Problematic Internet use in drug addicts under treatment in public rehab centers (2019)

World J Psychiatry. 2019 Jun 10; 9(3): 55–64.

Published online 2019 Jun 10. doi: 10.5498/wjp.v9.i3.55

PMCID: PMC6560498

PMID: 31211113

Stefano Baroni, Donatella Marazziti, Federico Mucci, Elisa Diadema, and Liliana Dell’Osso

Abstract

BACKGROUND

Problematic Internet use (PIU) or Internet addiction has been recognized to be a behavioral addiction characterized by excessive or poorly controlled preoccupations, urges, or behaviors regarding computer use and Internet access that leads to impairment or distress resembling substance abuse.

AIM

To investigate the prevalence and characteristics of Internet use and abuse in a group of drug addicts from Southern Italy, by means of a specific questionnaire [“Questionario sull’Utilizzo delle Nuove Tecnologie” (QUNT)].

METHODS

All subjects (183) were heavy smokers, almost 50% of them used heroin and/or opioid compounds, 30% alcohol, 10% cannabis, 8% cocaine, and 5% were polydrug users. Almost 10% of the individuals were also suffering from gambling disorder.

RESULTS

The time spent online was more than 4 hours a day in the total sample, with a slight prevalence in male subjects. Cocaine and cannabis users spent more than 6 hours online, significantly more than opioid and alcohol abusers. Distribution of the QUNT factors was not different in both sexes. Cocaine users showed higher scores at the “loss of control”, “pornography addiction”, and “addiction to social networks” factors, for the stimulant effect of this substance. Moreover, 15 out of the total 17 cocaine users were pathological gamblers. Positive and statistically significant relationships were observed between some QUNT factors and body mass index.

CONCLUSION

These findings indicate that PIU is less severe in subjects taking sedative substances, such as heroin/opioids and alcohol, than in subjects taking stimulants. Alternatively, it may be used as a “stimulant” trigger in cocaine and cannabis users. Flattening effect of abuse drugs was noted on possible sex-related differences in QUNT items. We observed a sort of “protective” effect of a love relationship and/or living together with a partner, as those engaged subjects showed lower scores on different items than single subjects or those living alone. The relationship between time spent online (and related sedentary lifestyle) and body mass index would suggest that Internet use might be a contributing factor to increasing weight gain and obesity amongst adolescents and young adults worldwide. Our findings also highlighted the specific vulnerability of drug addicts who use stimulants, rather than sedative compounds, to other kinds of behavioral addictions, such as gambling disorder.

Keywords: Internet, Problematic Internet use, Behavioral addictions, Drug abuse, Rehab centers

Core tip: This study investigated the characteristics of Internet use and problematic Internet use (PIU) in drug addicts through a specific questionnaire. The findings indicated that PIU is more common in subjects taking cocaine and cannabis than in subjects taking opioids or alcohol, and that the also affected by pathological gambling disorder. This suggests a favoring role of stimulant drugs towards the development of behavioral addictions. The relationship between time spent online and body mass index indicates that Internet use might be a factor that promotes weight gain and obesity. Addiction prevention should take into consideration PIU, which currently represents a worldwide epidemic.

INTRODUCTION

New technologies, when used appropriately, undoubtedly constitute a resource that can greatly improve the quality of an individual’s life. The Internet is probably one of the biggest revolutions of the last few years because it has transformed the way of communicating, exchanging information, participating in real-time events thousands of kilometers away, and finding easily and rapidly any kind of information[,]. In the same way, it should be noted that the mismatched use of the Internet constitutes, especially where predisposing psychopathological factors are present, a real risk for a subject’s mental health, as it may become a problem out of his/her control.

In particular, the abuse of the Internet represents the most dangerous and probable threat that may cause serious impairment to the social, psychological, working, and emotional individual adjustments. Over the last 15 years, the number of Internet users has increased by 1000%[] , as documented by the Internet World Stats, Pigdom, a society that features up to date world Internet usage, population statistics, and other issues[]. Not surprisingly, as a result, studies on abuse of the Internet have proli-ferated. This problem is not yet well understood, and research on its etiology is still at its beginning[].

Problematic Internet use (PIU) or Internet addiction is a behavioral addiction[] that can be defined as “use of the Internet that creates psychological, social, school, and/or work difficulties in a person’s life”[].

Increasing literature on PIU led the American Psychiatric Association to include Internet Gaming Disorder in section 3 of the Diagnostic and Statistical Manual for Mental Disorders (DSM-5), but the current opinion is that more data are needed before incorporating it in the manual as a condition with a nosological dignity[]. In 2008, Block[] suggested four diagnostic criteria essential to a possible diagnosis of PIU as an addictive behavior, as follows: “Excessive Internet use associated with a loss of sense of time; withdrawal, including feelings of anger, depression and tension when Internet is not accessible; tolerance, including the need for better computer equipment, more software, or more hours of use, and adverse consequences, includ-ing arguments, lying, poor school/work or vocational achievement, social isolation, and fatigue”[].

