Significant outcomes
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∙ The false alarm rate, total response errors, perseverative errors, failure to maintain set and Barratt Impulsiveness Scale-11 (BIS-11) scores of the Internet addiction disorder (IAD) and pathological gambling (PG) groups were significantly higher than those of the control group.
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∙ In addition, the forward scores and backwards scores, percentage of conceptual level responses, number of categories completed and hit rates of the IAD and PG group were significantly lower than those of the controls.
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∙ Furthermore, the false alarm rate and BIS-11 scores of the IAD group were significantly higher than those of PG patients, and the hit rate was significantly lower than that of the PG patients.
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∙ Individuals with IAD and PG present deficiencies in working memory, executive dysfunction and impulsivity, and individuals with IAD are more impulsive than PG patients.
Limitations
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∙ A limitation of this study is that our results are preliminary owing to the small sample size.
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∙ It is necessary to replicate these findings with larger sample sizes in further studies.
Introduction
Most people use the Internet as a functional tool in their private or business life because it provides multifarious possibilities for entertainment, communication and managing daily life tasks. However, the number of people experiencing massive negative consequences in their lives owing to Internet use has also grown. Internet addiction disorder (IAD) is defined as an individual’s inability to control his or her use of the Internet, which leads to psychological, academic, family, social and work difficulties or dysfunction in his or her life (Reference KS1,Reference Davis2). As IAD is known to be linked with academic and social dysfunction, it is currently becoming a serious mental health issue around the globe (Reference Wee, Zhao and Yap3). Until now, many studies support that IAD is a mental disorder. For example, a previous study using the cue-related go/no-go switching task indicated that people with IAD present cognitive biases towards information related to Internet gaming and poor executive functioning skills (i.e. lower mental flexibility as well as response inhibition) (Reference Zhou, Yuan and JJ4). Another study that investigated deficient inhibitory control in persons with IAD using a go/no-go task showed that individuals with IAD were more impulsive than controls and displayed neuropsychological characteristics of compulsive–impulsive spectrum disorder (Reference Zhou, Yuan, JJ, Li and Cheng5); impairments in response monitoring have been suggested to be a hallmark feature of impulse control disorders. A recent study using error-related negativity examined whether individuals with IAD displayed response monitoring functional deficit characteristics in a modified Eriksen flanker task. The results indicated that individuals with IAD display response monitoring functional deficit characteristics and share the error-related negativity characteristics of individuals with compulsive–impulsive spectrum disorder (Reference Zhou, Li and Zhu6). In addition, a study on brain structure and function in relation to IAD showed that IAD demonstrated widespread reductions of fractional anisotropy in major white matter pathways, and such abnormal white matter structures may be linked to some behavioural impairments (Reference Lin, Zhou and Du7). Another recent study on the effects of IAD on the microstructural integrity of major neuronal fibre pathways showed that long-term internet addiction resulted in brain structural alterations (Reference Yuan, Qin and Wang8).
