoiIGSantoroGGranieriABillieuxJet al. Dissociation in problematic gaming: a systematic review. Curr Addict Rep. (2019) 6:1–14. 10.1007/s40429-019-0237-z
104.
GundogduUErogluM. The relationship between dissociation symptoms, sleep disturbances, problematic internet use and online gaming in adolescents. Psychol Health Med. (2021) 1–12. 10.1080/13548506.2021.1984542
105.
SchimmentiAMusettiACostanzoATerroneGMaganucoNRAglieri RinellaCet al. The unfabulous four: Maladaptive personality functioning, insecure attachment, dissociative experiences, and problematic internet use among young adults. Int J Ment Health Addict. (2021) 19:447–61. 10.1007/s11469-019-00079-0
106.
AwanHAAamirADiwanMNUllahIPereira-SanchezVRamalhoRet al. Internet and pornography use during the COVID-19 pandemic: presumed impact and what can be done. Front Psychiatry. (2021) 12:220. 10.3389/fpsyt.2021.623508
107.
KaramanoliETantarosSPavlopoulosV. Internet use in emerging adulthood: associations with life satisfaction, identity development, and attachment style. Psychology. (2020) 25:93–108. 10.12681/psy_hps.25589
108.
VaneaMO. Intensive/excessive use of internet and risks of internet addiction among specialized workers-gender and online activities differences. Procedia-Social and Behavioral Sciences. (2011) 30:757–64. 10.1016/j.sbspro.2011.10.148
109.
ShinSEKimNSJangEY. Comparison of problematic internet and alcohol use and attachment styles among industrial workers in Korea. Cyberpsychol Behav Soc Netw.. (2011) 14:665–72. 10.1089/cyber.2010.0470
110.
ShrivastavaASharmaMKMarimuthuP. Internet addiction at workplace and it implication for workers life style: exploration from Southern India. Asian J Psychiatr. (2018) 32:151–5. 10.1016/j.ajp.2017.11.014
111.
PohlMFeherGKapusKFeherANagyGDKissJet al. The association of internet addiction with burnout, depression, insomnia, and quality of life among hungarian high school teachers. Int J Environ Res Public Health. (2022) 19:438. 10.3390/ijerph19010438
112.
OstovarSAllahyarNAminpoorHMoafianFNorMBMGriffithsMD. Internet addiction and its psychosocial risks (depression, anxiety, stress and loneliness) among Iranian adolescents and young adults: A structural equation model in a cross-sectional study. Int J Ment Health Addict. (2016) 14:257–67. 10.1007/s11469-015-9628-0
113.
Dieris-HircheJBottelLBielefeldMSteinbüchelTKehyayanADierisBet al. Media use and Internet addiction in adult depression: a case-control study. Comput Human Behav. (2017) 68:96–103. 10.1016/j.chb.2016.11.016
114.
PrzepiorkaABlachnioACudoA. The role of depression, personality, and future time perspective in internet addiction in adolescents and emerging adults. Psychiatry Res. (2019) 272:340–8. 10.1016/j.psychres.2018.12.086
115.
WangBQYaoNQZhouXLiuJLvZT. The association between attention deficit/hyperactivity disorder and internet addiction: a systematic review and meta-analysis. BMC Psychiatry. (2017) 17:1–12. 10.1186/s12888-017-1408-x
116.
MutluerBTOrumTYSertcelikS. Incidence of Internet addiction in adult attention deficit hyperactivity disorder. Eur Psychiat. (2017) 41:S396–7. 10.1016/j.eurpsy.2017.02.457
117.
PanagiotidiMOvertonP. The relationship between internet addiction, attention deficit hyperactivity symptoms and online activities in adults. Compr Psychiatry. (2018) 87:7–11. 10.1016/j.comppsych.2018.08.004
118.
EvrenBEvrenCDalbudakETopcuMKutluN. The impact of depression, anxiety, neuroticism, and severity of Internet addiction symptoms on the relationship between probable ADHD and severity of insomnia among young adults. Psychiatry Res. (2019) 271:726–31. 10.1016/j.psychres.2018.12.010
119.
KandreDDPatelAVMehtaPI. Analytical study of adult attention deficit hyperactivity disorder symptoms and internet addiction among medical students. Neuropsychiatry & Neuropsychology/Neuropsychiatria i Neuropsychologia. (2020) 15. 10.5114/nan.2020.97398
120.
SongPZhaMYangQZhangYLiXRudanI. The prevalence of adult attention-deficit hyperactivity disorder: A global systematic review and meta-analysis. J Glob Health. (2021) 11:04009. 10.7189/jogh.11.04009
121.
BarkleyRABrownTE. Unrecognized attention-deficit/hyperactivity disorder in adults presenting with other psychiatric disorders. CNS Spectr. (2008) 13:977–84. 10.1017/S1092852900014036
122.
VolkowNDSwansonJM. Adult attention deficit–hyperactivity disorder. New England Journal of Medicine. (2013) 369:1935–44. 10.1056/NEJMcp1212625
123.