Generally, PIU subjects are not aware that they have a problem[] that may progressively impair family, school, work, or social life[] or lead to severe social withdrawal[,] and even suicide[,]. Several studies have documented the negative consequences of PIU, but the literature does not reflect a consistent conceptualization of this behavior[,]. Specifically, it is unclear whether PIU should be classified as a type of behavioral addiction[], an impulse control disorder, a subtype of obsessive-compulsive disorder[], or an impaired way of coping with stress[].

The most common symptoms of PIU are similar to those of substance use disorders (SUDs) according to DSM-5[] including unpredictable behavior and mood[,], craving, excessive concerns about Internet activities, and inability to reduce its use[,]. Some researchers made some parallelisms with behavioral addictions, including gambling disorder[,]. Again, neurobiological studies indicate that PIU shares with SUDs several neurobiological characteristics[,]. Although PIU has been found frequently comorbid with other psychiatric disorders[], the literature on the relationship between PIU and SUDs is meager.

The same is true for data on PIU prevalence and characteristics in our country. Therefore, the present study aimed at exploring these phenomena in a peculiar population constituted by individuals following a rehab program for drug addictions in public centers (Servizio Tossicodipendenze, SERT) through a questionnaire called “Questionario sull’Utilizzo delle Nuove Tecnologie” (QUNT) that we had created for this purpose.

MATERIALS AND METHODS

Self-assessment questionnaire

A specific interactive platform and website (http://dronet.araneus.it/questionario) on new technologies were created on an external server. The platform allowed access to the self-assessment questionnaire only via the Internet.

At the same time, a self-assessment questionnaire referred to the acronym QUNT was developed. The QUNT consists of two sections, one for demographic data and another consisting of 101 items (Appendix 1). Forty-five out of the total 101 items had five possible answers, according to a Likert five-point scale with 1 indicating “completely false” and 5 indicating “completely true”; three items were multiple-choice questions; ten were focused on the use of “instant messaging” (with five possible answers, according to a Likert five-point scale with 1 indicating “completely false” and 5 indicating “completely true”), and 42 items on the use of “social networks” (instant messaging: Whatsapp, Telegram, Skype, and social networks: Facebook, Twitter and Instagram) (with five possible answers, according to a Likert five-point scale with 1 indicating “completely false” and 5 indicating “completely true”). The item #101 was actually a question on the satisfaction/utility or not with the questionnaire. The items considered of greater relevance were put together in order to identify factors built according to a priori criteria extrapolated from the data available in the scientific literature[,,]. These factors were “time spent online” (item 2, 3, 4, 5, 6, 7, 25, 33), “social withdrawal” (item 8, 10, 18, 22, 30, 35), “abstraction from reality” (item 11, 13, 24), “loss of control” (item 19, 20, 32, 36), “addiction to pornography” (item 26, 27), “ludopathy” (item 40, 41, 42, 43), and “addiction to social networks” (49, 50, 51, 52, 53, 54, 55, 56, 57). The “addiction to social networks” factor was further divided into the following sub-factors: “Addiction to Facebook” (item 61-75), “addiction to Twitter” (item 76-86), and “addiction to Instagram” (item 86-97). The factor scores were calculated as the sum of the scores obtained in each item divided by the maximum score in percentage. We established the answer 4 (between 4 and 6 hr/d) or 5 (> 6 hr/d) of item 2 “time spent online”. As the cut-off points to identify the presence of, respectively, possible or certain/severe PIU, in agreement with current literature, although controversies do exist[]. In no way it was possible to identify the participants whose anonymity was warranted.

Data collection procedure

The link for QUNT was communicated to the offices in charge of the territorial outpatient’s services for drug-addicted individuals, SERTs, located in the Calabria region, in order to ask their patients to fill it in. A total of 1500 subjects were asked to fill in the questionnaire on a voluntary basis. The present study was approved by the Ethics Committee at Pisa University.

Statistical analysis

The independent t-test was applied to compare the mean scores of the factors on the basis of these variables: Sex (M/F); single (yes/no living together (yes/no). One-way analysis of variance followed by Bonferroni’s test for post-hoc was used to assess the comparisons of body mass index (BMI) categories. The χ2 analysis was used to compare categorical variables. All statistics were carried out by the Statistical Package for Social Sciences (SPSS), version 22 (Armonk, NY, United States)[].

RESULTS

Characteristics of the study population

The returned questionnaires numbered 183, of which 148 (80.87%) were from men and 35 (19.13%) were from women, out of the total 1500 invitations. The majority of the subjects (86, 47%) had completed 8 years of school, 73 (39.9%) the high school, 14 (7.7%) 5 years of primary school, and 10 (5.5%) were graduated. Ninety-two (50.3%) subjects were single, 64 (14.8%) were married, and 27 (14.8%) were involved in a love relationship. The mean length of attendance at the public rehab center was between 1 and 60 mo (mean ± standard deviation (SD): 32 ± 20).