Pathological gambling (PG) is a mental disorder that is associated with both family and social costs. It presents an urge to continuously gamble despite harmful negative consequences or a desire to stop. Previous studies showed that impulsivity is a core characteristic of PG. A study that compared PG patients with healthy volunteers regarding the reaction time and number of errors in go/no-go tasks demonstrated that PG patients committed more errors in the go/no-go tasks and that impulsivity seemed to be a multi-dimensional phenomenon (Reference Fuentes, Tavares, Artes and Gorenstein9). A recent study that used a comprehensive assessment, including the impulsivity-related and antipodal parameters of the Stop Signal Task, Stroop Task, Tower of London Task, Card Playing Task, Iowa Gambling Task and Barratt Impulsiveness Scale-11 (BIS-11), indicated that PG is associated with generally heightened impulsivity profiles compared with a healthy control group (Reference Kraplin, Buhringer, Oosterlaan, Van Den Brink, Goschke and Goudriaan10). Neuropsychological studies indicate deficiencies in certain executive functions for PG patients (Reference Goudriaan, Oosterlaan, De Beurs and Van Den Brink11). For example, a study investigating neurocognitive impairments of executive functions in pathological gamblers indicated that both the PG and the alcohol-dependent groups were characterised by diminished performance on inhibition, cognitive flexibility, time estimation and planning tasks, and concluded that pathological gamblers and alcohol-dependent subjects were characterised by diminished executive functioning (Reference Goudriaan, Oosterlaan, De Beurs and Van Den Brink12). Another study showed that pronounced deficits in the Game of Dice Task and the frequency of risky decisions were correlated with executive functions and feedback processing in PG patients (Reference Brand, Kalbe, Labudda, Esther, Josef Kesslerb and Markowitsch13). Neuroimaging studies, including event-related potentials, point to abnormalities in brain functioning (Reference Stojanov, Karayanidis, Johnston, Bailey, Carr and Schall14–Reference Boileau, Payer and Chugani16).
It is well known that PG patients and individuals with IAD share common components, that is, preoccupation, unplanned use, mood modification, withdrawal, functional impairment and tolerance (Reference Block17). By using criteria from the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition, DSM-IV, American Psychiatric Association, 1994) (18), some authors suggest that IAD is an impulse disorder or at least related to an impulse control disorder (Reference Beard and Wolf19,Reference Shaw and Black20). In DSM-V (American Psychiatric Association, 2013), Internet gaming disorder (IGD) has been introduced as a condition warranting further study, and PG has been included in the DMS-V under a new classification titled addiction and related disorders. Many scholars have suggested that IAD has similar features to PG (Reference Guertler, Rumpf and Bischof21,Reference Tonioni, Mazza and Autullo22).
Working memory is the capacity to retain and manipulate information, and its integrity is required for many cognitive functions; individuals with working memory dysfunctions may more easily give in to the urge to use (Reference Cousijin, Vingerhoets and Koenders23). Previous research indicated that working memory deficits appear to be common across different substance-addictive populations and behaviourally disordered individuals (Reference Femandez-Serrano, Perez-Garcia and Verdejo-Garcia24,Reference Mcclernon, Froeliger and Rose25). In addition, executive dysfunction and impulsivity are major characteristics of both IAD and PG (Reference Ochoa, Alvarez-Moya and Penelo26,Reference Zhou, Zhu, Li and Wang27).
Neuropsychological studies have contributed to our understanding of the natural properties of IAD. Using the same experimental conditions to assess working memory, executive function and impulsivity in IAD and PG patients may not only help guide decisions as to whether IAD should be grouped together with PG, but may also play a key role in the investigation of the neurobiological mechanisms of IAD. However, to date, no study has compared the above three aspects between IAD and PG. Our hypothesis is that individuals with IAD share deficits in working memory, executive dysfunction and impulsivity with PG patients.
In this study, the participants are individuals with IAD, patients with PG and normal controls. All of the participants were measured with the digit span task, Wisconsin Card Sorting Test (WCST), go/no-go task and BIS-11 under the same experimental conditions. The purpose of the present study was to test whether individuals with IAD presented similar characteristic of working memory, executive function and impulsivity compared with PG patients.
Subjects and methods
Time and setting
The experiment was completed at the Department of Psychiatry at Wuxi Mental Health Center of Nanjing Medical University, China, from May 2008 to April 2011.