AnbarasanDKitchinMAdlerLA. Screening for adult ADHD. Curr Psychiatry Rep. (2020) 22:1–5. 10.1007/s11920-020-01194-9
124.
AdlerLAFaraoneSVSpencerTJBerglundPAlperinSKesslerRC. The structure of adult ADHD. Int J Methods Psychiatr Res. (2017) 26:e1555. 10.1002/mpr.1555
125.
KatzmanMABilkeyTSChokkaPRFalluAKlassenLJ. Adult ADHD and comorbid disorders: clinical implications of a dimensional approach. BMC Psychiatry. (2017) 17:1–15. 10.1186/s12888-017-1463-3
126.
XuLXWuLLGengXMWangZLGuoXYSongKRet al. A review of psychological interventions for internet addiction. Psychiatry Res. (2021) 302:114016. 10.1016/j.psychres.2021.114016
127.
IrvineMAWorbeYBoltonSHarrisonNABullmoreETVoonV. Impaired decisional impulsivity in pathological videogamers. PLoS ONE. (2013) 8:e75914. 10.1371/journal.pone.0075914
128.
DongGPotenzaMN. A cognitive-behavioral model of Internet gaming disorder: Theoretical underpinnings and clinical implications. J Psychiatr Res. (2014) 58:7–11. 10.1016/j.jpsychires.2014.07.005
129.
WubboldingRE. Evolution of Psychotherapy: A Conference of Inner Control. Int J Reality Ther. (2006) 26:35–7.
130.
KimJU. The effect of a R/T group counseling program on the Internet addiction level and self-esteem of Internet addiction university students. Int J Realty Therap. (2008) 27:4–12.
131.
AliverniniFLucidiFManganelliS. Assessment of academic motivation: A mixed methods study. Int J Mult Res Approaches. (2008) 2:71–82. 10.5172/mra.455.2.1.71
132.
AliverniniFManganelliSLucidiF. Personal and Classroom Achievement Goals: Their Structures and Relationships. J Psychoeduc Assess. (2018) 36:354–65. 10.1177/0734282916679758
133.
KimJU. A reality therapy group counseling program as an Internet addiction recovery method for college students in Korea. Int J Reality Therapy. (2007) 26:3–9.
134.
JahromiMKMosallanejadL. The impact of reality therapy on metacognition, stress and hope in addicts. Glob J Health Sci. (2014) 6:281. 10.5539/gjhs.v6n6p281
135.
LawFMGuoGJ. The impact of reality therapy on self-efficacy for substance-involved female offenders in Taiwan. Int J Offender Ther Comp Criminol. (2015) 59:631–53. 10.1177/0306624X13518385
136.
YuLShekDTL. Internet addiction in Hong Kong adolescents: a three-year longitudinal study. J Pediatr Adolesc Gynecol. (2013) 26:S10–7. 10.1016/j.jpag.2013.03.010
137.
HanDHKimSMLeeYSRenshawPF. The effect of family therapy on the changes in the severity of on-line game play and brain activity in adolescents with on-line game addiction. Psychiatry Res. (2012) 202:126–31. 10.1016/j.pscychresns.2012.02.011
138.
LiuQXFangXYYanNZhouZKYuanXJLanJet al. Multi-family group therapy for adolescent Internet addiction: exploring the underlying mechanisms. Addict Behav. (2015) 42:1–8. 10.1016/j.addbeh.2014.10.021
139.
ShekDTTangVMLoCY. Evaluation of an Internet addiction treatment program for Chinese adolescents in Hong Kong. Adolescence. (2009) 44.
140.
DiotaiutiPValenteGManconeSGramboneA. Psychometric properties and a preliminary validation study of the Italian brief version of the communication styles inventory (CSI-B/I). Front Psychol. (2020) 11:1421. 10.3389/fpsyg.2020.01421
141.
LiWGarlandELHowardMO. Therapeutic mechanisms of Mindfulness-Oriented Recovery Enhancement for internet gaming disorder: Reducing craving and addictive behavior by targeting cognitive processes. J Addict Dis. (2018) 37:5–13. 10.1080/10550887.2018.1442617
142.
YaoYWChenPRChiang-shanRLHareTALiSZhangJTet al. Combined reality therapy and mindfulness meditation decrease intertemporal decisional impulsivity in young adults with Internet gaming disorder. Comput Hum Behav. (2017) 68:210–6. 10.1016/j.chb.2016.11.038
143.
LangarizadehMNaghipourMTabatabaeiSMMirzaeiAVagharME. Prediction of internet addiction based on information literacy among students of Iran University of Medical Sciences. Electron Phys. (2018) 10:6333. 10.19082/6333
144.
DeonisiusRFLestariISarkadiS. The effect of digital literacy to internet addiction. J Educ. (2019) 5:71–5. 10.29210/120192333
145.