Types of substance abuse and/or behavioral addiction

The most abused drugs were heroin or opioids (n = 88, 48.1%), alcohol (n = 55, 30.1%), cannabis (n = 20, 9.8%), cocaine (n = 17, 7.7%), and amphetamines (n = 3, 1.6%). Polydrug abuse (amphetamine, cannabis, cocaine, ecstasy) was present in nine (4.9%) individuals, while gambling disorder was diagnosed in 18 (9.3%). All 183 subjects were heavy smokers (Table (Table11).

Table 1

Types of substance abuse and/or behavioral addiction

n (%)
Heroin or opioids 88 (48.1)
Alcohol 55 (30.1)
Cannabis 20 (9.8)
Cocaine 17 (7.7)
Amphetamines 3 (1.6)
Polydrug abuse 9 (4.9)
Gambling disorder 18 (9.3)
Smokers 183 (100)

The smartphone was found to be the most common device utilized by all subjects to access the Internet. The time spent online was similar in men and women, 4.12 ± 2.9 h. Interestingly, the time spent online by 30% of cocaine and 25% of cannabis users was significantly higher (> 6 h) than that of the other groups.

QUNT factors and gender

The distribution of the QUNT factors was not different in the two sexes; however, men using cannabis showed a trend towards higher scores (mean ± SD) at the following factors: “Social withdrawal” (2.44 ± 0.38 vs 2.23 ± 0.39, P < 0.001) and “abstraction from reality” (3.12 ± 1.74 vs 2.24 ± 0.46, P < 0.001). Cocaine users showed a higher score than the other subjects at the “loss of control” (3.64 ± 1.12 vs 2.51 ± 0.36, P < 0.001), “pornography addiction” (3.59 ± 1.44 vs 2.54 ± 0.41, P < 0.001), and “addiction to social networks” (3.22 ± 0.98 vs 2.66 ± 0.76, P < 0.001) factors.

QUNT factors and affective relationship

The analysis of the difference in QUNT factors regarding being single (n = 92) or involved in a love relationship (n = 91) showed that single subjects had higher scores at the following factors (mean ± SD): “Time spent online” (2.95 ± 0.47 vs 2.17 ± 0.44, P < 0.001); “social withdrawal” (1.40 ± 0.35 vs 1.34 ± 0.32, P < 0.001); “abstraction from reality” (1.90 ± 0.40 vs 1.56 ± 0.62, P < 0.001); “addiction to pornography” (3.12 ± 0.88 vs 1.99 ± 0.79, P < 0.001); and “addiction to social networks” (2.89 ± 1.08 vs 2.06 ± 0.33, P < 0.001).

The analysis of the differences between partners living (72) or not living together (17) with the partner showed some significant differences. The following factors showed higher scores in subjects who did not live with the partner vs those who lived with the partner: ”Time spent online” (3.03 ± 0.53 vs 2.16 ± 0.76, P < 0.001), “addiction to pornography” (3.15 ± 0.99 vs 2.33 ± 0.71, P < 0.001), “ludopathy” (3.42 ± 1.08 vs 2.96 ± 0.66, P < 0.001), and “addiction to social networks” (2.99 ± 0.91 vs 2.01 ± 0.44, P < 0.001).

QUNT factors and BMI

The total sample was then subdivided according to the BMI values. Fifteen subjects had a BMI below 18.50 (underweight, UW), 69 between 18.51 and 24.9 (normal weight, NW), 60 between 25 and 30 (overweight, OW), 26 between 30.1 and 34.9 (first degree of obesity, OB1), and 13 greater than 35 (second degree of obesity, OB2). The categories OB1 and OB2 were merged in the category “Obese” (OB). The comparisons of QUNT factor scores in the four BMI categories are reported in Table Table2,2, which shows that the greater the BMI values the greater the scores. Moreover, as shown in Figure Figure1,1, as BMI increased the percentage scores of the five factors, “time spent online”, “social withdrawal”, “abstraction from reality”, “ludopathy”, and “addiction to social network”, also trended upward. Finally, fifteen of the total cocaine users were also pathological gamblers (mainly online gamers) and showed a significantly higher score at the “ludopathy” factor (3.20 ± 0.45 vs 2.86 ± 0.51, P < 0.001).

An external file that holds a picture, illustration, etc. Object name is WJP-9-55-g001.jpg

Trend of the percentage scores of some QUNT factors and body mass index. A: Time spent online; B: Social withdrawal; C: Abstraction from reality; D: Ludopathy; E: Addiction to social networks. BMI: Body mass index; UW: Underweight; NW: Normal weight; OW: Overweight; OB: Obesity; QUNT: Questionario sull’Utilizzo delle Nuove Tecnologie.