Diagnostic approaches and participants
IAD group
The diagnostic criteria of IAD included: (1) met the criteria of the modified Diagnostic Questionnaire for Internet Addiction (YDQ) (Reference Beard and Wolf19), that is, individuals who answered ‘yes’ to questions 1 through 5 and at least any one of the remaining three questions were defined as suffering from IAD; (2) were >18 years old; (3) did not meet the criteria of any of the DSM-IV axis I disorders or personality disorders, as determined by administering a structured clinical interview (Chinese version); (4) were not smokers or alcohol-dependent individuals; and (5) did not have a diagnosis of substance dependence, neurological disorders, any type of head injury or systemic diseases that might affect the central nervous system. The duration of the IAD was confirmed via a retrospective diagnosis. Individuals who suffered from IAD were asked to recall their lifestyle when they were initially addicted to the Internet. To confirm that they were suffering from IAD, we retested them with the criteria of the modified YDQ. The reliability of these self-reports from the IAD individuals were confirmed by talking with their parents and relatives via telephone. The IAD subjects spent 11.29±1.78 h/day on online activities (including gaming, Internet shopping, pornography, virtual societies, Internet social interactions and obtaining information). The days of Internet use per week were 6.42±0.6. We verified the information from the parents, relatives and roommates of the IAD individuals who often insisted on using the Internet late at night, disrupting others’ lives despite the consequences. The IAD group was recruited from Psychology Department of Wuxi Mental Health Center of Nanjing Medical University, China. They had regulated sleep patterns and did not ingest large quantities of caffeinated or energetic drinks by medical staffs’ management. In total, 23 subjects were recruited for the IAD group.
PG group
The diagnostic criteria of the PG group included: (1) met the criteria of the DSM-IV for PG; (2) received no medication 2 weeks before the study; (3) were not smokers or alcohol-dependent individuals; and (4) did not have a diagnosis of substance dependence or neurological disorders or a history of head injury. PG patients were in-patients at the Psychology Department of Wuxi Mental Health Center of Nanjing Medical University, China. In total, 23 subjects were recruited for the PG group.
Controls
The normal controls were selected from citizens living in Wuxi City, China, through local advertisement. The controls were excluded from the study if they were smokers or alcohol-dependents individuals or if they had a diagnosis of substance dependence, neurological disorders, or all types of head injury or systemic disease that might affect the central nervous system. In total, 23 healthy persons were recruited for the control group. Referred from a previous IAD study (Reference CH, GC and Hsiao28), we chose normal controls who spent <2 h/day on the Internet. The controls were tested with the modified YDQ and PG diagnostic criteria to confirm that they did not suffer from IAD and PG. All of the participants were Chinese.
All of the participants underwent a clinical assessment by a psychiatric resident to collect information on the medications used and their socio-demographic data, as well as to exclude or confirm an IAD and PG diagnosis. In this study, we obtained written informed consent from all of the subjects, all of whom were paid to participate. The protocol for the research project was approved by the Ethics Committee of Wuxi Mental Health Center of Nanjing Medical University, China.
The demographic characteristics of the participants are detailed in Table 1.
Table 1 Demographic characteristics and clinical data of the participants
F, female; HAMD, Hamilton Depression Scale; IAD, Internet-addictive individual group; M, male; NS, not significant; PG, pathological gambling patient group.
Neuropsychological tests and procedures
IAD individuals, PG patients and controls completed the Hamilton Depression Scale (HAMD, version of 17 items, Hamilton, 1967) (Reference Hamilton29) to assess depressive symptoms and the BIS-11 to test impulsivity. The BIS-11 is a questionnaire on which subjects rate their frequency of several common impulsive or non-impulsive traits/behaviours on a scale from 1 (never/rarely) to 4 (always/almost always). It consists of 30 items and is divided into three subscales, including an attentional key, non-planning key and motor key, to decide overall the impulsiveness scores. All of the items are summed, with higher scores indicating greater impulsivity.
The Neuropsychological tests were as follows:
Digit span task
Wechsler Adult Intelligence Scale-Revised China (computerised version), provided by Beijing Ka Yip Wise Development Co. Ltd., was used to measure the digit span task. Individuals with IAD, PG patients and normal controls were given sets of digits to repeat initially forward then backward. This task is a test of immediate auditory recall and freedom from distraction. The participants were told to listen carefully as the testers provided a series of numbers and asked the participants to repeat them back in the same order. The first series included three numbers, such as ‘4, 7, 1’. Each number was said in a dull voice, one second apart. The participant repeated those numbers back. The next procedure was to speak a series of four numbers, such as, ‘2, 8, 3, 5’. In the same manner, the participant repeated those back. The task continued by increasing the series of numbers to five and asking the participant to repeat the numbers back in the same manner.