GarcíaLCGómezMC. Approaches and guidelines for creating educational projects based on Internet addiction: Presentation of a new approach linked to digital competence. In: Eighth International Conference on Technological Ecosystems for Enhancing Multiculturality. (2020). p. 605–10. 10.1145/3434780.3436657
146.
JormandHBashirianSBaratiMRezapur-ShahkolaiFBabamiriM. Exploration of media literacy about substance abuse among students: a qualitative study. Turkish J Addict. (2020) 7:234–40. 10.5152/ADDICTA.2020.20073
147.
BaratiMBashirianSJormandHBabamiriMRezapur-ShahkolaiF. Can substance abuse media literacy increase prediction of drug use in students?. BMC Psychol. (2022) 10:1–15. 10.1186/s40359-022-00860-2
148.
DaiHDRatnapradipaKMichaudTLKingKMGuenzelNTamrakarNet al. Vaping media literacy, harm perception, and susceptibility of e-cigarette use among youth. Am J Prevent Med. (2022). 10.1016/j.amepre.2022.05.012 [Epub ahead of print].
149.
LucidiFMalliaLAliverniniFChiricoAManganelliSGalliFet al. The effectiveness of a new school-based media literacy intervention on adolescents' doping attitudes and supplements use. Front Psychol. (2017) 8:749. 10.3389/fpsyg.2017.00749
150.
MalliaLChiricoAZelliAGalliFPalombiTBortoliLet al. The implementation and evaluation of a media literacy intervention about PAES use in sport science students. Front Psychol. (2020) 11:368. 10.3389/fpsyg.2020.00368
151.
McLeanSAWertheimEHMastersJPaxtonSJ. A pilot evaluation of a social media literacy intervention to reduce risk factors for eating disorders. Int J Eat Disord. (2017) 50:847–51. 10.1002/eat.22708
152.
DiotaiutiPGirelliLManconeSValenteGBellizziFMisitiFet al. Psychometric properties and measurement invariance across gender of the Italian version of the tempest self-regulation questionnaire for eating adapted for young adults. Front Psychol. (2022) 13:941784. 10.3389/fpsyg.2022.941784
153.
PaxtonSJMcLeanSARodgersRF. “My critical filter buffers your app filter”: Social media literacy as a protective factor for body image. Body Image. (2022) 40:158–64. 10.1016/j.bodyim.2021.12.009
154.
KapucuMSÖzcanHÖzyerKK. (2021). The relationship between secondary school students' digital literacy levels, social media usage purposes and cyberbullying threat level. Int J Modern Educ Stud. (2021) 5:537–66. 10.51383/ijonmes.2021.136
155.
ChengACS. Adolescent co-researchers design media literacy lessons to address cyberbullying through design thinking: encouraging passive bystanders to protect cyber-victims. In: Research Anthology on Combating Cyber-Aggression and Online Negativity. IGI Global. (2022). p. 285–311. 10.4018/978-1-6684-5594-4.ch017
156.
XieXGaiXZhouY. A meta-analysis of media literacy interventions for deviant behaviors. Comput. Educ. (2019) 139:146–56. 10.1016/j.compedu.2019.05.008
157.
MoorhouseEABrooksH. Critical media literacy approaches to violence prevention: A research note. J Media Liter Educ. (2020) 12:84–99. 10.23860/JMLE-2020-12-1-7
158.
JosephJVargheseAVrVDhandapaniMGroverSSharmaSet al. Prevalence of internet addiction among college students in the Indian setting: a systematic review and meta-analysis. General Psychiat. (2021) 34:e100496. 10.1136/gpsych-2021-100496
159.
ShaoYJZhengTWangYQLiuLChenYYaoYS. Internet addiction detection rate among college students in the People's Republic of China: a meta-analysis. Child Adolesc Psychiatry Ment Health. (2018) 12:25. 10.1186/s13034-018-0231-6
Summary
Keywords
Internet addiction, young adults, impulsivity, motor impulsivity, attentional impulsivity, codependency
Citation
Diotaiuti P, Mancone S, Corrado S, De Risio A, Cavicchiolo E, Girelli L and Chirico A (2022) Internet addiction in young adults: The role of impulsivity and codependency. Front. Psychiatry 13:893861. doi: 10.3389/fpsyt.2022.893861
Received
10 March 2022
Accepted
12 August 2022
Published
06 September 2022
Volume
13 - 2022
Edited by
Wing Fai Yeung, Hong Kong Polytechnic University, Hong Kong SAR, China
Reviewed by
Neven Ricijas, University of Zagreb, Croatia; Martina Benvenuti, University of Bologna, Italy; Mohamad Noorman Masrek, Universiti Teknologi MARA Puncak Alam, Malaysia; Dora Dodig Hundric, University of Zagreb, Croatia
Updates
Copyright
© 2022 Diotaiuti, Mancone, Corrado, De Risio, Cavicchiolo, Girelli and Chirico.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Pierluigi Diotaiuti p.diotaiuti@unicas.it
This article was submitted to Public Mental Health, a section of the journal Frontiers in Psychiatry
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.