Table 2

Comparisons of the QUNT factor scores in the four BMI categories

Factors UW NW OW OB F P value Post-hoc comparison: Significant for P < 0.05
Time spent online 53.44 ± 13.68 53.80 ± 13.12 54.91 ± 12.71 55.83 ± 14.10 3.87 0.009 OW > UW
Social withdrawal 25.39 ± 6.35 27.55 ± 7.61 28.73 ± 8.94 30.81 ± 10.14 9.91 0.001 OW > UW; OB > UW; OB > NW
Abstraction from reality 32.33 ± 10.02 34.90 ± 10.13 35.11 ± 12.98 36.11 ± 13.44 2.69 0.045 None
Loss of control 28,10 ± 9.11 29.79 ± 10.11 31.04 ± 12.49 31.21 ± 10.87 1.95 1.98 None
Addiction to pornography 43.32 ± 12.28 41.95 ± 13.70 41.34 ± 11.03 42.09 ± 13.45 1.55 0.250 None
Ludopathy 33.26 ± 13.17 36.23 ± 10.85 39.88 ± 22.91 41.16 ± 22.39 4.28 0.005 OW > NW
Addiction to instant messaging 54.05 ± 18.33 56.02 ± 16.47 56.24 ± 18.36 55.60 ± 17.09 1.72 0.197 None
Addiction to social networks 41.60 ± 12.61 42.13 ± 13.15 41.80 ± 12.19 44.14 ± 18.90 1.81 0.187 None

QUNT: Questionario sull’Utilizzo delle Nuove Tecnologie; BMI: Body mass Index; UW: Underweight; NW: Normal weight; OW: Overweight; OB: Obesity.

DISCUSSION

The present study reports the results of a collaborative survey investigating the prevalence and characteristics of Internet use by new technologies (PCs, smartphones and tablets), as well as of PIU, amongst subjects undergoing a program of rehabili-tation in public rehab centers in a region from southern Italy. According to our knowledge, this is the first study carried out in this peculiar adult population, as previously only samples of adolescents were investigated[].

Several subjects received the invitation from their psychiatrists/psychologists to fill in a questionnaire, the so-called QUNT, which was developed by us for this purpose. The specificity of the QUNT, as compared with those utilized in different studies, is that it is very detailed in order to assess the variety of individual features of both Internet use and PIU. The item 2 “time spent online” was considered crucial to identify the possible presence of PIU when it was between 4 and 6 hr/d (answer 4), or of severe PIU, when it was > 6 hr/d (answer 5).

About 10% of the subjects returned the QUNTs correctly filled in that were valid for statistical analyses. This can be ascribed to the peculiar personality of drug addicts, especially chronic ones that represent the majority of our sample, and it would indicate both a low propensity to collaborative studies and compliance as well as amotivation[]. The most used device (100% of subjects) to access the Internet was the smartphone. There was a high preponderance of men over women, which reflects the distribution of sexes in public rehab centers in Italy, in agreement with national data showing that the ratio male:female is 4:1[].

All subjects were heavy smokers, almost 50% of them used heroin and/or opioid compounds, 30% alcohol, 10% cannabis, 8% cocaine, and 5% were polydrug users. Only three subjects were amphetamine users and, therefore, were not included in the statistical analyses. Almost 10% of individuals were also suffering from gambling disorder, while the presence of other psychiatric disorders was set as an exclusion criterion.

The time spent online was quite high, more than 4 hr/d in the total sample, with a slight, albeit not significant prevalence in male subjects. Cocaine and cannabis users spent more than 6 hr/d online, significantly more than opioid and alcohol abusers. Therefore, they were probably affected by a severe PIU, according to the setpoint defined by us (answer 5 of item 2) and literature data[,]. Taken together, these findings indicate that although PIU is possibly present in all categories of drug addicts, it is less severe in subjects taking sedative substances, such as heroin/opioids and alcohol. Alternatively, it may be used as a “stimulant” trigger in cocaine and cannabis users. This is supported by the high prevalence of gaming disorder amongst cocaine abusers, in agreement with literature data[].

The analysis of the distribution of the QUNT factors showed no sex-related differences and a slight trend towards higher scores at the “social withdrawal” and “abstraction from reality” items in men. This is in contrast with a previous study carried out in healthy subjects that revealed significant differences between men and women. A possible explanation might be the flattening effects of abused drugs that tend to “minimize” sex differences[]. As compared with the other groups, cocaine users showed higher scores at the “loss of control”, “pornography addiction”, and “addiction to social networks” factors. This is not surprising given the stimulant effect of this substance[].

Our findings confirmed the “protective” effects of a love relationship and/or living together with a partner[], as single subjects or those living alone with no family support showed higher scores on different items, specifically “time spent online”, “social withdrawal”, “abstraction from reality”, “addiction to pornography”, and “addiction to social networks”. This clearly indicates that Internet was mainly used for passing time or recreation.

Not surprisingly, those subjects who spent more time online, as shown by the higher score of the “time spent online”, “social withdrawal”, “abstraction from reality”, and “addiction to social network” factors, had a higher BMI. Therefore, the excessive use of the Internet can be considered another factor that increases sedentary behaviors[], and it may be particularly risky in drug addicts who are already more vulnerable subjects already exposed to different medical diseases[]. Reduced sleeping time and altered circadian rhythms due to PIU are other factors that may increase the probability of metabolic, medical, and psychiatric disorders[,,] as well as of a disruption of work, family, social, or school performance[,].