WCST
The WCST (computerised version VI), which was provided by Beijing Ka Yip Wise Development Co., Ltd, was presented graphically on a computer screen. The WCST included matching stimulus cards with one of four category cards in which the stimuli were multi-dimensional according to shape, colour and number, with each dimension determining a sorting rule. After 10 consecutive correct sorts, the rule changed. There were up to six attempts to derive a rule, providing five rule shifts in the following sequence (colour – shape – number – colour – shape – number), with each rule attainment referred to as ‘completing a category’. By trial and error, the subject had to determine a preordained sorting rule given only the feedback ‘Wrong’ or ‘Right’ on the screen after each sort. Participants were not told of the correct sorting principle and that the sorting principal shifted during the measurement; it continued to measure until all 128 cards were sorted, irrespective of whether the subject achieved completing all of the rule shifts. During the measurement, there were two types of errors, including preservative errors (i.e. the participant made a response in which they persist with an incorrect sorting rule) and non-preservative errors. In our study, five main types of WCST were used for analysis: (1) total response errors; (2) percentage of conceptual level responses; (3) perseverative errors; (4) number of categories completed; and (5) failure to maintain set.
Go/no-go task
The go/no-go task was referred from a previous study (Reference Zhou, Yuan, JJ, Li and Cheng5). E-Prime software 2.0, which was provided by Psychology Software Tools Inc., Sharpsburg, NC, USA, was used for the task. The task involved the serial presentation on a computer screen of eight different two-digit numerical stimuli (including four go stimuli and four no-go stimuli) displayed in white on a black background (1.5×1.5 cm in size). In total, 20 blocks including 160 stimuli were presented on the computer screen. Each block included eight trials and were pseudo-randomly presented with no more than three consecutive trials with either a go or no-go stimulus. The go stimuli in any blocks were ‘63’, ‘08’, ‘25’ and ‘74’; the no-go were ‘19’, ‘58’, ‘79’ and ‘14’. The IAD group, PG group and normal controls were instructed that the task involved learning when to go (press bar as quickly as possible) or not to go (withhold response) and that responses after some numbers would result in winning money ($0.32 per trial), but responses after others would result in losing money ($0.32 per response). The response window period was 1000 ms and the inter-trial interval (ITI) period was 1500 ms. Reward contingencies (blue background with+$0.32 in white) or punishment contingencies (red background with−$0.32 in white) were presented on the computer screen for 1000 ms, immediately after a response (within the 1500 ms ITI). A practice phase and a recording phase were included in the experiment procedure. The practice phase included 16 go and no-go trials. The percentage of hits and reactive time (RT) to go stimuli and the percentage of false alarms to no-go stimuli were employed for analysis. When the button was pressed within 200–1000 ms after the presentation of a go stimulus, the response was verified as correct. Lack of a response in this period was defined as a miss, whereas a response made within this period to a no-go stimuli was defined as a false alarm. False alarms were defined for each modality separately. The percentages of correct responses to go stimuli were confirmed as 100×N (target detections) divided by the total number of go stimuli. The percentages of false alarms to no-go stimuli were confirmed as 100×N divided by the sum of no-go stimuli presented. RT was measured from the onset of the go stimulus to the button press.
Statistical analysis
Data were calculated and analysed using SPSS (SPSS, Chicago, IL, USA). Comparisons of years of addiction between the IAD and PG groups were performed using independent sample t-tests. The sex ratio among the IAD group, PG group and controls were analysed with χ 2 tests. Comparisons of the HAMD scores, BIS-11 scores, digit span task, WCST and data of the go/no-go task among the IAD, PG and control groups were performed using one-way analysis of variance (ANOVA). Least square difference (LSD) tests were performed as post hoc analyses if indicated.