Finally, the majority (15 out of the total 17) of cocaine users were also pathological gamblers (mainly online gamers), and showed a significantly higher score at the “ludopathy” factor. This would suggest a specific vulnerability of drug addicts to other kinds of addictions, especially if they use stimulants rather than sedative drugs[]. Our study has some limitations that should be acknowledged. The QUNT questionnaire was not validated, although this is quite common in studies in this field[,]. The prevalence of PIU was inferred from one item only, but it was a corollary of the main objective of the study exploring primarily the characteristics of Internet use. Similarly, no information was gathered on emotional distress or disturbed behaviors that are currently under investigation.

Taken together, our results suggest that the excessive use of Internet through smartphones is very common in drug addicts, as shown by their time spent online, and that PIU is very common in these individuals, especially in those taking cocaine and cannabis. The relationship between time spent online (and related sedentary lifestyle) and BMI would suggest that Internet use might be a contributing factor for increased weight and obesity amongst adolescents and young adults world-wide[,]. Our findings would suggest specific vulnerability of drug addicts, mainly if they use stimulants rather than sedative compounds, not only to other kinds of pharma-cological but also to behavioral addictions, such as PIU or pathological gaming. Prevention of addictions should take into consideration the novel, and still poorly explored, domain of behavioral addictions, especially of PIU that today represents a worldwide epidemic[,].

ARTICLE HIGHLIGHTS

Research background

Problematic Internet use (PIU) is a novel behavioral addiction characterized by excessive Internet use that is becoming an increasing problem worldwide. Although no agreement exists on precise diagnostic criteria, PIU is considered a behavioral addiction sharing with substance use disorders (SUDs) and other addictions several features and perhaps neurobiological under-pinnings.

Research motivation

Unfortunately, no information is available on the prevalence of PIU amongst drug-addicted subjects, in spite of the given evidence, that these individuals tend to be affected by polydrug use and also by behavioral addictions, as if the presence of one or more addictions would represent a sort of vulnerability towards a worsening of the clinical picture through the onset of other kinds of these disorders.

Research objectives

The investigation of the possible existence and prevalence of PIU amongst drug-addicts under treatment in rehab centers would permit the implementation of specific treatments to prevent the onset of other kind of addictions that could worsen the clinical picture and the rehabilitation programs.

Research methods

A specific questionnaire to be filled online, the so-called Questionario sull’Utilizzo delle Nuove Tecnologie (QUNT), was developed to explore the prevalence and characteristics of both Internet use and PIU. The QUNT consists of two sections, one for demographic data and another consisting of 101 items grouped in factors built according to a priori criteria extrapolated from the data available in scientific literature. All subjects who volunteered to participate in the study (n = 183) reported that the QUNT was useful and were satisfied with it. The factor scores were calculated as the sum of the scores obtained in each item divided by the maximum score in percentage. We chose the answer 4 (between 4 and 6 hr/d), and the answer 5 (> 6 hr/d) of item 2 “time spent online”. In order to identify the body mass index (points for, respectively, the possible or certain (and severe) presence of PIU.

Research results

The time spent online was more than 4 hr/d in the total sample, with a slight, although not significant, prevalence amongst male subjects. Cocaine and cannabis users spent more than 6 hours online, significantly more than opioid and alcohol users. The distribution of the QUNT factors was not different in both sexes. Cocaine users showed higher scores at the “loss of control”, “pornography addiction”, and “addiction to social networks”, probably because of the stimulant effect of this substance. Moreover, 15 out of the total 17 cocaine users were also pathological gamblers. Positive and statistically significant relationships were also observed between some QUNT factors and body mass index (BMI). These results, while showing that PIU is common amongst stimulant drug abusers, require to be replicated in larger samples from other countries. Nevertheless, they underline the risk of behavioral addictions in drug addicts, a problem that should be taken into account when planning prevention and intervention strate-gies.

Research conclusions

The new findings of this study are represented by the large percentage of PIU amongst drug addicts, especially if they use cocaine or cannabis. This suggests that, although the abuse of Internet is present in all drug addicts, PIU is less common in subjects taking sedative substances, such as heroin/opioids and alcohol, while it may become a sort of “stimulant” trigger in cocaine and cannabis users, as supported by the high prevalence of pathological gaming amongst cocaine abusers. Further, PIU is more frequent in single subjects or subjects living alone, a result stressing the protective effects of loving or social relationships in general against the onset of addictions. Those subjects who spent more time online, as shown by the higher score of the “time spent online”, “social withdrawal”, “abstraction from reality”, and “addiction to social network” factors, had a higher BMI. Therefore, the excessive use of the Internet can be considered as another factor increasing sedentary behaviors that may be particularly risky in drug addicts, subjects already prone to different medical diseases. Reduced sleeping time and disrupted circadian rhythms due to PIU are other factors that may increase the probability of metabolic, medical, and psychiatric disorders as well as the impairment of work, family, social, or school performance.