Results
Comparisons of digit span task scores among the IAD group, PG group and control group
Using forward scores and backward scores as dependent variables, one-way ANOVA revealed a significant main effect of group (IAD, PG and control groups). Post hoc LSD tests displayed that the forward and backward scores of the IAD and PG groups were significantly lower than those of the control group (for forwards scores, p=0.013 and 0.022; for backwards scores, p=0.016 and 0.038), whereas the forward scores and backward scores were not significantly different between the IAD and PG groups (all p>0.05) (Table 2).
Table 2 Digit span scores [mean (SD)] in IAD group (n=23), PG group (n=23) and control group (n=23)
IAD, Internet-addictive individual group; PG, pathological gambling patient group.
Comparisons of the WCST data among the IAD group, PG group and control group
Using the total response errors, percentage of conceptual level responses, perseverative errors, number of categories completed and failure to maintain set as dependent variables, one-way ANOVA showed a significant main effect of group (IAD, PG and control groups). Post hoc LSD tests displayed that the total response errors, perseverative errors and failure to maintain set of the IAD and PG groups were significantly higher than those of the control group, and that the number of categories completed and the percentage of conceptual level responses of the IAD and PG groups were significantly lower than those of the control group (for total response errors, p=0.038 and 0.019; for perseverative errors, p=0.036 and 0.042; for failure to maintain set, p=0.021 and 0.026; for the number of categories completed, p=0.039 and 0.037; for percentage of conceptual level responses, p=0.010 and 0.019, respectively), whereas the above five data results were not significantly different between the IAD and PG groups (all p>0.05) (Table 3).
Table 3 WCST data [mean (SD)] in IAD group (n=23), PG group (n=23) and control group (n=23)
IAD, Internet-addictive individual group; PG, pathological gambling patient group; WCST, Wisconsin Card Sorting Test.
Comparisons of the BIS-11 scores among the IAD group, PG group and control group
Using attentional key scores, non-planning key scores, motor key scores and BIS-11 total scores as dependent variables, one-way ANOVA revealed a significant main effect of group (IAD, PG and control groups). Post hoc LSD tests showed that the attentional key scores, non-planning key scores, motor key scores and BIS-11 total scores of the IAD and PG group were significantly higher than those of the control group (for attentional key scores, p=0.030 and 0.027; for non-planning key scores, p=0.014 and 0.043; for motor key scores, p=0.028 and 0.033; for BIS-11 total scores, p=0.020 and 0.031, respectively). The attentional key scores, non-planning key scores, motor key scores and BIS-11 total scores of the IAD group were significantly higher than those of the PG group (for attentional key scores, p=0.029; for non-planning key scores, p=0.019; for motor key scores, p=0.040; for BIS-11 total scores, p=0.036) (Table 4).
Table 4 BIS-11 scores [mean (SD)] in IAD group (n=23), PG group (n=23) and control group (n=23)
BIS-11, Barratt Impulsiveness Scale-11; IAD, Internet-addictive individual group; PG, pathological gambling patient group.
Comparisons of RTs, false alarm rate and hit rate among the IAD group, PG group and control group
Using RTs as the dependent variable, one-way ANOVA revealed no main effect of group (IAD, PG and control groups). Using the false alarm rate and hit rate as dependent variables, one-way ANOVA revealed a significant main effect of group (IAD, PG and control groups). Post hoc LSD tests showed that the false alarm rate of the IAD and PG groups was significantly higher than that of the control group and that the hit rate was significantly lower than that of the control group (for false alarm rate, p=0.019 and 0.023; for hit rate, p=0.016 and 0.025, respectively); the false alarm rate of the IAD group was significantly higher than that of the PG group, and the hit rate was significantly lower than that of the PG group (for false alarm rate, p=0.032; for hit rate, p=0.038) (Table 5).
Table 5 RTs, false alarm rate and hit rate [mean (SD)] in the IAD group (n=23), PG group (n=23) and control group (n=23)
IAD, Internet-addictive individual group; PG, pathological gambling patient group; RT, reactive time.
Discussion
This study measured working memory, executive function and impulsivity among individuals with IAD, PG patients and controls under the same experimental conditions. In this study, working memory was tested using the digit span task, executive function was assessed with the WCST and impulsivity was measured with a go/no-go task and BIS-11. Our results displayed working memory dysfunction and deficiencies in executive function and impulsivity in individuals with IAD and PG patients and higher impulsivity in individuals with IAD than in PG patients.
Working memory is responsible for the transient holding and processing of new and already stored messages, an important process for comprehension, reasoning, memory updating and learning. Many studies in animals and functional imaging of humans identified that the parietal cortex, frontal cortex, anterior cingulate and parts of the basal ganglia are involved in working memory (Reference Luna, Minshew and Garver30–Reference Luerdin, Weigand, Bogdahn and Schmid -Wilcke33). Our study results showed that the forward scores and backward scores of individuals with IAD and PG patients were significantly lower than those of controls by the measurement of the digit span task. However, the forward scores and backward scores were not significantly different between individuals with IAD and PG patients. These results indicated that Internet-addictive individuals share working memory dysfunction with PG patients.
Executive function is an umbrella term for the regulation or control of cognitive processes, including reasoning, problem solving, planning, decision making and task flexibility as well as the inhibition of impulsive responses. Previous studies identified the prefrontal areas of the frontal lobe as necessary but not solely sufficient for carrying out these functions (Reference Alvarez and Emoryv34). The WCST reflects the ability to display flexibility in the face of changing schedules of reinforcement. It is an assessment of executive function because of its reported sensitivity to frontal lobe dysfunctions, which include strategic planning, utilising environmental feedback to shift cognitive sets, organised searching, directing behaviour towards achieving a goal and modulating impulsive responses (Reference Alvarez and Emoryv34,Reference Miyake, Friedman, Emerson, Witzki, Howerter and Wager35). Many studies that used the WCST displayed that individuals with IAD and PG patients presented executive dysfunctions. For example, a study that investigated several measures of executive function, including measures of response inhibition, working memory, cognitive flexibility and perseveration, planning and decision making, between pathological gamblers and controls indicated that PG patients exhibited specific deficits for planning, decision making and perseveration (Reference Ledgerwood, Orr, Kaploun, Frish, Rupcich and Lundahl36). Another study showed that PG patients were associated with a frontotemporal dysfunction, such as impulsivity and novelty seeking (Reference Forbush, Shaw and Graebe37). A recent study that used the WCST to examine whether individuals with IAD share executive dysfunction with alcohol-dependent patients indicated that individuals with IAD share executive dysfunction with alcohol-dependent patients (Reference Zhou, Zhu, Li and Wang27). Consistent with previous studies, our results showed that the total response errors, perseverative errors and failure to maintain set of IAD and PG groups were significantly higher than those of controls, and that the number of categories completed and the percentage of conceptual level responses of the IAD and PG groups was significantly lower than those of the controls. This result confirmed that individuals with IAD and PG patients exhibit the same degree of executive dysfunction.