Research perspectives

The findings of the present study indicate that behavioral addictions, such as PIU, can broaden polydrug use, especially in subjects taking stimulants or cannabis. In addition, PIU may be considered another factor increasing negative life habits, already impaired in drug addicts, while promoting sedentary behaviors and maladjustments in different individual’s domains. Future studies should take into consideration the impact of PIU on drug addicts by means of specific instru-ments to assess it, in order to prevent, not only its detrimental consequences, but also those related to a broadening of addictive behaviors.

ACKNOWLEDGEMENTS

We thank all the accountable managers of the SERT from Calabria for their fruitful collaboration.

Footnotes

Institutional review board statement: The study was approved by the Ethics Committee of Pisa University.

Informed consent statement: The study was approved by the Ethics Committee of Pisa University, and the participants decided to take part in it on a voluntary basis and in an anonymous way, so that it was not possible to identify them.

Conflict-of-interest statement: The authors have no conflicts of interest to declare.

Manuscript source: Invited manuscript

Peer-review started: April 26, 2018

First decision: June 15, 2018

Article in press: May 15, 2019

P-Reviewer: Hosak L, Seeman MV S-Editor: Ji FF L-Editor: Filipodia E-Editor: Wang J

Specialty type: Psychiatry

Country of origin: Italy

Peer-review report classification

Grade A (Excellent): 0

Grade B (Very good): 0

Grade C (Good): C, C

Grade D (Fair): 0

Grade E (Poor): 0

Contributor Information

Stefano Baroni, Dipartimento di Medicina Clinica e Sperimentale, Section of Psychiatry, University of Pisa, Pisa 56100, Italy.

Donatella Marazziti, Dipartimento di Medicina Clinica e Sperimentale, Section of Psychiatry, University of Pisa, Pisa 56100, Italy. [email protected].

Federico Mucci, Dipartimento di Medicina Clinica e Sperimentale, Section of Psychiatry, University of Pisa, Pisa 56100, Italy.

Elisa Diadema, Dipartimento di Medicina Clinica e Sperimentale, Section of Psychiatry, University of Pisa, Pisa 56100, Italy.

Liliana Dell’Osso, Dipartimento di Medicina Clinica e Sperimentale, Section of Psychiatry, University of Pisa, Pisa 56100, Italy.

References

1. Valkenburg PM, Peter J. Online communication among adolescents: an integrated model of its attraction, opportunities, and risks. J Adolesc Health. 2011;48:121–127. [PubMed] []
2. Ryan T, Chester A, Reece J, Xenos S. The uses and abuses of Facebook: A review of Facebook addiction. J Behav Addict. 2014;3:133–148. [PMC free article] [PubMed] []
3. Miniwatts Marketing Group. 2017. Internet world stats: usage and population statistics. Available from: http://www.internetworldstats.com/stats.htm/ []
4. King DL, Delfabbro PH. Internet gaming disorder treatment: a review of definitions of diagnosis and treatment outcome. J Clin Psychol. 2014;70:942–955. [PubMed] []
5. Christakis DA, Moreno MM, Jelenchick L, Myaing MT, Zhou C. Problematic internet usage in US college students: a pilot study. BMC Med. 2011;9:77. [PMC free article] [PubMed] []
6. Beard KW, Wolf EM. Modification in the proposed diagnostic criteria for Internet addiction. Cyberpsychol Behav. 2001;4:377–383. [PubMed] []
7. Block JJ. Issues for DSM-V: internet addiction. Am J Psychiatry. 2008;165:306–307. [PubMed] []
8. American Psychiatric Association. Diagnostic and statistical manual of mental disorders: DSM-5. 5th ed. Arlington, VA: American Psychiatric Association; 2013. []
9. Kuss DJ, Griffiths MD, Karila L, Billieux J. Internet addiction: a systematic review of epidemiological research for the last decade. Curr Pharm Des. 2014;20:4026–4052. [PubMed] []
10. Young KS. Internet addiction: Symptoms, evaluation, and treatment. In: Vande-Creek L, Jackson T, editors. Innovations in Clinical Practice: A Source Book. Sarasota, FL: Professional Resource Press; 1999. pp. 19–31. []
11. Spada MM. An overview of problematic internet use. Addict Behav. 2014;39:3–6. [PubMed] []
12. Li W, O’Brien JE, Snyder SM, Howard MO. Characteristics of internet addiction/pathological internet use in U.S. university students: a qualitative-method investigation. PLoS One. 2015;10:e0117372. [PMC free article] [PubMed] []
13. Dong G, Lu Q, Zhou H, Zhao X. Precursor or sequela: pathological disorders in people with Internet addiction disorder. PLoS One. 2011;6:e14703. [PMC free article] [PubMed] []
14. Wei HT, Chen MH, Huang PC, Bai YM. The association between online gaming, social phobia, and depression: an internet survey. BMC Psychiatry. 2012;12:92. [PMC free article] [PubMed] []
15. Yen JY, Ko CH, Yen CF, Chen CS, Chen CC. The association between harmful alcohol use and Internet addiction among college students: comparison of personality. Psychiatry Clin Neurosci. 2009;63:218–224. [PubMed] []
16. Lam LT, Peng Z, Mai J, Jing J. The association between internet addiction and self-injurious behaviour among adolescents. Inj Prev. 2009;15:403–408. [PubMed] []
17. Sun P, Johnson CA, Palmer P, Arpawong TE, Unger JB, Xie B, Rohrbach LA, Spruijt-Metz D, Sussman S. Concurrent and predictive relationships between compulsive internet use and substance use: findings from vocational high school students in China and the USA. Int J Environ Res Public Health. 2012;9:660–673. [PMC free article] [PubMed] []
18. Weinstein A, Feder LC, Rosenberg KP, Dannon P. Internet addiction disorder: Overview and controsersies. In: Rosenberg KP, Feder LC, editors. Behavioral addictions: Criteria, evidence, and treatment. Cambridge (MA): Academic Press; 2014. pp. 99–118. []
19. Starcevic V. Is Internet addiction a useful concept? Aust N Z J Psychiatry. 2013;47:16–19. [PubMed] []
20. Van Rooij AJ, Prause N. A critical review of “Internet addiction” criteria with suggestions for the future. J Behav Addict. 2014;3:203–213. [PMC free article] [PubMed] []
21. van Rooij AJ, Schoenmakers TM, van de Eijnden RJ, van de Mheen D. Compulsive Internet use: the role of online gaming and other internet applications. J Adolesc Health. 2010;47:51–57. [PubMed] []
22. Tao R, Huang X, Wang J, Zhang H, Zhang Y, Li M. Proposed diagnostic criteria for internet addiction. Addiction. 2010;105:556–564. [PubMed] []
23. Zhang L, Amos C, McDowell WC. A comparative study of Internet addiction between the United States and China. Cyberpsychol Behav. 2008;11:727–729. [PubMed] []
24. Shapira NA, Lessig MC, Goldsmith TD, Szabo ST, Lazoritz M, Gold MS, Stein DJ. Problematic internet use: proposed classification and diagnostic criteria. Depress Anxiety. 2003;17:207–216. [PubMed] []
25. Chakraborty K, Basu D, Vijaya Kumar KG. Internet addiction: consensus, controversies, and the way ahead. East Asian Arch Psychiatry. 2010;20:123–132. [PubMed] []
26. Caselli G, Soliani M, Spada MM. The effect of desire thinking on craving: an experimental investigation. Psychol Addict Behav. 2013;27:301–306. [PubMed] []
27. Carli V, Durkee T, Wasserman D, Hadlaczky G, Despalins R, Kramarz E, Wasserman C, Sarchiapone M, Hoven CW, Brunner R, Kaess M. The association between pathological internet use and comorbid psychopathology: a systematic review. Psychopathology. 2013;46:1–13. [PubMed] []
28. Li W, O’Brien JE, Snyder SM, Howard MO. Diagnostic criteria for problematic internet use among U.S. university students: A mixed-methods evaluation. PLoS One. 2016;11:e0145981. [PMC free article] [PubMed] []
29. Lortie CL, Guitton MJ. Internet addiction assessment tools: dimensional structure and methodological status. Addiction. 2013;108:1207–1216. [PubMed] []