Individuals with IAD and PG patients involve continued gambling behavioural and Internet use, respectively, despite negative consequences, that is, loss of gambling behavioural control and Internet use. Impulsivity involves a tendency to act on an impulse and displays behaviour characterised by a lack of forethought or consideration of the consequences. Impulsivity includes dysfunction in attention, lack of reflection or insensitivity to consequences, all of which may occur in all types of addiction. In addition, impulsivity is a major component of various disorders, such as attention-deficit hyperactivity disorder, substance use disorders, bipolar disorder and so on. (Reference Nigg38–Reference Henry, Mitropoulou, New, Koenigsberg, Silveman and Siever40). In this study, there were significant differences in the BIS-11 scores among the IAD, PG and control groups. Furthermore, the BIS-11 scores in the IAD group were significantly higher than those of the PG group. Simultaneously, in the go/no-go task, there were significant differences in the false alarm rate and hit rate among the three groups. The false alarm rate of individuals with IAD was significantly higher than that of PG patients, and the hit rate was significantly lower than that of PG patients. The above two experiments indicate that although individuals with IAD share impulsivity with PG patients, individuals with IAD are more impulsive than PG patients. A previous study also displayed that IAD individuals showed increased levels of trait impulsivity compared with PG patients (Reference Lee, Choi, Shin, Lee, Jung and Kwon41). Our results support the above research outcome. From these findings, we deduce that there are some differences in trait impulsivity between IAD individuals and PG patients. The mechanism of differences in trait impulsivity should be researched in other aspects in the future, such as with neuroimaging, neurobiochemistry and genetics.
In this study, we used the DSM-IV criteria for PG. However, the DSM-IV classification of PG differs from the DSM-V classification of gambling disorder (GD). Although PG was classified as an impulse control disorder in the DSM-IV, GD is classified as a behavioural addiction in the DSM-V under the Substance use disorder chapter. Our results displayed that PG patients present working memory impairments, executive dysfunction and impulsivity, which is similar to research findings of the substance use disorder (Reference Verdejo-Garcia, Alonso-Maroto and Fernandez-Calderon42–Reference Cousijin, Wisers and Ridderinkhof46). Our findings support PG as classified as a behavioural addiction in the DSM-V under the Substance use disorder chapter.
In conclusion, our study shows the existence of deficiencies in working memory, impulsivity and executive dysfunction in an IAD and a PG sample. We confirmed our hypothesis that individuals with IAD share a deficit of working memory, executive dysfunction and impulsivity with PG patients. Furthermore, individuals with IAD are more impulsive than PG patients. Understanding the neuropsychological characteristic of IAD on the human brain may provide insight into the aetiology classification of IAD. Previous studies indicated that IAD included five specific subtypes: cybersex addiction, cyber-relationship addiction, net compulsions, information overload and IGD. These five subtypes share core components of addiction, including salience, mood modification, tolerance, withdrawal, conflict and relapse (Reference Grenard, Ames and Wiser45). At present, only IGD is included in the DSM-V. In this study, because IAD covers a broader range of Internet additive behaviours (including IGD), we chose individuals with IAD, not IGD, for inclusion in the research samples. However, the potential drawback is that IAD has not been designated as a formal diagnosis criterion in DSM-V. Our results suggest that IAD, just as PG, should be designated as a formal diagnosis criterion in DSM. By now, cognitive behavioural therapy is the mainstay of treatment for IAD. Owing to the lack of methodologically adequate research of pharmacological treatment, it is currently impossible to recommend any evidence-based treatment of Internet addiction. As for PG, most treatment involves counselling, step-based programs, self-help, peer support or medication. However, no one treatment is considered to be most efficacious. Our results suggest that both IAD and PG should employ the analogous therapeutic intervention. The other limitation of this study is that our results are preliminary because of the small sample size. It is necessary to replicate these findings with larger sample sizes in further studies.
Acknowledgements
This study was supported by the National Natural Science Foundation, China (No. 81471354).
Authors’ contributions
Conceived and designed the experiments: Zhou Z. and Zhou H. Performed the experiments: Zhou Z., Zhou H. and Zhu H. Analysed the data: Zhou Z., Zhou H. and Zhu H. Contributed reagents/materials/analysis tools: Zhou Z., Zhou H. and Zhu H. Wrote the paper: Zhou Z. Read and approved the final manuscript: Zhou Z., Zhou H. and Zhu H.
Conflicts 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 conflicts of interest.