30. Marazziti D, Presta S, Baroni S, Silvestri S, Dell’Osso L. Behavioral addictions: a novel challenge for psychopharmacology. CNS Spectr. 2014;19:486–495. [PubMed] []
31. Lee HW, Choi JS, Shin YC, Lee JY, Jung HY, Kwon JS. Impulsivity in internet addiction: a comparison with pathological gambling. Cyberpsychol Behav Soc Netw. 2012;15:373–377. [PubMed] []
32. Kim SH, Baik SH, Park CS, Kim SJ, Choi SW, Kim SE. Reduced striatal dopamine D2 receptors in people with Internet addiction. Neuroreport. 2011;22:407–411. [PubMed] []
33. Kühn S, Gallinat J. Brains online: structural and functional correlates of habitual Internet use. Addict Biol. 2015;20:415–422. [PubMed] []
34. Petry NM, Rehbein F, Gentile DA, Lemmens JS, Rumpf HJ, Mößle T, Bischof G, Tao R, Fung DS, Borges G, Auriacombe M, González Ibáñez A, Tam P, O’Brien CP. An international consensus for assessing internet gaming disorder using the new DSM-5 approach. Addiction. 2014;109:1399–1406. [PubMed] []
35. Ko CH, Yen JY, Yen CF, Chen CS, Chen CC. The association between Internet addiction and psychiatric disorder: a review of the literature. Eur Psychiatry. 2012;27:1–8. [PubMed] []
36. IBM Statistical Package for Social Sciences (SPSS) Version 22.0. Armonk, NY: IBM Corp; 2013. []
37. Rücker J, Akre C, Berchtold A, Suris JC. Problematic Internet use is associated with substance use in young adolescents. Acta Paediatr. 2015;104:504–507. [PubMed] []
38. Meyer PJ, King CP, Ferrario CR. Motivational processes underlying substance abuse disorder. Curr Top Behav Neurosci. 2016;27:473–506. [PMC free article] [PubMed] []
39. Istituto Superiore di Sanità Indagine sulle caratteristiche e sull’operatività dei servizi e delle strutture per il trattamento del disturbo da gioco di azzardo. 2017. Available from: http://old.iss.it/binary/ogap/cont/Indagine_sulle_caratteristiche_e_sull_operativita_768_.pdf. []
40. Durkee T, Kaess M, Carli V, Parzer P, Wasserman C, Floderus B, Apter A, Balazs J, Barzilay S, Bobes J, Brunner R, Corcoran P, Cosman D, Cotter P, Despalins R, Graber N, Guillemin F, Haring C, Kahn JP, Mandelli L, Marusic D, Mészáros G, Musa GJ, Postuvan V, Resch F, Saiz PA, Sisask M, Varnik A, Sarchiapone M, Hoven CW, Wasserman D. Prevalence of pathological internet use among adolescents in Europe: demographic and social factors. Addiction. 2012;107:2210–2222. [PubMed] []
41. Canan F, Ataoglu A, Ozcetin A, Icmeli C. The association between Internet addiction and dissociation among Turkish college students. Compr Psychiatry. 2012;53:422–426. [PubMed] []
42. Ni X, Yan H, Chen S, Liu Z. Factors influencing internet addiction in a sample of freshmen university students in China. Cyberpsychol Behav. 2009;12:327–330. [PubMed] []
43. Hall GW, Carriero NJ, Takushi RY, Montoya ID, Preston KL, Gorelick DA. Pathological gambling among cocaine-dependent outpatients. Am J Psychiatry. 2000;157:1127–1133. [PubMed] []
44. Worhunsky PD, Potenza MN, Rogers RD. Alterations in functional brain networks associated with loss-chasing in gambling disorder and cocaine-use disorder. Drug Alcohol Depend. 2017;178:363–371. [PMC free article] [PubMed] []
45. Dufour M, Nguyen N, Bertrand K, Perreault M, Jutras-Aswad D, Morvannou A, Bruneau J, Berbiche D, Roy É. Gambling Problems Among Community Cocaine Users. J Gambl Stud. 2016;32:1039–1053. [PubMed] []
46. Koob GF, Le Moal M. Drug abuse: hedonic homeostatic dysregulation. Science. 1997;278:52–58. [PubMed] []
47. Tucker J. The healing power of love. J Fam Health. 2015;25:23–26. [PubMed] []
48. McCreary AC, Müller CP, Filip M. Psychostimulants: Basic and Clinical Pharmacology. Int Rev Neurobiol. 2015;120:41–83. [PubMed] []
49. Hoare E, Milton K, Foster C, Allender S. The associations between sedentary behaviour and mental health among adolescents: a systematic review. Int J Behav Nutr Phys Act. 2016;13:108. [PMC free article] [PubMed] []
50. Sridhar GR, Sanjana NS. Sleep, circadian dysrhythmia, obesity and diabetes. World J Diabetes. 2016;7:515–522. [PMC free article] [PubMed] []
51. Catena-Dell’Osso M, Rotella F, Dell’Osso A, Fagiolini A, Marazziti D. Inflammation, serotonin and major depression. Curr Drug Targets. 2013;14:571–577. [PubMed] []
52. Derbyshire KL, Lust KA, Schreiber LR, Odlaug BL, Christenson GA, Golden DJ, Grant JE. Problematic Internet use and associated risks in a college sample. Compr Psychiatry. 2013;54:415–422. [PubMed] []
53. Senormancı O, Saraçlı O, Atasoy N, Senormancı G, Koktürk F, Atik L. Relationship of Internet addiction with cognitive style, personality, and depression in university students. Compr Psychiatry. 2014;55:1385–1390. [PubMed] []
54. Vandelanotte C, Sugiyama T, Gardiner P, Owen N. Associations of leisure-time internet and computer use with overweight and obesity, physical activity and sedentary behaviors: cross-sectional study. J Med Internet Res. 2009;11:e28. [PMC free article] [PubMed] []
55. Frangos CC, Frangos CC, Sotiropoulos I. Problematic Internet Use among Greek university students: an ordinal logistic regression with risk factors of negative psychological beliefs, pornographic sites, and online games. Cyberpsychol Behav Soc Netw. 2011;14:51–58. [PubMed] []
56. Carbonell X, Chamarro A, Oberst U, Rodrigo B, Prades M. Problematic use of the internet and smartphones in university students: 2006-2017. Int J Environ Res Public Health. 2018;15:pii: E475. [PMC free article] [PubMed] []